diff --git a/.dockerignore b/.dockerignore
new file mode 100644
index 00000000..9bb1ca3d
--- /dev/null
+++ b/.dockerignore
@@ -0,0 +1,3 @@
+/.git*
+build*
+/third_party
diff --git a/.gitignore b/.gitignore
index 7eb1c34a..22284b0b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,8 @@
 build/
-docker/jetpack_files/*
+/demo/BERT/models
+/demo/BERT/engines
+/demo/BERT/squad/*.json
+/docker/jetpack_files/*
+*.nvmk
+*.sln
+*.vcxproj
diff --git a/.gitmodules b/.gitmodules
index 19c5e52e..0ef10f1a 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -1,7 +1,3 @@
-[submodule "parsers/onnx"]
-	path = parsers/onnx
-	url = https://github.com/onnx/onnx-tensorrt.git
-	branch = 7.0
 [submodule "third_party/protobuf"]
 	path = third_party/protobuf
 	url = https://github.com/protocolbuffers/protobuf.git
@@ -10,3 +6,7 @@
 	path = third_party/cub
 	url = https://github.com/NVlabs/cub.git
 	branch = 1.8.0
+[submodule "parsers/onnx"]
+	path = parsers/onnx
+	url = https://github.com/onnx/onnx-tensorrt.git
+	branch = 7.1
diff --git a/CMakeLists.txt b/CMakeLists.txt
index b6b189c3..90d9eb15 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -19,7 +19,7 @@ include(cmake/modules/set_ifndef.cmake)
 include(cmake/modules/find_library_create_target.cmake)
 
 set_ifndef(TRT_LIB_DIR ${CMAKE_BINARY_DIR})
-set_ifndef(TRT_BIN_DIR ${CMAKE_BINARY_DIR})
+set_ifndef(TRT_OUT_DIR ${CMAKE_BINARY_DIR})
 
 file(STRINGS "${CMAKE_CURRENT_SOURCE_DIR}/include/NvInferVersion.h" VERSION_STRINGS REGEX "#define NV_TENSORRT_.*")
 
@@ -33,8 +33,8 @@ foreach(TYPE MAJOR MINOR PATCH)
     string(REGEX MATCH "[0-9]" TRT_SO_${TYPE} ${TRT_TYPE_STRING})
 endforeach(TYPE)
 
-set(TRT_VERSION "${TRT_MAJOR}.${TRT_MINOR}.${TRT_PATCH}.${TRT_BUILD}" CACHE STRING "TRT project version")
-set(TRT_SOVERSION "${TRT_SO_MAJOR}.${TRT_SO_MINOR}.${TRT_SO_PATCH}" CACHE STRING "TRT library so version")
+set(TRT_VERSION "${TRT_MAJOR}.${TRT_MINOR}.${TRT_PATCH}" CACHE STRING "TensorRT project version")
+set(TRT_SOVERSION "${TRT_SO_MAJOR}" CACHE STRING "TensorRT library so version")
 message("Building for TensorRT version: ${TRT_VERSION}, library version: ${TRT_SOVERSION}")
 
 if(NOT DEFINED CMAKE_TOOLCHAIN_FILE)
@@ -56,26 +56,81 @@ endif(CMAKE_INSTALL_PREFIX_INITIALIZED_TO_DEFAULT)
 option(BUILD_PLUGINS "Build TensorRT plugin" ON)
 option(BUILD_PARSERS "Build TensorRT parsers" ON)
 option(BUILD_SAMPLES "Build TensorRT samples" ON)
-option(NVPARTNER "Build partner repos from source" OFF)
-option(NVINTERNAL "Build in NVIDIA internal source tree" OFF)
 
 set(CMAKE_CXX_STANDARD 11)
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
 set(CMAKE_CXX_EXTENSIONS OFF)
+set(CMAKE_CXX_FLAGS "-Wno-deprecated-declarations ${CMAKE_CXX_FLAGS} -DBUILD_SYSTEM=cmake_oss")
 
-############################# CROSS COMPILATION SETTINGS ##################################
+############################################################################################
+# Cross-compilation settings
 
 set_ifndef(TRT_PLATFORM_ID "x86_64")
 message(STATUS "Targeting TRT Platform: ${TRT_PLATFORM_ID}")
 
 ############################################################################################
+# Debug settings
+
 set(TRT_DEBUG_POSTFIX _debug CACHE STRING "suffix for debug builds")
 
 if (CMAKE_BUILD_TYPE STREQUAL "Debug")
     message("Building in debug mode ${DEBUG_POSTFIX}")
 endif()
 
-set(CMAKE_CXX_FLAGS "-Wno-deprecated-declarations ${CMAKE_CXX_FLAGS} -DBUILD_SYSTEM=cmake_oss")
+############################################################################################
+# Dependencies
+
+set(DEFAULT_CUDA_VERSION 11.0)
+set(DEFAULT_CUDNN_VERSION 8.0)
+set(DEFAULT_PROTOBUF_VERSION 3.0.0)
+set(DEFAULT_CUB_VERSION 1.8.0)
+
+# Dependency Version Resolution
+set_ifndef(CUDA_VERSION ${DEFAULT_CUDA_VERSION})
+message(STATUS "CUDA version set to ${CUDA_VERSION}")
+set_ifndef(CUDNN_VERSION ${DEFAULT_CUDNN_VERSION})
+message(STATUS "cuDNN version set to ${CUDNN_VERSION}")
+set_ifndef(PROTOBUF_VERSION ${DEFAULT_PROTOBUF_VERSION})
+message(STATUS "Protobuf version set to ${PROTOBUF_VERSION}")
+
+find_package(Threads REQUIRED)
+if (BUILD_PLUGINS OR BUILD_PARSERS)
+    include(third_party/zlib.cmake)
+    include(third_party/protobuf.cmake)
+endif()
+if(NOT CUB_ROOT_DIR)
+    set(CUB_ROOT_DIR ${CMAKE_CURRENT_SOURCE_DIR}/third_party/cub CACHE STRING "directory of CUB installation")
+endif()
+
+## find_package(CUDA) is broken for cross-compilation. Enable CUDA language instead.
+if(NOT DEFINED CMAKE_TOOLCHAIN_FILE)
+    find_package(CUDA ${CUDA_VERSION} REQUIRED)
+endif()
+
+include_directories(
+    ${CUDA_INCLUDE_DIRS}
+    ${CUDNN_ROOT_DIR}/include
+)
+find_library(CUDNN_LIB cudnn HINTS
+    ${CUDA_TOOLKIT_ROOT_DIR} ${CUDNN_ROOT_DIR} PATH_SUFFIXES lib64 lib)
+find_library(CUBLAS_LIB cublas HINTS
+    ${CUDA_TOOLKIT_ROOT_DIR} PATH_SUFFIXES lib64 lib lib/stubs)
+find_library(CUBLASLT_LIB cublasLt HINTS
+    ${CUDA_TOOLKIT_ROOT_DIR} PATH_SUFFIXES lib64 lib lib/stubs)
+if(BUILD_PARSERS)
+    configure_protobuf(${PROTOBUF_VERSION})
+endif()
+
+find_library_create_target(nvinfer nvinfer SHARED ${TRT_LIB_DIR})
+find_library_create_target(nvuffparser nvparsers SHARED ${TRT_LIB_DIR})
+
+find_library(CUDART_LIB cudart HINTS ${CUDA_TOOLKIT_ROOT_DIR} PATH_SUFFIXES lib lib64)
+find_library(RT_LIB rt)
+
+set(CUDA_LIBRARIES ${CUDART_LIB})
+
+############################################################################################
+# CUDA targets
 
 if (DEFINED GPU_ARCHS)
   message(STATUS "GPU_ARCHS defined as ${GPU_ARCHS}. Generating CUDA code for SM ${GPU_ARCHS}")
@@ -88,6 +143,12 @@ else()
       70
       75
     )
+  if (CUDA_VERSION VERSION_GREATER_EQUAL 11.0)
+    # Ampere GPU (SM80) support is only available in CUDA versions > 11.0
+    list(APPEND GPU_ARCHS 80)
+  else()
+    message(WARNING "Detected CUDA version is < 11.0. SM80 not supported.")
+  endif()
   message(STATUS "GPU_ARCHS is not defined. Generating CUDA code for default SMs: ${GPU_ARCHS}")
 endif()
 set(BERT_GENCODES)
@@ -106,140 +167,23 @@ if (${LATEST_SM} GREATER_EQUAL 70)
 endif()
 set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Xcompiler -Wno-deprecated-declarations")
 
-################################### DEPENDENCIES ##########################################
-set(DEFAULT_CUDA_VERSION 10.2)
-set(DEFAULT_CUDNN_VERSION 7.6)
-set(DEFAULT_PROTOBUF_VERSION 3.0.0)
-set(DEFAULT_PROTOBUF_INTERNAL_VERSION 10.0)
-set(DEFAULT_CUB_VERSION 1.8.0)
-
-# Dependency Version Resolution
-set_ifndef(CUDA_VERSION ${DEFAULT_CUDA_VERSION})
-message(STATUS "CUDA version set to ${CUDA_VERSION}")
-set_ifndef(CUDNN_VERSION ${DEFAULT_CUDNN_VERSION})
-message(STATUS "cuDNN version set to ${CUDNN_VERSION}")
-
-if (NVINTERNAL)
-    #TODO: Change this to set_ifndef(PROTOBUF_INTERNAL_VERSION ${DEFAULT_PROTOBUF_INTERNAL_VERSION}) once onnx-tensorrts build system is fixed
-    set_ifndef(PROTOBUF_VERSION ${DEFAULT_PROTOBUF_VERSION})
-    message(STATUS "Protobuf version set to ${PROTOBUF_INTERNAL_VERSION}")
-    set_ifndef(CUB_VERSION ${DEFAULT_CUB_VERSION})
-    message(STATUS "CUB version set to ${CUB_VERSION}")
-    #TODO: Remove this once CMake is fully intergrated in the P4 build system
-    set_ifndef(NVINTERNAL_SUFFIX "V2")
-else()
-    set_ifndef(PROTOBUF_VERSION ${DEFAULT_PROTOBUF_VERSION})
-    message(STATUS "Protobuf version set to ${PROTOBUF_VERSION}")
-endif()
-
-
-find_package(Threads REQUIRED)
-if (BUILD_PLUGINS OR BUILD_PARSERS)
-    include(third_party/zlib.cmake)
-    include(third_party/protobuf.cmake)
-endif()
-if (NVINTERNAL)
-########################################### DEPENDENCIES FOR BUILDING IN NVIDIA's TREE ############################################
-    set(EXTERNALS ${PROJECT_SOURCE_DIR}/../externals)
-    set(CUB_ROOT_DIR ${EXTERNALS}/cub/${CUB_VERSION} CACHE STRING "directory of CUB installation")
-    set(Protobuf_DIR ${EXTERNALS}/protobuf/${TRT_PLATFORM_ID} CACHE STRING "directory of PROTOBUF installation")
-
-    ## This needs to be fixed to work with externals
-    if(NOT DEFINED CMAKE_TOOLCHAIN_FILE)
-        find_package(CUDA REQUIRED)
-    endif()
-
-    # Set this for ONNX Parser
-    set(CUDNN_ROOT_DIR ${EXTERNALS}/cudnn/${TRT_PLATFORM_ID}/${CUDNN_VERSION}/cuda-${CUDA_VERSION})
-
-    include_directories(
-        ${CUDNN_ROOT_DIR}/include
-        ${CUDA_TOOLKIT_ROOT_DIR}/include
-        /usr/local/cuda-${CUDA_VERSION}/include
-    )
-
-
-    #Check externals before using system
-    find_library(CUDNN_LIB cudnn HINTS
-        ${CUDNN_ROOT_DIR}/lib64 NO_DEFAULT_PATH)
-    find_library(CUDNN_LIB cudnn HINTS
-        ${CUDNN_ROOT_DIR}/lib64)
-
-    find_library(CUBLAS_LIB cublas HINTS
-        ${CUDA_TOOLKIT_ROOT_DIR}/lib NO_DEFAULT_PATH)
-    find_library(CUBLAS_LIB cublas HINTS
-        ${CUDA_TOOLKIT_ROOT_DIR}/lib)
-
-    if(BUILD_PARSERS)
-        #TODO: Change this to configure_protobuf_internal(${PROTOBUF_INTERNAL_VERSION}) once onnx-tensorrts build system is fixed
-        configure_protobuf(${PROTOBUF_VERSION})
-    endif()
-########################################### DEPENDENCIES FOR BUILDING IN NVIDIA's TREE ############################################
-else()
-########################################### DEPENDENCIES FOR BUILDING OUTSIDE OF NVIDIA ############################################
-    if(NOT CUB_ROOT_DIR)
-        set(CUB_ROOT_DIR ${CMAKE_CURRENT_SOURCE_DIR}/third_party/cub CACHE STRING "directory of CUB installation")
-    endif()
-
-    ## find_package(CUDA) is broken for cross-compilation. Enable CUDA language instead.
-    if(NOT DEFINED CMAKE_TOOLCHAIN_FILE)
-        find_package(CUDA ${CUDA_VERSION} REQUIRED)
-    endif()
-
-    include_directories(
-        ${CUDA_INCLUDE_DIRS}
-        ${CUDNN_ROOT_DIR}/include
-    )
-    find_library(CUDNN_LIB cudnn HINTS
-        ${CUDA_TOOLKIT_ROOT_DIR} ${CUDNN_ROOT_DIR} PATH_SUFFIXES lib64 lib)
-    find_library(CUBLAS_LIB cublas HINTS
-        ${CUDA_TOOLKIT_ROOT_DIR} PATH_SUFFIXES lib64 lib lib/stubs)
-    # CUBLASLT libraries are only available in CUDA versions > 10. Check for CUDA version here and
-    # remove dependency on the libarary and unset BERT_GENCODES.
-    if (CUDA_VERSION VERSION_LESS_EQUAL 10.0)
-        message(WARNING "Detected CUDA version is <= 10.0! Removing BERT plugins from compilation list.")
-        unset(BERT_GENCODES)
-    else()
-        find_library(CUBLASLT_LIB cublasLt HINTS
-            ${CUDA_TOOLKIT_ROOT_DIR} PATH_SUFFIXES lib64 lib lib/stubs)
-    endif()
-    if(BUILD_PARSERS)
-        configure_protobuf(${PROTOBUF_VERSION})
-    endif()
-########################################### DEPENDENCIES FOR BUILDING OUTSIDE OF NVIDIA ############################################
-endif()
-
-find_library_create_target(nvinfer nvinfer SHARED ${TRT_LIB_DIR})
-
-if (NOT (NVINTERNAL OR NVPARTNER))
-    find_library_create_target(nvuffparser nvparsers SHARED ${TRT_LIB_DIR})
-endif()
-
-find_library(CUDART_LIB cudart HINTS ${CUDA_TOOLKIT_ROOT_DIR} PATH_SUFFIXES lib lib64)
-find_library(RT_LIB rt)
-
-set(CUDA_LIBRARIES ${CUDART_LIB})
 ############################################################################################
 # TensorRT
 
 if(BUILD_PLUGINS)
-    add_subdirectory(plugin${NVINTERNAL_SUFFIX})
+    add_subdirectory(plugin)
 else()
-    find_library_create_target(nvinfer_plugin nvinfer_plugin SHARED ${TRT_BIN_DIR} ${TRT_LIB_DIR})
+    find_library_create_target(nvinfer_plugin nvinfer_plugin SHARED ${TRT_OUT_DIR} ${TRT_LIB_DIR})
 endif()
 
 if(BUILD_PARSERS)
-    add_subdirectory(parsers${NVINTERNAL_SUFFIX})
+    add_subdirectory(parsers)
 else()
-    if(NVPARTNER OR NVINTERNAL)
-        find_library_create_target(nvuffparser nvparsers SHARED ${TRT_BIN_DIR} ${TRT_LIB_DIR})
-    endif()
-
-    find_library_create_target(nvcaffeparser nvparsers SHARED ${TRT_BIN_DIR} ${TRT_LIB_DIR})
-    find_library_create_target(nvonnxparser nvonnxparser SHARED ${TRT_BIN_DIR} ${TRT_LIB_DIR})
+    find_library_create_target(nvcaffeparser nvparsers SHARED ${TRT_OUT_DIR} ${TRT_LIB_DIR})
+    find_library_create_target(nvonnxparser nvonnxparser SHARED ${TRT_OUT_DIR} ${TRT_LIB_DIR})
 endif()
 
 if(BUILD_SAMPLES)
-    add_subdirectory(samples${NVINTERNAL_SUFFIX})
+    add_subdirectory(samples)
 endif()
 
diff --git a/CODING-GUIDELINES.md b/CODING-GUIDELINES.md
index 958f52fa..569ad941 100644
--- a/CODING-GUIDELINES.md
+++ b/CODING-GUIDELINES.md
@@ -419,7 +419,7 @@ char const * const errStr = getErrorStr(status);
 1. All TensorRT Open Source Software code should contain an NVIDIA copyright header that includes the current year.  The following block of text should be prepended to the top of all OSS files.  This includes .cpp, .h, .cu, .py, and any other source files which are compiled or interpreted.
 ```cpp
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/LICENSE b/LICENSE
index 813b3f72..77fdb153 100644
--- a/LICENSE
+++ b/LICENSE
@@ -176,7 +176,7 @@
 
    END OF TERMS AND CONDITIONS
 
-   Copyright 2019 NVIDIA Corporation
+   Copyright 2020 NVIDIA Corporation
 
    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.
@@ -264,4 +264,4 @@
      THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
      LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
      FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
-     DEALINGS IN THE SOFTWARE.
\ No newline at end of file
+     DEALINGS IN THE SOFTWARE.
diff --git a/README.md b/README.md
index 0834d90b..847375dd 100644
--- a/README.md
+++ b/README.md
@@ -15,8 +15,8 @@ To build the TensorRT OSS components, ensure you meet the following package requ
 
 * [CUDA](https://developer.nvidia.com/cuda-toolkit)
   * Recommended versions:
-  * [cuda-10.2](https://developer.nvidia.com/cuda-10.2-download-archive-base) + cuDNN-7.6
-  * [cuda-10.0](https://developer.nvidia.com/cuda-10.0-download-archive) + cuDNN-7.6
+  * cuda-11.0 + cuDNN-8.0
+  * cuda-10.2 + cuDNN-8.0
 
 * [GNU Make](https://ftp.gnu.org/gnu/make/) >= v4.1
 
@@ -28,31 +28,39 @@ To build the TensorRT OSS components, ensure you meet the following package requ
   * [Python3](https://www.python.org/downloads/release/python-365/) >= v3.6.5
 
 * [PIP](https://pypi.org/project/pip/#history) >= v19.0
+  * PyPI packages
+  * [numpy](https://pypi.org/project/numpy/)
+  * [onnx](https://pypi.org/project/onnx/1.6.0/) 1.6.0
+  * [onnxruntime](https://pypi.org/project/onnxruntime/) >= 1.3.0
+  * [pytest](https://pypi.org/project/pytest/)
 
 * Essential libraries and utilities
   * [Git](https://git-scm.com/downloads), [pkg-config](https://www.freedesktop.org/wiki/Software/pkg-config/), [Wget](https://www.gnu.org/software/wget/faq.html#download), [Zlib](https://zlib.net/)
 
 * Cross compilation for Jetson platforms requires JetPack's host component installation
-  * [JetPack](https://developer.nvidia.com/embedded/jetpack) >= 4.2
+  * [JetPack](https://developer.nvidia.com/embedded/jetpack) >= 4.4
 
 **Optional Packages**
 
 * Containerized builds
-  * [Docker](https://docs.docker.com/install/) >= 1.12
-  * [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker) >= 2.0
+  * [Docker](https://docs.docker.com/install/) >= 19.03
+  * [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker) >= 2.0 or `nvidia-container-toolkit`
 
 * Code formatting tools
   * [Clang-format](https://clang.llvm.org/docs/ClangFormat.html)
   * [Git-clang-format](https://github.com/llvm-mirror/clang/blob/master/tools/clang-format/git-clang-format)
 
+* Required PyPI packages for Demos
+  * [Tensorflow-gpu](https://pypi.org/project/tensorflow/1.14.0/) == 1.15.0
+
 **TensorRT Release**
 
-* [TensorRT](https://developer.nvidia.com/nvidia-tensorrt-download) v7.0
+* [TensorRT](https://developer.nvidia.com/nvidia-tensorrt-download) v7.1
 
 
 NOTE: Along with the TensorRT OSS components, the following source packages will also be downloaded, and they are not required to be installed on the system.
 
-- [ONNX-TensorRT](https://github.com/onnx/onnx-tensorrt) v7.0
+- [ONNX-TensorRT](https://github.com/onnx/onnx-tensorrt) v7.1
 - [CUB](http://nvlabs.github.io/cub/) v1.8.0
 - [Protobuf](https://github.com/protocolbuffers/protobuf.git) v3.8.x
 
@@ -62,7 +70,7 @@ NOTE: Along with the TensorRT OSS components, the following source packages will
 1. #### Download TensorRT OSS sources.
 
 	```bash
-	git clone -b master https://github.com/nvidia/TensorRT TensorRT -b release/7.0
+	git clone -b master https://github.com/nvidia/TensorRT TensorRT
 	cd TensorRT
 	git submodule update --init --recursive
 	export TRT_SOURCE=`pwd`
@@ -70,28 +78,43 @@ NOTE: Along with the TensorRT OSS components, the following source packages will
 
 2. #### Download the TensorRT binary release.
 
-	To build the TensorRT OSS, obtain the corresponding TensorRT 7.0 binary release from [NVidia Developer Zone](https://developer.nvidia.com/nvidia-tensorrt-7x-download). For a list of key features, known and fixed issues, refer to the [TensorRT 7.0 Release Notes](https://docs.nvidia.com/deeplearning/sdk/tensorrt-release-notes/tensorrt-7.html#tensorrt-7).
+	To build the TensorRT OSS, obtain the corresponding TensorRT 7.1 binary release from [NVidia Developer Zone](https://developer.nvidia.com/nvidia-tensorrt-7x-download). For a list of key features, known and fixed issues, refer to the [TensorRT 7.1 Release Notes](https://docs.nvidia.com/deeplearning/tensorrt/release-notes/tensorrt-7.html#rel_7-1-0).
 
-	**Example: Ubuntu 18.04 with cuda-10.2**
+	**Example: Ubuntu 18.04 with cuda-11.0**
 
-	Download and extract the latest *TensorRT 7.0 GA package for Ubuntu 18.04 and CUDA 10.2*
+	Download and extract the latest *TensorRT 7.1 GA package for Ubuntu 18.04 and CUDA 11.0*
 	```bash
 	cd ~/Downloads
-	# Download TensorRT-7.0.0.11.Ubuntu-18.04.x86_64-gnu.cuda-10.2.cudnn7.6.tar.gz
-	tar -xvzf TensorRT-7.0.0.11.Ubuntu-18.04.x86_64-gnu.cuda-10.2.cudnn7.6.tar.gz
-	export TRT_RELEASE=`pwd`/TensorRT-7.0.0.11
-	export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$TRT_RELEASE/lib
+	tar -xvzf TensorRT-7.1.3.4.Ubuntu-18.04.x86_64-gnu.cuda-11.0.cudnn8.0.tar.gz
+	export TRT_RELEASE=`pwd`/TensorRT-7.1.3.4
 	```
 
-	**Example: CentOS/RedHat 7 with cuda-10.0**
+	**Example: Ubuntu 18.04 with cuda-11.0 on PowerPC**
 
-	Download and extract the *TensorRT 7.0 GA for CentOS/RedHat 7 and CUDA 10.0 tar package*
+	Download and extract the latest *TensorRT 7.1 GA package for Ubuntu 18.04 and CUDA 11.0*
 	```bash
 	cd ~/Downloads
-	# Download TensorRT-7.0.0.11.CentOS-7.6.x86_64-gnu.cuda-10.0.cudnn7.6.tar.gz
-	tar -xvzf TensorRT-7.0.0.11.CentOS-7.6.x86_64-gnu.cuda-10.0.cudnn7.6.tar.gz
-	export TRT_RELEASE=`pwd`/TensorRT-7.0.0.11
-	export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$TRT_RELEASE/lib
+	# Download TensorRT-7.1.3.4.Ubuntu-18.04.powerpc64le-gnu.cuda-11.0.cudnn8.0.tar.gz
+	tar -xvzf TensorRT-7.1.3.4.Ubuntu-18.04.powerpc64le-gnu.cuda-11.0.cudnn8.0.tar.gz
+	export TRT_RELEASE=`pwd`/TensorRT-7.1.3.4
+	```
+
+	**Example: CentOS/RedHat 7 with cuda-10.2**
+
+	Download and extract the *TensorRT 7.1 GA for CentOS/RedHat 7 and CUDA 10.2 tar package*
+	```bash
+	cd ~/Downloads
+	tar -xvzf TensorRT-7.1.3.4.CentOS-8.0.x86_64-gnu.cuda-10.2.cudnn8.0.tar.gz
+	export TRT_RELEASE=`pwd`/TensorRT-7.1.3.4
+	```
+
+	**Example: Ubuntu 16.04 with cuda-11.0**
+
+	Download and extract the *TensorRT 7.1 GA for Ubuntu 16.04 and CUDA 11.0 tar package*
+	```bash
+	cd ~/Downloads
+	tar -xvzf TensorRT-7.1.3.4.Ubuntu-16.04.x86_64-gnu.cuda-11.0.cudnn8.0.tar.gz
+	export TRT_RELEASE=`pwd`/TensorRT-7.1.3.4
 	```
 
 3. #### Download JetPack packages for cross-compilation.[OPTIONAL]
@@ -99,7 +122,7 @@ NOTE: Along with the TensorRT OSS components, the following source packages will
 Using the SDK manager, download the host componets of the PDK version or Jetpack specified in the name of the Dockerfile. To do this:
 
 1. [**SDK Manager Step 01**] Log into the SDK manager
-2. [**SDK Manager Step 01**] Select the correct platform and Target OS System  (should be corresponding to the name of the Dockerfile you are building (e.g. Jetson AGX Xavier, `Linux Jetpack 4.2.1`), then click `Continue`
+2. [**SDK Manager Step 01**] Select the correct platform and Target OS System  (should be corresponding to the name of the Dockerfile you are building (e.g. Jetson AGX Xavier, `Linux Jetpack 4.4`), then click `Continue`
 3. [**SDK Manager Step 02**] Under `Download & Install Options` make note of or change the download folder **and Select Download now. Install later.** then agree to the license terms and click `Continue`
 
 You should now have all expected files to build the container. Move these into the `docker/jetpack_files` folder.
@@ -112,33 +135,44 @@ You should now have all expected files to build the container. Move these into t
 
 1. #### Generate the TensorRT build container.
 
-  The docker container can be built using the included Dockerfile. The build container is configured with the environment and packages required for building TensorRT OSS.
+  The docker container can be built using the included Dockerfiles and build script. The build container is configured with the environment and packages required for building TensorRT OSS.
+
+  **Example: Ubuntu 18.04 with cuda-11.0**
+
+  ```bash
+  ./docker/build.sh --file docker/ubuntu.Dockerfile --tag tensorrt-ubuntu --os 18.04 --cuda 11.0
+  ```
 
-  **Example: Ubuntu 18.04 with cuda-10.2**
+  **Example: Ubuntu 16.04 with cuda-11.0**
 
   ```bash
-  docker build -f docker/ubuntu.Dockerfile --build-arg UBUNTU_VERSION=18.04 --build-arg CUDA_VERSION=10.2 --tag=tensorrt-ubuntu .
+  ./docker/build.sh --file docker/ubuntu.Dockerfile --tag tensorrt-ubuntu1604 --os 16.04 --cuda 11.0
   ```
 
-  **Example: CentOS/RedHat 7 with cuda-10.0**
+  **Example: CentOS/RedHat 7 with cuda-10.2**
 
   ```bash
-  docker build -f docker/centos.Dockerfile --build-arg CENTOS_VERSION=7 --build-arg CUDA_VERSION=10.0 --tag=tensorrt-centos .
+  ./docker/build.sh --file docker/centos.Dockerfile --tag tensorrt-centos --os 7 --cuda 10.2
   ```
 
-   **Example: Cross compile for JetPack 4.2.1 with cuda-10.0**
+   **Example: Cross compile for JetPack 4.4 with cuda-10.2**
+   ```bash
+   ./docker/build.sh --file docker/ubuntu-cross-aarch64.Dockerfile --tag tensorrt-ubuntu-jetpack --os 18.04 --cuda 10.2
+   ```
+
+   **Example: Cross compile for PowerPC with cuda-11.0**
    ```bash
-   docker build -f docker/ubuntu-cross-aarch64.Dockerfile --build-arg UBUNTU_VERSION=18.04 --build-arg CUDA_VERSION=10.0 --tag tensorrt-ubuntu-aarch64 .
-   `
+   ./docker/build.sh --file docker/ubuntu-cross-ppc64le.Dockerfile --tag tensorrt-ubuntu-ppc --os 18.04 --cuda 11.0
    ```
 
+
 2. #### Launch the TensorRT build container.
 
 	```bash
-	docker run -v $TRT_RELEASE:/tensorrt -v $TRT_SOURCE:/workspace/TensorRT -it tensorrt-ubuntu:latest
+	./docker/launch.sh --tag tensorrt-ubuntu --gpus all --release $TRT_RELEASE --source $TRT_SOURCE
 	```
 
-	> NOTE: To run TensorRT/CUDA programs within the build container, install [nvidia-docker](#prerequisites). Replace the `docker run` command with `nvidia-docker run` or `docker run --runtime=nvidia`.
+	> NOTE: To run TensorRT/CUDA programs in the build container, install [NVIDIA Docker support](#prerequisites). Docker versions < 19.03 require `nvidia-docker2` and `--runtime=nvidia` flag for docker run commands. On versions >= 19.03, you need the `nvidia-container-toolkit` package and `--gpus <NUM_GPUS>` flag.
 
 
 ## Building The TensorRT OSS Components
@@ -148,28 +182,30 @@ You should now have all expected files to build the container. Move these into t
 	```bash
 	cd $TRT_SOURCE
 	mkdir -p build && cd build
-	cmake .. -DTRT_LIB_DIR=$TRT_RELEASE/lib -DTRT_BIN_DIR=`pwd`/out
+	cmake .. -DTRT_LIB_DIR=$TRT_RELEASE/lib -DTRT_OUT_DIR=`pwd`/out
 	make -j$(nproc)
 	```
 
 	> NOTE:
-	> 1. The default CUDA version used by CMake is 10.2. To override this, for example to 10.0, append `-DCUDA_VERSION=10.0` to the cmake command.
+	> 1. The default CUDA version used by CMake is 11.0. To override this, for example to 10.2, append `-DCUDA_VERSION=10.2` to the cmake command.
 	> 2. Samples may fail to link on CentOS7. To work around this create the following symbolic link:
-	> `ln -s $TRT_BIN_DIR/libnvinfer_plugin.so $TRT_BIN_DIR/libnvinfer_plugin.so.7`
+	> `ln -s $TRT_OUT_DIR/libnvinfer_plugin.so $TRT_OUT_DIR/libnvinfer_plugin.so.7`
 
 	The required CMake arguments are:
 
 	- `TRT_LIB_DIR`: Path to the TensorRT installation directory containing libraries.
 
-	- `TRT_BIN_DIR`: Output directory where generated build artifacts will be copied.
+	- `TRT_OUT_DIR`: Output directory where generated build artifacts will be copied.
 
 	The following CMake build parameters are *optional*:
 
 	- `CMAKE_BUILD_TYPE`: Specify if binaries generated are for release or debug (contain debug symbols). Values consists of [`Release`] | `Debug`
 
-	- `CUDA_VERISON`: The version of CUDA to target, for example [`10.2`].
+	- `CUDA_VERISON`: The version of CUDA to target, for example [`11.0`].
+
+	- `CUDNN_VERSION`: The version of cuDNN to target, for example [`8.0`].
 
-	- `CUDNN_VERSION`: The version of cuDNN to target, for example [`7.6`].
+	- `NVCR_SUFFIX`: Optional nvcr/cuda image suffix. Set to "-rc" for CUDA11 RC builds until general availability. Blank by default.
 
 	- `PROTOBUF_VERSION`:  The version of Protobuf to use, for example [`3.8.x`]. Note: Changing this will not configure CMake to use a system version of Protobuf, it will configure CMake to download and try building that version.
 
@@ -183,61 +219,27 @@ You should now have all expected files to build the container. Move these into t
 
 	Other build options with limited applicability:
 
-	- `NVINTERNAL`: Used by TensorRT team for internal builds. Values consists of [`OFF`] | `ON`.
-
-	- `PROTOBUF_INTERNAL_VERSION`: The version of protobuf to use, for example [`10.0`].  Only applicable if `NVINTERNAL` is also enabled.
-
-	- `NVPARTNER`: For use by NVIDIA partners with exclusive source access.  Values consists of [`OFF`] | `ON`.
-
 	- `CUB_VERSION`: The version of CUB to use, for example [`1.8.0`].
 
 	- `GPU_ARCHS`: GPU (SM) architectures to target. By default we generate CUDA code for all major SMs. Specific SM versions can be specified here as a quoted space-separated list to reduce compilation time and binary size. Table of compute capabilities of NVIDIA GPUs can be found [here](https://developer.nvidia.com/cuda-gpus). Examples:
-	  - Titan V: `-DGPU_ARCHS="70"`
-	  - Tesla V100: `-DGPU_ARCHS="70"`
-	  - GeForce RTX 2080: `-DGPU_ARCHS="75"`
-	  - Tesla T4: `-DGPU_ARCHS="75"`
-	  - Multiple SMs: `-DGPU_ARCHS="70 75"`
-
-## Install the TensorRT OSS Components [Optional]
+        - NVidia A100: `-DGPU_ARCHS="80"`
+        - Tesla T4, GeForce RTX 2080: `-DGPU_ARCHS="75"`
+        - Titan V, Tesla V100: `-DGPU_ARCHS="70"`
+        - Multiple SMs: `-DGPU_ARCHS="80 75"`
 
-* Copy the build artifacts into the TensorRT installation directory, updating the installation.
-  * TensorRT installation directory is determined as `$TRT_LIB_DIR/..`
-  * Installation might require superuser privileges depending on the path and permissions of files being replaced.
-  * Installation is not supported in cross compilation scenario. Please copy the result files from `build/out` folder into the target device.
-
-	```bash
-	sudo make install
-	```
-* Verify the TensorRT samples have been installed correctly.
-
-	```bash
-	cd $TRT_LIB_DIR/../bin/
-	./sample_googlenet
-	```
-
-	If the sample was installed correctly, the following information will be printed out in the terminal.
-
-	```bash
-	[08/23/2019-22:08:57] [I] Building and running a GPU inference engine for GoogleNet
-	[08/23/2019-22:08:59] [I] [TRT] Some tactics do not have sufficient workspace memory to run. Increasing workspace size may increase performance, please check verbose output.
-	[08/23/2019-22:09:05] [I] [TRT] Detected 1 inputs and 1 output network tensors.
-	[08/23/2019-22:09:05] [I] Ran /tensorrt/bin/sample_googlenet with: 
-	[08/23/2019-22:09:05] [I] Input(s): data 
-	[08/23/2019-22:09:05] [I] Output(s): prob 
-	&&&& PASSED TensorRT.sample_googlenet # /tensorrt/bin/sample_googlenet
-	```
 
 ## Useful Resources
 
 #### TensorRT
 
 * [TensorRT Homepage](https://developer.nvidia.com/tensorrt)
-* [TensorRT Developer Guide](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html)
-* [TensorRT Sample Support Guide](https://docs.nvidia.com/deeplearning/sdk/tensorrt-sample-support-guide/index.html)
+* [TensorRT Developer Guide](https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html)
+* [TensorRT Sample Support Guide](https://docs.nvidia.com/deeplearning/tensorrt/sample-support-guide/index.html)
 * [TensorRT Discussion Forums](https://devtalk.nvidia.com/default/board/304/tensorrt/)
 
 
 ## Known Issues
 
-#### TensorRT 7.0
-* See [Release Notes](https://docs.nvidia.com/deeplearning/sdk/tensorrt-release-notes/tensorrt-7.html#tensorrt-7).
+#### TensorRT 7.1
+* [demo/BERT](demo/BERT) has a known accuracy regression for Volta GPUs; F1 score dropped (from 90 in TensorRT 7.0) to 85. A fix is underway.
+* See [Release Notes](https://docs.nvidia.com/deeplearning/tensorrt/release-notes/tensorrt-7.html#rel_7-1-3).
diff --git a/VERSION b/VERSION
index dbdef6f9..08c98abb 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-7.0.0.11
+7.1.3.4
diff --git a/cmake/modules/find_library_create_target.cmake b/cmake/modules/find_library_create_target.cmake
index ea23ebe6..cadf75f5 100644
--- a/cmake/modules/find_library_create_target.cmake
+++ b/cmake/modules/find_library_create_target.cmake
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/cmake/modules/set_ifndef.cmake b/cmake/modules/set_ifndef.cmake
index 7e679ec0..c67f3d03 100644
--- a/cmake/modules/set_ifndef.cmake
+++ b/cmake/modules/set_ifndef.cmake
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/cmake/toolchains/cmake_aarch64-android.toolchain b/cmake/toolchains/cmake_aarch64-android.toolchain
index 53a53a0d..c4fa1d32 100644
--- a/cmake/toolchains/cmake_aarch64-android.toolchain
+++ b/cmake/toolchains/cmake_aarch64-android.toolchain
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/cmake/toolchains/cmake_aarch64.toolchain b/cmake/toolchains/cmake_aarch64.toolchain
index 5262506e..3ffa0bd0 100644
--- a/cmake/toolchains/cmake_aarch64.toolchain
+++ b/cmake/toolchains/cmake_aarch64.toolchain
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -31,12 +31,7 @@ set(CMAKE_CXX_COMPILER_TARGET aarch64)
 set(CMAKE_C_COMPILER_FORCED TRUE)
 set(CMAKE_CXX_COMPILER_FORCED TRUE)
 
-if(NVINTERNAL)
-    set(EXT_PATH ${PROJECT_SOURCE_DIR}/../externals)
-    set(CUDA_ROOT ${EXT_PATH}/cuda-${CUDA_VERSION}-${TRT_PLATFORM_ID}/${CUDA_PLATFORM_ID})
-else()
-    set(CUDA_ROOT /usr/local/cuda-${CUDA_VERSION}/targets/${CUDA_PLATFORM_ID} CACHE STRING "CUDA ROOT dir")
-endif()
+set(CUDA_ROOT /usr/local/cuda-${CUDA_VERSION}/targets/${CUDA_PLATFORM_ID} CACHE STRING "CUDA ROOT dir")
 
 set(CUDA_TOOLKIT_ROOT_DIR ${CUDA_ROOT})
 set(CUDA_INCLUDE_DIRS ${CUDA_ROOT}/include)
@@ -47,12 +42,7 @@ set(CMAKE_CUDA_HOST_COMPILER ${CMAKE_CXX_COMPILER} CACHE STRING "" FORCE)
 set(CMAKE_CUDA_FLAGS "-cudart none -I${CUDA_INCLUDE_DIRS} -Xcompiler=\"-fPIC ${CMAKE_CXX_FLAGS}\"" CACHE STRING "" FORCE)
 set(CMAKE_CUDA_COMPILER_FORCED TRUE)
 
-if(DEFINED ENV{VULCAN} AND NOT $ENV{VULCAN} STREQUAL "")
-	message("cmake_aarch64.toolchain  using VULCAN mode")
-	set(CUDA_LIBS ${DEVLIBPATHS} -L${CUDA_ROOT}/lib64)
-else()
-	set(CUDA_LIBS -L${CUDA_ROOT}/lib)
-endif()
+set(CUDA_LIBS -L${CUDA_ROOT}/lib)
 
 set(ADDITIONAL_PLATFORM_LIB_FLAGS ${CUDA_LIBS} -lcublas -lcudart -lstdc++ -lm)
 
diff --git a/cmake/toolchains/cmake_ppc64le.toolchain b/cmake/toolchains/cmake_ppc64le.toolchain
index 48247c70..5cb0cd5e 100644
--- a/cmake/toolchains/cmake_ppc64le.toolchain
+++ b/cmake/toolchains/cmake_ppc64le.toolchain
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -33,7 +33,4 @@ endif()
 
 set(CUDA_INCLUDE_DIRS ${CUDA_ROOT}/include)
 
-if(DEFINED ENV{VULCAN} AND NOT $ENV{VULCAN} STREQUAL "")
-  set(DISABLE_SWIG TRUE)
-endif()
 set(TRT_PLATFORM_ID "ppc64le")
diff --git a/cmake/toolchains/cmake_qnx.toolchain b/cmake/toolchains/cmake_qnx.toolchain
index 5c97d090..990b2ed8 100644
--- a/cmake/toolchains/cmake_qnx.toolchain
+++ b/cmake/toolchains/cmake_qnx.toolchain
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -21,7 +21,7 @@ if(DEFINED ENV{QNX_BASE})
   set(QNX_BASE $ENV{QNX_BASE})
   message(STATUS "Found QNX_BASE = ${QNX_BASE}")
 elseif(DEFINED ENV{TOOLS_BASE})
-  set(QNX_BASE $ENV{TOOLS_BASE}/embedded/qnx/qnx700-ga3)
+  set(QNX_BASE $ENV{TOOLS_BASE}/embedded/qnx/qnx700-ga4)
   message(STATUS "Found QNX_BASE = ${QNX_BASE}")
 else()
   message(FATAL_ERROR "QNX_BASE was not found")
diff --git a/cmake/toolchains/cmake_x64_win.toolchain b/cmake/toolchains/cmake_x64_win.toolchain
index 9fa7275c..e72e89ea 100644
--- a/cmake/toolchains/cmake_x64_win.toolchain
+++ b/cmake/toolchains/cmake_x64_win.toolchain
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -40,9 +40,9 @@ set(W10_LINKER ${MSVC_COMPILER_DIR}/bin/amd64/link)
 set(CMAKE_CUDA_HOST_COMPILER ${CMAKE_NVCC_COMPILER} CACHE STRING "" FORCE)
  
 set(ADDITIONAL_PLATFORM_INCL_FLAGS "-I${MSVC_COMPILER_DIR}/include -I${MSVC_COMPILER_DIR}/../ucrt/include")
-set(ADDITIONAL_PLATFORM_LIB_FLAGS ${ADDITIONAL_PLATFORM_LIB_FLAGS} "-LIBPATH:${NV_TOOLS}/ddk/wddmv2/dev/rs4/17130/Lib/10.0.17130.0/um/x64")
+set(ADDITIONAL_PLATFORM_LIB_FLAGS ${ADDITIONAL_PLATFORM_LIB_FLAGS} "-LIBPATH:${NV_TOOLS}/ddk/wddmv2/official/17134/Lib/10.0.17134.0/um/x64")
 set(ADDITIONAL_PLATFORM_LIB_FLAGS ${ADDITIONAL_PLATFORM_LIB_FLAGS} "-LIBPATH:${MSVC_COMPILER_DIR}/lib/amd64" )
 set(ADDITIONAL_PLATFORM_LIB_FLAGS ${ADDITIONAL_PLATFORM_LIB_FLAGS} "-LIBPATH:${MSVC_COMPILER_DIR}/../ucrt/lib/x64")
 set(ADDITIONAL_PLATFORM_LIB_FLAGS ${ADDITIONAL_PLATFORM_LIB_FLAGS} "-LIBPATH:${W10_CUDA_ROOT}/lib/x64 cudart.lib cublas.lib")
 
-set(TRT_PLATFORM_ID "win10")
\ No newline at end of file
+set(TRT_PLATFORM_ID "win10")
diff --git a/cmake/toolchains/cmake_x86_64.toolchain b/cmake/toolchains/cmake_x86_64.toolchain
index 69ce060a..75234d2c 100644
--- a/cmake/toolchains/cmake_x86_64.toolchain
+++ b/cmake/toolchains/cmake_x86_64.toolchain
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -26,8 +26,4 @@ endif()
 
 set(CUDA_INCLUDE_DIRS ${CUDA_ROOT}/include)
 
-if(DEFINED ENV{VULCAN} AND NOT $ENV{VULCAN} STREQUAL "")
-  set(DISABLE_SWIG TRUE)
-endif()
-
 set(TRT_PLATFORM_ID "x86_64")
diff --git a/demo/BERT/CMakeLists.txt b/demo/BERT/CMakeLists.txt
new file mode 100644
index 00000000..09c44ade
--- /dev/null
+++ b/demo/BERT/CMakeLists.txt
@@ -0,0 +1,72 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+cmake_minimum_required(VERSION 3.12 FATAL_ERROR)
+project(infer_c LANGUAGES CXX)
+find_package(CUDA)
+
+include(FetchContent)
+FetchContent_Declare(
+    pybind11
+    GIT_REPOSITORY https://github.com/pybind/pybind11
+    GIT_TAG        v2.2.3
+)
+
+FetchContent_GetProperties(pybind11)
+if(NOT pybind11_POPULATED)
+    FetchContent_Populate(pybind11)
+    add_subdirectory(${pybind11_SOURCE_DIR} ${pybind11_BINARY_DIR})
+endif()
+
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-deprecated-declarations")
+
+include($ENV{TRT_SOURCE}/cmake/modules/set_ifndef.cmake)
+set_ifndef(TRT_INC_DIR $ENV{TRT_SOURCE}/include)
+set_ifndef(TRT_LIB_DIR $ENV{TRT_RELEASE}/lib)
+set_ifndef(TRT_OUT_DIR $ENV{TRT_SOURCE}/build/out)
+
+include_directories(
+    infer_c
+    ${CUDA_INCLUDE_DIRS}
+    ${TRT_INC_DIR}
+)
+
+link_directories(
+    ${TRT_OUT_DIR}
+    ${TRT_LIB_DIR}
+)
+
+pybind11_add_module(infer_c 
+    infer_c/infer_c.cpp
+    infer_c/logging.cpp
+)
+target_link_libraries(infer_c PRIVATE
+    ${CUDA_LIBRARIES}
+    nvinfer
+    nvinfer_plugin
+)
+
+add_executable(perf
+    infer_c/perf.cpp
+    infer_c/logging.cpp
+)
+
+target_link_libraries(perf 
+    ${CUDA_LIBRARIES}
+    nvinfer
+    nvinfer_plugin
+)
+
diff --git a/demo/BERT/README.md b/demo/BERT/README.md
new file mode 100644
index 00000000..3e30b7af
--- /dev/null
+++ b/demo/BERT/README.md
@@ -0,0 +1,501 @@
+# BERT Inference Using TensorRT
+
+This subfolder of the BERT TensorFlow repository, tested and maintained by NVIDIA, provides scripts to perform high-performance inference using NVIDIA TensorRT.
+
+
+## Table Of Contents
+
+- [Model Overview](#model-overview)
+   * [Model Architecture](#model-architecture)
+   * [TensorRT Inference Pipeline](#tensorrt-inference-pipeline)
+   * [Version Info](#version-info)
+- [Setup](#setup)
+   * [Requirements](#requirements)
+- [Quick Start Guide](#quick-start-guide)
+  * [(Optional) Trying a different configuration](#optional-trying-a-different-configuration)
+- [Advanced](#advanced)
+  * [Scripts and sample code](#scripts-and-sample-code)
+  * [Command-line options](#command-line-options)
+  * [TensorRT inference process](#tensorrt-inference-process)
+- [Accuracy](#accuracy)
+  * [Evaluating Post-Training-Quantization INT8 accuracy](#evaluating-ptq-post-training-quantization-int8-accuracy-using-the-squad-dataset)
+  * [Evaluating Quantization-Aware-Training INT8 accuracy](#evaluating-qat-quantization-aware-training-int8-accuracy-using-the-squad-dataset)
+- [Performance](#performance)
+  * [Benchmarking](#benchmarking)
+       * [TensorRT inference benchmark](#tensorrt-inference-benchmark)
+  * [Results](#results)
+    * [Inference performance: NVIDIA A100](#inference-performance-nvidia-a100-40gb)
+      * [BERT Base](#bert-base)
+      * [BERT Large](#bert-large)
+    * [Inference performance: NVIDIA T4](#inference-performance-nvidia-t4-16gb)
+      * [BERT Base](#bert-base-1)
+      * [BERT Large](#bert-large-1)
+    * [Inference performance: NVIDIA V100](#inference-performance-nvidia-v100-16gb)
+      * [BERT Base](#bert-base-2)
+      * [BERT Large](#bert-large-2)
+
+
+## Model overview
+
+BERT, or Bidirectional Encoder Representations from Transformers, is a new method of pre-training language representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. This model is based on the [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) paper. NVIDIA's BERT is an optimized version of [Google's official implementation](https://github.com/google-research/bert), leveraging mixed precision arithmetic and Tensor Cores for faster inference times while maintaining target accuracy.
+
+Other publicly available implementations of BERT include:
+1. [NVIDIA PyTorch](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/LanguageModeling/BERT)
+2. [Hugging Face](https://github.com/huggingface/pytorch-pretrained-BERT)
+3. [codertimo](https://github.com/codertimo/BERT-pytorch)
+4. [gluon-nlp](https://github.com/dmlc/gluon-nlp/tree/master/scripts/bert)
+5. [Google's official implementation](https://github.com/google-research/bert)
+
+
+### Model architecture
+
+BERT's model architecture is a multi-layer bidirectional Transformer encoder. Based on the model size, we have the following two default configurations of BERT:
+
+| **Model** | **Hidden layers** | **Hidden unit size** | **Attention heads** | **Feed-forward filter size** | **Max sequence length** | **Parameters** |
+|:---------:|:----------:|:----:|:---:|:--------:|:---:|:----:|
+|BERT-Base |12 encoder| 768| 12|4 x  768|512|110M|
+|BERT-Large|24 encoder|1024| 16|4 x 1024|512|330M|
+
+Typically, the language model is followed by a few task-specific layers. The model used here includes layers for question answering.
+
+### TensorRT Inference Pipeline
+
+BERT inference consists of three main stages: tokenization, the BERT model, and finally a projection of the tokenized prediction onto the original text.
+Since the tokenizer and projection of the final predictions are not nearly as compute-heavy as the model itself, we run them on the host. The BERT model is GPU-accelerated via TensorRT.
+
+The tokenizer splits the input text into tokens that can be consumed by the model. For details on this process, see [this tutorial](https://mccormickml.com/2019/05/14/BERT-word-embeddings-tutorial/).
+
+To run the BERT model in TensorRT, we construct the model using TensorRT APIs and import the weights from a pre-trained TensorFlow checkpoint from [NGC](https://ngc.nvidia.com/models/nvidian:bert_tf_v2_large_fp16_128). Finally, a TensorRT engine is generated and serialized to the disk. The various inference scripts then load this engine for inference.
+
+Lastly, the tokens predicted by the model are projected back to the original text to get a final result.
+
+### Version Info
+
+The following software version configuration has been tested:
+
+|Software|Version|
+|--------|-------|
+|Python|3.6.9|
+|TensorRT|7.1.3.4|
+|CUDA|11.0.171|
+
+
+## Setup
+
+The following section lists the requirements that you need to meet in order to run the BERT model.
+
+### Requirements
+
+This demo BERT application can be run within the TensorRT Open Source build container. If running in a different environment, ensure you have the following packages installed.
+
+* [NGC CLI](https://ngc.nvidia.com/setup/installers/cli) - for downloading BERT checkpoints from NGC.
+* PyPI Packages:
+  * [pycuda](https://pypi.org/project/pycuda/) 2019.1.2
+  * [onnx](https://pypi.org/project/onnx/1.6.0/) 1.6.0
+  * [tensorflow](https://pypi.org/project/tensorflow/1.15.3/) 1.15
+* NVIDIA [Volta](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/), [Turing](https://www.nvidia.com/en-us/geforce/turing/) or [Ampere](https://www.nvidia.com/en-us/data-center/nvidia-ampere-gpu-architecture/) based GPU with NVIDIA Driver 450.37 or later.
+
+
+## Quick Start Guide
+
+1. Build and launch the TensorRT-OSS build container. On x86 with Ubuntu 18.04 for example:
+    ```bash
+    cd <TensorRT-OSS>
+    ./docker/build.sh --file docker/ubuntu.Dockerfile --tag tensorrt-ubuntu --os 18.04 --cuda 11.0
+    ./docker/launch.sh --tag tensorrt-ubuntu --gpus all --release $TRT_RELEASE --source $TRT_SOURCE
+    ```
+
+    **Note:** After this point, all commands should be run from within the container.
+
+2. Build the TensorRT Plugins library from source and install the TensorRT python bindings:
+    ```bash
+    cd $TRT_SOURCE
+    export LD_LIBRARY_PATH=`pwd`/build/out:$LD_LIBRARY_PATH:/tensorrt/lib
+    mkdir -p build && cd build
+    cmake .. -DTRT_LIB_DIR=$TRT_RELEASE/lib -DTRT_OUT_DIR=`pwd`/out
+    make -j$(nproc)
+
+    pip3 install /tensorrt/python/tensorrt-7.1*-cp36-none-linux_x86_64.whl
+    ```
+    **Note:** While the workflow and Performance Data presented here are based on plugin library built from source, the BERT sample is also expected to work with pre-compiled libraries shipped with TensorRT releases.
+
+3. Download the SQuAD dataset and BERT checkpoints:
+    ```bash
+    cd $TRT_SOURCE/demo/BERT
+    ```
+
+    Download SQuAD v1.1 training and dev dataset.
+    ```bash
+    bash scripts/download_squad.sh
+    ```
+
+    Download Tensorflow checkpoints for BERT large model with sequence length 128 and fp16 weights, fine-tuned for SQuAD v2.0.
+    ```bash
+    bash scripts/download_model.sh
+    ````
+
+**Note:** Since the datasets and checkpoints are stored in the directory mounted from the host, they do *not* need to be downloaded each time the container is launched. 
+
+4. Build a TensorRT engine. To build an engine, run the `builder.py` script. For example:
+    ```bash
+    mkdir -p /workspace/TensorRT/demo/BERT/engines && python3 builder.py -m /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_tf_v2_large_fp16_128_v2/model.ckpt-8144 -o /workspace/TensorRT/demo/BERT/engines/bert_large_128.engine -b 1 -s 128 --fp16 -c /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_tf_v2_large_fp16_128_v2
+    ```
+
+    This will build an engine with a maximum batch size of 1 (`-b 1`), and sequence length of 128 (`-s 128`) using mixed precision (`--fp16`) using the BERT Large V2 FP16 Sequence Length 128 checkpoint (`-c /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_tf_v2_large_fp16_128_v2`).
+
+5. Run inference. Two options are provided for running the model.
+
+    a. `inference.py` script
+    This script accepts a passage and question and then runs the engine to generate an answer.
+    For example:
+    ```bash
+    python3 inference.py -e /workspace/TensorRT/demo/BERT/engines/bert_large_128.engine -p "TensorRT is a high performance deep learning inference platform that delivers low latency and high throughput for apps such as recommenders, speech and image/video on NVIDIA GPUs. It includes parsers to import models, and plugins to support novel ops and layers before applying optimizations for inference. Today NVIDIA is open-sourcing parsers and plugins in TensorRT so that the deep learning community can customize and extend these components to take advantage of powerful TensorRT optimizations for your apps." -q "What is TensorRT?" -v /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_tf_v2_large_fp16_128_v2/vocab.txt
+    ```
+
+    b. `inference.ipynb` Jupyter Notebook
+    The Jupyter Notebook includes a passage and various example questions and allows you to interactively make modifications and see the outcome.
+    To launch the Jupyter Notebook from inside the container, run:
+    ```bash
+    jupyter notebook --ip 0.0.0.0 inference.ipynb
+    ```
+    Then, use your browser to open the link displayed. The link should look similar to: `http://127.0.0.1:8888/?token=<TOKEN>`
+    
+6. Run inference with CUDA Graph support.
+
+    A separate python `inference_c.py` script is provided to run inference with CUDA Graph support. This is necessary since CUDA Graph is only supported through CUDA C/C++ APIs, not pyCUDA. The `inference_c.py` script uses pybind11 to interface with C/C++ for CUDA graph capturing and launching. The cmdline interface is the same as `inference.py` except for an extra `--enable-graph` option.
+    
+    ```bash
+    mkdir -p build
+    cd build; cmake ..
+    make; cd ..
+    python3 inference_c.py -e /workspace/TensorRT/demo/BERT/engines/bert_large_128.engine --enable-graph -p "TensorRT is a high performance deep learning inference platform that delivers low latency and high throughput for apps such as recommenders, speech and image/video on NVIDIA GPUs. It includes parsers to import models, and plugins to support novel ops and layers before applying optimizations for inference. Today NVIDIA is open-sourcing parsers and plugins in TensorRT so that the deep learning community can customize and extend these components to take advantage of powerful TensorRT optimizations for your apps." -q "What is TensorRT?" -v /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_tf_v2_large_fp16_128_v2/vocab.txt
+    ```
+
+    A separate C/C++ inference benchmark executable `perf` (compiled from `perf.cpp`) is provided to run inference benchmarks with CUDA Graph. The cmdline interface is the same as `perf.py` except for an extra `--enable_graph` option.
+    
+    ```bash
+    build/perf -e /workspace/TensorRT/demo/BERT/engines/bert_large_128.engine -b 1 -s 128 -w 100 -i 1000 --enable_graph
+    ```
+    
+
+### (Optional) Trying a different configuration
+
+If you would like to run another configuration, you can manually download checkpoints using the included script. For example, run:
+```bash
+bash scripts/download_model.sh base
+```
+to download a BERT Base model instead of the default BERT Large model.
+
+To view all available model options, run:
+```bash
+bash scripts/download_model.sh -h
+```
+
+## Advanced
+
+The following sections provide greater details on inference with TensorRT.
+
+### Scripts and sample code
+
+In the `root` directory, the most important files are:
+
+- `builder.py` - Builds an engine for the specified BERT model
+- `Dockerfile` - Container which includes dependencies and model checkpoints to run BERT
+- `inference.ipynb` - Runs inference interactively
+- `inference.py` - Runs inference with a given passage and question
+- `perf.py` - Runs inference benchmarks
+
+The `scripts/` folder encapsulates all the one-click scripts required for running various supported functionalities, such as:
+
+- `build.sh` - Builds a Docker container that is ready to run BERT
+- `launch.sh` - Launches the container created by the `build.sh` script.
+- `download_model.sh` - Downloads pre-trained model checkpoints from NGC
+- `inference_benchmark.sh` - Runs an inference benchmark and prints results
+
+Other folders included in the `root` directory are:
+
+- `helpers` - Contains helpers for tokenization of inputs
+
+The `infer_c/` folder contains all the necessary C/C++ files required for CUDA Graph support.
+- `bert_infer.h` - Defines necessary data structures for running BERT inference
+- `infer_c.cpp` - Defines C/C++ interface using pybind11 that can be plugged into `inference_c.py`
+- `perf.cpp` - Runs inference benchmarks. It is equivalent to `perf.py`, with an extra option `--enable_graph` to enable CUDA Graph support.
+
+### Command-line options
+
+To view the available parameters for each script, you can use the help flag (`-h`).
+
+### TensorRT inference process
+
+As mentioned in the [Quick Start Guide](#quick-start-guide), two options are provided for running inference:
+1. The `inference.py` script which accepts a passage and a question and then runs the engine to generate an answer. Alternatively, this script can be used to run inference on the Squad dataset.
+2. The `inference.ipynb` Jupyter Notebook which includes a passage and various example questions and allows you to interactively make modifications and see the outcome.
+
+## Accuracy
+
+### Evaluating PTQ (post-training quantization) Int8 Accuracy Using The SQuAD Dataset
+1.  Download Tensorflow checkpoints for a BERT Large FP16 SQuAD v2 model with a sequence length of 384:
+    ```bash
+    bash scripts/download_model.sh large fp16 384 v2
+    ```
+
+2. Build an engine:
+
+    **Turing and Ampere GPUs**
+    ```bash
+    # QKVToContextPlugin and SkipLayerNormPlugin supported with INT8 I/O. To enable, use -imh and -iln builder flags respectively.
+    mkdir -p /workspace/TensorRT/demo/BERT/engines && python3 builder.py -m /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_tf_v2_large_fp16_384_v2/model.ckpt-8144 -o /workspace/TensorRT/demo/BERT/engines/bert_large_384_int8mix.engine -b 1 -s 384 --int8 --fp16 --strict -c /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_tf_v2_large_fp16_384_v2 --squad-json ./squad/train-v1.1.json -v /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_tf_v2_large_fp16_384_v2/vocab.txt --calib-num 100 -iln -imh
+    ```
+
+    **Xavier GPU**
+    ```bash
+    # Only supports SkipLayerNormPlugin running with INT8 I/O. Use -iln builder flag to enable.
+    mkdir -p /workspace/TensorRT/demo/BERT/engines && python3 builder.py -m /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_tf_v2_large_fp16_384_v2/model.ckpt-8144 -o /workspace/TensorRT/demo/BERT/engines/bert_large_384_int8mix.engine -b 1 -s 384 --int8 --fp16 --strict -c /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_tf_v2_large_fp16_384_v2 --squad-json ./squad/train-v1.1.json -v /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_tf_v2_large_fp16_384_v2/vocab.txt --calib-num 100 -iln 
+    ```
+
+    **Volta GPU**
+    ```bash
+    # No support for QKVToContextPlugin or SkipLayerNormPlugin running with INT8 I/O. Don't specify -imh or -iln in builder flags.
+    mkdir -p /workspace/TensorRT/demo/BERT/engines && python3 builder.py -m /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_tf_v2_large_fp16_384_v2/model.ckpt-8144 -o /workspace/TensorRT/demo/BERT/engines/bert_large_384_int8mix.engine -b 1 -s 384 --int8 --fp16 --strict -c /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_tf_v2_large_fp16_384_v2 --squad-json ./squad/train-v1.1.json -v /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_tf_v2_large_fp16_384_v2/vocab.txt --calib-num 100
+    ```
+
+    This will build an engine with a maximum batch size of 1 (`-b 1`), calibration dataset squad (`--squad-json ./squad/train-v1.1.json`), calibration sentences number 100 (`--calib-num 100`), and sequence length of 384 (`-s 384`) using INT8 mixed precision computation where possible (`--int8 --fp16 --strict`).
+
+3. Run inference using the squad dataset, and evaluate the F1 score and exact match score:
+    ```bash
+    python3 inference.py -e /workspace/TensorRT/demo/BERT/engines/bert_large_384_int8mix.engine -s 384 -sq ./squad/dev-v1.1.json -v /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_tf_v2_large_fp16_384_v2/vocab.txt -o ./predictions.json
+    python3 squad/evaluate-v1.1.py  squad/dev-v1.1.json  ./predictions.json 90
+    ```
+### Evaluating QAT (quantization aware training) Int8 Accuracy Using The SQuAD Dataset
+1.  Download checkpoint for BERT Large FP16 SQuAD v1.1 model with sequence length of 384:
+    ```bash
+    bash scripts/download_model.sh pyt v1_1
+    ```
+
+2. Build an engine:
+
+    **Turing and Ampere GPUs**
+    ```bash
+    # QKVToContextPlugin and SkipLayerNormPlugin supported with INT8 I/O. To enable, use -imh and -iln builder flags respectively.
+    mkdir -p /workspace/TensorRT/demo/BERT/engines && python3 builder.py -o /workspace/TensorRT/demo/BERT/engines/bert_large_384_int8mix.engine -b 1 -s 384 --int8 --fp16 --strict -c /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_tf_v2_large_fp16_384_v2 -v /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_tf_v2_large_fp16_384_v2/vocab.txt -x /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_pyt_onnx_large_qa_squad11_amp_fake_quant_v1/bert_large_v1_1_fake_quant.onnx -iln -imh
+    ```
+
+    **Xavier GPU**
+    ```bash
+    # Only supports SkipLayerNormPlugin running with INT8 I/O. Use -iln builder flag to enable.
+    mkdir -p /workspace/TensorRT/demo/BERT/engines && python3 builder.py -o /workspace/TensorRT/demo/BERT/engines/bert_large_384_int8mix.engine -b 1 -s 384 --int8 --fp16 --strict -c /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_tf_v2_large_fp16_384_v2 -v /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_tf_v2_large_fp16_384_v2/vocab.txt -x /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_pyt_onnx_large_qa_squad11_amp_fake_quant_v1/bert_large_v1_1_fake_quant.onnx -iln 
+    ```
+
+    **Volta GPU**
+    ```bash
+    # No support for QKVToContextPlugin or SkipLayerNormPlugin running with INT8 I/O. Don't specify -imh or -iln in builder flags.
+    mkdir -p /workspace/TensorRT/demo/BERT/engines && python3 builder.py -o /workspace/TensorRT/demo/BERT/engines/bert_large_384_int8mix.engine -b 1 -s 384 --int8 --fp16 --strict -c /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_tf_v2_large_fp16_384_v2 -v /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_tf_v2_large_fp16_384_v2/vocab.txt -x /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_pyt_onnx_large_qa_squad11_amp_fake_quant_v1/bert_large_v1_1_fake_quant.onnx 
+    ```
+
+    This will build and engine with a maximum batch size of 1 (`-b 1`) and sequence length of 384 (`-s 384`) using INT8 mixed precision computation where possible (`--int8 --fp16 --strict`).
+
+3. Run inference using the squad dataset, and evaluate the F1 score and exact match score:
+
+    ```bash
+    python3 inference.py -e /workspace/TensorRT/demo/BERT/engines/bert_large_384_int8mix.engine -s 384 -sq ./squad/dev-v1.1.json -v /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_tf_v2_large_fp16_384_v2/vocab.txt -o ./predictions.json
+    python3 squad/evaluate-v1.1.py  squad/dev-v1.1.json  ./predictions.json 90
+    ```
+
+## Performance
+
+### Benchmarking
+
+The following section shows how to run benchmarks measuring the model performance in inference modes.
+
+#### TensorRT inference benchmark
+
+The inference benchmark is performed on a single GPU by the `inference_benchmark.sh` script, which takes the following steps for each set of model parameters:
+
+1. Downloads checkpoints and builds a TensorRT engine if it does not already exist.
+
+2. Runs 100 warm-up iteration then runs inference for 1000 to 2000 iterations for each batch size specified in the script, selecting the profile best for each size.
+
+**Note:** The time measurements do not include the time required to copy inputs to the device and copy outputs to the host.
+
+To run the inference benchmark script, run:
+```bash
+bash scripts/inference_benchmark.sh --gpu <arch>
+```
+Options for `<arch>` are: 'Volta', 'Xavier', 'Turing', 'Ampere'
+
+Note: Some of the configurations in the benchmark script require 16GB of GPU memory. On GPUs with smaller amounts of memory, parts of the benchmark may fail to run.
+
+Also note that BERT Large engines, especially using mixed precision with large batch sizes and sequence lengths may take a couple hours to build.
+
+### Results
+
+The following sections provide details on how we achieved our performance and inference.
+
+#### Inference performance: NVIDIA A100 (40GB)
+
+Our results were obtained by running the `scripts/inference_benchmark.sh --gpu Ampere` script in the container generated by the TensorRT OSS Dockerfile on NVIDIA A100 with (1x A100 40G) GPUs.
+
+##### BERT Base
+
+| Sequence Length | Batch Size | INT8 Latency (ms) |               |         | FP16 Latency (ms) |               |         |
+|-----------------|------------|-----------------|-----------------|---------|-----------------|-----------------|---------|
+|                 |            | 95th Percentile | 99th Percentile | Average | 95th Percentile | 99th Percentile | Average |
+| 128 | 1 | 0.77 | 0.77 | 0.77 | 0.78 | 0.80 | 0.78 |
+| 128 | 2 | 0.76 | 0.77 | 0.76 | 0.92 | 0.93 | 0.92 |
+| 128 | 4 | 0.93 | 1.18 | 0.93 | 1.19 | 1.51 | 1.19 |
+| 128 | 8 | 1.19 | 1.20 | 1.19 | 1.78 | 1.78 | 1.77 |
+| 128 | 12 | 1.57 | 1.57 | 1.56 | 2.07 | 2.08 | 2.05 |
+| 128 | 16 | 1.88 | 1.89 | 1.88 | 2.54 | 2.60 | 2.52 |
+| 128 | 24 | 2.65 | 2.65 | 2.64 | 3.65 | 3.70 | 3.61 |
+| 128 | 32 | 3.21 | 3.22 | 3.21 | 4.71 | 4.74 | 4.67 |
+| 128 | 64 | 5.69 | 5.70 | 5.64 | 8.87 | 8.96 | 8.81 |
+| 128 | 128 | 10.84 | 10.85 | 10.70 | 17.61 | 17.62 | 17.44 |
+| 384 | 1 | 1.34 | 1.35 | 1.34 | 1.46 | 1.46 | 1.45 |
+| 384 | 2 | 1.56 | 1.79 | 1.56 | 1.85 | 1.85 | 1.84 |
+| 384 | 4 | 2.02 | 2.03 | 2.02 | 2.46 | 2.46 | 2.45 |
+| 384 | 8 | 2.94 | 2.95 | 2.94 | 3.91 | 3.92 | 3.86 |
+| 384 | 12 | 4.07 | 4.07 | 4.06 | 5.54 | 5.55 | 5.47 |
+| 384 | 16 | 5.22 | 5.23 | 5.21 | 7.78 | 7.79 | 7.69 |
+| 384 | 24 | 7.42 | 7.42 | 7.37 | 10.75 | 10.76 | 10.63 |
+| 384 | 32 | 9.92 | 9.93 | 9.77 | 14.58 | 14.73 | 14.52 |
+| 384 | 64 | 18.74 | 18.78 | 18.61 | 28.66 | 28.70 | 28.39 |
+| 384 | 128 | 36.40 | 36.42 | 36.05 | 55.36 | 55.90 | 55.21 |
+
+##### BERT Large
+
+| Sequence Length | Batch Size | INT8 Latency (ms) |               |         | FP16 Latency (ms) |               |         |
+|-----------------|------------|-----------------|-----------------|---------|-----------------|-----------------|---------|
+|                 |            | 95th Percentile | 99th Percentile | Average | 95th Percentile | 99th Percentile | Average |
+| 128 | 1 | 1.60 | 1.61 | 1.60 | 1.87 | 1.88 | 1.87 |
+| 128 | 2 | 1.94 | 1.95 | 1.94 | 2.36 | 2.37 | 2.35 |
+| 128 | 4 | 2.45 | 2.46 | 2.44 | 3.36 | 3.37 | 3.36 |
+| 128 | 8 | 3.82 | 3.83 | 3.79 | 4.98 | 5.00 | 4.95 |
+| 128 | 12 | 4.22 | 4.23 | 4.22 | 6.45 | 6.46 | 6.38 |
+| 128 | 16 | 5.75 | 5.75 | 5.74 | 8.50 | 8.53 | 8.43 |
+| 128 | 24 | 7.10 | 7.11 | 7.04 | 11.47 | 11.49 | 11.31 |
+| 128 | 32 | 9.61 | 9.61 | 9.51 | 15.49 | 15.50 | 15.25 |
+| 128 | 64 | 17.25 | 17.25 | 17.11 | 29.43 | 29.73 | 29.29 |
+| 128 | 128 | 33.25 | 33.58 | 33.05 | 56.98 | 57.17 | 56.68 |
+| 384 | 1 | 3.00 | 3.01 | 2.99 | 3.52 | 3.53 | 3.51 |
+| 384 | 2 | 3.71 | 3.72 | 3.71 | 4.97 | 4.99 | 4.97 |
+| 384 | 4 | 5.08 | 5.09 | 5.08 | 7.01 | 7.01 | 6.92 |
+| 384 | 8 | 9.04 | 9.05 | 9.04 | 12.71 | 12.72 | 12.67 |
+| 384 | 12 | 11.65 | 11.71 | 11.57 | 18.24 | 18.25 | 18.04 |
+| 384 | 16 | 15.63 | 15.63 | 15.49 | 24.24 | 24.28 | 23.94 |
+| 384 | 24 | 22.57 | 22.61 | 22.36 | 35.77 | 35.78 | 35.38 |
+| 384 | 32 | 29.66 | 29.66 | 29.33 | 47.09 | 47.11 | 46.81 |
+| 384 | 64 | 57.20 | 57.34 | 56.93 | 92.12 | 92.49 | 91.61 |
+| 384 | 128 | 112.00 | 112.42 | 111.24 | 180.61 | 181.02 | 179.56 |
+
+
+#### Inference performance: NVIDIA T4 (16GB)
+
+Our results were obtained by running the `scripts/inference_benchmark.sh --gpu Turing` script in the container generated by the TensorRT OSS Dockerfile on NVIDIA T4 with (1x T4 16G) GPUs.
+
+##### BERT Base
+
+| Sequence Length | Batch Size | INT8 Latency (ms) |               |         | FP16 Latency (ms) |               |         |
+|-----------------|------------|-----------------|-----------------|---------|-----------------|-----------------|---------|
+|                 |            | 95th Percentile | 99th Percentile | Average | 95th Percentile | 99th Percentile | Average |
+| 128 | 1 | 1.67 | 1.67 | 1.66 | 1.82 | 1.96 | 1.76 |
+| 128 | 2 | 1.94 | 1.95 | 1.89 | 2.58 | 2.67 | 2.50 |
+| 128 | 4 | 2.73 | 2.80 | 2.64 | 4.30 | 4.34 | 4.17 |
+| 128 | 8 | 4.93 | 4.96 | 4.81 | 8.85 | 9.74 | 8.36 |
+| 128 | 12 | 6.85 | 7.05 | 6.70 | 12.83 | 13.19 | 12.34 |
+| 128 | 16 | 9.65 | 9.89 | 9.43 | 17.70 | 18.27 | 17.01 |
+| 128 | 24 | 15.04 | 15.70 | 14.68 | 27.00 | 27.87 | 26.50 |
+| 128 | 32 | 20.55 | 21.01 | 19.88 | 34.51 | 34.81 | 33.83 |
+| 128 | 64 | 40.48 | 41.29 | 39.87 | 67.84 | 68.57 | 67.03 |
+| 128 | 128 | 82.17 | 82.53 | 80.95 | 132.78 | 133.23 | 131.64 |
+| 384 | 1 | 2.75 | 2.78 | 2.67 | 3.73 | 3.79 | 3.63 |
+| 384 | 2 | 4.22 | 4.38 | 4.09 | 6.68 | 7.27 | 6.53 |
+| 384 | 4 | 7.87 | 8.07 | 7.75 | 13.22 | 13.50 | 12.83 |
+| 384 | 8 | 16.07 | 16.13 | 15.77 | 28.01 | 28.72 | 27.48 |
+| 384 | 12 | 23.87 | 24.15 | 23.53 | 40.96 | 41.51 | 39.39 |
+| 384 | 16 | 31.87 | 32.25 | 30.99 | 51.56 | 51.83 | 51.00 |
+| 384 | 24 | 48.14 | 48.33 | 47.22 | 82.06 | 82.56 | 80.13 |
+| 384 | 32 | 64.07 | 64.48 | 63.19 | 102.64 | 103.33 | 101.20 |
+| 384 | 64 | 129.58 | 130.37 | 125.79 | 215.79 | 216.38 | 213.87 |
+| 384 | 128 | 258.69 | 259.74 | 245.91 | 414.96 | 415.57 | 413.16 |
+
+##### BERT Large
+
+| Sequence Length | Batch Size | INT8 Latency (ms) |               |         | FP16 Latency (ms) |               |         |
+|-----------------|------------|-----------------|-----------------|---------|-----------------|-----------------|---------|
+|                 |            | 95th Percentile | 99th Percentile | Average | 95th Percentile | 99th Percentile | Average |
+| 128 | 1 | 4.20 | 4.35 | 4.10 | 5.05 | 5.21 | 4.91 |
+| 128 | 2 | 5.41 | 5.70 | 5.30 | 7.99 | 8.31 | 7.79 |
+| 128 | 4 | 8.48 | 8.68 | 8.32 | 14.87 | 15.28 | 14.44 |
+| 128 | 8 | 15.20 | 15.22 | 14.91 | 29.66 | 30.20 | 28.97 |
+| 128 | 12 | 23.54 | 23.72 | 23.21 | 45.48 | 45.90 | 44.91 |
+| 128 | 16 | 31.04 | 31.38 | 30.46 | 62.06 | 62.61 | 60.27 |
+| 128 | 24 | 48.00 | 48.59 | 47.44 | 84.17 | 84.50 | 83.43 |
+| 128 | 32 | 64.41 | 64.77 | 63.54 | 113.60 | 113.98 | 112.32 |
+| 128 | 64 | 128.03 | 128.45 | 126.36 | 223.89 | 224.83 | 220.75 |
+| 128 | 128 | 246.96 | 247.80 | 245.00 | 441.52 | 442.26 | 439.65 |
+| 384 | 1 | 7.88 | 8.06 | 7.73 | 11.84 | 12.11 | 11.51 |
+| 384 | 2 | 13.00 | 13.18 | 12.80 | 23.59 | 24.13 | 23.12 |
+| 384 | 4 | 25.14 | 25.32 | 24.70 | 46.66 | 46.69 | 45.81 |
+| 384 | 8 | 50.14 | 50.65 | 49.41 | 86.74 | 87.47 | 85.40 |
+| 384 | 12 | 72.92 | 73.01 | 71.86 | 127.10 | 127.44 | 125.66 |
+| 384 | 16 | 97.00 | 97.26 | 95.47 | 169.41 | 169.93 | 167.55 |
+| 384 | 24 | 149.70 | 150.28 | 148.00 | 258.26 | 258.88 | 255.79 |
+| 384 | 32 | 192.74 | 193.85 | 190.59 | 339.87 | 340.55 | 337.86 |
+| 384 | 64 | 385.85 | 387.66 | 383.62 | 692.10 | 692.88 | 689.73 |
+| 384 | 128 | 780.95 | 781.81 | 778.82 | 1367.61 | 1368.85 | 1365.16 |
+
+
+#### Inference performance: NVIDIA V100 (16GB)
+
+Our results were obtained by running the `scripts/inference_benchmark.sh --gpu Volta` script in the container generated by the TensorRT OSS Dockerfile on NVIDIA V100 with (1x V100 16G) GPUs.
+
+##### BERT Base
+
+| Sequence Length | Batch Size | INT8 Latency (ms) |               |         | FP16 Latency (ms) |               |         |
+|-----------------|------------|-----------------|-----------------|---------|-----------------|-----------------|---------|
+|                 |            | 95th Percentile | 99th Percentile | Average | 95th Percentile | 99th Percentile | Average |
+| 128 | 1 | 1.39 | 1.39 | 1.39 | 1.23 | 1.23 | 1.23 |
+| 128 | 2 | 1.76 | 1.76 | 1.75 | 1.49 | 1.49 | 1.48 |
+| 128 | 4 | 2.35 | 2.36 | 2.34 | 2.12 | 2.13 | 2.11 |
+| 128 | 8 | 3.69 | 3.7 | 3.65 | 3.35 | 3.36 | 3.32 |
+| 128 | 12 | 4.79 | 4.83 | 4.75 | 4.65 | 4.67 | 4.61 |
+| 128 | 16 | 6.7 | 6.72 | 6.64 | 6.3 | 6.35 | 6.25 |
+| 128 | 24 | 8.95 | 8.96 | 8.9 | 8.68 | 8.71 | 8.6 |
+| 128 | 32 | 14.74 | 14.77 | 14.59 | 14.16 | 14.18 | 14.06 |
+| 128 | 64 | 24.12 | 24.14 | 23.98 | 22.57 | 22.63 | 22.47 |
+| 128 | 128 | 45.59 | 45.65 | 45.53 | 43.45 | 43.51 | 43.25 |
+| 384 | 1 | 2.17 | 2.18 | 2.16 | 1.98 | 1.98 | 1.97 |
+| 384 | 2 | 3.4 | 3.42 | 3.38 | 3.11 | 3.11 | 3.08 |
+| 384 | 4 | 5.61 | 5.62 | 5.57 | 5.5 | 5.52 | 5.46 |
+| 384 | 8 | 10.58 | 10.63 | 10.49 | 10.26 | 10.29 | 10.17 |
+| 384 | 12 | 16.55 | 16.57 | 16.43 | 15.8 | 15.83 | 15.69 |
+| 384 | 16 | 21.15 | 21.19 | 21.04 | 20.09 | 20.12 | 19.94 |
+| 384 | 24 | 30.95 | 31 | 30.77 | 29.44 | 29.51 | 29.24 |
+| 384 | 32 | 47.94 | 48.03 | 47.66 | 47.97 | 48.05 | 47.56 |
+| 384 | 64 | 81.8 | 81.91 | 81.62 | 76.84 | 77.05 | 76.4 |
+| 384 | 128 | 159.87 | 160.06 | 159.47 | 151.4 | 151.61 | 150.85 |
+
+##### BERT Large
+
+| Sequence Length | Batch Size | INT8 Latency (ms) |               |         | FP16 Latency (ms) |               |         |
+|-----------------|------------|-----------------|-----------------|---------|-----------------|-----------------|---------|
+|                 |            | 95th Percentile | 99th Percentile | Average | 95th Percentile | 99th Percentile | Average |
+| 128 | 1 | 3.43 | 3.44 | 3.42 | 3.06 | 3.07 | 3.05 |
+| 128 | 2 | 4.35 | 4.37 | 4.33 | 3.79 | 3.8 | 3.79 |
+| 128 | 4 | 6.8 | 6.83 | 6.74 | 6.02 | 6.05 | 5.98 |
+| 128 | 8 | 11 | 11.07 | 10.93 | 10.57 | 10.62 | 10.47 |
+| 128 | 12 | 16.28 | 16.31 | 16.15 | 15.06 | 15.1 | 14.96 |
+| 128 | 16 | 20.33 | 20.44 | 20.13 | 20.47 | 20.51 | 20.25 |
+| 128 | 24 | 30.63 | 30.66 | 30.33 | 28.65 | 28.8 | 28.48 |
+| 128 | 32 | 45.28 | 45.35 | 45.09 | 46.88 | 47.02 | 46.43 |
+| 128 | 64 | 75.33 | 75.57 | 74.82 | 71.88 | 71.97 | 71.47 |
+| 128 | 128 | 148.1 | 148.31 | 147.59 | 140.81 | 140.97 | 140.35 |
+| 384 | 1 | 6.16 | 6.17 | 6.12 | 5.7 | 5.72 | 5.66 |
+| 384 | 2 | 10.25 | 10.27 | 10.18 | 9.46 | 9.49 | 9.37 |
+| 384 | 4 | 18.44 | 18.5 | 18.27 | 17.22 | 17.28 | 17.09 |
+| 384 | 8 | 34.67 | 34.71 | 34.41 | 32.71 | 32.79 | 32.45 |
+| 384 | 12 | 49.04 | 49.13 | 48.79 | 47.53 | 47.77 | 47.27 |
+| 384 | 16 | 67.08 | 67.21 | 66.75 | 62.86 | 63.01 | 62.76 |
+| 384 | 24 | 94.22 | 94.39 | 94.04 | 92.08 | 92.2 | 91.86 |
+| 384 | 32 | 148.96 | 149.11 | 148.59 | 147.7 | 147.84 | 147.23 |
+| 384 | 64 | 245.91 | 246.09 | 244.67 | 240.16 | 240.43 | 239.07 |
diff --git a/demo/BERT/builder.py b/demo/BERT/builder.py
new file mode 100644
index 00000000..47dbe637
--- /dev/null
+++ b/demo/BERT/builder.py
@@ -0,0 +1,678 @@
+#!/usr/bin/env python3
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import ctypes
+import json
+import numpy as np
+import os
+import os.path
+import re
+import sys
+import time
+import onnx
+
+# TensorRT
+import tensorrt as trt
+from helpers.calibrator import BertCalibrator as BertCalibrator
+
+try:
+    from tensorflow.python import pywrap_tensorflow as pyTF
+except ImportError as err:
+    sys.stderr.write("""Error: Failed to import tensorflow module ({})\n""".format(err))
+    sys.exit()
+
+"""
+TensorRT Initialization
+"""
+TRT_LOGGER = trt.Logger(trt.Logger.INFO)
+
+handle = ctypes.CDLL("libnvinfer_plugin.so", mode=ctypes.RTLD_GLOBAL)
+if not handle:
+    raise RuntimeError("Could not load plugin library. Is `libnvinfer_plugin.so` on your LD_LIBRARY_PATH?")
+
+trt.init_libnvinfer_plugins(TRT_LOGGER, "")
+plg_registry = trt.get_plugin_registry()
+emln_plg_creator = plg_registry.get_plugin_creator("CustomEmbLayerNormPluginDynamic", "1", "")
+qkv2_plg_creator = plg_registry.get_plugin_creator("CustomQKVToContextPluginDynamic", "1", "")
+skln_plg_creator = plg_registry.get_plugin_creator("CustomSkipLayerNormPluginDynamic", "1", "")
+fc_plg_creator = plg_registry.get_plugin_creator("CustomFCPluginDynamic", "1", "")
+
+"""
+Attentions Keys
+"""
+WQ = "self_query_kernel"
+BQ = "self_query_bias"
+WK = "self_key_kernel"
+BK = "self_key_bias"
+WV = "self_value_kernel"
+BV = "self_value_bias"
+WQKV = "self_qkv_kernel"
+BQKV = "self_qkv_bias"
+
+"""
+Transformer Keys
+"""
+W_AOUT = "attention_output_dense_kernel"
+B_AOUT = "attention_output_dense_bias"
+AOUT_LN_BETA = "attention_output_layernorm_beta"
+AOUT_LN_GAMMA = "attention_output_layernorm_gamma"
+W_MID = "intermediate_dense_kernel"
+B_MID = "intermediate_dense_bias"
+W_LOUT = "output_dense_kernel"
+B_LOUT = "output_dense_bias"
+LOUT_LN_BETA = "output_layernorm_beta"
+LOUT_LN_GAMMA = "output_layernorm_gamma"
+
+"""
+Squad Output Keys
+"""
+SQD_W = "squad_output_weights"
+SQD_B = "squad_output_bias"
+
+class BertConfig:
+    def __init__(self, bert_config_path, use_fp16, use_int8, use_strict, use_fc2_gemm, use_int8_skipln, use_int8_multihead, use_qat):
+        with open(bert_config_path, "r") as f:
+            data = json.load(f)
+            self.num_attention_heads = data["num_attention_heads"]
+            self.hidden_size = data["hidden_size"]
+            self.intermediate_size = data["intermediate_size"]
+            self.num_hidden_layers = data["num_hidden_layers"]
+            self.head_size = self.hidden_size // self.num_attention_heads
+            self.use_fp16 = use_fp16
+            self.use_int8 = use_int8
+            self.use_fc2_gemm = use_fc2_gemm
+            self.use_strict = use_strict
+            self.use_int8_skipln = use_int8_skipln
+            self.use_int8_multihead = use_int8_multihead
+            self.is_calib_mode = False
+            self.use_qat = use_qat
+
+def set_tensor_name(tensor, prefix, name):
+    tensor.name = prefix + name
+
+def set_output_name(layer, prefix, name, out_idx = 0):
+    set_tensor_name(layer.get_output(out_idx), prefix, name)
+
+def set_output_range(layer, maxval, out_idx = 0):
+    layer.get_output(out_idx).set_dynamic_range(-maxval, maxval)
+
+def attention_layer_opt(prefix, config, init_dict, network, input_tensor, imask):
+    """
+    Add the attention layer
+    """
+    assert(len(input_tensor.shape) == 5)
+    B, S, hidden_size, _, _ = input_tensor.shape
+    num_heads = config.num_attention_heads
+    head_size = int(hidden_size / num_heads)
+
+    Wall = init_dict[prefix + WQKV]
+    Ball = init_dict[prefix + BQKV]
+
+    # FC_attention
+    if config.use_int8:
+        mult_all = network.add_convolution(input_tensor, 3 * hidden_size, (1, 1), Wall, Ball)
+    else:
+        mult_all = network.add_fully_connected(input_tensor, 3 * hidden_size, Wall, Ball)
+
+    if config.use_qat:
+        dr_qkv = max(
+            init_dict[prefix + 'self_qv_a_input_quantizer_amax'],
+            init_dict[prefix + 'self_qv_b_input_quantizer_amax'],
+            init_dict[prefix + 'self_av_b_input_quantizer_amax'],
+        )
+        set_output_range(mult_all, dr_qkv)
+    set_output_name(mult_all, prefix, "qkv_mult")
+
+    has_mask = imask is not None
+
+    # QKV2CTX
+    dtype = trt.float32
+    if config.use_fp16:
+        dtype = trt.float16
+    # Multi-head attention doesn't use INT8 inputs and output by default unless it is specified.
+    if config.use_int8 and config.use_int8_multihead and not config.is_calib_mode:
+        dtype = trt.int8
+
+    pf_type = trt.PluginField("type_id", np.array([int(dtype)], np.int32), trt.PluginFieldType.INT32)
+    pf_hidden_size = trt.PluginField("hidden_size", np.array([hidden_size], np.int32), trt.PluginFieldType.INT32)
+    pf_num_heads = trt.PluginField("num_heads", np.array([num_heads], np.int32), trt.PluginFieldType.INT32)
+    pf_has_mask = trt.PluginField("has_mask", np.array([has_mask], np.int32), trt.PluginFieldType.INT32)
+    if config.use_qat:
+        dr_probs = init_dict[prefix + 'self_av_a_input_quantizer_amax']
+        dq_probs = dr_probs / 127.0
+        pf_dq_probs =  trt.PluginField("dq_probs", np.array([dq_probs], np.float32), trt.PluginFieldType.FLOAT32)
+        pfc = trt.PluginFieldCollection([pf_hidden_size, pf_num_heads, pf_has_mask, pf_type, pf_dq_probs])
+    else:
+        pfc = trt.PluginFieldCollection([pf_hidden_size, pf_num_heads, pf_has_mask, pf_type])
+    qkv2ctx_plug = qkv2_plg_creator.create_plugin("qkv2ctx", pfc)
+
+    qkv_in = [mult_all.get_output(0)]
+    if has_mask:
+        qkv_in.append(imask)
+    qkv2ctx = network.add_plugin_v2(qkv_in, qkv2ctx_plug)
+
+    if config.use_qat:
+        dr_ctx = init_dict[prefix + 'output_dense_input_amax']
+        set_output_range(qkv2ctx, dr_ctx)
+    set_output_name(qkv2ctx, prefix, "context_layer")
+    return qkv2ctx
+
+def skipln(prefix, config, init_dict, network, input_tensor, skip, bias=None):
+    """
+    Add the skip layer
+    """
+    idims = input_tensor.shape
+    assert len(idims) == 5
+    hidden_size = idims[2]
+
+    dtype = trt.float32
+    if config.use_fp16:
+        dtype = trt.float16
+    # Skip layernorm doesn't use INT8 inputs and output by default unless it is specified.
+    if config.use_int8 and config.use_int8_skipln and not config.is_calib_mode:
+        dtype = trt.int8
+
+    pf_ld = trt.PluginField("ld", np.array([hidden_size], np.int32), trt.PluginFieldType.INT32)
+    wbeta = init_dict[prefix + "beta"]
+    pf_beta = trt.PluginField("beta", wbeta.numpy(), trt.PluginFieldType.FLOAT32)
+    wgamma = init_dict[prefix + "gamma"]
+    pf_gamma = trt.PluginField("gamma", wgamma.numpy(), trt.PluginFieldType.FLOAT32)
+    pf_type = trt.PluginField("type_id", np.array([int(dtype)], np.int32), trt.PluginFieldType.INT32)
+
+    fields = [pf_ld, pf_beta, pf_gamma, pf_type ]
+
+    if bias:
+        pf_bias = trt.PluginField("bias", bias.numpy(), trt.PluginFieldType.FLOAT32)
+        fields.append(pf_bias)
+
+    pfc = trt.PluginFieldCollection(fields)
+    skipln_plug = skln_plg_creator.create_plugin("skipln", pfc)
+
+    skipln_inputs = [input_tensor, skip]
+    layer = network.add_plugin_v2(skipln_inputs, skipln_plug)
+    return layer
+
+def custom_fc(config, network, input_tensor, out_dims, W):
+    pf_out_dims = trt.PluginField("out_dims", np.array([out_dims], dtype=np.int32), trt.PluginFieldType.INT32)
+    pf_W = trt.PluginField("W", W.numpy(), trt.PluginFieldType.FLOAT32)
+    pf_type = trt.PluginField("type_id", np.array([1 if config.use_fp16 else 0], np.int32), trt.PluginFieldType.INT32)
+    pfc = trt.PluginFieldCollection([pf_out_dims, pf_W, pf_type])
+    fc_plugin = fc_plg_creator.create_plugin("fcplugin", pfc)
+    plug_inputs = [input_tensor]
+    out_dense = network.add_plugin_v2(plug_inputs, fc_plugin)
+    return out_dense
+
+def transformer_layer_opt(prefix, config, init_dict, network, input_tensor, imask):
+    """
+    Add the transformer layer
+    """
+    idims = input_tensor.shape
+    assert len(idims) == 5
+    hidden_size = idims[2]
+
+    if config.use_qat:
+        dr_input = init_dict[prefix + 'attention_self_query_input_amax'] 
+        assert(dr_input ==init_dict[prefix + 'attention_self_key_input_amax'] )
+        assert(dr_input ==init_dict[prefix + 'attention_self_value_input_amax'] )
+        input_tensor.set_dynamic_range(-dr_input, dr_input)
+
+    context_transposed = attention_layer_opt(prefix + "attention_", config, init_dict, network, input_tensor, imask)
+    attention_heads = context_transposed.get_output(0)
+
+    # FC0
+    B_aout = init_dict[prefix + B_AOUT]
+    if config.use_int8:
+        W_aout = init_dict[prefix + W_AOUT]
+        attention_out_fc = network.add_convolution(attention_heads, hidden_size, (1, 1), W_aout, B_aout)
+        B_aout = None
+
+        if not config.use_int8_skipln:
+            attention_out_fc.set_output_type(0, trt.DataType.HALF if config.use_fp16 else trt.DataType.FLOAT)
+
+        if config.use_qat:
+            dr_fc_aout = init_dict[prefix + 'attention_output_add_local_input_quantizer_amax']
+            set_output_range(attention_out_fc, dr_fc_aout)
+    else:
+        W_aoutT = init_dict[prefix + W_AOUT + "_notrans"]
+        attention_out_fc = custom_fc(config, network, attention_heads, hidden_size, W_aoutT)
+
+    skiplayer = skipln(prefix + "attention_output_layernorm_",config, init_dict, network, attention_out_fc.get_output(0), input_tensor, B_aout)
+    attention_ln = skiplayer.get_output(0)
+    if config.use_qat:
+        dr_skln1 = init_dict[prefix + 'intermediate_dense_input_amax']
+        set_output_range(skiplayer, dr_skln1)
+
+    # FC1 + GELU
+    B_mid = init_dict[prefix + B_MID]
+    W_mid = init_dict[prefix + W_MID]
+    if config.use_int8:
+        mid_dense = network.add_convolution(attention_ln, config.intermediate_size, (1, 1), W_mid, B_mid)
+    else:
+        mid_dense = network.add_fully_connected(attention_ln, config.intermediate_size, W_mid, B_mid)
+
+    mid_dense_out = mid_dense.get_output(0)
+    POW = network.add_constant((1, 1, 1, 1, 1), trt.Weights(np.ascontiguousarray([3.0], dtype=np.float32)))
+    MULTIPLY = network.add_constant((1, 1, 1, 1, 1), trt.Weights(np.ascontiguousarray([0.044715], dtype=np.float32)))
+    SQRT = network.add_constant((1, 1, 1, 1, 1), trt.Weights((np.ascontiguousarray([0.79788456080286535587989211986876], dtype=np.float32))))
+    ONE = network.add_constant((1, 1, 1, 1, 1), trt.Weights((np.ascontiguousarray([1.0], dtype=np.float32))))
+    HALF = network.add_constant((1, 1, 1, 1, 1), trt.Weights((np.ascontiguousarray([0.5], dtype=np.float32))))
+    X_pow = network.add_elementwise(mid_dense_out, POW.get_output(0), trt.ElementWiseOperation.POW)
+    X_pow_t = X_pow.get_output(0)
+    X_mul = network.add_elementwise(X_pow_t, MULTIPLY.get_output(0), trt.ElementWiseOperation.PROD)
+    X_add = network.add_elementwise(mid_dense_out, X_mul.get_output(0), trt.ElementWiseOperation.SUM)
+    X_sqrt = network.add_elementwise(X_add.get_output(0), SQRT.get_output(0), trt.ElementWiseOperation.PROD)
+    X_sqrt_tensor = X_sqrt.get_output(0)
+    X_tanh = network.add_activation(X_sqrt_tensor, trt.ActivationType.TANH)
+    X_tanh_tensor = X_tanh.get_output(0)
+    X_one = network.add_elementwise(X_tanh_tensor, ONE.get_output(0), trt.ElementWiseOperation.SUM)
+    CDF = network.add_elementwise(X_one.get_output(0), HALF.get_output(0), trt.ElementWiseOperation.PROD)
+    gelu_layer = network.add_elementwise(CDF.get_output(0), mid_dense_out, trt.ElementWiseOperation.PROD)
+
+    intermediate_act = gelu_layer.get_output(0)
+    set_tensor_name(intermediate_act, prefix, "gelu")
+    if config.use_int8:
+        if config.use_qat:
+            dr_gelu = init_dict[prefix + 'output_dense_input_amax']
+            set_output_range(gelu_layer, dr_gelu)
+        else:
+            # use gelu10 according to whitepaper http://arxiv.org/abs/2004.09602 
+            set_output_range(gelu_layer, 10)
+
+    # FC2
+    # Dense to hidden size
+    B_lout = init_dict[prefix + B_LOUT]
+    if config.use_int8 and not config.use_fc2_gemm:
+        W_lout = init_dict[prefix + W_LOUT]
+        out_dense = network.add_convolution(intermediate_act, hidden_size, (1, 1), W_lout, B_lout)
+        B_lout = None
+
+        if not config.use_int8_skipln:
+            out_dense.set_output_type(0, trt.DataType.HALF if config.use_fp16 else trt.DataType.FLOAT)
+    else:
+        W_loutT = init_dict[prefix + W_LOUT + "_notrans"]
+        out_dense = custom_fc(config, network, intermediate_act, hidden_size, W_loutT)
+
+    if config.use_qat:
+        dr_fc_out = init_dict[prefix + 'output_add_local_input_quantizer_amax']
+        set_output_range(out_dense, dr_fc_out)
+    set_output_name(out_dense, prefix + "output_", "dense")
+
+    out_layer = skipln(prefix + "output_layernorm_", config, init_dict, network, out_dense.get_output(0), attention_ln, B_lout)
+    set_output_name(out_layer, prefix + "output_", "reshape")
+
+    return out_layer
+
+def bert_model(config, init_dict, network, input_tensor, input_mask):
+    """
+    Create the bert model
+    """
+    prev_input = input_tensor
+    for layer in range(0, config.num_hidden_layers):
+        ss = "l{}_".format(layer)
+        out_layer = transformer_layer_opt(ss, config,  init_dict, network, prev_input, input_mask)
+        prev_input = out_layer.get_output(0)
+
+    if config.use_qat:
+        dr_out = init_dict["bert_encoder_final_input_quantizer_amax"]
+        set_output_range(out_layer, dr_out)
+    return prev_input
+
+def squad_output(prefix, config, init_dict, network, input_tensor):
+    """
+    Create the squad output
+    """
+
+    idims = input_tensor.shape
+    assert len(idims) == 5
+    B, S, hidden_size, _, _ = idims
+
+    W_out = init_dict[prefix + SQD_W]
+    B_out = init_dict[prefix + SQD_B]
+
+    W = network.add_constant((1, hidden_size, 2), W_out)
+    dense = network.add_fully_connected(input_tensor, 2, W_out, B_out)
+
+    OUT = network.add_shuffle(dense.get_output(0))
+    OUT.second_transpose = (1, 0, 2, 3, 4)
+    set_output_name(OUT, prefix, "squad_logits")
+    return OUT
+
+def load_tf_weights(inputbase, config):
+    """
+    Load the weights from the tensorflow checkpoint
+    """
+    weights_dict = dict()
+
+    try:
+        reader = pyTF.NewCheckpointReader(inputbase)
+        tensor_dict = reader.get_variable_to_shape_map()
+
+        # There might be training-related variables in the checkpoint that can be discarded
+        param_names = [key for key in sorted(tensor_dict) if "adam" not in key and "global_step" not in key and "pooler" not in key]
+        count = len(param_names)
+        TRT_LOGGER.log(TRT_LOGGER.INFO, "Found {:} entries in weight map".format(count))
+
+        for pn in param_names:
+            toks = pn.lower().split("/")
+            if "encoder" in pn:
+                assert ("layer" in pn)
+                l = (re.findall("\d+", pn))[0]
+                outname = "l{}_".format(l) + "_".join(toks[3:])
+            else:
+                outname = "_".join(toks)
+
+            tensor = reader.get_tensor(pn)
+            shape = tensor.shape
+            if pn.find("kernel") != -1:
+                weights_dict[outname + "_notrans"] = trt.Weights(np.ascontiguousarray(tensor).flatten())
+
+                TRT_LOGGER.log(TRT_LOGGER.VERBOSE, "Transposing {}\n".format(np))
+                tensor = np.transpose(tensor)
+
+            shape = tensor.shape
+            flat_tensor = tensor.flatten()
+            shape_str = "{} ".format(len(shape)) + " ".join([str(d) for d in shape])
+            weights_dict[outname] = trt.Weights(flat_tensor)
+
+            TRT_LOGGER.log(TRT_LOGGER.VERBOSE, "Original name: {:}, TensorRT name: {:}, shape: {:}".format(pn, outname, shape_str))
+
+        N = config.num_attention_heads
+        H = config.head_size
+
+        additional_dict = dict()
+        for key, value in weights_dict.items():
+            pos = key.find(BQ)
+            if pos != -1:
+                hidden_size = value.size
+                prefix = key[:pos]
+
+                Bq_ = value
+                Bk_ = weights_dict[prefix + BK]
+                Bv_ = weights_dict[prefix + BV]
+                Wq_ = weights_dict[prefix + WQ]
+                Wk_ = weights_dict[prefix + WK]
+                Wv_ = weights_dict[prefix + WV]
+
+                mat_size = hidden_size * hidden_size
+                wcount = 3 * mat_size
+                Wall = np.zeros(wcount, np.float32)
+                bcount = 3 * hidden_size
+                Ball = np.zeros(bcount, np.float32)
+                Wall[0:mat_size] = Wq_.numpy()[0:mat_size]
+                Wall[mat_size:2*mat_size] = Wk_.numpy()[0:mat_size]
+                Wall[2*mat_size:3*mat_size] = Wv_.numpy()[0:mat_size]
+                Ball[0:hidden_size] = Bq_.numpy()[0:hidden_size]
+                Ball[hidden_size:2*hidden_size] = Bk_.numpy()[0:hidden_size]
+                Ball[2*hidden_size:3*hidden_size] = Bv_.numpy()[0:hidden_size]
+
+                Wall = np.ascontiguousarray(Wall.reshape((3, N, H, N, H)).transpose((1, 0, 2, 3, 4)), dtype=np.float32)
+                Ball = np.ascontiguousarray(Ball.reshape((3, N, H)).transpose((1, 0, 2)), dtype=np.float32)
+
+                additional_dict[prefix + WQKV] = trt.Weights(Wall)
+                additional_dict[prefix + BQKV] = trt.Weights(Ball)
+
+                additional_dict[prefix + WQKV + "_notrans"] = trt.Weights(Wall.T)
+
+    except Exception as error:
+        TRT_LOGGER.log(TRT_LOGGER.ERROR, str(error))
+
+    weights_dict.update(additional_dict)
+    return weights_dict
+
+def onnx_to_trt_name(onnx_name):
+    """
+    Converting variables in the onnx checkpoint to names corresponding to the naming convention used in the TF version, expected by the builder
+    """
+    onnx_name = onnx_name.lower()
+    toks = [t.strip('_') for t in onnx_name.split('.')]
+    if toks[0] == 'bert': #embeddings or encoder
+        if toks[1] == 'encoder': #transformer
+            
+            if toks[-2] == 'layernorm': #bias->beta, weight->gamma
+                toks[-1] = 'beta' if toks[-1] == 'bias' else 'gamma'
+            elif (toks[-2] == 'dense' or toks[-2] in {'key', 'value', 'query'}) and toks[-1] == 'weight':
+                toks[-1] = 'kernel'
+            elif (toks[-3] == 'dense' or toks[-3] in {'key', 'value', 'query'}) and toks[-1] == 'amax':
+                if toks[-2] == 'weight_quantizer':
+                    toks[-2] = 'kernel'
+                elif toks[-2] == 'input_quantizer':
+                    toks[-2] = 'input'
+            
+            if 'final_input_quantizer' not in toks[2]:
+                toks = toks[3:]
+                toks[0] = 'l{}'.format(int(toks[0]))
+        else:
+            if toks[-2] == 'layernorm': #bias->beta, weight->gamma
+                toks[-1] = 'beta' if toks[-1] == 'bias' else 'gamma'
+            else: #embeddings: drop "_weight" suffix
+                if toks[-1] == 'amax':
+                    toks[-2] = 'amax'
+                toks = toks[:-1]
+    elif 'qa' in onnx_name:
+        name = 'cls_squad_output_bias' if toks[-1] == 'bias' else 'cls_squad_output_weights'
+        return name
+    else:
+        print("Encountered unknown case:", onnx_name)
+        assert(False)
+    parsed = '_'.join(toks)
+    return parsed
+
+def load_onnx_weights_and_quant(path, config):
+    """
+    Load the weights from the onnx checkpoint
+    """
+    N = config.num_attention_heads
+    H = config.head_size
+    hidden_size = config.hidden_size
+
+    model = onnx.load(path)
+    weights = model.graph.initializer
+    tensor_dict = dict([(onnx_to_trt_name(w.name), np.frombuffer(w.raw_data, np.float32).reshape(w.dims)) for w in weights])
+
+    weights_dict = dict()
+    for outname, tensor in tensor_dict.items():
+        if outname.find("_amax") != -1:
+            weights_dict[outname] = tensor
+        elif outname.find(BQ) != -1:
+            prefix = outname[:outname.find(BQ)]
+
+            Wqkv = np.zeros((3, hidden_size, hidden_size), np.float32)
+            Bqkv = np.zeros((3, hidden_size), np.float32)
+
+            Wqkv[0,:,:] = tensor_dict[prefix + WQ]
+            Wqkv[1,:,:] = tensor_dict[prefix + WK]
+            Wqkv[2,:,:] = tensor_dict[prefix + WV]
+            Bqkv[0,:] = tensor
+            Bqkv[1,:] = tensor_dict[prefix + BK]
+            Bqkv[2,:] = tensor_dict[prefix + BV]
+    
+            Wqkv = np.ascontiguousarray(Wqkv.reshape((3, N, H, N, H)).transpose((1,0,2,3,4)))
+            Bqkv = np.ascontiguousarray(Bqkv.reshape((3, N, H)).transpose((1,0,2)))
+    
+            weights_dict[prefix + WQKV] = trt.Weights(Wqkv)
+            weights_dict[prefix + BQKV] = trt.Weights(Bqkv)
+            weights_dict[prefix + WQKV + "_notrans"] = trt.Weights(Wqkv.T)
+
+        elif outname.find(BK) != -1 or outname.find(BV) != -1 or outname.find(WQ) != -1 or outname.find(WK) != -1 or outname.find(WV) != -1:
+            pass
+        else:
+            flat_tensor = np.ascontiguousarray(tensor).flatten()
+            weights_dict[outname] = trt.Weights(flat_tensor)
+
+            if outname.find("kernel") != -1:
+                tensor = np.transpose(tensor)
+                weights_dict[outname + "_notrans"] = trt.Weights(np.ascontiguousarray(tensor).flatten())
+
+
+    TRT_LOGGER.log(TRT_LOGGER.INFO, "Found {:} entries in weight map".format(len(weights_dict)))
+    return weights_dict
+
+def emb_layernorm(builder, network, config, weights_dict, builder_config, sequence_length, batch_sizes):
+    if len(batch_sizes) > 1:
+        input_ids = network.add_input(name="input_ids", dtype=trt.int32, shape=(sequence_length, -1))
+        segment_ids = network.add_input(name="segment_ids", dtype=trt.int32, shape=(sequence_length, -1))
+        input_mask = network.add_input(name="input_mask", dtype=trt.int32, shape=(sequence_length, -1))
+
+        # Specify profiles for the batch sizes we're interested in.
+        # Make sure the profile also works for all sizes not covered by the previous profile.
+        prev_size = 0
+        for batch_size in sorted(batch_sizes):
+            profile = builder.create_optimization_profile()
+            min_shape = (sequence_length, prev_size + 1)
+            shape = (sequence_length, batch_size)
+            profile.set_shape("input_ids", min=min_shape, opt=shape, max=shape)
+            profile.set_shape("segment_ids", min=min_shape, opt=shape, max=shape)
+            profile.set_shape("input_mask", min=min_shape, opt=shape, max=shape)
+            builder_config.add_optimization_profile(profile)
+            prev_size = batch_size
+    else:
+        input_ids = network.add_input(name="input_ids", dtype=trt.int32, shape=(sequence_length, batch_sizes[0]))
+        segment_ids = network.add_input(name="segment_ids", dtype=trt.int32, shape=(sequence_length, batch_sizes[0]))
+        input_mask = network.add_input(name="input_mask", dtype=trt.int32, shape=(sequence_length, batch_sizes[0]))
+
+    wbeta = trt.PluginField("bert_embeddings_layernorm_beta", weights_dict["bert_embeddings_layernorm_beta"].numpy(), trt.PluginFieldType.FLOAT32)
+    wgamma = trt.PluginField("bert_embeddings_layernorm_gamma", weights_dict["bert_embeddings_layernorm_gamma"].numpy(), trt.PluginFieldType.FLOAT32)
+    wwordemb = trt.PluginField("bert_embeddings_word_embeddings", weights_dict["bert_embeddings_word_embeddings"].numpy(), trt.PluginFieldType.FLOAT32)
+    wtokemb = trt.PluginField("bert_embeddings_token_type_embeddings", weights_dict["bert_embeddings_token_type_embeddings"].numpy(), trt.PluginFieldType.FLOAT32)
+    wposemb = trt.PluginField("bert_embeddings_position_embeddings", weights_dict["bert_embeddings_position_embeddings"].numpy(), trt.PluginFieldType.FLOAT32)
+
+    output_fp16 = trt.PluginField("output_fp16", np.array([1 if config.use_fp16 else 0]).astype(np.int32), trt.PluginFieldType.INT32)
+    full_mask = trt.PluginField("full_mask", np.array([1 if config.use_fp16 else 0]).astype(np.int32), trt.PluginFieldType.INT32)
+
+    pfc = trt.PluginFieldCollection([wbeta, wgamma, wwordemb, wtokemb, wposemb, output_fp16, full_mask])
+    fn = emln_plg_creator.create_plugin("embeddings", pfc)
+
+    inputs = [input_ids, segment_ids, input_mask]
+    emb_layer = network.add_plugin_v2(inputs, fn)
+
+    if config.use_qat:
+        set_output_range(emb_layer, 1, 1)
+    set_output_name(emb_layer, "embeddings_", "output")
+    return emb_layer
+
+def build_engine(batch_sizes, workspace_size, sequence_length, config, weights_dict, squad_json, vocab_file, calibrationCacheFile, calib_num):
+    explicit_batch_flag = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
+
+    with trt.Builder(TRT_LOGGER) as builder, builder.create_network(explicit_batch_flag) as network, builder.create_builder_config() as builder_config:
+        builder_config.max_workspace_size = workspace_size * (1024 * 1024)
+        if config.use_fp16:
+            builder_config.set_flag(trt.BuilderFlag.FP16)
+        if config.use_int8:
+            builder_config.set_flag(trt.BuilderFlag.INT8)
+            if not config.use_qat:
+                calibrator = BertCalibrator(squad_json, vocab_file, calibrationCacheFile, 1, sequence_length, calib_num)
+                builder_config.set_quantization_flag(trt.QuantizationFlag.CALIBRATE_BEFORE_FUSION)
+                builder_config.int8_calibrator = calibrator
+        if config.use_strict:
+            builder_config.set_flag(trt.BuilderFlag.STRICT_TYPES)
+
+        # Create the network
+        emb_layer = emb_layernorm(builder, network, config, weights_dict, builder_config, sequence_length, batch_sizes)
+        embeddings = emb_layer.get_output(0)
+        mask_idx = emb_layer.get_output(1)
+
+        bert_out = bert_model(config, weights_dict, network, embeddings, mask_idx)
+
+        squad_logits = squad_output("cls_", config, weights_dict, network, bert_out)
+        squad_logits_out = squad_logits.get_output(0)
+
+        network.mark_output(squad_logits_out)
+
+        build_start_time = time.time()
+        engine = builder.build_engine(network, builder_config)
+        build_time_elapsed = (time.time() - build_start_time)
+        TRT_LOGGER.log(TRT_LOGGER.INFO, "build engine in {:.3f} Sec".format(build_time_elapsed))
+        if config.use_int8 and not config.use_qat:
+            calibrator.free()
+        return engine
+
+def generate_calibration_cache(sequence_length, workspace_size, config, weights_dict, squad_json, vocab_file, calibrationCacheFile, calib_num):
+    """
+    BERT demo needs a separate engine building path to generate calibration cache.
+    This is because we need to configure SLN and MHA plugins in FP32 mode when
+    generating calibration cache, and INT8 mode when building the actual engine.
+    This cache could be generated by examining certain training data and can be
+    reused across different configurations.
+    """
+    # dynamic shape not working with calibration, so we need generate a calibration cache first using fulldims network
+    if not config.use_int8 or os.path.exists(calibrationCacheFile):
+        return calibrationCacheFile
+
+    # generate calibration cache
+    saved_use_fp16 = config.use_fp16
+    config.use_fp16 = False
+    config.is_calib_mode = True
+
+    with build_engine([1], workspace_size, sequence_length, config, weights_dict, squad_json, vocab_file, calibrationCacheFile, calib_num) as engine:
+        TRT_LOGGER.log(TRT_LOGGER.INFO, "calibration cache generated in {:}".format(calibrationCacheFile))
+
+    config.use_fp16 = saved_use_fp16
+    config.is_calib_mode = False
+
+def main():
+    parser = argparse.ArgumentParser(description="TensorRT BERT Sample", formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    parser.add_argument("-m", "--ckpt", required=False,
+                        help="The checkpoint file basename, e.g.: basename(model.ckpt-766908.data-00000-of-00001) is model.ckpt-766908")
+    parser.add_argument("-x", "--onnx", required=False, help="The ONNX model file path.")
+    parser.add_argument("-o", "--output", required=True, default="bert_base_384.engine", help="The bert engine file, ex bert.engine")
+    parser.add_argument("-b", "--batch-size", default=[], action="append", help="Batch size(s) to optimize for. The engine will be usable with any batch size below this, but may not be optimal for smaller sizes. Can be specified multiple times to optimize for more than one batch size.", type=int)
+    parser.add_argument("-s", "--sequence-length", default=128, help="Sequence length of the BERT model", type=int)
+    parser.add_argument("-c", "--config-dir", required=True,
+                        help="The folder containing the bert_config.json, which can be downloaded e.g. from https://github.com/google-research/bert#pre-trained-models or by running download_models.py in dle/TensorFlow/LanguageModeling/BERT/data/pretrained_models_google")
+    parser.add_argument("-f", "--fp16", action="store_true", help="Indicates that inference should be run in FP16 precision", required=False)
+    parser.add_argument("-i", "--int8", action="store_true", help="Indicates that inference should be run in INT8 precision", required=False)
+    parser.add_argument("-t", "--strict", action="store_true", help="Indicates that inference should be run in strict precision mode", required=False)
+    parser.add_argument("-w", "--workspace-size", default=1000, help="Workspace size in MiB for building the BERT engine", type=int)
+    parser.add_argument("-j", "--squad-json", default="squad/dev-v1.1.json", help="squad json dataset used for int8 calibration", required=False)
+    parser.add_argument("-v", "--vocab-file", default="./pre-trained_model/uncased_L-24_H-1024_A-16/vocab.txt", help="Path to file containing entire understandable vocab", required=False)
+    parser.add_argument("-n", "--calib-num", default=100, help="calibration batch numbers", type=int)
+    parser.add_argument("-p", "--calib-path", help="calibration cache path", required=False)
+    parser.add_argument("-g", "--force-fc2-gemm", action="store_true", help="Force use gemm to implement FC2 layer", required=False)
+    parser.add_argument("-iln", "--force-int8-skipln", action="store_true", help="Run skip layernorm with INT8 (FP32 or FP16 by default) inputs and output", required=False)
+    parser.add_argument("-imh", "--force-int8-multihead", action="store_true", help="Run multi-head attention with INT8 (FP32 or FP16 by default) input and output", required=False)
+
+    args, _ = parser.parse_known_args()
+    args.batch_size = args.batch_size or [1]
+
+    bert_config_path = os.path.join(args.config_dir, "bert_config.json")
+    TRT_LOGGER.log(TRT_LOGGER.INFO, "Using configuration file: {:}".format(bert_config_path))
+
+    config = BertConfig(bert_config_path, args.fp16, args.int8, args.strict, args.force_fc2_gemm, args.force_int8_skipln, args.force_int8_multihead, args.int8 and args.onnx != None)
+
+    if args.calib_path != None:
+        calib_cache = args.calib_path
+    else:
+        calib_cache = "BertSquadL{}H{}A{}S{}CalibCache".format(config.num_hidden_layers, config.head_size, config.num_attention_heads, args.sequence_length)
+
+    if args.onnx != None:
+        weights_dict = load_onnx_weights_and_quant(args.onnx, config)
+    elif args.ckpt != None:
+        weights_dict = load_tf_weights(args.ckpt, config)
+        generate_calibration_cache(args.sequence_length, args.workspace_size, config, weights_dict, args.squad_json, args.vocab_file, calib_cache, args.calib_num)
+    else:
+        raise RuntimeError("You need either specify TF checkpoint using option --ckpt or ONNX using option --onnx to build TRT BERT model.")
+
+    with build_engine(args.batch_size, args.workspace_size, args.sequence_length, config, weights_dict, args.squad_json, args.vocab_file, calib_cache, args.calib_num) as engine:
+        TRT_LOGGER.log(TRT_LOGGER.VERBOSE, "Serializing Engine...")
+        serialized_engine = engine.serialize()
+        TRT_LOGGER.log(TRT_LOGGER.INFO, "Saving Engine to {:}".format(args.output))
+        with open(args.output, "wb") as fout:
+            fout.write(serialized_engine)
+        TRT_LOGGER.log(TRT_LOGGER.INFO, "Done.")
+
+if __name__ == "__main__":
+    main()
diff --git a/docker/jetpack_files/.gitkeep b/demo/BERT/helpers/__init__.py
similarity index 100%
rename from docker/jetpack_files/.gitkeep
rename to demo/BERT/helpers/__init__.py
diff --git a/demo/BERT/helpers/calibrator.py b/demo/BERT/helpers/calibrator.py
new file mode 100644
index 00000000..33d801c9
--- /dev/null
+++ b/demo/BERT/helpers/calibrator.py
@@ -0,0 +1,111 @@
+#!/usr/bin/env python3
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import tensorrt as trt
+import os
+
+import pycuda.driver as cuda
+import pycuda.autoinit
+import numpy as np
+import helpers.tokenization as tokenization
+import helpers.data_processing as dp
+
+class BertCalibrator(trt.IInt8LegacyCalibrator):
+    def __init__(self, squad_json, vocab_file, cache_file, batch_size, max_seq_length, num_inputs):
+        # Whenever you specify a custom constructor for a TensorRT class,
+        # you MUST call the constructor of the parent explicitly.
+        trt.IInt8LegacyCalibrator.__init__(self)
+
+        self.cache_file = cache_file
+
+        # Every time get_batch is called, the next batch of size batch_size will be copied to the device and returned.
+        self.data = dp.read_squad_json(squad_json)
+        self.max_seq_length = max_seq_length
+        self.batch_size = batch_size
+        self.current_index = 0
+        self.num_inputs = num_inputs
+        self.tokenizer = tokenization.BertTokenizer(vocab_file=vocab_file, do_lower_case=True)
+        self.doc_stride = 128
+        self.max_query_length = 64
+
+        # Allocate enough memory for a whole batch.
+        self.device_inputs = [cuda.mem_alloc(self.max_seq_length * trt.int32.itemsize * self.batch_size) for binding in range(3)]
+
+    def free(self):
+        for dinput in self.device_inputs:
+            dinput.free()
+
+    def get_batch_size(self):
+        return self.batch_size
+
+    # TensorRT passes along the names of the engine bindings to the get_batch function.
+    # You don't necessarily have to use them, but they can be useful to understand the order of
+    # the inputs. The bindings list is expected to have the same ordering as 'names'.
+    def get_batch(self, names):
+        if self.current_index + self.batch_size > self.num_inputs:
+            print("Calibrating index {:} batch size {:} exceed max input limit {:} sentences".format(self.current_index, self.batch_size, self.num_inputs))
+            return None
+
+        current_batch = int(self.current_index / self.batch_size)
+        if current_batch % 10 == 0:
+            print("Calibrating batch {:}, containing {:} sentences".format(current_batch, self.batch_size))
+
+        input_ids = []
+        segment_ids = []
+        input_mask = []
+        for i in range(self.batch_size):
+            example = self.data[self.current_index + i]
+            features = dp.convert_example_to_features(example.doc_tokens, example.question_text, self.tokenizer, self.max_seq_length, self.doc_stride, self.max_query_length)
+            if len(input_ids) and len(segment_ids) and len(input_mask):
+                input_ids = np.concatenate((input_ids, features[0].input_ids))
+                segment_ids = np.concatenate((segment_ids, features[0].segment_ids))
+                input_mask = np.concatenate((input_mask, features[0].input_mask))
+            else:
+                input_ids = features[0].input_ids
+                segment_ids = features[0].segment_ids
+                input_mask = features[0].input_mask
+
+        cuda.memcpy_htod(self.device_inputs[0], input_ids.ravel())
+        cuda.memcpy_htod(self.device_inputs[1], segment_ids.ravel())
+        cuda.memcpy_htod(self.device_inputs[2], input_mask.ravel())
+
+        self.current_index += self.batch_size
+        return self.device_inputs
+
+    def read_calibration_cache(self):
+        # If there is a cache, use it instead of calibrating again. Otherwise, implicitly return None.
+        if os.path.exists(self.cache_file):
+            with open(self.cache_file, "rb") as f:
+                return f.read()
+
+    def write_calibration_cache(self, cache):
+        with open(self.cache_file, "wb") as f:
+            f.write(cache)
+            f.flush()
+            os.fsync(f)
+
+    def get_quantile(self):
+        return 0.9999
+
+    def get_regression_cutoff(self):
+        return 1.0
+
+    def read_histogram_cache(self, length):
+        return None
+
+    def write_histogram_cache(self, ptr, length):
+        return None
+
diff --git a/demo/BERT/helpers/data_processing.py b/demo/BERT/helpers/data_processing.py
new file mode 100644
index 00000000..cb7b661f
--- /dev/null
+++ b/demo/BERT/helpers/data_processing.py
@@ -0,0 +1,495 @@
+#!/usr/bin/env python3
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import helpers.tokenization as tokenization
+import collections
+import numpy as np
+import six
+import math
+import json
+
+
+def convert_doc_tokens(paragraph_text):
+
+    """ Return the list of tokens from the doc text """
+    def is_whitespace(c):
+        if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
+            return True
+        return False
+
+    doc_tokens = []
+    prev_is_whitespace = True
+    for c in paragraph_text:
+        if is_whitespace(c):
+            prev_is_whitespace = True
+        else:
+            if prev_is_whitespace:
+                doc_tokens.append(c)
+            else:
+                doc_tokens[-1] += c
+            prev_is_whitespace = False
+
+    return doc_tokens
+
+
+def _check_is_max_context(doc_spans, cur_span_index, position):
+    """Check if this is the 'max context' doc span for the token."""
+
+    # Because of the sliding window approach taken to scoring documents, a single
+    # token can appear in multiple documents. E.g.
+    #  Doc: the man went to the store and bought a gallon of milk
+    #  Span A: the man went to the
+    #  Span B: to the store and bought
+    #  Span C: and bought a gallon of
+    #  ...
+    #
+    # Now the word 'bought' will have two scores from spans B and C. We only
+    # want to consider the score with "maximum context", which we define as
+    # the *minimum* of its left and right context (the *sum* of left and
+    # right context will always be the same, of course).
+    #
+    # In the example the maximum context for 'bought' would be span C since
+    # it has 1 left context and 3 right context, while span B has 4 left context
+    # and 0 right context.
+    best_score = None
+    best_span_index = None
+    for (span_index, doc_span) in enumerate(doc_spans):
+        end = doc_span.start + doc_span.length - 1
+        if position < doc_span.start:
+            continue
+        if position > end:
+            continue
+        num_left_context = position - doc_span.start
+        num_right_context = end - position
+        score = min(num_left_context, num_right_context) + 0.01 * doc_span.length
+        if best_score is None or score > best_score:
+            best_score = score
+            best_span_index = span_index
+
+    return cur_span_index == best_span_index
+
+
+def convert_example_to_features(doc_tokens, question_text, tokenizer, max_seq_length,
+                                 doc_stride, max_query_length):
+    """Loads a data file into a list of `InputBatch`s."""
+
+    query_tokens = tokenizer.tokenize(question_text)
+
+    if len(query_tokens) > max_query_length:
+        query_tokens = query_tokens[0:max_query_length]
+
+    tok_to_orig_index = []
+    orig_to_tok_index = []
+    all_doc_tokens = []
+    for (i, token) in enumerate(doc_tokens):
+        orig_to_tok_index.append(len(all_doc_tokens))
+        sub_tokens = tokenizer.tokenize(token)
+        for sub_token in sub_tokens:
+            tok_to_orig_index.append(i)
+            all_doc_tokens.append(sub_token)
+
+    # The -3 accounts for [CLS], [SEP] and [SEP]
+    max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
+
+    # We can have documents that are longer than the maximum sequence length.
+    # To deal with this we do a sliding window approach, where we take chunks
+    # of the up to our max length with a stride of `doc_stride`.
+    _DocSpan = collections.namedtuple(  # pylint: disable=invalid-name
+        "DocSpan", ["start", "length"])
+    doc_spans = []
+    start_offset = 0
+    while start_offset < len(all_doc_tokens):
+        length = len(all_doc_tokens) - start_offset
+        if length > max_tokens_for_doc:
+            length = max_tokens_for_doc
+        doc_spans.append(_DocSpan(start=start_offset, length=length))
+        if start_offset + length == len(all_doc_tokens):
+            break
+        start_offset += min(length, doc_stride)
+
+    _Feature = collections.namedtuple(  # pylint: disable=invalid-name
+            "Feature",
+            ["input_ids", "input_mask", "segment_ids", "tokens", "token_to_orig_map", "token_is_max_context"])
+
+        
+    features = []
+    for (doc_span_index, doc_span) in enumerate(doc_spans):
+        tokens = []
+        token_to_orig_map = {}
+        token_is_max_context = {}
+        segment_ids = []
+        tokens.append("[CLS]")
+        segment_ids.append(0)
+        for token in query_tokens:
+            tokens.append(token)
+            segment_ids.append(0)
+        tokens.append("[SEP]")
+        segment_ids.append(0)
+
+        for i in range(doc_span.length):
+            split_token_index = doc_span.start + i
+            token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]
+
+            is_max_context = _check_is_max_context(doc_spans, doc_span_index, split_token_index)
+            token_is_max_context[len(tokens)] = is_max_context
+            tokens.append(all_doc_tokens[split_token_index])
+            segment_ids.append(1)
+        tokens.append("[SEP]")
+        segment_ids.append(1)
+
+        input_ids = tokenizer.convert_tokens_to_ids(tokens)
+
+        # The mask has 1 for real tokens and 0 for padding tokens. Only real
+        # tokens are attended to.
+        input_mask = [1] * len(input_ids)
+
+        # Zero-pad up to the sequence length.
+        while len(input_ids) < max_seq_length:
+            input_ids.append(0)
+            input_mask.append(0)
+            segment_ids.append(0)
+
+        assert len(input_ids) == max_seq_length
+        assert len(input_mask) == max_seq_length
+        assert len(segment_ids) == max_seq_length
+
+        def create_int_feature(values):
+            feature = np.asarray(values, dtype=np.int32, order=None)
+            return feature
+
+
+        features.append(_Feature(
+            input_ids = create_int_feature(input_ids),
+            input_mask = create_int_feature(input_mask),
+            segment_ids = create_int_feature(segment_ids),
+            tokens = tokens,
+            token_to_orig_map = token_to_orig_map,
+            token_is_max_context = token_is_max_context
+            ))
+    return features
+
+
+def read_squad_json(input_file):
+    """read from squad json into a list of examples"""
+    with open(input_file, "r", encoding='utf-8') as reader:
+        input_data = json.load(reader)["data"]
+
+    _Example = collections.namedtuple(  # pylint: disable=invalid-name
+            "Example",
+            ["id", "question_text", "doc_tokens"])
+
+    examples = []
+    for entry in input_data:
+        for paragraph in entry["paragraphs"]:
+            paragraph_text = paragraph["context"]
+            doc_tokens = convert_doc_tokens(paragraph_text)
+
+            for qa in paragraph["qas"]:
+                examples.append(_Example(
+                    id = qa["id"],
+                    question_text = qa["question"],
+                    doc_tokens = doc_tokens
+                    ))
+
+    return examples
+
+
+def _get_best_indexes(logits, n_best_size):
+    """Get the n-best logits from a list."""
+
+    index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
+
+    best_indexes = []
+    for i in range(len(index_and_score)):
+        if i >= n_best_size:
+            break
+        best_indexes.append(index_and_score[i][0])
+    return best_indexes
+
+
+def get_final_text(pred_text, orig_text, do_lower_case):
+    """Project the tokenized prediction back to the original text."""
+
+    # When we created the data, we kept track of the alignment between original
+    # (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
+    # now `orig_text` contains the span of our original text corresponding to the
+    # span that we predicted.
+    #
+    # However, `orig_text` may contain extra characters that we don't want in
+    # our prediction.
+    #
+    # For example, let's say:
+    #   pred_text = steve smith
+    #   orig_text = Steve Smith's
+    #
+    # We don't want to return `orig_text` because it contains the extra "'s".
+    #
+    # We don't want to return `pred_text` because it's already been normalized
+    # (the SQuAD eval script also does punctuation stripping/lower casing but
+    # our tokenizer does additional normalization like stripping accent
+    # characters).
+    #
+    # What we really want to return is "Steve Smith".
+    #
+    # Therefore, we have to apply a semi-complicated alignment heruistic between
+    # `pred_text` and `orig_text` to get a character-to-charcter alignment. This
+    # can fail in certain cases in which case we just return `orig_text`.
+
+    def _strip_spaces(text):
+        ns_chars = []
+        ns_to_s_map = collections.OrderedDict()
+        for (i, c) in enumerate(text):
+            if c == " ":
+                continue
+            ns_to_s_map[len(ns_chars)] = i
+            ns_chars.append(c)
+        ns_text = "".join(ns_chars)
+        return (ns_text, ns_to_s_map)
+
+    # We first tokenize `orig_text`, strip whitespace from the result
+    # and `pred_text`, and check if they are the same length. If they are
+    # NOT the same length, the heuristic has failed. If they are the same
+    # length, we assume the characters are one-to-one aligned.
+    tokenizer = tokenization.BasicTokenizer(do_lower_case=do_lower_case)
+
+    tok_text = " ".join(tokenizer.tokenize(orig_text))
+
+    start_position = tok_text.find(pred_text)
+    if start_position == -1:
+        return orig_text
+    end_position = start_position + len(pred_text) - 1
+
+    (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
+    (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
+
+    if len(orig_ns_text) != len(tok_ns_text):
+        return orig_text
+
+    # We then project the characters in `pred_text` back to `orig_text` using
+    # the character-to-character alignment.
+    tok_s_to_ns_map = {}
+    for (i, tok_index) in six.iteritems(tok_ns_to_s_map):
+        tok_s_to_ns_map[tok_index] = i
+
+    orig_start_position = None
+    if start_position in tok_s_to_ns_map:
+        ns_start_position = tok_s_to_ns_map[start_position]
+        if ns_start_position in orig_ns_to_s_map:
+            orig_start_position = orig_ns_to_s_map[ns_start_position]
+
+    if orig_start_position is None:
+        return orig_text
+
+    orig_end_position = None
+    if end_position in tok_s_to_ns_map:
+        ns_end_position = tok_s_to_ns_map[end_position]
+        if ns_end_position in orig_ns_to_s_map:
+            orig_end_position = orig_ns_to_s_map[ns_end_position]
+
+    if orig_end_position is None:
+        return orig_text
+
+    output_text = orig_text[orig_start_position:(orig_end_position + 1)]
+    return output_text
+
+
+def _compute_softmax(scores):
+    """Compute softmax probability over raw logits."""
+    if not scores:
+        return []
+
+    max_score = None
+    for score in scores:
+        if max_score is None or score > max_score:
+            max_score = score
+
+    exp_scores = []
+    total_sum = 0.0
+    for score in scores:
+        x = math.exp(score - max_score)
+        exp_scores.append(x)
+        total_sum += x
+
+    probs = []
+    for score in exp_scores:
+        probs.append(score / total_sum)
+    return probs
+
+
+def get_predictions(doc_tokens, features, results, n_best_size, max_answer_length):
+    _PrelimPrediction = collections.namedtuple(  # pylint: disable=invalid-name
+        "PrelimPrediction",
+        ["feature_index", "start_index", "end_index", "start_logit", "end_logit"])
+
+    prediction = ""
+    scores_diff_json = 0.0
+
+    prelim_predictions = []
+    # keep track of the minimum score of null start+end of position 0
+    score_null = 1000000  # large and positive
+    min_null_feature_index = 0  # the paragraph slice with min mull score
+    null_start_logit = 0  # the start logit at the slice with min null score
+    null_end_logit = 0  # the end logit at the slice with min null score
+    version_2_with_negative = False
+    
+    for result in results:
+        start_indexes = _get_best_indexes(result.start_logits, n_best_size)
+        end_indexes = _get_best_indexes(result.end_logits, n_best_size)
+        feature = features[result.feature_index]
+
+        # if we could have irrelevant answers, get the min score of irrelevant
+        if version_2_with_negative:
+            feature_null_score = result.start_logits[0] + result.end_logits[0]
+            if feature_null_score < score_null:
+                score_null = feature_null_score
+                min_null_feature_index = 0
+                null_start_logit = result.start_logits[0]
+                null_end_logit = result.end_logits[0]
+
+        for start_index in start_indexes:
+            for end_index in end_indexes:
+                # We could hypothetically create invalid predictions, e.g., predict
+                # that the start of the span is in the question. We throw out all
+                # invalid predictions.
+                if start_index >= len(feature.tokens):
+                    continue
+                if end_index >= len(feature.tokens):
+                    continue
+                if start_index not in feature.token_to_orig_map:
+                    continue
+                if end_index not in feature.token_to_orig_map:
+                    continue
+                if not feature.token_is_max_context.get(start_index, False):
+                    continue
+                if end_index < start_index:
+                    continue
+                length = end_index - start_index + 1
+                if length > max_answer_length:
+                    continue
+                prelim_predictions.append(
+                    _PrelimPrediction(
+                        feature_index=result.feature_index,
+                        start_index=start_index,
+                        end_index=end_index,
+                        start_logit=result.start_logits[start_index],
+                        end_logit=result.end_logits[end_index]))
+
+    if version_2_with_negative:
+        prelim_predictions.append(
+            _PrelimPrediction(
+                feature_index=result.feature_index,
+                start_index=0,
+                end_index=0,
+                start_logit=null_start_logit,
+                end_logit=null_end_logit))
+
+    prelim_predictions = sorted(
+        prelim_predictions,
+        key=lambda x: (x.start_logit + x.end_logit),
+        reverse=True)
+
+    _NbestPrediction = collections.namedtuple(  # pylint: disable=invalid-name
+        "NbestPrediction", ["text", "start_logit", "end_logit"])
+
+    seen_predictions = {}
+    nbest = []
+    for pred in prelim_predictions:
+        if len(nbest) >= n_best_size:
+            break
+
+        if pred.start_index > 0:  # this is a non-null prediction
+            feature = features[pred.feature_index]
+            tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1)]
+            orig_doc_start = feature.token_to_orig_map[pred.start_index]
+            orig_doc_end = feature.token_to_orig_map[pred.end_index]
+            orig_tokens = doc_tokens[orig_doc_start:(orig_doc_end + 1)]
+            tok_text = " ".join(tok_tokens)
+
+            # De-tokenize WordPieces that have been split off.
+            tok_text = tok_text.replace(" ##", "")
+            tok_text = tok_text.replace("##", "")
+
+            # Clean whitespace
+            tok_text = tok_text.strip()
+            tok_text = " ".join(tok_text.split())
+            orig_text = " ".join(orig_tokens)
+
+            final_text = get_final_text(tok_text, orig_text, True)
+            if final_text in seen_predictions:
+                continue
+
+            seen_predictions[final_text] = True
+        else:
+            final_text = ""
+            seen_predictions[final_text] = True
+
+        if len(final_text):
+            nbest.append(
+                _NbestPrediction(
+                    text=final_text,
+                    start_logit=pred.start_logit,
+                    end_logit=pred.end_logit))
+
+    # if we didn't inlude the empty option in the n-best, inlcude it
+    if version_2_with_negative:
+        if "" not in seen_predictions:
+            nbest.append(
+                _NbestPrediction(
+                    text="", start_logit=null_start_logit,
+                    end_logit=null_end_logit))
+    # In very rare edge cases we could have no valid predictions. So we
+    # just create a nonce prediction in this case to avoid failure.
+    if not nbest:
+        nbest.append(
+            _NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
+
+    assert len(nbest) >= 1
+
+    total_scores = []
+    best_non_null_entry = None
+    for entry in nbest:
+        total_scores.append(entry.start_logit + entry.end_logit)
+        if not best_non_null_entry:
+            if entry.text:
+                best_non_null_entry = entry
+
+    probs = _compute_softmax(total_scores)
+
+    nbest_json = []
+    for (i, entry) in enumerate(nbest):
+        output = collections.OrderedDict()
+        output["text"] = entry.text
+        output["probability"] = probs[i]
+        output["start_logit"] = entry.start_logit
+        output["end_logit"] = entry.end_logit
+        nbest_json.append(output)
+
+    assert len(nbest_json) >= 1
+
+    null_score_diff_threshold = 0.0
+    if not version_2_with_negative:
+        prediction = nbest_json[0]["text"]
+    else:
+        # predict "" iff the null score - the score of best non-null > threshold
+        score_diff = score_null - best_non_null_entry.start_logit - (
+            best_non_null_entry.end_logit)
+        scores_diff_json = score_diff
+        if score_diff > null_score_diff_threshold:
+            prediction = ""
+        else:
+            prediction = best_non_null_entry.text
+
+    return prediction, nbest_json, scores_diff_json
diff --git a/demo/BERT/helpers/tokenization.py b/demo/BERT/helpers/tokenization.py
new file mode 100644
index 00000000..e3609332
--- /dev/null
+++ b/demo/BERT/helpers/tokenization.py
@@ -0,0 +1,429 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tokenization classes."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import re
+import unicodedata
+import six
+
+
+def validate_case_matches_checkpoint(do_lower_case, init_checkpoint):
+  """Checks whether the casing config is consistent with the checkpoint name."""
+
+  # The casing has to be passed in by the user and there is no explicit check
+  # as to whether it matches the checkpoint. The casing information probably
+  # should have been stored in the bert_config.json file, but it's not, so
+  # we have to heuristically detect it to validate.
+
+  if not init_checkpoint:
+    return
+
+  m = re.match("^.*?([A-Za-z0-9_-]+)/bert_model.ckpt", init_checkpoint)
+  if m is None:
+    return
+
+  model_name = m.group(1)
+
+  lower_models = [
+      "uncased_L-24_H-1024_A-16", "uncased_L-12_H-768_A-12",
+      "multilingual_L-12_H-768_A-12", "chinese_L-12_H-768_A-12"
+  ]
+
+  cased_models = [
+      "cased_L-12_H-768_A-12", "cased_L-24_H-1024_A-16",
+      "multi_cased_L-12_H-768_A-12"
+  ]
+
+  is_bad_config = False
+  if model_name in lower_models and not do_lower_case:
+    is_bad_config = True
+    actual_flag = "False"
+    case_name = "lowercased"
+    opposite_flag = "True"
+
+  if model_name in cased_models and do_lower_case:
+    is_bad_config = True
+    actual_flag = "True"
+    case_name = "cased"
+    opposite_flag = "False"
+
+  if is_bad_config:
+    raise ValueError(
+        "You passed in `--do_lower_case=%s` with `--init_checkpoint=%s`. "
+        "However, `%s` seems to be a %s model, so you "
+        "should pass in `--do_lower_case=%s` so that the fine-tuning matches "
+        "how the model was pre-training. If this error is wrong, please "
+        "just comment out this check." % (actual_flag, init_checkpoint,
+                                          model_name, case_name, opposite_flag))
+
+
+def convert_to_unicode(text):
+  """Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
+  if six.PY3:
+    if isinstance(text, str):
+      return text
+    elif isinstance(text, bytes):
+      return text.decode("utf-8", "ignore")
+    else:
+      raise ValueError("Unsupported string type: %s" % (type(text)))
+  elif six.PY2:
+    if isinstance(text, str):
+      return text.decode("utf-8", "ignore")
+    elif isinstance(text, unicode):
+      return text
+    else:
+      raise ValueError("Unsupported string type: %s" % (type(text)))
+  else:
+    raise ValueError("Not running on Python2 or Python 3?")
+
+
+def printable_text(text):
+  """Returns text encoded in a way suitable for print or `tf.logging`."""
+
+  # These functions want `str` for both Python2 and Python3, but in one case
+  # it's a Unicode string and in the other it's a byte string.
+  if six.PY3:
+    if isinstance(text, str):
+      return text
+    elif isinstance(text, bytes):
+      return text.decode("utf-8", "ignore")
+    else:
+      raise ValueError("Unsupported string type: %s" % (type(text)))
+  elif six.PY2:
+    if isinstance(text, str):
+      return text
+    elif isinstance(text, unicode):
+      return text.encode("utf-8")
+    else:
+      raise ValueError("Unsupported string type: %s" % (type(text)))
+  else:
+    raise ValueError("Not running on Python2 or Python 3?")
+
+
+def load_vocab(vocab_file):
+  """Loads a vocabulary file into a dictionary."""
+  vocab = collections.OrderedDict()
+  index = 0
+  with open(vocab_file, "r", encoding='utf-8') as reader:
+    while True:
+      token = convert_to_unicode(reader.readline())
+      if not token:
+        break
+      token = token.strip()
+      vocab[token] = index
+      index += 1
+  return vocab
+
+
+def convert_by_vocab(vocab, items):
+  """Converts a sequence of [tokens|ids] using the vocab."""
+  output = []
+  for item in items:
+    output.append(vocab[item])
+  return output
+
+
+def convert_tokens_to_ids(vocab, tokens):
+  return convert_by_vocab(vocab, tokens)
+
+
+def convert_ids_to_tokens(inv_vocab, ids):
+  return convert_by_vocab(inv_vocab, ids)
+
+
+def whitespace_tokenize(text):
+  """Runs basic whitespace cleaning and splitting on a piece of text."""
+  text = text.strip()
+  if not text:
+    return []
+  tokens = text.split()
+  return tokens
+
+
+class FullTokenizer(object):
+  """Runs end-to-end tokenziation."""
+
+  def __init__(self, vocab_file, do_lower_case=True):
+    self.vocab = load_vocab(vocab_file)
+    self.inv_vocab = {v: k for k, v in self.vocab.items()}
+    self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
+    self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
+
+  def tokenize(self, text):
+    split_tokens = []
+    for token in self.basic_tokenizer.tokenize(text):
+      for sub_token in self.wordpiece_tokenizer.tokenize(token):
+        split_tokens.append(sub_token)
+
+    return split_tokens
+
+  def convert_tokens_to_ids(self, tokens):
+    return convert_by_vocab(self.vocab, tokens)
+
+  def convert_ids_to_tokens(self, ids):
+    return convert_by_vocab(self.inv_vocab, ids)
+
+
+class BertTokenizer(object):
+    """Runs end-to-end tokenization: punctuation splitting + wordpiece"""
+
+    def __init__(self, vocab_file, do_lower_case=True):
+        self.vocab = load_vocab(vocab_file)
+        self.ids_to_tokens = collections.OrderedDict(
+            [(ids, tok) for tok, ids in self.vocab.items()])
+        self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
+        self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
+
+    def tokenize(self, text):
+        split_tokens = []
+        for token in self.basic_tokenizer.tokenize(text):
+            for sub_token in self.wordpiece_tokenizer.tokenize(token):
+                split_tokens.append(sub_token)
+        return split_tokens
+
+    def convert_tokens_to_ids(self, tokens):
+        """Converts a sequence of tokens into ids using the vocab."""
+        ids = []
+        for token in tokens:
+            ids.append(self.vocab[token])
+        return ids
+
+    def convert_ids_to_tokens(self, ids):
+        """Converts a sequence of ids in wordpiece tokens using the vocab."""
+        tokens = []
+        for i in ids:
+            tokens.append(self.ids_to_tokens[i])
+        return tokens
+
+class BasicTokenizer(object):
+  """Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
+
+  def __init__(self, do_lower_case=True):
+    """Constructs a BasicTokenizer.
+
+    Args:
+      do_lower_case: Whether to lower case the input.
+    """
+    self.do_lower_case = do_lower_case
+
+  def tokenize(self, text):
+    """Tokenizes a piece of text."""
+    text = convert_to_unicode(text)
+    text = self._clean_text(text)
+
+    # This was added on November 1st, 2018 for the multilingual and Chinese
+    # models. This is also applied to the English models now, but it doesn't
+    # matter since the English models were not trained on any Chinese data
+    # and generally don't have any Chinese data in them (there are Chinese
+    # characters in the vocabulary because Wikipedia does have some Chinese
+    # words in the English Wikipedia.).
+    text = self._tokenize_chinese_chars(text)
+
+    orig_tokens = whitespace_tokenize(text)
+    split_tokens = []
+    for token in orig_tokens:
+      if self.do_lower_case:
+        token = token.lower()
+        token = self._run_strip_accents(token)
+      split_tokens.extend(self._run_split_on_punc(token))
+
+    output_tokens = whitespace_tokenize(" ".join(split_tokens))
+    return output_tokens
+
+  def _run_strip_accents(self, text):
+    """Strips accents from a piece of text."""
+    text = unicodedata.normalize("NFD", text)
+    output = []
+    for char in text:
+      cat = unicodedata.category(char)
+      if cat == "Mn":
+        continue
+      output.append(char)
+    return "".join(output)
+
+  def _run_split_on_punc(self, text):
+    """Splits punctuation on a piece of text."""
+    chars = list(text)
+    i = 0
+    start_new_word = True
+    output = []
+    while i < len(chars):
+      char = chars[i]
+      if _is_punctuation(char):
+        output.append([char])
+        start_new_word = True
+      else:
+        if start_new_word:
+          output.append([])
+        start_new_word = False
+        output[-1].append(char)
+      i += 1
+
+    return ["".join(x) for x in output]
+
+  def _tokenize_chinese_chars(self, text):
+    """Adds whitespace around any CJK character."""
+    output = []
+    for char in text:
+      cp = ord(char)
+      if self._is_chinese_char(cp):
+        output.append(" ")
+        output.append(char)
+        output.append(" ")
+      else:
+        output.append(char)
+    return "".join(output)
+
+  def _is_chinese_char(self, cp):
+    """Checks whether CP is the codepoint of a CJK character."""
+    # This defines a "chinese character" as anything in the CJK Unicode block:
+    #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
+    #
+    # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
+    # despite its name. The modern Korean Hangul alphabet is a different block,
+    # as is Japanese Hiragana and Katakana. Those alphabets are used to write
+    # space-separated words, so they are not treated specially and handled
+    # like the all of the other languages.
+    if ((cp >= 0x4E00 and cp <= 0x9FFF) or  #
+        (cp >= 0x3400 and cp <= 0x4DBF) or  #
+        (cp >= 0x20000 and cp <= 0x2A6DF) or  #
+        (cp >= 0x2A700 and cp <= 0x2B73F) or  #
+        (cp >= 0x2B740 and cp <= 0x2B81F) or  #
+        (cp >= 0x2B820 and cp <= 0x2CEAF) or
+        (cp >= 0xF900 and cp <= 0xFAFF) or  #
+        (cp >= 0x2F800 and cp <= 0x2FA1F)):  #
+      return True
+
+    return False
+
+  def _clean_text(self, text):
+    """Performs invalid character removal and whitespace cleanup on text."""
+    output = []
+    for char in text:
+      cp = ord(char)
+      if cp == 0 or cp == 0xfffd or _is_control(char):
+        continue
+      if _is_whitespace(char):
+        output.append(" ")
+      else:
+        output.append(char)
+    return "".join(output)
+
+
+class WordpieceTokenizer(object):
+  """Runs WordPiece tokenziation."""
+
+  def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=200):
+    self.vocab = vocab
+    self.unk_token = unk_token
+    self.max_input_chars_per_word = max_input_chars_per_word
+
+  def tokenize(self, text):
+    """Tokenizes a piece of text into its word pieces.
+
+    This uses a greedy longest-match-first algorithm to perform tokenization
+    using the given vocabulary.
+
+    For example:
+      input = "unaffable"
+      output = ["un", "##aff", "##able"]
+
+    Args:
+      text: A single token or whitespace separated tokens. This should have
+        already been passed through `BasicTokenizer.
+
+    Returns:
+      A list of wordpiece tokens.
+    """
+
+    text = convert_to_unicode(text)
+
+    output_tokens = []
+    for token in whitespace_tokenize(text):
+      chars = list(token)
+      if len(chars) > self.max_input_chars_per_word:
+        output_tokens.append(self.unk_token)
+        continue
+
+      is_bad = False
+      start = 0
+      sub_tokens = []
+      while start < len(chars):
+        end = len(chars)
+        cur_substr = None
+        while start < end:
+          substr = "".join(chars[start:end])
+          if start > 0:
+            substr = "##" + substr
+          if substr in self.vocab:
+            cur_substr = substr
+            break
+          end -= 1
+        if cur_substr is None:
+          is_bad = True
+          break
+        sub_tokens.append(cur_substr)
+        start = end
+
+      if is_bad:
+        output_tokens.append(self.unk_token)
+      else:
+        output_tokens.extend(sub_tokens)
+    return output_tokens
+
+
+def _is_whitespace(char):
+  """Checks whether `chars` is a whitespace character."""
+  # \t, \n, and \r are technically contorl characters but we treat them
+  # as whitespace since they are generally considered as such.
+  if char == " " or char == "\t" or char == "\n" or char == "\r":
+    return True
+  cat = unicodedata.category(char)
+  if cat == "Zs":
+    return True
+  return False
+
+
+def _is_control(char):
+  """Checks whether `chars` is a control character."""
+  # These are technically control characters but we count them as whitespace
+  # characters.
+  if char == "\t" or char == "\n" or char == "\r":
+    return False
+  cat = unicodedata.category(char)
+  if cat.startswith("C"):
+    return True
+  return False
+
+
+def _is_punctuation(char):
+  """Checks whether `chars` is a punctuation character."""
+  cp = ord(char)
+  # We treat all non-letter/number ASCII as punctuation.
+  # Characters such as "^", "$", and "`" are not in the Unicode
+  # Punctuation class but we treat them as punctuation anyways, for
+  # consistency.
+  if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or
+      (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
+    return True
+  cat = unicodedata.category(char)
+  if cat.startswith("P"):
+    return True
+  return False
diff --git a/demo/BERT/infer.py b/demo/BERT/infer.py
new file mode 100644
index 00000000..ab50ba82
--- /dev/null
+++ b/demo/BERT/infer.py
@@ -0,0 +1,164 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import argparse
+import ctypes
+import numpy as np
+import tensorrt as trt
+import pycuda.driver as cuda
+import pycuda.autoinit
+
+import numpy as np
+
+import helpers.tokenization as tokenization
+import helpers.data_processing as dp
+import pdb
+
+TRT_LOGGER = trt.Logger(trt.Logger.ERROR)
+
+class DeviceBuffer(object):
+    def __init__(self, shape, dtype=trt.int32):
+        self.buf = cuda.mem_alloc(trt.volume(shape) * dtype.itemsize)
+
+    def binding(self):
+        return int(self.buf)
+
+    def free(self):
+        self.buf.free()
+
+
+def main():
+    parser = argparse.ArgumentParser(description='BERT Inference Benchmark')
+    parser.add_argument("-e", "--engine", help='Path to BERT TensorRT engine')
+    parser.add_argument('-b', '--batch-size', default=[], action="append", help='Batch size(s) to benchmark. Can be specified multiple times for more than one batch size. This script assumes that the engine has been built with one optimization profile for each batch size, and that these profiles are in order of increasing batch size.', type=int)
+    parser.add_argument('-s', '--sequence-length', default=128, help='Sequence length of the BERT model', type=int)
+    parser.add_argument('-i', '--iterations', default=1, help='Number of iterations to run when benchmarking each batch size.', type=int)
+    parser.add_argument('-w', '--warm-up-runs', default=0, help='Number of iterations to run prior to benchmarking.', type=int)
+    parser.add_argument('-r', '--random-seed', required=False, default=12345, help='Random seed.', type=int)
+    parser.add_argument('-p', '--passage', nargs='*', help='Text for paragraph/passage for BERT QA', default='')
+    parser.add_argument('-q', '--question', nargs='*', help='Text for query/question for BERT QA', default='')
+    parser.add_argument('-v', '--vocab-file', help='Path to file containing entire understandable vocab')
+
+    args, _ = parser.parse_known_args()
+    args.batch_size = args.batch_size or [1]
+
+    # Import necessary plugins for BERT TensorRT
+    ctypes.CDLL("libnvinfer_plugin.so", mode=ctypes.RTLD_GLOBAL)
+
+    with open(args.engine, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(f.read()) as engine, engine.create_execution_context() as context:
+        # Allocate buffers large enough to store the largest batch size
+        max_input_shape = (args.sequence_length, max(args.batch_size))
+        max_output_shape = (args.sequence_length, max(args.batch_size), 2, 1, 1)
+        buffers = [
+            DeviceBuffer(max_input_shape),
+            DeviceBuffer(max_input_shape),
+            DeviceBuffer(max_input_shape),
+            DeviceBuffer(max_output_shape)
+        ]
+
+        def question_features(tokens, question):
+            # Extract features from the paragraph and question
+            tokenizer = tokenization.FullTokenizer(vocab_file=args.vocab_file, do_lower_case=True)
+            return dp.convert_example_to_features(tokens, question, tokenizer, args.sequence_length, 128, 64)
+
+        # Prepare random input
+        pseudo_vocab_size = 30522
+        pseudo_type_vocab_size = 2
+        np.random.seed(args.random_seed)
+
+        paragraph_text = ' '.join(args.passage)
+        question_text = ' '.join(args.question)
+        print("\nPassage: {}".format(paragraph_text))
+        print("\nQuestion: {}".format(question_text))
+        doc_tokens = dp.convert_doc_tokens(paragraph_text)
+        features = question_features(doc_tokens, question_text)
+        test_word_ids = features[0].input_ids
+        test_segment_ids = features[0].segment_ids
+        test_input_mask = features[0].input_mask
+
+        # Copy input h2d
+        cuda.memcpy_htod(buffers[0].buf, test_word_ids.ravel())
+        cuda.memcpy_htod(buffers[1].buf, test_segment_ids.ravel())
+        cuda.memcpy_htod(buffers[2].buf, test_input_mask.ravel())
+
+        num_binding_per_profile = engine.num_bindings // engine.num_optimization_profiles
+
+        bench_times = {}
+
+        for idx, batch_size in enumerate(sorted(args.batch_size)):
+            context.active_optimization_profile = idx
+
+            # Each profile has unique bindings
+            binding_idx_offset = idx * num_binding_per_profile
+            bindings = [0] * binding_idx_offset + [buf.binding() for buf in buffers]
+
+            shapes = {
+                "input_ids": (args.sequence_length, batch_size),
+                "segment_ids": (args.sequence_length, batch_size),
+                "input_mask": (args.sequence_length, batch_size),
+            }
+
+            for binding, shape in shapes.items():
+                context.set_binding_shape(engine[binding] + binding_idx_offset, shape)
+            assert context.all_binding_shapes_specified
+
+            # Inference
+            total_time = 0
+            start = cuda.Event()
+            end = cuda.Event()
+            stream = cuda.Stream()
+
+            # Warmup
+            for _ in range(args.warm_up_runs):
+                context.execute_async_v2(bindings=bindings, stream_handle=stream.handle)
+                stream.synchronize()
+
+            # Timing loop
+            times = []
+            for _ in range(args.iterations):
+                start.record(stream)
+                context.execute_async_v2(bindings=bindings, stream_handle=stream.handle)
+                end.record(stream)
+                stream.synchronize()
+                times.append(end.time_since(start))
+
+            # Transfer predictions back from GPU
+            cuda.memcpy_dtoh_async(h_output, d_output, stream)
+
+                for index, batch in enumerate(h_output):
+                    # Data Post-processing
+                    networkOutputs.append(_NetworkOutput(
+                        start_logits = np.array(batch.squeeze()[:, 0]),
+                        end_logits = np.array(batch.squeeze()[:, 1]),
+                        feature_index = feature_index
+                        ))
+
+            # Compute average time, 95th percentile time and 99th percentile time.
+            bench_times[batch_size] = times
+
+        [b.free() for b in buffers]
+
+        for batch_size, times in bench_times.items():
+            total_time = sum(times)
+            avg_time = total_time / float(len(times))
+            times.sort()
+            percentile95 = times[int(len(times) * 0.95)]
+            percentile99 = times[int(len(times) * 0.99)]
+            print("Running {:} iterations with Batch Size: {:}\n\tTotal Time: {:} ms \tAverage Time: {:} ms\t95th Percentile Time: {:} ms\t99th Percentile Time: {:}".format(args.iterations, batch_size, total_time, avg_time, percentile95, percentile99))
+
+
+if __name__ == '__main__':
+    main()
diff --git a/demo/BERT/infer_c/bert_infer.h b/demo/BERT/infer_c/bert_infer.h
new file mode 100644
index 00000000..fb05ec5c
--- /dev/null
+++ b/demo/BERT/infer_c/bert_infer.h
@@ -0,0 +1,295 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef INFER_C_BERT_INFER_H
+#define INFER_C_BERT_INFER_H
+
+#include "common.h"
+#include "logging.h"
+#include <NvInfer.h>
+#include <NvInferPlugin.h>
+#include <algorithm>
+#include <cuda_runtime.h>
+#include <fstream>
+#include <numeric>
+#include <vector>
+
+using namespace nvinfer1;
+
+struct BertInference
+{
+    BertInference(
+        const std::string& enginePath, const int maxBatchSize, const int seqLength, const bool enableGraph = false)
+        : mSeqLength(seqLength)
+        , mEnableGraph(enableGraph)
+    {
+        gLogInfo << "--------------------\n";
+        gLogInfo << "Using BERT inference C++\n";
+        if (enableGraph)
+        {
+            gLogInfo << "CUDA Graph is enabled\n";
+        }
+        else
+        {
+            gLogInfo << "CUDA Graph is disabled\n";
+        }
+
+        gLogInfo << "--------------------\n";
+
+        initLibNvInferPlugins(&gLogger, "");
+
+        gLogInfo << "Loading BERT Inference Engine ... \n";
+        std::ifstream input(enginePath, std::ios::binary);
+        if (!input)
+        {
+            gLogError << "Error opening engine file: " << enginePath << "\n";
+            exit(-1);
+        }
+
+        input.seekg(0, input.end);
+        const size_t fsize = input.tellg();
+        input.seekg(0, input.beg);
+
+        std::vector<char> bytes(fsize);
+        input.read(bytes.data(), fsize);
+
+        auto runtime = TrtUniquePtr<IRuntime>(createInferRuntime(gLogger));
+        if (runtime == nullptr)
+        {
+            gLogError << "Error creating TRT runtime\n";
+            exit(-1);
+        }
+
+        mEngine = TrtUniquePtr<ICudaEngine>(runtime->deserializeCudaEngine(bytes.data(), bytes.size(), nullptr));
+        if (mEngine == nullptr)
+        {
+            gLogError << "Error deserializing CUDA engine\n";
+            exit(-1);
+        }
+        gLogInfo << "Done\n";
+        mContext = TrtUniquePtr<IExecutionContext>(mEngine->createExecutionContext());
+        if (!mContext)
+        {
+            gLogError << "Error creating execution context\n";
+            exit(-1);
+        }
+
+        gpuErrChk(cudaStreamCreate(&mStream));
+
+        allocateBindings(maxBatchSize);
+    }
+
+    void allocateBindings(const int maxBatchSize)
+    {
+        const size_t allocationSize = mSeqLength * maxBatchSize * sizeof(int32_t);
+
+        // Static sizes with implicit batch size: allocation sizes known to engine
+        for (int i = 0; i < kBERT_INPUT_NUM; i++)
+        {
+            void* devBuf;
+            gpuErrChk(cudaMalloc(&devBuf, allocationSize));
+            gpuErrChk(cudaMemset(devBuf, 0, allocationSize));
+            mDeviceBuffers.emplace_back(devBuf);
+            mInputSizes.emplace_back(allocationSize);
+        }
+
+        const size_t numOutputItems = maxBatchSize * mSeqLength * 2;
+        mOutputSize = numOutputItems * sizeof(float);
+        mOutputDims = {maxBatchSize, mSeqLength, 2, 1, 1};
+        void* devBuf;
+        gpuErrChk(cudaMalloc(&devBuf, mOutputSize));
+        gpuErrChk(cudaMemset(devBuf, 0, mOutputSize));
+        mDeviceBuffers.emplace_back(devBuf);
+        mHostOutput.resize(numOutputItems);
+
+        mBindings.resize(mEngine->getNbBindings());
+    }
+
+    void prepare(int profIdx, int batchSize)
+    {
+
+        mContext->setOptimizationProfile(profIdx);
+        const int numBindingPerProfile = mEngine->getNbBindings() / mEngine->getNbOptimizationProfiles();
+        const int bindingIdxOffset = profIdx * numBindingPerProfile;
+        std::copy(mDeviceBuffers.begin(), mDeviceBuffers.end(), mBindings.begin() + bindingIdxOffset);
+
+        for (int i = 0; i < kBERT_INPUT_NUM; i++)
+        {
+            mContext->setBindingDimensions(i + bindingIdxOffset, Dims2(mSeqLength, batchSize));
+        }
+
+        if (!mContext->allInputDimensionsSpecified())
+        {
+            gLogError << "Not all input dimensions are specified for the exeuction context\n";
+            exit(-1);
+        }
+
+        if (mEnableGraph)
+        {
+            cudaGraph_t graph;
+            cudaGraphExec_t exec;
+            // warm up and let mContext do cublas initialization
+            bool status = mContext->enqueueV2(mBindings.data(), mStream, nullptr);
+            if (!status)
+            {
+                gLogError << "Enqueue failed\n";
+                exit(-1);
+            }
+            gLogVerbose << "Capturing graph\n";
+
+            gpuErrChk(cudaStreamBeginCapture(mStream, cudaStreamCaptureModeRelaxed));
+            status = mContext->enqueueV2(mBindings.data(), mStream, nullptr);
+            if (!status)
+            {
+                gLogError << "Enqueue failed\n";
+                exit(-1);
+            }
+
+            gpuErrChk(cudaStreamEndCapture(mStream, &graph));
+            gpuErrChk(cudaStreamSynchronize(mStream));
+
+            gpuErrChk(cudaGraphInstantiate(&exec, graph, NULL, NULL, 0));
+            mExecGraph = exec;
+        }
+    }
+
+    void run(const void* inputIds, const void* segmentIds, const void* inputMask, int warmUps, int iterations)
+    {
+
+        const std::vector<const void*> inputBuffers = {inputIds, segmentIds, inputMask};
+
+        for (int i = 0; i < kBERT_INPUT_NUM; i++)
+        {
+            gpuErrChk(
+                cudaMemcpyAsync(mDeviceBuffers[i], inputBuffers[i], mInputSizes[i], cudaMemcpyHostToDevice, mStream));
+        }
+
+        gLogInfo << "Warming up " << warmUps << " iterations ...\n";
+        for (int it = 0; it < warmUps; it++)
+        {
+            if (mEnableGraph)
+            {
+                gpuErrChk(cudaGraphLaunch(mExecGraph, mStream));
+            }
+            else
+            {
+                bool status = mContext->enqueueV2(mBindings.data(), mStream, nullptr);
+                if (!status)
+                {
+                    gLogError << "Enqueue failed\n";
+                    exit(-1);
+                }
+            }
+        }
+        gpuErrChk(cudaStreamSynchronize(mStream));
+
+        cudaEvent_t start, stop;
+        gpuErrChk(cudaEventCreate(&start));
+        gpuErrChk(cudaEventCreate(&stop));
+
+        std::vector<float> times;
+        gLogInfo << "Running " << iterations << " iterations ...\n";
+        for (int it = 0; it < iterations; it++)
+        {
+            gpuErrChk(cudaEventRecord(start, mStream));
+            if (mEnableGraph)
+            {
+                gpuErrChk(cudaGraphLaunch(mExecGraph, mStream));
+            }
+            else
+            {
+                bool status = mContext->enqueueV2(mBindings.data(), mStream, nullptr);
+                if (!status)
+                {
+                    gLogError << "Enqueue failed\n";
+                    exit(-1);
+                }
+            }
+            gpuErrChk(cudaEventRecord(stop, mStream));
+            gpuErrChk(cudaStreamSynchronize(mStream));
+            float time;
+            gpuErrChk(cudaEventElapsedTime(&time, start, stop));
+            times.push_back(time);
+        }
+
+        gpuErrChk(cudaMemcpyAsync(
+            mHostOutput.data(), mDeviceBuffers[kBERT_INPUT_NUM], mOutputSize, cudaMemcpyDeviceToHost, mStream));
+
+        gpuErrChk(cudaStreamSynchronize(mStream));
+
+        mTimes.push_back(times);
+    }
+
+    void run(int profIdx, int batchSize, const void* inputIds, const void* segmentIds, const void* inputMask,
+        int warmUps, int iterations)
+    {
+
+        prepare(profIdx, batchSize);
+        run(inputIds, segmentIds, inputMask, warmUps, iterations);
+    }
+
+    void reportTiming(int batchIndex, int batchSize)
+    {
+
+        std::vector<float>& times = mTimes[batchIndex];
+        const float totalTime = std::accumulate(times.begin(), times.end(), 0.0);
+        const float avgTime = totalTime / times.size();
+
+        sort(times.begin(), times.end());
+        const float percentile95 = times[(int) ((float) times.size() * 0.95)];
+        const float percentile99 = times[(int) ((float) times.size() * 0.99)];
+        const int throughput = (int) ((float) batchSize * (1000.0 / avgTime));
+        gLogInfo << "Running " << times.size() << " iterations with Batch Size: " << batchSize << "\n";
+        gLogInfo << "\tTotal Time: " << totalTime << " ms \n";
+        gLogInfo << "\tAverage Time: " << avgTime << " ms\n";
+        gLogInfo << "\t95th Percentile Time: " << percentile95 << " ms\n";
+        gLogInfo << "\t99th Percentile Time: " << percentile99 << " ms\n";
+        gLogInfo << "\tThroughtput: " << throughput << " sentences/s\n";
+    }
+
+    ~BertInference()
+    {
+
+        gpuErrChk(cudaStreamDestroy(mStream));
+
+        for (auto& buf : mDeviceBuffers)
+        {
+            gpuErrChk(cudaFree(buf));
+        }
+    }
+
+    static const int kBERT_INPUT_NUM = 3;
+
+    const int mSeqLength;
+    const bool mEnableGraph;
+
+    TrtUniquePtr<ICudaEngine> mEngine{nullptr};
+    TrtUniquePtr<IExecutionContext> mContext{nullptr};
+    std::vector<void*> mBindings;
+
+    cudaStream_t mStream{NULL};
+    std::vector<void*> mDeviceBuffers;
+    std::vector<float> mHostOutput;
+    std::vector<size_t> mInputSizes;
+    size_t mOutputSize;
+    std::vector<int> mOutputDims;
+
+    std::vector<std::vector<float>> mTimes;
+
+    cudaGraphExec_t mExecGraph;
+};
+
+#endif // INFER_C_BERT_INFER_H
diff --git a/demo/BERT/infer_c/common.h b/demo/BERT/infer_c/common.h
new file mode 100644
index 00000000..d6aa057b
--- /dev/null
+++ b/demo/BERT/infer_c/common.h
@@ -0,0 +1,178 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef INFER_C_COMMON_H
+#define INFER_C_COMMON_H
+
+#include "logging.h"
+#include <cuda_runtime_api.h>
+#include <getopt.h>
+#include <memory>
+#include <vector>
+
+struct Args
+{
+    bool help{false};
+    std::string engine{};
+    std::vector<int> batchSize;
+    int sequenceLength{128};
+    int iterations{200};
+    int warmUpRuns{10};
+    int randomSeed{12345};
+    bool enableGraph{false};
+};
+
+//!
+//! \brief Populates the Args struct with the provided command-line parameters.
+//!
+//! \throw invalid_argument if any of the arguments are not valid
+//!
+//! \return boolean If return value is true, execution can continue, otherwise program should exit
+//!
+inline bool parseArgs(Args& args, int argc, char* argv[])
+{
+    while (1)
+    {
+        int arg;
+        // clang-format off
+        static struct option long_options[] = 
+        {
+            {"help", no_argument, 0, 'h'}, 
+            {"engine", required_argument, 0, 'e'},
+            {"batch_size", required_argument, 0, 'b'}, 
+            {"sequence_length", no_argument, 0, 's'},
+            {"iterations", required_argument, 0, 'i'}, 
+            {"warm_up_runs", required_argument, 0, 'w'},
+            {"ramdon_seed", required_argument, 0, 'r'}, 
+            {"enable_graph", no_argument, 0, 'g'},
+            {nullptr, 0, nullptr, 0}
+        };
+        // clang-format on
+        int option_index = 0;
+        arg = getopt_long(argc, argv, "he:b:s:i:w:r:g", long_options, &option_index);
+        if (arg == -1)
+        {
+            break;
+        }
+
+        switch (arg)
+        {
+        case 'h': args.help = true; return false;
+        case 'e':
+            if (optarg)
+            {
+                args.engine = optarg;
+            }
+            else
+            {
+                std::cerr << "ERROR: --engine requires option argument" << std::endl;
+                return false;
+            }
+            break;
+        case 'b':
+            if (optarg)
+            {
+                args.batchSize.push_back(std::stoi(optarg));
+            }
+            else
+            {
+                std::cerr << "ERROR: --batch_size requires option argument" << std::endl;
+                return false;
+            }
+            break;
+        case 's':
+            if (optarg)
+            {
+                args.sequenceLength = std::stoi(optarg);
+            }
+            else
+            {
+                std::cerr << "ERROR: --sequence_length requires option argument" << std::endl;
+                return false;
+            }
+            break;
+        case 'i':
+            if (optarg)
+            {
+                args.iterations = std::stoi(optarg);
+            }
+            else
+            {
+                std::cerr << "ERROR: --iterations requires option argument" << std::endl;
+                return false;
+            }
+            break;
+        case 'w':
+            if (optarg)
+            {
+                args.warmUpRuns = std::stoi(optarg);
+            }
+            else
+            {
+                std::cerr << "ERROR: --warm_up_runs requires option argument" << std::endl;
+                return false;
+            }
+            break;
+        case 'r':
+            if (optarg)
+            {
+                args.randomSeed = std::stoi(optarg);
+            }
+            else
+            {
+                std::cerr << "ERROR: --random_seed requires option argument" << std::endl;
+                return false;
+            }
+            break;
+        case 'g': args.enableGraph = true; break;
+        default: return false;
+        }
+    }
+    return true;
+}
+
+// clang-format off
+#define gpuErrChk(ans)                        \
+    {                                         \
+        gpuAssert((ans), __FILE__, __LINE__); \
+    }
+// clang-format on
+
+inline void gpuAssert(cudaError_t code, const char* file, int line, bool abort = true)
+{
+    if (code != cudaSuccess)
+    {
+        gLogError << "GPUassert: " << cudaGetErrorString(code) << " " << file << " " << line << "\n";
+        if (abort)
+        {
+            exit(code);
+        }
+    }
+}
+
+template <typename T>
+struct TrtDestroyer
+{
+    void operator()(T* t)
+    {
+        t->destroy();
+    }
+};
+
+template <typename T>
+using TrtUniquePtr = std::unique_ptr<T, TrtDestroyer<T>>;
+
+#endif // INFER_C_COMMON_H
diff --git a/demo/BERT/infer_c/infer_c.cpp b/demo/BERT/infer_c/infer_c.cpp
new file mode 100644
index 00000000..94ed8d97
--- /dev/null
+++ b/demo/BERT/infer_c/infer_c.cpp
@@ -0,0 +1,61 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "bert_infer.h"
+#include <pybind11/numpy.h>
+#include <pybind11/pybind11.h>
+
+namespace py = pybind11;
+
+struct BertInferenceRunner
+{
+    BertInferenceRunner(
+        const std::string& enginePath, const int maxBatchSize, const int maxSeqLength, const bool enableGraph)
+        : bert{enginePath, maxBatchSize, maxSeqLength, enableGraph}
+    {
+    }
+
+    void prepare(const int batchSize)
+    {
+        bert.prepare(0, batchSize);
+    }
+
+    py::array_t<float> run(py::array_t<int> inputIds, py::array_t<int> segmentIds, py::array_t<int> inputMask)
+    {
+
+        const void* inputIdsPtr = inputIds.request().ptr;
+        const void* segmentIdsPtr = segmentIds.request().ptr;
+        const void* inputMaskPtr = inputMask.request().ptr;
+
+        bert.run(inputIdsPtr, segmentIdsPtr, inputMaskPtr, 0, 1);
+
+        auto output = py::array_t<float>(bert.mOutputDims, (float*) bert.mHostOutput.data());
+
+        return output;
+    }
+
+    BertInference bert;
+};
+
+PYBIND11_MODULE(infer_c, m)
+{
+    m.doc() = "Pybind11 plugin for Bert inference";
+
+    py::class_<BertInferenceRunner>(m, "bert_inf")
+        .def(py::init<const std::string&, const int, const int, const bool>())
+        .def("prepare", &BertInferenceRunner::prepare)
+        .def("run", &BertInferenceRunner::run);
+}
diff --git a/demo/BERT/infer_c/logging.cpp b/demo/BERT/infer_c/logging.cpp
new file mode 100644
index 00000000..8d198f9a
--- /dev/null
+++ b/demo/BERT/infer_c/logging.cpp
@@ -0,0 +1,19 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "logging.h"
+
+Logger gLogger(Severity::kINFO);
diff --git a/demo/BERT/infer_c/logging.h b/demo/BERT/infer_c/logging.h
new file mode 100644
index 00000000..3e373f4e
--- /dev/null
+++ b/demo/BERT/infer_c/logging.h
@@ -0,0 +1,85 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef INFER_C_LOGGING_H
+#define INFER_C_LOGGING_H
+
+#include <NvInfer.h>
+#include <cassert>
+#include <iostream>
+#include <map>
+
+using namespace nvinfer1;
+using Severity = nvinfer1::ILogger::Severity;
+
+class Logger : public ILogger
+{
+public:
+    Logger(Severity severity)
+        : mOstream(&std::cout)
+        , mReportableSeverity(severity)
+    {
+    }
+
+    template <typename T>
+    Logger& operator<<(T const& obj)
+    {
+        if (mOstream != nullptr)
+        {
+            *mOstream << obj;
+        }
+        return *this;
+    }
+
+    Logger& report(Severity severity, const char* msg)
+    {
+
+        if (severity <= mReportableSeverity)
+        {
+            const std::map<Severity, std::string> prefixMapping = {{Severity::kINTERNAL_ERROR, "[DemoBERT][F] "},
+                {Severity::kERROR, "[DemoBERT][E] "}, {Severity::kWARNING, "[DemoBERT][W] "},
+                {Severity::kINFO, "[DemoBERT][I] "}, {Severity::kVERBOSE, "[DemoBERT][V] "}};
+
+            assert(prefixMapping.find(severity) != prefixMapping.end());
+
+            mOstream = &std::cout;
+
+            *this << prefixMapping.at(severity) << msg;
+
+            return *this;
+        }
+        mOstream = nullptr;
+        return *this;
+    }
+
+private:
+    void log(Severity severity, const char* msg) override
+    {
+        report(severity, msg) << "\n";
+    }
+
+    std::ostream* mOstream;
+    Severity mReportableSeverity;
+};
+
+extern Logger gLogger;
+#define gLogFatal gLogger.report(Severity::kINTERNAL_ERROR, "")
+#define gLogError gLogger.report(Severity::kERROR, "")
+#define gLogWarning gLogger.report(Severity::kWARNING, "")
+#define gLogInfo gLogger.report(Severity::kINFO, "")
+#define gLogVerbose gLogger.report(Severity::kVERBOSE, "")
+
+#endif // INFER_C_LOGGING_H
diff --git a/demo/BERT/infer_c/perf.cpp b/demo/BERT/infer_c/perf.cpp
new file mode 100644
index 00000000..034186e6
--- /dev/null
+++ b/demo/BERT/infer_c/perf.cpp
@@ -0,0 +1,110 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "bert_infer.h"
+#include "common.h"
+#include <limits>
+#include <random>
+
+using namespace nvinfer1;
+
+void printHelpInfo()
+{
+    std::cout << "usage: ./perf [-h] [-e ENGINE] [-b BATCH_SIZE] [-s SEQUENCE_LENGTH]\n";
+    std::cout << "               [-i ITERATIONS] [-w WARM_UP_RUNS] [-r RANDOM_SEED] [--enable_graph]\n";
+    std::cout << "\n";
+    std::cout << "BERT Inference Benchmark\n";
+    std::cout << "\n";
+    std::cout << "optional arguments:\n";
+    std::cout << "  -h, --help            show this help message and exit\n";
+    std::cout << "  -e ENGINE, --engine ENGINE\n";
+    std::cout << "                        Path to BERT TensorRT engine\n";
+    std::cout << "  -b BATCH_SIZE, --batch_size BATCH_SIZE\n";
+    std::cout << "                        Batch size(s) to benchmark. Can be specified multiple\n";
+    std::cout << "                        times for more than one batch size. This script\n";
+    std::cout << "                        assumes that the engine has been built with one\n";
+    std::cout << "                        optimization profile for each batch size, and that\n";
+    std::cout << "                        these profiles are in order of increasing batch size.\n";
+    std::cout << "  -s SEQUENCE_LENGTH, --sequence_length SEQUENCE_LENGTH\n";
+    std::cout << "                        Sequence length of the BERT model\n";
+    std::cout << "  -i ITERATIONS, --iterations ITERATIONS\n";
+    std::cout << "                        Number of iterations to run when benchmarking.\n";
+    std::cout << "  -w WARM_UP_RUNS, --warm_up_runs WARM_UP_RUNS\n";
+    std::cout << "                        Number of iterations to run prior to benchmarking.\n";
+    std::cout << "  -r RANDOM_SEED, --random_seed RANDOM_SEED\n";
+    std::cout << "                        Random seed.\n";
+    std::cout << "  --enable_graph\n";
+    std::cout << "                        Enable CUDA Graph.\n";
+    std::cout << std::endl;
+}
+
+int main(int argc, char* argv[])
+{
+
+    Args args;
+
+    const bool argsOK = parseArgs(args, argc, argv);
+    if (args.help)
+    {
+        printHelpInfo();
+        return EXIT_SUCCESS;
+    }
+
+    if (!argsOK)
+    {
+        std::cerr << "Invalid arguments" << std::endl;
+        printHelpInfo();
+        return EXIT_FAILURE;
+    }
+
+    if (args.batchSize.empty())
+    {
+        args.batchSize.push_back(1);
+    }
+
+    const int maxBatchSize = *std::max_element(args.batchSize.begin(), args.batchSize.end());
+
+    BertInference bert(args.engine, maxBatchSize, args.sequenceLength, args.enableGraph);
+
+    std::default_random_engine generator(args.randomSeed);
+    std::uniform_int_distribution<int> distribution(0, std::numeric_limits<int>::max());
+
+    const int pseudoVocabSize = 30522;
+    const int pseudoTypeVocabSize = 2;
+    const int maxInputSize = args.sequenceLength * maxBatchSize;
+
+    std::vector<int> testWordIds(maxInputSize);
+    std::vector<int> testSegmentIds(maxInputSize);
+    std::vector<int> testInputMask(maxInputSize);
+    std::generate(
+        testWordIds.begin(), testWordIds.end(), [&] { return distribution(generator) % pseudoVocabSize; });
+    std::generate(testSegmentIds.begin(), testSegmentIds.end(),
+        [&] { return distribution(generator) % pseudoTypeVocabSize; });
+    std::generate(testInputMask.begin(), testInputMask.end(), [&] { return 1; });
+
+    for (int i = 0; i < args.batchSize.size(); i++)
+    {
+        bert.run(i, args.batchSize[i], (void*) (testWordIds.data()), (void*) (testSegmentIds.data()),
+            (void*) (testInputMask.data()), args.warmUpRuns, args.iterations);
+    }
+
+    for (int i = 0; i < args.batchSize.size(); i++)
+    {
+        bert.reportTiming(i, args.batchSize[i]);
+    }
+
+    return EXIT_SUCCESS;
+}
diff --git a/demo/BERT/inference.ipynb b/demo/BERT/inference.ipynb
new file mode 100644
index 00000000..0c553a3c
--- /dev/null
+++ b/demo/BERT/inference.ipynb
@@ -0,0 +1,357 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Copyright 2019 NVIDIA Corporation. All Rights Reserved.\n",
+    "#\n",
+    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "# you may not use this file except in compliance with the License.\n",
+    "# You may obtain a copy of the License at\n",
+    "#\n",
+    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
+    "#\n",
+    "# Unless required by applicable law or agreed to in writing, software\n",
+    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "# See the License for the specific language governing permissions and\n",
+    "# limitations under the License.\n",
+    "# =============================================================================="
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"https://upload.wikimedia.org/wikipedia/en/6/6d/Nvidia_image_logo.svg\" style=\"width: 90px; float: right;\">\n",
+    "\n",
+    "# QA Inference on BERT using TensorRT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Overview\n",
+    "\n",
+    "Bidirectional Embedding Representations from Transformers (BERT), is a method of pre-training language representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. \n",
+    "\n",
+    "The original paper can be found here: https://arxiv.org/abs/1810.04805.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.a Learning objectives\n",
+    "\n",
+    "This notebook demonstrates:\n",
+    "- Inference on Question Answering (QA) task with BERT Base/Large model\n",
+    "- The use fine-tuned NVIDIA BERT models\n",
+    "- Use of BERT model with TRT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Requirements\n",
+    "\n",
+    "Please refer to the ReadMe file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. BERT Inference: Question Answering\n",
+    "\n",
+    "We can run inference on a fine-tuned BERT model for tasks like Question Answering.\n",
+    "\n",
+    "Here we use a BERT model fine-tuned on a [SQuaD 2.0 Dataset](https://rajpurkar.github.io/SQuAD-explorer/) which contains 100,000+ question-answer pairs on 500+ articles combined with over 50,000 new, unanswerable questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.a Paragraph and Queries\n",
+    "\n",
+    "The paragraph and the questions can be customized by changing the text below. Note that when using models with small sequence lengths, you should use a shorter paragraph:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Paragraph:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "paragraph_text = \"The Apollo program, also known as Project Apollo, was the third United States human spaceflight program carried out by the National Aeronautics and Space Administration (NASA), which accomplished landing the first humans on the Moon from 1969 to 1972. First conceived during Dwight D. Eisenhower's administration as a three-man spacecraft to follow the one-man Project Mercury which put the first Americans in space, Apollo was later dedicated to President John F. Kennedy's national goal of landing a man on the Moon and returning him safely to the Earth by the end of the 1960s, which he proposed in a May 25, 1961, address to Congress. Project Mercury was followed by the two-man Project Gemini. The first manned flight of Apollo was in 1968. Apollo ran from 1961 to 1972, and was supported by the two-man Gemini program which ran concurrently with it from 1962 to 1966. Gemini missions developed some of the space travel techniques that were necessary for the success of the Apollo missions. Apollo used Saturn family rockets as launch vehicles. Apollo/Saturn vehicles were also used for an Apollo Applications Program, which consisted of Skylab, a space station that supported three manned missions in 1973-74, and the Apollo-Soyuz Test Project, a joint Earth orbit mission with the Soviet Union in 1975.\"\n",
+    "\n",
+    "# Short paragraph version for BERT models with max sequence length of 128\n",
+    "short_paragraph_text = \"The Apollo program was the third United States human spaceflight program. First conceived as a three-man spacecraft to follow the one-man Project Mercury which put the first Americans in space, Apollo was dedicated to President John F. Kennedy's national goal of landing a man on the Moon. The first manned flight of Apollo was in 1968. Apollo ran from 1961 to 1972 followed by the Apollo-Soyuz Test Project a joint Earth orbit mission with the Soviet Union in 1975.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Question:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "question_text = \"What project put the first Americans into space?\"\n",
+    "#question_text =  \"What year did the first manned Apollo flight occur?\"\n",
+    "#question_text =  \"What President is credited with the original notion of putting Americans in space?\"\n",
+    "#question_text =  \"Who did the U.S. collaborate with on an Earth orbit mission in 1975?\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this example we ask our BERT model questions related to the following paragraph:\n",
+    "\n",
+    "**The Apollo Program**\n",
+    "_\"The Apollo program, also known as Project Apollo, was the third United States human spaceflight program carried out by the National Aeronautics and Space Administration (NASA), which accomplished landing the first humans on the Moon from 1969 to 1972. First conceived during Dwight D. Eisenhower's administration as a three-man spacecraft to follow the one-man Project Mercury which put the first Americans in space, Apollo was later dedicated to President John F. Kennedy's national goal of landing a man on the Moon and returning him safely to the Earth by the end of the 1960s, which he proposed in a May 25, 1961, address to Congress. Project Mercury was followed by the two-man Project Gemini. The first manned flight of Apollo was in 1968. Apollo ran from 1961 to 1972, and was supported by the two-man Gemini program which ran concurrently with it from 1962 to 1966. Gemini missions developed some of the space travel techniques that were necessary for the success of the Apollo missions. Apollo used Saturn family rockets as launch vehicles. Apollo/Saturn vehicles were also used for an Apollo Applications Program, which consisted of Skylab, a space station that supported three manned missions in 1973-74, and the Apollo-Soyuz Test Project, a joint Earth orbit mission with the Soviet Union in 1975.\"_\n",
+    "\n",
+    "The questions and relative answers expected are shown below:\n",
+    "\n",
+    " - **Q1:** \"What project put the first Americans into space?\" \n",
+    "  - **A1:** \"Project Mercury\"\n",
+    " - **Q2:** \"What program was created to carry out these projects and missions?\"\n",
+    "  - **A2:** \"The Apollo program\"\n",
+    " - **Q3:** \"What year did the first manned Apollo flight occur?\"\n",
+    "  - **A3:** \"1968\"\n",
+    " - **Q4:** \"What President is credited with the original notion of putting Americans in space?\"\n",
+    "  - **A4:** \"John F. Kennedy\"\n",
+    " - **Q5:** \"Who did the U.S. collaborate with on an Earth orbit mission in 1975?\"\n",
+    "  - **A5:** \"Soviet Union\"\n",
+    " - **Q6:** \"How long did Project Apollo run?\"\n",
+    "  - **A6:** \"1961 to 1972\"\n",
+    " - **Q7:** \"What program helped develop space travel techniques that Project Apollo used?\"\n",
+    "  - **A7:** \"Gemini Mission\"\n",
+    " - **Q8:** \"What space station supported three manned missions in 1973-1974?\"\n",
+    "  - **A8:** \"Skylab\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Preprocessing\n",
+    "Let's convert the paragraph and the question to BERT input with the help of the tokenizer:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import helpers.data_processing as dp\n",
+    "import helpers.tokenization as tokenization\n",
+    "\n",
+    "tokenizer = tokenization.FullTokenizer(vocab_file=\"/workspace/bert/models/fine-tuned/bert_tf_v2_large_fp16_128_v2/vocab.txt\", do_lower_case=True)\n",
+    "\n",
+    "# The maximum number of tokens for the question. Questions longer than this will be truncated to this length.\n",
+    "max_query_length = 64\n",
+    "\n",
+    "# When splitting up a long document into chunks, how much stride to take between chunks.\n",
+    "doc_stride = 128\n",
+    "\n",
+    "# The maximum total input sequence length after WordPiece tokenization. \n",
+    "# Sequences longer than this will be truncated, and sequences shorter \n",
+    "max_seq_length = 128\n",
+    "\n",
+    "# Extract tokens from the paragraph\n",
+    "doc_tokens = dp.convert_doc_tokens(short_paragraph_text)\n",
+    "\n",
+    "# Extract features from the paragraph and question\n",
+    "features = dp.convert_example_to_features(doc_tokens, question_text, tokenizer, max_seq_length, doc_stride, max_query_length)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## TensorRT Inference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorrt as trt\n",
+    "TRT_LOGGER = trt.Logger(trt.Logger.INFO)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ctypes\n",
+    "import os\n",
+    "\n",
+    "ctypes.CDLL(\"libnvinfer_plugin.so\", mode=ctypes.RTLD_GLOBAL)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pycuda.driver as cuda\n",
+    "import pycuda.autoinit\n",
+    "import collections\n",
+    "import numpy as np\n",
+    "import time\n",
+    "\n",
+    "# Load the BERT-Large Engine\n",
+    "with open(\"/workspace/bert/engines/bert_large_128.engine\", \"rb\") as f, \\\n",
+    "    trt.Runtime(TRT_LOGGER) as runtime, \\\n",
+    "    runtime.deserialize_cuda_engine(f.read()) as engine, \\\n",
+    "    engine.create_execution_context() as context:\n",
+    "\n",
+    "     # We always use batch size 1.\n",
+    "    input_shape = (max_seq_length, 1)\n",
+    "    input_nbytes = trt.volume(input_shape) * trt.int32.itemsize\n",
+    "    \n",
+    "    # Allocate device memory for inputs.\n",
+    "    d_inputs = [cuda.mem_alloc(input_nbytes) for binding in range(3)]\n",
+    "    # Create a stream in which to copy inputs/outputs and run inference.\n",
+    "    stream = cuda.Stream()\n",
+    "\n",
+    "    # Specify input shapes. These must be within the min/max bounds of the active profile (0th profile in this case)\n",
+    "    # Note that input shapes can be specified on a per-inference basis, but in this case, we only have a single shape.\n",
+    "    for binding in range(3):\n",
+    "        context.set_binding_shape(binding, input_shape)\n",
+    "    assert context.all_binding_shapes_specified\n",
+    "\n",
+    "    # Allocate output buffer by querying the size from the context. This may be different for different input shapes.\n",
+    "    h_output = cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.float32)\n",
+    "    d_output = cuda.mem_alloc(h_output.nbytes)\n",
+    "\n",
+    "    print(\"\\nRunning Inference...\")\n",
+    "\n",
+    "    _NetworkOutput = collections.namedtuple(  # pylint: disable=invalid-name\n",
+    "        \"NetworkOutput\",\n",
+    "        [\"start_logits\", \"end_logits\", \"feature_index\"])\n",
+    "    networkOutputs = []\n",
+    "\n",
+    "    eval_time_elapsed = 0\n",
+    "    for feature_index, feature in enumerate(features):\n",
+    "        # Copy inputs\n",
+    "        input_ids = cuda.register_host_memory(np.ascontiguousarray(feature.input_ids.ravel()))\n",
+    "        segment_ids = cuda.register_host_memory(np.ascontiguousarray(feature.segment_ids.ravel()))\n",
+    "        input_mask = cuda.register_host_memory(np.ascontiguousarray(feature.input_mask.ravel()))\n",
+    "\n",
+    "        eval_start_time = time.time()\n",
+    "        cuda.memcpy_htod_async(d_inputs[0], input_ids, stream)\n",
+    "        cuda.memcpy_htod_async(d_inputs[1], segment_ids, stream)\n",
+    "        cuda.memcpy_htod_async(d_inputs[2], input_mask, stream)\n",
+    "\n",
+    "        # Run inference\n",
+    "        context.execute_async_v2(bindings=[int(d_inp) for d_inp in d_inputs] + [int(d_output)], stream_handle=stream.handle)\n",
+    "        # Synchronize the stream\n",
+    "        stream.synchronize()\n",
+    "        eval_time_elapsed += (time.time() - eval_start_time)\n",
+    "\n",
+    "        # Transfer predictions back from GPU\n",
+    "        cuda.memcpy_dtoh_async(h_output, d_output, stream)\n",
+    "        stream.synchronize()\n",
+    "\n",
+    "        for index, batch in enumerate(h_output):\n",
+    "            # Data Post-processing\n",
+    "            networkOutputs.append(_NetworkOutput(\n",
+    "                start_logits = np.array(batch.squeeze()[:, 0]),\n",
+    "                end_logits = np.array(batch.squeeze()[:, 1]),\n",
+    "                feature_index = feature_index\n",
+    "                ))\n",
+    "\n",
+    "    eval_time_elapsed /= len(features)\n",
+    "    \n",
+    "    print(\"-----------------------------\")\n",
+    "    print(\"Running Inference at {:.3f} Sentences/Sec\".format(1.0/eval_time_elapsed))\n",
+    "    print(\"-----------------------------\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Post-Processing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have the inference results let's extract the actual answer to our question"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "    # The total number of n-best predictions to generate in the nbest_predictions.json output file\n",
+    "    n_best_size = 20\n",
+    "\n",
+    "    # The maximum length of an answer that can be generated. This is needed \n",
+    "    #  because the start and end predictions are not conditioned on one another\n",
+    "    max_answer_length = 30\n",
+    "\n",
+    "    prediction, nbest_json, scores_diff_json = dp.get_predictions(doc_tokens, features,\n",
+    "        networkOutputs, n_best_size, max_answer_length)\n",
+    "    \n",
+    "    for index, output in enumerate(networkOutputs):\n",
+    "        print(\"Processing output\")\n",
+    "        print(\"Answer: '{}'\".format(prediction))\n",
+    "        print(\"with prob: {:.3f}%\".format(nbest_json[0]['probability'] * 100.0))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/demo/BERT/inference.py b/demo/BERT/inference.py
new file mode 100644
index 00000000..9f82c924
--- /dev/null
+++ b/demo/BERT/inference.py
@@ -0,0 +1,254 @@
+#!/usr/bin/env python3
+# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+This script uses a prebuilt TensorRT BERT QA Engine to answer a question 
+based on the provided passage. It additionally includes an interactive mode 
+where multiple questions can be asked.
+"""
+
+import time
+import json
+import ctypes
+import argparse
+import collections
+import numpy as np
+import tensorrt as trt
+import pycuda.driver as cuda
+import pycuda.autoinit
+
+import helpers.tokenization as tokenization
+import helpers.data_processing as dp
+
+TRT_LOGGER = trt.Logger(trt.Logger.INFO)
+
+def parse_args():
+    """
+    Parse command line arguments
+    """
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument('-e', '--engine',
+            help='Path to BERT TensorRT engine')
+    parser.add_argument("-b", "--batch-size", default=1, help="Batch size for inference.", type=int)
+    parser.add_argument('-p', '--passage', nargs='*',
+            help='Text for paragraph/passage for BERT QA',
+            default='')
+    parser.add_argument('-pf', '--passage-file',
+            help='File containing input passage',
+            default='')
+    parser.add_argument('-q', '--question', nargs='*',
+            help='Text for query/question for BERT QA',
+            default='')
+    parser.add_argument('-qf', '--question-file',
+            help='File containing input question',
+            default='')
+    parser.add_argument('-sq', '--squad-json',
+            help='SQuAD json file',
+            default='')
+    parser.add_argument('-o', '--output-prediction-file',
+            help='Output prediction file for SQuAD evaluation',
+            default='./predictions.json')
+    parser.add_argument('-v', '--vocab-file',
+            help='Path to file containing entire understandable vocab')
+    parser.add_argument('-s', '--sequence-length',
+            help='The sequence length to use. Defaults to 128',
+            default=128, type=int)
+    parser.add_argument('--max-query-length',
+            help='The maximum length of a query in number of tokens. Queries longer than this will be truncated',
+            default=64, type=int)
+    parser.add_argument('--max-answer-length',
+            help='The maximum length of an answer that can be generated',
+            default=30, type=int)
+    parser.add_argument('--n-best-size',
+            help='Total number of n-best predictions to generate in the nbest_predictions.json output file',
+            default=20, type=int)
+    args, _ = parser.parse_known_args()
+    return args
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    paragraph_text = None
+    squad_examples = None
+    output_prediction_file = None
+
+    if not args.passage == '':
+        paragraph_text = ' '.join(args.passage)
+    elif not args.passage_file == '':
+        f = open(args.passage_file, 'r')
+        paragraph_text = f.read()
+    elif not args.squad_json == '':
+        squad_examples = dp.read_squad_json(args.squad_json)
+        output_prediction_file = args.output_prediction_file
+    else:
+        paragraph_text = input("Paragraph: ")
+
+    question_text = None
+    if not args.question == '':
+        question_text = ' '.join(args.question)
+    elif not args.question_file == '':
+        f = open(args.question_file, 'r')
+        question_text = f.read()
+
+    tokenizer = tokenization.FullTokenizer(vocab_file=args.vocab_file, do_lower_case=True)
+    # When splitting up a long document into chunks, how much stride to take between chunks.
+    doc_stride = 128
+    # The maximum total input sequence length after WordPiece tokenization.
+    # Sequences longer than this will be truncated, and sequences shorter
+    max_seq_length = args.sequence_length
+
+    def question_features(tokens, question):
+        # Extract features from the paragraph and question
+        return dp.convert_example_to_features(tokens, question, tokenizer, max_seq_length, doc_stride, args.max_query_length)
+
+    # Import necessary plugins for BERT TensorRT
+    handle = ctypes.CDLL("libnvinfer_plugin.so", mode=ctypes.RTLD_GLOBAL)
+    if not handle:
+        raise RuntimeError("Could not load plugin library. Is `libnvinfer_plugin.so` on your LD_LIBRARY_PATH?")
+
+    # The first context created will use the 0th profile. A new context must be created
+    # for each additional profile needed. Here, we only use batch size 1, thus we only need the first profile.
+    with open(args.engine, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime, \
+        runtime.deserialize_cuda_engine(f.read()) as engine, engine.create_execution_context() as context:
+
+        # select engine profile
+        selected_profile = -1
+        num_binding_per_profile = engine.num_bindings // engine.num_optimization_profiles
+        for idx in range(engine.num_optimization_profiles):
+            profile_shape = engine.get_profile_shape(profile_index = idx, binding = idx * num_binding_per_profile)
+            if profile_shape[0][1] <= args.batch_size and profile_shape[2][1] >= args.batch_size:
+                selected_profile = idx
+                break
+        if selected_profile == -1:
+            raise RuntimeError("Could not find any profile that can run batch size {}.".format(args.batch_size))
+
+        context.active_optimization_profile = selected_profile
+        binding_idx_offset = selected_profile * num_binding_per_profile
+
+        # Specify input shapes. These must be within the min/max bounds of the active profile 
+        # Note that input shapes can be specified on a per-inference basis, but in this case, we only have a single shape.
+        input_shape = (max_seq_length, args.batch_size)
+        input_nbytes = trt.volume(input_shape) * trt.int32.itemsize
+        for binding in range(3):
+            context.set_binding_shape(binding_idx_offset + binding, input_shape)
+        assert context.all_binding_shapes_specified
+
+        # Create a stream in which to copy inputs/outputs and run inference.
+        stream = cuda.Stream()
+
+        # Allocate device memory for inputs.
+        d_inputs = [cuda.mem_alloc(input_nbytes) for binding in range(3)]
+
+        # Allocate output buffer by querying the size from the context. This may be different for different input shapes.
+        h_output = cuda.pagelocked_empty(tuple(context.get_binding_shape(binding_idx_offset + 3)), dtype=np.float32)
+        d_output = cuda.mem_alloc(h_output.nbytes)
+
+        def inference(features, tokens):
+            global h_output
+
+            _NetworkOutput = collections.namedtuple(  # pylint: disable=invalid-name
+                    "NetworkOutput",
+                    ["start_logits", "end_logits", "feature_index"])
+            networkOutputs = []
+
+            eval_time_elapsed = 0
+            for feature_index, feature in enumerate(features):
+                # Copy inputs
+                input_ids_batch = np.dstack([feature.input_ids] * args.batch_size).squeeze()
+                segment_ids_batch = np.dstack([feature.segment_ids] * args.batch_size).squeeze()
+                input_mask_batch = np.dstack([feature.input_mask] * args.batch_size).squeeze()
+
+                input_ids = cuda.register_host_memory(np.ascontiguousarray(input_ids_batch.ravel()))
+                segment_ids = cuda.register_host_memory(np.ascontiguousarray(segment_ids_batch.ravel()))
+                input_mask = cuda.register_host_memory(np.ascontiguousarray(input_mask_batch.ravel()))
+
+                eval_start_time = time.time()
+                cuda.memcpy_htod_async(d_inputs[0], input_ids, stream)
+                cuda.memcpy_htod_async(d_inputs[1], segment_ids, stream)
+                cuda.memcpy_htod_async(d_inputs[2], input_mask, stream)
+
+                # Run inference
+                context.execute_async_v2(bindings=[0 for i in range(binding_idx_offset)] + [int(d_inp) for d_inp in d_inputs] + [int(d_output)], stream_handle=stream.handle)
+                # Synchronize the stream
+                stream.synchronize()
+                eval_time_elapsed += (time.time() - eval_start_time)
+
+                # Transfer predictions back from GPU
+                cuda.memcpy_dtoh_async(h_output, d_output, stream)
+                stream.synchronize()
+
+                # Only retrieve and post-process the first batch
+                batch = h_output[0]
+                networkOutputs.append(_NetworkOutput(
+                    start_logits = np.array(batch.squeeze()[:, 0]),
+                    end_logits = np.array(batch.squeeze()[:, 1]),
+                    feature_index = feature_index
+                    ))
+
+            eval_time_elapsed /= len(features)
+
+            # Total number of n-best predictions to generate in the nbest_predictions.json output file
+            n_best_size = 20
+
+            # The maximum length of an answer that can be generated. This is needed
+            # because the start and end predictions are not conditioned on one another
+            max_answer_length = 30
+
+            prediction, nbest_json, scores_diff_json = dp.get_predictions(tokens, features,
+                    networkOutputs, args.n_best_size, args.max_answer_length)
+
+            return eval_time_elapsed, prediction, nbest_json
+
+        def print_single_query(eval_time_elapsed, prediction, nbest_json):
+            print("------------------------")
+            print("Running inference in {:.3f} Sentences/Sec".format(args.batch_size/eval_time_elapsed))
+            print("------------------------")
+
+            print("Answer: '{}'".format(prediction))
+            print("With probability: {:.3f}".format(nbest_json[0]['probability'] * 100.0))
+
+        if squad_examples:
+            all_predictions = collections.OrderedDict()
+
+            for example in squad_examples:
+                features = question_features(example.doc_tokens, example.question_text)
+                eval_time_elapsed, prediction, nbest_json = inference(features, example.doc_tokens)
+                all_predictions[example.id] = prediction
+
+            with open(output_prediction_file, "w") as f:
+                f.write(json.dumps(all_predictions, indent=4))
+                print("\nOutput dump to {}".format(output_prediction_file))
+        else:
+            # Extract tokecs from the paragraph
+            doc_tokens = dp.convert_doc_tokens(paragraph_text)
+
+            if question_text:
+                print("\nPassage: {}".format(paragraph_text))
+                print("\nQuestion: {}".format(question_text))
+
+                features = question_features(doc_tokens, question_text)
+                eval_time_elapsed, prediction, nbest_json = inference(features, doc_tokens)
+                print_single_query(eval_time_elapsed, prediction, nbest_json)
+
+            else:
+                # If no question text is provided, loop until the question is 'exit'
+                EXIT_CMDS = ["exit", "quit"]
+                question_text = input("Question (to exit, type one of {:}): ".format(EXIT_CMDS))
+
+                while question_text.strip() not in EXIT_CMDS:
+                    features = question_features(doc_tokens, question_text)
+                    eval_time_elapsed, prediction, nbest_json = inference(features, doc_tokens)
+                    print_single_query(eval_time_elapsed, prediction, nbest_json)
+                    question_text = input("Question (to exit, type one of {:}): ".format(EXIT_CMDS))
diff --git a/demo/BERT/inference_c.py b/demo/BERT/inference_c.py
new file mode 100644
index 00000000..d0a27fe1
--- /dev/null
+++ b/demo/BERT/inference_c.py
@@ -0,0 +1,214 @@
+#!/usr/bin/env python3
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+This script uses a prebuilt TensorRT BERT QA Engine to answer a question 
+based on the provided passage. It additionally includes an interactive mode 
+where multiple questions can be asked.
+"""
+
+import os
+import sys
+import time
+import json
+import argparse
+import collections
+import numpy as np
+
+import helpers.tokenization as tokenization
+import helpers.data_processing as dp
+
+sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), 'build'))
+
+import infer_c
+
+def parse_args():
+    """
+    Parse command line arguments
+    """
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument('-e', '--engine',
+            help='Path to BERT TensorRT engine')
+    parser.add_argument('-p', '--passage', nargs='*',
+            help='Text for paragraph/passage for BERT QA',
+            default='')
+    parser.add_argument('-pf', '--passage-file',
+            help='File containing input passage',
+            default='')
+    parser.add_argument('-q', '--question', nargs='*',
+            help='Text for query/question for BERT QA',
+            default='')
+    parser.add_argument('-qf', '--question-file',
+            help='File containing input question',
+            default='')
+    parser.add_argument('-sq', '--squad-json',
+            help='SQuAD json file',
+            default='')
+    parser.add_argument('-o', '--output-prediction-file',
+            help='Output prediction file for SQuAD evaluation',
+            default='./predictions.json')
+    parser.add_argument('-v', '--vocab-file',
+            help='Path to file containing entire understandable vocab')
+    parser.add_argument('-s', '--sequence-length',
+            help='The sequence length to use. Defaults to 128',
+            default=128, type=int)
+    parser.add_argument('--max-query-length',
+            help='The maximum length of a query in number of tokens. Queries longer than this will be truncated',
+            default=64, type=int)
+    parser.add_argument('--max-answer-length',
+            help='The maximum length of an answer that can be generated',
+            default=30, type=int)
+    parser.add_argument('--n-best-size',
+            help='Total number of n-best predictions to generate in the nbest_predictions.json output file',
+            default=20, type=int)
+    parser.add_argument('--enable-graph',
+            help='Enable CUDA Graph support',
+            action='store_true',
+            default=False)
+    args = parser.parse_args()
+    return args
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    paragraph_text = None
+    squad_examples = None
+    output_prediction_file = None
+
+    if not args.passage == '':
+        paragraph_text = ' '.join(args.passage)
+    elif not args.passage_file == '':
+        f = open(args.passage_file, 'r')
+        paragraph_text = f.read()
+    elif not args.squad_json == '':
+        squad_examples = dp.read_squad_json(args.squad_json)
+        output_prediction_file = args.output_prediction_file
+    else:
+        paragraph_text = input("Paragraph: ")
+
+    question_text = None
+    if not args.question == '':
+        question_text = ' '.join(args.question)
+    elif not args.question_file == '':
+        f = open(args.question_file, 'r')
+        question_text = f.read()
+
+    tokenizer = tokenization.FullTokenizer(vocab_file=args.vocab_file, do_lower_case=True)
+    # When splitting up a long document into chunks, how much stride to take between chunks.
+    doc_stride = 128
+    # The maximum total input sequence length after WordPiece tokenization.
+    # Sequences longer than this will be truncated, and sequences shorter
+    max_seq_length = args.sequence_length
+
+    def question_features(tokens, question):
+        # Extract features from the paragraph and question
+        return dp.convert_example_to_features(tokens, question, tokenizer, max_seq_length, doc_stride, args.max_query_length)
+
+    # The first context created will use the 0th profile. A new context must be created
+    # for each additional profile needed. Here, we only use batch size 1, thus we only need the first profile.
+        
+    # We always use batch size 1.
+    # Specify input shapes as (max_seq_length, 1). 
+    # These must be within the min/max bounds of the active profile (0th profile in this case)
+    # Note that input shapes can be specified on a per-inference basis, but in this case, we only have a single shape.
+    bert = infer_c.bert_inf(args.engine, 1, max_seq_length, args.enable_graph)
+    bert.prepare(1)
+
+    def inference(features, tokens):
+
+        _NetworkOutput = collections.namedtuple(  # pylint: disable=invalid-name
+                "NetworkOutput",
+                ["start_logits", "end_logits", "feature_index"])
+        networkOutputs = []
+
+        eval_time_elapsed = 0
+        for feature_index, feature in enumerate(features):
+            # Copy inputs
+            input_ids = np.ascontiguousarray(feature.input_ids.ravel())
+            segment_ids = np.ascontiguousarray(feature.segment_ids.ravel())
+            input_mask = np.ascontiguousarray(feature.input_mask.ravel())
+
+            eval_start_time = time.time()
+
+            # Run inference
+            h_output = bert.run(input_ids, segment_ids, input_mask)
+            eval_time_elapsed += (time.time() - eval_start_time)
+
+
+            for index, batch in enumerate(h_output):
+                # Data Post-processing
+                networkOutputs.append(_NetworkOutput(
+                    start_logits = np.array(batch.squeeze()[:, 0]),
+                    end_logits = np.array(batch.squeeze()[:, 1]),
+                    feature_index = feature_index
+                    ))
+
+        eval_time_elapsed /= len(features)
+
+        # Total number of n-best predictions to generate in the nbest_predictions.json output file
+        n_best_size = 20
+
+        # The maximum length of an answer that can be generated. This is needed
+        # because the start and end predictions are not conditioned on one another
+        max_answer_length = 30
+
+        prediction, nbest_json, scores_diff_json = dp.get_predictions(tokens, features,
+                networkOutputs, args.n_best_size, args.max_answer_length)
+
+        return eval_time_elapsed, prediction, nbest_json
+
+    def print_single_query(eval_time_elapsed, prediction, nbest_json):
+        print("------------------------")
+        print("Running inference in {:.3f} Sentences/Sec".format(1.0/eval_time_elapsed))
+        print("------------------------")
+
+        print("Answer: '{}'".format(prediction))
+        print("With probability: {:.3f}".format(nbest_json[0]['probability'] * 100.0))
+
+    if squad_examples:
+        all_predictions = collections.OrderedDict()
+
+        for example_index, example in enumerate(squad_examples):
+            print("Processing example {} of {}".format(example_index+1, len(squad_examples)), end="\r")
+            features = question_features(example.doc_tokens, example.question_text)
+            eval_time_elapsed, prediction, nbest_json = inference(features, example.doc_tokens)
+            all_predictions[example.id] = prediction
+
+        with open(output_prediction_file, "w") as f:
+            f.write(json.dumps(all_predictions, indent=4))
+            print("\nOutput dump to {}".format(output_prediction_file))
+    else:
+        # Extract tokecs from the paragraph
+        doc_tokens = dp.convert_doc_tokens(paragraph_text)
+
+        if question_text:
+            print("\nPassage: {}".format(paragraph_text))
+            print("\nQuestion: {}".format(question_text))
+
+            features = question_features(doc_tokens, question_text)
+            eval_time_elapsed, prediction, nbest_json = inference(features, doc_tokens)
+            print_single_query(eval_time_elapsed, prediction, nbest_json)
+
+        else:
+            # If no question text is provided, loop until the question is 'exit'
+            EXIT_CMDS = ["exit", "quit"]
+            question_text = input("Question (to exit, type one of {:}): ".format(EXIT_CMDS))
+
+            while question_text.strip() not in EXIT_CMDS:
+                features = question_features(doc_tokens, question_text)
+                eval_time_elapsed, prediction, nbest_json = inference(features, doc_tokens)
+                print_single_query(eval_time_elapsed, prediction, nbest_json)
+                question_text = input("Question (to exit, type one of {:}): ".format(EXIT_CMDS))
diff --git a/demo/BERT/perf.py b/demo/BERT/perf.py
new file mode 100644
index 00000000..afa5131a
--- /dev/null
+++ b/demo/BERT/perf.py
@@ -0,0 +1,133 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import argparse
+import ctypes
+import numpy as np
+import tensorrt as trt
+import pycuda.driver as cuda
+import pycuda.autoinit
+
+import numpy as np
+
+TRT_LOGGER = trt.Logger(trt.Logger.ERROR)
+
+class DeviceBuffer(object):
+    def __init__(self, shape, dtype=trt.int32):
+        self.buf = cuda.mem_alloc(trt.volume(shape) * dtype.itemsize)
+
+    def binding(self):
+        return int(self.buf)
+
+    def free(self):
+        self.buf.free()
+
+
+def main():
+    parser = argparse.ArgumentParser(description='BERT Inference Benchmark')
+    parser.add_argument("-e", "--engine", help='Path to BERT TensorRT engine')
+    parser.add_argument('-b', '--batch-size', default=[], action="append", help='Batch size(s) to benchmark. Can be specified multiple times for more than one batch size. This script assumes that the engine has been built with one optimization profile for each batch size, and that these profiles are in order of increasing batch size.', type=int)
+    parser.add_argument('-s', '--sequence-length', default=128, help='Sequence length of the BERT model', type=int)
+    parser.add_argument('-i', '--iterations', default=200, help='Number of iterations to run when benchmarking each batch size.', type=int)
+    parser.add_argument('-w', '--warm-up-runs', default=10, help='Number of iterations to run prior to benchmarking.', type=int)
+    parser.add_argument('-r', '--random-seed', required=False, default=12345, help='Random seed.', type=int)
+    args, _ = parser.parse_known_args()
+    args.batch_size = args.batch_size or [1]
+
+    # Import necessary plugins for BERT TensorRT
+    ctypes.CDLL("libnvinfer_plugin.so", mode=ctypes.RTLD_GLOBAL)
+
+    with open(args.engine, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(f.read()) as engine, engine.create_execution_context() as context:
+        # Allocate buffers large enough to store the largest batch size
+        max_input_shape = (args.sequence_length, max(args.batch_size))
+        max_output_shape = (args.sequence_length, max(args.batch_size), 2, 1, 1)
+        buffers = [
+            DeviceBuffer(max_input_shape),
+            DeviceBuffer(max_input_shape),
+            DeviceBuffer(max_input_shape),
+            DeviceBuffer(max_output_shape)
+        ]
+
+        # Prepare random input
+        pseudo_vocab_size = 30522
+        pseudo_type_vocab_size = 2
+        np.random.seed(args.random_seed)
+        test_word_ids = np.random.randint(0, pseudo_vocab_size, (args.sequence_length, max(args.batch_size)), dtype=np.int32)
+        test_segment_ids = np.random.randint(0, pseudo_type_vocab_size, (args.sequence_length, max(args.batch_size)), dtype=np.int32)
+        test_input_mask = np.ones((args.sequence_length, max(args.batch_size)), dtype=np.int32)
+
+        # Copy input h2d
+        cuda.memcpy_htod(buffers[0].buf, test_word_ids.ravel())
+        cuda.memcpy_htod(buffers[1].buf, test_segment_ids.ravel())
+        cuda.memcpy_htod(buffers[2].buf, test_input_mask.ravel())
+
+        num_binding_per_profile = engine.num_bindings // engine.num_optimization_profiles
+
+        bench_times = {}
+
+        for idx, batch_size in enumerate(sorted(args.batch_size)):
+            context.active_optimization_profile = idx
+
+            # Each profile has unique bindings
+            binding_idx_offset = idx * num_binding_per_profile
+            bindings = [0] * binding_idx_offset + [buf.binding() for buf in buffers]
+
+            shapes = {
+                "input_ids": (args.sequence_length, batch_size),
+                "segment_ids": (args.sequence_length, batch_size),
+                "input_mask": (args.sequence_length, batch_size),
+            }
+
+            for binding, shape in shapes.items():
+                context.set_binding_shape(engine[binding] + binding_idx_offset, shape)
+            assert context.all_binding_shapes_specified
+
+            # Inference
+            total_time = 0
+            start = cuda.Event()
+            end = cuda.Event()
+            stream = cuda.Stream()
+
+            # Warmup
+            for _ in range(args.warm_up_runs):
+                context.execute_async_v2(bindings=bindings, stream_handle=stream.handle)
+                stream.synchronize()
+
+            # Timing loop
+            times = []
+            for _ in range(args.iterations):
+                start.record(stream)
+                context.execute_async_v2(bindings=bindings, stream_handle=stream.handle)
+                end.record(stream)
+                stream.synchronize()
+                times.append(end.time_since(start))
+
+            # Compute average time, 95th percentile time and 99th percentile time.
+            bench_times[batch_size] = times
+
+        [b.free() for b in buffers]
+
+        for batch_size, times in bench_times.items():
+            total_time = sum(times)
+            avg_time = total_time / float(len(times))
+            times.sort()
+            percentile95 = times[int(len(times) * 0.95)]
+            percentile99 = times[int(len(times) * 0.99)]
+            print("Running {:} iterations with Batch Size: {:}\n\tTotal Time: {:} ms \tAverage Time: {:} ms\t95th Percentile Time: {:} ms\t99th Percentile Time: {:}".format(args.iterations, batch_size, total_time, avg_time, percentile95, percentile99))
+
+
+if __name__ == '__main__':
+    main()
diff --git a/demo/BERT/scripts/download_model.sh b/demo/BERT/scripts/download_model.sh
new file mode 100755
index 00000000..04d15ac8
--- /dev/null
+++ b/demo/BERT/scripts/download_model.sh
@@ -0,0 +1,74 @@
+#!/bin/bash
+
+# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+# Setup default parameters (if no command-line parameters given)
+VERSION='v2'
+MODEL='large'
+FT_PRECISION='fp16'
+SEQ_LEN='128'
+FW='tf'
+
+while test $# -gt 0
+do
+    case "$1" in
+        -h) echo "Usage: sh download_model.sh [tf|pyt] [base|large] [fp16|fp32] [128|384] [v2|v1_1]"
+            exit 0
+            ;;
+        base) MODEL='base'
+            ;;
+        large) MODEL='large'
+            ;;
+        fp16) FT_PRECISION='fp16'
+            ;;
+        fp32) FT_PRECISION='fp32'
+            ;;
+        128) SEQ_LEN='128'
+            ;;
+        384) SEQ_LEN='384'
+            ;;
+        v2) VERSION='v2'
+            ;;
+        v1_1) VERSION='v1_1'
+            ;;
+        tf) FW='tf'
+            ;;
+        pyt) FW='pyt'
+            ;;
+        *) echo "Invalid argument $1...exiting"
+            exit 0
+            ;;
+    esac
+    shift
+done
+
+# Prepare the download directory
+mkdir -p /workspace/TensorRT/demo/BERT/models/fine-tuned
+cd /workspace/TensorRT/demo/BERT/models/fine-tuned
+
+# Download the BERT fine-tuned model
+echo "Downloading BERT-${FW} ${MODEL} checkpoints with precision ${FT_PRECISION} and sequence length ${SEQ_LEN} and fine-tuned for SQuAD ${VERSION} from NGC"
+if [ "${FW}" = 'tf' ]; then
+    ngc registry model download-version nvidia/bert_tf_${VERSION}_${MODEL}_${FT_PRECISION}_${SEQ_LEN}:2
+elif [ "${FW}" = 'pyt' ]; then
+    if [ "${MODEL}" != 'large' ] || [ "${VERSION}" != 'v1_1' ]; then
+        echo "Skipping. Currently only BERT-large checkpoint fine-tuned for SQuAD v1.1 available in QAT (PyTorch) workflow."
+    else
+        ngc registry model download-version nvidia/bert_pyt_onnx_large_qa_squad11_amp_fake_quant:1
+    fi
+else
+    echo "Invalid framework specified for checkpoint. Run download_model.sh -h for help."
+fi
diff --git a/demo/BERT/scripts/download_squad.sh b/demo/BERT/scripts/download_squad.sh
new file mode 100755
index 00000000..72afc611
--- /dev/null
+++ b/demo/BERT/scripts/download_squad.sh
@@ -0,0 +1,42 @@
+#!/bin/bash
+
+# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Setup default parameters (if no command-line parameters given)
+VERSION='v1.1'
+
+while test $# -gt 0
+do
+    case "$1" in
+        -h) echo "Usage: sh download_squad.sh [v2_0|v1_1]"
+            exit 0
+            ;;
+        v2_0) VERSION='v2.0'
+            ;;
+        v1_1) VERSION='v1.1'
+            ;;
+        *) echo "Invalid argument $1...exiting"
+            exit 0
+            ;;
+    esac
+    shift
+done
+
+# Download the SQuAD training and dev datasets
+echo "Downloading SQuAD-${VERSION} training and dev datasets"
+mkdir -p /workspace/TensorRT/demo/BERT/squad
+cd /workspace/TensorRT/demo/BERT/squad
+wget https://rajpurkar.github.io/SQuAD-explorer/dataset/train-${VERSION}.json
+wget https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-${VERSION}.json
diff --git a/demo/BERT/scripts/inference_benchmark.sh b/demo/BERT/scripts/inference_benchmark.sh
new file mode 100755
index 00000000..da66dad5
--- /dev/null
+++ b/demo/BERT/scripts/inference_benchmark.sh
@@ -0,0 +1,283 @@
+#!/bin/bash
+
+# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Usage: run_benchmark(batch_sizes, model_variant: (base/large), precision: (int8/int8-qat/fp16/fp32), sequence_length, max_batch_size, gpu_arch)
+run_benchmark() {
+BATCH_SIZES="${1}"
+MODEL_VARIANT="${2}"
+PRECISION="${3}"
+SEQUENCE_LENGTH="${4}"
+MAX_BATCH="${5}"
+GPU_ARCH="${6}"
+
+if [ "${PRECISION}" == "int8" ] || [ "${PRECISION}" == "int8-qat" ]; then
+    CHECKPOINT_PRECISION="fp16"
+else
+    CHECKPOINT_PRECISION="${PRECISION}"
+fi;
+CHECKPOINTS_DIR="/workspace/TensorRT/demo/BERT/models/fine-tuned/bert_tf_v2_${MODEL_VARIANT}_${CHECKPOINT_PRECISION}_${SEQUENCE_LENGTH}_v2"
+SQUAD_DIR="/workspace/TensorRT/demo/BERT/squad"
+ENGINE_NAME="/workspace/TensorRT/demo/BERT/engines/bert_${MODEL_VARIANT}_${PRECISION}_bs${MAX_BATCH}_seqlen${SEQUENCE_LENGTH}_benchmark.engine"
+# QAT Checkpoint - available only for BERT-Large
+QAT_CHECKPOINT="/workspace/TensorRT/demo/BERT/models/fine-tuned/bert_pyt_onnx_large_qa_squad11_amp_fake_quant_v1/bert_large_v1_1_fake_quant.onnx"
+CUDAGRAPH_PERFBIN="/workspace/TensorRT/demo/BERT/build/perf"
+
+echo "==== Benchmarking BERT ${MODEL_VARIANT} ${PRECISION} SEQLEN ${SEQUENCE_LENGTH} on ${GPU_ARCH} ===="
+if [ ! -f ${ENGINE_NAME} ]; then
+    if [ ! -d ${CHECKPOINTS_DIR} ]; then
+        echo "Downloading checkpoints: scripts/download_model.sh ${MODEL_VARIANT} ${CHECKPOINT_PRECISION} ${SEQUENCE_LENGTH}"
+        scripts/download_model.sh "${MODEL_VARIANT}" "${CHECKPOINT_PRECISION}" "${SEQUENCE_LENGTH}"
+    fi;
+    if [ "${PRECISION}" == "int8-qat" ]; then
+        if [ ${MODEL_VARIANT} != "large" ]; then
+            echo "Skipping: BERT-base not supported for int8 (QAT)"
+            return
+        fi;
+        if [ ! -f ${QAT_CHECKPOINT} ]; then
+            echo "Downloading QAT checkpoint: scripts/download_model.sh pyt v1_1 ${MODEL_VARIANT}"
+            scripts/download_model.sh pyt v1_1 "${MODEL_VARIANT}"
+        fi;
+        PRECISION="int8"
+        BUILDER_ARGS="-x ${QAT_CHECKPOINT}"
+    else
+        BUILDER_ARGS="-m ${CHECKPOINTS_DIR}/model.ckpt-8144"
+    fi;
+    BUILDER_ARGS="${BUILDER_ARGS} -o ${ENGINE_NAME} ${BATCH_SIZES} -s ${SEQUENCE_LENGTH} -c ${CHECKPOINTS_DIR} -v ${CHECKPOINTS_DIR}/vocab.txt --${PRECISION}"
+    if [ "${PRECISION}" == "int8" ]; then
+        BUILDER_ARGS="${BUILDER_ARGS} --fp16 --strict --calib-num 1"
+        if [ "${GPU_ARCH}" == "Ampere" ] || [ "${GPU_ARCH}" == "Turing" ]; then
+            BUILDER_ARGS="${BUILDER_ARGS} -iln -imh"
+        elif [ "${GPU_ARCH}" == "Xavier" ]; then
+            BUILDER_ARGS="${BUILDER_ARGS} -iln"
+        fi;
+    fi;
+
+    echo "Building engine: python3 builder.py ${BUILDER_ARGS}"
+    python3 builder.py ${BUILDER_ARGS}
+fi;
+
+
+if [ "${GPU_ARCH}" == "Ampere" ]; then
+    # Use more iterations for faster GPUs
+    NUM_ITERATIONS=2000
+else
+    NUM_ITERATIONS=1000
+fi;
+if [ -f ${CUDAGRAPH_PERFBIN} ]; then
+    echo "Running benchmark with CUDA graph acceleration: perf ${BATCH_SIZES} -s ${SEQUENCE_LENGTH} -e ${ENGINE_NAME} -w 100 -i ${NUM_ITERATIONS} --enable_graph"
+    ${CUDAGRAPH_PERFBIN} ${BATCH_SIZES} -s ${SEQUENCE_LENGTH} -e ${ENGINE_NAME} -w 100 -i ${NUM_ITERATIONS} --enable_graph
+else
+    echo "Running benchmark: perf.py ${BATCH_SIZES} -s ${SEQUENCE_LENGTH} -e ${ENGINE_NAME} -w 100 -i ${NUM_ITERATIONS}"
+    python3 perf.py ${BATCH_SIZES} -s ${SEQUENCE_LENGTH} -e ${ENGINE_NAME} -w 100 -i ${NUM_ITERATIONS}
+fi;
+echo
+}
+
+arg_gpu="Volta"
+arg_help=0
+while [[ "$#" -gt 0 ]]; do case $1 in
+  --gpu) arg_gpu="$2"; shift;;
+  -h|--help) arg_help=1;;
+  *) echo "Unknown parameter passed: $1"; echo "For help type: $0 --help"; exit 1;
+esac; shift; done
+if [ "$arg_help" -eq "1" ]; then
+    echo "Usage: $0 [options]"
+    echo " --help or -h  : Print this help menu."
+    echo " --gpu <arch>  : GPU arch. Options: 'Volta', 'Xavier', 'Turing', 'Ampere'"
+    exit;
+fi
+
+mkdir -p /workspace/TensorRT/demo/BERT/engines
+
+# BERT BASE
+
+## INT8
+# run_benchmark "-b 1 -b 2 -b 4 -b 8 -b 12 -b 16 -b 24 -b 32" "base" "int8" "128" "32" "${arg_gpu}"
+run_benchmark "-b 1" "base" "int8" "128" "1" "${arg_gpu}"
+run_benchmark "-b 2" "base" "int8" "128" "2" "${arg_gpu}"
+run_benchmark "-b 4" "base" "int8" "128" "4" "${arg_gpu}"
+run_benchmark "-b 8" "base" "int8" "128" "8" "${arg_gpu}"
+run_benchmark "-b 12" "base" "int8" "128" "12" "${arg_gpu}"
+run_benchmark "-b 16" "base" "int8" "128" "16" "${arg_gpu}"
+run_benchmark "-b 24" "base" "int8" "128" "24" "${arg_gpu}"
+run_benchmark "-b 32" "base" "int8" "128" "32" "${arg_gpu}"
+run_benchmark "-b 64" "base" "int8" "128" "64" "${arg_gpu}"
+run_benchmark "-b 128" "base" "int8" "128" "128" "${arg_gpu}"
+
+# run_benchmark "-b 1 -b 2 -b 4 -b 8 -b 12 -b 16 -b 24 -b 32" "base" "int8" "384" "32" "${arg_gpu}"
+run_benchmark "-b 1" "base" "int8" "384" "1" "${arg_gpu}"
+run_benchmark "-b 2" "base" "int8" "384" "2" "${arg_gpu}"
+run_benchmark "-b 4" "base" "int8" "384" "4" "${arg_gpu}"
+run_benchmark "-b 8" "base" "int8" "384" "8" "${arg_gpu}"
+run_benchmark "-b 12" "base" "int8" "384" "12" "${arg_gpu}"
+run_benchmark "-b 16" "base" "int8" "384" "16" "${arg_gpu}"
+run_benchmark "-b 24" "base" "int8" "384" "24" "${arg_gpu}"
+run_benchmark "-b 32" "base" "int8" "384" "32" "${arg_gpu}"
+run_benchmark "-b 64" "base" "int8" "384" "64" "${arg_gpu}"
+run_benchmark "-b 128" "base" "int8" "384" "128" "${arg_gpu}"
+
+## FP16
+# run_benchmark "-b 1 -b 2 -b 4 -b 8 -b 12 -b 16 -b 24 -b 32" "base" "fp16" "128" "32" "${arg_gpu}"
+run_benchmark "-b 1" "base" "fp16" "128" "1" "${arg_gpu}"
+run_benchmark "-b 2" "base" "fp16" "128" "2" "${arg_gpu}"
+run_benchmark "-b 4" "base" "fp16" "128" "4" "${arg_gpu}"
+run_benchmark "-b 8" "base" "fp16" "128" "8" "${arg_gpu}"
+run_benchmark "-b 12" "base" "fp16" "128" "12" "${arg_gpu}"
+run_benchmark "-b 16" "base" "fp16" "128" "16" "${arg_gpu}"
+run_benchmark "-b 24" "base" "fp16" "128" "24" "${arg_gpu}"
+run_benchmark "-b 32" "base" "fp16" "128" "32" "${arg_gpu}"
+run_benchmark "-b 64" "base" "fp16" "128" "64" "${arg_gpu}"
+run_benchmark "-b 128" "base" "fp16" "128" "128" "${arg_gpu}"
+
+# run_benchmark "-b 1 -b 2 -b 4 -b 8 -b 12 -b 16 -b 24 -b 32" "base" "fp16" "384" "32" "${arg_gpu}"
+run_benchmark "-b 1" "base" "fp16" "384" "1" "${arg_gpu}"
+run_benchmark "-b 2" "base" "fp16" "384" "2" "${arg_gpu}"
+run_benchmark "-b 4" "base" "fp16" "384" "4" "${arg_gpu}"
+run_benchmark "-b 8" "base" "fp16" "384" "8" "${arg_gpu}"
+run_benchmark "-b 12" "base" "fp16" "384" "12" "${arg_gpu}"
+run_benchmark "-b 16" "base" "fp16" "384" "16" "${arg_gpu}"
+run_benchmark "-b 24" "base" "fp16" "384" "24" "${arg_gpu}"
+run_benchmark "-b 32" "base" "fp16" "384" "32" "${arg_gpu}"
+run_benchmark "-b 64" "base" "fp16" "384" "64" "${arg_gpu}"
+run_benchmark "-b 128" "base" "fp16" "384" "128" "${arg_gpu}"
+
+## FP32
+#run_benchmark "-b 1 -b 2 -b 4 -b 8 -b 12 -b 16 -b 24 -b 32" "base" "fp32" "128" "32" "${arg_gpu}"
+run_benchmark "-b 1" "base" "fp32" "128" "1" "${arg_gpu}"
+run_benchmark "-b 2" "base" "fp32" "128" "2" "${arg_gpu}"
+run_benchmark "-b 4" "base" "fp32" "128" "4" "${arg_gpu}"
+run_benchmark "-b 8" "base" "fp32" "128" "8" "${arg_gpu}"
+run_benchmark "-b 12" "base" "fp32" "128" "12" "${arg_gpu}"
+run_benchmark "-b 16" "base" "fp32" "128" "16" "${arg_gpu}"
+run_benchmark "-b 24" "base" "fp32" "128" "24" "${arg_gpu}"
+run_benchmark "-b 32" "base" "fp32" "128" "32" "${arg_gpu}"
+run_benchmark "-b 64" "base" "fp32" "128" "64" "${arg_gpu}"
+run_benchmark "-b 128" "base" "fp32" "128" "128" "${arg_gpu}"
+
+# run_benchmark "-b 1 -b 2 -b 4 -b 8 -b 12 -b 16 -b 24 -b 32" "base" "fp32" "384" "32" "${arg_gpu}"
+run_benchmark "-b 1" "base" "fp32" "384" "1" "${arg_gpu}"
+run_benchmark "-b 2" "base" "fp32" "384" "2" "${arg_gpu}"
+run_benchmark "-b 4" "base" "fp32" "384" "4" "${arg_gpu}"
+run_benchmark "-b 8" "base" "fp32" "384" "8" "${arg_gpu}"
+run_benchmark "-b 12" "base" "fp32" "384" "12" "${arg_gpu}"
+run_benchmark "-b 16" "base" "fp32" "384" "16" "${arg_gpu}"
+run_benchmark "-b 24" "base" "fp32" "384" "24" "${arg_gpu}"
+run_benchmark "-b 32" "base" "fp32" "384" "32" "${arg_gpu}"
+run_benchmark "-b 64" "base" "fp32" "384" "64" "${arg_gpu}"
+run_benchmark "-b 128" "base" "fp32" "384" "128" "${arg_gpu}"
+
+# BERT LARGE
+
+## INT8-QAT
+# run_benchmark "-b 1 -b 2 -b 4 -b 8 -b 12 -b 16 -b 24 -b 32" "large" "int8-qat" "128" "32" "${arg_gpu}"
+run_benchmark "-b 1" "large" "int8-qat" "128" "1" "${arg_gpu}"
+run_benchmark "-b 2" "large" "int8-qat" "128" "2" "${arg_gpu}"
+run_benchmark "-b 4" "large" "int8-qat" "128" "4" "${arg_gpu}"
+run_benchmark "-b 8" "large" "int8-qat" "128" "8" "${arg_gpu}"
+run_benchmark "-b 12" "large" "int8-qat" "128" "12" "${arg_gpu}"
+run_benchmark "-b 16" "large" "int8-qat" "128" "16" "${arg_gpu}"
+run_benchmark "-b 24" "large" "int8-qat" "128" "24" "${arg_gpu}"
+run_benchmark "-b 32" "large" "int8-qat" "128" "32" "${arg_gpu}"
+run_benchmark "-b 64" "large" "int8-qat" "128" "64" "${arg_gpu}"
+run_benchmark "-b 128" "large" "int8-qat" "128" "128" "${arg_gpu}"
+
+# run_benchmark "-b 1 -b 2 -b 4 -b 8 -b 12 -b 16 -b 24 -b 32" "large" "int8-qat" "384" "32" "${arg_gpu}"
+run_benchmark "-b 1" "large" "int8-qat" "384" "1" "${arg_gpu}"
+run_benchmark "-b 2" "large" "int8-qat" "384" "2" "${arg_gpu}"
+run_benchmark "-b 4" "large" "int8-qat" "384" "4" "${arg_gpu}"
+run_benchmark "-b 8" "large" "int8-qat" "384" "8" "${arg_gpu}"
+run_benchmark "-b 12" "large" "int8-qat" "384" "12" "${arg_gpu}"
+run_benchmark "-b 16" "large" "int8-qat" "384" "16" "${arg_gpu}"
+run_benchmark "-b 24" "large" "int8-qat" "384" "24" "${arg_gpu}"
+run_benchmark "-b 32" "large" "int8-qat" "384" "32" "${arg_gpu}"
+run_benchmark "-b 64" "large" "int8-qat" "384" "64" "${arg_gpu}"
+run_benchmark "-b 128" "large" "int8-qat" "384" "128" "${arg_gpu}"
+
+## INT8
+# run_benchmark "-b 1 -b 2 -b 4 -b 8 -b 12 -b 16 -b 24 -b 32" "large" "int8" "128" "32" "${arg_gpu}"
+run_benchmark "-b 1" "large" "int8" "128" "1" "${arg_gpu}"
+run_benchmark "-b 2" "large" "int8" "128" "2" "${arg_gpu}"
+run_benchmark "-b 4" "large" "int8" "128" "4" "${arg_gpu}"
+run_benchmark "-b 8" "large" "int8" "128" "8" "${arg_gpu}"
+run_benchmark "-b 12" "large" "int8" "128" "12" "${arg_gpu}"
+run_benchmark "-b 16" "large" "int8" "128" "16" "${arg_gpu}"
+run_benchmark "-b 24" "large" "int8" "128" "24" "${arg_gpu}"
+run_benchmark "-b 32" "large" "int8" "128" "32" "${arg_gpu}"
+run_benchmark "-b 64" "large" "int8" "128" "64" "${arg_gpu}"
+run_benchmark "-b 128" "large" "int8" "128" "128" "${arg_gpu}"
+
+# run_benchmark "-b 1 -b 2 -b 4 -b 8 -b 12 -b 16 -b 24 -b 32" "large" "int8" "384" "32" "${arg_gpu}"
+run_benchmark "-b 1" "large" "int8" "384" "1" "${arg_gpu}"
+run_benchmark "-b 2" "large" "int8" "384" "2" "${arg_gpu}"
+run_benchmark "-b 4" "large" "int8" "384" "4" "${arg_gpu}"
+run_benchmark "-b 8" "large" "int8" "384" "8" "${arg_gpu}"
+run_benchmark "-b 12" "large" "int8" "384" "12" "${arg_gpu}"
+run_benchmark "-b 16" "large" "int8" "384" "16" "${arg_gpu}"
+run_benchmark "-b 24" "large" "int8" "384" "24" "${arg_gpu}"
+run_benchmark "-b 32" "large" "int8" "384" "32" "${arg_gpu}"
+run_benchmark "-b 64" "large" "int8" "384" "64" "${arg_gpu}"
+run_benchmark "-b 128" "large" "int8" "384" "128" "${arg_gpu}"
+
+## FP16
+# run_benchmark "-b 1 -b 2 -b 4 -b 8 -b 12 -b 16 -b 24 -b 32" "large" "fp16" "128" "32" "${arg_gpu}"
+run_benchmark "-b 1" "large" "fp16" "128" "1" "${arg_gpu}"
+run_benchmark "-b 2" "large" "fp16" "128" "2" "${arg_gpu}"
+run_benchmark "-b 4" "large" "fp16" "128" "4" "${arg_gpu}"
+run_benchmark "-b 8" "large" "fp16" "128" "8" "${arg_gpu}"
+run_benchmark "-b 12" "large" "fp16" "128" "12" "${arg_gpu}"
+run_benchmark "-b 16" "large" "fp16" "128" "16" "${arg_gpu}"
+run_benchmark "-b 24" "large" "fp16" "128" "24" "${arg_gpu}"
+run_benchmark "-b 32" "large" "fp16" "128" "32" "${arg_gpu}"
+run_benchmark "-b 64" "large" "fp16" "128" "64" "${arg_gpu}"
+run_benchmark "-b 128" "large" "fp16" "128" "128" "${arg_gpu}"
+
+# run_benchmark "-b 1 -b 2 -b 4 -b 8 -b 12 -b 16 -b 24 -b 32" "large" "fp16" "384" "32" "${arg_gpu}"
+run_benchmark "-b 1" "large" "fp16" "384" "1" "${arg_gpu}"
+run_benchmark "-b 2" "large" "fp16" "384" "2" "${arg_gpu}"
+run_benchmark "-b 4" "large" "fp16" "384" "4" "${arg_gpu}"
+run_benchmark "-b 8" "large" "fp16" "384" "8" "${arg_gpu}"
+run_benchmark "-b 12" "large" "fp16" "384" "12" "${arg_gpu}"
+run_benchmark "-b 16" "large" "fp16" "384" "16" "${arg_gpu}"
+run_benchmark "-b 24" "large" "fp16" "384" "24" "${arg_gpu}"
+run_benchmark "-b 32" "large" "fp16" "384" "32" "${arg_gpu}"
+run_benchmark "-b 64" "large" "fp16" "384" "64" "${arg_gpu}"
+run_benchmark "-b 128" "large" "fp16" "384" "128" "${arg_gpu}"
+
+## FP32
+# run_benchmark "-b 1 -b 2 -b 4 -b 8 -b 12 -b 16 -b 24 -b 32" "large" "fp32" "128" "32" "${arg_gpu}"
+run_benchmark "-b 1" "large" "fp32" "128" "1" "${arg_gpu}"
+run_benchmark "-b 2" "large" "fp32" "128" "2" "${arg_gpu}"
+run_benchmark "-b 4" "large" "fp32" "128" "4" "${arg_gpu}"
+run_benchmark "-b 8" "large" "fp32" "128" "8" "${arg_gpu}"
+run_benchmark "-b 12" "large" "fp32" "128" "12" "${arg_gpu}"
+run_benchmark "-b 16" "large" "fp32" "128" "16" "${arg_gpu}"
+run_benchmark "-b 24" "large" "fp32" "128" "24" "${arg_gpu}"
+run_benchmark "-b 32" "large" "fp32" "128" "32" "${arg_gpu}"
+run_benchmark "-b 64" "large" "fp32" "128" "64" "${arg_gpu}"
+run_benchmark "-b 128" "large" "fp32" "128" "128" "${arg_gpu}"
+
+# run_benchmark "-b 1 -b 2 -b 4 -b 8 -b 12 -b 16 -b 24 -b 32" "large" "fp32" "384" "32" "${arg_gpu}"
+run_benchmark "-b 1" "large" "fp32" "384" "1" "${arg_gpu}"
+run_benchmark "-b 2" "large" "fp32" "384" "2" "${arg_gpu}"
+run_benchmark "-b 4" "large" "fp32" "384" "4" "${arg_gpu}"
+run_benchmark "-b 8" "large" "fp32" "384" "8" "${arg_gpu}"
+run_benchmark "-b 12" "large" "fp32" "384" "12" "${arg_gpu}"
+run_benchmark "-b 16" "large" "fp32" "384" "16" "${arg_gpu}"
+run_benchmark "-b 24" "large" "fp32" "384" "24" "${arg_gpu}"
+run_benchmark "-b 32" "large" "fp32" "384" "32" "${arg_gpu}"
+run_benchmark "-b 64" "large" "fp32" "384" "64" "${arg_gpu}"
+run_benchmark "-b 128" "large" "fp32" "384" "128" "${arg_gpu}"
+
diff --git a/demo/BERT/squad/evaluate-v1.1.py b/demo/BERT/squad/evaluate-v1.1.py
new file mode 100644
index 00000000..25c93a8d
--- /dev/null
+++ b/demo/BERT/squad/evaluate-v1.1.py
@@ -0,0 +1,115 @@
+#!/usr/bin/env python3
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# Obtained from https://rajpurkar.github.io/SQuAD-explorer/
+
+""" Official evaluation script for v1.1 of the SQuAD dataset. """
+from __future__ import print_function
+from collections import Counter
+import string
+import re
+import argparse
+import json
+import sys
+
+def normalize_answer(s):
+    """Lower text and remove punctuation, articles and extra whitespace."""
+    def remove_articles(text):
+        return re.sub(r'\b(a|an|the)\b', ' ', text)
+
+    def white_space_fix(text):
+        return ' '.join(text.split())
+
+    def remove_punc(text):
+        exclude = set(string.punctuation)
+        return ''.join(ch for ch in text if ch not in exclude)
+
+    def lower(text):
+        return text.lower()
+
+    return white_space_fix(remove_articles(remove_punc(lower(s))))
+
+
+def f1_score(prediction, ground_truth):
+    prediction_tokens = normalize_answer(prediction).split()
+    ground_truth_tokens = normalize_answer(ground_truth).split()
+    common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
+    num_same = sum(common.values())
+    if num_same == 0:
+        return 0
+    precision = 1.0 * num_same / len(prediction_tokens)
+    recall = 1.0 * num_same / len(ground_truth_tokens)
+    f1 = (2 * precision * recall) / (precision + recall)
+    return f1
+
+
+def exact_match_score(prediction, ground_truth):
+    return (normalize_answer(prediction) == normalize_answer(ground_truth))
+
+
+def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
+    scores_for_ground_truths = []
+    for ground_truth in ground_truths:
+        score = metric_fn(prediction, ground_truth)
+        scores_for_ground_truths.append(score)
+    return max(scores_for_ground_truths)
+
+def evaluate(dataset, predictions, f1_acc):
+    f1 = exact_match = total = 0
+    for article in dataset:
+        for paragraph in article['paragraphs']:
+            for qa in paragraph['qas']:
+                total += 1
+                if qa['id'] not in predictions:
+                    message = 'Unanswered question ' + qa['id'] + \
+                              ' will receive score 0.'
+                    print(message, file=sys.stderr)
+                    continue
+                ground_truths = list(map(lambda x: x['text'], qa['answers']))
+                prediction = predictions[qa['id']]
+                exact_match += metric_max_over_ground_truths(
+                    exact_match_score, prediction, ground_truths)
+                f1 += metric_max_over_ground_truths(
+                    f1_score, prediction, ground_truths)
+
+    exact_match = 100.0 * exact_match / total
+    f1 = 100.0 * f1 / total
+    if (f1 < f1_acc - 0.5):
+        print("&&&& FAILED TensorRT BERT Squad Accuracy matches reference.")
+    else:
+        print("&&&& PASSED TensorRT BERT Squad Accuracy matches reference.")
+    return {'exact_match': exact_match, 'f1': f1}
+
+if __name__ == '__main__':
+    expected_version = '1.1'
+    parser = argparse.ArgumentParser(
+        description='Evaluation for SQuAD ' + expected_version)
+    parser.add_argument('dataset_file', help='Dataset file')
+    parser.add_argument('prediction_file', help='Prediction File')
+    parser.add_argument('f1_acc', help='Reference Accuracy')
+    args = parser.parse_args()
+    with open(args.dataset_file) as dataset_file:
+        dataset_json = json.load(dataset_file)
+        if (dataset_json['version'] != expected_version):
+            print('Evaluation expects v-' + expected_version +
+                  ', but got dataset with v-' + dataset_json['version'],
+                  file=sys.stderr)
+        dataset = dataset_json['data']
+    with open(args.prediction_file) as prediction_file:
+        predictions = json.load(prediction_file)
+        f1_acc = float(args.f1_acc)
+    print(json.dumps(evaluate(dataset, predictions, f1_acc)))
+
diff --git a/docker/build.sh b/docker/build.sh
new file mode 100755
index 00000000..dbc4015f
--- /dev/null
+++ b/docker/build.sh
@@ -0,0 +1,54 @@
+#!/usr/bin/env bash
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+arg_dockerfile=docker/ubuntu.Dockerfile
+arg_imagename=tensorrt-ubuntu
+arg_osversion=18.04
+arg_cudaversion=11.0
+arg_cudnnversion=8.0
+arg_trtversion=7.1.3
+arg_help=0
+
+while [[ "$#" -gt 0 ]]; do case $1 in
+  --file) arg_dockerfile="$2"; shift;;
+  --tag) arg_imagename="$2"; shift;;
+  --os) arg_osversion="$2"; shift;;
+  --cuda) arg_cudaversion="$2"; shift;;
+  -h|--help) arg_help=1;;
+  *) echo "Unknown parameter passed: $1"; echo "For help type: $0 --help"; exit 1;
+esac; shift; done
+
+if [ "$arg_help" -eq "1" ]; then
+    echo "Usage: $0 [options]"
+    echo " --help or -h         : Print this help menu."
+    echo " --file  <dockerfile> : Docker file to use for build."
+    echo " --tag   <imagename>  : Image name for the generated container."
+    echo " --os    <version>    : OS version to use."
+    echo " --cuda  <version>    : CUDA version to use."
+    exit;
+fi
+
+extra_args=""
+# Use RC builds for CUDA 11.0
+if [ "$arg_cudaversion" = "11.0" ]; then
+    extra_args="$extra_args --build-arg NVCR_SUFFIX=-rc"
+fi
+docker_args="-f $arg_dockerfile --build-arg OS_VERSION=$arg_osversion --build-arg CUDA_VERSION=$arg_cudaversion $extra_args --build-arg uid=$(id -u) --build-arg gid=$(id -g) --tag=$arg_imagename ."
+
+echo "Building container:"
+echo "> docker build $docker_args"
+docker build $docker_args
diff --git a/docker/centos.Dockerfile b/docker/centos.Dockerfile
index e1a4c545..fe1482b2 100644
--- a/docker/centos.Dockerfile
+++ b/docker/centos.Dockerfile
@@ -1,4 +1,4 @@
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,9 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-ARG CUDA_VERSION=10.2
-ARG CENTOS_VERSION=7
-FROM nvidia/cuda:${CUDA_VERSION}-cudnn7-devel-centos${CENTOS_VERSION}
+ARG CUDA_VERSION=11.0
+ARG OS_VERSION=7
+ARG NVCR_SUFFIX=
+FROM nvidia/cuda:${CUDA_VERSION}-devel-centos${OS_VERSION}${NVCR_SUFFIX}
 
 LABEL maintainer="NVIDIA CORPORATION"
 
@@ -24,6 +25,7 @@ RUN groupadd -r -f -g ${gid} trtuser && useradd -r -u ${uid} -g ${gid} -ms /bin/
 RUN usermod -aG wheel trtuser
 RUN echo 'trtuser:nvidia' | chpasswd
 RUN mkdir -p /workspace && chown trtuser /workspace
+
 # Install requried libraries
 RUN yum -y install \
     libcurl4-openssl-dev \
@@ -34,14 +36,18 @@ RUN yum -y install \
     python3 \
     python3-pip \
     python3-dev \
-    python3-setuptools \
+    python3-devel \
     python3-wheel \
+    unzip \
     sudo \
-    make
+    make \
+    build-essential
 
 RUN cd /usr/local/bin &&\
     ln -s /usr/bin/python3 python &&\
     ln -s /usr/bin/pip3 pip
+RUN pip3 install --upgrade pip
+RUN pip3 install setuptools>=41.0.0
 
 # Install Cmake
 RUN cd /tmp && \
@@ -50,6 +56,13 @@ RUN cd /tmp && \
     ./cmake-3.14.4-Linux-x86_64.sh --prefix=/usr/local --exclude-subdir --skip-license && \
     rm ./cmake-3.14.4-Linux-x86_64.sh
 
+# Install PyPI packages
+COPY requirements.txt /tmp/requirements.txt
+RUN pip3 install -r /tmp/requirements.txt
+
+# Download NGC client
+RUN cd /usr/local/bin && wget https://ngc.nvidia.com/downloads/ngccli_bat_linux.zip && unzip ngccli_bat_linux.zip && chmod u+x ngc && rm ngccli_bat_linux.zip ngc.md5 && echo "no-apikey\nascii\nno-org\nno-team\nno-ace\n" | ngc config set
+
 # Set environment and working directory
 ENV TRT_RELEASE /tensorrt
 ENV TRT_SOURCE /workspace/TensorRT
diff --git a/docker/launch.sh b/docker/launch.sh
new file mode 100755
index 00000000..402beb1e
--- /dev/null
+++ b/docker/launch.sh
@@ -0,0 +1,53 @@
+#!/usr/bin/env bash
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+arg_dockerfile=docker/ubuntu
+arg_imagename=tensorrt-ubuntu
+arg_gpus=all
+arg_trtrelease=$TRT_RELEASE
+arg_trtsource=$TRT_SOURCE
+arg_help=0
+
+while [[ "$#" -gt 0 ]]; do case $1 in
+  --tag) arg_imagename="$2"; shift;;
+  --gpus) arg_gpus="$2"; shift;;
+  --release) arg_trtrelease="$2"; shift;;
+  --source) arg_trtsource="$2"; shift;;
+  -h|--help) arg_help=1;;
+  *) echo "Unknown parameter passed: $1"; echo "For help type: $0 --help"; exit 1;
+esac; shift; done
+
+if [ "$arg_help" -eq "1" ]; then
+    echo "Usage: $0 [options]"
+    echo " --help or -h          : Print this help menu."
+    echo " --tag     <imagename> : Image name for the generated container."
+    echo " --gpus    <number>    : Number of GPUs visible in container. Set 'none' to disable, and 'all' to make all visible."
+    echo " --release <path>      : Path to TensorRT release build."
+    echo " --source  <path>      : Path to TensorRT open source codebase."
+    exit;
+fi
+
+extra_args=""
+if [ "$arg_gpus" != "none" ]; then
+    extra_args="$extra_args --gpus $arg_gpus"
+fi
+
+docker_args="$extra_args -v $arg_trtrelease:/tensorrt -v $arg_trtsource:/workspace/TensorRT -it $arg_imagename:latest"
+
+echo "Launching container:"
+echo "> docker run $docker_args"
+docker run $docker_args
diff --git a/docker/ubuntu-cross-aarch64.Dockerfile b/docker/ubuntu-cross-aarch64.Dockerfile
index 0376a0d4..3715791f 100644
--- a/docker/ubuntu-cross-aarch64.Dockerfile
+++ b/docker/ubuntu-cross-aarch64.Dockerfile
@@ -1,4 +1,4 @@
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,9 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-ARG CUDA_VERSION=10.0
-ARG UBUNTU_VERSION=18.04
-FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
+ARG CUDA_VERSION=10.2
+ARG OS_VERSION=18.04
+ARG NVCR_SUFFIX=
+FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${OS_VERSION}${NVCR_SUFFIX}
 
 LABEL maintainer="NVIDIA CORPORATION"
 
@@ -24,6 +25,7 @@ RUN groupadd -r -f -g ${gid} trtuser && useradd -r -u ${uid} -g ${gid} -ms /bin/
 RUN usermod -aG sudo trtuser
 RUN echo 'trtuser:nvidia' | chpasswd
 RUN mkdir -p /workspace && chown trtuser /workspace
+
 # Install requried libraries
 RUN apt-get update && apt-get install -y software-properties-common
 RUN add-apt-repository ppa:ubuntu-toolchain-r/test
@@ -36,18 +38,20 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
     python3 \
     python3-pip \
     python3-dev \
-    python3-setuptools \
     python3-wheel \
     sudo \
     ssh \
     pbzip2 \
     pv \
     bzip2 \
-    unzip
+    unzip \
+    build-essential
 
 RUN cd /usr/local/bin &&\
     ln -s /usr/bin/python3 python &&\
     ln -s /usr/bin/pip3 pip
+RUN pip3 install --upgrade pip
+RUN pip3 install setuptools>=41.0.0
 
 # Install Cmake
 RUN cd /tmp && \
@@ -56,37 +60,42 @@ RUN cd /tmp && \
     ./cmake-3.14.4-Linux-x86_64.sh --prefix=/usr/local --exclude-subdir --skip-license && \
     rm ./cmake-3.14.4-Linux-x86_64.sh
 
+# Install PyPI packages
+COPY requirements.txt /tmp/requirements.txt
+RUN pip3 install -r /tmp/requirements.txt
+
+# Download NGC client
+RUN cd /usr/local/bin && wget https://ngc.nvidia.com/downloads/ngccli_bat_linux.zip && unzip ngccli_bat_linux.zip && chmod u+x ngc && rm ngccli_bat_linux.zip ngc.md5 && echo "no-apikey\nascii\nno-org\nno-team\nno-ace\n" | ngc config set
 
 COPY docker/jetpack_files /pdk_files
 COPY scripts/stubify.sh /pdk_files
 
 # Install CUDA cross compile toolchain
-RUN dpkg -i /pdk_files/cuda-repo-cross-aarch64-10-0-local-10.0.326_1.0-1_all.deb /pdk_files/cuda-repo-ubuntu1804-10-0-local-10.0.326-410.108_1.0-1_amd64.deb \
+RUN dpkg -i /pdk_files/cuda-repo-cross-aarch64*.deb /pdk_files/cuda-repo-ubuntu*_amd64.deb \
     && apt-get update \
     && apt-get install -y cuda-cross-aarch64 \
     && rm -rf /var/lib/apt/lists/*
 
 # Unpack cudnn
-RUN dpkg -x /pdk_files/libcudnn7_7.5.0.56-1+cuda10.0_arm64.deb /pdk_files/cudnn  \
-    && dpkg -x /pdk_files/libcudnn7-dev_7.5.0.56-1+cuda10.0_arm64.deb /pdk_files/cudnn \
+RUN  dpkg -x /pdk_files/libcudnn[7-8]_*-1+cuda10.[0-9]_arm64.deb /pdk_files/cudnn \ 
+    && dpkg -x /pdk_files/libcudnn[7-8]-dev_*-1+cuda10.[0-9]_arm64.deb /pdk_files/cudnn \
     && cd /pdk_files/cudnn/usr/include/aarch64-linux-gnu \
     && cd /pdk_files/cudnn/usr/lib/aarch64-linux-gnu \
     && ln -s libcudnn.so.7 libcudnn.so \
     && cd /pdk_files/cudnn \
     && ln -s usr/include/aarch64-linux-gnu include \
     && ln -s usr/lib/aarch64-linux-gnu lib \
-    && ln -s /pdk_files/cudnn/usr/include/aarch64-linux-gnu/cudnn_v7.h /usr/include/cudnn.h
+    && ln -s /pdk_files/cudnn/usr/include/aarch64-linux-gnu/cudnn_v[7-9].h /usr/include/cudnn.h
 
 # Unpack libnvinfer
-#
-RUN dpkg -x /pdk_files/libnvinfer6_6.0.1-1+cuda10.0_arm64.deb /pdk_files/tensorrt \
-    && dpkg -x /pdk_files/libnvinfer-dev_6.0.1-1+cuda10.0_arm64.deb /pdk_files/tensorrt \
-    && dpkg -x /pdk_files/libnvparsers6_6.0.1-1+cuda10.0_arm64.deb /pdk_files/tensorrt \
-    && dpkg -x /pdk_files/libnvparsers-dev_6.0.1-1+cuda10.0_arm64.deb /pdk_files/tensorrt \
-    && dpkg -x /pdk_files/libnvinfer-plugin6_6.0.1-1+cuda10.0_arm64.deb /pdk_files/tensorrt \
-    && dpkg -x /pdk_files/libnvinfer-plugin-dev_6.0.1-1+cuda10.0_arm64.deb /pdk_files/tensorrt \
-    && dpkg -x /pdk_files/libnvonnxparsers6_6.0.1-1+cuda10.0_arm64.deb /pdk_files/tensorrt \
-    && dpkg -x /pdk_files/libnvonnxparsers-dev_6.0.1-1+cuda10.0_arm64.deb /pdk_files/tensorrt
+RUN dpkg -x /pdk_files/libnvinfer[0-7]_*-1+cuda10.[0-9]_arm64.deb /pdk_files/tensorrt \
+    && dpkg -x /pdk_files/libnvinfer-dev_*-1+cuda10.[0-9]_arm64.deb /pdk_files/tensorrt \
+    && dpkg -x /pdk_files/libnvparsers[6-7]_*-1+cuda10.[0-9]_arm64.deb /pdk_files/tensorrt \
+    && dpkg -x /pdk_files/libnvparsers-dev_*-1+cuda10.[0-9]_arm64.deb /pdk_files/tensorrt \
+    && dpkg -x /pdk_files/libnvinfer-plugin[6-7]_*-1+cuda10.[0-9]_arm64.deb /pdk_files/tensorrt \
+    && dpkg -x /pdk_files/libnvinfer-plugin-dev_*-1+cuda10.[0-9]_arm64.deb /pdk_files/tensorrt \
+    && dpkg -x /pdk_files/libnvonnxparsers[6-7]_*-1+cuda10.[0-9]_arm64.deb /pdk_files/tensorrt \
+    && dpkg -x /pdk_files/libnvonnxparsers-dev_*-1+cuda10.[0-9]_arm64.deb /pdk_files/tensorrt
 
 # create stub libraries
 RUN cd /pdk_files/tensorrt \
diff --git a/docker/ubuntu-cross-ppc64le.Dockerfile b/docker/ubuntu-cross-ppc64le.Dockerfile
new file mode 100755
index 00000000..2299fb7a
--- /dev/null
+++ b/docker/ubuntu-cross-ppc64le.Dockerfile
@@ -0,0 +1,110 @@
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+ARG CUDA_VERSION=11.0
+ARG OS_VERSION=18.04
+ARG NVCR_SUFFIX=
+FROM nvidia/cuda:${CUDA_VERSION}-cudnn8-devel-ubuntu${OS_VERSION}${NVCR_SUFFIX}
+
+LABEL maintainer="NVIDIA CORPORATION"
+
+ARG uid=1000
+ARG gid=1000
+RUN groupadd -r -f -g ${gid} trtuser && useradd -r -u ${uid} -g ${gid} -ms /bin/bash trtuser
+RUN usermod -aG sudo trtuser
+RUN echo 'trtuser:nvidia' | chpasswd
+RUN mkdir -p /workspace && chown -R trtuser:trtuser /workspace
+
+# Install requried libraries
+RUN apt-get update && apt-get install -y software-properties-common
+RUN add-apt-repository ppa:ubuntu-toolchain-r/test
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    libcurl4-openssl-dev \
+    wget \
+    zlib1g-dev \
+    git \
+    pkg-config \
+    python3 \
+    python3-pip \
+    python3-dev \
+    python3-setuptools \
+    python3-wheel \
+    sudo \
+    ssh \
+    pbzip2 \
+    pv \
+    bzip2 \
+    unzip \
+    g++-powerpc64le-linux-gnu \
+    libc6-powerpc-cross
+
+RUN cd /usr/local/bin &&\
+    ln -s /usr/bin/python3 python &&\
+    ln -s /usr/bin/pip3 pip
+
+RUN cd /tmp && \
+    wget https://github.com/Kitware/CMake/releases/download/v3.14.4/cmake-3.14.4-Linux-x86_64.sh && \
+    chmod +x cmake-3.14.4-Linux-x86_64.sh && \
+    ./cmake-3.14.4-Linux-x86_64.sh --prefix=/usr/local --exclude-subdir --skip-license && \
+    rm ./cmake-3.14.4-Linux-x86_64.sh
+# Download ppc Cudnn, Cublas, Cudart, RT, nvprof
+# TODO Remove once packages are added to cuda cross compiler
+RUN wget http://cuda-repo/release-candidates/Libraries/cuDNN/v8.0/8.0.2.5_20200617_28575977/11.0.x-r445/Installer/Ubuntu18_04-ppc64le/libcudnn8_8.0.2.5-1+cuda11.0_ppc64el.deb && \
+    wget http://cuda-repo/release-candidates/Libraries/cuDNN/v8.0/8.0.2.5_20200617_28575977/11.0.x-r445/Installer/Ubuntu18_04-ppc64le/libcudnn8-dev_8.0.2.5-1+cuda11.0_ppc64el.deb && \
+    wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/ppc64el/libcublas-dev-11-0_11.0.0.191-1_ppc64el.deb && \
+    wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/ppc64el/libcublas-11-0_11.0.0.191-1_ppc64el.deb && \
+    wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/ppc64el/cuda-cudart-11-0_11.0.171-1_ppc64el.deb && \
+    wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/ppc64el/cuda-cudart-dev-11-0_11.0.171-1_ppc64el.deb && \
+    wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/ppc64el/cuda-nvrtc-11-0_11.0.167-1_ppc64el.deb && \
+    wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/ppc64el/cuda-nvrtc-dev-11-0_11.0.167-1_ppc64el.deb && \
+    wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/ppc64el/cuda-nvcc-11-0_11.0.167-1_ppc64el.deb && \
+    wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/ppc64el/cuda-nvprof-11-0_11.0.167-1_ppc64el.deb
+
+# Unpack Cublas
+RUN dpkg -x libcublas-11-0_11.0.0.191-1_ppc64el.deb cublas && \
+    dpkg -x libcublas-dev-11-0_11.0.0.191-1_ppc64el.deb cublas && \
+    cp -r cublas/* /
+
+# Unpack Cudart
+RUN dpkg -x cuda-cudart-11-0_11.0.171-1_ppc64el.deb cudart && \
+    dpkg -x cuda-cudart-dev-11-0_11.0.171-1_ppc64el.deb cudart && \
+    cp -r cudart/* /
+
+# Unpack RT
+RUN dpkg -x cuda-nvrtc-11-0_11.0.167-1_ppc64el.deb rt && \
+    dpkg -x cuda-nvrtc-dev-11-0_11.0.167-1_ppc64el.deb rt && \
+    cp -r rt/* /
+
+# Unpack Cudnn
+RUN dpkg -x libcudnn8_8.0.2.5-1+cuda11.0_ppc64el.deb cudnn && \
+    dpkg -x libcudnn8-dev_8.0.2.5-1+cuda11.0_ppc64el.deb cudnn && \
+    cp -r cudnn/* /
+
+# Unpack NVCC, and copy headers
+RUN dpkg -x cuda-nvcc-11-0_11.0.167-1_ppc64el.deb nvcc && \
+    cp -r nvcc/usr/local/cuda-11.0/targets/ppc64le-linux/include/*  /usr/local/cuda-11.0/targets/ppc64le-linux/include/
+
+# Install nvprof
+RUN dpkg -x cuda-nvprof-11-0_11.0.167-1_ppc64el.deb prof && \
+    cp -r prof/* /
+
+# Clean up temporary files
+RUN rm -rf cublas cudart rt prof nvcc
+RUN rm *.deb
+
+WORKDIR /workspace
+ENV TRT_RELEASE /tensorrt
+ENV TRT_SOURCE /workspace/TensorRT
+USER trtuser
+RUN ["/bin/bash"]
diff --git a/docker/ubuntu.Dockerfile b/docker/ubuntu.Dockerfile
index e29f0245..e97b342d 100644
--- a/docker/ubuntu.Dockerfile
+++ b/docker/ubuntu.Dockerfile
@@ -1,4 +1,4 @@
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,9 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-ARG CUDA_VERSION=10.2
-ARG UBUNTU_VERSION=18.04
-FROM nvidia/cuda:${CUDA_VERSION}-cudnn7-devel-ubuntu${UBUNTU_VERSION}
+ARG CUDA_VERSION=11.0
+ARG OS_VERSION=18.04
+ARG NVCR_SUFFIX=
+FROM nvidia/cuda:${CUDA_VERSION}-cudnn8-devel-ubuntu${OS_VERSION}${NVCR_SUFFIX}
 
 LABEL maintainer="NVIDIA CORPORATION"
 
@@ -24,6 +25,7 @@ RUN groupadd -r -f -g ${gid} trtuser && useradd -r -u ${uid} -g ${gid} -ms /bin/
 RUN usermod -aG sudo trtuser
 RUN echo 'trtuser:nvidia' | chpasswd
 RUN mkdir -p /workspace && chown trtuser /workspace
+
 # Install requried libraries
 RUN apt-get update && apt-get install -y software-properties-common
 RUN add-apt-repository ppa:ubuntu-toolchain-r/test
@@ -36,18 +38,20 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
     python3 \
     python3-pip \
     python3-dev \
-    python3-setuptools \
     python3-wheel \
     sudo \
     ssh \
     pbzip2 \
     pv \
     bzip2 \
-    unzip
+    unzip \
+    build-essential
 
 RUN cd /usr/local/bin &&\
     ln -s /usr/bin/python3 python &&\
     ln -s /usr/bin/pip3 pip
+RUN pip3 install --upgrade pip
+RUN pip3 install setuptools>=41.0.0
 
 # Install Cmake
 RUN cd /tmp && \
@@ -56,6 +60,13 @@ RUN cd /tmp && \
     ./cmake-3.14.4-Linux-x86_64.sh --prefix=/usr/local --exclude-subdir --skip-license && \
     rm ./cmake-3.14.4-Linux-x86_64.sh
 
+# Install PyPI packages
+COPY requirements.txt /tmp/requirements.txt
+RUN pip3 install -r /tmp/requirements.txt
+
+# Download NGC client
+RUN cd /usr/local/bin && wget https://ngc.nvidia.com/downloads/ngccli_bat_linux.zip && unzip ngccli_bat_linux.zip && chmod u+x ngc && rm ngccli_bat_linux.zip ngc.md5 && echo "no-apikey\nascii\nno-org\nno-team\nno-ace\n" | ngc config set
+
 # Set environment and working directory
 ENV TRT_RELEASE /tensorrt
 ENV TRT_SOURCE /workspace/TensorRT
diff --git a/include/NvCaffeParser.h b/include/NvCaffeParser.h
index 2bbd8f63..b42f5dab 100644
--- a/include/NvCaffeParser.h
+++ b/include/NvCaffeParser.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -19,6 +19,12 @@
 
 #include "NvInfer.h"
 
+//!
+//! \file NvCaffeParser.h
+//!
+//! This is the API for the Caffe Parser
+//!
+
 //!
 //! \namespace nvcaffeparser1
 //!
diff --git a/include/NvInfer.h b/include/NvInfer.h
index 6363bc60..4c0d2c25 100644
--- a/include/NvInfer.h
+++ b/include/NvInfer.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -423,7 +423,11 @@ constexpr inline int EnumMax<LayerType>()
 //!
 //! \brief A tensor in a network definition.
 //!
-//! to remove a tensor from a network definition, use INetworkDefinition::removeTensor()
+//! To remove a tensor from a network definition, use INetworkDefinition::removeTensor().
+//!
+//! When using the DLA, the cumulative size of all Tensors that are not marked as Network Input or Output tensors,
+//! must be less than 1GB in size to fit into a single subgraph. If the build option kGPU_FALLBACK is specified, then
+//! multiple subgraphs can be created, with each subgraph limited to less than 1GB of internal tensors data.
 //!
 //! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI.
 //!
@@ -635,6 +639,23 @@ class ITensor
     //!
     //! \brief Whether the tensor is a shape tensor.
     //!
+    //! A shape tensor is a tensor that is related to shape calculations.
+    //! It must be 0D or 1D, have type Int32 or Bool, and its shape must be determinable at build time.
+    //! Furthermore, it must be needed as a shape tensor, either marked as a network shape
+    //! output via markOutputForShapes(), or as an input that is required to be a shape
+    //! tensor, such as the second input to IShuffleLayer. Some layers are "polymorphic" in
+    //! this respect. For example, the inputs to IElementWiseLayer must be shape tensors
+    //! if the output is a shape tensor.
+    //!
+    //! The TensorRT Developer Guide give the formal rules for what tensors are shape tensors.
+    //!
+    //! The result of isShapeTensor() is reliable only when network construction is complete.
+    //! For example, if a partially built network sums two tensors T1 and T2 to create
+    //! tensor T3, and none are yet needed as shape tensors, isShapeTensor() returns false
+    //! for all three tensors.  Setting the second input of IShuffleLayer to be T3 would
+    //! cause all three tensors to be shape tensors, because IShuffleLayer requires that its
+    //! second optional input be a shape tensor, and IElementWiseLayer is "polymorphic".
+    //!
     //! If a tensor is a shape tensor and becomes an engine input or output,
     //! then ICudaEngine::isShapeBinding will be true for that tensor.
     //!
@@ -642,17 +663,23 @@ class ITensor
     //!
     //! \return True if tensor is a shape tensor, false otherwise.
     //!
+    //! \see INetworkDefinition::markOutputForShapes(), ICudaEngine::isShapeBinding()
+    //!
     virtual bool isShapeTensor() const TRTNOEXCEPT = 0;
 
     //!
     //! \brief Whether the tensor is an execution tensor.
     //!
-    //! If a tensor is an execution tensor and becomes an engine input or output,
-    //! then ICudaEngine::isExecutionBinding will be true for that tensor.
-    //!
     //! Tensors are usually execution tensors.  The exceptions are tensors used
     //! solely for shape calculations or whose contents not needed to compute the outputs.
     //!
+    //! The result of isExecutionTensor() is reliable only when network construction is complete.
+    //! For example, if a partially built network has no path from a tensor to a network output,
+    //! isExecutionTensor() returns false. Completing the path would cause it to become true.
+    //!
+    //! If a tensor is an execution tensor and becomes an engine input or output,
+    //! then ICudaEngine::isExecutionBinding will be true for that tensor.
+    //!
     //! A tensor with isShapeTensor() == false and isExecutionTensor() == false
     //! can still show up as an input to the engine if its dimensions are required.
     //! In that case, only its dimensions need to be set at runtime and a nullptr
@@ -863,36 +890,37 @@ class ILayer
 //!     O = output
 //!     D = dilation
 //!     M = I + B + A ; The image data plus any padding
-//!     E = F - S ; The discarded remainder on the right border
-//!     K = 1 + D * (E - 1)
+//!     DK = 1 + D * (F - 1)
 //! \endcode
 //!
 //! Formulas for Convolution:
 //!     - EXPLICIT_ROUND_DOWN:
 //! \code
-//!         O = floor((M - K) / S)
+//!         O = floor((M - DK) / S) + 1
 //! \endcode
 //!     - CAFFE_ROUND_DOWN:
 //! \code
-//!         O = floor((I + B * 2 - K) / S)
+//!         O = floor((I + B * 2 - DK) / S)
 //! \endcode
 //!     - EXPLICIT_ROUND_UP:
 //! \code
-//!         O = ceil((M - K) / S)
+//!         O = ceil((M - DK) / S) + 1
 //! \endcode
 //!     - CAFFE_ROUND_UP:
 //! \code
-//!         O = ceil((I + B * 2 - K) / S)
+//!         O = ceil((I + B * 2 - DK) / S)
 //! \endcode
 //!     - SAME_UPPER:
 //! \code
-//!         P = (I - ceil(I / S))
+//!         O = ceil(I / S)
+//!         P = floor((I - 1) / S) * S + DK - I;
 //!         B = floor(P / 2)
 //!         A = P - B
 //! \endcode
 //!     - SAME_LOWER:
 //! \code
-//!         P = (I - ceil(I / S))
+//!         O = ceil(I / S)
+//!         P = floor((I - 1) / S) * S + DK - I;
 //!         A = floor(P / 2)
 //!         B = P - A
 //! \endcode
@@ -903,19 +931,19 @@ class ILayer
 //!     - EXPLICIT_ROUND_UP:
 //!     - CAFFE_ROUND_UP:
 //! \code
-//!         O = (I - 1) * S + K - (B + A)
+//!         O = (I - 1) * S + DK - (B + A)
 //! \endcode
 //!     - SAME_UPPER:
 //! \code
-//!         O = min(I * S, (I - 1) * S + K)
-//!         P = max(K - S, 0)
+//!         O = min(I * S, (I - 1) * S + DK)
+//!         P = max(DK - S, 0)
 //!         B = floor(P / 2)
 //!         A = P - B
 //! \endcode
 //!     - SAME_LOWER:
 //! \code
-//!         O = min(I * S, (I - 1) * S + K)
-//!         P = max(K - S, 0)
+//!         O = min(I * S, (I - 1) * S + DK)
+//!         P = max(DK - S, 0)
 //!         A = floor(P / 2)
 //!         B = P - A
 //! \endcode
@@ -923,23 +951,23 @@ class ILayer
 //! Formulas for Pooling:
 //!     - EXPLICIT_ROUND_DOWN:
 //! \code
-//!         O = floor((M - E) / S)
+//!         O = floor((M - F) / S) + 1
 //! \endcode
 //!     - EXPLICIT_ROUND_UP:
 //! \code
-//!         O = ceil((M - E) / S)
+//!         O = ceil((M - F) / S) + 1
 //! \endcode
 //!     - SAME_UPPER:
 //! \code
 //!         O = ceil(I / S)
-//!         P = (I - ceil(I / S))
+//!         P = floor((I - 1) / S) * S + F - I;
 //!         B = floor(P / 2)
 //!         A = P - B
 //! \endcode
 //!     - SAME_LOWER:
 //! \code
 //!         O = ceil(I / S)
-//!         P = (I - ceil(I / S))
+//!         P = floor((I - 1) / S) * S + F - I;
 //!         A = floor(P / 2)
 //!         B = P - A
 //! \endcode
@@ -955,26 +983,25 @@ class ILayer
 //! Pooling Example 1:
 //! \code
 //!     Given I = {6, 6}, B = {3, 3}, A = {2, 2}, S = {2, 2}, F = {3, 3}. What is O?
+//!     (B, A can be calculated for SAME_UPPER and SAME_LOWER mode)
 //! \endcode
 //!
 //! - EXPLICIT_ROUND_DOWN:
 //! \code
 //!     Computation:
 //!         M = {6, 6} + {3, 3} + {2, 2} ==> {11, 11}
-//!         E = {3, 3} - {2, 2} ==> {1, 1}
-//!         O ==> floor(({11, 11} - {1, 1}) / {2, 2})
-//!           ==> floor({10, 10} / {2, 2})
-//!           ==> floor({5, 5})
+//!         O ==> floor((M - F) / S) + 1
+//!           ==> floor(({11, 11} - {3, 3}) / {2, 2}) + {1, 1}
+//!           ==> floor({8, 8} / {2, 2}) + {1, 1}
 //!           ==> {5, 5}
 //! \endcode
 //! - EXPLICIT_ROUND_UP:
 //! \code
 //!     Computation:
 //!         M = {6, 6} + {3, 3} + {2, 2} ==> {11, 11}
-//!         E = {3, 3} - {2, 2} ==> {1, 1}
-//!         O ==> ceil(({11, 11} - {1, 1}) / {2, 2})
-//!           ==> ceil({10, 10} / {2, 2})
-//!           ==> ceil({5, 5})
+//!         O ==> ceil((M - F) / S) + 1
+//!           ==> ceil(({11, 11} - {3, 3}) / {2, 2}) + {1, 1}
+//!           ==> ceil({8, 8} / {2, 2}) + {1, 1}
 //!           ==> {5, 5}
 //! \endcode
 //!     The sample points are {0, 2, 4, 6, 8} in each dimension.
@@ -984,32 +1011,32 @@ class ILayer
 //!     Computation:
 //!         I = {6, 6}
 //!         S = {2, 2}
-//!         O = {3, 3}
-//!         P = ({6, 6} - ceil({6, 6} / {2, 2}))
-//!             ==> ({6, 6} - {3, 3})
-//!             ==> {3, 3}
-//!         B = floor({3, 3} / {2, 2})
+//!         O = ceil(I / S) = {3, 3}
+//!         P = floor((I - 1) / S) * S + F - I
+//!             ==> floor(({6, 6} - {1, 1}) / {2, 2}) * {2, 2} + {3, 3} - {6, 6}
+//!             ==> {4, 4} + {3, 3} - {6, 6}
+//!             ==> {1, 1}
+//!         B = floor({1, 1} / {2, 2})
+//!             ==> {0, 0}
+//!         A = {1, 1} - {0, 0}
 //!             ==> {1, 1}
-//!         A = {3, 3} - {1, 1}
-//!             ==> {2, 2}
 //! \endcode
 //! - SAME_LOWER:
 //! \code
 //!     Computation:
 //!         I = {6, 6}
 //!         S = {2, 2}
-//!         O = {6, 6}
-//!         P = ({6, 6} - ceil({6, 6} / {2, 2}))
-//!           ==> ({6, 6} - {3, 3})
-//!           ==> {3, 3}
-//!         B = floor({3, 3} / {2, 2})
+//!         O = ceil(I / S) = {3, 3}
+//!         P = floor((I - 1) / S) * S + F - I
+//!           ==> {1, 1}
+//!         A = floor({1, 1} / {2, 2})
+//!           ==> {0, 0}
+//!         B = {1, 1} - {0, 0}
 //!           ==> {1, 1}
-//!         A = {3, 3} - {1, 1}
-//!           ==> {2, 2}
 //! \endcode
 //!     The sample pointers are {0, 2, 4} in each dimension.
-//!     SAMPLE_UPPER has {pad, O0, O1, O2, pad, pad} in output in each dimension.
-//!     SAMPLE_LOWER has {pad, pad, O0, O1, O2, pad} in output in each dimension.
+//!     SAMPLE_UPPER has {O0, O1, O2, pad} in output in each dimension.
+//!     SAMPLE_LOWER has {pad, O0, O1, O2} in output in each dimension.
 //!
 //! Pooling Example 2:
 //! \code
@@ -1020,9 +1047,9 @@ class ILayer
 //! \code
 //!     Computation:
 //!         M = {6, 6} + {3, 3} + {3, 3} ==> {12, 12}
-//!         E = {3, 3} - {2, 2} ==> {1, 1}
-//!         EXPLICIT_ROUND_DOWN = floor(M - E, S) ==> {5, 5}
-//!
+//!         EXPLICIT_ROUND_DOWN ==> floor((M - F) / S) + 1
+//!                             ==> floor(({12, 12} - {3, 3}) / {2, 2}) + {1, 1}
+//!                             ==> {5, 5}
 //!         DIFF = (((EXPLICIT_ROUND_DOWN - 1) * S >= I + B) ? {1, 1} : {0, 0})
 //!           ==> ({5, 5} - {1, 1}) * {2, 2} >= {6, 6} + {3, 3} ? {1, 1} : {0,0}
 //!           ==> {0, 0}
@@ -1034,9 +1061,9 @@ class ILayer
 //! \code
 //!     Computation:
 //!         M = {6, 6} + {3, 3} + {3, 3} ==> {12, 12}
-//!         E = {3, 3} - {2, 2} ==> {1, 1}
-//!         EXPLICIT_ROUND_UP = CEIL(M - E, S) ==> {6, 6}
-//!
+//!         EXPLICIT_ROUND_UP ==> ceil((M - F) / S) + 1
+//!                           ==> ceil(({12, 12} - {3, 3}) / {2, 2}) + {1, 1}
+//!                           ==> {6, 6}
 //!         DIFF = (((EXPLICIT_ROUND_UP - 1) * S >= I + B) ? {1, 1} : {0, 0})
 //!           ==> ({6, 6} - {1, 1}) * {2, 2} >= {6, 6} + {3, 3} ? {1, 1} : {0,0}
 //!           ==> {1, 1}
@@ -1098,7 +1125,7 @@ class IConvolutionLayer : public ILayer
     //!
     //! \brief Set the HW kernel size of the convolution.
     //!
-    //! If executing this layer on DLA, both height and width of kernel size must be in the range [1,16].
+    //! If executing this layer on DLA, both height and width of kernel size must be in the range [1,32].
     //!
     //! \see getKernelSize()
     //!
@@ -1159,7 +1186,8 @@ class IConvolutionLayer : public ILayer
     //!
     //! Default: (0,0)
     //!
-    //! If executing this layer on DLA, both height and width of padding must be in the range [0,15].
+    //! If executing this layer on DLA, both height and width of padding must be in the range [0,31],
+    //! and the padding size must be less than the kernel size.
     //!
     //! \see getPadding()
     //!
@@ -1187,6 +1215,8 @@ class IConvolutionLayer : public ILayer
     //!
     //! Default: 1
     //!
+    //! If executing this layer on DLA, the max number of groups is 8192.
+    //!
     //! \see getNbGroups()
     //!
     virtual void setNbGroups(int nbGroups) TRTNOEXCEPT = 0;
@@ -1240,6 +1270,8 @@ class IConvolutionLayer : public ILayer
     //!
     //! Default: (1,1)
     //!
+    //! If executing this layer on DLA, both height and width must be in the range [1,32].
+    //!
     //! \see getDilation()
     //!
     //! \deprecated Superseded by setDilationNd
@@ -1266,7 +1298,8 @@ class IConvolutionLayer : public ILayer
     //!
     //! Default: 0
     //!
-    //! If executing this layer on DLA, both height and width of padding must be in the range [0,15].
+    //! If executing this layer on DLA, both height and width of padding must be in the range [0,31],
+    //! and the padding must be less than the kernel size.
     //!
     //! \see getPrePadding()
     //!
@@ -1286,7 +1319,8 @@ class IConvolutionLayer : public ILayer
     //!
     //! Default: (0,0)
     //!
-    //! If executing this layer on DLA, both height and width of padding must be in the range [0,15].
+    //! If executing this layer on DLA, both height and width of padding must be in the range [0,31],
+    //! and the padding must be less than the kernel size.
     //!
     //! \see getPostPadding()
     //!
@@ -1322,7 +1356,8 @@ class IConvolutionLayer : public ILayer
     //!
     //! \brief Set the multi-dimension kernel size of the convolution.
     //!
-    //! If executing this layer on DLA, only support 2D kernel size, both height and width of kernel size must be in the range [1,16].
+    //! If executing this layer on DLA, only support 2D kernel size, both height and width of kernel size must be in the
+    //! range [1,32].
     //!
     //! \see getKernelSizeNd()
     //!
@@ -1361,7 +1396,8 @@ class IConvolutionLayer : public ILayer
     //!
     //! Default: (0, 0, ..., 0)
     //!
-    //! If executing this layer on DLA, only support 2D padding, both height and width of padding must be in the range [0,15].
+    //! If executing this layer on DLA, only support 2D padding, both height and width of padding must be in the range
+    //! [0,31], and the padding must be less than the kernel size.
     //!
     //! \see getPaddingNd() setPadding() getPadding()
     //!
@@ -1381,6 +1417,8 @@ class IConvolutionLayer : public ILayer
     //!
     //! Default: (1, 1, ..., 1)
     //!
+    //! If executing this layer on DLA, only support 2D padding, both height and width must be in the range [1,32].
+    //!
     //! \see getDilation()
     //!
     virtual void setDilationNd(Dims dilation) TRTNOEXCEPT = 0;
@@ -1398,14 +1436,15 @@ class IConvolutionLayer : public ILayer
     //! \param index the index of the input to modify.
     //! \param tensor the new input tensor
     //!
-    //! For a convolution layer, the values 0-2 are valid. The value 1 override kernel weights, and the value
-    //! 2 override bias weights. Conversely, this input tensor can be overridden via appropriate set call.
+    //! For a IConvolutionLayer, only index 0 is valid unless explicit precision mode is enabled.
+    //! With explicit precision mode, values 0-1 are valid where value 1 overrides kernel weights.
+    //! Kernel weights tensor (computed at build-time) must be an output of dequantize scale layer (i.e. a scale layer with int8 input and float output)
+    //! in explicit precision network. Conversely, this input tensor can be overridden via appropriate set call.
     //! The indices are as follows:
     //!
     //! Index | Description
     //!   0   | The input activation tensor.
-    //!   1   | The kernel weights tensor.
-    //!   2   | The bias weights tensor.
+    //!   1   | The kernel weights tensor (a constant tensor).
     //!
     //! If this function is called with a value greater than 0, then the function getNbInputs() changes
     void setInput(int index, ITensor& tensor) _TENSORRT_OVERRIDE TRTNOEXCEPT = 0;
@@ -1499,14 +1538,15 @@ class IFullyConnectedLayer : public ILayer
     //! \param index the index of the input to modify.
     //! \param tensor the new input tensor
     //!
-    //! For a fulyconnected layer, the values 0-2 are valid. The value 1 override kernel weights, and the value
-    //! 2 override bias weights. Conversely, this input tensor can be overridden via appropriate set call.
+    //! For a IFullyConnectedLayer, only index 0 is valid unless explicit precision mode is enabled.
+    //! With explicit precision mode, values 0-1 are valid where value 1 overrides kernel weights.
+    //! Kernel weights tensor (computed at build-time) must be an output of dequantize scale layer (i.e. a scale layer with int8 input and float output)
+    //! in explicit precision network. Conversely, this input tensor can be overridden via appropriate set call.
     //! The indices are as follows:
     //!
     //! Index | Description
     //!   0   | The input activation tensor.
-    //!   1   | The kernel weights tensor.
-    //!   2   | The bias weights tensor.
+    //!   1   | The kernel weights tensor (a constant tensor).
     //!
     //! If this function is called with a value greater than 0, then the function getNbInputs() changes
     void setInput(int index, ITensor& tensor) _TENSORRT_OVERRIDE TRTNOEXCEPT = 0;
@@ -1529,6 +1569,8 @@ class IActivationLayer : public ILayer
     //!
     //! \brief Set the type of activation to be performed.
     //!
+    //! On the DLA, the valid activation types are kRELU, kSIGMOID, kTANH, and kCLIP.
+    //!
     //! \see getActivationType(), ActivationType
     //!
     virtual void setActivationType(ActivationType type) TRTNOEXCEPT = 0;
@@ -1603,6 +1645,9 @@ constexpr inline int EnumMax<PoolingType>()
 //!
 //! The layer applies a reduction operation within a window over the input.
 //!
+//! \warning When running pooling layer with DeviceType::kDLA in Int8 mode, the dynamic ranges
+//! for input and output tensors must be equal.
+//!
 //! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI.
 //!
 class IPoolingLayer : public ILayer
@@ -1611,7 +1656,7 @@ class IPoolingLayer : public ILayer
     //!
     //! \brief Set the type of activation to be performed.
     //!
-    //! DLA only supports kMAX and kAVERAGE.
+    //! DLA only supports kMAX and kAVERAGE pooling types.
     //!
     //! \see getPoolingType(), PoolingType
     //!
@@ -1696,6 +1741,8 @@ class IPoolingLayer : public ILayer
     //! blendFactor is a user value in [0,1] with the default value of 0.0
     //! This value only applies for the kMAX_AVERAGE_BLEND mode.
     //!
+    //! Since DLA does not support kMAX_AVERAGE_BLEND, blendFactor is ignored on the DLA.
+    //!
     //! \see getBlendFactor()
     //!
     virtual void setBlendFactor(float blendFactor) TRTNOEXCEPT = 0;
@@ -1715,8 +1762,12 @@ class IPoolingLayer : public ILayer
     //! and the unpadded input.
     //! If this is not set, the denominator is the overlap between the pooling window and the padded input.
     //!
+    //! If executing this layer on the DLA, only inclusive padding is supported.
+    //!
     //! Default: true
     //!
+    //! If executing this layer on the DLA, this is ignored as the DLA does not support exclusive padding.
+    //!
     //! \see getAverageCountExcludesPadding()
     //!
     virtual void setAverageCountExcludesPadding(bool exclusive) TRTNOEXCEPT = 0;
@@ -1740,7 +1791,7 @@ class IPoolingLayer : public ILayer
     //!
     //! Default: 0
     //!
-    //! If executing this layer on DLA, both height and width of padding must be in the range [0,15].
+    //! If executing this layer on DLA, both height and width of padding must be in the range [0,7].
     //!
     //! \see getPadding()
     //!
@@ -1760,7 +1811,7 @@ class IPoolingLayer : public ILayer
     //!
     //! Default: (0,0)
     //!
-    //! If executing this layer on DLA, both height and width of padding must be in the range [0,15].
+    //! If executing this layer on DLA, both height and width of padding must be in the range [0,7].
     //!
     //! \see getPadding()
     //!
@@ -1865,6 +1916,9 @@ class ILRNLayer : public ILayer
     //! \brief Set the LRN window size.
     //!
     //! The window size must be odd and in the range of [1, 15].
+    //!
+    //! If executing this layer on the DLA, only values in the set, [3, 5, 7, 9], are valid.
+    //!
     //! \see setWindowStride()
     //!
     virtual void setWindowSize(int windowSize) TRTNOEXCEPT = 0;
@@ -2121,6 +2175,8 @@ class IConcatenationLayer : public ILayer
     //! 0 is the major axis (excluding the batch dimension). The default is the number of non-batch axes in the tensor
     //! minus three (e.g. for an NCHW input it would be 0), or 0 if there are fewer than 3 non-batch axes.
     //!
+    //! When running this layer on the DLA, only concat across the Channel axis is valid.
+    //!
     //! \param axis The axis along which concatenation occurs.
     //!
     virtual void setAxis(int axis) TRTNOEXCEPT = 0;
@@ -2148,7 +2204,9 @@ class IDeconvolutionLayer : public ILayer
     //!
     //! \brief Set the HW kernel size of the convolution.
     //!
-    //! If executing this layer on DLA, both height and width of kernel size must be in the range [1,16].
+    //! If executing this layer on DLA, both height and width of kernel size must be in the range [1,32], or the
+    //! combinations of [64, 96, 128] in one dimension and 1 in the other dimensions, i.e. [1x64] or [64x1] are valid,
+    //! but not [64x64].
     //!
     //! \see getKernelSize()
     //!
@@ -2184,7 +2242,9 @@ class IDeconvolutionLayer : public ILayer
     //!
     //! \brief Get the stride of the deconvolution.
     //!
-    //! If executing this layer on DLA, both height and width of stride must be in the range [1,8].
+    //! If executing this layer on DLA, both height and width of stride must be in the range [1,32] or the combinations
+    //! of [64, 96, 128] in one dimension and 1 in the other dimensions, i.e. [1x64] or [64x1] are valid, but not
+    //! [64x64].
     //!
     //! \see setStride()
     //!
@@ -2210,7 +2270,7 @@ class IDeconvolutionLayer : public ILayer
     //!
     //! Default: (0,0)
     //!
-    //! If executing this layer on DLA, both height and width of padding must be in the range [0,15].
+    //! If executing this layer on DLA, both height and width of padding must be 0.
     //!
     //! \see getPadding()
     //!
@@ -2235,6 +2295,8 @@ class IDeconvolutionLayer : public ILayer
     //! The input tensor channels are divided into \p nbGroups groups, and a deconvolution is executed for each group,
     //! using a filter per group. The results of the group convolutions are concatenated to form the output.
     //!
+    //! If executing this layer on DLA, nbGroups must be one
+    //!
     //! \note When using groups in int8 mode, the size of the groups (i.e. the channel count divided by the group count)
     //! must be a multiple of 4 for both input and output.
     //!
@@ -2299,7 +2361,7 @@ class IDeconvolutionLayer : public ILayer
     //!
     //! Default: 0
     //!
-    //! If executing this layer on DLA, both height and width of padding must be in the range [0,15].
+    //! If executing this layer on DLA, both height and width of padding must be 0.
     //!
     //! \see getPadding()
     //!
@@ -2319,7 +2381,7 @@ class IDeconvolutionLayer : public ILayer
     //!
     //! Default: (0,0)
     //!
-    //! If executing this layer on DLA, both height and width of padding must be in the range [0,15].
+    //! If executing this layer on DLA, both height and width of padding must be 0.
     //!
     //! \see getPadding()
     //!
@@ -2344,7 +2406,7 @@ class IDeconvolutionLayer : public ILayer
     virtual void setPaddingMode(PaddingMode paddingMode) TRTNOEXCEPT = 0;
 
     //!
-    //! \brief Set the padding mode.
+    //! \brief Get the padding mode.
     //!
     //! Default: kEXPLICIT_ROUND_DOWN
     //!
@@ -2355,7 +2417,8 @@ class IDeconvolutionLayer : public ILayer
     //!
     //! \brief Set the multi-dimension kernel size of the deconvolution.
     //!
-    //! If executing this layer on DLA, only support 2D kernel size, both height and width of kernel size must be in the range [1,16].
+    //! If executing this layer on DLA, only support 2D kernel size, both height and width of kernel size must be in
+    //! the range [1-32].
     //!
     //! \see getKernelSizeNd() setKernelSize() getKernelSize()
     //!
@@ -2373,7 +2436,8 @@ class IDeconvolutionLayer : public ILayer
     //!
     //! Default: (1, 1, ..., 1)
     //!
-    //! If executing this layer on DLA, only support 2D stride, both height and width of stride must be in the range [1,8].
+    //! If executing this layer on DLA, only support 2D stride, both height and width of stride must be in the range
+    //! [1-32].
     //!
     //! \see getStrideNd() setStride() getStride()
     //!
@@ -2394,7 +2458,7 @@ class IDeconvolutionLayer : public ILayer
     //!
     //! Default: (0, 0, ..., 0)
     //!
-    //! If executing this layer on DLA, only support 2D padding, both height and width of padding must be in the range [0,15].
+    //! If executing this layer on DLA, padding must be 0.
     //!
     //! \see getPaddingNd() setPadding() getPadding()
     //!
@@ -2415,18 +2479,34 @@ class IDeconvolutionLayer : public ILayer
     //! \param index the index of the input to modify.
     //! \param tensor the new input tensor
     //!
-    //! For a deconvolution layer, the values 0-2 are valid. The value 1 override kernel weights, and the value
-    //! 2 override bias weights. Conversely, this input tensor can be overridden via appropriate set call.
+    //! For a IDeconvolutionLayer, only index 0 is valid unless explicit precision mode is enabled.
+    //! With explicit precision mode, values 0-1 are valid where value 1 overrides kernel weights.
+    //! Kernel weights tensor (computed at build-time) must be an output of dequantize scale layer (i.e. a scale layer with int8 input and float output)
+    //! in explicit precision network. Conversely, this input tensor can be overridden via appropriate set call.
     //! The indices are as follows:
     //!
     //! Index | Description
     //!   0   | The input activation tensor.
-    //!   1   | The kernel weights tensor.
-    //!   2   | The bias weights tensor.
+    //!   1   | The kernel weights tensor (a constant tensor).
     //!
     //! If this function is called with a value greater than 0, then the function getNbInputs() changes
     //!
     void setInput(int index, ITensor& tensor) _TENSORRT_OVERRIDE TRTNOEXCEPT = 0;
+
+    //! \brief Set the multi-dimension dilation of the deconvolution.
+    //!
+    //! Default: (1, 1, ..., 1)
+    //!
+    //! \see getDilationNd()
+    //!
+    virtual void setDilationNd(Dims dilation) TRTNOEXCEPT = 0;
+
+    //!
+    //! \brief Get the multi-dimension dilation of the deconvolution.
+    //!
+    //! \see setDilationNd()
+    //!
+    virtual Dims getDilationNd() const TRTNOEXCEPT = 0;
 };
 
 //!
@@ -2468,6 +2548,9 @@ constexpr inline int EnumMax<ElementWiseOperation>()
 //! This layer applies a per-element binary operation between corresponding elements of two tensors.
 //!
 //! The input dimensions of the two input tensors must be equal, and the output tensor is the same size as each input.
+//�
+//! \warning When running this layer on the DLA with Int8 data type, the dynamic ranges of two input tensors shall be
+//! equal. If the dynamic ranges are generated using calibrator, the largest value shall be used.
 //!
 //! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI.
 //!
@@ -3362,6 +3445,19 @@ class IUnaryLayer : public ILayer
 //!
 //! \brief Enumerates the reduce operations that may be performed by a Reduce layer.
 //!
+//! The table shows the result of reducing across an empty volume of a given type.
+//!
+//! Operation | kFLOAT and kHALF  | kINT32  | kINT8
+//! --------- | ----------------- | ------- | -----
+//! kSUM      | 0                 | 0       | 0
+//! kPROD     | 1                 | 1       | 1
+//! kMAX      | negative infinity | INT_MIN | -128
+//! kMIN      | positive infinity | INT_MAX | 127
+//! kAVG      | NaN               | 0       | -128
+//!
+//! The current version of TensorRT usually performs reduction for kINT8 via kFLOAT or kHALF.
+//! The kINT8 values show the quantized representations of the floating-point values.
+//!
 enum class ReduceOperation : int
 {
     kSUM = 0,
@@ -3624,7 +3720,7 @@ class IShuffleLayer : public ILayer
     //!
     //! Index | Description
     //!   0   | Data or Shape tensor to be shuffled.
-    //!   1   | The dimensions for the reshape operation, as a 1D shape tensor.
+    //!   1   | The dimensions for the reshape operation, as a 1D Int32 shape tensor.
     //!
     //! If this function is called with a value 1, then the function getNbInputs() changes
     //! from returning 1 to 2.
@@ -3656,6 +3752,30 @@ class IShuffleLayer : public ILayer
 
 protected:
     virtual ~IShuffleLayer() {}
+
+public:
+    //!
+    //! \brief Set meaning of 0 in reshape dimensions.
+    //!
+    //! If true, then a 0 in the reshape dimensions denotes copying the corresponding
+    //! dimension from the first input tensor.  If false, then a 0 in the reshape
+    //! dimensions denotes a zero-length dimension.
+    //!
+    //! Default: true
+    //!
+    //! \see getZeroIsPlaceholder();
+    //!
+    virtual void setZeroIsPlaceholder(bool zeroIsPlaceholder) = 0;
+
+    //!
+    //! \brief Get meaning of 0 in reshape dimensions.
+    //!
+    //! \return true if 0 is placeholder for corresponding input dimension,
+    //!         false if 0 denotes a zero-length dimension.
+    //!
+    //! \see setZeroIsPlaceholder
+    //!
+    virtual bool getZeroIsPlaceholder() const = 0;
 };
 
 //!
@@ -3689,7 +3809,7 @@ constexpr inline int EnumMax<SliceMode>()
 //!
 //! The slice layer selects for each dimension a start location from within the input tensor, and
 //! copies elements to the output tensor using the specified stride across the input tensor.
-//! Start, size, and stride tensors must be 1D shape tensors if not specified via Dims.
+//! Start, size, and stride tensors must be 1D Int32 shape tensors if not specified via Dims.
 //!
 //! Furthermore, if the slice layer must produce a shape tensor, then start, size, and stride must be
 //! build time constants, i.e. as static Dims, or be computable by constant folding.
@@ -3801,9 +3921,9 @@ class ISliceLayer : public ILayer
     //!
     //! Index | Description
     //!   0   | Data or Shape tensor to be sliced.
-    //!   1   | The start tensor to begin slicing, as a 1D shape tensor.
-    //!   2   | The size tensor of the resulting slice, as a 1D shape tensor.
-    //!   3   | The stride of the slicing operation, as a 1D shape tensor.
+    //!   1   | The start tensor to begin slicing, as a 1D Int32 shape tensor.
+    //!   2   | The size tensor of the resulting slice, as a 1D Int32 shape tensor.
+    //!   3   | The stride of the slicing operation, as a 1D Int32 shape tensor.
     //!
     //! If this function is called with a value greater than 0, then the function getNbInputs() changes
     //! from returning 1 to index + 1.
@@ -4044,6 +4164,7 @@ class IIdentityLayer : public ILayer
 //! \class IConstantLayer
 //!
 //! \brief Layer that represents a constant value.
+//! \note This layer does not support boolean types.
 //!
 //! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI.
 //!
@@ -4252,7 +4373,7 @@ class IResizeLayer : public ILayer
     //!
     //! Index | Description
     //!   0   | Data or Shape tensor to be resized.
-    //!   1   | The output dimensions, as a 1D shape tensor.
+    //!   1   | The output dimensions, as a 1D Int32 shape tensor.
     //!
     //! If this function is called with a value 1, then the function getNbInputs() changes
     //! from returning 1 to 2.
@@ -4385,7 +4506,7 @@ class ILoopOutputLayer : public ILoopBoundaryLayer
     //!
     //! Index | Description
     //!   0   | Contribution to the output tensor.  The contribution must come from inside the loop.
-    //!   1   | The concatenation length scalar value, must come from outside the loop, as a 0D shape tensor.
+    //!   1   | The concatenation length scalar value, must come from outside the loop, as a 0D Int32 shape tensor.
     //!
     //! If this function is called with a value 1, then the function getNbInputs() changes
     //! from returning 1 to 2.
@@ -4529,7 +4650,7 @@ constexpr inline int EnumMax<FillOperation>()
 //! a corresponding input.  The corresponding static parameter is used if an input is missing or null.
 //!
 //! The shape of the output is specified by the parameter \p Dimension, or if non-null and present,
-//! the first input, which must be a 1D shape tensor.  Thus an application can determine if the
+//! the first input, which must be a 1D Int32 shape tensor.  Thus an application can determine if the
 //! IFillLayer has a dynamic output shape based on whether it has a non-null first input.
 //!
 //! Alpha and Beta are treated differently based on the Fill Operation specified. See details in
@@ -4692,13 +4813,13 @@ class INetworkDefinition
 
     //! For networks with an implicit batch dimension, this volume includes the batch dimension with its length set
     //! to the maximum batch size. For networks with all explicit dimensions and with wildcard dimensions, the volume
-    //! is based on the maxima specified by an IOptimizationProfile.Dimensions are normally positive integers. The
+    //! is based on the maxima specified by an IOptimizationProfile.Dimensions are normally non-negative integers. The
     //! exception is that in networks with all explicit dimensions, -1 can be used as a wildcard for a dimension to
     //! be specified at runtime. Input tensors with such a wildcard must have a corresponding entry in the
     //! IOptimizationProfiles indicating the permitted extrema, and the input dimensions must be set by
     //! IExecutionContext::setBindingDimensions. Different IExecutionContext instances can have different dimensions.
     //! Wildcard dimensions are only supported for EngineCapability::kDEFAULT. They are not
-    //! supported in safety contexts. DLA does not support Wildcard dimensions in {C, H, W} dimensions.
+    //! supported in safety contexts. DLA does not support Wildcard dimensions.
     //!
     //! Tensor dimensions are specified independent of format.  For example, if a
     //! tensor is formatted in "NHWC" or a vectorized format, the dimensions are
@@ -4712,6 +4833,9 @@ class INetworkDefinition
     //!
     //! \warning It is an error to specify a wildcard value on a dimension that is determined by trained parameters.
     //!
+    //! \warning If run on DLA with explicit dimensions, only leading dimension can be a wildcard. And provided profile
+    //! must have same minimum, optimum, and maximum dimensions.
+    //!
     //! \see ITensor
     //!
     //! \return The new tensor or nullptr if there is an error.
@@ -4753,8 +4877,8 @@ class INetworkDefinition
     //!
     //! \param input The input tensor to the layer.
     //! \param nbOutputs The number of outputs of the layer.
-    //! \param kernelWeights The kernel weights for the convolution.
-    //! \param biasWeights The optional bias weights for the convolution.
+    //! \param kernelWeights The kernel weights for the fully connected layer.
+    //! \param biasWeights The optional bias weights for the fully connected layer.
     //!
     //! \see IFullyConnectedLayer
     //!
@@ -4825,10 +4949,11 @@ class INetworkDefinition
     //! \param power The power value.
     //!
     //! If the weights are available, then the size of weights are dependent on the ScaleMode.
-    //! For ::kUNIFORM, the number of weights is equal to 1.
-    //! For ::kCHANNEL, the number of weights is equal to the channel dimension.
-    //! For ::kELEMENTWISE, the number of weights is equal to the volume of the input.
+    //! For ::kUNIFORM, the number of weights equals 1.
+    //! For ::kCHANNEL, the number of weights equals the channel dimension.
+    //! For ::kELEMENTWISE, the number of weights equals the product of the last three dimensions of the input.
     //!
+    //! \see addScaleNd
     //! \see IScaleLayer
     //! \warning Int32 tensors are not valid input tensors.
     //!
@@ -4918,7 +5043,8 @@ class INetworkDefinition
     //! \param weights The weights for the weight matrix parameters of the RNN.
     //! \param bias The weights for the bias vectors parameters of the RNN.
     //!
-    //! The input tensors must be of the type DataType::kFLOAT or DataType::kHALF.
+    //! The inputs tensor must be of the type DataType::kFLOAT or DataType::kHALF,
+    //! and have non-zero volume.
     //!
     //! See IRNNLayer::setWeights() and IRNNLayer::setBias() for details on the required input
     //! format for \p weights and \p bias.
@@ -4993,6 +5119,8 @@ class INetworkDefinition
     //!
     //! \warning Int32 tensors are not valid input tensors.
     //!
+    //! \warning Shape tensors are not supported as outputs.
+    //!
     //! \return The new unary layer, or nullptr if it could not be created
     //!
     virtual IUnaryLayer* addUnary(ITensor& input, UnaryOperation operation) TRTNOEXCEPT = 0;
@@ -5198,7 +5326,7 @@ class INetworkDefinition
     //!
     //! \see IReduceLayer
     //!
-    //! \warning If input is a shape tensor, ReduceOperation::kAVG is unsupported.
+    //! \warning If output is a shape tensor, ReduceOperation::kAVG is unsupported.
     //!
     //! \return The new reduce layer, or nullptr if it could not be created.
     //!
@@ -5636,9 +5764,14 @@ class INetworkDefinition
     //! \param channelAxis The channel axis.
     //!
     //! If the weights are available, then the size of weights are dependent on the ScaleMode.
+    //! For ::kUNIFORM, the number of weights equals 1.
+    //! For ::kCHANNEL, the number of weights equals the channel dimension.
+    //! For ::kELEMENTWISE, the number of weights equals the product of all input dimensions at channelAxis and beyond.
+    //!
+    //! For example, if the inputs dimensions are [A,B,C,D,E,F], and channelAxis=2:
     //! For ::kUNIFORM, the number of weights is equal to 1.
-    //! For ::kCHANNEL, the number of weights is equal to the channel dimension.
-    //! For ::kELEMENTWISE, the number of weights is equal to the volume of the input.
+    //! For ::kCHANNEL, the number of weights is C.
+    //! For ::kELEMENTWISE, the number of weights is C*D*E*F.
     //!
     //! \see IScaleLayer
     //! \warning Int32 tensors are not valid input tensors.
@@ -5716,7 +5849,7 @@ class INetworkDefinition
     //!
     //! \return The new padding layer, or nullptr if it could not be created.
     //!
-    TRT_DEPRECATED virtual IPaddingLayer* addPaddingNd(
+    virtual IPaddingLayer* addPaddingNd(
         ITensor& input, Dims prePadding, Dims postPadding) TRTNOEXCEPT = 0;
 };
 
@@ -5857,9 +5990,10 @@ class IInt8MinMaxCalibrator : public IInt8Calibrator
 };
 
 //!
-//! \deprecated Legacy calibrator left for backward compatibility with TensorRT 2.0.
+//! Legacy calibrator left for backward compatibility with TensorRT 2.0. This calibrator requires user parameterization,
+//! and is provided as a fallback option if the other calibrators yield poor results.
 //!
-class TRT_DEPRECATED IInt8LegacyCalibrator : public IInt8Calibrator
+class IInt8LegacyCalibrator : public IInt8Calibrator
 {
 public:
     //!
@@ -5911,7 +6045,216 @@ class TRT_DEPRECATED IInt8LegacyCalibrator : public IInt8Calibrator
 };
 
 //!
-//! \brief It is capable of representing one or more BuilderFlags by binary OR
+//! \class IAlgorithmIOInfo
+//!
+//! \brief Carries information about input or output of the algorithm.
+//!        IAlgorithmIOInfo for all the input and output along with IAlgorithmVariant denotes the variation of algorithm
+//!        and can be used to select or reproduce an algorithm using IAlgorithmSelector::selectAlgorithms().
+//! \see IAlgorithmVariant, IAlgorithm, IAlgorithmSelector::selectAlgorithms()
+//!
+//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI.
+//!
+class IAlgorithmIOInfo
+{
+public:
+    //!
+    //! \brief Return TensorFormat of the input/output of algorithm.
+    //!
+    virtual TensorFormat getTensorFormat() const TRTNOEXCEPT = 0;
+
+    //!
+    //! \brief Return DataType of the input/output of algorithm.
+    //!
+    virtual DataType getDataType() const TRTNOEXCEPT = 0;
+
+    //!
+    //! \brief Return strides of the input/output tensor of algorithm.
+    //!
+    virtual Dims getStrides() const TRTNOEXCEPT = 0;
+
+protected:
+    virtual ~IAlgorithmIOInfo() {}
+};
+
+//!
+//! \class IAlgorithmVariant
+//!
+//! \brief provides a unique 128-bit identifier, which along with the input and output information
+//!        denotes the variation of algorithm and can be used to select or reproduce an algorithm,
+//!        using IAlgorithmSelector::selectAlgorithms()
+//! \see IAlgorithmIOInfo, IAlgorithm, IAlgorithmSelector::selectAlgorithms()
+//! \note A single implementation can have multiple tactics.
+//!
+//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI.
+//!
+class IAlgorithmVariant
+{
+public:
+    //!
+    //! \brief Return implementation of the algorithm.
+    //!
+    virtual int64_t getImplementation() const TRTNOEXCEPT = 0;
+
+    //!
+    //! \brief Return tactic of the algorithm.
+    //!
+    virtual int64_t getTactic() const TRTNOEXCEPT = 0;
+
+protected:
+    virtual ~IAlgorithmVariant() {}
+};
+
+//!
+//! \class IAlgorithmContext
+//!
+//! \brief Describes the context and requirements, that could be fulfilled by one or more instances of IAlgorithm.
+//! \see IAlgorithm
+//!
+//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI.
+//!
+class IAlgorithmContext
+{
+public:
+    //!
+    //! \brief Return name of the algorithm node.
+    //! This is a unique identifier for the IAlgorithmContext.
+    //!
+    virtual const char* getName() const TRTNOEXCEPT = 0;
+
+    //!
+    //! \brief Get the minimum / optimum / maximum dimensions for input or output tensor.
+    //! \param index Index of the input or output of the algorithm. Incremental numbers assigned to indices of inputs
+    //!              and the outputs.
+    //! \param select Which of the minimum, optimum, or maximum dimensions to be queried.
+    //!
+    virtual Dims getDimensions(int32_t index, OptProfileSelector select) const TRTNOEXCEPT = 0;
+
+    //!
+    //! \brief Return number of inputs of the algorithm.
+    //!
+    virtual int32_t getNbInputs() const TRTNOEXCEPT = 0;
+
+    //!
+    //! \brief Return number of outputs of the algorithm.
+    //!
+    virtual int32_t getNbOutputs() const TRTNOEXCEPT = 0;
+
+protected:
+    virtual ~IAlgorithmContext() {}
+};
+
+//!
+//! \class IAlgorithm
+//! \brief Describes a variation of execution of a layer.
+//!        An algorithm is represented by IAlgorithmVariant and the IAlgorithmIOInfo for each of its inputs and outputs.
+//!        An algorithm can be selected or reproduced using AlgorithmSelector::selectAlgorithms()."
+//! \see IAlgorithmIOInfo, IAlgorithmVariant, IAlgorithmSelector::selectAlgorithms()
+//!
+//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI.
+//!
+class IAlgorithm
+{
+public:
+    //!
+    //! \brief Returns the format of an Algorithm input or output. Algorithm inputs are incrementally numbered first,
+    //!        followed by algorithm outputs.
+    //! \param index Index of the input or output of the algorithm. Incremental numbers assigned to indices of inputs
+    //!              and the outputs.
+    //!
+    virtual const IAlgorithmIOInfo& getAlgorithmIOInfo(int32_t index) const TRTNOEXCEPT = 0;
+
+    //!
+    //! \brief Returns the algorithm variant.
+    //!
+    virtual const IAlgorithmVariant& getAlgorithmVariant() const TRTNOEXCEPT = 0;
+
+    //!
+    //! \brief The time in milliseconds to execute the algorithm.
+    //!
+    virtual float getTimingMSec() const TRTNOEXCEPT = 0;
+
+    //!
+    //! \brief The size of the GPU temporary memory in bytes which the algorithm uses at execution time.
+    //!
+    virtual std::size_t getWorkspaceSize() const TRTNOEXCEPT = 0;
+
+protected:
+    virtual ~IAlgorithm() {}
+};
+
+//!
+//! \class IAlgorithmSelector
+//!
+//! \brief Interface implemented by application for selecting and reporting algorithms of a layer provided by the
+//!        builder.
+//! \note A layer in context of algorithm selection may be different from ILayer in INetworkDefiniton.
+//!       For example, an algorithm might be implementing a conglomeration of multiple ILayers in INetworkDefinition.
+//!
+class  IAlgorithmSelector
+{
+public:
+    //!
+    //! \brief Select Algorithms for a layer from the given list of algorithm choices.
+    //!
+    //! \return The number of choices selected from [0, nbChoices-1].
+    //! \param context The context for which the algorithm choices are valid.
+    //! \param choices The list of algorithm choices to select for implementation of this layer.
+    //! \param nbChoices Number of algorithm choices.
+    //! \param selection The user writes indices of selected choices in to selection buffer which is of size nbChoices.
+    //!
+    //! \note TRT uses its default algorithm selection to choose from the list provided.
+    //!       If return value is 0, TRT’s default algorithm selection is used unless strict type constraints are set.
+    //!       The list of choices is valid only for this specific algorithm context.
+    //!
+    virtual int32_t selectAlgorithms(const IAlgorithmContext& context, const IAlgorithm* const* choices,
+                                     int32_t nbChoices, int32_t* selection) TRTNOEXCEPT = 0;
+    //!
+    //! \brief Called by TensorRT to report choices it made.
+    //!
+    //! \note For a given optimization profile, this call comes after all calls to selectAlgorithms.
+    //! algoChoices[i] is the choice that TensorRT made for algoContexts[i], for i in [0, nbAlgorithms-1]
+    //!
+    //! \param algoContexts The list of all algorithm contexts.
+    //! \param algoChoices The list of algorithm choices made by TensorRT
+    //! \param nbAlgorithms The size of algoContexts as well as algoChoices.
+    //!
+    virtual void reportAlgorithms(const IAlgorithmContext* const* algoContexts, const IAlgorithm* const* algoChoices,
+        int32_t nbAlgorithms) TRTNOEXCEPT = 0;
+
+    virtual ~IAlgorithmSelector() {}
+};
+
+//!
+//! \brief Represents a collection of one or more QuantizationFlag values using binary OR
+//! operations.
+//!
+//! \see IBuilderConfig::getQuantizationFlags(), IBuilderConfig::setQuantizationFlags()
+//!
+typedef uint32_t QuantizationFlags;
+
+//!
+//! \enum QuantizationFlag
+//!
+//! \brief List of valid flags for quantizing the network to int8
+//!
+//! \see IBuilderConfig::setQuantizationFlag(), IBuilderConfig::getQuantizationFlag()
+//!
+enum class QuantizationFlag : int32_t
+{
+    //!< Run int8 calibration pass before layer fusion. Only valid for IInt8LegacyCalibrator and
+    //! IInt8EntropyCalibrator. We always run int8 calibration pass before layer fusion for
+    //! IInt8MinMaxCalibrator and IInt8EntropyCalibrator2. Disabled by default.
+    kCALIBRATE_BEFORE_FUSION = 0
+};
+
+template <>
+constexpr inline int EnumMax<QuantizationFlag>()
+{
+    return 1;
+} //!< Maximum number of quantization flags in QuantizationFlag enum. \see QuantizationFlag
+
+//!
+//! \brief Represents a collection of one or more QuantizationFlag values using binary OR
 //! operations, e.g., 1U << BuilderFlag::kFP16 | 1U << BuilderFlag::kDEBUG.
 //!
 //! \see IBuilderConfig::getFlags(), ITensor::setFlags(),
@@ -5919,28 +6262,55 @@ class TRT_DEPRECATED IInt8LegacyCalibrator : public IInt8Calibrator
 typedef uint32_t BuilderFlags;
 
 //!
-//! \enum BuilderFlags
+//! \enum BuilderFlag
 //!
 //! \brief List of valid modes that the builder can enable when creating an engine from a network definition.
 //!
 //! \see IBuilderConfig::setFlag(), IBuilderConfig::getFlag()
 //!
-enum class BuilderFlag : int
+enum class BuilderFlag : int32_t
 {
-    kFP16 = 0,         //!< Enable FP16 layer selection, with FP32 fallback.
-    kINT8 = 1,         //!< Enable Int8 layer selection, with FP32 fallback with FP16 fallback if kFP16 also specified.
-    kDEBUG = 2,        //!< Enable debugging of layers via synchronizing after every layer.
-    kGPU_FALLBACK = 3, //!< Enable layers marked to execute on GPU if layer cannot execute on DLA.
-    kSTRICT_TYPES = 4, //!< Enables strict type constraints.
-    kREFIT = 5,        //!< Enable building a refittable engine.
+    kFP16 = 0,                 //!< Enable FP16 layer selection, with FP32 fallback.
+    kINT8 = 1,                 //!< Enable Int8 layer selection, with FP32 fallback with FP16 fallback if kFP16 also specified.
+    kDEBUG = 2,                //!< Enable debugging of layers via synchronizing after every layer.
+    kGPU_FALLBACK = 3,         //!< Enable layers marked to execute on GPU if layer cannot execute on DLA.
+    kSTRICT_TYPES = 4,         //!< Enables strict type constraints.
+    kREFIT = 5,                //!< Enable building a refittable engine.
+    kDISABLE_TIMING_CACHE = 6, //!< Disable reuse of timing information across identical layers.
+
+    //! Allow (but not require) computations on tensors of type DataType::kFLOAT to use TF32.
+    //! TF32 computes inner products by rounding the inputs to 10-bit mantissas before
+    //! multiplying, but accumulates the sum using 23-bit mantissas. Enabled by default.
+    kTF32 = 7
 };
 
 template <>
 constexpr inline int EnumMax<BuilderFlag>()
 {
-    return 6;
+    return 8;
 } //!< Maximum number of builder flags in BuilderFlag enum. \see BuilderFlag
 
+//!
+//! \enum ProfilingVerbosity
+//!
+//! \brief List of verbosity levels of layer information exposed in NVTX annotations.
+//!
+//! \see IBuilderConfig::setProfilingVerbosity(),
+//!      IBuilderConfig::getProfilingVerbosity()
+//!
+enum class ProfilingVerbosity : int32_t
+{
+   kDEFAULT = 0,      //!< Register layer names in NVTX message field.
+   kNONE = 1,         //!< Turn off NVTX traces.
+   kVERBOSE = 2,      //!< Register layer names in NVTX message field and register layer detail in NVTX JSON payload field.
+};
+
+template <>
+constexpr inline int32_t EnumMax<ProfilingVerbosity>()
+{
+    return 3;
+} //!< Maximum number of profile verbosity levels in ProfilingVerbosity enum. \see ProfilingVerbosity
+
 //!
 //! \class IBuilderConfig
 //!
@@ -6048,14 +6418,14 @@ class IBuilderConfig
     //!
     //! \param builderFlags The build option for an engine.
     //!
-    //! \note This function will override the previous set flags, rather than bitwise adding the new flag.
+    //! \note This function will override the previous set flags, rather than bitwise ORing the new flag.
     //!
     //! \see getFlags()
     //!
     virtual void setFlags(BuilderFlags builderFlags) TRTNOEXCEPT = 0;
 
     //!
-    //! \brief Get the set of build mode flags for this builder config. Defaults to BuildMode::kDEFAULT.
+    //! \brief Get the build mode flags for this builder config. Defaults to 0.
     //!
     //! \return The build options as a bitmask.
     //!
@@ -6066,25 +6436,23 @@ class IBuilderConfig
     //!
     //! \brief clear a single build mode flag.
     //!
-    //! clears the builder flag from the set of enabled flags.
+    //! clears the builder mode flag from the enabled flags.
     //!
-    //! \see setFlags
+    //! \see setFlags()
     //!
     virtual void clearFlag(BuilderFlag builderFlag) TRTNOEXCEPT = 0;
 
     //!
     //! \brief Set a single build mode flag.
     //!
-    //! Sets the build mode flags on top of the flags already specified.
+    //! Add the input builder mode flag to the already enabled flags.
     //!
-    //! \see setFlags
+    //! \see setFlags()
     //!
     virtual void setFlag(BuilderFlag builderFlag) TRTNOEXCEPT = 0;
 
     //!
-    //! \brief returns true if the build mode flag is set
-    //!
-    //! Check if a build mode flag is set.
+    //! \brief Returns true if the build mode flag is set
     //!
     //! \see getFlags()
     //!
@@ -6092,7 +6460,6 @@ class IBuilderConfig
     //!
     virtual bool getFlag(BuilderFlag builderFlag) const TRTNOEXCEPT = 0;
 
-
     //!
     //! \brief Set the device that this layer must execute on.
     //! \param deviceType that this layer must execute on.
@@ -6139,12 +6506,16 @@ class IBuilderConfig
     //!
     //! \see IRuntime::setDLACore() getDLACore()
     //!
+    //! \warning Starting with TensorRT 8, the default value will be -1 if the DLA is not specified or unused.
+    //!
     virtual void setDLACore(int dlaCore) TRTNOEXCEPT = 0;
 
     //!
     //! \brief Get the DLA core that the engine executes on.
     //! \return If setDLACore is called, returns DLA core from 0 to N-1, else returns 0.
     //!
+    //! \warning Starting with TensorRT 8, the default value will be -1 if the DLA is not specified or unused.
+    //!
     virtual int getDLACore() const TRTNOEXCEPT = 0;
 
     //!
@@ -6207,17 +6578,118 @@ class IBuilderConfig
     virtual int addOptimizationProfile(const IOptimizationProfile* profile) noexcept = 0;
 
     //!
-    //! \brief Get number of optimization profiles
+    //! \brief Get number of optimization profiles.
     //!
     //! This is one higher than the index of the last optimization profile that has be defined (or
     //! zero, if none has been defined yet).
     //!
+    //! \return The number of the optimization profiles.
+    //!
     virtual int getNbOptimizationProfiles() const noexcept = 0;
 
 protected:
     virtual ~IBuilderConfig()
     {
     }
+
+public:
+    //!
+    //! \brief Set verbosity level of layer information exposed in NVTX annotations.
+    //!
+    //! Control how much layer information will be exposed in NVTX annotations.
+    //!
+    //! \see ProfilingVerbosity, getProfilingVerbosity()
+    //!
+    virtual void setProfilingVerbosity(ProfilingVerbosity verbosity) TRTNOEXCEPT = 0;
+
+    //!
+    //! \brief Get verbosity level of layer information exposed in NVTX annotations.
+    //!
+    //! Get the current setting of verbosity level of layer information exposed in
+    //! NVTX annotations. Default value is ProfilingVerbosity::kDEFAULT.
+    //!
+    //! \see ProfilingVerbosity, setProfilingVerbosity()
+    //!
+    virtual ProfilingVerbosity getProfilingVerbosity() const TRTNOEXCEPT = 0;
+
+    //!
+    //! \brief Set Algorithm Selector.
+    //!
+    //! \param selector The algorithm slector to be set in the build config.
+    virtual void setAlgorithmSelector(IAlgorithmSelector* selector) TRTNOEXCEPT = 0;
+
+    //!
+    //! \brief Get Algorithm Selector.
+    //!
+    virtual IAlgorithmSelector* getAlgorithmSelector() const TRTNOEXCEPT = 0;
+
+    //!
+    //! \brief Add a calibration profile.
+    //!
+    //! Calibration optimization profile must be set if int8 calibration is used to set scales for a network with runtime dimensions.
+    //!
+    //! \param profile The new calibration profile, which must satisfy profile->isValid() == true or be nullptr.
+    //! MIN and MAX values will be overwritten by kOPT.
+    //! \return True if the calibration profile was set correctly.
+    //!
+    virtual bool setCalibrationProfile(const IOptimizationProfile* profile) noexcept = 0;
+
+    //!
+    //! \brief Get the current calibration profile.
+    //!
+    //! \return A pointer to the current calibration profile or nullptr if calibration profile is unset.
+    //!
+    virtual const IOptimizationProfile* getCalibrationProfile() noexcept = 0;
+
+    //!
+    //! \brief Set the quantization flags.
+    //!
+    //! The flags are listed in the QuantizationFlag enum.
+    //! The flags set configuration options to quantize the network in int8.
+    //!
+    //! \param flags The quantization flags.
+    //!
+    //! \note This function will override the previous set flags, rather than bitwise ORing the new flag.
+    //!
+    //! \see getQuantizationFlags()
+    //!
+    virtual void setQuantizationFlags(QuantizationFlags flags) TRTNOEXCEPT = 0;
+
+    //!
+    //! \brief Get the quantization flags.
+    //!
+    //! \return The quantization flags as a bitmask.
+    //!
+    //! \see setQuantizationFlag()
+    //!
+    virtual QuantizationFlags getQuantizationFlags() const TRTNOEXCEPT = 0;
+
+    //!
+    //! \brief clear a quantization flag.
+    //!
+    //! Clears the quantization flag from the enabled quantization flags.
+    //!
+    //! \see setQuantizationFlags()
+    //!
+    virtual void clearQuantizationFlag(QuantizationFlag flag) TRTNOEXCEPT = 0;
+
+    //!
+    //! \brief Set a single quantization flag.
+    //!
+    //! Add the input quantization flag to the already enabled quantization flags.
+    //!
+    //! \see setQuantizationFlags()
+    //!
+    virtual void setQuantizationFlag(QuantizationFlag flag) TRTNOEXCEPT = 0;
+
+    //!
+    //! \brief Returns true if the quantization flag is set.
+    //!
+    //! \see getQuantizationFlags()
+    //!
+    //! \return True if quantization flag is set, false if unset.
+    //!
+    virtual bool getQuantizationFlag(QuantizationFlag flag) const TRTNOEXCEPT = 0;
 };
 
 
@@ -6423,7 +6895,7 @@ class IBuilder
     //!
     //! \see INetworkDefinition ICudaEngine
     //!
-    //! \depercated API will be removed in a future release, use IBuilderConfig::buildEngineWithConfig instead.
+    //! \deprecated API will be removed in a future release, use IBuilder::buildEngineWithConfig instead.
     //!
     TRT_DEPRECATED virtual nvinfer1::ICudaEngine* buildCudaEngine(
         nvinfer1::INetworkDefinition& network) TRTNOEXCEPT = 0;
@@ -6516,6 +6988,8 @@ class IBuilder
     //! \brief Checks if a layer can run on DLA.
     //! \return status true if the layer can on DLA else returns false.
     //!
+    //! \deprecated API will be removed in a future release, use IBuilderConfig::canRunOnDLA instead.
+    //!
     TRT_DEPRECATED virtual bool canRunOnDLA(const ILayer* layer) const TRTNOEXCEPT = 0;
 
     //!
@@ -6760,6 +7234,11 @@ class IBuilder
     //! \brief Resets the builder state to default values.
     //!
     virtual void reset() TRTNOEXCEPT = 0;
+
+    //!
+    //! \brief Determine whether the platform has TF32 support.
+    //!
+    virtual bool platformHasTf32() const TRTNOEXCEPT = 0;
 };
 
 } // namespace nvinfer1
diff --git a/include/NvInferPlugin.h b/include/NvInferPlugin.h
index 5d06aa92..01ba3f1b 100644
--- a/include/NvInferPlugin.h
+++ b/include/NvInferPlugin.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -35,6 +35,7 @@ extern "C"
 //! \param libNamespace Namespace used to register all the plugins in this library
 //!
 TENSORRTAPI bool initLibNvInferPlugins(void* logger, const char* libNamespace);
+
 } // extern "C"
 
 #endif // NV_INFER_PLUGIN_H
diff --git a/include/NvInferPluginUtils.h b/include/NvInferPluginUtils.h
index fbe8197d..a92488cf 100644
--- a/include/NvInferPluginUtils.h
+++ b/include/NvInferPluginUtils.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -13,12 +13,13 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
- 
+
 #ifndef NV_INFER_PLUGIN_UTILS_H
 #define NV_INFER_PLUGIN_UTILS_H
 #include "NvInferRuntimeCommon.h"
+
 //!
-//! \file NvPluginUtils.h
+//! \file NvInferPluginUtils.h
 //!
 //! This is the API for the Nvidia provided TensorRT plugin utilities.
 //! It lists all the parameters utilized by the TensorRT plugins.
diff --git a/include/NvInferRuntime.h b/include/NvInferRuntime.h
index 3277e660..52c8d15e 100644
--- a/include/NvInferRuntime.h
+++ b/include/NvInferRuntime.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -37,14 +37,20 @@ class IPluginFactory; //!< Forward declaration of IPluginFactory for use by othe
 //!
 //! \brief List of supported engine capability flows.
 //!
-//! \note at present, kSAFE_DLA flow doesn't strictly limit execution to DLA devices - it simply
-//! restricts the engine capabilities to DLA support levels anticipated in future releases.
+//! The EngineCapability determines the restrictions of a network during build time for what can be executed
+//! at runtime. EngineCapability::kDEFAULT does not provide any restrictions on functionality and the
+//! resulting serialized engine can be executed with TensorRT's standard runtime APIs in the nvinfer1 namespace.
+//! EngineCapabiltiy::kSAFE_GPU provides a restricted subset of network operations that are safety certified and
+//! the resulting serialized engine can be executed with TensorRT's safe runtime APIs in the nvinfer1::safe namespace.
+//! EngineCapability::kSAFE_DLA provides a restricted subset of network operations that are DLA compatible and
+//! the resulting serialized engine can be executed using NvMediaDLA's runtime APIs. See sampleNvmedia for an
+//! example of integrating NvMediaDLA APIs with TensorRT APIs.
 //!
 enum class EngineCapability : int
 {
-    kDEFAULT = 0,  //!< Full capability, TensorRT mode without any restrictions.
-    kSAFE_GPU = 1, //!< Safety restricted capability, TensorRT flow that can only run on GPU devices.
-    kSAFE_DLA = 2, //!< Safety restricted capability, TensorRT flow that can only run on DLA devices.
+    kDEFAULT = 0,  //!< Full capability, TensorRT mode without any restrictions using TensorRT nvinfer1 APIs.
+    kSAFE_GPU = 1, //!< Safety restricted capability, TensorRT flow that can only run on GPU devices via TensorRT nvinfer1::safe APIs.
+    kSAFE_DLA = 2, //!< Safety restricted capability, TensorRT flow that can only run on DLA devices via NvMediaDLA APIs.
 };
 
 template <>
@@ -53,12 +59,15 @@ constexpr inline int EnumMax<EngineCapability>()
     return 3;
 } //!< Maximum number of elements in EngineCapability enum. \see EngineCapability
 
-
 //!
 //! \class Weights
 //!
 //! \brief An array of weights used as a layer parameter.
 //!
+//! When using the DLA, the cumulative size of all Weights used in a network
+//! must be less than 512MB in size. If the build option kGPU_FALLBACK is specified,
+//! then multiple DLA sub-networks may be generated from the single original network.
+//!
 //! The weights are held by reference until the engine has been built. Therefore the data referenced
 //! by \p values field should be preserved until the build is complete.
 //!
@@ -225,6 +234,8 @@ class IPluginExt : public IPlugin
     //! This function is called by the implementations of INetworkDefinition, IBuilder, and ICudaEngine.
     //! In particular, it is called when creating an engine and when deserializing an engine.
     //!
+    //! \warning DataType:kBOOL not supported.
+    //!
     virtual bool supportsFormat(DataType type, PluginFormat format) const TRTNOEXCEPT = 0;
 
     //!
@@ -243,6 +254,8 @@ class IPluginExt : public IPlugin
     //!
     //! The dimensions passed here do not include the outermost batch size (i.e. for 2-D image networks, they will be 3-dimensional CHW dimensions).
     //!
+    //! \warning DataType:kBOOL not supported.
+    //!
     virtual void configureWithFormat(const Dims* inputDims, int nbInputs, const Dims* outputDims, int nbOutputs, DataType type, PluginFormat format, int maxBatchSize) TRTNOEXCEPT = 0;
 
     virtual ~IPluginExt() {}
@@ -677,12 +690,16 @@ class IRuntime
     //! \param dlaCore The DLA core to execute the engine on (0 to N-1, where N is the maximum number of DLA's present on the device). Default value is 0.
     //! \see getDLACore()
     //!
+    //! \warning Starting with TensorRT 8, the default value will be -1 if the DLA is not specified or unused.
+    //!
     virtual void setDLACore(int dlaCore) noexcept = 0;
 
     //!
     //! \brief Get the DLA core that the engine executes on.
     //! \return If setDLACore is called, returns DLA core from 0 to N-1, else returns 0.
     //!
+    //! \warning Starting with TensorRT 8, the default value will be -1 if the DLA is not specified or unused.
+    //!
     virtual int getDLACore() const noexcept = 0;
 
     //!
@@ -973,7 +990,7 @@ class IOptimizationProfile
     //! then the following conditions must all hold:
     //!
     //! (1) minDims.nbDims == optDims.nbDims == maxDims.nbDims == networkDims.nbDims
-    //! (2) 1 <= minDims.d[i] <= optDims.d[i] <= maxDims.d[i] for i = 0, ..., networkDims.nbDims-1
+    //! (2) 0 <= minDims.d[i] <= optDims.d[i] <= maxDims.d[i] for i = 0, ..., networkDims.nbDims-1
     //! (3) if networkDims.d[i] != -1, then minDims.d[i] == optDims.d[i] == maxDims.d[i] == networkDims.d[i]
     //!
     //! This function may (but need not be) called for an input tensor that does not have dynamic dimensions. In this
@@ -987,6 +1004,8 @@ class IOptimizationProfile
     //!         previously set for the same input), true if no inconsistency was detected. Note that inputs can be
     //!         validated only partially; a full validation is performed at engine build time.
     //!
+    //! \warning If run on DLA, minimum, optimum, and maximum dimensions must to be the same.
+    //!
     virtual bool setDimensions(const char* inputName, OptProfileSelector select, Dims dims) noexcept = 0;
 
     //!
@@ -1017,6 +1036,8 @@ class IOptimizationProfile
     //!         tensor), else true. As for setDimensions(), a full validation can only be performed at engine build
     //!         time.
     //!
+    //! \warning If run on DLA, minimum, optimum, and maximum shape values must to be the same.
+    //!
     virtual bool setShapeValues(
         const char* inputName, OptProfileSelector select, const int32_t* values, int nbValues) noexcept = 0;
 
@@ -1085,6 +1106,8 @@ class ICudaEngine
     //!
     //! \brief Get the number of binding indices.
     //!
+    //! There are separate binding indices for each optimization profile.
+    //! This method returns the total over all profiles.
     //! If the engine has been built for K profiles, the first getNbBindings() / K bindings are used by profile
     //! number 0, the following getNbBindings() / K bindings are used by profile number 1 etc.
     //!
@@ -1100,10 +1123,13 @@ class ICudaEngine
     //! Engine bindings map from tensor names to indices in this array.
     //! Binding indices are assigned at engine build time, and take values in the range [0 ... n-1] where n is the total number of inputs and outputs.
     //!
+    //! To get the binding index of the name in an optimization profile with index k > 0,
+    //! mangle the name by appending " [profile k]", as described for method getBindingName().
+    //!
     //! \param name The tensor name.
     //! \return The binding index for the named tensor, or -1 if the name is not found.
     //!
-    //! see getNbBindings() getBindingIndex()
+    //! \see getNbBindings() getBindingName()
     //!
     virtual int getBindingIndex(const char* name) const noexcept = 0;
 
@@ -1112,6 +1138,11 @@ class ICudaEngine
     //!
     //! This is the reverse mapping to that provided by getBindingIndex().
     //!
+    //! For optimization profiles with an index k > 0, the name is mangled by appending
+    //! " [profile k]", with k written in decimal.  For example, if the tensor in the
+    //! INetworkDefinition had the name "foo", and bindingIndex refers to that tensor in the
+    //! optimization profile with index 3, getBindingName returns "foo [profile 3]".
+    //!
     //! \param bindingIndex The binding index.
     //! \return The name corresponding to the index, or nullptr if the index is out of range.
     //!
@@ -1133,8 +1164,19 @@ class ICudaEngine
     //! \brief Get the dimensions of a binding.
     //!
     //! \param bindingIndex The binding index.
-    //! \return The dimensions of the binding if the index is in range, otherwise Dims()
-    //!         Has -1 for any dimension with a dynamic value.
+    //! \return The dimensions of the binding if the index is in range, otherwise Dims().
+    //!         Has -1 for any dimension that varies within the optimization profile.
+    //!
+    //! For example, suppose an INetworkDefinition has an input with shape [-1,-1]
+    //! that becomes a binding b in the engine.  If the associated optimization profile
+    //! specifies that b has minimum dimensions as [6,9] and maximum dimensions [7,9],
+    //! getBindingDimensions(b) returns [-1,9], despite the second dimension being
+    //! dynamic in the INetworkDefinition.
+    //!
+    //! Because each optimization profile has separate bindings, the returned value can
+    //! differ across profiles. Consider another binding b' for the same network input,
+    //! but for another optimization profile.  If that other profile specifies minimum
+    //! dimensions [5,8] and maximum dimensions [5,9], getBindingDimensions(b') returns [5,-1].
     //!
     //! \see getBindingIndex()
     //!
@@ -1192,7 +1234,12 @@ class ICudaEngine
     //!
     //! \brief Create an execution context.
     //!
+    //! If the engine supports dynamic shapes, each execution context in concurrent use must use a separate optimization
+    //! profile. The first execution context created will call setOptimizationProfile(0) implicitly. For other execution
+    //! contexts, setOptimizationProfile() must be called with unique profile index before calling execute or enqueue.
+    //!
     //! \see IExecutionContext.
+    //! \see IExecutionContext::setOptimizationProfile()
     //!
     virtual IExecutionContext* createExecutionContext() noexcept = 0;
 
@@ -1316,13 +1363,25 @@ class ICudaEngine
     //!
     //! \brief Get the minimum / optimum / maximum dimensions for a particular binding under an optimization profile.
     //!
-    //! \param bindingIndex The binding index (must be between 0 and getNbBindings() - 1)
+    //! \param bindingIndex The binding index, which must belong to the given profile,
+    //!        or be between 0 and bindingsPerProfile-1 as described below.
     //!
-    //! \param profileIndex The profile index (must be between 0 and getNbOptimizationProfiles()-1)
+    //! \param profileIndex The profile index, which must be between 0 and getNbOptimizationProfiles()-1.
     //!
     //! \param select Whether to query the minimum, optimum, or maximum dimensions for this binding.
     //!
     //! \return The minimum / optimum / maximum dimensions for this binding in this profile.
+    //!         If the profileIndex or bindingIndex are invalid, return Dims with nbDims=-1.
+    //!
+    //! For backwards compatibility with earlier versions of TensorRT, if the bindingIndex
+    //! does not belong to the current optimization profile, but is between 0 and bindingsPerProfile-1,
+    //! where bindingsPerProfile = getNbBindings()/getNbOptimizationProfiles,
+    //! then a corrected bindingIndex is used instead, computed by:
+    //!
+    //!     profileIndex * bindingsPerProfile + bindingIndex % bindingsPerProfile
+    //!
+    //! Otherwise the bindingIndex is considered invalid.
+    //!
     virtual Dims getProfileDimensions(int bindingIndex, int profileIndex, OptProfileSelector select) const noexcept = 0;
 
     //!
@@ -1340,6 +1399,12 @@ class ICudaEngine
     //!         the elementwise minimum / optimum / maximum values for this shape binding under the profile.
     //!         If either of the indices is out of range, or if the binding is not an input shape binding, return
     //!         nullptr.
+    //!
+    //! For backwards compatibility with earlier versions of TensorRT, a bindingIndex that does not belong
+    //! to the profile is corrected as described for getProfileDimensions.
+    //!
+    //! \see ICudaEngine::getProfileDimensions
+    //!
     virtual const int32_t* getProfileShapeValues(int profileIndex, int inputIndex, OptProfileSelector select) const
         noexcept
         = 0;
@@ -1545,13 +1610,13 @@ class IExecutionContext
     virtual const char* getName() const noexcept = 0;
 
     //!
-    //! \brief set the device memory for use by this execution context.
+    //! \brief Set the device memory for use by this execution context.
     //!
-    //! The memory must be aligned with cuda memory alignment property (using cudaGetDeviceProperties()), and its size must be at least that
-    //! returned by getDeviceMemorySize(). If using enqueue() to run the network, The memory is in
-    //! use from the invocation of enqueue() until network execution is complete. If using execute(),
-    //! it is in use until execute() returns. Releasing or otherwise using the memory for other
-    //! purposes during this time will result in undefined behavior.
+    //! The memory must be aligned with cuda memory alignment property (using cudaGetDeviceProperties()), and its size
+    //! must be at least that returned by getDeviceMemorySize(). Setting memory to nullptr is acceptable if
+    //! getDeviceMemorySize() returns 0. If using enqueue() to run the network, the memory is in use from the invocation
+    //! of enqueue() until network execution is complete. If using execute(), it is in use until execute() returns.
+    //! Releasing or otherwise using the memory for other purposes during this time will result in undefined behavior.
     //!
     //! \see ICudaEngine::getDeviceMemorySize() ICudaEngine::createExecutionContextWithoutDeviceMemory()
     //!
@@ -1560,11 +1625,19 @@ class IExecutionContext
     //!
     //! \brief Return the strides of the buffer for the given binding.
     //!
+    //! The strides are in units of elements, not components or bytes.
+    //! For example, for TensorFormat::kHWC8, a stride of one spans 8 scalars.
+    //!
     //! Note that strides can be different for different execution contexts
     //! with dynamic shapes.
     //!
+    //! If the bindingIndex is invalid or there are dynamic dimensions that have not been
+    //! set yet, returns Dims with Dims::nbDims = -1.
+    //!
     //! \param bindingIndex The binding index.
     //!
+    //! \see getBindingComponentsPerElement
+    //!
     virtual Dims getStrides(int bindingIndex) const noexcept = 0;
 
 public:
@@ -1613,6 +1686,7 @@ class IExecutionContext
     //! new dimension > 0). Furthermore, the dimensions must be in the valid range for the
     //! currently selected optimization profile, and the corresponding engine must not be
     //! safety-certified.
+    //!
     //! This method will fail unless a valid optimization profile is defined for the current
     //! execution context (getOptimizationProfile() must not be -1).
     //!
@@ -1622,6 +1696,8 @@ class IExecutionContext
     //!
     //! \return false if an error occurs (e.g. index out of range), else true
     //!
+    //! \see ICudaEngine::getBindingIndex
+    //!
     virtual bool setBindingDimensions(int bindingIndex, Dims dimensions) noexcept = 0;
 
     //!
@@ -1644,6 +1720,11 @@ class IExecutionContext
     //!
     //! \return Currently selected binding dimensions
     //!
+    //! For backwards compatibility with earlier versions of TensorRT, a bindingIndex that does not belong
+    //! to the current profile is corrected as described for ICudaEngine::getProfileDimensions.
+    //!
+    //! \see ICudaEngine::getProfileDimensions
+    //!
     virtual Dims getBindingDimensions(int bindingIndex) const noexcept = 0;
 
     //!
@@ -1759,6 +1840,10 @@ class IExecutionContext
     //!
     //! \see ICudaEngine::getBindingIndex() ICudaEngine::getMaxBatchSize()
     //!
+    //! \note Calling enqueueV2() with a stream in CUDA graph capture mode has a known issue. If dynamic shapes are
+    //!       used, the first enqueueV2() call after a setInputShapeBinding() call will cause failure in stream capture
+    //!       due to resource allocation. Please call enqueueV2() once before capturing the graph.
+    //!
     virtual bool enqueueV2(void** bindings, cudaStream_t stream, cudaEvent_t* inputConsumed) noexcept = 0;
 };
 }
diff --git a/include/NvInferRuntimeCommon.h b/include/NvInferRuntimeCommon.h
index 27316036..fb3c139d 100644
--- a/include/NvInferRuntimeCommon.h
+++ b/include/NvInferRuntimeCommon.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -124,15 +124,25 @@ constexpr inline int EnumMax<ActivationType>()
 
 //!
 //! \enum DataType
+//!
 //! \brief The type of weights and tensors.
 //!
 enum class DataType : int
 {
-    kFLOAT = 0, //!< FP32 format.
-    kHALF = 1,  //!< FP16 format.
-    kINT8 = 2,  //!< quantized INT8 format.
-    kINT32 = 3, //!< INT32 format.
-    kBOOL = 4   //!< BOOL format.
+    //! 32-bit floating point format.
+    kFLOAT = 0,
+
+    //! IEEE 16-bit floating-point format.
+    kHALF = 1,
+
+    //! 8-bit integer representing a quantized floating-point value.
+    kINT8 = 2,
+
+    //! Signed 32-bit integer format.
+    kINT32 = 3,
+
+    //! 8-bit boolean. 0 = false, 1 = true, other values undefined.
+    kBOOL = 4
 };
 
 template <>
@@ -170,6 +180,9 @@ constexpr inline int EnumMax<DimensionType>()
 //! TensorRT can also return an invalid dims structure. This structure is represented by nbDims == -1
 //! and d[i] == 0 for all d.
 //!
+//! TensorRT can also return an "unknown rank" dims structure. This structure is represented by nbDims == -1
+//! and d[i] == -1 for all d.
+//!
 class Dims
 {
 public:
@@ -206,8 +219,11 @@ enum class TensorFormat : int
     //! For a tensor with dimensions {N, C, H, W} or {numbers, channels,
     //! columns, rows}, the dimensional index corresponds to {3, 2, 1, 0}
     //! and thus the order is W minor.
+    //!
+    //! For DLA usage, the tensor sizes are limited to C,H,W in the range [1,8192].
+    //!
     kLINEAR = 0,
-    kNCHW TRT_DEPRECATED_ENUM = kLINEAR, //! <-- Deprecated, used for backward compatibility
+    kNCHW TRT_DEPRECATED_ENUM = kLINEAR, //!< Deprecated name of kLINEAR, provided for backwards compatibility
 
     //! Two wide channel vectorized row major format. This format is bound to
     //! FP16. It is only available for dimensions >= 3.
@@ -216,7 +232,7 @@ enum class TensorFormat : int
     //! [N][(C+1)/2][H][W][2], with the tensor coordinates (n, c, h, w)
     //! mapping to array subscript [n][c/2][h][w][c%2].
     kCHW2 = 1,
-    kNC2HW2 TRT_DEPRECATED_ENUM = kCHW2, //! <-- Deprecated, used for backward compatibility
+    kNC2HW2 TRT_DEPRECATED_ENUM = kCHW2, //!< Deprecated name of kCHW2, provided for backwards compatibility
 
     //! Eight channel format where C is padded to a multiple of 8. This format
     //! is bound to FP16. It is only available for dimensions >= 3.
@@ -225,14 +241,17 @@ enum class TensorFormat : int
     //! [N][H][W][(C+7)/8*8], with the tensor coordinates (n, h, w, c)
     //! mapping to array subscript [n][h][w][c].
     kHWC8 = 2,
-    kNHWC8 TRT_DEPRECATED_ENUM = kHWC8, //! <-- Deprecated, used for backward compatibility
+    kNHWC8 TRT_DEPRECATED_ENUM = kHWC8, //!< Deprecated name of kHWC8, provided for backwards compatibility
 
     //! Four wide channel vectorized row major format. This format is bound to
     //! INT8 or FP16. It is only available for dimensions >= 3.
+    //! For INT8, the C dimension must be a build-time constant.
     //! For a tensor with dimensions {N, C, H, W},
     //! the memory layout is equivalent to a C array with dimensions
     //! [N][(C+3)/4][H][W][4], with the tensor coordinates (n, c, h, w)
     //! mapping to array subscript [n][c/4][h][w][c%4].
+    //! If running on the DLA, this format can be used for acceleration
+    //! with the caveat that C must equal 4.
     kCHW4 = 3,
 
     //! Sixteen wide channel vectorized row major format. This format is bound
@@ -241,6 +260,10 @@ enum class TensorFormat : int
     //! the memory layout is equivalent to a C array with dimensions
     //! [N][(C+15)/16][H][W][16], with the tensor coordinates (n, c, h, w)
     //! mapping to array subscript [n][c/16][h][w][c%16].
+    //!
+    //! For DLA usage, this format maps to the native image format for FP16,
+    //! and the tensor sizes are limited to C,H,W in the range [1,8192].
+    //!
     kCHW16 = 4,
 
     //! Thirty-two wide channel vectorized row major format. This format is
@@ -249,6 +272,10 @@ enum class TensorFormat : int
     //! the memory layout is equivalent to a C array with dimensions
     //! [N][(C+31)/32][H][W][32], with the tensor coordinates (n, c, h, w)
     //! mapping to array subscript [n][c/32][h][w][c%32].
+    //!
+    //! For DLA usage, this format maps to the native image format for INT8,
+    //! and the tensor sizes are limited to C,H,W in the range [1,8192].
+    //!
     kCHW32 = 5
 };
 
@@ -278,7 +305,7 @@ constexpr inline int EnumMax<TensorFormat>()
 struct PluginTensorDesc
 {
     Dims dims;
-    DataType type;
+    DataType type; //!< \warning DataType:kBOOL not supported.
     TensorFormat format;
     float scale;
 };
@@ -367,6 +394,8 @@ class IPluginV2
     //! will not be passed in, this is to keep backward compatibility with TensorRT 5.x series.  Use PluginV2IOExt
     //! or PluginV2DynamicExt for other PluginFormats.
     //!
+    //! \warning DataType:kBOOL not supported.
+    //!
     virtual bool supportsFormat(DataType type, PluginFormat format) const TRTNOEXCEPT = 0;
 
     //!
@@ -389,6 +418,8 @@ class IPluginV2
     //! will not be passed in, this is to keep backward compatibility with TensorRT 5.x series.  Use PluginV2IOExt
     //! or PluginV2DynamicExt for other PluginFormats.
     //!
+    //! \warning DataType:kBOOL not supported.
+    //!
     virtual void configureWithFormat(const Dims* inputDims, int nbInputs, const Dims* outputDims, int nbOutputs, DataType type, PluginFormat format, int maxBatchSize) TRTNOEXCEPT = 0;
 
     //!
@@ -487,6 +518,8 @@ class IPluginV2Ext : public IPluginV2
     //! The returned data type must have a format that is supported by the plugin.
     //! \see supportsFormat()
     //!
+    //! \warning DataType:kBOOL not supported.
+    //!
     virtual nvinfer1::DataType getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const TRTNOEXCEPT = 0;
 
     //! \brief Return true if output tensor is broadcast across a batch.
@@ -724,8 +757,9 @@ enum class PluginFieldType : int
 //! This information can be parsed to decode necessary plugin metadata
 //!
 //!
-struct PluginField
+class PluginField
 {
+public:
     //!
     //! \brief Plugin field attribute name
     //!
@@ -1084,7 +1118,6 @@ constexpr inline int EnumMax<ErrorCode>()
     return 11;
 } //!< Maximum number of elements in ErrorCode enum. \see ErrorCode
 
-
 //!
 //! \class IErrorRecorder
 //!
diff --git a/include/NvInferVersion.h b/include/NvInferVersion.h
index 6d94b788..ab02b239 100644
--- a/include/NvInferVersion.h
+++ b/include/NvInferVersion.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -14,16 +14,22 @@
  * limitations under the License.
  */
 
+ //!
+ //! \file NvInferVersion.h
+ //!
+ //! Defines the TensorRT version
+ //!
+
 #ifndef NV_INFER_VERSION_H
 #define NV_INFER_VERSION_H
 
 #define NV_TENSORRT_MAJOR 7 //!< TensorRT major version.
-#define NV_TENSORRT_MINOR 0 //!< TensorRT minor version.
-#define NV_TENSORRT_PATCH 0 //!< TensorRT patch version.
-#define NV_TENSORRT_BUILD 11 //!< TensorRT build number.
+#define NV_TENSORRT_MINOR 1 //!< TensorRT minor version.
+#define NV_TENSORRT_PATCH 3 //!< TensorRT patch version.
+#define NV_TENSORRT_BUILD 4 //!< TensorRT build number.
 
 #define NV_TENSORRT_SONAME_MAJOR 7 //!< Shared object library major version number.
-#define NV_TENSORRT_SONAME_MINOR 0 //!< Shared object library minor version number.
-#define NV_TENSORRT_SONAME_PATCH 0 //!< Shared object library patch version number.
+#define NV_TENSORRT_SONAME_MINOR 1 //!< Shared object library minor version number.
+#define NV_TENSORRT_SONAME_PATCH 3 //!< Shared object library patch version number.
 
 #endif // NV_INFER_VERSION_H
diff --git a/include/NvOnnxConfig.h b/include/NvOnnxConfig.h
index efa7cf7d..8e77c03a 100644
--- a/include/NvOnnxConfig.h
+++ b/include/NvOnnxConfig.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -14,8 +14,8 @@
  * limitations under the License.
  */
 
-#ifndef NV_ONNX_CONFIG_H
-#define NV_ONNX_CONFIG_H
+#ifndef NV_OnnxConfig_H
+#define NV_OnnxConfig_H
 
 #include "NvInfer.h"
 
@@ -185,4 +185,4 @@ TENSORRTAPI IOnnxConfig* createONNXConfig();
 
 } // namespace nvonnxparser
 
-#endif // NV_ONNX_CONFIG_H
+#endif
diff --git a/include/NvOnnxParser.h b/include/NvOnnxParser.h
index e9ab8bcc..e8b96346 100644
--- a/include/NvOnnxParser.h
+++ b/include/NvOnnxParser.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -21,6 +21,12 @@
 #include <stddef.h>
 #include <vector>
 
+//!
+//! \file NvOnnxParser.h
+//!
+//! This is the API for the ONNX Parser
+//!
+
 #define NV_ONNX_PARSER_MAJOR 0
 #define NV_ONNX_PARSER_MINOR 1
 #define NV_ONNX_PARSER_PATCH 0
@@ -116,7 +122,7 @@ class IParser
      *         This method has very limited diagnostic. If parsing the serialized model
      *         fails for any reason (e.g. unsupported IR version, unsupported opset, etc.)
      *         it the user responsibility to intercept and report the error.
-     *         To obtain a better diagnostic, use the parseFromFile method below. 
+     *         To obtain a better diagnostic, use the parseFromFile method below.
      *
      * \param serialized_onnx_model Pointer to the serialized ONNX model
      * \param serialized_onnx_model_size Size of the serialized ONNX model
@@ -128,14 +134,14 @@ class IParser
                        size_t serialized_onnx_model_size)
         = 0;
 
-    /** \brief Parse an onnx model file, can be a binary protobuf or a text onnx model 
-     *         calls parse method inside.  
+    /** \brief Parse an onnx model file, can be a binary protobuf or a text onnx model
+     *         calls parse method inside.
      *
      * \param File name
      * \param Verbosity Level
-     *        
+     *
      * \return true if the model was parsed successfully
-     * 
+     *
      */
     virtual bool parseFromFile(const char* onnxModelFile, int verbosity) = 0;
 
diff --git a/include/NvUffParser.h b/include/NvUffParser.h
index 1e957ed7..c9b3b245 100644
--- a/include/NvUffParser.h
+++ b/include/NvUffParser.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -19,10 +19,16 @@
 
 #include "NvInfer.h"
 
+//!
+//! \file NvUffParser.h
+//!
+//! This is the API for the UFF Parser
+//!
+
 // Current supported Universal Framework Format (UFF) version for the parser.
 #define UFF_REQUIRED_VERSION_MAJOR 0
 #define UFF_REQUIRED_VERSION_MINOR 6
-#define UFF_REQUIRED_VERSION_PATCH 5
+#define UFF_REQUIRED_VERSION_PATCH 9
 
 //!
 //! \namespace nvuffparser
@@ -277,4 +283,4 @@ TENSORRTAPI void shutdownProtobufLibrary(void) TRTNOEXCEPT;
 //!
 extern "C" TENSORRTAPI void* createNvUffParser_INTERNAL() TRTNOEXCEPT;
 
-#endif /* !NV_UFF_PARSER_H */
\ No newline at end of file
+#endif /* !NV_UFF_PARSER_H */
diff --git a/include/NvUtils.h b/include/NvUtils.h
index 61b89b5d..dece2df9 100644
--- a/include/NvUtils.h
+++ b/include/NvUtils.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -19,6 +19,12 @@
 
 #include "NvInfer.h"
 
+//!
+//! \file NvUtils.h
+//!
+//! This file includes various utility functions
+//!
+
 namespace nvinfer1
 {
 namespace utils
diff --git a/parsers/CMakeLists.txt b/parsers/CMakeLists.txt
index f6c7ad29..e63ebc9e 100644
--- a/parsers/CMakeLists.txt
+++ b/parsers/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -17,23 +17,15 @@
 add_custom_target(parsers DEPENDS 
     nvcaffeparserlibs
     nvonnxparser
-    if (NVINTERNAL OR NVPARTNER)
-        nvuffparserlibs
-        nvparserslibs
-    endif()
 )
 
 add_subdirectory(caffe)
-if (NVINTERNAL OR NVPARTNER)
-    add_subdirectory(uff)
-    include(uff/NvParsersCMakeLists.txt)
-endif()
 
 add_definitions("-D_PROTOBUF_INSTALL_DIR=${Protobuf_INSTALL_DIR}")
 add_compile_options("-Dgoogle=google_private")
 set(TENSORRT_ROOT ${PROJECT_SOURCE_DIR})
-set(TENSORRT_BUILD ${TRT_BIN_DIR} ${TRT_LIB_DIR})
-set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${TRT_BIN_DIR})
+set(TENSORRT_BUILD ${TRT_OUT_DIR} ${TRT_LIB_DIR})
+set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${TRT_OUT_DIR})
 
 include_directories(
    ${Protobuf_INCLUDE_DIR}
diff --git a/parsers/caffe/CMakeLists.txt b/parsers/caffe/CMakeLists.txt
index 14a46b94..285da1b6 100644
--- a/parsers/caffe/CMakeLists.txt
+++ b/parsers/caffe/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -55,9 +55,9 @@ set_target_properties(${SHARED_TARGET}
     CXX_STANDARD 11
     CXX_STANDARD_REQUIRED YES
     CXX_EXTENSIONS NO
-    ARCHIVE_OUTPUT_DIRECTORY "${TRT_BIN_DIR}"
-    LIBRARY_OUTPUT_DIRECTORY "${TRT_BIN_DIR}"
-    RUNTIME_OUTPUT_DIRECTORY "${TRT_BIN_DIR}"
+    ARCHIVE_OUTPUT_DIRECTORY "${TRT_OUT_DIR}"
+    LIBRARY_OUTPUT_DIRECTORY "${TRT_OUT_DIR}"
+    RUNTIME_OUTPUT_DIRECTORY "${TRT_OUT_DIR}"
 )
 
 target_link_libraries(${SHARED_TARGET} 
@@ -105,9 +105,9 @@ set_target_properties(${STATIC_TARGET}
     CXX_STANDARD 11
     CXX_STANDARD_REQUIRED YES
     CXX_EXTENSIONS NO
-    ARCHIVE_OUTPUT_DIRECTORY "${TRT_BIN_DIR}"
-    LIBRARY_OUTPUT_DIRECTORY "${TRT_BIN_DIR}"
-    RUNTIME_OUTPUT_DIRECTORY "${TRT_BIN_DIR}"
+    ARCHIVE_OUTPUT_DIRECTORY "${TRT_OUT_DIR}"
+    LIBRARY_OUTPUT_DIRECTORY "${TRT_OUT_DIR}"
+    RUNTIME_OUTPUT_DIRECTORY "${TRT_OUT_DIR}"
 )
 
 target_link_libraries(${STATIC_TARGET} 
diff --git a/parsers/caffe/CaffeParserSources.txt b/parsers/caffe/CaffeParserSources.txt
index bcfaf44c..0b8949bf 100644
--- a/parsers/caffe/CaffeParserSources.txt
+++ b/parsers/caffe/CaffeParserSources.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/NvCaffeParser.cpp b/parsers/caffe/NvCaffeParser.cpp
index 55bfb7be..492bad02 100644
--- a/parsers/caffe/NvCaffeParser.cpp
+++ b/parsers/caffe/NvCaffeParser.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/binaryProtoBlob.h b/parsers/caffe/binaryProtoBlob.h
index e13b5ad7..9dc410d7 100644
--- a/parsers/caffe/binaryProtoBlob.h
+++ b/parsers/caffe/binaryProtoBlob.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/blobNameToTensor.h b/parsers/caffe/blobNameToTensor.h
index 814c126c..52497285 100644
--- a/parsers/caffe/blobNameToTensor.h
+++ b/parsers/caffe/blobNameToTensor.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/caffeMacros.h b/parsers/caffe/caffeMacros.h
index 7a5d5c50..33a066ef 100644
--- a/parsers/caffe/caffeMacros.h
+++ b/parsers/caffe/caffeMacros.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/caffeParser/caffeParser.cpp b/parsers/caffe/caffeParser/caffeParser.cpp
index 511bbf21..9fb28d6f 100644
--- a/parsers/caffe/caffeParser/caffeParser.cpp
+++ b/parsers/caffe/caffeParser/caffeParser.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/caffeParser/caffeParser.h b/parsers/caffe/caffeParser/caffeParser.h
index b639d9a1..122bf604 100644
--- a/parsers/caffe/caffeParser/caffeParser.h
+++ b/parsers/caffe/caffeParser/caffeParser.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/caffeParser/opParsers/opParsers.h b/parsers/caffe/caffeParser/opParsers/opParsers.h
index 22ab66c1..b58caac9 100644
--- a/parsers/caffe/caffeParser/opParsers/opParsers.h
+++ b/parsers/caffe/caffeParser/opParsers/opParsers.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/caffeParser/opParsers/parseAbsVal.cpp b/parsers/caffe/caffeParser/opParsers/parseAbsVal.cpp
index d5e267b7..79d9d446 100644
--- a/parsers/caffe/caffeParser/opParsers/parseAbsVal.cpp
+++ b/parsers/caffe/caffeParser/opParsers/parseAbsVal.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/caffeParser/opParsers/parseBNLL.cpp b/parsers/caffe/caffeParser/opParsers/parseBNLL.cpp
index ce4a1e98..536689f8 100644
--- a/parsers/caffe/caffeParser/opParsers/parseBNLL.cpp
+++ b/parsers/caffe/caffeParser/opParsers/parseBNLL.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/caffeParser/opParsers/parseBatchNorm.cpp b/parsers/caffe/caffeParser/opParsers/parseBatchNorm.cpp
index c709a320..77a70034 100644
--- a/parsers/caffe/caffeParser/opParsers/parseBatchNorm.cpp
+++ b/parsers/caffe/caffeParser/opParsers/parseBatchNorm.cpp
@@ -1,4 +1,4 @@
-/* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/caffeParser/opParsers/parseClip.cpp b/parsers/caffe/caffeParser/opParsers/parseClip.cpp
index 1b6da18d..b2b86e86 100644
--- a/parsers/caffe/caffeParser/opParsers/parseClip.cpp
+++ b/parsers/caffe/caffeParser/opParsers/parseClip.cpp
@@ -1,4 +1,4 @@
-/* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/caffeParser/opParsers/parseConcat.cpp b/parsers/caffe/caffeParser/opParsers/parseConcat.cpp
index 565e2695..7eb44056 100644
--- a/parsers/caffe/caffeParser/opParsers/parseConcat.cpp
+++ b/parsers/caffe/caffeParser/opParsers/parseConcat.cpp
@@ -1,4 +1,4 @@
-/* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/caffeParser/opParsers/parseConv.cpp b/parsers/caffe/caffeParser/opParsers/parseConv.cpp
index 036c9028..67d77275 100644
--- a/parsers/caffe/caffeParser/opParsers/parseConv.cpp
+++ b/parsers/caffe/caffeParser/opParsers/parseConv.cpp
@@ -1,4 +1,4 @@
-/* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/caffeParser/opParsers/parseCrop.cpp b/parsers/caffe/caffeParser/opParsers/parseCrop.cpp
index 6acee157..7f3fe05d 100644
--- a/parsers/caffe/caffeParser/opParsers/parseCrop.cpp
+++ b/parsers/caffe/caffeParser/opParsers/parseCrop.cpp
@@ -1,4 +1,4 @@
-/* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/caffeParser/opParsers/parseDeconv.cpp b/parsers/caffe/caffeParser/opParsers/parseDeconv.cpp
index 354d6811..a56de106 100644
--- a/parsers/caffe/caffeParser/opParsers/parseDeconv.cpp
+++ b/parsers/caffe/caffeParser/opParsers/parseDeconv.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/caffeParser/opParsers/parseELU.cpp b/parsers/caffe/caffeParser/opParsers/parseELU.cpp
index 2844943a..4c14e9d7 100644
--- a/parsers/caffe/caffeParser/opParsers/parseELU.cpp
+++ b/parsers/caffe/caffeParser/opParsers/parseELU.cpp
@@ -1,4 +1,4 @@
-/* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/caffeParser/opParsers/parseEltwise.cpp b/parsers/caffe/caffeParser/opParsers/parseEltwise.cpp
index d88d38dc..9963fb11 100644
--- a/parsers/caffe/caffeParser/opParsers/parseEltwise.cpp
+++ b/parsers/caffe/caffeParser/opParsers/parseEltwise.cpp
@@ -1,4 +1,4 @@
-/* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/caffeParser/opParsers/parseInnerProduct.cpp b/parsers/caffe/caffeParser/opParsers/parseInnerProduct.cpp
index 2f4df384..85ae2542 100644
--- a/parsers/caffe/caffeParser/opParsers/parseInnerProduct.cpp
+++ b/parsers/caffe/caffeParser/opParsers/parseInnerProduct.cpp
@@ -1,4 +1,4 @@
-/* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/caffeParser/opParsers/parseLRN.cpp b/parsers/caffe/caffeParser/opParsers/parseLRN.cpp
index 13a71f52..e7caa03f 100644
--- a/parsers/caffe/caffeParser/opParsers/parseLRN.cpp
+++ b/parsers/caffe/caffeParser/opParsers/parseLRN.cpp
@@ -1,4 +1,4 @@
-/* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/caffeParser/opParsers/parsePReLU.cpp b/parsers/caffe/caffeParser/opParsers/parsePReLU.cpp
index 0c9c3c21..85289a2b 100644
--- a/parsers/caffe/caffeParser/opParsers/parsePReLU.cpp
+++ b/parsers/caffe/caffeParser/opParsers/parsePReLU.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/caffeParser/opParsers/parsePermute.cpp b/parsers/caffe/caffeParser/opParsers/parsePermute.cpp
index 4eebce32..8d3b75fa 100644
--- a/parsers/caffe/caffeParser/opParsers/parsePermute.cpp
+++ b/parsers/caffe/caffeParser/opParsers/parsePermute.cpp
@@ -1,4 +1,4 @@
-/* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/caffeParser/opParsers/parsePooling.cpp b/parsers/caffe/caffeParser/opParsers/parsePooling.cpp
index ef144cf7..e4ebfd45 100644
--- a/parsers/caffe/caffeParser/opParsers/parsePooling.cpp
+++ b/parsers/caffe/caffeParser/opParsers/parsePooling.cpp
@@ -1,4 +1,4 @@
-/* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/caffeParser/opParsers/parsePower.cpp b/parsers/caffe/caffeParser/opParsers/parsePower.cpp
index 14c2b85f..898dcc15 100644
--- a/parsers/caffe/caffeParser/opParsers/parsePower.cpp
+++ b/parsers/caffe/caffeParser/opParsers/parsePower.cpp
@@ -1,4 +1,4 @@
-/* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/caffeParser/opParsers/parseReLU.cpp b/parsers/caffe/caffeParser/opParsers/parseReLU.cpp
index af962cf6..1cc25c51 100644
--- a/parsers/caffe/caffeParser/opParsers/parseReLU.cpp
+++ b/parsers/caffe/caffeParser/opParsers/parseReLU.cpp
@@ -1,4 +1,4 @@
-/* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/caffeParser/opParsers/parseReduction.cpp b/parsers/caffe/caffeParser/opParsers/parseReduction.cpp
index 8358cb9a..ac507cf4 100644
--- a/parsers/caffe/caffeParser/opParsers/parseReduction.cpp
+++ b/parsers/caffe/caffeParser/opParsers/parseReduction.cpp
@@ -1,4 +1,4 @@
-/* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/caffeParser/opParsers/parseReshape.cpp b/parsers/caffe/caffeParser/opParsers/parseReshape.cpp
index ed29399f..81d225c8 100644
--- a/parsers/caffe/caffeParser/opParsers/parseReshape.cpp
+++ b/parsers/caffe/caffeParser/opParsers/parseReshape.cpp
@@ -1,4 +1,4 @@
-/* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/caffeParser/opParsers/parseScale.cpp b/parsers/caffe/caffeParser/opParsers/parseScale.cpp
index 1fa1c9be..452c30f9 100644
--- a/parsers/caffe/caffeParser/opParsers/parseScale.cpp
+++ b/parsers/caffe/caffeParser/opParsers/parseScale.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/caffeParser/opParsers/parseSigmoid.cpp b/parsers/caffe/caffeParser/opParsers/parseSigmoid.cpp
index a5e7ad5b..d48ce98c 100644
--- a/parsers/caffe/caffeParser/opParsers/parseSigmoid.cpp
+++ b/parsers/caffe/caffeParser/opParsers/parseSigmoid.cpp
@@ -1,4 +1,4 @@
-/* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/caffeParser/opParsers/parseSoftMax.cpp b/parsers/caffe/caffeParser/opParsers/parseSoftMax.cpp
index 13953c8a..2d11bb77 100644
--- a/parsers/caffe/caffeParser/opParsers/parseSoftMax.cpp
+++ b/parsers/caffe/caffeParser/opParsers/parseSoftMax.cpp
@@ -1,4 +1,4 @@
-/* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/caffeParser/opParsers/parseTanH.cpp b/parsers/caffe/caffeParser/opParsers/parseTanH.cpp
index cc4efdf3..1e20e79b 100644
--- a/parsers/caffe/caffeParser/opParsers/parseTanH.cpp
+++ b/parsers/caffe/caffeParser/opParsers/parseTanH.cpp
@@ -1,4 +1,4 @@
-/* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/caffeParser/readProto.h b/parsers/caffe/caffeParser/readProto.h
index 731eab2b..3ece85c5 100644
--- a/parsers/caffe/caffeParser/readProto.h
+++ b/parsers/caffe/caffeParser/readProto.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/caffeWeightFactory/caffeWeightFactory.cpp b/parsers/caffe/caffeWeightFactory/caffeWeightFactory.cpp
index 11cd112f..19c73d95 100644
--- a/parsers/caffe/caffeWeightFactory/caffeWeightFactory.cpp
+++ b/parsers/caffe/caffeWeightFactory/caffeWeightFactory.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/caffeWeightFactory/caffeWeightFactory.h b/parsers/caffe/caffeWeightFactory/caffeWeightFactory.h
index 12c7acbb..43b91a44 100644
--- a/parsers/caffe/caffeWeightFactory/caffeWeightFactory.h
+++ b/parsers/caffe/caffeWeightFactory/caffeWeightFactory.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/caffeWeightFactory/weightType.h b/parsers/caffe/caffeWeightFactory/weightType.h
index 2778c75b..93550459 100644
--- a/parsers/caffe/caffeWeightFactory/weightType.h
+++ b/parsers/caffe/caffeWeightFactory/weightType.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/caffe/proto/trtcaffe.proto b/parsers/caffe/proto/trtcaffe.proto
index 0168dece..d79980b8 100644
--- a/parsers/caffe/proto/trtcaffe.proto
+++ b/parsers/caffe/proto/trtcaffe.proto
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/parsers/common/ParserCommonSrcs.txt b/parsers/common/ParserCommonSrcs.txt
index 00629dba..d99e6fab 100644
--- a/parsers/common/ParserCommonSrcs.txt
+++ b/parsers/common/ParserCommonSrcs.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/plugin/CMakeLists.txt b/plugin/CMakeLists.txt
index 9dccd460..ba786aed 100644
--- a/plugin/CMakeLists.txt
+++ b/plugin/CMakeLists.txt
@@ -51,6 +51,8 @@ set(PLUGIN_LISTS
     resizeNearestPlugin
     specialSlicePlugin
     instanceNormalizationPlugin
+    groupNormalizationPlugin
+    coordConvACPlugin
     )
 
 # Add BERT sources if ${BERT_GENCODES} was populated
@@ -105,9 +107,9 @@ set_target_properties(${SHARED_TARGET} PROPERTIES
     CXX_STANDARD "11"
     CXX_STANDARD_REQUIRED "YES"
     CXX_EXTENSIONS "NO"
-    ARCHIVE_OUTPUT_DIRECTORY "${TRT_BIN_DIR}"
-    LIBRARY_OUTPUT_DIRECTORY "${TRT_BIN_DIR}"
-    RUNTIME_OUTPUT_DIRECTORY "${TRT_BIN_DIR}"
+    ARCHIVE_OUTPUT_DIRECTORY "${TRT_OUT_DIR}"
+    LIBRARY_OUTPUT_DIRECTORY "${TRT_OUT_DIR}"
+    RUNTIME_OUTPUT_DIRECTORY "${TRT_OUT_DIR}"
 )
 
 set_target_properties(${SHARED_TARGET} PROPERTIES LINK_FLAGS "-Wl,--exclude-libs,ALL -Wl,--version-script=${PLUGIN_EXPORT_MAP} -Wl,--no-undefined")
@@ -124,6 +126,7 @@ target_link_libraries(${SHARED_TARGET}
     ${CUDART_LIB}
     ${CUDNN_LIB}
     nvinfer
+    ${CMAKE_DL_LIBS}
 )
 
 ################################## STATIC LIBRARY #######################################
@@ -144,9 +147,9 @@ set_target_properties(${STATIC_TARGET} PROPERTIES
     CXX_STANDARD "11"
     CXX_STANDARD_REQUIRED "YES"
     CXX_EXTENSIONS "NO"
-    ARCHIVE_OUTPUT_DIRECTORY "${TRT_BIN_DIR}"
-    LIBRARY_OUTPUT_DIRECTORY "${TRT_BIN_DIR}"
-    RUNTIME_OUTPUT_DIRECTORY "${TRT_BIN_DIR}"
+    ARCHIVE_OUTPUT_DIRECTORY "${TRT_OUT_DIR}"
+    LIBRARY_OUTPUT_DIRECTORY "${TRT_OUT_DIR}"
+    RUNTIME_OUTPUT_DIRECTORY "${TRT_OUT_DIR}"
 )
 
 set_target_properties(${STATIC_TARGET} PROPERTIES LINK_FLAGS "-Wl,--exclude-libs,ALL")
diff --git a/plugin/InferPlugin.cpp b/plugin/InferPlugin.cpp
index 78997bc1..f973bb8e 100644
--- a/plugin/InferPlugin.cpp
+++ b/plugin/InferPlugin.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -15,6 +15,7 @@
  */
 #include "NvInfer.h"
 #include "NvInferPlugin.h"
+#include "checkMacrosPlugin.h"
 #include <algorithm>
 #include <array>
 #include <iostream>
@@ -25,28 +26,28 @@
 using namespace nvinfer1;
 using namespace nvinfer1::plugin;
 
-#include "batchedNMSPlugin/batchedNMSPlugin.h"
-#include "cropAndResizePlugin/cropAndResizePlugin.h"
-#include "flattenConcat/flattenConcat.h"
-#include "gridAnchorPlugin/gridAnchorPlugin.h"
-#include "nmsPlugin/nmsPlugin.h"
-#include "normalizePlugin/normalizePlugin.h"
-#include "nvFasterRCNN/nvFasterRCNNPlugin.h"
-#include "priorBoxPlugin/priorBoxPlugin.h"
-#include "proposalPlugin/proposalPlugin.h"
-#include "regionPlugin/regionPlugin.h"
-#include "reorgPlugin/reorgPlugin.h"
-
-#include "batchTilePlugin/batchTilePlugin.h"
-#include "detectionLayerPlugin/detectionLayerPlugin.h"
-#include "proposalLayerPlugin/proposalLayerPlugin.h"
-#include "pyramidROIAlignPlugin/pyramidROIAlignPlugin.h"
-#include "resizeNearestPlugin/resizeNearestPlugin.h"
-#include "specialSlicePlugin/specialSlicePlugin.h"
-#include "instanceNormalizationPlugin/instanceNormalizationPlugin.h"
-#include "generateDetectionPlugin/generateDetectionPlugin.h"
-#include "multilevelProposeROI/multilevelProposeROIPlugin.h"
-#include "multilevelCropAndResizePlugin/multilevelCropAndResizePlugin.h"
+#include "batchTilePlugin.h"
+#include "batchedNMSPlugin.h"
+#include "coordConvACPlugin.h"
+#include "cropAndResizePlugin.h"
+#include "detectionLayerPlugin.h"
+#include "flattenConcat.h"
+#include "generateDetectionPlugin.h"
+#include "gridAnchorPlugin.h"
+#include "instanceNormalizationPlugin.h"
+#include "multilevelCropAndResizePlugin.h"
+#include "multilevelProposeROIPlugin.h"
+#include "nmsPlugin.h"
+#include "normalizePlugin.h"
+#include "nvFasterRCNNPlugin.h"
+#include "priorBoxPlugin.h"
+#include "proposalLayerPlugin.h"
+#include "proposalPlugin.h"
+#include "pyramidROIAlignPlugin.h"
+#include "regionPlugin.h"
+#include "reorgPlugin.h"
+#include "resizeNearestPlugin.h"
+#include "specialSlicePlugin.h"
 
 using nvinfer1::plugin::RPROIParams;
 
@@ -55,7 +56,8 @@ namespace nvinfer1
 
 namespace plugin
 {
-ILogger* gLogger{};
+
+extern ILogger* gLogger;
 
 // This singleton ensures that each plugin is only registered once for a given
 // namespace and type, and attempts of duplicate registration are ignored.
@@ -81,8 +83,9 @@ class PluginCreatorRegistry
         pluginCreator->setPluginNamespace(libNamespace);
 
         nvinfer1::plugin::gLogger = static_cast<nvinfer1::ILogger*>(logger);
-        std::string pluginType
-            = std::string(pluginCreator->getPluginNamespace()) + "::" + std::string(pluginCreator->getPluginName());
+        std::string pluginType = std::string{pluginCreator->getPluginNamespace()}
+            + "::" + std::string{pluginCreator->getPluginName()} + " version "
+            + std::string{pluginCreator->getPluginVersion()};
 
         if (mRegistryList.find(pluginType) == mRegistryList.end())
         {
@@ -91,11 +94,11 @@ class PluginCreatorRegistry
             {
                 mRegistry.push(std::move(pluginCreator));
                 mRegistryList.insert(pluginType);
-                verboseMsg = "Plugin creator registration succeeded - " + pluginType;
+                verboseMsg = "Registered plugin creator - " + pluginType;
             }
             else
             {
-                errorMsg = "Could not register plugin creator:  " + pluginType;
+                errorMsg = "Could not register plugin creator -  " + pluginType;
             }
         }
         else
@@ -148,6 +151,7 @@ void initializePlugin(void* logger, const char* libNamespace)
 
 } // namespace plugin
 } // namespace nvinfer1
+// New Plugin APIs
 
 extern "C" {
 bool initLibNvInferPlugins(void* logger, const char* libNamespace)
@@ -162,9 +166,8 @@ bool initLibNvInferPlugins(void* logger, const char* libNamespace)
     initializePlugin<nvinfer1::plugin::BatchedNMSPluginCreator>(logger, libNamespace);
     initializePlugin<nvinfer1::plugin::FlattenConcatPluginCreator>(logger, libNamespace);
     initializePlugin<nvinfer1::plugin::CropAndResizePluginCreator>(logger, libNamespace);
-    initializePlugin<nvinfer1::plugin::ProposalPluginCreator>(logger, libNamespace);
-    initializePlugin<nvinfer1::plugin::BatchTilePluginCreator>(logger, libNamespace);
     initializePlugin<nvinfer1::plugin::DetectionLayerPluginCreator>(logger, libNamespace);
+    initializePlugin<nvinfer1::plugin::ProposalPluginCreator>(logger, libNamespace);
     initializePlugin<nvinfer1::plugin::ProposalLayerPluginCreator>(logger, libNamespace);
     initializePlugin<nvinfer1::plugin::PyramidROIAlignPluginCreator>(logger, libNamespace);
     initializePlugin<nvinfer1::plugin::ResizeNearestPluginCreator>(logger, libNamespace);
@@ -173,6 +176,7 @@ bool initLibNvInferPlugins(void* logger, const char* libNamespace)
     initializePlugin<nvinfer1::plugin::GenerateDetectionPluginCreator>(logger, libNamespace);
     initializePlugin<nvinfer1::plugin::MultilevelProposeROIPluginCreator>(logger, libNamespace);
     initializePlugin<nvinfer1::plugin::MultilevelCropAndResizePluginCreator>(logger, libNamespace);
+    initializePlugin<nvinfer1::plugin::CoordConvACPluginCreator>(logger, libNamespace);
     return true;
 }
 } // extern "C"
diff --git a/plugin/README.md b/plugin/README.md
index 3836aaf4..5f9f5c4f 100644
--- a/plugin/README.md
+++ b/plugin/README.md
@@ -7,14 +7,19 @@
 | [batchTilePlugin](batchTilePlugin) | BatchTilePlugin_TRT | 1 |
 | [batchedNMSPlugin](batchedNMSPlugin) | BatchedNMS_TRT | 1 |
 | [bertQKVToContextPlugin](bertQKVToContextPlugin) | CustomQKVToContextPluginDynamic | 1 |
+| [coordConvACPlugin](coordConvACPlugin) | CoordConvAC | 1 |
 | [cropAndResizePlugin](cropAndResizePlugin) | CropAndResize | 1 |
 | [detectionLayerPlugin](detectionLayerPlugin) | DetectionLayer_TRT | 1 |
 | [embLayerNormPlugin](embLayerNormPlugin) | CustomEmbLayerNormPluginDynamic | 1 |
 | [fcPlugin](fcPlugin) | CustomFCPluginDynamic | 1 |
 | [flattenConcat](flattenConcat) | FlattenConcat_TRT | 1 |
 | [geluPlugin](geluPlugin) | CustomGeluPluginDynamic | 1 |
+| [generateDetectionPlugin](generateDetectionPlugin) | GenerateDetection_TRT | 1 |
 | [gridAnchorPlugin](gridAnchorPlugin) | GridAnchor_TRT | 1 |
+| [groupNormalizationPlugin](groupNormalizationPlugin) | GroupNormalizationPlugin | 1 |
 | [instanceNormalizationPlugin](instanceNormalizationPlugin) | InstanceNormalization_TRT | 1 |
+| [multilevelCropAndResizePlugin](multilevelCropAndResizePlugin) | MultilevelCropAndResize_TRT | 1 |
+| [multilevelProposeROI](multilevelProposeROI) | MultilevelProposeROI_TRT | 1 |
 | [nmsPlugin](nmsPlugin) | NMS_TRT | 1 |
 | [normalizePlugin](normalizePlugin) | Normalize_TRT | 1 |
 | [nvFasterRCNN](nvFasterRCNN) | RPROI_TRT | 1 |
diff --git a/plugin/batchTilePlugin/CMakeLists.txt b/plugin/batchTilePlugin/CMakeLists.txt
index de8fff17..85d8ce4e 100644
--- a/plugin/batchTilePlugin/CMakeLists.txt
+++ b/plugin/batchTilePlugin/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/plugin/batchTilePlugin/batchTilePlugin.cpp b/plugin/batchTilePlugin/batchTilePlugin.cpp
index 5121d773..aa5deb75 100644
--- a/plugin/batchTilePlugin/batchTilePlugin.cpp
+++ b/plugin/batchTilePlugin/batchTilePlugin.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/plugin/batchTilePlugin/batchTilePlugin.h b/plugin/batchTilePlugin/batchTilePlugin.h
index c495455e..abbaf274 100644
--- a/plugin/batchTilePlugin/batchTilePlugin.h
+++ b/plugin/batchTilePlugin/batchTilePlugin.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/plugin/batchedNMSPlugin/CMakeLists.txt b/plugin/batchedNMSPlugin/CMakeLists.txt
index 264adf76..a6e90128 100644
--- a/plugin/batchedNMSPlugin/CMakeLists.txt
+++ b/plugin/batchedNMSPlugin/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/plugin/batchedNMSPlugin/batchedNMSInference.cpp b/plugin/batchedNMSPlugin/batchedNMSInference.cu
similarity index 98%
rename from plugin/batchedNMSPlugin/batchedNMSInference.cpp
rename to plugin/batchedNMSPlugin/batchedNMSInference.cu
index 71c31ac2..e2f7197f 100644
--- a/plugin/batchedNMSPlugin/batchedNMSInference.cpp
+++ b/plugin/batchedNMSPlugin/batchedNMSInference.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/plugin/batchedNMSPlugin/batchedNMSInference.h b/plugin/batchedNMSPlugin/batchedNMSInference.h
deleted file mode 100644
index b7490860..00000000
--- a/plugin/batchedNMSPlugin/batchedNMSInference.h
+++ /dev/null
@@ -1,28 +0,0 @@
-/*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#ifndef TRT_BATCHED_NMS_INFERENCE_H
-#define TRT_BATCHED_NMS_INFERENCE_H
-#include "plugin.h"
-
-using namespace nvinfer1;
-using namespace nvinfer1::plugin;
-
-pluginStatus_t nmsInference(cudaStream_t stream, int N, int boxesSize, int scoresSize, bool shareLocation,
-    int backgroundLabelId, int numPredsPerClass, int numClasses, int topK, int keepTopK, float scoreThreshold,
-    float iouThreshold, DataType DT_BBOX, const void* locData, DataType DT_SCORE, const void* confData, void* keepCount,
-    void* nmsedBoxes, void* nmsedScores, void* nmsedClasses, void* workspace, bool isNormalized = true,
-    bool confSigmoid = false, bool clipBoxes = true);
-#endif
diff --git a/plugin/batchedNMSPlugin/batchedNMSPlugin.cpp b/plugin/batchedNMSPlugin/batchedNMSPlugin.cpp
index 99ae293c..ee0c8b93 100644
--- a/plugin/batchedNMSPlugin/batchedNMSPlugin.cpp
+++ b/plugin/batchedNMSPlugin/batchedNMSPlugin.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/plugin/batchedNMSPlugin/batchedNMSPlugin.h b/plugin/batchedNMSPlugin/batchedNMSPlugin.h
index 69322b08..ca0aaf13 100644
--- a/plugin/batchedNMSPlugin/batchedNMSPlugin.h
+++ b/plugin/batchedNMSPlugin/batchedNMSPlugin.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -15,8 +15,7 @@
  */
 #ifndef TRT_BATCHED_NMS_PLUGIN_H
 #define TRT_BATCHED_NMS_PLUGIN_H
-#include "batchedNMSPlugin/batchedNMSInference.h"
-#include "batchedNMSPlugin/gatherNMSOutputs.h"
+#include "gatherNMSOutputs.h"
 #include "kernel.h"
 #include "nmsUtils.h"
 #include "plugin.h"
diff --git a/plugin/batchedNMSPlugin/gatherNMSOutputs.cu b/plugin/batchedNMSPlugin/gatherNMSOutputs.cu
index b84e87a4..08aa8ca2 100644
--- a/plugin/batchedNMSPlugin/gatherNMSOutputs.cu
+++ b/plugin/batchedNMSPlugin/gatherNMSOutputs.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -16,7 +16,7 @@
 #include "kernel.h"
 #include "plugin.h"
 #include "gatherNMSOutputs.h"
-#include <vector>
+#include <array>
 
 template <typename T_BBOX, typename T_SCORE, unsigned nthds_per_cta>
 __launch_bounds__(nthds_per_cta)
@@ -158,18 +158,8 @@ struct nmsOutLaunchConfig
 
 using nvinfer1::DataType;
 
-static std::vector<nmsOutLaunchConfig> nmsOutFuncVec;
-
-bool nmsOutputInit()
-{
-    nmsOutFuncVec.push_back(nmsOutLaunchConfig(DataType::kFLOAT, DataType::kFLOAT,
-                                         gatherNMSOutputs_gpu<float, float>));
-    return true;
-}
-
-static bool initialized = nmsOutputInit();
-
-//}}}
+static std::array<nmsOutLaunchConfig, 1> nmsOutLCOptions = {
+  nmsOutLaunchConfig(DataType::kFLOAT, DataType::kFLOAT, gatherNMSOutputs_gpu<float, float>)};
 
 pluginStatus_t gatherNMSOutputs(
     cudaStream_t stream,
@@ -192,12 +182,12 @@ pluginStatus_t gatherNMSOutputs(
     )
 {
     nmsOutLaunchConfig lc = nmsOutLaunchConfig(DT_BBOX, DT_SCORE);
-    for (unsigned i = 0; i < nmsOutFuncVec.size(); ++i)
+    for (unsigned i = 0; i < nmsOutLCOptions.size(); ++i)
     {
-        if (lc == nmsOutFuncVec[i])
+        if (lc == nmsOutLCOptions[i])
         {
             DEBUG_PRINTF("gatherNMSOutputs kernel %d\n", i);
-            return nmsOutFuncVec[i].function(stream,
+            return nmsOutLCOptions[i].function(stream,
                                           shareLocation,
                                           numImages,
                                           numPredsPerClass,
@@ -216,4 +206,4 @@ pluginStatus_t gatherNMSOutputs(
         }
     }
     return STATUS_BAD_PARAM;
-}
\ No newline at end of file
+}
diff --git a/plugin/batchedNMSPlugin/gatherNMSOutputs.h b/plugin/batchedNMSPlugin/gatherNMSOutputs.h
index 0479dda4..f3c291ff 100644
--- a/plugin/batchedNMSPlugin/gatherNMSOutputs.h
+++ b/plugin/batchedNMSPlugin/gatherNMSOutputs.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/plugin/bertQKVToContextPlugin/CMakeLists.txt b/plugin/bertQKVToContextPlugin/CMakeLists.txt
index 39f2d485..443b76ac 100644
--- a/plugin/bertQKVToContextPlugin/CMakeLists.txt
+++ b/plugin/bertQKVToContextPlugin/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,6 +13,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
+file(GLOB SRCS *.cpp)
+set(PLUGIN_SOURCES ${PLUGIN_SOURCES} ${SRCS})
+set(PLUGIN_SOURCES ${PLUGIN_SOURCES} PARENT_SCOPE)
 file(GLOB CU_SRCS *.cu)
 set(BERT_CU_SOURCES ${BERT_CU_SOURCES} ${CU_SRCS})
 set(BERT_CU_SOURCES ${BERT_CU_SOURCES} PARENT_SCOPE)
diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention.h b/plugin/bertQKVToContextPlugin/fused_multihead_attention.h
new file mode 100644
index 00000000..9b4e44ec
--- /dev/null
+++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention.h
@@ -0,0 +1,277 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#pragma once
+#include "cudaDriverWrapper.h"
+#include "cuda_runtime_api.h"
+#include <mutex>
+#include <memory>
+#include <assert.h>
+#include <stdint.h>
+#include <unordered_map>
+#include <vector>
+
+namespace bert
+{
+
+static constexpr int32_t kSM_TURING = 75;
+static constexpr int32_t kSM_AMPERE = 80;
+
+enum Data_type
+{
+    DATA_TYPE_BOOL,
+    DATA_TYPE_E8M10,
+    DATA_TYPE_E8M7,
+    DATA_TYPE_FP16,
+    DATA_TYPE_FP32,
+    DATA_TYPE_INT4,
+    DATA_TYPE_INT8,
+    DATA_TYPE_INT32
+};
+
+static inline size_t get_size_in_bytes(size_t n, Data_type dtype)
+{
+    switch (dtype)
+    {
+    case DATA_TYPE_E8M10: return n * 4;
+    case DATA_TYPE_FP32: return n * 4;
+    case DATA_TYPE_FP16: return n * 2;
+    case DATA_TYPE_INT32: return n * 4;
+    case DATA_TYPE_INT8: return n;
+    case DATA_TYPE_INT4: return n / 2;
+    case DATA_TYPE_BOOL: return n / 8;
+    case DATA_TYPE_E8M7: return n * 2;
+    default: assert(false); return 0;
+    }
+}
+
+////////////////////////////////////////////////////////////////////////////////////////////////////
+
+struct Fused_multihead_attention_params
+{
+    // The QKV matrices.
+    void* qkv_ptr;
+    // The mask to implement drop-out.
+    void* packed_mask_ptr;
+    // The O matrix (output).
+    void* o_ptr;
+
+    // The stride between rows of the Q, K and V matrices.
+    int64_t qkv_stride_in_bytes;
+    // The stride between matrices of packed mask.
+    int64_t packed_mask_stride_in_bytes;
+    // The stride between rows of O.
+    int64_t o_stride_in_bytes;
+
+#if defined(STORE_P)
+    // The pointer to the P matrix (for debugging).
+    void* p_ptr;
+    // The stride between rows of the P matrix (for debugging).
+    int64_t p_stride_in_bytes;
+#endif // defined(STORE_P)
+
+#if defined(STORE_S)
+    // The pointer to the S matrix (for debugging).
+    void* s_ptr;
+    // The stride between rows of the S matrix (for debugging).
+    int64_t s_stride_in_bytes;
+#endif // defined(STORE_S)
+
+    // The dimensions.
+    int b, h, s, d;
+    // The scaling factors for the kernel.
+    uint32_t scale_bmm1, scale_softmax, scale_bmm2;
+
+    // Do we use Niall's trick to avoid I2F/F2I in the INT8 kernel.
+    // See https://confluence.nvidia.com/pages/viewpage.action?pageId=302779721 for details.
+    bool enable_i2f_trick;
+};
+
+////////////////////////////////////////////////////////////////////////////////////////////////////
+extern unsigned char fused_multihead_attention_fp16_128_64_kernel_sm75_cu_o[];
+extern unsigned char fused_multihead_attention_fp16_384_64_kernel_sm75_cu_o[];
+extern unsigned char fused_multihead_attention_int8_128_64_kernel_sm75_cu_o[];
+extern unsigned char fused_multihead_attention_int8_384_64_kernel_sm75_cu_o[];
+extern unsigned char fused_multihead_attention_int8_384_64_kernel_sm80_cu_o[];
+extern unsigned char fused_multihead_attention_int8_128_64_kernel_sm80_cu_o[];
+extern unsigned char fused_multihead_attention_fp16_128_64_kernel_sm80_cu_o[];
+extern unsigned char fused_multihead_attention_fp16_384_64_kernel_sm80_cu_o[];
+
+extern unsigned int fused_multihead_attention_fp16_128_64_kernel_sm75_cu_o_len;
+extern unsigned int fused_multihead_attention_fp16_384_64_kernel_sm75_cu_o_len;
+extern unsigned int fused_multihead_attention_int8_128_64_kernel_sm75_cu_o_len;
+extern unsigned int fused_multihead_attention_int8_384_64_kernel_sm75_cu_o_len;
+extern unsigned int fused_multihead_attention_int8_384_64_kernel_sm80_cu_o_len;
+extern unsigned int fused_multihead_attention_int8_128_64_kernel_sm80_cu_o_len;
+extern unsigned int fused_multihead_attention_fp16_128_64_kernel_sm80_cu_o_len;
+extern unsigned int fused_multihead_attention_fp16_384_64_kernel_sm80_cu_o_len;
+
+static const struct FusedMultiHeadAttentionKernelMetaInfo
+{
+    Data_type mDataType;
+    unsigned int mS;
+    unsigned int mD;
+    unsigned int mSM;
+    const unsigned char* mCubin;
+    unsigned int mCubinSize;
+    const char* mFuncName;
+    unsigned int mSharedMemBytes;
+    unsigned int mThreadsPerCTA;
+} sMhaKernelMetaInfos[] = {
+    // Turing
+    {DATA_TYPE_FP16, 128, 64, kSM_TURING, fused_multihead_attention_fp16_128_64_kernel_sm75_cu_o,
+        fused_multihead_attention_fp16_128_64_kernel_sm75_cu_o_len, "fused_multihead_attention_fp16_128_64_kernel_sm75",
+        32768, 128},
+    {DATA_TYPE_FP16, 384, 64, kSM_TURING, fused_multihead_attention_fp16_384_64_kernel_sm75_cu_o,
+        fused_multihead_attention_fp16_384_64_kernel_sm75_cu_o_len, "fused_multihead_attention_fp16_384_64_kernel_sm75",
+        57344, 256},
+    {DATA_TYPE_INT8, 128, 64, kSM_TURING, fused_multihead_attention_int8_128_64_kernel_sm75_cu_o,
+        fused_multihead_attention_int8_128_64_kernel_sm75_cu_o_len, "fused_multihead_attention_int8_128_64_kernel_sm75",
+        16384, 128},
+    {DATA_TYPE_INT8, 384, 64, kSM_TURING, fused_multihead_attention_int8_384_64_kernel_sm75_cu_o,
+        fused_multihead_attention_int8_384_64_kernel_sm75_cu_o_len, "fused_multihead_attention_int8_384_64_kernel_sm75",
+        53284, 256},
+#if CUDA_VERSION >= 11000
+    // Ampere
+    {DATA_TYPE_FP16, 128, 64, kSM_AMPERE, fused_multihead_attention_fp16_128_64_kernel_sm80_cu_o,
+        fused_multihead_attention_fp16_128_64_kernel_sm80_cu_o_len, "fused_multihead_attention_fp16_128_64_kernel_sm80",
+        49152, 128},
+    {DATA_TYPE_FP16, 384, 64, kSM_AMPERE, fused_multihead_attention_fp16_384_64_kernel_sm80_cu_o,
+        fused_multihead_attention_fp16_384_64_kernel_sm80_cu_o_len, "fused_multihead_attention_fp16_384_64_kernel_sm80",
+        114688, 256},
+    {DATA_TYPE_INT8, 128, 64, kSM_AMPERE, fused_multihead_attention_int8_128_64_kernel_sm80_cu_o,
+        fused_multihead_attention_int8_128_64_kernel_sm80_cu_o_len, "fused_multihead_attention_int8_128_64_kernel_sm80",
+        24576, 128},
+    {DATA_TYPE_INT8, 384, 64, kSM_AMPERE, fused_multihead_attention_int8_384_64_kernel_sm80_cu_o,
+        fused_multihead_attention_int8_384_64_kernel_sm80_cu_o_len, "fused_multihead_attention_int8_384_64_kernel_sm80",
+        57344, 256},
+#endif
+};
+
+struct FusedMultiHeadAttentionXMMAKernel
+{
+    inline uint64_t hashID(unsigned int s, unsigned int d) const
+    {
+        return (uint64_t) s << 32 | d;
+    }
+
+    FusedMultiHeadAttentionXMMAKernel(Data_type type, unsigned int sm)
+        : mDataType(type)
+        , mSM(sm)
+    {
+        for (unsigned int i = 0; i < sizeof(sMhaKernelMetaInfos) / sizeof(sMhaKernelMetaInfos[0]); ++i)
+        {
+            const auto& kernelMeta = sMhaKernelMetaInfos[i];
+            if (kernelMeta.mSM == sm && kernelMeta.mDataType == type)
+            {
+                CUmodule hmod{0};
+                cuErrCheck(mDriver.cuModuleLoadData(&hmod, kernelMeta.mCubin), mDriver);
+                mModules.push_back(hmod);
+
+                FusedMultiHeadAttentionKernelInfo funcInfo;
+                funcInfo.mMetaInfoIndex = i;
+                cuErrCheck(mDriver.cuModuleGetFunction(&funcInfo.mDeviceFunction, hmod, kernelMeta.mFuncName), mDriver);
+                if (kernelMeta.mSharedMemBytes >= 48 * 1024)
+                {
+                    cuErrCheck(mDriver.cuFuncSetAttribute(funcInfo.mDeviceFunction,
+                                   CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES, kernelMeta.mSharedMemBytes),
+                        mDriver);
+                }
+                mFunctions.insert(std::make_pair(hashID(kernelMeta.mS, kernelMeta.mD), funcInfo));
+            }
+        }
+    }
+
+    ~FusedMultiHeadAttentionXMMAKernel()
+    {
+        for (auto mod : mModules)
+        {
+            mDriver.cuModuleUnload(mod);
+        }
+        mFunctions.clear();
+        mModules.clear();
+    }
+
+    bool isValid() const
+    {
+        return !mFunctions.empty();
+    }
+
+    void run(Fused_multihead_attention_params& params, size_t s, size_t d, cudaStream_t ss) const
+    {
+        const auto findIter = mFunctions.find(hashID(s, d));
+        ASSERT(findIter != mFunctions.end());
+
+        const auto& kernelMeta = sMhaKernelMetaInfos[findIter->second.mMetaInfoIndex];
+        const CUfunction func = findIter->second.mDeviceFunction;
+
+        void* kernelParams[] = {&params, nullptr};
+        cuErrCheck(mDriver.cuLaunchKernel(func, params.h, params.b, 1, kernelMeta.mThreadsPerCTA, 1, 1,
+                       kernelMeta.mSharedMemBytes, ss, kernelParams, nullptr),
+            mDriver);
+    }
+
+    nvinfer1::CUDADriverWrapper mDriver;
+
+    Data_type mDataType;
+    unsigned int mSM;
+    std::vector<CUmodule> mModules;
+    struct FusedMultiHeadAttentionKernelInfo
+    {
+        unsigned int mMetaInfoIndex;
+        CUfunction mDeviceFunction;
+    };
+    std::unordered_map<uint64_t, FusedMultiHeadAttentionKernelInfo> mFunctions;
+};
+
+
+class FusedMHAKernelFactory
+{
+public:
+    const FusedMultiHeadAttentionXMMAKernel* getXMMAKernels(Data_type type, unsigned int sm)
+    {
+        static std::mutex s_mutex;
+        std::lock_guard<std::mutex> lg(s_mutex);
+
+        const auto id = hashID(type, sm);
+        const auto findIter = mKernels.find(id);
+        if (findIter == mKernels.end())
+        {
+            FusedMultiHeadAttentionXMMAKernel* newKernel = new FusedMultiHeadAttentionXMMAKernel{type, sm};
+            mKernels.insert(std::make_pair(id, std::unique_ptr<FusedMultiHeadAttentionXMMAKernel>(newKernel)));
+            return newKernel;
+        }
+        return findIter->second.get();
+    }
+
+    static FusedMHAKernelFactory& Get()
+    {
+        static FusedMHAKernelFactory s_factory;
+        return s_factory;
+    }
+
+private:
+    FusedMHAKernelFactory() = default;
+
+    inline uint64_t hashID(Data_type type, unsigned int sm) const
+    {
+        return (uint64_t) type << 32 | sm;
+    }
+
+    std::unordered_map<uint64_t, const std::unique_ptr<FusedMultiHeadAttentionXMMAKernel> > mKernels;
+};
+
+} // namespace bert
diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_128_64_kernel.sm75.cpp b/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_128_64_kernel.sm75.cpp
new file mode 100644
index 00000000..d50d95be
--- /dev/null
+++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_128_64_kernel.sm75.cpp
@@ -0,0 +1,2703 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+namespace bert
+{
+
+unsigned char fused_multihead_attention_fp16_128_64_kernel_sm75_cu_o[] = {0x50, 0xed, 0x55, 0xba, 0x01, 0x00, 0x10,
+    0x00, 0xe8, 0xc6, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x01, 0x01, 0x40, 0x00, 0x00, 0x00, 0xa8, 0xc6,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0x00, 0x01, 0x00, 0x4b,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7f, 0x45, 0x4c,
+    0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xc6, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80,
+    0xc2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4b, 0x05, 0x4b, 0x00, 0x40, 0x00, 0x38, 0x00, 0x03, 0x00, 0x40, 0x00,
+    0x0e, 0x00, 0x01, 0x00, 0x00, 0x2e, 0x73, 0x68, 0x73, 0x74, 0x72, 0x74, 0x61, 0x62, 0x00, 0x2e, 0x73, 0x74, 0x72,
+    0x74, 0x61, 0x62, 0x00, 0x2e, 0x73, 0x79, 0x6d, 0x74, 0x61, 0x62, 0x00, 0x2e, 0x73, 0x79, 0x6d, 0x74, 0x61, 0x62,
+    0x5f, 0x73, 0x68, 0x6e, 0x64, 0x78, 0x00, 0x2e, 0x6e, 0x76, 0x2e, 0x69, 0x6e, 0x66, 0x6f, 0x00, 0x2e, 0x74, 0x65,
+    0x78, 0x74, 0x2e, 0x5f, 0x5f, 0x63, 0x75, 0x64, 0x61, 0x5f, 0x73, 0x6d, 0x37, 0x30, 0x5f, 0x77, 0x61, 0x72, 0x70,
+    0x73, 0x79, 0x6e, 0x63, 0x00, 0x2e, 0x74, 0x65, 0x78, 0x74, 0x2e, 0x5f, 0x5f, 0x63, 0x75, 0x64, 0x61, 0x5f, 0x73,
+    0x6d, 0x37, 0x30, 0x5f, 0x73, 0x68, 0x66, 0x6c, 0x73, 0x79, 0x6e, 0x63, 0x5f, 0x62, 0x66, 0x6c, 0x79, 0x5f, 0x70,
+    0x00, 0x2e, 0x74, 0x65, 0x78, 0x74, 0x2e, 0x5f, 0x5f, 0x63, 0x75, 0x64, 0x61, 0x5f, 0x73, 0x6d, 0x37, 0x30, 0x5f,
+    0x73, 0x68, 0x66, 0x6c, 0x73, 0x79, 0x6e, 0x63, 0x5f, 0x69, 0x64, 0x78, 0x5f, 0x70, 0x00, 0x2e, 0x74, 0x65, 0x78,
+    0x74, 0x2e, 0x66, 0x75, 0x73, 0x65, 0x64, 0x5f, 0x6d, 0x75, 0x6c, 0x74, 0x69, 0x68, 0x65, 0x61, 0x64, 0x5f, 0x61,
+    0x74, 0x74, 0x65, 0x6e, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x66, 0x70, 0x31, 0x36, 0x5f, 0x31, 0x32, 0x38, 0x5f, 0x36,
+    0x34, 0x5f, 0x6b, 0x65, 0x72, 0x6e, 0x65, 0x6c, 0x5f, 0x73, 0x6d, 0x37, 0x35, 0x00, 0x2e, 0x6e, 0x76, 0x2e, 0x69,
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+    0x50, 0x0b, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x30, 0xb7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x30, 0xb7, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x80,
+    0xc2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00};
+unsigned int fused_multihead_attention_fp16_128_64_kernel_sm75_cu_o_len = 50936;
+} // namespace bert
diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_128_64_kernel.sm80.cpp b/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_128_64_kernel.sm80.cpp
new file mode 100644
index 00000000..667cbf87
--- /dev/null
+++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_128_64_kernel.sm80.cpp
@@ -0,0 +1,2284 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+namespace bert
+{
+
+unsigned char fused_multihead_attention_fp16_128_64_kernel_sm80_cu_o[] = {0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01,
+    0x33, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x40, 0xa7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0xa4, 0x00, 0x00, 0x00,
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+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x04,
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+    0xe2, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x90, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xb0, 0x01, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x32, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x06, 0x00, 0x10, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x9d, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0xe6, 0x80, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xa5, 0x00, 0x00, 0x00, 0x08,
+    0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x80, 0xa4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x09, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x40, 0xa7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xa8, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0xa8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x01, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x90, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xf0, 0x9e, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0xf0, 0x9e, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x01, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x80, 0xa4, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00};
+unsigned int fused_multihead_attention_fp16_128_64_kernel_sm80_cu_o_len = 42984;
+} // namespace bert
diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_384_64_kernel.sm75.cpp b/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_384_64_kernel.sm75.cpp
new file mode 100644
index 00000000..5ee20bcc
--- /dev/null
+++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_384_64_kernel.sm75.cpp
@@ -0,0 +1,1820 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+namespace bert
+{
+
+unsigned char fused_multihead_attention_fp16_384_64_kernel_sm75_cu_o[] = {0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01,
+    0x33, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xc0, 0x84, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x82, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x4b, 0x05, 0x4b, 0x00, 0x40, 0x00, 0x38, 0x00, 0x03, 0x00, 0x40, 0x00, 0x0b, 0x00, 0x01, 0x00,
+    0x00, 0x2e, 0x73, 0x68, 0x73, 0x74, 0x72, 0x74, 0x61, 0x62, 0x00, 0x2e, 0x73, 0x74, 0x72, 0x74, 0x61, 0x62, 0x00,
+    0x2e, 0x73, 0x79, 0x6d, 0x74, 0x61, 0x62, 0x00, 0x2e, 0x73, 0x79, 0x6d, 0x74, 0x61, 0x62, 0x5f, 0x73, 0x68, 0x6e,
+    0x64, 0x78, 0x00, 0x2e, 0x6e, 0x76, 0x2e, 0x69, 0x6e, 0x66, 0x6f, 0x00, 0x2e, 0x74, 0x65, 0x78, 0x74, 0x2e, 0x66,
+    0x75, 0x73, 0x65, 0x64, 0x5f, 0x6d, 0x75, 0x6c, 0x74, 0x69, 0x68, 0x65, 0x61, 0x64, 0x5f, 0x61, 0x74, 0x74, 0x65,
+    0x6e, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x66, 0x70, 0x31, 0x36, 0x5f, 0x33, 0x38, 0x34, 0x5f, 0x36, 0x34, 0x5f, 0x6b,
+    0x65, 0x72, 0x6e, 0x65, 0x6c, 0x5f, 0x73, 0x6d, 0x37, 0x35, 0x00, 0x2e, 0x6e, 0x76, 0x2e, 0x69, 0x6e, 0x66, 0x6f,
+    0x2e, 0x66, 0x75, 0x73, 0x65, 0x64, 0x5f, 0x6d, 0x75, 0x6c, 0x74, 0x69, 0x68, 0x65, 0x61, 0x64, 0x5f, 0x61, 0x74,
+    0x74, 0x65, 0x6e, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x66, 0x70, 0x31, 0x36, 0x5f, 0x33, 0x38, 0x34, 0x5f, 0x36, 0x34,
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+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xa8, 0x7c,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xa8, 0x7c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x00, 0x82, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00};
+unsigned int fused_multihead_attention_fp16_384_64_kernel_sm75_cu_o_len = 34152;
+} // namespace bert
diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_384_64_kernel.sm80.cpp b/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_384_64_kernel.sm80.cpp
new file mode 100644
index 00000000..05c68eac
--- /dev/null
+++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_384_64_kernel.sm80.cpp
@@ -0,0 +1,1665 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+namespace bert
+{
+
+unsigned char fused_multihead_attention_fp16_384_64_kernel_sm80_cu_o[] = {0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01,
+    0x33, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, 0x00, 0x00,
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+    0x00, 0x00, 0x48, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03,
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+    0x00, 0x00, 0x00, 0x00, 0xe2, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x58, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xb0, 0x01,
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+    0x06, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0x07, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x04, 0x00,
+    0x00, 0xee, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xa5,
+    0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x80, 0x76, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x40, 0x79, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0xa8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xa8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x58, 0x05, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x28,
+    0x71, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x28, 0x71, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x80, 0x76, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00};
+unsigned int fused_multihead_attention_fp16_384_64_kernel_sm80_cu_o_len = 31208;
+} // namespace bert
diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_int8_128_64_kernel.sm75.cpp b/plugin/bertQKVToContextPlugin/fused_multihead_attention_int8_128_64_kernel.sm75.cpp
new file mode 100644
index 00000000..ccc2aa48
--- /dev/null
+++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_int8_128_64_kernel.sm75.cpp
@@ -0,0 +1,3591 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+namespace bert
+{
+
+unsigned char fused_multihead_attention_int8_128_64_kernel_sm75_cu_o[] = {0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01,
+    0x33, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x40, 0x08, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0x05, 0x01, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x4b, 0x05, 0x4b, 0x00, 0x40, 0x00, 0x38, 0x00, 0x03, 0x00, 0x40, 0x00, 0x0b, 0x00, 0x01, 0x00,
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+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x32, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x06, 0x00, 0x10, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0xfe, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0xfd, 0x80, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xa5, 0x00, 0x00, 0x00, 0x08, 0x00,
+    0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80,
+    0x05, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x09, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x06, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x40, 0x08, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xa8, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0xa8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x01, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x90, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xf0, 0xff, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0xf0, 0xff, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01,
+    0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x80, 0x05, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00};
+unsigned int fused_multihead_attention_int8_128_64_kernel_sm75_cu_o_len = 67816;
+} // namespace bert
diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_int8_128_64_kernel.sm80.cpp b/plugin/bertQKVToContextPlugin/fused_multihead_attention_int8_128_64_kernel.sm80.cpp
new file mode 100644
index 00000000..0be5356f
--- /dev/null
+++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_int8_128_64_kernel.sm80.cpp
@@ -0,0 +1,3268 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+namespace bert
+{
+
+unsigned char fused_multihead_attention_int8_128_64_kernel_sm80_cu_o[] = {0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01,
+    0x33, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x40, 0xf0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0xed, 0x00, 0x00, 0x00,
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+    0x40, 0x4b, 0x00, 0x00, 0x01, 0x00, 0x00, 0xe2, 0x0f, 0x04, 0x02, 0x78, 0x0b, 0x00, 0x80, 0xff, 0x3f, 0x4b, 0x00,
+    0x0f, 0x00, 0x00, 0x00, 0xe4, 0x0f, 0x00, 0x0b, 0x38, 0x00, 0x13, 0x00, 0x00, 0xfe, 0x42, 0x00, 0x40, 0xf3, 0x03,
+    0x00, 0xe4, 0x0f, 0x00, 0x07, 0x28, 0x0b, 0x05, 0x7f, 0x00, 0x40, 0x4b, 0x00, 0x00, 0x00, 0x04, 0x00, 0xe4, 0x0f,
+    0x00, 0x16, 0x78, 0x0e, 0x06, 0x40, 0x00, 0x00, 0x00, 0x0d, 0x00, 0x00, 0x00, 0x00, 0xc4, 0x0f, 0x00, 0x16, 0x78,
+    0x09, 0x09, 0x10, 0x04, 0x00, 0x00, 0x0a, 0x00, 0x00, 0x00, 0x00, 0xe4, 0x0f, 0x00, 0x10, 0x7c, 0x02, 0x02, 0x07,
+    0x00, 0x00, 0x00, 0xff, 0xe0, 0xf9, 0x0f, 0x00, 0xc8, 0x2f, 0x00, 0x10, 0x7c, 0x03, 0x03, 0x04, 0x00, 0x00, 0x00,
+    0xff, 0xe4, 0x7f, 0x0a, 0x00, 0xe4, 0x0f, 0x00, 0x10, 0x7c, 0x04, 0x02, 0x07, 0x00, 0x00, 0x00, 0xff, 0xe0, 0xf9,
+    0x0f, 0x00, 0xe4, 0x0f, 0x00, 0x02, 0x78, 0x0a, 0x00, 0x80, 0xff, 0x3f, 0x4b, 0x00, 0x0f, 0x00, 0x00, 0x00, 0xe4,
+    0x0f, 0x00, 0x07, 0x38, 0x0a, 0x0c, 0x7f, 0x00, 0x40, 0x4b, 0x00, 0x00, 0x80, 0x04, 0x00, 0xe4, 0x0f, 0x00, 0x10,
+    0x7c, 0x05, 0x03, 0x04, 0x00, 0x00, 0x00, 0xff, 0xe4, 0x7f, 0x0a, 0x00, 0xe4, 0x0f, 0x00, 0x10, 0x7c, 0x06, 0x04,
+    0x07, 0x00, 0x00, 0x00, 0xff, 0xe0, 0xf1, 0x0f, 0x00, 0xc4, 0x0f, 0x00, 0x16, 0x78, 0x0d, 0x0e, 0x10, 0x04, 0x00,
+    0x00, 0x0b, 0x00, 0x00, 0x00, 0x00, 0xe4, 0x0f, 0x00, 0x16, 0x78, 0x09, 0x09, 0x10, 0x42, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0xe4, 0x0f, 0x00, 0x16, 0x78, 0x0b, 0x08, 0x10, 0x42, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x00,
+    0xe4, 0x0f, 0x00, 0x10, 0x7c, 0x07, 0x05, 0x04, 0x00, 0x00, 0x00, 0xff, 0xe4, 0x7f, 0x08, 0x00, 0xe4, 0x0f, 0x00,
+    0x16, 0x78, 0x0d, 0x0d, 0x10, 0x42, 0x00, 0x00, 0x0a, 0x00, 0x00, 0x00, 0x00, 0xe2, 0x0f, 0x00, 0x86, 0x79, 0x00,
+    0x02, 0x09, 0x00, 0x00, 0x00, 0x08, 0x19, 0x10, 0x0c, 0x00, 0xe8, 0x0f, 0x00, 0x86, 0x79, 0x00, 0x04, 0x0b, 0x00,
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+    0x19, 0x10, 0x0c, 0x00, 0xe2, 0x0f, 0x00, 0x4d, 0x79, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0x03,
+    0x00, 0xea, 0x0f, 0x00, 0x47, 0x79, 0x00, 0x00, 0xf0, 0xff, 0xff, 0xff, 0xff, 0xff, 0x83, 0x03, 0x00, 0xc0, 0x0f,
+    0x00, 0x18, 0x79, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xc0, 0x0f, 0x00, 0x18, 0x79,
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+    0x00, 0x00, 0x00, 0x00, 0x00, 0xc0, 0x0f, 0x00, 0x18, 0x79, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0xc0, 0x0f, 0x00, 0x18, 0x79, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xc0,
+    0x0f, 0x00, 0x18, 0x79, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xc0, 0x0f, 0x00, 0x18,
+    0x79, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xc0, 0x0f, 0x00, 0x18, 0x79, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xc0, 0x0f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x40, 0x01, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0xbc, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x13, 0x00, 0x00, 0x00, 0x02,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x40, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x78, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00,
+    0x00, 0x03, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x22, 0x01, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xb8, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x70, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x29, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x70, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x28, 0x04, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x24, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x6a, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x70, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x4c, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x34, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03,
+    0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x2f, 0x01, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xe2, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
+    0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x90, 0x05, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0xb0, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x00,
+    0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x32,
+    0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x06, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x80, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xe6, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x03, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0xd1, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xa5, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0xed, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x05, 0x00,
+    0x00, 0x00, 0x40, 0xf0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xa8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xa8, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00,
+    0x00, 0x90, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xf0, 0xe7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xf0, 0xe7, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00,
+    0x80, 0xed, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00};
+unsigned int fused_multihead_attention_int8_128_64_kernel_sm80_cu_o_len = 61672;
+} // namespace bert
diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_int8_384_64_kernel.sm75.cpp b/plugin/bertQKVToContextPlugin/fused_multihead_attention_int8_384_64_kernel.sm75.cpp
new file mode 100644
index 00000000..898269f9
--- /dev/null
+++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_int8_384_64_kernel.sm75.cpp
@@ -0,0 +1,2689 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+namespace bert
+{
+
+unsigned char fused_multihead_attention_int8_384_64_kernel_sm75_cu_o[] = {0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01,
+    0x33, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x40, 0xc5, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0xc2, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x4b, 0x05, 0x4b, 0x00, 0x40, 0x00, 0x38, 0x00, 0x03, 0x00, 0x40, 0x00, 0x0b, 0x00, 0x01, 0x00,
+    0x00, 0x2e, 0x73, 0x68, 0x73, 0x74, 0x72, 0x74, 0x61, 0x62, 0x00, 0x2e, 0x73, 0x74, 0x72, 0x74, 0x61, 0x62, 0x00,
+    0x2e, 0x73, 0x79, 0x6d, 0x74, 0x61, 0x62, 0x00, 0x2e, 0x73, 0x79, 0x6d, 0x74, 0x61, 0x62, 0x5f, 0x73, 0x68, 0x6e,
+    0x64, 0x78, 0x00, 0x2e, 0x6e, 0x76, 0x2e, 0x69, 0x6e, 0x66, 0x6f, 0x00, 0x2e, 0x74, 0x65, 0x78, 0x74, 0x2e, 0x66,
+    0x75, 0x73, 0x65, 0x64, 0x5f, 0x6d, 0x75, 0x6c, 0x74, 0x69, 0x68, 0x65, 0x61, 0x64, 0x5f, 0x61, 0x74, 0x74, 0x65,
+    0x6e, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x69, 0x6e, 0x74, 0x38, 0x5f, 0x33, 0x38, 0x34, 0x5f, 0x36, 0x34, 0x5f, 0x6b,
+    0x65, 0x72, 0x6e, 0x65, 0x6c, 0x5f, 0x73, 0x6d, 0x37, 0x35, 0x00, 0x2e, 0x6e, 0x76, 0x2e, 0x69, 0x6e, 0x66, 0x6f,
+    0x2e, 0x66, 0x75, 0x73, 0x65, 0x64, 0x5f, 0x6d, 0x75, 0x6c, 0x74, 0x69, 0x68, 0x65, 0x61, 0x64, 0x5f, 0x61, 0x74,
+    0x74, 0x65, 0x6e, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x69, 0x6e, 0x74, 0x38, 0x5f, 0x33, 0x38, 0x34, 0x5f, 0x36, 0x34,
+    0x5f, 0x6b, 0x65, 0x72, 0x6e, 0x65, 0x6c, 0x5f, 0x73, 0x6d, 0x37, 0x35, 0x00, 0x2e, 0x6e, 0x76, 0x2e, 0x73, 0x68,
+    0x61, 0x72, 0x65, 0x64, 0x2e, 0x66, 0x75, 0x73, 0x65, 0x64, 0x5f, 0x6d, 0x75, 0x6c, 0x74, 0x69, 0x68, 0x65, 0x61,
+    0x64, 0x5f, 0x61, 0x74, 0x74, 0x65, 0x6e, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x69, 0x6e, 0x74, 0x38, 0x5f, 0x33, 0x38,
+    0x34, 0x5f, 0x36, 0x34, 0x5f, 0x6b, 0x65, 0x72, 0x6e, 0x65, 0x6c, 0x5f, 0x73, 0x6d, 0x37, 0x35, 0x00, 0x2e, 0x6e,
+    0x76, 0x2e, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x61, 0x6e, 0x74, 0x30, 0x2e, 0x66, 0x75, 0x73, 0x65, 0x64, 0x5f, 0x6d,
+    0x75, 0x6c, 0x74, 0x69, 0x68, 0x65, 0x61, 0x64, 0x5f, 0x61, 0x74, 0x74, 0x65, 0x6e, 0x74, 0x69, 0x6f, 0x6e, 0x5f,
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+    0x00, 0x00, 0x88, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03,
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+    0x00, 0xf5, 0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xa5,
+    0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x80, 0xc2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x40, 0xc5, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0xa8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xa8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x98, 0x05, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xe8,
+    0xbc, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xe8, 0xbc, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x80, 0xc2, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00};
+unsigned int fused_multihead_attention_int8_384_64_kernel_sm75_cu_o_len = 50664;
+} // namespace bert
diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_int8_384_64_kernel.sm80.cpp b/plugin/bertQKVToContextPlugin/fused_multihead_attention_int8_384_64_kernel.sm80.cpp
new file mode 100644
index 00000000..28a6914c
--- /dev/null
+++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_int8_384_64_kernel.sm80.cpp
@@ -0,0 +1,2722 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+namespace bert
+{
+
+unsigned char fused_multihead_attention_int8_384_64_kernel_sm80_cu_o[] = {0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01,
+    0x33, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xc0, 0xc7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xc5, 0x00, 0x00, 0x00,
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+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0x05, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00,
+    0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xe2, 0x00,
+    0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x90, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xb0, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x32, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x06, 0x00, 0x10, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80,
+    0xbd, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0xf0, 0x80, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xa5, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00,
+    0x00, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xc5,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09,
+    0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x06, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0xc0, 0xc7, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xa8, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0xa8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01,
+    0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x90, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x70, 0xbf, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x70, 0xbf, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00,
+    0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x00, 0xc5, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00};
+unsigned int fused_multihead_attention_int8_384_64_kernel_sm80_cu_o_len = 51304;
+} // namespace bert
diff --git a/plugin/bertQKVToContextPlugin/qkvToContext.cu b/plugin/bertQKVToContextPlugin/qkvToContext.cu
new file mode 100644
index 00000000..b0dd3dd3
--- /dev/null
+++ b/plugin/bertQKVToContextPlugin/qkvToContext.cu
@@ -0,0 +1,846 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+// Need 10.1 for cublasGemmStridedBatchedEx
+#include <cuda.h>
+#if CUDA_VERSION >= 10010
+
+#include "NvInfer.h"
+#include "bertCommon.h"
+#include "common.cuh"
+#include "qkvToContextPlugin.h"
+#include "serialize.hpp"
+
+#include <cassert>
+#include <cstring>
+#include <iostream>
+#include <vector>
+#include <tuple>
+
+#include <fused_multihead_attention.h>
+using namespace nvinfer1;
+
+namespace bert
+{
+
+template <typename T, int TPB, int VPT>
+__global__ void maskedSoftmax(const float rsqrtHeadSize, const T* input, T* output, const int* maskIdx)
+{
+    using BlockReduce = cub::BlockReduce<float, TPB>;
+
+    union SMem
+    {
+        T shm[VPT * TPB];
+        typename BlockReduce::TempStorage reduce;
+        SMem() {}
+    };
+    __shared__ SMem tmp;
+
+    // grid: (NxS, B)
+    const int b = blockIdx.y;
+    const int blockOffset = (b * gridDim.x + blockIdx.x) * TPB;
+    __shared__ int lastValid;
+    if (threadIdx.x == 0)
+    {
+        lastValid = min(TPB, maskIdx[b]);
+    }
+    __syncthreads();
+    float local[VPT];
+
+    __shared__ float rZ;
+
+    const int idx = (blockOffset + threadIdx.x) * VPT;
+    T* myshm = &tmp.shm[threadIdx.x * VPT];
+    copy<sizeof(T) * VPT>(&input[idx], myshm);
+
+    __syncthreads();
+
+#pragma unroll
+    for (int it = 0; it < VPT; it++)
+    {
+        local[it]
+            = (threadIdx.x < lastValid) ? myExp<float>((rsqrtHeadSize) * float(tmp.shm[it * TPB + threadIdx.x])) : 0.f;
+    }
+    __syncthreads();
+
+#pragma unroll
+    for (int it = 0; it < VPT; it++)
+    {
+
+        const auto Z = BlockReduce(tmp.reduce).Reduce(local[it], cub::Sum());
+
+        if (threadIdx.x == 0)
+        {
+            rZ = (1.f) / Z;
+        }
+        __syncthreads();
+        local[it] *= rZ;
+    }
+
+#pragma unroll
+    for (int it = 0; it < VPT; it++)
+    {
+        tmp.shm[it * TPB + threadIdx.x] = local[it];
+    }
+    __syncthreads();
+    copy<sizeof(T) * VPT>(myshm, &output[idx]);
+}
+
+template <typename T, int TPB, int VPT>
+__global__ void softmax(const float rsqrtHeadSize, const T* input, T* output)
+{
+    float local[VPT];
+
+    using BlockReduce = cub::BlockReduce<float, TPB>;
+
+    union SMem
+    {
+        T shm[VPT * TPB];
+        typename BlockReduce::TempStorage reduce;
+        SMem() {}
+    };
+    __shared__ SMem tmp;
+
+    __shared__ float rZ;
+
+    const int idx = (TPB * blockIdx.x + threadIdx.x) * VPT;
+    T* myshm = &tmp.shm[threadIdx.x * VPT];
+    copy<sizeof(T) * VPT>(&input[idx], myshm);
+
+    __syncthreads();
+
+#pragma unroll
+    for (int it = 0; it < VPT; it++)
+    {
+        local[it] = myExp<float>(rsqrtHeadSize * float(tmp.shm[it * TPB + threadIdx.x]));
+    }
+    __syncthreads();
+
+#pragma unroll
+    for (int it = 0; it < VPT; it++)
+    {
+
+        const auto Z = BlockReduce(tmp.reduce).Reduce(local[it], cub::Sum());
+
+        if (threadIdx.x == 0)
+        {
+            rZ = 1.f / Z;
+        }
+        __syncthreads();
+        local[it] *= rZ;
+    }
+
+#pragma unroll
+    for (int it = 0; it < VPT; it++)
+    {
+        tmp.shm[it * TPB + threadIdx.x] = local[it];
+    }
+    __syncthreads();
+    copy<sizeof(T) * VPT>(myshm, &output[idx]);
+}
+
+template <typename T, unsigned TPB>
+__global__ void scaledSoftmaxKernelSmall(const int ld, const float rsqrtHeadSize, const T* input, T* output)
+{
+    scaledSoftmaxSmall<T, TPB>(ld, ld, rsqrtHeadSize, input, output);
+}
+
+template <typename T, unsigned TPB>
+__global__ void scaledSoftmaxKernel(const int ld, const float rsqrtHeadSize, const T* input, T* output)
+{
+    scaledSoftmax<T, TPB>(ld, ld, rsqrtHeadSize, input, output);
+}
+
+template <typename T>
+int computeScaledSoftmax(
+    cudaStream_t stream, const int ld, const int B, const int N, const float rsqrtHeadSize, const T* input, T* output)
+{
+
+    constexpr int VPT = 16 / sizeof(T);
+
+    const dim3 grid(ld * N, B, 1);
+
+    if (ld <= 32)
+    {
+        const int blockSize = 32;
+        scaledSoftmaxKernelSmall<T, blockSize><<<grid, blockSize, 0, stream>>>(ld, rsqrtHeadSize, input, output);
+    }
+    else if (ld < 128)
+    {
+        const int blockSize = 128;
+        scaledSoftmaxKernelSmall<T, blockSize><<<grid, blockSize, 0, stream>>>(ld, rsqrtHeadSize, input, output);
+    }
+    else if (ld == 128)
+    {
+        const int grid = B * N * ld / (VPT);
+        softmax<T, 128, VPT><<<grid, 128, 0, stream>>>(rsqrtHeadSize, input, output);
+    }
+
+    else if (ld == 384)
+    {
+        const int grid = B * N * ld / (VPT);
+        softmax<T, 384, VPT><<<grid, 384, 0, stream>>>(rsqrtHeadSize, input, output);
+    }
+    else
+    {
+        const int blockSize = 256;
+
+        scaledSoftmaxKernel<T, blockSize><<<grid, blockSize, 0, stream>>>(ld, rsqrtHeadSize, input, output);
+    }
+
+    CHECK(cudaPeekAtLastError());
+    return 0;
+}
+
+template <typename T, unsigned TPB>
+__global__ void maskedScaledSoftmaxKernelSmall(
+    const int ld, const float rsqrtHeadSize, const int* maskIdx, const T* input, T* output)
+{
+    __shared__ int lastValid;
+
+    if (threadIdx.x == 0)
+    {
+        lastValid = min(ld, maskIdx[blockIdx.y]);
+    }
+    __syncthreads();
+
+    scaledSoftmaxSmall<T, TPB>(ld, lastValid, rsqrtHeadSize, input, output);
+}
+
+template <typename T, unsigned TPB>
+__global__ void maskedScaledSoftmaxKernel(
+    const int ld, const float rsqrtHeadSize, const int* maskIdx, const T* input, T* output)
+{
+
+    __shared__ int lastValid;
+
+    if (threadIdx.x == 0)
+    {
+        lastValid = min(ld, maskIdx[blockIdx.y]);
+    }
+    __syncthreads();
+    scaledSoftmax<T, TPB>(ld, lastValid, rsqrtHeadSize, input, output);
+}
+
+template <typename T>
+int computeMaskedScaledSoftmax(cudaStream_t stream, const int ld, const int B, const int N, const float rsqrtHeadSize,
+    const int* maskIdx, const T* input, T* output)
+{
+    // Mask idx is of length B and assumes the valid region is contiguous starting
+    // from the beginning of the sequence
+
+    const dim3 grid(ld * N, B, 1);
+    // for smaller problems, e.g. BERT base B=1, this is not optimal
+    if (ld <= 32)
+    {
+        constexpr int blockSize = 32;
+        maskedScaledSoftmaxKernelSmall<T, blockSize>
+            <<<grid, blockSize, 0, stream>>>(ld, rsqrtHeadSize, maskIdx, input, output);
+    }
+    else if (ld < 128)
+    {
+        constexpr int blockSize = 128;
+        maskedScaledSoftmaxKernelSmall<T, blockSize>
+            <<<grid, blockSize, 0, stream>>>(ld, rsqrtHeadSize, maskIdx, input, output);
+    }
+    else if (ld == 128)
+    {
+        if (B == 1)
+        {
+            constexpr int VPT = 4 / sizeof(T);
+            constexpr int blockSize = 128;
+            const dim3 grid(ld * N / VPT, B, 1);
+            maskedSoftmax<T, blockSize, VPT><<<grid, blockSize, 0, stream>>>(rsqrtHeadSize, input, output, maskIdx);
+        }
+        else
+        {
+            constexpr int VPT = 16 / sizeof(T);
+            constexpr int blockSize = 128;
+            const dim3 grid(ld * N / VPT, B, 1);
+            maskedSoftmax<T, blockSize, VPT><<<grid, blockSize, 0, stream>>>(rsqrtHeadSize, input, output, maskIdx);
+        }
+    }
+    else if (ld == 384)
+    {
+        if (B == 1)
+        {
+            constexpr int VPT = 4 / sizeof(T);
+            constexpr int blockSize = 384;
+            const dim3 grid(ld * N / VPT, B, 1);
+            maskedSoftmax<T, blockSize, VPT><<<grid, blockSize, 0, stream>>>(rsqrtHeadSize, input, output, maskIdx);
+        }
+        else
+        {
+            constexpr int VPT = 16 / sizeof(T);
+            constexpr int blockSize = 384;
+            const dim3 grid(ld * N / VPT, B, 1);
+            maskedSoftmax<T, blockSize, VPT><<<grid, blockSize, 0, stream>>>(rsqrtHeadSize, input, output, maskIdx);
+        }
+    }
+    else
+    {
+        constexpr int blockSize = 256;
+        maskedScaledSoftmaxKernel<T, blockSize>
+            <<<grid, blockSize, 0, stream>>>(ld, rsqrtHeadSize, maskIdx, input, output);
+    }
+
+    CHECK(cudaPeekAtLastError());
+    return 0;
+}
+
+std::pair<int, int> tuneBatchedGemm(const int B, const int S, const int numHeads, const int headSize)
+{
+    const int nruns = 500;
+    cublasHandle_t cublas;
+    cublasCreate(&cublas);
+    cudaStream_t stream;
+    cudaStreamCreate(&stream);
+    cudaEvent_t start, stop;
+    cudaEventCreate(&start);
+    cudaEventCreate(&stop);
+    cublasSetStream(cublas, stream);
+    cublasSetMathMode(cublas, CUBLAS_TENSOR_OP_MATH);
+
+    using T = half;
+    const int omatSize = S * S;
+    const int numMats = B * numHeads;
+    const int ldQKV = 3 * B * numHeads * headSize;
+    const int strideQKV = 3 * headSize;
+    const int ldOut = B * numHeads * headSize;
+    const int strideOut = headSize;
+
+    const size_t inBytes = S * B * 3 * numHeads * headSize * sizeof(T);
+    const size_t qkBytes = S * S * B * numHeads * sizeof(T);
+    const size_t outBytes = S * B * numHeads * headSize * sizeof(T);
+
+    T* input = nullptr;
+    T* qkptr = nullptr;
+    T* output = nullptr;
+    cudaMalloc(&input, inBytes);
+    cudaMalloc(&qkptr, qkBytes);
+    cudaMalloc(&output, outBytes);
+    cudaMemset(input, 1, inBytes);
+    cudaMemset(qkptr, 1, qkBytes);
+
+    // input: SxBx3xNxH
+    const T* qptr = input;
+    const T* kptr = qptr + headSize;
+    const T* vptr = kptr + headSize;
+
+    const int startAlgo = (int) CUBLAS_GEMM_DEFAULT_TENSOR_OP;
+    const int endAlgo = (int) CUBLAS_GEMM_ALGO15_TENSOR_OP;
+    int best1 = startAlgo;
+    int best2 = startAlgo;
+    float ms1 = 1000000;
+    float ms2 = 1000000;
+    for (int a = startAlgo; a <= endAlgo; a++)
+    {
+        cublasGemmAlgo_t algo = static_cast<cublasGemmAlgo_t>(a);
+        float ms1_, ms2_;
+        // qkptr: BxNxSxS
+        cudaEventRecord(start, stream);
+        for (int r = 0; r < nruns; r++)
+        {
+            CUBLASASSERT(cublasGemmStridedBatchedEx<T>(cublas, CUBLAS_OP_T, CUBLAS_OP_N, S, S, headSize, T(1.f), kptr,
+                ldQKV, strideQKV, qptr, ldQKV, strideQKV, T(0.f), qkptr, S, omatSize, numMats, algo));
+        }
+
+        cudaEventRecord(stop, stream);
+        cudaStreamSynchronize(stream);
+        cudaEventElapsedTime(&ms1_, start, stop);
+        if (ms1_ < ms1)
+        {
+            best1 = algo;
+            ms1 = ms1_;
+        }
+
+        // pptr: BxNxSxS
+        // output: SxBxNxH
+        cudaEventRecord(start, stream);
+        for (int r = 0; r < nruns; r++)
+        {
+            CUBLASASSERT(cublasGemmStridedBatchedEx<T>(cublas, CUBLAS_OP_N, CUBLAS_OP_N, headSize, S, S, 1.f, vptr,
+                ldQKV, strideQKV, qkptr, S, omatSize, 0.f, output, ldOut, strideOut, numMats, algo));
+        }
+
+        cudaEventRecord(stop, stream);
+        cudaStreamSynchronize(stream);
+        cudaEventElapsedTime(&ms2_, start, stop);
+
+        if (ms2_ < ms2)
+        {
+            best2 = algo;
+            ms2 = ms2_;
+        }
+    }
+
+    cudaFree(input);
+    cudaFree(qkptr);
+    cudaFree(output);
+    cudaEventDestroy(start);
+    cudaEventDestroy(stop);
+    cudaStreamDestroy(stream);
+    cublasDestroy(cublas);
+    return std::make_pair(best1, best2);
+}
+
+template int computeScaledSoftmax<float>(cudaStream_t stream, const int ld, const int B, const int N,
+    const float rsqrtHeadSize, const float* input, float* output);
+template int computeScaledSoftmax<half>(cudaStream_t stream, const int ld, const int B, const int N,
+    const float rsqrtHeadSize, const half* input, half* output);
+
+template int computeMaskedScaledSoftmax<float>(cudaStream_t stream, const int ld, const int B, const int N,
+    const float rsqrtHeadSize, const int* maskIdx, const float* input, float* output);
+template int computeMaskedScaledSoftmax<half>(cudaStream_t stream, const int ld, const int B, const int N,
+    const float rsqrtHeadSize, const int* maskIdx, const half* input, half* output);
+
+size_t MHARunner::getSerializationSize() const
+{
+    return sizeof(mS) + sizeof(mB);
+}
+
+void MHARunner::serialize(void* buffer) const
+{
+    serialize_value(&buffer, mS);
+    serialize_value(&buffer, mB);
+}
+
+void MHARunner::deserialize(const void* data, size_t length)
+{
+    deserialize_value(&data, &length, &mS);
+    deserialize_value(&data, &length, &mB);
+    setup(mS, mB);
+}
+
+UnfusedMHARunner::UnfusedMHARunner(const nvinfer1::DataType type, const int numHeads, const int headSize)
+    : MHARunner(type, numHeads, headSize)
+    , mAlgoBatchedEx1(CUBLAS_GEMM_DEFAULT_TENSOR_OP)
+    , mAlgoBatchedEx2(CUBLAS_GEMM_DEFAULT_TENSOR_OP)
+{
+    CUBLASASSERT(cublasCreate(&mCublas));
+}
+
+UnfusedMHARunner::~UnfusedMHARunner()
+{
+    CUBLASASSERT(cublasDestroy(mCublas));
+}
+
+size_t UnfusedMHARunner::getSerializationSize() const
+{
+    return sizeof(mAlgoBatchedEx1) + sizeof(mAlgoBatchedEx2) + MHARunner::getSerializationSize();
+}
+
+void UnfusedMHARunner::serialize(void* buffer) const
+{
+    serialize_value(&buffer, mAlgoBatchedEx1);
+    serialize_value(&buffer, mAlgoBatchedEx2);
+    MHARunner::serialize(buffer);
+}
+
+void UnfusedMHARunner::deserialize(const void* data, size_t length)
+{
+    deserialize_value(&data, &length, &mAlgoBatchedEx1);
+    deserialize_value(&data, &length, &mAlgoBatchedEx2);
+    MHARunner::deserialize(data, length);
+}
+
+void UnfusedMHARunner::setup(const int S, const int B)
+{
+    MHARunner::setup(S, B);
+    if (mType == DataType::kHALF)
+    {
+        std::tie(mAlgoBatchedEx1, mAlgoBatchedEx2) = tuneBatchedGemm(B, S, mNumHeads, mHeadSize);
+
+        gLogVerbose << "QKV Plugin - Selected Algos for batch gemms: " << mAlgoBatchedEx1 << ", " << mAlgoBatchedEx2
+            << "\n";
+    }
+}
+
+size_t UnfusedMHARunner::getWorkspaceSize() const
+{
+    return 2UL * mWordSize * mOmatSize * mNumMats;
+}
+
+void UnfusedMHARunner::run(const PluginTensorDesc& inputDesc, const PluginTensorDesc& outputDesc, const void* qkvPtr,
+    const void* maskPtr, void* output, void* workspace, cudaStream_t stream)
+{
+    const int* maskIdx = static_cast<const int*>(maskPtr);
+
+    cublasSetStream(mCublas, stream);
+
+    // Q, K, V: BxNxSxH (inputs)
+    // Q * K': BxNxSxS (-> scratch1)
+    // P: BxNxSxS (-> scratch2)
+    // P * V: BxNxSxH (output)
+
+    if (mType == DataType::kHALF)
+    {
+        CublasConfigHelper helper(mCublas);
+        const half* qptr = static_cast<const half*>(qkvPtr);
+        const half* kptr = qptr + mHeadSize;
+        const half* vptr = kptr + mHeadSize;
+        half* qkptr = static_cast<half*>(workspace);
+        half* pptr = qkptr + mOmatSize * mNumMats;
+        half alpha = 1.f;
+        half beta = 0.f;
+        CUBLASASSERT(::cublasGemmStridedBatchedEx(mCublas, CUBLAS_OP_T, CUBLAS_OP_N, mS, mS, mHeadSize, &alpha, kptr,
+            CUDA_R_16F, mLdQKV, mStrideQKV, qptr, CUDA_R_16F, mLdQKV, mStrideQKV, &beta, qkptr, CUDA_R_16F, mS,
+            mOmatSize, mNumMats, CUDA_R_16F, static_cast<cublasGemmAlgo_t>(mAlgoBatchedEx1)));
+
+        // apply softmax
+        if (maskIdx)
+        { // if we have a mask
+            computeMaskedScaledSoftmax<half>(stream, mS, mB, mNumHeads, mRsqrtHeadSize, maskIdx, qkptr, pptr);
+        }
+        else
+        { // if we don't have a mask
+            computeScaledSoftmax<half>(stream, mS, mB, mNumHeads, mRsqrtHeadSize, qkptr, pptr);
+        }
+
+        // compute P*V (as V*P)
+        CUBLASASSERT(cublasGemmStridedBatchedEx(mCublas, CUBLAS_OP_N, CUBLAS_OP_N, mHeadSize, mS, mS, &alpha, vptr,
+            CUDA_R_16F, mLdQKV, mStrideQKV, pptr, CUDA_R_16F, mS, mOmatSize, &beta, output, CUDA_R_16F, mLdOut,
+            mStrideOut, mNumMats, CUDA_R_16F, static_cast<cublasGemmAlgo_t>(mAlgoBatchedEx2)));
+    }
+    else
+    {
+
+        const float* qptr = static_cast<const float*>(qkvPtr);
+        const float* kptr = qptr + mHeadSize;
+        const float* vptr = kptr + mHeadSize;
+        float* qkptr = static_cast<float*>(workspace);
+        float* pptr = qkptr + mOmatSize * mNumMats;
+        float* outptr = static_cast<float*>(output);
+        CUBLASASSERT(cublasGemmStridedBatched<float>(mCublas, CUBLAS_OP_T, CUBLAS_OP_N, mS, mS, mHeadSize, 1.f, kptr,
+            mLdQKV, mStrideQKV, qptr, mLdQKV, mStrideQKV, 0.f, qkptr, mS, mOmatSize, mNumMats));
+
+        // apply softmax
+        if (maskIdx)
+        { // if we have a mask
+            computeMaskedScaledSoftmax<float>(stream, mS, mB, mNumHeads, mRsqrtHeadSize, maskIdx, qkptr, pptr);
+        }
+        else
+        { // if we don't have a mask
+            computeScaledSoftmax<float>(stream, mS, mB, mNumHeads, mRsqrtHeadSize, qkptr, pptr);
+        }
+
+        CUBLASASSERT(cublasGemmStridedBatched<float>(mCublas, CUBLAS_OP_N, CUBLAS_OP_N, mHeadSize, mS, mS, 1.f, vptr,
+            mLdQKV, mStrideQKV, pptr, mS, mOmatSize, 0.f, outptr, mLdOut, mStrideOut, mNumMats));
+    }
+}
+
+bool UnfusedMHARunner::isValid() const
+{
+    return mType != DataType::kINT8;
+}
+
+static inline void set_alpha(uint32_t& alpha, float norm, Data_type dtype)
+{
+    if (dtype == DATA_TYPE_FP16)
+    {
+        half2 h2 = __float2half2_rn(norm);
+        alpha = reinterpret_cast<const uint32_t&>(h2);
+    }
+    else if (dtype == DATA_TYPE_FP32)
+    {
+        alpha = reinterpret_cast<const uint32_t&>(norm);
+    }
+    else if (dtype == DATA_TYPE_INT32)
+    {
+        int32_t inorm = static_cast<int32_t>(norm);
+        alpha = reinterpret_cast<const uint32_t&>(inorm);
+    }
+    else
+    {
+        assert(false);
+    }
+}
+
+class FusedMHARunnerFP16::mhaImpl
+{
+public:
+    mhaImpl(FusedMHARunnerFP16* interface)
+        : interface(interface)
+        , sm(interface->mSm)
+        , xmmaKernel(FusedMHAKernelFactory::Get().getXMMAKernels(DATA_TYPE_FP16, sm))
+    {
+        memset(&params, 0, sizeof(params));
+    }
+
+    ~mhaImpl() {}
+
+    size_t getPackedMaskSizeInBytes() const
+    {
+        // check that we initialized
+        assert(xmmas_m > 0);
+        assert(threads_per_cta > 0);
+        assert(interface->mB > 0);
+        return interface->mB * xmmas_m * threads_per_cta * sizeof(uint32_t);
+    }
+
+    void setup(const int S, const int B)
+    {
+        // TODO these implementation details might be better centralized into the XMMA code, since they are needed in
+        // several places (also outside of this plugin)
+        size_t warps_m, warps_n, warps_k = 1;
+        if (S == 128)
+        {
+            warps_m = 2;
+            warps_n = 2;
+        }
+        else if (S == 384)
+        {
+            warps_m = 1;
+            warps_n = 8;
+        }
+        else
+        {
+            assert(false && "Unsupporte seqlen");
+        }
+        // The number of threads per CTA.
+        threads_per_cta = warps_m * warps_n * warps_k * 32;
+        // The number of xmmas in the M dimension. We use one uint32_t per XMMA in the M dimension.
+        xmmas_m = (S + 16 * warps_m - 1) / (16 * warps_m);
+        // The number of xmmas in the N dimension.
+        xmmas_n = (S + 16 * warps_n - 1) / (16 * warps_n);
+
+        const float scale_bmm1 = interface->mRsqrtHeadSize;
+        const float scale_softmax = 1.f; // Seems to be only required for int8
+        const float scale_bmm2 = 1.f;
+
+        Data_type scale_type = DATA_TYPE_FP16;
+        set_alpha(params.scale_bmm1, scale_bmm1, scale_type);
+        set_alpha(params.scale_softmax, scale_softmax, scale_type);
+        set_alpha(params.scale_bmm2, scale_bmm2, scale_type);
+
+        params.b = B;
+        params.h = interface->mNumHeads;
+        params.s = S;
+        params.d = interface->mHeadSize;
+
+        params.qkv_stride_in_bytes = get_size_in_bytes(interface->mLdQKV, DATA_TYPE_FP16);
+        params.packed_mask_stride_in_bytes = xmmas_m * threads_per_cta * sizeof(uint32_t);
+        params.o_stride_in_bytes = get_size_in_bytes(interface->mLdOut, DATA_TYPE_FP16);
+    }
+
+    void run(const PluginTensorDesc& inputDesc, const PluginTensorDesc& outputDesc, const void* qkvPtr,
+        const void* maskPtr, void* output, void* workspace, cudaStream_t stream)
+    {
+        params.qkv_ptr = const_cast<void*>(qkvPtr);
+
+        params.packed_mask_ptr = const_cast<void*>(maskPtr);
+
+        params.o_ptr = output;
+
+        xmmaKernel->run(params, interface->mS, 64, stream);
+
+        CHECK(cudaPeekAtLastError());
+    }
+
+    bool isValid() const
+    {
+        return xmmaKernel->isValid();
+    }
+
+private:
+    FusedMHARunnerFP16* interface;
+    Fused_multihead_attention_params params;
+    int sm;
+    const FusedMultiHeadAttentionXMMAKernel* xmmaKernel;
+    size_t xmmas_m;
+    size_t xmmas_n;
+    size_t threads_per_cta;
+};
+
+FusedMHARunnerFP16::FusedMHARunnerFP16(const int numHeads, const int headSize, const int sm)
+    : MHARunner(DataType::kHALF, numHeads, headSize)
+    , mSm(sm)
+    , pimpl(new mhaImpl(this))
+{
+}
+
+void FusedMHARunnerFP16::setup(const int S, const int B)
+{
+    MHARunner::setup(S, B);
+    pimpl->setup(S, B);
+}
+
+size_t FusedMHARunnerFP16::getWorkspaceSize() const
+{
+    return 0;
+}
+
+void FusedMHARunnerFP16::deserialize(const void* data, size_t length)
+{
+    MHARunner::deserialize(data, length);
+    setup(mS, mB);
+}
+
+void FusedMHARunnerFP16::run(const PluginTensorDesc& inputDesc, const PluginTensorDesc& outputDesc, const void* qkvPtr,
+    const void* maskPtr, void* output, void* workspace, cudaStream_t stream)
+{
+    pimpl->run(inputDesc, outputDesc, qkvPtr, maskPtr, output, workspace, stream);
+}
+
+bool FusedMHARunnerFP16::isValid() const
+{
+    return pimpl->isValid();
+}
+
+// Int8 starts here: TODO refactor the duplicate stuff
+
+class FusedMHARunnerInt8::mhaImpl
+{
+
+public:
+    mhaImpl(FusedMHARunnerInt8* interface)
+        : interface(interface)
+        , sm(interface->mSm)
+        , xmmaKernel(FusedMHAKernelFactory::Get().getXMMAKernels(DATA_TYPE_INT8, sm))
+        , mDqProbs(interface->mDqProbs)
+    {
+        memset(&params, 0, sizeof(params));
+
+    }
+
+    ~mhaImpl() {}
+
+    size_t getPackedMaskSizeInBytes() const
+    {
+        assert(xmmas_m > 0);
+        assert(threads_per_cta > 0);
+        assert(interface->mB > 0);
+        return interface->mB * xmmas_m * threads_per_cta * sizeof(uint32_t);
+    }
+
+    void setup(const int S, const int B)
+    {
+        size_t warps_m, warps_n, warps_k = 1;
+        if (S == 128)
+        {
+            warps_m = 2;
+            warps_n = 2;
+        }
+        else if (S == 384)
+        {
+            warps_m = 1;
+            warps_n = 8;
+        }
+        else
+        {
+            assert(false && "Unsupporte seqlen");
+        }
+        // The number of threads per CTA.
+        threads_per_cta = warps_m * warps_n * warps_k * 32;
+        // The number of xmmas in the M dimension. We use one uint32_t per XMMA in the M dimension.
+        xmmas_m = (S + 16 * warps_m - 1) / (16 * warps_m);
+        // The number of xmmas in the N dimension.
+        xmmas_n = (S + 16 * warps_n - 1) / (16 * warps_n);
+
+
+        params.b = B;
+        params.h = interface->mNumHeads;
+        params.s = S;
+        params.d = interface->mHeadSize;
+
+        params.qkv_stride_in_bytes = get_size_in_bytes(interface->mLdQKV, DATA_TYPE_INT8);
+        params.packed_mask_stride_in_bytes = xmmas_m * threads_per_cta * sizeof(uint32_t);
+        params.o_stride_in_bytes = get_size_in_bytes(interface->mLdOut, DATA_TYPE_INT8);
+    }
+
+    void run(const PluginTensorDesc& inputDesc, const PluginTensorDesc& outputDesc, const void* qkvPtr,
+        const void* maskPtr, void* output, void* workspace, cudaStream_t stream)
+    {
+        float scaleQkv = inputDesc.scale;
+        float scaleCtx = outputDesc.scale;
+
+        float scaleBmm1 = scaleQkv * scaleQkv * interface->mRsqrtHeadSize;
+        float scaleBmm2 = mDqProbs * scaleQkv / scaleCtx;
+        float scaleSoftmax = 1.f / mDqProbs;
+
+        params.scale_bmm1 = reinterpret_cast<const uint32_t&>(scaleBmm1);
+        params.scale_bmm2 = reinterpret_cast<const uint32_t&>(scaleBmm2);
+        params.scale_softmax = reinterpret_cast<const uint32_t&>(scaleSoftmax);
+
+        params.enable_i2f_trick = -double(1 << 22) * double(scaleBmm2) <= -128.f
+            && double(1 << 22) * double(scaleBmm2) >= 127.f;
+
+        params.qkv_ptr = const_cast<void*>(qkvPtr);
+
+        params.packed_mask_ptr = const_cast<void*>(maskPtr);
+
+        params.o_ptr = output;
+
+        xmmaKernel->run(params, interface->mS, 64, stream);
+        CHECK(cudaPeekAtLastError());
+
+    }
+
+    bool isValid() const
+    {
+        return xmmaKernel->isValid();
+    }
+
+private:
+
+    float mDqProbs;
+    FusedMHARunnerInt8* interface;
+    Fused_multihead_attention_params params;
+    int sm;
+    const FusedMultiHeadAttentionXMMAKernel* xmmaKernel;
+    size_t xmmas_m;
+    size_t xmmas_n;
+    size_t threads_per_cta;
+};
+
+FusedMHARunnerInt8::FusedMHARunnerInt8(const int numHeads, const int headSize, const int sm, const float dqProbs)
+    : MHARunner(DataType::kINT8, numHeads, headSize)
+    , mSm(sm)
+    , pimpl(new mhaImpl(this))
+    , mDqProbs(dqProbs)
+{
+}
+
+void FusedMHARunnerInt8::setup(const int S, const int B)
+{
+    MHARunner::setup(S, B);
+    pimpl->setup(S, B);
+}
+
+size_t FusedMHARunnerInt8::getWorkspaceSize() const
+{
+    return 0;
+}
+
+void FusedMHARunnerInt8::deserialize(const void* data, size_t length)
+{
+    MHARunner::deserialize(data, length);
+    setup(mS, mB);
+}
+
+void FusedMHARunnerInt8::run(const PluginTensorDesc& inputDesc, const PluginTensorDesc& outputDesc, const void* qkvPtr,
+    const void* maskPtr, void* output, void* workspace, cudaStream_t stream)
+{
+    pimpl->run(inputDesc, outputDesc,qkvPtr, maskPtr, output, workspace, stream);
+}
+
+bool FusedMHARunnerInt8::isValid() const
+{
+    return pimpl->isValid();
+}
+
+} // namespace bert
+
+#endif // CUDA_VERSION >= 10010
diff --git a/plugin/bertQKVToContextPlugin/qkvToContextPlugin.cpp b/plugin/bertQKVToContextPlugin/qkvToContextPlugin.cpp
new file mode 100644
index 00000000..59004852
--- /dev/null
+++ b/plugin/bertQKVToContextPlugin/qkvToContextPlugin.cpp
@@ -0,0 +1,479 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// Need 10.1 for cublasGemmStridedBatchedEx
+#include <cuda.h>
+#if CUDA_VERSION >= 10010
+
+#include "NvInfer.h"
+#include "bertCommon.h"
+#include "fused_multihead_attention.h"
+#include "qkvToContextPlugin.h"
+#include "serialize.hpp"
+
+#include <cassert>
+#include <cstring>
+#include <iostream>
+#include <tuple>
+#include <vector>
+
+using namespace nvinfer1;
+
+namespace bert
+{
+
+namespace
+{
+static const char* QKV_TO_CONTEXT_PLUGIN_VERSION{"1"};
+static const char* QKV_TO_CONTEXT_PLUGIN_NAME{"CustomQKVToContextPluginDynamic"};
+} // namespace
+
+// Static class fields initialization
+PluginFieldCollection QKVToContextPluginDynamicCreator::mFC{};
+std::vector<PluginField> QKVToContextPluginDynamicCreator::mPluginAttributes;
+
+REGISTER_TENSORRT_PLUGIN(QKVToContextPluginDynamicCreator);
+
+constexpr size_t kAlignment = 256;
+constexpr uint32_t IIDX = 0; // index of the input tensor
+constexpr uint32_t MIDX = 1; // index of the mask
+
+QKVToContextPluginDynamic::QKVToContextPluginDynamic(const std::string name, const DataType type, const int hiddenSize,
+    const int numHeads, const float dqProbs, bool hasImask)
+    : mLayerName(name)
+    , mS(0)
+    , mB(0)
+    , mHeadSize(hiddenSize / numHeads)
+    , mHiddenSize(hiddenSize)
+    , mNumHeads(numHeads)
+    , mHasImask(hasImask)
+    , mType(type)
+    , mDqProbs(dqProbs)
+
+{
+    mSM = getSMVersion();
+}
+
+QKVToContextPluginDynamic::QKVToContextPluginDynamic(const std::string name, const void* data, size_t length)
+    : mLayerName(name)
+{
+    gLogVerbose << "QKV Deser Start" << std::endl;
+    deserialize_value(&data, &length, &mType);
+    deserialize_value(&data, &length, &mNumHeads);
+    deserialize_value(&data, &length, &mHeadSize);
+    deserialize_value(&data, &length, &mHasImask);
+    deserialize_value(&data, &length, &mHiddenSize);
+    deserialize_value(&data, &length, &mSM);
+    deserialize_value(&data, &length, &mS);
+    deserialize_value(&data, &length, &mB);
+
+    deserialize_value(&data, &length, &mDqProbs);
+
+    createMHARunner();
+    dispatcher->deserialize(data, length);
+
+    gLogVerbose << "QKV Deser done" << std::endl;
+}
+
+int QKVToContextPluginDynamic::getSMVersion() const
+{
+    int device{-1};
+    CHECK(cudaGetDevice(&device));
+    cudaDeviceProp props;
+    CHECK(cudaGetDeviceProperties(&props, device));
+    return props.major * 10 + props.minor;
+}
+
+void QKVToContextPluginDynamic::createMHARunner()
+{
+    assert(getSMVersion() == mSM);
+
+    if (mType == DataType::kHALF)
+    {
+        dispatcher.reset(new FusedMHARunnerFP16(mNumHeads, mHeadSize, mSM));
+    }
+    else if (mType == DataType::kINT8)
+    {
+        dispatcher.reset(new FusedMHARunnerInt8(mNumHeads, mHeadSize, mSM, mDqProbs));
+    }
+    if (!dispatcher || !dispatcher->isValid())
+    {
+        dispatcher.reset(new UnfusedMHARunner(mType, mNumHeads, mHeadSize));
+    }
+}
+
+// IPluginV2DynamicExt Methods
+nvinfer1::IPluginV2DynamicExt* QKVToContextPluginDynamic::clone() const
+{
+    gLogVerbose << "QKV Clone" << std::endl;
+
+    QKVToContextPluginDynamic* ret = nullptr;
+    if (dispatcher.get())
+    {
+        std::vector<char> buff;
+        buff.resize(getSerializationSize());
+        serialize(buff.data());
+
+        ret = new QKVToContextPluginDynamic(mLayerName, buff.data(), buff.size());
+    }
+    else
+    {
+        ret = new QKVToContextPluginDynamic(mLayerName, mType, mHiddenSize, mNumHeads, mDqProbs, mHasImask);
+    }
+
+    ret->setPluginNamespace(mNamespace.c_str());
+    gLogVerbose << "QKV Clone done" << std::endl;
+    return ret;
+}
+
+DimsExprs QKVToContextPluginDynamic::getOutputDimensions(
+    int outputIndex, const DimsExprs* inputs, int nbInputs, IExprBuilder& exprBuilder)
+{
+    // Input is BxSx3*N*H, output should be BxSxN*H
+    assert(outputIndex == 0);
+    // Copy over everything
+    DimsExprs output(inputs[IIDX]);
+    // Divide last dim by three
+    auto three = exprBuilder.constant(3);
+    output.d[HDIM] = exprBuilder.operation(DimensionOperation::kFLOOR_DIV, *inputs[IIDX].d[HDIM], *three);
+    return output;
+}
+bool QKVToContextPluginDynamic::supportsFormatCombination(
+    int pos, const PluginTensorDesc* inOut, int nbInputs, int nbOutputs)
+{
+
+    // TODO cleanup
+    // TODO add CHW32
+    assert(pos >= 0);
+    assert(pos < 2 + mHasImask);
+    assert(nbInputs == 1 + mHasImask);
+    const auto* in = inOut;
+    const auto* out = inOut + nbInputs;
+
+    // we only support int8 IO in fused mha runner, and we only support fused mha runner on Turing and Ampere
+    if (mType == DataType::kINT8 && mSM != bert::kSM_AMPERE && mSM != bert::kSM_TURING)
+    {
+        gLogVerbose << "INT8 IO is only supported on Turing and Ampere for plugin " << QKV_TO_CONTEXT_PLUGIN_NAME
+                    << std::endl;
+        return false;
+    }
+
+    if (pos == 0)
+    {
+        bool isFormatSupported = in->format == TensorFormat::kLINEAR;
+        if (mType == DataType::kINT8)
+        {
+            if (in->dims.d[HDIM] % 32 == 0)
+            {
+                isFormatSupported = in->format == TensorFormat::kCHW32;
+            }
+            else
+            {
+                isFormatSupported = in->format == TensorFormat::kCHW4;
+            }
+        }
+
+        // must not check descriptions > pos
+        return (in->type == mType) &&        // precision
+            isFormatSupported &&             // format
+            (in->dims.nbDims == 5) &&        // num dims
+            ((in->dims.d[HDIM] % 3) == 0) && // see getOutputDimensions
+            ((in->dims.d[3]) == 1) &&        // for fc
+            ((in->dims.d[4]) == 1)           // for fc
+            ;
+    }
+    else
+    {                                // pos==1
+        if ((mHasImask && pos == 1)) // pos 1 is the mask
+        {
+            const auto* inMask = &inOut[1];
+            // detect full mask and check that it was produced
+            const bool useFullMask = (mType == DataType::kHALF || mType == DataType::kINT8)
+                && (in->dims.d[SDIM] == 128 || in->dims.d[SDIM] == 384);
+
+            if (useFullMask)
+            {
+                // return inOut[pos].type == DataType::kHALF && inOut[pos].format == TensorFormat::kLINEAR;
+                return (inMask->type == DataType::kHALF) &&      // precision
+                    (inMask->format == TensorFormat::kLINEAR) && // format
+                    (inMask->dims.nbDims == 2) &&                // Bx2*maskSize
+                    ((inMask->dims.d[0]) == in->dims.d[BDIM])
+                    // TODO && ((inMask->dims.d[0]) == 2*mask size)
+                    ;
+            }
+            return (inMask->type == DataType::kINT32) &&     // precision
+                (inMask->format == TensorFormat::kLINEAR) && // format
+                (inMask->dims.nbDims == 1) &&                // num dims
+                ((inMask->dims.d[0]) == in->dims.d[BDIM])    // check B
+                ;
+        }
+
+        if (!mHasImask || pos == 2) // output pos
+        {
+            bool isFormatSupported = out->format == TensorFormat::kLINEAR;
+            if (mType == DataType::kINT8)
+            {
+                if (out->dims.d[HDIM] % 32 == 0)
+                {
+                    isFormatSupported = out->format == TensorFormat::kCHW32;
+                }
+                else
+                {
+                    isFormatSupported = out->format == TensorFormat::kCHW4;
+                }
+            }
+
+            return (in->type == out->type) &&                      // precision
+                isFormatSupported &&                               // format
+                (out->dims.nbDims == 5) &&                         // num dims
+                ((in->dims.d[HDIM] / 3) == (out->dims.d[HDIM])) && // div 3
+                ((out->dims.d[3]) == 1) &&                         // for fc
+                ((out->dims.d[4]) == 1) &&                         // for fc
+                ((out->dims.d[BDIM]) == in->dims.d[BDIM]) &&       // check B
+                ((out->dims.d[SDIM]) == in->dims.d[SDIM])          // check S
+                ;
+        }
+    }
+    return false;
+}
+void QKVToContextPluginDynamic::configurePlugin(
+    const DynamicPluginTensorDesc* in, int nbInputs, const DynamicPluginTensorDesc* out, int nbOutputs)
+{
+    assert(nbInputs == 1 + mHasImask);
+    assert(nbOutputs == 1);
+    const PluginTensorDesc& inDesc = in[IIDX].desc;
+    TRT_UNUSED inDesc;
+    const PluginTensorDesc& outDesc = out->desc;
+    TRT_UNUSED outDesc;
+    assert(mType == inDesc.type);
+    assert(mType == outDesc.type);
+    assert(inDesc.dims.d[BDIM] == outDesc.dims.d[BDIM]);
+    assert(inDesc.dims.d[SDIM] == outDesc.dims.d[SDIM]);
+    assert(inDesc.dims.d[HDIM] == 3 * outDesc.dims.d[HDIM]);
+    if (mHasImask)
+    {
+        const PluginTensorDesc& maskDesc = in[MIDX].desc;
+        TRT_UNUSED maskDesc;
+        assert(maskDesc.dims.d[0] == inDesc.dims.d[BDIM]);
+    }
+
+    const int S = inDesc.dims.d[SDIM] <= 0 ? in->max.d[SDIM] : inDesc.dims.d[SDIM];
+    const int B = inDesc.dims.d[BDIM] <= 0 ? in->max.d[BDIM] : inDesc.dims.d[BDIM];
+
+    if (S != mS || B != mB)
+    {
+        createMHARunner();
+        this->dispatcher->setup(S, B);
+        mS = S;
+        mB = B;
+    }
+}
+
+size_t QKVToContextPluginDynamic::getWorkspaceSize(
+    const PluginTensorDesc* inputs, int nbInputs, const PluginTensorDesc* outputs, int nbOutputs) const
+{
+    return this->dispatcher->getWorkspaceSize();
+}
+
+// IPluginV2Ext Methods
+DataType QKVToContextPluginDynamic::getOutputDataType(
+    int index, const nvinfer1::DataType* inputTypes, int nbInputs) const
+{
+    assert(index == 0);
+    assert(inputTypes[0] == DataType::kFLOAT || inputTypes[0] == DataType::kHALF || inputTypes[0] == DataType::kINT8);
+    return inputTypes[0];
+}
+
+// IPluginV2 Methods
+const char* QKVToContextPluginDynamic::getPluginType() const
+{
+    return QKV_TO_CONTEXT_PLUGIN_NAME;
+}
+
+const char* QKVToContextPluginDynamic::getPluginVersion() const
+{
+    return QKV_TO_CONTEXT_PLUGIN_VERSION;
+}
+
+int QKVToContextPluginDynamic::getNbOutputs() const
+{
+    return 1;
+}
+
+int QKVToContextPluginDynamic::initialize()
+{
+    return 0;
+}
+
+void QKVToContextPluginDynamic::terminate() {}
+
+size_t QKVToContextPluginDynamic::getSerializationSize() const
+{
+    return sizeof(mNumHeads) + sizeof(mHeadSize) + sizeof(DataType) + sizeof(mHasImask) + sizeof(mHiddenSize)
+        + sizeof(mSM) + sizeof(mS) + sizeof(mB) + sizeof(mDqProbs) + dispatcher->getSerializationSize();
+}
+
+void QKVToContextPluginDynamic::serialize(void* buffer) const
+{
+    serialize_value(&buffer, mType);
+    serialize_value(&buffer, mNumHeads);
+    serialize_value(&buffer, mHeadSize);
+    serialize_value(&buffer, mHasImask);
+    serialize_value(&buffer, mHiddenSize);
+    serialize_value(&buffer, mSM);
+    serialize_value(&buffer, mS);
+    serialize_value(&buffer, mB);
+
+    serialize_value(&buffer, mDqProbs);
+    dispatcher->serialize(buffer);
+}
+
+void QKVToContextPluginDynamic::destroy()
+{
+    delete this;
+}
+
+void QKVToContextPluginDynamic::setPluginNamespace(const char* libNamespace)
+{
+    mNamespace = libNamespace;
+}
+
+const char* QKVToContextPluginDynamic::getPluginNamespace() const
+{
+    return mNamespace.c_str();
+}
+
+int QKVToContextPluginDynamic::enqueue(const PluginTensorDesc* inputDesc, const PluginTensorDesc* outputDesc,
+    const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream)
+{
+    assert(mS == inputDesc->dims.d[SDIM]);
+    assert(mB == inputDesc->dims.d[BDIM]);
+
+    const void* maskPtr = mHasImask ? inputs[1] : nullptr;
+    this->dispatcher->run(inputDesc[0], outputDesc[0], inputs[0], maskPtr, outputs[0], workspace, stream);
+    return 0;
+}
+
+QKVToContextPluginDynamicCreator::QKVToContextPluginDynamicCreator()
+{
+    mFC.nbFields = mPluginAttributes.size();
+    mFC.fields = mPluginAttributes.data();
+}
+
+const char* QKVToContextPluginDynamicCreator::getPluginName() const
+{
+    return QKV_TO_CONTEXT_PLUGIN_NAME;
+}
+
+const char* QKVToContextPluginDynamicCreator::getPluginVersion() const
+{
+    return QKV_TO_CONTEXT_PLUGIN_VERSION;
+}
+
+const PluginFieldCollection* QKVToContextPluginDynamicCreator::getFieldNames()
+{
+    return &mFC;
+}
+
+IPluginV2* QKVToContextPluginDynamicCreator::createPlugin(const char* name, const PluginFieldCollection* fc)
+{
+    gLogVerbose << "Creating QKV2ContextPlugin...\n";
+
+    int hiddenSize = 0;
+    int numHeads = 0;
+    bool hasMask = false;
+    int typeId = -1;
+
+    float dqProbs = -1;
+
+    for (int i = 0; i < fc->nbFields; i++)
+    {
+        std::string field_name(fc->fields[i].name);
+
+        if (field_name.compare("type_id") == 0)
+        {
+            typeId = *static_cast<const int*>(fc->fields[i].data);
+            gLogVerbose << "Building typeId: " << typeId << std::endl;
+        }
+        if (field_name.compare("hidden_size") == 0)
+        {
+            hiddenSize = *static_cast<const int*>(fc->fields[i].data);
+            gLogVerbose << "Building hiddenSize: " << hiddenSize << std::endl;
+        }
+        if (field_name.compare("num_heads") == 0)
+        {
+            numHeads = *static_cast<const int*>(fc->fields[i].data);
+            gLogVerbose << "Building numHeads: " << numHeads << std::endl;
+        }
+        if (field_name.compare("has_mask") == 0)
+        {
+            hasMask = *static_cast<const bool*>(fc->fields[i].data);
+            gLogVerbose << "Building hasMask: " << hasMask << std::endl;
+        }
+
+        if (field_name.compare("dq_probs") == 0)
+        {
+            dqProbs = *static_cast<const float*>(fc->fields[i].data);
+            gLogVerbose << "Building dqProbs: " << dqProbs << std::endl;
+        }
+    }
+    if (typeId < 0 || typeId > 3)
+    {
+        gLogError << "QKV: Invalid TypeId " << typeId << std::endl;
+    }
+
+    if (hiddenSize <= 0)
+    {
+        gLogError << "QKV: Invalid hiddenSize " << hiddenSize << std::endl;
+    }
+
+    if (numHeads <= 0)
+    {
+        gLogError << "QKV: Invalid numHeads " << numHeads << std::endl;
+    }
+
+    gLogVerbose << "Building the Plugin...\n";
+    DataType type = static_cast<DataType>(typeId);
+    if (type == DataType::kINT8 && dqProbs < 0)
+    {
+        gLogInfo << "Using default scale factor\n";
+        dqProbs = 1.f / 127.f;
+    }
+
+    QKVToContextPluginDynamic* p = new QKVToContextPluginDynamic(name, type, hiddenSize, numHeads, dqProbs, hasMask);
+    return p;
+}
+
+IPluginV2* QKVToContextPluginDynamicCreator::deserializePlugin(
+    const char* name, const void* serialData, size_t serialLength)
+{
+    // This object will be deleted when the network is destroyed, which will
+    // call QKVToContextPluginDynamic::destroy()
+    return new QKVToContextPluginDynamic(name, serialData, serialLength);
+}
+
+void QKVToContextPluginDynamicCreator::setPluginNamespace(const char* libNamespace)
+{
+    mNamespace = libNamespace;
+}
+
+const char* QKVToContextPluginDynamicCreator::getPluginNamespace() const
+{
+    return mNamespace.c_str();
+}
+} // namespace bert
+
+#endif // CUDA_VERSION >= 10010
diff --git a/plugin/bertQKVToContextPlugin/qkvToContextPlugin.cu b/plugin/bertQKVToContextPlugin/qkvToContextPlugin.cu
deleted file mode 100644
index d97f9f52..00000000
--- a/plugin/bertQKVToContextPlugin/qkvToContextPlugin.cu
+++ /dev/null
@@ -1,822 +0,0 @@
-/*
- * Copyright (c) 2019, NVIDIA CORPORATION.  All rights reserved.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "NvInfer.h"
-#include "bertCommon.h"
-#include "common.h"
-#include "qkvToContextPlugin.h"
-#include "serialize.hpp"
-
-#include <cassert>
-#include <cstring>
-#include <iostream>
-#include <vector>
-
-using namespace nvinfer1;
-
-namespace bert
-{
-
-template <typename T, int TPB, int VPT>
-__global__ void maskedSoftmax(const float rsqrtHeadSize, const T* input, T* output, const int* maskIdx)
-{
-    using BlockReduce = cub::BlockReduce<float, TPB>;
-    __shared__ union
-    {
-        T shm[VPT * TPB];
-        typename BlockReduce::TempStorage reduce;
-    } tmp;
-
-    // grid: (NxS, B)
-    const int b = blockIdx.y;
-    const int blockOffset = (b * gridDim.x + blockIdx.x) * TPB;
-    __shared__ int lastValid;
-    if (threadIdx.x == 0)
-    {
-        lastValid = min(TPB, maskIdx[b]);
-    }
-    __syncthreads();
-    float local[VPT];
-
-    __shared__ float rZ;
-
-    const int idx = (blockOffset + threadIdx.x) * VPT;
-    T* myshm = &tmp.shm[threadIdx.x * VPT];
-    copy<sizeof(T) * VPT>(&input[idx], myshm);
-
-    __syncthreads();
-
-#pragma unroll
-    for (int it = 0; it < VPT; it++)
-    {
-        local[it]
-            = (threadIdx.x < lastValid) ? myExp<float>((rsqrtHeadSize) * float(tmp.shm[it * TPB + threadIdx.x])) : 0.f;
-    }
-    __syncthreads();
-
-#pragma unroll
-    for (int it = 0; it < VPT; it++)
-    {
-
-        const auto Z = BlockReduce(tmp.reduce).Reduce(local[it], cub::Sum());
-
-        if (threadIdx.x == 0)
-        {
-            rZ = (1.f) / Z;
-        }
-        __syncthreads();
-        local[it] *= rZ;
-    }
-
-#pragma unroll
-    for (int it = 0; it < VPT; it++)
-    {
-        tmp.shm[it * TPB + threadIdx.x] = local[it];
-    }
-    __syncthreads();
-    copy<sizeof(T) * VPT>(myshm, &output[idx]);
-}
-
-template <typename T, int TPB, int VPT>
-__global__ void softmax(const float rsqrtHeadSize, const T* input, T* output)
-{
-    float local[VPT];
-
-    using BlockReduce = cub::BlockReduce<float, TPB>;
-
-    __shared__ union
-    {
-        T shm[VPT * TPB];
-        typename BlockReduce::TempStorage reduce;
-    } tmp;
-
-    __shared__ float rZ;
-
-    const int idx = (TPB * blockIdx.x + threadIdx.x) * VPT;
-    T* myshm = &tmp.shm[threadIdx.x * VPT];
-    copy<sizeof(T) * VPT>(&input[idx], myshm);
-
-    __syncthreads();
-
-#pragma unroll
-    for (int it = 0; it < VPT; it++)
-    {
-        local[it] = myExp<float>(rsqrtHeadSize * float(tmp.shm[it * TPB + threadIdx.x]));
-    }
-    __syncthreads();
-
-#pragma unroll
-    for (int it = 0; it < VPT; it++)
-    {
-
-        const auto Z = BlockReduce(tmp.reduce).Reduce(local[it], cub::Sum());
-
-        if (threadIdx.x == 0)
-        {
-            rZ = 1.f / Z;
-        }
-        __syncthreads();
-        local[it] *= rZ;
-    }
-
-#pragma unroll
-    for (int it = 0; it < VPT; it++)
-    {
-        tmp.shm[it * TPB + threadIdx.x] = local[it];
-    }
-    __syncthreads();
-    copy<sizeof(T) * VPT>(myshm, &output[idx]);
-}
-
-template <typename T, unsigned TPB>
-__global__ void scaledSoftmaxKernelSmall(const int ld, const float rsqrtHeadSize, const T* input, T* output)
-{
-    scaledSoftmaxSmall<T, TPB>(ld, ld, rsqrtHeadSize, input, output);
-}
-
-template <typename T, unsigned TPB>
-__global__ void scaledSoftmaxKernel(const int ld, const float rsqrtHeadSize, const T* input, T* output)
-{
-    scaledSoftmax<T, TPB>(ld, ld, rsqrtHeadSize, input, output);
-}
-
-template <typename T>
-int computeScaledSoftmax(
-    cudaStream_t stream, const int ld, const int B, const int N, const float rsqrtHeadSize, const T* input, T* output)
-{
-
-    constexpr int VPT = 16 / sizeof(T);
-
-    const dim3 grid(ld * N, B, 1);
-
-    if (ld <= 32)
-    {
-        const int blockSize = 32;
-        scaledSoftmaxKernelSmall<T, blockSize><<<grid, blockSize, 0, stream>>>(ld, rsqrtHeadSize, input, output);
-    }
-    else if (ld < 128)
-    {
-        const int blockSize = 128;
-        scaledSoftmaxKernelSmall<T, blockSize><<<grid, blockSize, 0, stream>>>(ld, rsqrtHeadSize, input, output);
-    }
-    else if (ld == 128)
-    {
-        const int grid = B * N * ld / (VPT);
-        softmax<T, 128, VPT><<<grid, 128, 0, stream>>>(rsqrtHeadSize, input, output);
-    }
-
-    else if (ld == 384)
-    {
-
-        const int grid = B * N * ld / (VPT);
-        softmax<T, 384, VPT><<<grid, 384, 0, stream>>>(rsqrtHeadSize, input, output);
-    }
-    else
-    {
-        const int blockSize = 256;
-
-        scaledSoftmaxKernel<T, blockSize><<<grid, blockSize, 0, stream>>>(ld, rsqrtHeadSize, input, output);
-    }
-
-    CHECK(cudaPeekAtLastError());
-    return 0;
-}
-
-template <typename T, unsigned TPB>
-__global__ void maskedScaledSoftmaxKernelSmall(
-    const int ld, const float rsqrtHeadSize, const int* maskIdx, const T* input, T* output)
-{
-    __shared__ int lastValid;
-
-    if (threadIdx.x == 0)
-    {
-        lastValid = min(ld, maskIdx[blockIdx.y]);
-    }
-    __syncthreads();
-
-    scaledSoftmaxSmall<T, TPB>(ld, lastValid, rsqrtHeadSize, input, output);
-}
-
-template <typename T, unsigned TPB>
-__global__ void maskedScaledSoftmaxKernel(
-    const int ld, const float rsqrtHeadSize, const int* maskIdx, const T* input, T* output)
-{
-
-    __shared__ int lastValid;
-
-    if (threadIdx.x == 0)
-    {
-        lastValid = min(ld, maskIdx[blockIdx.y]);
-    }
-    __syncthreads();
-    scaledSoftmax<T, TPB>(ld, lastValid, rsqrtHeadSize, input, output);
-}
-
-template <typename T>
-int computeMaskedScaledSoftmax(cudaStream_t stream, const int ld, const int B, const int N, const float rsqrtHeadSize,
-    const int* maskIdx, const T* input, T* output)
-{
-    // Mask idx is of length B and assumes the valid region is contiguous starting
-    // from the beginning of the sequence
-
-    const dim3 grid(ld * N, B, 1);
-    // for smaller problems, e.g. BERT base B=1, this is not optimal
-    if (ld <= 32)
-    {
-        constexpr int blockSize = 32;
-        maskedScaledSoftmaxKernelSmall<T, blockSize>
-            <<<grid, blockSize, 0, stream>>>(ld, rsqrtHeadSize, maskIdx, input, output);
-    }
-    else if (ld < 128)
-    {
-        constexpr int blockSize = 128;
-        maskedScaledSoftmaxKernelSmall<T, blockSize>
-            <<<grid, blockSize, 0, stream>>>(ld, rsqrtHeadSize, maskIdx, input, output);
-    }
-    else if (ld == 128)
-    {
-        if (B == 1)
-        {
-            constexpr int VPT = 4 / sizeof(T);
-            constexpr int blockSize = 128;
-            const dim3 grid(ld * N / VPT, B, 1);
-            maskedSoftmax<T, blockSize, VPT><<<grid, blockSize, 0, stream>>>(rsqrtHeadSize, input, output, maskIdx);
-        }
-        else
-        {
-            constexpr int VPT = 16 / sizeof(T);
-            constexpr int blockSize = 128;
-            const dim3 grid(ld * N / VPT, B, 1);
-            maskedSoftmax<T, blockSize, VPT><<<grid, blockSize, 0, stream>>>(rsqrtHeadSize, input, output, maskIdx);
-        }
-    }
-    else if (ld == 384)
-    {
-        if (B == 1)
-        {
-            constexpr int VPT = 4 / sizeof(T);
-            constexpr int blockSize = 384;
-            const dim3 grid(ld * N / VPT, B, 1);
-            maskedSoftmax<T, blockSize, VPT><<<grid, blockSize, 0, stream>>>(rsqrtHeadSize, input, output, maskIdx);
-        }
-        else
-        {
-            constexpr int VPT = 16 / sizeof(T);
-            constexpr int blockSize = 384;
-            const dim3 grid(ld * N / VPT, B, 1);
-            maskedSoftmax<T, blockSize, VPT><<<grid, blockSize, 0, stream>>>(rsqrtHeadSize, input, output, maskIdx);
-        }
-    }
-    else
-    {
-        constexpr int blockSize = 256;
-        maskedScaledSoftmaxKernel<T, blockSize>
-            <<<grid, blockSize, 0, stream>>>(ld, rsqrtHeadSize, maskIdx, input, output);
-    }
-
-    CHECK(cudaPeekAtLastError());
-    return 0;
-}
-
-std::pair<int, int> tuneBatchedGemm(const int B, const int S, const int numHeads, const int headSize)
-{
-    const int nruns = 500;
-    cublasHandle_t cublas;
-    cublasCreate(&cublas);
-    cudaStream_t stream;
-    cudaStreamCreate(&stream);
-    cudaEvent_t start, stop;
-    cudaEventCreate(&start);
-    cudaEventCreate(&stop);
-    cublasSetStream(cublas, stream);
-    cublasSetMathMode(cublas, CUBLAS_TENSOR_OP_MATH);
-
-    using T = half;
-    const int omatSize = S * S;
-    const int numMats = B * numHeads;
-    const int ldQKV = 3 * B * numHeads * headSize;
-    const int strideQKV = 3 * headSize;
-    const int ldOut = B * numHeads * headSize;
-    const int strideOut = headSize;
-
-    const size_t inBytes = S * B * 3 * numHeads * headSize * sizeof(T);
-    const size_t qkBytes = S * S * B * numHeads * sizeof(T);
-    const size_t outBytes = S * B * numHeads * headSize * sizeof(T);
-
-    T* input = nullptr;
-    T* qkptr = nullptr;
-    T* output = nullptr;
-    cudaMalloc(&input, inBytes);
-    cudaMalloc(&qkptr, qkBytes);
-    cudaMalloc(&output, outBytes);
-    cudaMemset(input, 1, inBytes);
-    cudaMemset(qkptr, 1, qkBytes);
-
-    // input: SxBx3xNxH
-    const T* qptr = input;
-    const T* kptr = qptr + headSize;
-    const T* vptr = kptr + headSize;
-
-    const int startAlgo = (int) CUBLAS_GEMM_DEFAULT_TENSOR_OP;
-    const int endAlgo = (int) CUBLAS_GEMM_ALGO15_TENSOR_OP;
-    int best1 = startAlgo;
-    int best2 = startAlgo;
-    float ms1 = 1000000;
-    float ms2 = 1000000;
-    for (int a = startAlgo; a <= endAlgo; a++)
-    {
-        cublasGemmAlgo_t algo = static_cast<cublasGemmAlgo_t>(a);
-        float ms1_, ms2_;
-        // qkptr: BxNxSxS
-        cudaEventRecord(start, stream);
-        for (int r = 0; r < nruns; r++)
-        {
-            CHECK(cublasGemmStridedBatchedEx<T>(cublas, CUBLAS_OP_T, CUBLAS_OP_N, S, S, headSize, T(1.f), kptr, ldQKV,
-                strideQKV, qptr, ldQKV, strideQKV, T(0.f), qkptr, S, omatSize, numMats, algo));
-        }
-
-        cudaEventRecord(stop, stream);
-        cudaStreamSynchronize(stream);
-        cudaEventElapsedTime(&ms1_, start, stop);
-        if (ms1_ < ms1)
-        {
-            best1 = algo;
-            ms1 = ms1_;
-        }
-
-        // pptr: BxNxSxS
-        // output: SxBxNxH
-        cudaEventRecord(start, stream);
-        for (int r = 0; r < nruns; r++)
-        {
-            CHECK(cublasGemmStridedBatchedEx<T>(cublas, CUBLAS_OP_N, CUBLAS_OP_N, headSize, S, S, 1.f, vptr, ldQKV,
-                strideQKV, qkptr, S, omatSize, 0.f, output, ldOut, strideOut, numMats, algo));
-        }
-
-        cudaEventRecord(stop, stream);
-        cudaStreamSynchronize(stream);
-        cudaEventElapsedTime(&ms2_, start, stop);
-
-        if (ms2_ < ms2)
-        {
-            best2 = algo;
-            ms2 = ms2_;
-        }
-    }
-
-    cudaFree(input);
-    cudaFree(qkptr);
-    cudaFree(output);
-    cudaEventDestroy(start);
-    cudaEventDestroy(stop);
-    cudaStreamDestroy(stream);
-    cublasDestroy(cublas);
-    return std::make_pair(best1, best2);
-}
-
-template <typename T>
-int QKVToContextPluginDynamic::qkvToCtx(cublasHandle_t& cublas, const int B, const int S, const int numHeads,
-    const int headSize, const float rsqrtHeadSize, const T* input, T* output, T* qkptr, T* pptr, cudaStream_t stream,
-    const int* maskIdx)
-{
-
-    const int omatSize = S * S;
-    const int numMats = B * numHeads;
-    const T* qptr = input;
-    const T* kptr = qptr + headSize;
-    const T* vptr = kptr + headSize;
-
-    cublasSetStream(cublas, stream);
-    CublasConfigHelper helper(cublas);
-
-    // Q, K, V: BxNxSxH (inputs)
-    // Q * K': BxNxSxS (-> scratch1)
-    // P: BxNxSxS (-> scratch2)
-    // P * V: BxNxSxH (output)
-
-    const int ldQKV = 3 * B * numHeads * headSize;
-    const int strideQKV = 3 * headSize;
-    
-    if (mType == DataType::kHALF)
-    {
-        CHECK(cublasGemmStridedBatchedEx<T>(cublas, CUBLAS_OP_T, CUBLAS_OP_N, S, S, headSize, 1.f, kptr, ldQKV,
-            strideQKV, qptr, ldQKV, strideQKV, 0.f, qkptr, S, omatSize, numMats,
-            static_cast<cublasGemmAlgo_t>(mAlgoBatchedEx1)));
-    }
-    else 
-    {
-
-        CHECK(cublasGemmStridedBatched<T>(cublas, CUBLAS_OP_T, CUBLAS_OP_N, S, S, headSize, 1.f, kptr, ldQKV, strideQKV,
-            qptr, ldQKV, strideQKV, 0.f, qkptr, S, omatSize, numMats));
-    }
-
-    // apply softmax
-    if (maskIdx)
-    { // if we have a mask
-        computeMaskedScaledSoftmax<T>(stream, S, B, numHeads, rsqrtHeadSize, maskIdx, qkptr, pptr);
-    }
-    else
-    { // if we don't have a mask
-        computeScaledSoftmax<T>(stream, S, B, numHeads, rsqrtHeadSize, qkptr, pptr);
-    }
-
-    // compute P*V (as V*P)
-
-    const int ldOut = B * numHeads * headSize;
-    const int strideOut = headSize;
-    if (mType == DataType::kHALF)
-    {
-
-        CHECK(cublasGemmStridedBatchedEx<T>(cublas, CUBLAS_OP_N, CUBLAS_OP_N, headSize, S, S, 1.f, vptr, ldQKV,
-            strideQKV, pptr, S, omatSize, 0.f, output, ldOut, strideOut, numMats,
-            static_cast<cublasGemmAlgo_t>(mAlgoBatchedEx2)));
-    }
-    else 
-    {
-
-        CHECK(cublasGemmStridedBatched<T>(cublas, CUBLAS_OP_N, CUBLAS_OP_N, headSize, S, S, 1.f, vptr, ldQKV, strideQKV,
-            pptr, S, omatSize, 0.f, output, ldOut, strideOut, numMats));
-    }
-    return 0;
-}
-
-namespace
-{
-static const char* QKV_TO_CONTEXT_PLUGIN_VERSION{"1"};
-static const char* QKV_TO_CONTEXT_PLUGIN_NAME{"CustomQKVToContextPluginDynamic"};
-} // namespace
-
-// Static class fields initialization
-PluginFieldCollection QKVToContextPluginDynamicCreator::mFC{};
-std::vector<PluginField> QKVToContextPluginDynamicCreator::mPluginAttributes;
-
-REGISTER_TENSORRT_PLUGIN(QKVToContextPluginDynamicCreator);
-
-constexpr size_t kAlignment = 256;
-constexpr uint32_t IIDX = 0; // index of the input tensor
-constexpr uint32_t MIDX = 1; // index of the mask
-
-QKVToContextPluginDynamic::QKVToContextPluginDynamic(
-    const std::string name, const DataType type, const int hiddenSize, const int numHeads, bool hasImask)
-    : mLayerName(name)
-    , mHiddenSize(hiddenSize)
-    , mNumHeads(numHeads)
-    , mHasImask(hasImask)
-    , mType(type)
-      , mAlgoBatchedEx1(CUBLAS_GEMM_DEFAULT_TENSOR_OP)
-      , mAlgoBatchedEx2(CUBLAS_GEMM_DEFAULT_TENSOR_OP)
-{
-    assert(hiddenSize % numHeads == 0);
-    mHeadSize = hiddenSize / numHeads;
-    mRsqrtHeadSize = 1.f / sqrt(float(mHeadSize));
-}
-
-QKVToContextPluginDynamic::QKVToContextPluginDynamic(const std::string name, const void* data, size_t length)
-    : mLayerName(name)
-{
-    gLogVerbose << "QKV Deser Start" << std::endl;
-    deserialize_value(&data, &length, &mType);
-    deserialize_value(&data, &length, &mNumHeads);
-    deserialize_value(&data, &length, &mHeadSize);
-    deserialize_value(&data, &length, &mRsqrtHeadSize);
-    deserialize_value(&data, &length, &mHasImask);
-    deserialize_value(&data, &length, &mHiddenSize);
-    deserialize_value(&data, &length, &mAlgoBatchedEx1);
-    deserialize_value(&data, &length, &mAlgoBatchedEx2);
-    gLogVerbose << "QKV Deser done" << std::endl;
-}
-
-// IPluginV2DynamicExt Methods
-nvinfer1::IPluginV2DynamicExt* QKVToContextPluginDynamic::clone() const
-{
-    gLogVerbose << "QKV Clone" << std::endl;
-    auto ret = new QKVToContextPluginDynamic(mLayerName, mType, mHiddenSize, mNumHeads, mHasImask);
-    ret->initialize();
-    gLogVerbose << "QKV Clone done" << std::endl;
-    return ret;
-}
-
-DimsExprs QKVToContextPluginDynamic::getOutputDimensions(
-    int outputIndex, const DimsExprs* inputs, int nbInputs, IExprBuilder& exprBuilder)
-{
-    // Input is BxSx3*N*H, output should be BxSxN*H
-    assert(outputIndex == 0);
-    // Copy over everything
-    DimsExprs output(inputs[IIDX]);
-    // Divide last dim by three
-    auto three = exprBuilder.constant(3);
-    output.d[HDIM] = exprBuilder.operation(DimensionOperation::kFLOOR_DIV, *inputs[IIDX].d[HDIM], *three);
-    return output;
-}
-bool QKVToContextPluginDynamic::supportsFormatCombination(
-    int pos, const PluginTensorDesc* inOut, int nbInputs, int nbOutputs)
-{
-    assert(pos >= 0);
-    assert(pos < 2 + mHasImask);
-    assert(nbInputs == 1 + mHasImask);
-    const auto* in = inOut;
-    const auto* out = inOut + nbInputs;
-    if (pos == 0)
-    {
-        // must not check descriptions > pos
-        return (in->type == mType) &&                // precision
-            (in->format == TensorFormat::kLINEAR) && // format
-            (in->dims.nbDims == 5) &&                // num dims
-            ((in->dims.d[HDIM] % 3) == 0) &&         // see getOutputDimensions
-            ((in->dims.d[3]) == 1) &&                // for fc
-            ((in->dims.d[4]) == 1)                   // for fc
-            ;
-    }
-    else
-    { // pos==1
-        if ((mHasImask && pos == 1))
-        {
-            const auto* inMask = &inOut[1];
-            return (inMask->type == DataType::kINT32) &&     // precision
-                (inMask->format == TensorFormat::kLINEAR) && // format
-                (inMask->dims.nbDims == 1) &&                // num dims
-                ((inMask->dims.d[0]) == in->dims.d[BDIM])    // check B
-                ;
-        }
-        if (!mHasImask || (pos == 2))
-        {
-            return (in->type == out->type) &&                      // precision
-                (out->format == TensorFormat::kLINEAR) &&          // format
-                (out->dims.nbDims == 5) &&                         // num dims
-                ((in->dims.d[HDIM] / 3) == (out->dims.d[HDIM])) && // div 3
-                ((out->dims.d[3]) == 1) &&                         // for fc
-                ((out->dims.d[4]) == 1) &&                         // for fc
-                ((out->dims.d[BDIM]) == in->dims.d[BDIM]) &&       // check B
-                ((out->dims.d[SDIM]) == in->dims.d[SDIM])          // check S
-                ;
-        }
-    }
-    return false;
-}
-void QKVToContextPluginDynamic::configurePlugin(
-    const DynamicPluginTensorDesc* in, int nbInputs, const DynamicPluginTensorDesc* out, int nbOutputs)
-{
-    assert(nbInputs == 1 + mHasImask);
-    assert(nbOutputs == 1);
-    const PluginTensorDesc& inDesc = in[IIDX].desc;
-    TRT_UNUSED inDesc;
-    const PluginTensorDesc& outDesc = out->desc;
-    TRT_UNUSED outDesc;
-    assert(mType == inDesc.type);
-    assert(mType == outDesc.type);
-    assert(inDesc.dims.d[BDIM] == outDesc.dims.d[BDIM]);
-    assert(inDesc.dims.d[SDIM] == outDesc.dims.d[SDIM]);
-    assert(inDesc.dims.d[HDIM] == 3 * outDesc.dims.d[HDIM]);
-    if (mHasImask)
-    {
-        const PluginTensorDesc& maskDesc = in[MIDX].desc;
-        TRT_UNUSED maskDesc;
-        assert(maskDesc.type == DataType::kINT32);
-        assert(maskDesc.dims.d[0] == inDesc.dims.d[BDIM]);
-    }
-
-    const int S = in->max.d[SDIM];
-    const int B = in->max.d[BDIM];
-    std::tie(mAlgoBatchedEx1, mAlgoBatchedEx2) = tuneBatchedGemm(B, S, mNumHeads, mHeadSize);
-    gLogVerbose << "QKV Plugin - Selected Algos for batch gemms: " << mAlgoBatchedEx1 << ", " << mAlgoBatchedEx2 << "\n";
-}
-
-size_t QKVToContextPluginDynamic::scratchSize(const int B, const int S) const
-{
-    const size_t wordSize = samplesCommon::getElementSize(mType);
-    const size_t len = B * mNumHeads * S * S;
-    const size_t bytes = len * wordSize;
-
-    return bytes;
-}
-
-size_t QKVToContextPluginDynamic::getWorkspaceSize(
-    const PluginTensorDesc* inputs, int nbInputs, const PluginTensorDesc* outputs, int nbOutputs) const
-{
-    const int B = inputs->dims.d[BDIM];
-    const int S = inputs->dims.d[SDIM];
-
-    const size_t bytesAligned = alignTo<size_t>(scratchSize(B, S), kAlignment);
-    const size_t ws = 2UL * bytesAligned;
-
-    return ws;
-}
-
-// IPluginV2Ext Methods
-DataType QKVToContextPluginDynamic::getOutputDataType(
-    int index, const nvinfer1::DataType* inputTypes, int nbInputs) const
-{
-    assert(index == 0);
-    assert(inputTypes[0] == DataType::kFLOAT || inputTypes[0] == DataType::kHALF);
-    return inputTypes[0];
-}
-
-// IPluginV2 Methods
-const char* QKVToContextPluginDynamic::getPluginType() const
-{
-    return QKV_TO_CONTEXT_PLUGIN_NAME;
-}
-
-const char* QKVToContextPluginDynamic::getPluginVersion() const
-{
-    return QKV_TO_CONTEXT_PLUGIN_VERSION;
-}
-
-int QKVToContextPluginDynamic::getNbOutputs() const
-{
-    return 1;
-}
-
-int QKVToContextPluginDynamic::initialize()
-{
-    cublasCreate(&cublas);
-    return 0;
-}
-
-void QKVToContextPluginDynamic::terminate()
-{
-    CHECK(cublasDestroy(cublas));
-}
-
-size_t QKVToContextPluginDynamic::getSerializationSize() const
-{
-    return sizeof(mNumHeads) + sizeof(mHeadSize) + sizeof(DataType) + sizeof(mRsqrtHeadSize) + sizeof(mHasImask)
-        + sizeof(mHiddenSize) + sizeof(mAlgoBatchedEx1) + sizeof(mAlgoBatchedEx2);
-}
-
-void QKVToContextPluginDynamic::serialize(void* buffer) const
-{
-    serialize_value(&buffer, mType);
-    serialize_value(&buffer, mNumHeads);
-    serialize_value(&buffer, mHeadSize);
-    serialize_value(&buffer, mRsqrtHeadSize);
-    serialize_value(&buffer, mHasImask);
-    serialize_value(&buffer, mHiddenSize);
-    serialize_value(&buffer, mAlgoBatchedEx1);
-    serialize_value(&buffer, mAlgoBatchedEx2);
-}
-
-void QKVToContextPluginDynamic::destroy()
-{
-    delete this;
-}
-
-void QKVToContextPluginDynamic::setPluginNamespace(const char* libNamespace)
-{
-    mNamespace = libNamespace;
-}
-
-const char* QKVToContextPluginDynamic::getPluginNamespace() const
-{
-    return mNamespace.c_str();
-}
-
-int QKVToContextPluginDynamic::enqueue(const PluginTensorDesc* inputDesc, const PluginTensorDesc* outputDesc,
-    const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream)
-{
-
-    const int batchSize = inputDesc->dims.d[BDIM];
-    const int S = inputDesc->dims.d[SDIM];
-
-    const size_t bytesAligned = alignTo<size_t>(scratchSize(batchSize, S), kAlignment);
-    char* scratch1 = static_cast<char*>(workspace);
-    char* scratch2 = scratch1 + bytesAligned;
-
-    const int* maskIdx = mHasImask ? static_cast<const int*>(inputs[1]) : nullptr;
-
-    int status = -1;
-    if (mType == DataType::kFLOAT)
-    {
-        const float* input = static_cast<const float*>(inputs[0]);
-        float* output = static_cast<float*>(outputs[0]);
-        float* scr1 = reinterpret_cast<float*>(scratch1);
-        float* scr2 = reinterpret_cast<float*>(scratch2);
-
-        status = qkvToCtx(
-            cublas, batchSize, S, mNumHeads, mHeadSize, mRsqrtHeadSize, input, output, scr1, scr2, stream, maskIdx);
-    }
-    else if (mType == DataType::kHALF)
-    {
-        const half* input = static_cast<const half*>(inputs[0]);
-        half* output = static_cast<half*>(outputs[0]);
-        half* scr1 = reinterpret_cast<half*>(scratch1);
-        half* scr2 = reinterpret_cast<half*>(scratch2);
-
-        status = qkvToCtx(
-            cublas, batchSize, S, mNumHeads, mHeadSize, mRsqrtHeadSize, input, output, scr1, scr2, stream, maskIdx);
-    }
-    else
-    {
-        assert(false);
-    }
-
-    return status;
-}
-
-QKVToContextPluginDynamicCreator::QKVToContextPluginDynamicCreator()
-{
-    mFC.nbFields = mPluginAttributes.size();
-    mFC.fields = mPluginAttributes.data();
-}
-
-const char* QKVToContextPluginDynamicCreator::getPluginName() const
-{
-    return QKV_TO_CONTEXT_PLUGIN_NAME;
-}
-
-const char* QKVToContextPluginDynamicCreator::getPluginVersion() const
-{
-    return QKV_TO_CONTEXT_PLUGIN_VERSION;
-}
-
-const PluginFieldCollection* QKVToContextPluginDynamicCreator::getFieldNames()
-{
-    return &mFC;
-}
-
-IPluginV2* QKVToContextPluginDynamicCreator::createPlugin(const char* name, const PluginFieldCollection* fc)
-{
-    gLogVerbose << "Creating QKV2ContextPlugin...\n";
-
-    int hiddenSize = 0;
-    int numHeads = 0;
-    bool hasMask = false;
-    int typeId = -1;
-
-    for (int i = 0; i < fc->nbFields; i++)
-    {
-        std::string field_name(fc->fields[i].name);
-
-        if (field_name.compare("type_id") == 0)
-        {
-            typeId = *static_cast<const int*>(fc->fields[i].data);
-            gLogVerbose << "Building typeId: " << typeId << std::endl;
-        }
-        if (field_name.compare("hidden_size") == 0)
-        {
-            hiddenSize = *static_cast<const int*>(fc->fields[i].data);
-            gLogVerbose << "Building hiddenSize: " << hiddenSize << std::endl;
-        }
-        if (field_name.compare("num_heads") == 0)
-        {
-            numHeads = *static_cast<const int*>(fc->fields[i].data);
-            gLogVerbose << "Building numHeads: " << numHeads << std::endl;
-        }
-        if (field_name.compare("has_mask") == 0)
-        {
-            hasMask = *static_cast<const bool*>(fc->fields[i].data);
-            gLogVerbose << "Building hasMask: " << hasMask << std::endl;
-        }
-    }
-    if (typeId < 0 || typeId > 3)
-    {
-        gLogError << "QKV: Invalid TypeId " << typeId << std::endl;
-    }
-
-    if (hiddenSize <= 0)
-    {
-        gLogError << "QKV: Invalid hiddenSize " << hiddenSize << std::endl;
-    }
-
-    if (numHeads <= 0)
-    {
-        gLogError << "QKV: Invalid numHeads " << numHeads << std::endl;
-    }
-
-    gLogVerbose << "Building the Plugin...\n";
-    DataType type = static_cast<DataType>(typeId);
-    QKVToContextPluginDynamic* p = new QKVToContextPluginDynamic(name, type, hiddenSize, numHeads, hasMask);
-    return p;
-}
-
-IPluginV2* QKVToContextPluginDynamicCreator::deserializePlugin(
-    const char* name, const void* serialData, size_t serialLength)
-{
-    // This object will be deleted when the network is destroyed, which will
-    // call QKVToContextPluginDynamic::destroy()
-    return new QKVToContextPluginDynamic(name, serialData, serialLength);
-}
-
-void QKVToContextPluginDynamicCreator::setPluginNamespace(const char* libNamespace)
-{
-    mNamespace = libNamespace;
-}
-
-const char* QKVToContextPluginDynamicCreator::getPluginNamespace() const
-{
-    return mNamespace.c_str();
-}
-} // namespace bert
diff --git a/plugin/bertQKVToContextPlugin/qkvToContextPlugin.h b/plugin/bertQKVToContextPlugin/qkvToContextPlugin.h
index c3aee687..870b6194 100644
--- a/plugin/bertQKVToContextPlugin/qkvToContextPlugin.h
+++ b/plugin/bertQKVToContextPlugin/qkvToContextPlugin.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -14,6 +14,10 @@
  * limitations under the License.
  */
 
+// Need 10.1 for cublasGemmStridedBatchedEx
+#include <cuda.h>
+#if CUDA_VERSION >= 10010
+
 #ifndef TRT_QKV_TO_CONTEXT_PLUGIN_H
 #define TRT_QKV_TO_CONTEXT_PLUGIN_H
 
@@ -25,6 +29,77 @@
 namespace bert
 {
 
+// Multi Head Attention runner
+class MHARunner
+{
+public:
+    MHARunner(const nvinfer1::DataType type, const int numHeads, const int headSize)
+        : mType(type)
+        , mNumHeads(numHeads)
+        , mHeadSize(headSize)
+        , mWordSize(getElementSize(type))
+        , mRsqrtHeadSize(1.f / sqrtf(headSize))
+    {
+    }
+
+    virtual ~MHARunner() = default;
+
+    virtual void setup(const int S, const int B)
+    {
+        assert(S);
+        assert(B);
+        mB = B;
+        mS = S;
+
+        mLdQKV = 3 * B * mNumHeads * mHeadSize;
+        mStrideQKV = 3 * mHeadSize;
+
+        mLdOut = B * mNumHeads * mHeadSize;
+        mStrideOut = mHeadSize;
+        mOmatSize = S * S;
+        mNumMats = B * mNumHeads;
+    }
+
+    virtual void run(const nvinfer1::PluginTensorDesc& inputDesc, const nvinfer1::PluginTensorDesc& outputDesc,
+        const void* qkvPtr, const void* maskPtr, void* output, void* workspace, cudaStream_t stream)
+        = 0;
+
+    virtual size_t getSerializationSize() const;
+    virtual void serialize(void* buffer) const;
+    virtual void deserialize(const void* data, size_t length);
+
+    virtual size_t getWorkspaceSize() const = 0;
+
+    virtual bool isValid() const = 0;
+
+protected:
+    nvinfer1::DataType mType;
+
+    int mS;
+    int mB;
+    int mOmatSize;
+    int mNumMats;
+    int mNumHeads;
+    int mHeadSize;
+    int mWordSize;
+    int mLdQKV;
+    int mStrideQKV;
+    int mLdOut;
+    int mStrideOut;
+
+    float mRsqrtHeadSize;
+};
+
+std::pair<int, int> tuneBatchedGemm(const int B, const int S, const int numHeads, const int headSize);
+
+template <typename T>
+int computeScaledSoftmax(
+    cudaStream_t stream, const int ld, const int B, const int N, const float rsqrtHeadSize, const T* input, T* output);
+
+template <typename T>
+int computeMaskedScaledSoftmax(cudaStream_t stream, const int ld, const int B, const int N, const float rsqrtHeadSize,
+    const int* maskIdx, const T* input, T* output);
+
 // One of the preferred ways of making TensorRT to be able to see
 // our custom layer requires extending IPluginV2 and IPluginCreator classes.
 // For requirements for overriden functions, check TensorRT API docs.
@@ -32,8 +107,8 @@ namespace bert
 class QKVToContextPluginDynamic : public nvinfer1::IPluginV2DynamicExt
 {
 public:
-    QKVToContextPluginDynamic(const std::string name, const nvinfer1::DataType type, const int hiddenSize, const int numHeads,
-        bool hasImask = false);
+    QKVToContextPluginDynamic(const std::string name, const nvinfer1::DataType type, const int hiddenSize,
+        const int numHeads, const float dqProbs, bool hasImask = false);
 
     QKVToContextPluginDynamic(const std::string name, const void* data, size_t length);
 
@@ -69,37 +144,36 @@ class QKVToContextPluginDynamic : public nvinfer1::IPluginV2DynamicExt
     void setPluginNamespace(const char* pluginNamespace) override;
     const char* getPluginNamespace() const override;
 
+protected:
+    void createMHARunner();
+    int getSMVersion() const;
+
 private:
-    size_t scratchSize(const int B, const int S) const;
-    float mRsqrtHeadSize;
-    int mHeadSize;
-    int mB;
+    const std::string mLayerName;
+    std::string mNamespace;
+
+    std::unique_ptr<MHARunner> dispatcher;
+
     int mS;
+    int mB;
+    int mSM;
+    int mHeadSize;
     int mHiddenSize;
     int mNumHeads;
     bool mHasImask;
-    const std::string mLayerName;
-    std::string mNamespace;
-    int mAlgoBatchedEx1;
-    int mAlgoBatchedEx2;
-
     nvinfer1::DataType mType;
-    cublasHandle_t cublas;
 
-    template <typename T>
-    int qkvToCtx(cublasHandle_t& cublas, const int B, const int S, const int numHeads, const int headSize,
-        const float rsqrtHeadSize, const T* input, T* output, T* qkptr, T* pptr, cudaStream_t stream,
-        const int* maskIdx = nullptr);
+    float mDqProbs;
 
 protected:
     // To prevent compiler warnings.
-    using nvinfer1::IPluginV2DynamicExt::getOutputDimensions;
-    using nvinfer1::IPluginV2DynamicExt::isOutputBroadcastAcrossBatch;
     using nvinfer1::IPluginV2DynamicExt::canBroadcastInputAcrossBatch;
-    using nvinfer1::IPluginV2DynamicExt::supportsFormat;
     using nvinfer1::IPluginV2DynamicExt::configurePlugin;
-    using nvinfer1::IPluginV2DynamicExt::getWorkspaceSize;
     using nvinfer1::IPluginV2DynamicExt::enqueue;
+    using nvinfer1::IPluginV2DynamicExt::getOutputDimensions;
+    using nvinfer1::IPluginV2DynamicExt::getWorkspaceSize;
+    using nvinfer1::IPluginV2DynamicExt::isOutputBroadcastAcrossBatch;
+    using nvinfer1::IPluginV2DynamicExt::supportsFormat;
 };
 
 class QKVToContextPluginDynamicCreator : public nvinfer1::IPluginCreator
@@ -126,5 +200,79 @@ class QKVToContextPluginDynamicCreator : public nvinfer1::IPluginCreator
     static std::vector<nvinfer1::PluginField> mPluginAttributes;
     std::string mNamespace;
 };
-}
+
+class UnfusedMHARunner : public MHARunner
+{
+public:
+    UnfusedMHARunner(const nvinfer1::DataType type, const int numHeads, const int headSize);
+    virtual ~UnfusedMHARunner();
+
+    virtual void setup(const int S, const int B) override;
+
+    void run(const nvinfer1::PluginTensorDesc& inputDesc, const nvinfer1::PluginTensorDesc& outputDesc,
+        const void* qkvPtr, const void* maskPtr, void* output, void* workspace, cudaStream_t stream) override;
+
+    size_t getWorkspaceSize() const override;
+
+    size_t getSerializationSize() const override;
+    void serialize(void* buffer) const override;
+    void deserialize(const void* data, size_t length) override;
+    bool isValid() const override;
+
+private:
+    int mAlgoBatchedEx1;
+    int mAlgoBatchedEx2;
+    cublasHandle_t mCublas;
+};
+
+class FusedMHARunnerFP16 : public MHARunner
+{
+public:
+    FusedMHARunnerFP16(const int numHeads, const int headSize, const int sm);
+    ~FusedMHARunnerFP16() = default; // for pimpl
+
+    virtual void setup(const int S, const int B) override;
+
+    void run(const nvinfer1::PluginTensorDesc& inputDesc, const nvinfer1::PluginTensorDesc& outputDesc,
+        const void* qkvPtr, const void* maskPtr, void* output, void* workspace, cudaStream_t stream) override;
+
+    size_t getWorkspaceSize() const override;
+
+    void deserialize(const void* data, size_t length) override;
+
+    bool isValid() const override;
+
+private:
+    int mSm;
+    class mhaImpl;
+    std::unique_ptr<mhaImpl> pimpl;
+};
+
+class FusedMHARunnerInt8 : public MHARunner
+{
+public:
+    FusedMHARunnerInt8(const int numHeads, const int headSize, const int sm, const float dqProbs);
+    ~FusedMHARunnerInt8() = default; // for pimpl
+
+    virtual void setup(const int S, const int B) override;
+
+    void run(const nvinfer1::PluginTensorDesc& inputDesc, const nvinfer1::PluginTensorDesc& outputDesc,
+        const void* qkvPtr, const void* maskPtr, void* output, void* workspace, cudaStream_t stream) override;
+
+    size_t getWorkspaceSize() const override;
+
+    void deserialize(const void* data, size_t length) override;
+
+    bool isValid() const override;
+
+private:
+    float mDqProbs;
+    int mSm;
+    class mhaImpl;
+    std::unique_ptr<mhaImpl> pimpl;
+};
+
+} // namespace bert
 #endif // TRT_QKV_TO_CONTEXT_PLUGIN_H
+
+#endif // CUDA_VERSION >= 10010
diff --git a/plugin/common/CMakeLists.txt b/plugin/common/CMakeLists.txt
index 57b2fc28..48bd64e8 100644
--- a/plugin/common/CMakeLists.txt
+++ b/plugin/common/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/plugin/common/bboxUtils.h b/plugin/common/bboxUtils.h
index 17e2c110..09f0d7d1 100644
--- a/plugin/common/bboxUtils.h
+++ b/plugin/common/bboxUtils.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/plugin/common/bertCommon.h b/plugin/common/bertCommon.h
index dde8be22..423e25d9 100644
--- a/plugin/common/bertCommon.h
+++ b/plugin/common/bertCommon.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -14,289 +14,100 @@
  * limitations under the License.
  */
 
-#ifndef TRT_PLUGIN_UTIL_H
-#define TRT_PLUGIN_UTIL_H
+#include <cuda.h>
+#if CUDA_VERSION >= 10010
+
+#ifndef BERT_COMMON_H
+#define BERT_COMMON_H
 
 #include "NvInfer.h"
 #include "NvInferRuntimeCommon.h"
-#include "common.h"
 #include "cublas_v2.h"
-#include "cuda_fp16.hpp"
-#include "half.h"
-#include "logging.h"
-#include <cub/cub.cuh>
-
-extern Logger gLogger;
-extern LogStreamConsumer gLogVerbose;
-extern LogStreamConsumer gLogInfo;
-extern LogStreamConsumer gLogWarning;
-extern LogStreamConsumer gLogError;
-extern LogStreamConsumer gLogFatal;
+#include "cuda_fp16.h"
+#include "plugin.h"
+#include "pluginLogger.h"
 
-void setReportableSeverity(Logger::Severity severity);
+#include <algorithm>
+#include <cuda_runtime_api.h>
+#include <memory>
+#include <numeric>
+#include <stdexcept>
+#include <vector>
 
 #define TRT_UNUSED (void)
 
-#include <numeric>
-#include <vector>
+using half = __half;
 
-typedef __half half;
-namespace bert
-{
 constexpr uint32_t BDIM = 1; // batch dimension
 constexpr uint32_t SDIM = 0; // seq len dimension
 constexpr uint32_t HDIM = 2; // hidden dimension
 
-#define HDI inline __host__ __device__
-
-template <typename T>
-inline T* deserToDev(const char*& buffer, size_t nbElem)
-{
-    T* dev = nullptr;
-    const size_t len = sizeof(T) * nbElem;
-    CHECK(cudaMalloc(&dev, len));
-    CHECK(cudaMemcpy(dev, buffer, len, cudaMemcpyHostToDevice));
-
-    buffer += len;
-    return dev;
-}
-
-template <typename T>
-inline void serFromDev(char*& buffer, const T* data, size_t nbElem)
+inline unsigned int getElementSize(nvinfer1::DataType t)
 {
-    const size_t len = sizeof(T) * nbElem;
-    CHECK(cudaMemcpy(buffer, data, len, cudaMemcpyDeviceToHost));
-    buffer += len;
+    switch (t)
+    {
+    case nvinfer1::DataType::kINT32: return 4;
+    case nvinfer1::DataType::kFLOAT: return 4;
+    case nvinfer1::DataType::kHALF: return 2;
+    case nvinfer1::DataType::kBOOL:
+    case nvinfer1::DataType::kINT8: return 1;
+    }
+    throw std::runtime_error("Invalid DataType.");
+    return 0;
 }
 
-template <typename T>
-__device__ inline T rsqrt(const T& x);
-
-template <>
-__device__ inline float rsqrt(const float& x)
+inline int64_t getWeightsSize(const nvinfer1::Weights& w, nvinfer1::DataType type)
 {
-    return rsqrtf(x);
+    return w.count * getElementSize(type);
 }
 
-template <>
-__device__ inline half rsqrt(const half& x)
+inline int64_t volume(const nvinfer1::Dims& d)
 {
-    return hrsqrt(x);
+    return std::accumulate(d.d, d.d + d.nbDims, 1, std::multiplies<int64_t>());
 }
 
-template <typename T>
-__device__ inline T tanh(const T& x);
-
-template <>
-__device__ inline float tanh(const float& x)
+namespace bert
 {
-    return tanhf(x);
-}
 
-template <>
-__device__ inline half tanh(const half& x)
+template <typename IntType>
+constexpr IntType ceildiv(IntType a, IntType b)
 {
-    const float tmp = tanhf(__half2float(x));
-    return __float2half(tmp);
+    return (a + b - 1) / b;
 }
-
-template <>
-__device__ inline half2 tanh(const half2& x)
+template <typename IntType>
+constexpr IntType alignTo(IntType a, IntType b)
 {
-    // at the moment, there is no half2 tanh builtin
-    float2 tmp = (__half22float2(x));
-    tmp.x = tanhf(tmp.x);
-    tmp.y = tanhf(tmp.y);
-    return __float22half2_rn(tmp);
+    return ceildiv(a, b) * b;
 }
 
 template <typename T>
-__device__ inline T exp(const T x);
-
-template <>
-__device__ inline float exp(const float x)
-{
-    return expf(x);
-}
-
-template <>
-__device__ inline half exp(const half x)
-{
-    return hexp(x);
-}
-
-using kv_float = cub::KeyValuePair<float, float>;
-using kv_half = cub::KeyValuePair<half, half>;
-using kv_half2 = cub::KeyValuePair<half2, half2>;
-
-__device__ inline kv_float operator+(const kv_float& a, const kv_float& b)
-{
-    return kv_float(a.key + b.key, a.value + b.value);
-}
-
-__device__ inline kv_half operator+(const kv_half& a, const kv_half& b)
+inline T* deserToDev(const char*& buffer, size_t nbElem)
 {
-    const half2 a2 = __halves2half2(a.key, a.value);
-    const half2 b2 = __halves2half2(b.key, b.value);
-    const half2 res = __hadd2(a2, b2);
-    return kv_half(res.x, res.y);
-}
+    void* dev{nullptr};
+    const size_t len = sizeof(T) * nbElem;
+    CUASSERT(cudaMalloc(&dev, len));
+    CUASSERT(cudaMemcpy(dev, buffer, len, cudaMemcpyHostToDevice));
 
-__device__ inline kv_half2 operator+(const kv_half2& a, const kv_half2& b)
-{
-    return kv_half2(__hadd2(a.key, b.key), __hadd2(a.value, b.value));
+    buffer += len;
+    return static_cast<T*>(dev);
 }
 
 template <typename T>
-using kvp = cub::KeyValuePair<T, T>;
-
-template <typename T, typename R, typename P, int TPB>
-__device__ inline void layerNorm(
-    const kvp<R>& threadData, const int ld, const int offset, const P* beta, const P* gamma, T* output)
-{
-    // Assuming threadData is already divided by ld
-
-    using BlockReduce = cub::BlockReduce<kvp<R>, TPB>;
-    __shared__ typename BlockReduce::TempStorage temp_storage;
-    __shared__ R mu;     // mean
-    __shared__ R rsigma; // 1 / std.dev.
-
-    const auto sumKV = BlockReduce(temp_storage).Reduce(threadData, cub::Sum());
-
-    if (threadIdx.x == 0)
-    {
-        mu = sumKV.key;
-        rsigma = rsqrt(sumKV.value - mu * mu);
-    }
-    __syncthreads();
-
-    for (int i = threadIdx.x; i < ld; i += TPB)
-    {
-        const int idx = offset + i;
-        const R val = output[idx];
-        const R g(gamma[i]);
-        const R b(beta[i]);
-        output[idx] = g * (val - mu) * rsigma + b;
-    }
-}
-
-template <typename T, typename P, int TPB>
-__device__ inline void layerNormSmall(
-    const T val, const kvp<T>& threadData, const int ld, const int idx, const P* beta, const P* gamma, T* output)
-{
-    // Assuming threadData is already divided by ld
-    // Small settings: the block covers the leading dimension TPB >= ld. The input
-    // value is available in a register
-
-    using BlockReduce = cub::BlockReduce<kvp<T>, TPB>;
-    __shared__ typename BlockReduce::TempStorage temp_storage;
-    __shared__ T mu;     // mean
-    __shared__ T rsigma; // 1 / std.dev.
-
-    const auto sumKV = BlockReduce(temp_storage).Reduce(threadData, cub::Sum());
-
-    if (threadIdx.x == 0)
-    {
-        mu = sumKV.key;
-        rsigma = rsqrt(sumKV.value - mu * mu);
-    }
-    __syncthreads();
-
-    if (threadIdx.x < ld)
-    {
-        const T g(gamma[threadIdx.x]);
-        const T b(beta[threadIdx.x]);
-        output[idx] = g * (val - mu) * rsigma + b;
-    }
-}
-
-template <typename T, unsigned TPB>
-__device__ inline void scaledSoftmaxSmall(
-    const int ld, const int lastValid, const float rsqrtHeadSize, const T* input, T* output)
-{
-
-    using BlockReduce = cub::BlockReduce<float, TPB>;
-
-    __shared__ typename BlockReduce::TempStorage tmpStorage;
-
-    __shared__ float rZ;
-
-    const int offset = (blockIdx.y * gridDim.x + blockIdx.x) * ld;
-
-    const float w(rsqrtHeadSize);
-    cub::Sum sum;
-    float threadData(0);
-
-    const int idx = offset + threadIdx.x;
-    if (threadIdx.x < lastValid)
-    {
-        const float val = input[idx];
-        threadData = exp(val * w);
-    }
-
-    const auto Z = BlockReduce(tmpStorage).Reduce(threadData, sum);
-
-    if (threadIdx.x == 0)
-    {
-        rZ = (1.f) / Z;
-    }
-    __syncthreads();
-
-    if (threadIdx.x < ld)
-    {
-        // this will be 0 for threadIdx.x >= lastValid
-        output[idx] = T(threadData * rZ);
-    }
-}
-
-template <typename T, unsigned TPB>
-__device__ inline void scaledSoftmax(
-    const int ld, const int lastValid, const float rsqrtHeadSize, const T* input, T* output)
+inline void serFromDev(char*& buffer, const T* data, size_t nbElem)
 {
-
-    using BlockReduce = cub::BlockReduce<float, TPB>;
-    __shared__ typename BlockReduce::TempStorage tmpStorage;
-
-    __shared__ float rZ;
-
-    const int offset = (blockIdx.y * gridDim.x + blockIdx.x) * ld;
-
-    const float w(rsqrtHeadSize);
-    cub::Sum sum;
-    float threadData(0);
-
-    for (int i = threadIdx.x; i < lastValid; i += TPB)
-    {
-        const int idx = offset + i;
-        const float val = input[idx];
-        threadData += exp(val * w);
-    }
-
-    const auto Z = BlockReduce(tmpStorage).Reduce(threadData, sum);
-
-    if (threadIdx.x == 0)
-    {
-        rZ = 1.f / Z;
-    }
-    __syncthreads();
-
-    for (int i = threadIdx.x; i < ld; i += TPB)
-    {
-        const int idx = offset + i;
-        const float val = (i < lastValid) ? exp(float(input[idx]) * w) * rZ : 0.f;
-        output[idx] = T(val);
-    }
+    const size_t len = sizeof(T) * nbElem;
+    CUASSERT(cudaMemcpy(buffer, static_cast<const void*>(data), len, cudaMemcpyDeviceToHost));
+    buffer += len;
 }
 
-template <typename IntType>
-constexpr HDI IntType ceildiv(IntType a, IntType b)
-{
-    return (a + b - 1) / b;
-}
-template <typename IntType>
-constexpr HDI IntType alignTo(IntType a, IntType b)
+template <typename T>
+inline T* devToDev(const T* data, size_t nbElem)
 {
-    return ceildiv(a, b) * b;
+    void* dev{nullptr};
+    const size_t len = sizeof(T) * nbElem;
+    CUASSERT(cudaMalloc(&dev, len));
+    CUASSERT(cudaMemcpy(dev, static_cast<const void*>(data), len, cudaMemcpyDeviceToDevice));
+    return static_cast<T*>(dev);
 }
 
 template <typename T>
@@ -332,7 +143,7 @@ cublasStatus_t inline cublasGemmStridedBatchedEx(cublasHandle_t handle, cublasOp
     int batchCount, cublasGemmAlgo_t algo)
 {
 
-    return cublasGemmStridedBatchedEx(handle, transa, transb, m, n, k, &alpha, A, CUDA_R_32F, lda, strideA, B,
+    return ::cublasGemmStridedBatchedEx(handle, transa, transb, m, n, k, &alpha, A, CUDA_R_32F, lda, strideA, B,
         CUDA_R_32F, ldb, strideB, &beta, C, CUDA_R_32F, ldc, strideC, batchCount, CUDA_R_32F, algo);
 }
 
@@ -342,7 +153,7 @@ cublasStatus_t inline cublasGemmStridedBatchedEx(cublasHandle_t handle, cublasOp
     const half* B, int ldb, long long int strideB, const half beta, half* C, int ldc, long long int strideC,
     int batchCount, cublasGemmAlgo_t algo)
 {
-    return cublasGemmStridedBatchedEx(handle, transa, transb, m, n, k, &alpha, A, CUDA_R_16F, lda, strideA, B,
+    return ::cublasGemmStridedBatchedEx(handle, transa, transb, m, n, k, &alpha, A, CUDA_R_16F, lda, strideA, B,
         CUDA_R_16F, ldb, strideB, &beta, C, CUDA_R_16F, ldc, strideC, batchCount, CUDA_R_16F, algo);
 }
 
@@ -392,6 +203,128 @@ struct CublasConfigHelper
     }
 };
 
+template <typename T>
+struct CudaDeleter
+{
+    void operator()(T* buf)
+    {
+        CUASSERT(cudaFree(buf));
+    }
+};
+
+template <typename T>
+using cuda_unique_ptr = std::unique_ptr<T, bert::CudaDeleter<T>>;
+
+template <typename T>
+using cuda_shared_ptr = std::shared_ptr<T>;
+
+template <typename T>
+void make_cuda_shared(cuda_shared_ptr<T>& ptr, void* cudaMem)
+{
+    ptr.reset(static_cast<T*>(cudaMem), bert::CudaDeleter<T>());
+}
+
+struct WeightsWithOwnership : public nvinfer1::Weights
+{
+    WeightsWithOwnership()
+    {
+        values = nullptr;
+        count = 0;
+    }
+    ~WeightsWithOwnership()
+    {
+        operator delete[](const_cast<void*>(values));
+    }
+
+    WeightsWithOwnership(const WeightsWithOwnership&) = delete;
+    WeightsWithOwnership operator=(const WeightsWithOwnership&) = delete;
+    WeightsWithOwnership(const WeightsWithOwnership&&) = delete;
+    WeightsWithOwnership operator=(const WeightsWithOwnership&&) = delete;
+
+    void convertAndCopy(const nvinfer1::Weights& src, nvinfer1::DataType type)
+    {
+        this->type = type;
+        this->count = src.count;
+
+        if (type == nvinfer1::DataType::kFLOAT)
+        {
+            auto destBuf = new float[src.count];
+            this->values = destBuf;
+
+            if (src.type == nvinfer1::DataType::kFLOAT)
+            {
+                gLogVerbose << "Float Weights(Host) => Float Array(Host)\n";
+                std::copy_n(static_cast<const float*>(src.values), src.count, destBuf);
+            }
+            else
+            {
+                assert(src.type == nvinfer1::DataType::kHALF);
+
+                gLogVerbose << "Half Weights(Host) => Float Array(Host)\n";
+                const auto s = static_cast<const half*>(src.values);
+                auto d = static_cast<float*>(const_cast<void*>(this->values));
+
+                for (auto it = 0; it < src.count; it++)
+                {
+                    d[it] = __half2float(s[it]);
+                }
+            }
+        }
+        else if (type == nvinfer1::DataType::kHALF)
+        {
+            auto destBuf = new half[src.count];
+            this->values = destBuf;
+
+            if (src.type == nvinfer1::DataType::kHALF)
+            {
+                gLogVerbose << "Half Weights(Host) => Half Array(Host)\n";
+                std::copy_n(static_cast<const half*>(src.values), src.count, destBuf);
+            }
+            else
+            {
+                assert(src.type == nvinfer1::DataType::kFLOAT);
+
+                gLogVerbose << "Float Weights(Host) => Half Array(Host)\n";
+                const auto s = static_cast<const float*>(src.values);
+                auto d = static_cast<half*>(const_cast<void*>(this->values));
+
+                for (auto it = 0; it < src.count; it++)
+                {
+                    d[it] = __float2half(s[it]);
+                }
+            }
+        }
+        else
+        {
+            throw std::runtime_error("Unsupported DataType specified for plugin.");
+        }
+    }
+
+    void convertAndCopy(const char*& srcBuf, size_t count, nvinfer1::DataType type)
+    {
+        this->type = type;
+        this->count = count;
+        const auto nbBytes = getWeightsSize(*this, type);
+        auto destBuf = new char[nbBytes];
+        this->values = destBuf;
+
+        std::copy_n(srcBuf, nbBytes, destBuf);
+        srcBuf += nbBytes;
+    }
+};
+
+template <typename T>
+inline void copyToDevice(WeightsWithOwnership& hostWeights, size_t nbBytes, cuda_unique_ptr<T>& cudaWeights)
+{
+    if (hostWeights.values)
+    {
+        void* cudaMem{nullptr};
+        CUASSERT(cudaMalloc(&cudaMem, nbBytes));
+        CUASSERT(cudaMemcpy(cudaMem, hostWeights.values, nbBytes, cudaMemcpyHostToDevice));
+        cudaWeights.reset(static_cast<T*>(cudaMem));
+    }
+}
+
 inline void convertAndCopyToDevice(const nvinfer1::Weights& src, float* destDev)
 {
 
@@ -400,7 +333,7 @@ inline void convertAndCopyToDevice(const nvinfer1::Weights& src, float* destDev)
     if (src.type == nvinfer1::DataType::kFLOAT)
     {
         gLogVerbose << "Float Weights(Host) => Float Array(Device)" << std::endl;
-        CHECK(cudaMemcpy(destDev, src.values, nbBytes, cudaMemcpyHostToDevice));
+        CUASSERT(cudaMemcpy(destDev, src.values, nbBytes, cudaMemcpyHostToDevice));
     }
     else
     {
@@ -408,12 +341,12 @@ inline void convertAndCopyToDevice(const nvinfer1::Weights& src, float* destDev)
         std::vector<float> tmp(src.count);
         const half* values = reinterpret_cast<const half*>(src.values);
 
-        for (int it = 0; it < tmp.size(); it++)
+        for (size_t it = 0; it < tmp.size(); it++)
         {
             tmp[it] = __half2float(values[it]);
         }
 
-        CHECK(cudaMemcpy(destDev, &tmp[0], nbBytes, cudaMemcpyHostToDevice));
+        CUASSERT(cudaMemcpy(destDev, &tmp[0], nbBytes, cudaMemcpyHostToDevice));
     }
 }
 
@@ -424,7 +357,7 @@ inline void convertAndCopyToDevice(const nvinfer1::Weights& src, half* destDev)
     if (src.type == nvinfer1::DataType::kHALF)
     {
         gLogVerbose << "Half Weights(Host) => Half Array(Device)" << std::endl;
-        CHECK(cudaMemcpy(destDev, src.values, nbBytes, cudaMemcpyHostToDevice));
+        CUASSERT(cudaMemcpy(destDev, src.values, nbBytes, cudaMemcpyHostToDevice));
     }
     else
     {
@@ -432,11 +365,11 @@ inline void convertAndCopyToDevice(const nvinfer1::Weights& src, half* destDev)
         std::vector<half> tmp(src.count);
         const float* values = reinterpret_cast<const float*>(src.values);
 
-        for (int it = 0; it < tmp.size(); it++)
+        for (size_t it = 0; it < tmp.size(); it++)
         {
             tmp[it] = __float2half(values[it]);
         }
-        CHECK(cudaMemcpy(destDev, &tmp[0], nbBytes, cudaMemcpyHostToDevice));
+        CUASSERT(cudaMemcpy(destDev, &tmp[0], nbBytes, cudaMemcpyHostToDevice));
     }
 }
 
@@ -444,19 +377,23 @@ inline nvinfer1::DataType fieldTypeToDataType(const nvinfer1::PluginFieldType ft
 {
     switch (ftype)
     {
-    case nvinfer1::PluginFieldType::kFLOAT32: {
+    case nvinfer1::PluginFieldType::kFLOAT32:
+    {
         gLogVerbose << "PluginFieldType is Float32" << std::endl;
         return nvinfer1::DataType::kFLOAT;
     }
-    case nvinfer1::PluginFieldType::kFLOAT16: {
+    case nvinfer1::PluginFieldType::kFLOAT16:
+    {
         gLogVerbose << "PluginFieldType is Float16" << std::endl;
         return nvinfer1::DataType::kHALF;
     }
-    case nvinfer1::PluginFieldType::kINT32: {
+    case nvinfer1::PluginFieldType::kINT32:
+    {
         gLogVerbose << "PluginFieldType is Int32" << std::endl;
         return nvinfer1::DataType::kINT32;
     }
-    case nvinfer1::PluginFieldType::kINT8: {
+    case nvinfer1::PluginFieldType::kINT8:
+    {
         gLogVerbose << "PluginFieldType is Int8" << std::endl;
         return nvinfer1::DataType::kINT8;
     }
@@ -464,58 +401,26 @@ inline nvinfer1::DataType fieldTypeToDataType(const nvinfer1::PluginFieldType ft
     }
 }
 
-inline int64_t volume(const nvinfer1::Dims& d)
-{
-    return std::accumulate(d.d, d.d + d.nbDims, 1, std::multiplies<int64_t>());
-}
-
-template <int VPT>
-struct BytesToType;
-
-template <>
-struct BytesToType<2>
-{
-    using type = uint16_t;
-};
-template <>
-struct BytesToType<4>
-{
-    using type = uint32_t;
-};
-template <>
-struct BytesToType<8>
-{
-    using type = uint64_t;
-};
-template <>
-struct BytesToType<16>
-{
-    using type = float4;
-};
-
-template <int Bytes>
-__device__ inline void copy(const void* local, void* data)
+inline unsigned int getElementSize(nvinfer1::DataType t)
 {
-    using T = typename BytesToType<Bytes>::type;
-
-    const T* in = static_cast<const T*>(local);
-    T* out = static_cast<T*>(data);
-    *out = *in;
+    switch (t)
+    {
+    case nvinfer1::DataType::kINT32: return 4;
+    case nvinfer1::DataType::kFLOAT: return 4;
+    case nvinfer1::DataType::kHALF: return 2;
+    case nvinfer1::DataType::kBOOL:
+    case nvinfer1::DataType::kINT8: return 1;
+    }
+    throw std::runtime_error("Invalid DataType.");
+    return 0;
 }
 
-template <typename T>
-__device__ inline T myExp(const T x);
-
-template <>
-__device__ inline half myExp<half>(const half x)
-{
-    return hexp(x);
-}
-template <>
-__device__ inline float myExp<float>(const float x)
+inline int64_t volume(const nvinfer1::Dims& d)
 {
-    return __expf(x);
+    return std::accumulate(d.d, d.d + d.nbDims, 1, std::multiplies<int64_t>());
 }
 
 } // namespace bert
-#endif // TRT_PLUGIN_UTIL_H
+#endif // BERT_COMMON_H
+
+#endif // CUDA_VERSION >= 10010
diff --git a/plugin/common/checkMacrosPlugin.cpp b/plugin/common/checkMacrosPlugin.cpp
new file mode 100644
index 00000000..17dddbe7
--- /dev/null
+++ b/plugin/common/checkMacrosPlugin.cpp
@@ -0,0 +1,119 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "checkMacrosPlugin.h"
+#include <cstdlib>
+#include <cublas_v2.h>
+#include <cuda_runtime.h>
+
+namespace nvinfer1
+{
+namespace plugin
+{
+
+ILogger* gLogger{};
+
+template <ILogger::Severity kSeverity>
+int LogStream<kSeverity>::Buf::sync()
+{
+    std::string s = str();
+    while (!s.empty() && s.back() == '\n')
+    {
+        s.pop_back();
+    }
+    if (gLogger != nullptr)
+    {
+        gLogger->log(kSeverity, s.c_str());
+    }
+    str("");
+    return 0;
+}
+
+LogStream<ILogger::Severity::kERROR> gLogError;
+LogStream<ILogger::Severity::kWARNING> gLogWarning;
+LogStream<ILogger::Severity::kINFO> gLogInfo;
+
+// break-pointable
+void throwCudaError(const char* file, const char* function, int line, int status, const char* msg)
+{
+    CudaError error(file, function, line, status, msg);
+    error.log(gLogError);
+    throw error;
+}
+
+// break-pointable
+void throwCublasError(const char* file, const char* function, int line, int status, const char* msg)
+{
+    if (msg == nullptr)
+    {
+        auto s_ = static_cast<cublasStatus_t>(status);
+        switch (s_)
+        {
+        case CUBLAS_STATUS_SUCCESS: msg = "CUBLAS_STATUS_SUCCESS"; break;
+        case CUBLAS_STATUS_NOT_INITIALIZED: msg = "CUBLAS_STATUS_NOT_INITIALIZED"; break;
+        case CUBLAS_STATUS_ALLOC_FAILED: msg = "CUBLAS_STATUS_ALLOC_FAILED"; break;
+        case CUBLAS_STATUS_INVALID_VALUE: msg = "CUBLAS_STATUS_INVALID_VALUE"; break;
+        case CUBLAS_STATUS_ARCH_MISMATCH: msg = "CUBLAS_STATUS_ARCH_MISMATCH"; break;
+        case CUBLAS_STATUS_MAPPING_ERROR: msg = "CUBLAS_STATUS_MAPPING_ERROR"; break;
+        case CUBLAS_STATUS_EXECUTION_FAILED: msg = "CUBLAS_STATUS_EXECUTION_FAILED"; break;
+        case CUBLAS_STATUS_INTERNAL_ERROR: msg = "CUBLAS_STATUS_INTERNAL_ERROR"; break;
+        case CUBLAS_STATUS_NOT_SUPPORTED: msg = "CUBLAS_STATUS_NOT_SUPPORTED"; break;
+        case CUBLAS_STATUS_LICENSE_ERROR: msg = "CUBLAS_STATUS_LICENSE_ERROR"; break;
+        }
+    }
+    CublasError error(file, function, line, status, msg);
+    error.log(gLogError);
+    throw error;
+}
+
+// break-pointable
+void throwCudnnError(const char* file, const char* function, int line, int status, const char* msg)
+{
+    CudnnError error(file, function, line, status, msg);
+    error.log(gLogError);
+    throw error;
+}
+
+void logError(const char* msg, const char* file, const char* fn, int line)
+{
+    gLogError << "Parameter check failed at: " << file << "::" << fn << "::" << line;
+    gLogError << ", condition: " << msg << std::endl;
+}
+
+// break-pointable
+void reportAssertion(const char* msg, const char* file, int line)
+{
+    std::ostringstream stream;
+    stream << "Assertion failed: " << msg << std::endl
+           << file << ':' << line << std::endl
+           << "Aborting..." << std::endl;
+    getLogger()->log(nvinfer1::ILogger::Severity::kINTERNAL_ERROR, stream.str().c_str());
+    cudaDeviceReset();
+    abort();
+}
+
+void TRTException::log(std::ostream& logStream) const
+{
+    logStream << file << " (" << line << ") - " << name << " Error in " << function << ": " << status;
+    if (message != nullptr)
+    {
+        logStream << " (" << message << ")";
+    }
+    logStream << std::endl;
+}
+
+} // namespace plugin
+
+} // namespace nvinfer1
diff --git a/plugin/common/checkMacrosPlugin.h b/plugin/common/checkMacrosPlugin.h
new file mode 100644
index 00000000..48eca1a0
--- /dev/null
+++ b/plugin/common/checkMacrosPlugin.h
@@ -0,0 +1,248 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef CHECK_MACROS_PLUGIN_H
+#define CHECK_MACROS_PLUGIN_H
+
+#include "NvInfer.h"
+#include <sstream>
+
+#ifdef _MSC_VER
+#define FN_NAME __FUNCTION__
+#else
+#define FN_NAME __func__
+#endif
+#if __cplusplus < 201103L
+#define OVERRIDE
+#define NORETURN
+#else
+#define OVERRIDE override
+#define NORETURN [[noreturn]]
+#endif
+
+namespace nvinfer1
+{
+namespace plugin
+{
+template <ILogger::Severity kSeverity>
+class LogStream : public std::ostream
+{
+    class Buf : public std::stringbuf
+    {
+    public:
+        int sync() override;
+    };
+
+    Buf buffer;
+
+public:
+    LogStream()
+        : std::ostream(&buffer){};
+};
+
+extern LogStream<ILogger::Severity::kERROR> gLogError;
+extern LogStream<ILogger::Severity::kWARNING> gLogWarning;
+extern LogStream<ILogger::Severity::kINFO> gLogInfo;
+
+void reportAssertion(const char* msg, const char* file, int line);
+void logError(const char* msg, const char* file, const char* fn, int line);
+
+NORETURN void throwCudaError(const char* file, const char* function, int line, int status, const char* msg = nullptr);
+NORETURN void throwCudnnError(const char* file, const char* function, int line, int status, const char* msg = nullptr);
+NORETURN void throwCublasError(const char* file, const char* function, int line, int status, const char* msg = nullptr);
+
+class TRTException : public std::exception
+{
+public:
+    TRTException(const char* fl, const char* fn, int ln, int st, const char* msg, const char* nm)
+        : file(fl)
+        , function(fn)
+        , line(ln)
+        , status(st)
+        , message(msg)
+        , name(nm)
+    {
+    }
+    virtual void log(std::ostream& logStream) const;
+    void setMessage(const char* msg)
+    {
+        message = msg;
+    }
+
+protected:
+    const char* file{nullptr};
+    const char* function{nullptr};
+    int line{0};
+    int status{0};
+    const char* message{nullptr};
+    const char* name{nullptr};
+};
+
+class CudaError : public TRTException
+{
+public:
+    CudaError(const char* fl, const char* fn, int ln, int stat, const char* msg = nullptr)
+        : TRTException(fl, fn, ln, stat, msg, "Cuda")
+    {
+    }
+};
+
+class CudnnError : public TRTException
+{
+public:
+    CudnnError(const char* fl, const char* fn, int ln, int stat, const char* msg = nullptr)
+        : TRTException(fl, fn, ln, stat, msg, "Cudnn")
+    {
+    }
+};
+
+class CublasError : public TRTException
+{
+public:
+    CublasError(const char* fl, const char* fn, int ln, int stat, const char* msg = nullptr)
+        : TRTException(fl, fn, ln, stat, msg, "cuBLAS")
+    {
+    }
+};
+
+} // namespace plugin
+
+} // namespace nvinfer1
+
+#define API_CHECK(condition)                                                                                           \
+    {                                                                                                                  \
+        if ((condition) == false)                                                                                      \
+        {                                                                                                              \
+            nvinfer1::plugin::logError(#condition, __FILE__, FN_NAME, __LINE__);                                       \
+            return;                                                                                                    \
+        }                                                                                                              \
+    }
+
+#define API_CHECK_RETVAL(condition, retval)                                                                            \
+    {                                                                                                                  \
+        if ((condition) == false)                                                                                      \
+        {                                                                                                              \
+            nvinfer1::plugin::logError(#condition, __FILE__, FN_NAME, __LINE__);                                       \
+            return retval;                                                                                             \
+        }                                                                                                              \
+    }
+
+#define API_CHECK_WEIGHTS(Name)                                                                                        \
+    API_CHECK((Name).values != nullptr);                                                                               \
+    API_CHECK((Name).count > 0);                                                                                       \
+    API_CHECK(int((Name).type) >= 0 && int((Name).type) < EnumMax<DataType>());
+
+#define API_CHECK_WEIGHTS0(Name)                                                                                       \
+    API_CHECK((Name).count >= 0);                                                                                      \
+    API_CHECK((Name).count > 0 ? ((Name).values != nullptr) : ((Name).values == nullptr));                             \
+    API_CHECK(int((Name).type) >= 0 && int((Name).type) < EnumMax<DataType>());
+
+#define API_CHECK_WEIGHTS_RETVAL(Name, retval)                                                                         \
+    API_CHECK_RETVAL((Name).values != nullptr, retval);                                                                \
+    API_CHECK_RETVAL((Name).count > 0, retval);                                                                        \
+    API_CHECK_RETVAL(int((Name).type) >= 0 && int((Name).type) < EnumMax<DataType>(), retval);
+
+#define API_CHECK_WEIGHTS0_RETVAL(Name, retval)                                                                        \
+    API_CHECK_RETVAL((Name).count >= 0, retval);                                                                       \
+    API_CHECK_RETVAL((Name).count > 0 ? ((Name).values != nullptr) : ((Name).values == nullptr), retval);              \
+    API_CHECK_RETVAL(int((Name).type) >= 0 && int((Name).type) < EnumMax<DataType>(), retval);
+
+#define API_CHECK_NULL(param) API_CHECK((param) != nullptr)
+#define API_CHECK_NULL_RETVAL(param, retval) API_CHECK_RETVAL((param) != nullptr, retval)
+#define API_CHECK_NULL_RET_NULL(ptr) API_CHECK_NULL_RETVAL(ptr, nullptr)
+
+#define API_CHECK_ENUM_RANGE(Type, val) API_CHECK(int(val) >= 0 && int(val) < EnumMax<Type>())
+#define API_CHECK_ENUM_RANGE_RETVAL(Type, val, retval)                                                                 \
+    API_CHECK_RETVAL(int(val) >= 0 && int(val) < EnumMax<Type>(), retval)
+
+#ifndef TRT_PLUGIN_H
+#define CUBLASASSERTMSG(status_, msg)                                                                                  \
+    {                                                                                                                  \
+        auto s_ = status_;                                                                                             \
+        if (s_ != CUBLAS_STATUS_SUCCESS)                                                                               \
+        {                                                                                                              \
+            nvinfer1::plugin::throwCublasError(__FILE__, FN_NAME, __LINE__, s_, msg);                                  \
+        }                                                                                                              \
+    }
+
+#define CUBLASASSERT(status_)                                                                                          \
+    {                                                                                                                  \
+        auto s_ = status_;                                                                                             \
+        if (s_ != CUBLAS_STATUS_SUCCESS)                                                                               \
+        {                                                                                                              \
+            nvinfer1::plugin::throwCublasError(__FILE__, FN_NAME, __LINE__, s_);                                       \
+        }                                                                                                              \
+    }
+
+#define CUDNNASSERTMSG(status_, msg)                                                                                   \
+    {                                                                                                                  \
+        auto s_ = status_;                                                                                             \
+        if (s_ != CUDNN_STATUS_SUCCESS)                                                                                \
+        {                                                                                                              \
+            nvinfer1::plugin::throwCudnnError(__FILE__, FN_NAME, __LINE__, s_, msg);                                   \
+        }                                                                                                              \
+    }
+
+#define CUDNNASSERT(status_)                                                                                           \
+    {                                                                                                                  \
+        auto s_ = status_;                                                                                             \
+        if (s_ != CUDNN_STATUS_SUCCESS)                                                                                \
+        {                                                                                                              \
+            const char* msg = cudnnGetErrorString(s_);                                                                 \
+            nvinfer1::plugin::throwCudnnError(__FILE__, FN_NAME, __LINE__, s_, msg);                                   \
+        }                                                                                                              \
+    }
+
+#define CUASSERTMSG(status_, msg)                                                                                      \
+    {                                                                                                                  \
+        auto s_ = status_;                                                                                             \
+        if (s_ != cudaSuccess)                                                                                         \
+        {                                                                                                              \
+            nvinfer1::plugin::throwCudaError(__FILE__, FN_NAME, __LINE__, s_, msg);                                    \
+        }                                                                                                              \
+    }
+
+#define CUASSERT(status_)                                                                                              \
+    {                                                                                                                  \
+        auto s_ = status_;                                                                                             \
+        if (s_ != cudaSuccess)                                                                                         \
+        {                                                                                                              \
+            const char* msg = cudaGetErrorString(s_);                                                                  \
+            nvinfer1::plugin::throwCudaError(__FILE__, FN_NAME, __LINE__, s_, msg);                                    \
+        }                                                                                                              \
+    }
+
+#define ASSERT(assertion)                                                                                              \
+    {                                                                                                                  \
+        if (!(assertion))                                                                                              \
+        {                                                                                                              \
+            nvinfer1::plugin::reportAssertion(#assertion, __FILE__, __LINE__);                                         \
+        }                                                                                                              \
+    }
+
+#define FAIL(msg)                                                                                                      \
+    {                                                                                                                  \
+        nvinfer1::plugin::reportAssertion(msg, __FILE__, __LINE__);                                                    \
+    }
+
+#define CUERRORMSG(status_)                                                                                            \
+    {                                                                                                                  \
+        auto s_ = status_;                                                                                             \
+        if (s_ != 0)                                                                                                   \
+            nvinfer1::plugin::logError(#status_ " failure.", __FILE__, FN_NAME, __LINE__);                             \
+    }
+
+#endif // TRT_PLUGIN_H
+#endif /*CHECK_MACROS_PLUGIN_H*/
diff --git a/plugin/common/common.cuh b/plugin/common/common.cuh
new file mode 100644
index 00000000..89dbc64d
--- /dev/null
+++ b/plugin/common/common.cuh
@@ -0,0 +1,407 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef COMMON_CUH
+#define COMMON_CUH
+
+#include "cublas_v2.h"
+#include <cub/cub.cuh>
+
+#define HDI inline __host__ __device__
+
+using kv_float = cub::KeyValuePair<float, float>;
+using kv_half = cub::KeyValuePair<half, half>;
+using kv_half2 = cub::KeyValuePair<half2, half2>;
+
+template <typename T>
+__device__ inline T tanh(const T& x);
+
+template <typename T>
+__device__ inline T rsqrt(const T& x);
+
+template <typename T>
+__device__ inline T exp(const T& x);
+
+
+// Float32 Operations
+template <>
+__device__ inline float tanh(const float& x)
+{
+    return tanhf(x);
+}
+
+template <>
+__device__ inline float rsqrt(const float& x)
+{
+    return rsqrtf(x);
+}
+
+template <>
+__device__ inline float exp(const float& x)
+{
+    return expf(x);
+}
+
+
+__device__ inline kv_float operator+(const kv_float& a, const kv_float& b)
+{
+    return kv_float(a.key + b.key, a.value + b.value);
+}
+
+
+// Half Operations
+
+__device__ inline half2 __hadd2_with_fallback(const half2 a, const half2 b)
+{
+#if __CUDA_ARCH__ >= 530
+    return __hadd2(a, b);
+#else
+    float2 out{};
+    out.x = __half2float(a.x) + __half2float(b.x);
+    out.y = __half2float(a.y) + __half2float(b.y);
+    return __float22half2_rn(out);
+#endif
+}
+
+#if __CUDA_ARCH__ < 530
+
+template <typename T>
+__device__ inline T operator+(const T& a, const T& b);
+
+template <typename T>
+__device__ inline T operator*(const T& a, const T& b);
+
+
+template <>
+__device__ inline half2 operator+(const half2& a, const half2& b)
+{
+    return __hadd2_with_fallback(a, b);
+}
+
+template <>
+__device__ inline half2 operator*(const half2& a, const half2& b)
+{
+    float2 out{};
+    out.x = __half2float(a.x) * __half2float(b.x);
+    out.y = __half2float(a.y) * __half2float(b.y);
+    return __float22half2_rn(out);
+}
+
+template <typename T>
+__device__ inline T operator+(const T& a, const T& b);
+
+
+template <typename T>
+__device__ inline T operator/(const T& a, const T& b);
+
+
+template <typename T>
+__device__ inline T& operator+=(T& a, const T& b);
+
+template <typename T>
+__device__ inline T operator-(const T& a, const T& b);
+
+template <typename T>
+__device__ inline T operator*(const T& a, const T& b);
+
+
+template <>
+__device__ inline half operator+(const half& a, const half& b)
+{
+    return __float2half(__half2float(a) + __half2float(b));
+}
+
+template <>
+__device__ inline half& operator+=(half& a, const half& b)
+{
+    a = __float2half(__half2float(a) + __half2float(b));
+    return a;
+}
+
+template <>
+__device__ inline half operator-(const half& a, const half& b)
+{
+    return __float2half(__half2float(a) - __half2float(b));
+}
+
+template <>
+__device__ inline half operator*(const half& a, const half& b)
+{
+    return __float2half(__half2float(a) * __half2float(b));
+}
+
+template <>
+__device__ inline half operator/(const half& a, const half& b)
+{
+    return __float2half(__half2float(a) / __half2float(b));
+}
+
+#endif
+
+template <>
+__device__ inline half tanh(const half& x)
+{
+    const float tmp = tanhf(__half2float(x));
+    return __float2half(tmp);
+}
+
+template <>
+__device__ inline half2 tanh(const half2& x)
+{
+    // at the moment, there is no half2 tanh builtin
+    float2 tmp = (__half22float2(x));
+    tmp.x = tanhf(tmp.x);
+    tmp.y = tanhf(tmp.y);
+    return __float22half2_rn(tmp);
+}
+
+template <>
+__device__ inline half rsqrt(const half& x)
+{
+#if __CUDA_ARCH__ >= 530
+    return hrsqrt(x);
+#else
+    return __float2half(rsqrt(__half2float(x)));
+#endif
+}
+
+template <>
+__device__ inline half exp(const half& x)
+{
+#if __CUDA_ARCH__ >= 530
+    return hexp(x);
+#else
+    return __float2half(exp(__half2float(x)));
+#endif
+}
+
+__device__ inline kv_half operator+(const kv_half& a, const kv_half& b)
+{
+    const half2 a2 = __halves2half2(a.key, a.value);
+    const half2 b2 = __halves2half2(b.key, b.value);
+    const half2 res = __hadd2_with_fallback(a2, b2);
+    return kv_half(res.x, res.y);
+}
+
+__device__ inline kv_half2 operator+(const kv_half2& a, const kv_half2& b)
+{
+    return kv_half2(__hadd2_with_fallback(a.key, b.key), __hadd2_with_fallback(a.value, b.value));
+}
+
+
+// Helper Functions
+
+template <typename T>
+using kvp = cub::KeyValuePair<T, T>;
+
+template <typename T, typename R, typename P, int TPB>
+__device__ inline void layerNorm(
+    const kvp<R>& threadData, const int ld, const int offset, const P* beta, const P* gamma, T* output)
+{
+    // Assuming threadData is already divided by ld
+
+    using BlockReduce = cub::BlockReduce<kvp<R>, TPB>;
+    __shared__ typename BlockReduce::TempStorage temp_storage;
+    __shared__ R mu;     // mean
+    __shared__ R rsigma; // 1 / std.dev.
+
+    const auto sumKV = BlockReduce(temp_storage).Reduce(threadData, cub::Sum());
+
+    if (threadIdx.x == 0)
+    {
+        mu = sumKV.key;
+        rsigma = rsqrt(sumKV.value - mu * mu);
+    }
+    __syncthreads();
+
+    for (int i = threadIdx.x; i < ld; i += TPB)
+    {
+        const int idx = offset + i;
+        const R val = output[idx];
+        const R g(gamma[i]);
+        const R b(beta[i]);
+        output[idx] = g * (val - mu) * rsigma + b;
+    }
+}
+
+template <typename T, typename P, int TPB>
+__device__ inline void layerNormSmall(
+    const T val, const kvp<T>& threadData, const int ld, const int idx, const P* beta, const P* gamma, T* output)
+{
+    // Assuming threadData is already divided by ld
+    // Small settings: the block covers the leading dimension TPB >= ld. The input
+    // value is available in a register
+
+    using BlockReduce = cub::BlockReduce<kvp<T>, TPB>;
+    __shared__ typename BlockReduce::TempStorage temp_storage;
+    __shared__ T mu;     // mean
+    __shared__ T rsigma; // 1 / std.dev.
+
+    const auto sumKV = BlockReduce(temp_storage).Reduce(threadData, cub::Sum());
+
+    if (threadIdx.x == 0)
+    {
+        mu = sumKV.key;
+        rsigma = rsqrt(sumKV.value - mu * mu);
+    }
+    __syncthreads();
+
+    if (threadIdx.x < ld)
+    {
+        const T g(gamma[threadIdx.x]);
+        const T b(beta[threadIdx.x]);
+        output[idx] = g * (val - mu) * rsigma + b;
+    }
+}
+
+template <typename T, unsigned TPB>
+__device__ inline void scaledSoftmaxSmall(
+    const int ld, const int lastValid, const float rsqrtHeadSize, const T* input, T* output)
+{
+
+    using BlockReduce = cub::BlockReduce<float, TPB>;
+
+    __shared__ typename BlockReduce::TempStorage tmpStorage;
+
+    __shared__ float rZ;
+
+    const int offset = (blockIdx.y * gridDim.x + blockIdx.x) * ld;
+
+    const float w(rsqrtHeadSize);
+    cub::Sum sum;
+    float threadData(0);
+
+    const int idx = offset + threadIdx.x;
+    if (threadIdx.x < lastValid)
+    {
+        const float val = input[idx];
+        threadData = exp(val * w);
+    }
+
+    const auto Z = BlockReduce(tmpStorage).Reduce(threadData, sum);
+
+    if (threadIdx.x == 0)
+    {
+        rZ = (1.f) / Z;
+    }
+    __syncthreads();
+
+    if (threadIdx.x < ld)
+    {
+        // this will be 0 for threadIdx.x >= lastValid
+        output[idx] = T(threadData * rZ);
+    }
+}
+
+template <typename T, unsigned TPB>
+__device__ inline void scaledSoftmax(
+    const int ld, const int lastValid, const float rsqrtHeadSize, const T* input, T* output)
+{
+
+    using BlockReduce = cub::BlockReduce<float, TPB>;
+    __shared__ typename BlockReduce::TempStorage tmpStorage;
+
+    __shared__ float rZ;
+
+    const int offset = (blockIdx.y * gridDim.x + blockIdx.x) * ld;
+
+    const float w(rsqrtHeadSize);
+    cub::Sum sum;
+    float threadData(0);
+
+    for (int i = threadIdx.x; i < lastValid; i += TPB)
+    {
+        const int idx = offset + i;
+        const float val = input[idx];
+        threadData += exp(val * w);
+    }
+
+    const auto Z = BlockReduce(tmpStorage).Reduce(threadData, sum);
+
+    if (threadIdx.x == 0)
+    {
+        rZ = 1.f / Z;
+    }
+    __syncthreads();
+
+    for (int i = threadIdx.x; i < ld; i += TPB)
+    {
+        const int idx = offset + i;
+        const float val = (i < lastValid) ? exp(float(input[idx]) * w) * rZ : 0.f;
+        output[idx] = T(val);
+    }
+}
+
+template <typename IntType>
+constexpr HDI IntType ceildiv(IntType a, IntType b)
+{
+    return (a + b - 1) / b;
+}
+template <typename IntType>
+constexpr HDI IntType alignTo(IntType a, IntType b)
+{
+    return ceildiv(a, b) * b;
+}
+
+template <int VPT>
+struct BytesToType;
+
+template <>
+struct BytesToType<2>
+{
+    using type = uint16_t;
+};
+template <>
+struct BytesToType<4>
+{
+    using type = uint32_t;
+};
+template <>
+struct BytesToType<8>
+{
+    using type = uint64_t;
+};
+template <>
+struct BytesToType<16>
+{
+    using type = float4;
+};
+
+template <int Bytes>
+__device__ inline void copy(const void* local, void* data)
+{
+    using T = typename BytesToType<Bytes>::type;
+
+    const T* in = static_cast<const T*>(local);
+    T* out = static_cast<T*>(data);
+    *out = *in;
+}
+
+template <typename T>
+__device__ inline T myExp(const T x);
+
+template <>
+__device__ inline half myExp<half>(const half x)
+{
+    return exp(x);
+}
+template <>
+__device__ inline float myExp<float>(const float x)
+{
+    return __expf(x);
+}
+
+#endif // #ifndef COMMON_CUH
diff --git a/plugin/common/cub_helper.h b/plugin/common/cub_helper.h
index 616f450c..2be4ca57 100644
--- a/plugin/common/cub_helper.h
+++ b/plugin/common/cub_helper.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/plugin/common/cudaDriverWrapper.cpp b/plugin/common/cudaDriverWrapper.cpp
new file mode 100644
index 00000000..947b825d
--- /dev/null
+++ b/plugin/common/cudaDriverWrapper.cpp
@@ -0,0 +1,137 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#define CUDA_LIB_NAME "cuda"
+
+#if defined(_WIN32)
+#if !defined(WIN32_LEAN_AND_MEAN)
+#define WIN32_LEAN_AND_MEAN
+#endif // defined(WIN32_LEAN_AND_MEAN)
+#include <windows.h>
+#define dllOpen(name) (void*) LoadLibraryA("nv" name ".dll")
+#define dllClose(handle) FreeLibrary(static_cast<HMODULE>(handle))
+#define dllGetSym(handle, name) GetProcAddress(static_cast<HMODULE>(handle), name)
+#else
+#include <dlfcn.h>
+#define dllOpen(name) dlopen("lib" name ".so", RTLD_LAZY)
+#define dllClose(handle) dlclose(handle)
+#define dllGetSym(handle, name) dlsym(handle, name)
+#endif
+
+#include "cudaDriverWrapper.h"
+#include "plugin.h"
+#include <cuda.h>
+#include <stdio.h>
+
+using namespace nvinfer1;
+
+CUDADriverWrapper::CUDADriverWrapper()
+{
+    handle = dllOpen(CUDA_LIB_NAME);
+    ASSERT(handle != nullptr);
+
+    auto load_sym = [](void* handle, const char* name) {
+        void* ret = dllGetSym(handle, name);
+        ASSERT(ret != nullptr);
+        return ret;
+    };
+
+    *(void**) (&_cuGetErrorName) = load_sym(handle, "cuGetErrorName");
+    *(void**) (&_cuFuncSetAttribute) = load_sym(handle, "cuFuncSetAttribute");
+    *(void**) (&_cuLinkComplete) = load_sym(handle, "cuLinkComplete");
+    *(void**) (&_cuModuleUnload) = load_sym(handle, "cuModuleUnload");
+    *(void**) (&_cuLinkDestroy) = load_sym(handle, "cuLinkDestroy");
+    *(void**) (&_cuModuleLoadData) = load_sym(handle, "cuModuleLoadData");
+    *(void**) (&_cuLinkCreate) = load_sym(handle, "cuLinkCreate_v2");
+    *(void**) (&_cuModuleGetFunction) = load_sym(handle, "cuModuleGetFunction");
+    *(void**) (&_cuLinkAddFile) = load_sym(handle, "cuLinkAddFile_v2");
+    *(void**) (&_cuLinkAddData) = load_sym(handle, "cuLinkAddData_v2");
+    *(void**) (&_cuLaunchCooperativeKernel) = load_sym(handle, "cuLaunchCooperativeKernel");
+    *(void**) (&_cuLaunchKernel) = load_sym(handle, "cuLaunchKernel");
+}
+
+CUDADriverWrapper::~CUDADriverWrapper()
+{
+    dllClose(handle);
+}
+
+CUresult CUDADriverWrapper::cuGetErrorName(CUresult error, const char** pStr) const
+{
+    return (*_cuGetErrorName)(error, pStr);
+}
+
+CUresult CUDADriverWrapper::cuFuncSetAttribute(CUfunction hfunc, CUfunction_attribute attrib, int value) const
+{
+    return (*_cuFuncSetAttribute)(hfunc, attrib, value);
+}
+
+CUresult CUDADriverWrapper::cuLinkComplete(CUlinkState state, void** cubinOut, size_t* sizeOut) const
+{
+    return (*_cuLinkComplete)(state, cubinOut, sizeOut);
+}
+
+CUresult CUDADriverWrapper::cuModuleUnload(CUmodule hmod) const
+{
+    return (*_cuModuleUnload)(hmod);
+}
+
+CUresult CUDADriverWrapper::cuLinkDestroy(CUlinkState state) const
+{
+    return (*_cuLinkDestroy)(state);
+}
+
+CUresult CUDADriverWrapper::cuModuleLoadData(CUmodule* module, const void* image) const
+{
+    return (*_cuModuleLoadData)(module, image);
+}
+
+CUresult CUDADriverWrapper::cuLinkCreate(
+    unsigned int numOptions, CUjit_option* options, void** optionValues, CUlinkState* stateOut) const
+{
+    return (*_cuLinkCreate)(numOptions, options, optionValues, stateOut);
+}
+
+CUresult CUDADriverWrapper::cuModuleGetFunction(CUfunction* hfunc, CUmodule hmod, const char* name) const
+{
+    return (*_cuModuleGetFunction)(hfunc, hmod, name);
+}
+
+CUresult CUDADriverWrapper::cuLinkAddFile(CUlinkState state, CUjitInputType type, const char* path,
+    unsigned int numOptions, CUjit_option* options, void** optionValues) const
+{
+    return (*_cuLinkAddFile)(state, type, path, numOptions, options, optionValues);
+}
+
+CUresult CUDADriverWrapper::cuLinkAddData(CUlinkState state, CUjitInputType type, void* data, size_t size,
+    const char* name, unsigned int numOptions, CUjit_option* options, void** optionValues) const
+{
+    return (*_cuLinkAddData)(state, type, data, size, name, numOptions, options, optionValues);
+}
+
+CUresult CUDADriverWrapper::cuLaunchCooperativeKernel(CUfunction f, unsigned int gridDimX, unsigned int gridDimY,
+    unsigned int gridDimZ, unsigned int blockDimX, unsigned int blockDimY, unsigned int blockDimZ,
+    unsigned int sharedMemBytes, CUstream hStream, void** kernelParams) const
+{
+    return (*_cuLaunchCooperativeKernel)(
+        f, gridDimX, gridDimY, gridDimZ, blockDimX, blockDimY, blockDimZ, sharedMemBytes, hStream, kernelParams);
+}
+
+CUresult CUDADriverWrapper::cuLaunchKernel(CUfunction f, unsigned int gridDimX, unsigned int gridDimY,
+    unsigned int gridDimZ, unsigned int blockDimX, unsigned int blockDimY, unsigned int blockDimZ,
+    unsigned int sharedMemBytes, CUstream hStream, void** kernelParams, void** extra) const
+{
+    return (*_cuLaunchKernel)(
+        f, gridDimX, gridDimY, gridDimZ, blockDimX, blockDimY, blockDimZ, sharedMemBytes, hStream, kernelParams, extra);
+}
diff --git a/plugin/common/cudaDriverWrapper.cu b/plugin/common/cudaDriverWrapper.cu
deleted file mode 100644
index eec1ad2a..00000000
--- a/plugin/common/cudaDriverWrapper.cu
+++ /dev/null
@@ -1,114 +0,0 @@
-/*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifdef __linux__
-#ifdef __x86_64__
-#include <cuda.h>
-#include <stdio.h>
-#include <dlfcn.h>
-#include "cudaDriverWrapper.h"
-#include <assert.h>
-
-using namespace nvinfer1;
-
-CUDADriverWrapper::CUDADriverWrapper()
-{
-    handle = dlopen("libcuda.so.1", RTLD_LAZY);
-    assert(handle != nullptr);
-
-    auto load_sym = [](void *handle, const char *name) {
-        void *ret = dlsym(handle, name);
-        assert(ret != nullptr);
-        return ret;
-    };
-
-    *(void**)(&_cuGetErrorName) = load_sym(handle, "cuGetErrorName");
-    *(void**)(&_cuFuncSetAttribute) = load_sym(handle, "cuFuncSetAttribute");
-    *(void**)(&_cuLinkComplete) = load_sym(handle, "cuLinkComplete");
-    *(void**)(&_cuModuleUnload) = load_sym(handle, "cuModuleUnload");
-    *(void**)(&_cuLinkDestroy) = load_sym(handle, "cuLinkDestroy");
-    *(void**)(&_cuModuleLoadData) = load_sym(handle, "cuModuleLoadData");
-    *(void**)(&_cuLinkCreate) = load_sym(handle, "cuLinkCreate_v2");
-    *(void**)(&_cuModuleGetFunction) = load_sym(handle, "cuModuleGetFunction");
-    *(void**)(&_cuLinkAddFile) = load_sym(handle, "cuLinkAddFile_v2");
-    *(void**)(&_cuLinkAddData) = load_sym(handle, "cuLinkAddData_v2");
-    *(void**)(&_cuLaunchCooperativeKernel) = load_sym(handle, "cuLaunchCooperativeKernel");
-}
-
-CUDADriverWrapper::~CUDADriverWrapper()
-{
-    dlclose(handle);
-}
-
-CUresult CUDADriverWrapper::cuGetErrorName(CUresult error, const char** pStr) const
-{
-    return (*_cuGetErrorName)(error, pStr);
-}
-
-CUresult CUDADriverWrapper::cuFuncSetAttribute(CUfunction hfunc, CUfunction_attribute attrib, int  value) const
-{
-    return (*_cuFuncSetAttribute)(hfunc, attrib, value);
-}
-
-CUresult CUDADriverWrapper::cuLinkComplete(CUlinkState state, void** cubinOut, size_t* sizeOut) const
-{
-    return (*_cuLinkComplete)(state, cubinOut, sizeOut);
-}
-
-CUresult CUDADriverWrapper::cuModuleUnload(CUmodule hmod) const
-{
-    return (*_cuModuleUnload)(hmod);
-}
-
-CUresult CUDADriverWrapper::cuLinkDestroy(CUlinkState state) const
-{
-    return (*_cuLinkDestroy)(state);
-}
-
-CUresult CUDADriverWrapper::cuModuleLoadData(CUmodule* module, const void* image) const
-{
-    return (*_cuModuleLoadData)(module, image);
-}
-
-CUresult CUDADriverWrapper::cuLinkCreate(unsigned int numOptions, CUjit_option* options, void** optionValues, CUlinkState* stateOut) const
-{
-    return (*_cuLinkCreate)(numOptions, options, optionValues, stateOut);
-}
-
-CUresult CUDADriverWrapper::cuModuleGetFunction(CUfunction* hfunc, CUmodule hmod, const char* name) const
-{
-    return (*_cuModuleGetFunction)(hfunc, hmod, name);
-}
-
-CUresult CUDADriverWrapper::cuLinkAddFile(CUlinkState state, CUjitInputType type, const char* path, unsigned int numOptions, CUjit_option* options, void** optionValues) const
-{
-    return (*_cuLinkAddFile)(state, type, path, numOptions, options, optionValues);
-}
-
-CUresult CUDADriverWrapper::cuLinkAddData(CUlinkState state, CUjitInputType type, void* data, size_t size, const char* name, unsigned int numOptions, CUjit_option* options, void** optionValues) const
-{
-    return (*_cuLinkAddData)(state, type, data, size, name, numOptions, options, optionValues);
-}
-
-CUresult CUDADriverWrapper::cuLaunchCooperativeKernel (CUfunction f, unsigned int  gridDimX, unsigned int  gridDimY, unsigned int  gridDimZ,
-    unsigned int  blockDimX, unsigned int  blockDimY, unsigned int  blockDimZ, unsigned int  sharedMemBytes, CUstream hStream, void** kernelParams) const
-{
-    return (*_cuLaunchCooperativeKernel)(f, gridDimX, gridDimY, gridDimZ,
-        blockDimX, blockDimY, blockDimZ, sharedMemBytes, hStream, kernelParams);
-}
-
-#endif // __x86_64__
-#endif //__linux__
diff --git a/plugin/common/cudaDriverWrapper.h b/plugin/common/cudaDriverWrapper.h
index da74420e..c24b578c 100644
--- a/plugin/common/cudaDriverWrapper.h
+++ b/plugin/common/cudaDriverWrapper.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -17,11 +17,13 @@
 #ifndef CUDA_DRIVER_WRAPPER_H
 #define CUDA_DRIVER_WRAPPER_H
 
-#ifdef __linux__
-#ifdef __x86_64__
 #include <cstdio>
 #include <cuda.h>
-#include <dlfcn.h>
+
+#define cuErrCheck(stat, wrap)                                                                                         \
+    {                                                                                                                  \
+        nvinfer1::cuErrCheck_((stat), wrap, __FILE__, __LINE__);                                                       \
+    }
 
 namespace nvinfer1
 {
@@ -59,6 +61,10 @@ class CUDADriverWrapper
         unsigned int gridDimZ, unsigned int blockDimX, unsigned int blockDimY, unsigned int blockDimZ,
         unsigned int sharedMemBytes, CUstream hStream, void** kernelParams) const;
 
+    CUresult cuLaunchKernel(CUfunction f, unsigned int gridDimX, unsigned int gridDimY, unsigned int gridDimZ,
+        unsigned int blockDimX, unsigned int blockDimY, unsigned int blockDimZ, unsigned int sharedMemBytes,
+        CUstream hStream, void** kernelParams, void** extra) const;
+
 private:
     void* handle;
     CUresult (*_cuGetErrorName)(CUresult, const char**);
@@ -74,8 +80,21 @@ class CUDADriverWrapper
         CUlinkState, CUjitInputType, void*, size_t, const char*, unsigned int, CUjit_option*, void**);
     CUresult (*_cuLaunchCooperativeKernel)(CUfunction, unsigned int, unsigned int, unsigned int, unsigned int,
         unsigned int, unsigned int, unsigned int, CUstream, void**);
+    CUresult (*_cuLaunchKernel)(CUfunction f, unsigned int gridDimX, unsigned int gridDimY, unsigned int gridDimZ,
+        unsigned int blockDimX, unsigned int blockDimY, unsigned int blockDimZ, unsigned int sharedMemBytes,
+        CUstream hStream, void** kernelParams, void** extra);
 };
+
+inline void cuErrCheck_(CUresult stat, const CUDADriverWrapper& wrap, const char* file, int line)
+{
+    if (stat != CUDA_SUCCESS)
+    {
+        const char* msg = nullptr;
+        wrap.cuGetErrorName(stat, &msg);
+        fprintf(stderr, "CUDA Error: %s %s %d\n", msg, file, line);
+    }
+}
+
 } // namespace nvinfer1
-#endif // __x86_64__
-#endif //__linux__
+
 #endif // CUDA_DRIVER_WRAPPER_H
diff --git a/plugin/common/kernels/CMakeLists.txt b/plugin/common/kernels/CMakeLists.txt
index f6e65f88..117cac00 100644
--- a/plugin/common/kernels/CMakeLists.txt
+++ b/plugin/common/kernels/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/plugin/common/kernels/allClassNMS.cu b/plugin/common/kernels/allClassNMS.cu
index 8dca8693..bcaf0806 100644
--- a/plugin/common/kernels/allClassNMS.cu
+++ b/plugin/common/kernels/allClassNMS.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -15,7 +15,7 @@
  */
 #include "kernel.h"
 #include "bboxUtils.h"
-#include <vector>
+#include <array>
 
 template <typename T_BBOX>
 __device__ T_BBOX bboxSize(
@@ -66,6 +66,19 @@ __device__ void intersectBbox(
     }
 }
 
+
+template <typename T_BBOX>
+__device__ Bbox<T_BBOX> getDiagonalMinMaxSortedBox(const Bbox<T_BBOX>& bbox1)
+{
+    Bbox<T_BBOX> result;
+    result.xmin = min(bbox1.xmin, bbox1.xmax);
+    result.xmax = max(bbox1.xmin, bbox1.xmax);
+
+    result.ymin = min(bbox1.ymin, bbox1.ymax);
+    result.ymax = max(bbox1.ymin, bbox1.ymax);
+    return result;
+}
+
 template <typename T_BBOX>
 __device__ float jaccardOverlap(
     const Bbox<T_BBOX>& bbox1,
@@ -73,7 +86,11 @@ __device__ float jaccardOverlap(
     const bool normalized)
 {
     Bbox<T_BBOX> intersect_bbox;
-    intersectBbox(bbox1, bbox2, &intersect_bbox);
+
+    Bbox<T_BBOX> localbbox1 = getDiagonalMinMaxSortedBox(bbox1);
+    Bbox<T_BBOX> localbbox2 = getDiagonalMinMaxSortedBox(bbox2);
+
+    intersectBbox(localbbox1, localbbox2, &intersect_bbox);
     float intersect_width, intersect_height;
     if (normalized)
     {
@@ -88,8 +105,8 @@ __device__ float jaccardOverlap(
     if (intersect_width > 0 && intersect_height > 0)
     {
         float intersect_size = intersect_width * intersect_height;
-        float bbox1_size = bboxSize(bbox1, normalized);
-        float bbox2_size = bboxSize(bbox2, normalized);
+        float bbox1_size = bboxSize(localbbox1, normalized);
+        float bbox2_size = bboxSize(localbbox2, normalized);
         return intersect_size / (bbox1_size + bbox2_size - intersect_size);
     }
     else
@@ -139,7 +156,7 @@ __global__ void allClassNMS_kernel(
         Bbox<T_BBOX> loc_bbox[TSIZE];
 
 // initialize Bbox, Bboxinfo, kept_bboxinfo_flag
-        // Eliminate shared memory RAW hazard  
+        // Eliminate shared memory RAW hazard
         __syncthreads();
 #pragma unroll
         for (int t = 0; t < TSIZE; t++)
@@ -155,11 +172,11 @@ __global__ void allClassNMS_kernel(
                 {
                     const int bbox_data_idx = share_location ? (loc_bboxIndex[t] % num_preds_per_class + bbox_idx_offset) : loc_bboxIndex[t];
 
-                    loc_bbox[t].xmin = flipXY ? bbox_data[bbox_data_idx * 4 + 1] 
+                    loc_bbox[t].xmin = flipXY ? bbox_data[bbox_data_idx * 4 + 1]
                       : bbox_data[bbox_data_idx * 4 + 0];
-                    loc_bbox[t].ymin = flipXY ? bbox_data[bbox_data_idx * 4 + 0] 
+                    loc_bbox[t].ymin = flipXY ? bbox_data[bbox_data_idx * 4 + 0]
                       : bbox_data[bbox_data_idx * 4 + 1];
-                    loc_bbox[t].xmax = flipXY ? bbox_data[bbox_data_idx * 4 + 3] 
+                    loc_bbox[t].xmax = flipXY ? bbox_data[bbox_data_idx * 4 + 3]
                       : bbox_data[bbox_data_idx * 4 + 2];
                     loc_bbox[t].ymax = flipXY ? bbox_data[bbox_data_idx * 4 + 2]
                       : bbox_data[bbox_data_idx * 4 + 3];
@@ -188,7 +205,7 @@ __global__ void allClassNMS_kernel(
             ref_bbox.xmax = flipXY ? bbox_data[ref_bbox_idx * 4 + 3] : bbox_data[ref_bbox_idx * 4 + 2];
             ref_bbox.ymax = flipXY ? bbox_data[ref_bbox_idx * 4 + 2] : bbox_data[ref_bbox_idx * 4 + 3];
 
-            // Eliminate shared memory RAW hazard  
+            // Eliminate shared memory RAW hazard
             __syncthreads();
 
             for (int t = 0; t < TSIZE; t++)
@@ -268,7 +285,7 @@ pluginStatus_t allClassNMS_gpu(
     kernel[t_size - 1]<<<GS, BS, BS * t_size * sizeof(bool), stream>>>(num, num_classes, num_preds_per_class,
                                                                        top_k, nms_threshold, share_location, isNormalized,
                                                                        (T_BBOX*) bbox_data,
-                                                                       (T_SCORE*) beforeNMS_scores, 
+                                                                       (T_SCORE*) beforeNMS_scores,
                                                                        (int*) beforeNMS_index_array,
                                                                        (T_SCORE*) afterNMS_scores,
                                                                        (int*) afterNMS_index_array,
@@ -278,7 +295,7 @@ pluginStatus_t allClassNMS_gpu(
     return STATUS_SUCCESS;
 }
 
-// allClassNMS LAUNCH CONFIG 
+// allClassNMS LAUNCH CONFIG
 typedef pluginStatus_t (*nmsFunc)(cudaStream_t,
                                const int,
                                const int,
@@ -317,17 +334,8 @@ struct nmsLaunchConfigSSD
     }
 };
 
-static std::vector<nmsLaunchConfigSSD> nmsFuncVec;
-
-bool nmsInit()
-{
-    nmsFuncVec.push_back(nmsLaunchConfigSSD(DataType::kFLOAT, DataType::kFLOAT,
-                                            allClassNMS_gpu<float, float>));
-    return true;
-}
-
-static bool initialized = nmsInit();
-
+static std::array<nmsLaunchConfigSSD, 1> nmsSsdLCOptions = {
+    nmsLaunchConfigSSD(DataType::kFLOAT, DataType::kFLOAT, allClassNMS_gpu<float, float>)};
 
 pluginStatus_t allClassNMS(cudaStream_t stream,
                         const int num,
@@ -347,12 +355,12 @@ pluginStatus_t allClassNMS(cudaStream_t stream,
                         bool flipXY)
 {
     nmsLaunchConfigSSD lc = nmsLaunchConfigSSD(DT_SCORE, DT_BBOX, allClassNMS_gpu<float, float>);
-    for (unsigned i = 0; i < nmsFuncVec.size(); ++i)
+    for (unsigned i = 0; i < nmsSsdLCOptions.size(); ++i)
     {
-        if (lc == nmsFuncVec[i])
+        if (lc == nmsSsdLCOptions[i])
         {
             DEBUG_PRINTF("all class nms kernel %d\n", i);
-            return nmsFuncVec[i].function(stream,
+            return nmsSsdLCOptions[i].function(stream,
                                           num,
                                           num_classes,
                                           num_preds_per_class,
diff --git a/plugin/common/kernels/bboxDeltas2Proposals.cu b/plugin/common/kernels/bboxDeltas2Proposals.cu
index 01b3eae9..4bfd68b1 100755
--- a/plugin/common/kernels/bboxDeltas2Proposals.cu
+++ b/plugin/common/kernels/bboxDeltas2Proposals.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -14,9 +14,9 @@
  * limitations under the License.
  */
 #include <algorithm>
+#include <array>
 #include <math.h>
 #include <stdio.h>
-#include <vector>
 #include "kernel.h"
 
 using std::max;
@@ -235,21 +235,10 @@ struct bd2pLaunchConfig
     }
 };
 
-static std::vector<bd2pLaunchConfig> bd2pFunVec;
 #define FLOAT32 nvinfer1::DataType::kFLOAT
-bool init()
-{
-    bd2pFunVec.push_back(bd2pLaunchConfig(FLOAT32, NC4HW,
-                                          FLOAT32, NC4HW,
-                                          FLOAT32, NCHW,
-                                          bboxDeltas2Proposals_gpu<float, NC4HW, float4, NC4HW, float, NCHW>));
-    bd2pFunVec.push_back(bd2pLaunchConfig(FLOAT32, NCHW,
-                                          FLOAT32, NC4HW,
-                                          FLOAT32, NCHW,
-                                          bboxDeltas2Proposals_gpu<float, NCHW, float4, NC4HW, float, NCHW>));
-    return true;
-}
-static bool initialized = init();
+static std::array<bd2pLaunchConfig, 2> bd2pLCOptions = {
+    bd2pLaunchConfig(FLOAT32, NC4HW, FLOAT32, NC4HW, FLOAT32, NCHW, bboxDeltas2Proposals_gpu<float, NC4HW, float4, NC4HW, float, NCHW>),
+    bd2pLaunchConfig(FLOAT32, NCHW, FLOAT32, NC4HW, FLOAT32, NCHW, bboxDeltas2Proposals_gpu<float, NCHW, float4, NC4HW, float, NCHW>)};
 
 // BBD2P 
 pluginStatus_t bboxDeltas2Proposals(cudaStream_t stream,
@@ -272,12 +261,12 @@ pluginStatus_t bboxDeltas2Proposals(cudaStream_t stream,
                                    void* scores)
 {
     bd2pLaunchConfig lc = bd2pLaunchConfig(t_deltas, l_deltas, t_proposals, l_proposals, t_scores, l_scores);
-    for (unsigned i = 0; i < bd2pFunVec.size(); i++)
+    for (unsigned i = 0; i < bd2pLCOptions.size(); i++)
     {
-        if (lc == bd2pFunVec[i])
+        if (lc == bd2pLCOptions[i])
         {
             DEBUG_PRINTF("BBD2P kernel %d\n", i);
-            return bd2pFunVec[i].function(stream,
+            return bd2pLCOptions[i].function(stream,
                                           N, A, H, W,
                                           imInfo,
                                           featureStride,
diff --git a/plugin/common/kernels/common.cu b/plugin/common/kernels/common.cu
index 77f409e2..ddf19f93 100755
--- a/plugin/common/kernels/common.cu
+++ b/plugin/common/kernels/common.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/plugin/common/kernels/cropAndResizeKernel.cu b/plugin/common/kernels/cropAndResizeKernel.cu
index 61f712a6..37fe51f9 100755
--- a/plugin/common/kernels/cropAndResizeKernel.cu
+++ b/plugin/common/kernels/cropAndResizeKernel.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/plugin/common/kernels/decodeBBoxes.cu b/plugin/common/kernels/decodeBBoxes.cu
index 29165859..11a3571d 100644
--- a/plugin/common/kernels/decodeBBoxes.cu
+++ b/plugin/common/kernels/decodeBBoxes.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -13,7 +13,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include <vector>
+#include <array>
 #include "kernel.h"
 
 template <typename T_BBOX, unsigned nthds_per_cta>
@@ -278,16 +278,8 @@ struct dbbLaunchConfig
     }
 };
 
-static std::vector<dbbLaunchConfig> dbbFuncVec;
-
-bool decodeBBoxesInit()
-{
-    dbbFuncVec.push_back(dbbLaunchConfig(DataType::kFLOAT,
-                                         decodeBBoxes_gpu<float>));
-    return true;
-}
-
-static bool initialized = decodeBBoxesInit();
+static std::array<dbbLaunchConfig, 1> dbbLCOptions = {
+    dbbLaunchConfig(DataType::kFLOAT, decodeBBoxes_gpu<float>)};
 
 pluginStatus_t decodeBBoxes(
     cudaStream_t stream,
@@ -305,12 +297,12 @@ pluginStatus_t decodeBBoxes(
     void* bbox_data)
 {
     dbbLaunchConfig lc = dbbLaunchConfig(DT_BBOX);
-    for (unsigned i = 0; i < dbbFuncVec.size(); ++i)
+    for (unsigned i = 0; i < dbbLCOptions.size(); ++i)
     {
-        if (lc == dbbFuncVec[i])
+        if (lc == dbbLCOptions[i])
         {
             DEBUG_PRINTF("decodeBBox kernel %d\n", i);
-            return dbbFuncVec[i].function(stream,
+            return dbbLCOptions[i].function(stream,
                                           nthreads,
                                           code_type,
                                           variance_encoded_in_target,
diff --git a/plugin/common/kernels/detectionForward.cu b/plugin/common/kernels/detectionForward.cu
index b2f5ef54..e5810c65 100644
--- a/plugin/common/kernels/detectionForward.cu
+++ b/plugin/common/kernels/detectionForward.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -206,3 +206,199 @@ pluginStatus_t detectionInference(
     return STATUS_SUCCESS;
 }
 
+namespace nvinfer1
+{
+namespace plugin
+{
+    pluginStatus_t detectionInference(
+    cudaStream_t stream,
+    const int N,
+    const int C1,
+    const int C2,
+    const bool shareLocation,
+    const bool varianceEncodedInTarget,
+    const int backgroundLabelId,
+    const int numPredsPerClass,
+    const int numClasses,
+    const int topK,
+    const int keepTopK,
+    const float confidenceThreshold,
+    const float nmsThreshold,
+    const CodeTypeSSD codeType,
+    const DataType DT_BBOX,
+    const void* locData,
+    const void* priorData,
+    const DataType DT_SCORE,
+    const void* confData,
+    void* keepCount,
+    void* topDetections,
+    void* workspace,
+    bool isNormalized,
+    bool confSigmoid)
+{
+    // Batch size * number bbox per sample * 4 = total number of bounding boxes * 4
+    const int locCount = N * C1;
+    // Do not clip the bounding box that goes outside the image
+    const bool clipBBox = false;
+    /*
+     * shareLocation
+     * Bounding box are shared among all classes, i.e., a bounding box could be classified as any candidate class.
+     * Otherwise
+     * Bounding box are designed for specific classes, i.e., a bounding box could be classified as one certain class or not (binary classification).
+     */
+    const int numLocClasses = shareLocation ? 1 : numClasses;
+
+    size_t bboxDataSize = detectionForwardBBoxDataSize(N, C1, DataType::kFLOAT);
+    void* bboxDataRaw = workspace;
+
+    pluginStatus_t status = decodeBBoxes(stream,
+                                      locCount,
+                                      codeType,
+                                      varianceEncodedInTarget,
+                                      numPredsPerClass,
+                                      shareLocation,
+                                      numLocClasses,
+                                      backgroundLabelId,
+                                      clipBBox,
+                                      DataType::kFLOAT,
+                                      locData,
+                                      priorData,
+                                      bboxDataRaw);
+
+    ASSERT_FAILURE(status == STATUS_SUCCESS);
+
+    /*
+     * bboxDataRaw format:
+     * [batch size, numPriors (per sample), numLocClasses, 4]
+     */
+    // float for now
+    void* bboxData;
+    size_t bboxPermuteSize = detectionForwardBBoxPermuteSize(shareLocation, N, C1, DataType::kFLOAT);
+    void* bboxPermute = nextWorkspacePtr((int8_t*) bboxDataRaw, bboxDataSize);
+
+    /*
+     * After permutation, bboxData format:
+     * [batch_size, numLocClasses, numPriors (per sample) (numPredsPerClass), 4]
+     * This is equivalent to swapping axis
+     */
+    if (!shareLocation)
+    {
+        status = permuteData(stream,
+                             locCount,
+                             numLocClasses,
+                             numPredsPerClass,
+                             4,
+                             DataType::kFLOAT,
+                             false,
+                             bboxDataRaw,
+                             bboxPermute);
+        ASSERT_FAILURE(status == STATUS_SUCCESS);
+        bboxData = bboxPermute;
+    }
+    /*
+     * If shareLocation, numLocClasses = 1
+     * No need to permute data on linear memory
+     */
+    else
+    {
+        bboxData = bboxDataRaw;
+    }
+    /*
+     * Conf data format
+     * [batch size, numPriors * param.numClasses, 1, 1]
+     */
+    const int numScores = N * C2;
+    size_t scoresSize = detectionForwardPreNMSSize(N, C2);
+    void* scores = nextWorkspacePtr((int8_t*) bboxPermute, bboxPermuteSize);
+    // need a conf_scores
+    /*
+     * After permutation, bboxData format:
+     * [batch_size, numClasses, numPredsPerClass, 1]
+     */
+    status = permuteData(stream,
+                         numScores,
+                         numClasses,
+                         numPredsPerClass,
+                         1,
+                         DataType::kFLOAT,
+                         confSigmoid,
+                         confData,
+                         scores);
+    ASSERT_FAILURE(status == STATUS_SUCCESS);
+
+    size_t indicesSize = detectionForwardPreNMSSize(N, C2);
+    void* indices = nextWorkspacePtr((int8_t*) scores, scoresSize);
+
+    size_t postNMSScoresSize = detectionForwardPostNMSSize(N, numClasses, topK);
+    size_t postNMSIndicesSize = detectionForwardPostNMSSize(N, numClasses, topK);
+    void* postNMSScores = nextWorkspacePtr((int8_t*) indices, indicesSize);
+    void* postNMSIndices = nextWorkspacePtr((int8_t*) postNMSScores, postNMSScoresSize);
+
+    //size_t sortingWorkspaceSize = sortScoresPerClassWorkspaceSize(N, numClasses, numPredsPerClass, FLOAT32);
+    void* sortingWorkspace = nextWorkspacePtr((int8_t*) postNMSIndices, postNMSIndicesSize);
+    // Sort the scores so that the following NMS could be applied.
+    status = sortScoresPerClass(stream,
+                                N,
+                                numClasses,
+                                numPredsPerClass,
+                                backgroundLabelId,
+                                confidenceThreshold,
+                                DataType::kFLOAT,
+                                scores,
+                                indices,
+                                sortingWorkspace);
+    ASSERT_FAILURE(status == STATUS_SUCCESS);
+    
+    // NMS
+    status = allClassNMS(stream,
+                         N,
+                         numClasses,
+                         numPredsPerClass,
+                         topK,
+                         nmsThreshold,
+                         shareLocation,
+                         isNormalized,
+                         DataType::kFLOAT,
+                         DataType::kFLOAT,
+                         bboxData,
+                         scores,
+                         indices,
+                         postNMSScores,
+                         postNMSIndices,
+                         false);
+    ASSERT_FAILURE(status == STATUS_SUCCESS);
+
+    // Sort the bounding boxes after NMS using scores
+    status = sortScoresPerImage(stream,
+                                N,
+                                numClasses * topK,
+                                DataType::kFLOAT,
+                                postNMSScores,
+                                postNMSIndices,
+                                scores,
+                                indices,
+                                sortingWorkspace);
+    ASSERT_FAILURE(status == STATUS_SUCCESS);
+
+    // Gather data from the sorted bounding boxes after NMS
+    status = gatherTopDetections(stream,
+                                 shareLocation,
+                                 N,
+                                 numPredsPerClass,
+                                 numClasses,
+                                 topK,
+                                 keepTopK,
+                                 DataType::kFLOAT,
+                                 DataType::kFLOAT,
+                                 indices,
+                                 scores,
+                                 bboxData,
+                                 keepCount,
+                                 topDetections);
+    ASSERT_FAILURE(status == STATUS_SUCCESS);
+
+    return STATUS_SUCCESS;
+}
+
+} // namespace plugin
+} // namespace nvinfer1
diff --git a/plugin/common/kernels/extractFgScores.cu b/plugin/common/kernels/extractFgScores.cu
index e15e46d5..203056fc 100755
--- a/plugin/common/kernels/extractFgScores.cu
+++ b/plugin/common/kernels/extractFgScores.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/plugin/common/kernels/gatherTopDetections.cu b/plugin/common/kernels/gatherTopDetections.cu
index 5f5cad4e..802f82ea 100644
--- a/plugin/common/kernels/gatherTopDetections.cu
+++ b/plugin/common/kernels/gatherTopDetections.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -13,7 +13,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include <vector>
+#include <array>
 #include "plugin.h"
 #include "kernel.h"
 
@@ -149,16 +149,8 @@ struct gtdLaunchConfig
 
 using nvinfer1::DataType;
 
-static std::vector<gtdLaunchConfig> gtdFuncVec;
-
-bool gtdInit()
-{
-    gtdFuncVec.push_back(gtdLaunchConfig(DataType::kFLOAT, DataType::kFLOAT,
-                                         gatherTopDetections_gpu<float, float>));
-    return true;
-}
-
-static bool initialized = gtdInit();
+static std::array<gtdLaunchConfig, 1> gtdLCOptions = {
+    gtdLaunchConfig(DataType::kFLOAT, DataType::kFLOAT, gatherTopDetections_gpu<float, float>)};
 
 pluginStatus_t gatherTopDetections(
     cudaStream_t stream,
@@ -177,12 +169,12 @@ pluginStatus_t gatherTopDetections(
     void* topDetections)
 {
     gtdLaunchConfig lc = gtdLaunchConfig(DT_BBOX, DT_SCORE);
-    for (unsigned i = 0; i < gtdFuncVec.size(); ++i)
+    for (unsigned i = 0; i < gtdLCOptions.size(); ++i)
     {
-        if (lc == gtdFuncVec[i])
+        if (lc == gtdLCOptions[i])
         {
             DEBUG_PRINTF("gatherTopDetections kernel %d\n", i);
-            return gtdFuncVec[i].function(stream,
+            return gtdLCOptions[i].function(stream,
                                           shareLocation,
                                           numImages,
                                           numPredsPerClass,
diff --git a/plugin/common/kernels/generateAnchors.cu b/plugin/common/kernels/generateAnchors.cu
index b0fede47..d8cc3c38 100755
--- a/plugin/common/kernels/generateAnchors.cu
+++ b/plugin/common/kernels/generateAnchors.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/plugin/common/kernels/gridAnchorLayer.cu b/plugin/common/kernels/gridAnchorLayer.cu
index b26ca74d..6e342647 100644
--- a/plugin/common/kernels/gridAnchorLayer.cu
+++ b/plugin/common/kernels/gridAnchorLayer.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -14,10 +14,10 @@
  * limitations under the License.
  */
 #include "kernel.h"
-#include "reducedMath.h"
+#include "reducedMathPlugin.h"
 #include <iostream>
 
-using nvinfer1::rt::reduced_divisor;
+using nvinfer1::plugin::reduced_divisor;
 template <unsigned nthdsPerCTA>
         __launch_bounds__(nthdsPerCTA)
         __global__ void gridAnchorKernel(
@@ -81,36 +81,75 @@ template <unsigned nthdsPerCTA>
 
         }
 
-    pluginStatus_t anchorGridInference(
-            cudaStream_t stream,
-            const GridAnchorParameters param,
-            const int numAspectRatios,
-            const void* widths,
-            const void* heights,
-            void* outputData
-            )
+pluginStatus_t anchorGridInference(
+        cudaStream_t stream,
+        const GridAnchorParameters param,
+        const int numAspectRatios,
+        const void* widths,
+        const void* heights,
+        void* outputData
+        )
+{
+    const int dim = param.H * param.W * numAspectRatios;
+    reduced_divisor divObj(numAspectRatios);
+    if (dim > 5120)
     {
-        const int dim = param.H * param.W * numAspectRatios;
-        reduced_divisor divObj(numAspectRatios);
-        if (dim > 5120)
-        {
-            const int BS = 128;
-            const int GS = (dim + BS - 1) / BS;
-            gridAnchorKernel<BS><<<GS, BS, 0, stream>>>(param, numAspectRatios, divObj,
-                    (const float*) widths, (const float*) heights,
-                    (float*) outputData);
+        const int BS = 128;
+        const int GS = (dim + BS - 1) / BS;
+        gridAnchorKernel<BS><<<GS, BS, 0, stream>>>(param, numAspectRatios, divObj,
+                (const float*) widths, (const float*) heights,
+                (float*) outputData);
 
-        }
-        else
-        {
-            const int BS = 32;
-            const int GS = (dim + BS - 1) / BS;
-            gridAnchorKernel<BS><<<GS, BS, 0, stream>>>(param, numAspectRatios, divObj,
-                    (const float*) widths, (const float*) heights,
-                    (float*) outputData);
+    }
+    else
+    {
+        const int BS = 32;
+        const int GS = (dim + BS - 1) / BS;
+        gridAnchorKernel<BS><<<GS, BS, 0, stream>>>(param, numAspectRatios, divObj,
+                (const float*) widths, (const float*) heights,
+                (float*) outputData);
 
-        }
-        CSC(cudaGetLastError(), STATUS_FAILURE);
-        return STATUS_SUCCESS;
+    }
+    CSC(cudaGetLastError(), STATUS_FAILURE);
+    return STATUS_SUCCESS;
+}
+
+namespace nvinfer1
+{
+namespace plugin
+{
+pluginStatus_t anchorGridInference(
+    cudaStream_t stream,
+    const GridAnchorParameters param,
+    const int numAspectRatios,
+    const void* widths,
+    const void* heights,
+    void* outputData
+    )
+{
+    const int dim = param.H * param.W * numAspectRatios;
+    reduced_divisor divObj(numAspectRatios);
+    if (dim > 5120)
+    {
+        const int BS = 128;
+        const int GS = (dim + BS - 1) / BS;
+        gridAnchorKernel<BS><<<GS, BS, 0, stream>>>(param, numAspectRatios, divObj,
+                (const float*) widths, (const float*) heights,
+                (float*) outputData);
+
+    }
+    else
+    {
+        const int BS = 32;
+        const int GS = (dim + BS - 1) / BS;
+        gridAnchorKernel<BS><<<GS, BS, 0, stream>>>(param, numAspectRatios, divObj,
+                (const float*) widths, (const float*) heights,
+                (float*) outputData);
 
     }
+    CSC(cudaGetLastError(), STATUS_FAILURE);
+    return STATUS_SUCCESS;
+}
+
+}
+}
diff --git a/plugin/common/kernels/kernel.cpp b/plugin/common/kernels/kernel.cpp
index a328a680..142a9b9e 100644
--- a/plugin/common/kernels/kernel.cpp
+++ b/plugin/common/kernels/kernel.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -30,3 +30,24 @@ size_t detectionInferenceWorkspaceSize(bool shareLocation, int N, int C1, int C2
         sortScoresPerImageWorkspaceSize(N, numClasses * topK, DT_SCORE));
     return calculateTotalWorkspaceSize(wss, 7);
 }
+
+namespace nvinfer1
+{
+namespace plugin
+{
+size_t detectionInferenceWorkspaceSize(bool shareLocation, int N, int C1, int C2, int numClasses, int numPredsPerClass,
+    int topK, DataType DT_BBOX, DataType DT_SCORE)
+{
+    size_t wss[7];
+    wss[0] = detectionForwardBBoxDataSize(N, C1, DT_BBOX);
+    wss[1] = detectionForwardBBoxPermuteSize(shareLocation, N, C1, DT_BBOX);
+    wss[2] = detectionForwardPreNMSSize(N, C2);
+    wss[3] = detectionForwardPreNMSSize(N, C2);
+    wss[4] = detectionForwardPostNMSSize(N, numClasses, topK);
+    wss[5] = detectionForwardPostNMSSize(N, numClasses, topK);
+    wss[6] = std::max(sortScoresPerClassWorkspaceSize(N, numClasses, numPredsPerClass, DT_SCORE),
+        sortScoresPerImageWorkspaceSize(N, numClasses * topK, DT_SCORE));
+    return calculateTotalWorkspaceSize(wss, 7);
+}
+} // namespace plugin
+} // namespace nvinfer1
diff --git a/plugin/common/kernels/kernel.h b/plugin/common/kernels/kernel.h
index 7dad3171..03a190e5 100644
--- a/plugin/common/kernels/kernel.h
+++ b/plugin/common/kernels/kernel.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -18,6 +18,7 @@
 
 #include "cublas_v2.h"
 #include "plugin.h"
+#include <algorithm>
 #include <cassert>
 #include <cstdio>
 
@@ -25,6 +26,7 @@ using namespace nvinfer1;
 using namespace nvinfer1::plugin;
 #define DEBUG_ENABLE 0
 
+#ifndef TRT_RPNLAYER_H
 typedef enum
 {
     NCHW = 0,
@@ -44,6 +46,12 @@ pluginStatus_t detectionInference(cudaStream_t stream, int N, int C1, int C2, bo
 
 );
 
+pluginStatus_t nmsInference(cudaStream_t stream, int N, int boxesSize, int scoresSize, bool shareLocation,
+    int backgroundLabelId, int numPredsPerClass, int numClasses, int topK, int keepTopK, float scoreThreshold,
+    float iouThreshold, DataType DT_BBOX, const void* locData, DataType DT_SCORE, const void* confData, void* keepCount,
+    void* nmsedBoxes, void* nmsedScores, void* nmsedClasses, void* workspace, bool isNormalized = true,
+    bool confSigmoid = false, bool clipBoxes = true);
+
 pluginStatus_t gatherTopDetections(cudaStream_t stream, bool shareLocation, int numImages, int numPredsPerClass,
     int numClasses, int topK, int keepTopK, DataType DT_BBOX, DataType DT_SCORE, const void* indices,
     const void* scores, const void* bboxData, void* keepCount, void* topDetections);
@@ -210,4 +218,5 @@ int proposalInference_gpu(cudaStream_t stream, const void* rpn_prob, const void*
 
 size_t _get_workspace_size(int N, int anc_size_num, int anc_ratio_num, int H, int W, int nmsMaxOut);
 
+#endif // TRT_RPNLAYER_H
 #endif
diff --git a/plugin/common/kernels/maskRCNNKernels.cu b/plugin/common/kernels/maskRCNNKernels.cu
index 800bc621..393cd41f 100644
--- a/plugin/common/kernels/maskRCNNKernels.cu
+++ b/plugin/common/kernels/maskRCNNKernels.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -1444,7 +1444,10 @@ __global__ void extract_fg_kernel(int samples, const void* in_scores, void* outp
     for (int i = 0; i < totalItems; i++)
     {
         int cur_id = i * blockDim.x + threadIdx.x;
-        out[blockOffset + cur_id] = in[blockOffset + cur_id].fg;
+        if (cur_id < samples)
+        {
+            out[blockOffset + cur_id] = in[blockOffset + cur_id].fg;
+        }
     }
 }
 __global__ void set_offset_kernel(int stride, int size, int* output)
@@ -1855,46 +1858,48 @@ __global__ void apply_delta_kernel(int samples, const void* anchors, const void*
     for (int i = 0; i < totalItems; i++)
     {
         int cur_id = i * blockDim.x + threadIdx.x;
-
-        BBOX cur_anchor_yxyx = anchors_in[blockOffset + cur_id];
-        // convert yxyx -> cyxhw
-        // cy, cx, h, w
-        BBOX cur_anchor_cyxhw;
-
-        cur_anchor_cyxhw.y1 = (cur_anchor_yxyx.y1 + cur_anchor_yxyx.y2) / 2;
-        cur_anchor_cyxhw.x1 = (cur_anchor_yxyx.x1 + cur_anchor_yxyx.x2) / 2;
-        cur_anchor_cyxhw.y2 = (cur_anchor_yxyx.y2 - cur_anchor_yxyx.y1);
-        cur_anchor_cyxhw.x2 = (cur_anchor_yxyx.x2 - cur_anchor_yxyx.x1);
-
-        DELTA cur_delta = delta_in[blockOffset + cur_id];
-
-        // multiply std_dev
-        cur_delta.dy *= 0.1;
-        cur_delta.dx *= 0.1;
-        cur_delta.logdh *= 0.2;
-        cur_delta.logdw *= 0.2;
-
-        // apply delta
-        cur_anchor_cyxhw.y1 += cur_delta.dy * cur_anchor_cyxhw.y2;
-        cur_anchor_cyxhw.x1 += cur_delta.dx * cur_anchor_cyxhw.x2;
-        cur_anchor_cyxhw.y2 *= expf(cur_delta.logdh);
-        cur_anchor_cyxhw.x2 *= expf(cur_delta.logdw);
-
-        cur_anchor_yxyx.y1 = cur_anchor_cyxhw.y1 - 0.5 * cur_anchor_cyxhw.y2;
-        cur_anchor_yxyx.x1 = cur_anchor_cyxhw.x1 - 0.5 * cur_anchor_cyxhw.x2;
-        cur_anchor_yxyx.y2 = cur_anchor_yxyx.y1 + cur_anchor_cyxhw.y2;
-        cur_anchor_yxyx.x2 = cur_anchor_yxyx.x1 + cur_anchor_cyxhw.x2;
-
-        // clip bbox: a more precision clip method based on real window could be implemented
-        cur_anchor_yxyx.y1 = dMAX(dMIN(cur_anchor_yxyx.y1, 1.0), 0.0);
-        cur_anchor_yxyx.x1 = dMAX(dMIN(cur_anchor_yxyx.x1, 1.0), 0.0);
-        cur_anchor_yxyx.y2 = dMAX(dMIN(cur_anchor_yxyx.y2, 1.0), 0.0);
-        cur_anchor_yxyx.x2 = dMAX(dMIN(cur_anchor_yxyx.x2, 1.0), 0.0);
-
-        bbox_out[blockOffset + cur_id].y1 = cur_anchor_yxyx.y1;
-        bbox_out[blockOffset + cur_id].x1 = cur_anchor_yxyx.x1;
-        bbox_out[blockOffset + cur_id].y2 = cur_anchor_yxyx.y2;
-        bbox_out[blockOffset + cur_id].x2 = cur_anchor_yxyx.x2;
+        if (cur_id < samples)
+        {
+            BBOX cur_anchor_yxyx = anchors_in[blockOffset + cur_id];
+            // convert yxyx -> cyxhw
+            // cy, cx, h, w
+            BBOX cur_anchor_cyxhw;
+
+            cur_anchor_cyxhw.y1 = (cur_anchor_yxyx.y1 + cur_anchor_yxyx.y2) / 2;
+            cur_anchor_cyxhw.x1 = (cur_anchor_yxyx.x1 + cur_anchor_yxyx.x2) / 2;
+            cur_anchor_cyxhw.y2 = (cur_anchor_yxyx.y2 - cur_anchor_yxyx.y1);
+            cur_anchor_cyxhw.x2 = (cur_anchor_yxyx.x2 - cur_anchor_yxyx.x1);
+
+            DELTA cur_delta = delta_in[blockOffset + cur_id];
+
+            // multiply std_dev
+            cur_delta.dy *= 0.1;
+            cur_delta.dx *= 0.1;
+            cur_delta.logdh *= 0.2;
+            cur_delta.logdw *= 0.2;
+
+            // apply delta
+            cur_anchor_cyxhw.y1 += cur_delta.dy * cur_anchor_cyxhw.y2;
+            cur_anchor_cyxhw.x1 += cur_delta.dx * cur_anchor_cyxhw.x2;
+            cur_anchor_cyxhw.y2 *= expf(cur_delta.logdh);
+            cur_anchor_cyxhw.x2 *= expf(cur_delta.logdw);
+
+            cur_anchor_yxyx.y1 = cur_anchor_cyxhw.y1 - 0.5 * cur_anchor_cyxhw.y2;
+            cur_anchor_yxyx.x1 = cur_anchor_cyxhw.x1 - 0.5 * cur_anchor_cyxhw.x2;
+            cur_anchor_yxyx.y2 = cur_anchor_yxyx.y1 + cur_anchor_cyxhw.y2;
+            cur_anchor_yxyx.x2 = cur_anchor_yxyx.x1 + cur_anchor_cyxhw.x2;
+
+            // clip bbox: a more precision clip method based on real window could be implemented
+            cur_anchor_yxyx.y1 = dMAX(dMIN(cur_anchor_yxyx.y1, 1.0), 0.0);
+            cur_anchor_yxyx.x1 = dMAX(dMIN(cur_anchor_yxyx.x1, 1.0), 0.0);
+            cur_anchor_yxyx.y2 = dMAX(dMIN(cur_anchor_yxyx.y2, 1.0), 0.0);
+            cur_anchor_yxyx.x2 = dMAX(dMIN(cur_anchor_yxyx.x2, 1.0), 0.0);
+
+            bbox_out[blockOffset + cur_id].y1 = cur_anchor_yxyx.y1;
+            bbox_out[blockOffset + cur_id].x1 = cur_anchor_yxyx.x1;
+            bbox_out[blockOffset + cur_id].y2 = cur_anchor_yxyx.y2;
+            bbox_out[blockOffset + cur_id].x2 = cur_anchor_yxyx.x2;
+        }
     }
 }
 
@@ -2239,11 +2244,13 @@ __global__ void specialslice_kernel(int samples, const void* idata, void* odata)
     for (int i = 0; i < totalItems; i++)
     {
         int cur_id = i * blockDim.x + threadIdx.x;
-
-        out_bboxes[blockOffset + cur_id].y1 = in_detections[blockOffset + cur_id].y1;
-        out_bboxes[blockOffset + cur_id].x1 = in_detections[blockOffset + cur_id].x1;
-        out_bboxes[blockOffset + cur_id].y2 = in_detections[blockOffset + cur_id].y2;
-        out_bboxes[blockOffset + cur_id].x2 = in_detections[blockOffset + cur_id].x2;
+        if (cur_id < samples)
+        {
+            out_bboxes[blockOffset + cur_id].y1 = in_detections[blockOffset + cur_id].y1;
+            out_bboxes[blockOffset + cur_id].x1 = in_detections[blockOffset + cur_id].x1;
+            out_bboxes[blockOffset + cur_id].y2 = in_detections[blockOffset + cur_id].y2;
+            out_bboxes[blockOffset + cur_id].x2 = in_detections[blockOffset + cur_id].x2;
+        }
     }
 }
 
diff --git a/plugin/common/kernels/maskRCNNKernels.h b/plugin/common/kernels/maskRCNNKernels.h
index 5893301b..358f84f7 100644
--- a/plugin/common/kernels/maskRCNNKernels.h
+++ b/plugin/common/kernels/maskRCNNKernels.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -154,8 +154,8 @@ struct ProposalWorkSpace
 
 struct MultilevelProposeROIWorkSpace
 {
-    MultilevelProposeROIWorkSpace(const int batchSize, const int inputCnt, const int sampleCount, const RefineNMSParameters& param,
-        const nvinfer1::DataType type);
+    MultilevelProposeROIWorkSpace(const int batchSize, const int inputCnt, const int sampleCount,
+        const RefineNMSParameters& param, const nvinfer1::DataType type);
 
     MultilevelProposeROIWorkSpace() = default;
 
@@ -188,7 +188,7 @@ struct MultilevelProposeROIWorkSpace
 
 struct ConcatTopKWorkSpace
 {
-    ConcatTopKWorkSpace(const int batchSize, const int concatCnt, const int topK,const nvinfer1::DataType inType);
+    ConcatTopKWorkSpace(const int batchSize, const int concatCnt, const int topK, const nvinfer1::DataType inType);
 
     ConcatTopKWorkSpace() = default;
 
@@ -209,10 +209,10 @@ cudaError_t RefineBatchClassNMS(cudaStream_t stream, int N, int samples, nvinfer
     const RefineNMSParameters& param, const RefineDetectionWorkSpace& refineOffset, void* workspace,
     const void* inScores, const void* inDelta, const void* inCountValid, const void* inROI, void* outDetections);
 
-cudaError_t DetectionPostProcess(cudaStream_t stream, int N, int samples, const float* regWeight, 
-    const float inputHeight, const float inputWidth, nvinfer1::DataType dtype,
-    const RefineNMSParameters& param, const RefineDetectionWorkSpace& refineOffset, void* workspace,
-    const void* inScores, const void* inDelta, const void* inCountValid, const void* inROI, void* outDetections);
+cudaError_t DetectionPostProcess(cudaStream_t stream, int N, int samples, const float* regWeight,
+    const float inputHeight, const float inputWidth, nvinfer1::DataType dtype, const RefineNMSParameters& param,
+    const RefineDetectionWorkSpace& refineOffset, void* workspace, const void* inScores, const void* inDelta,
+    const void* inCountValid, const void* inROI, void* outDetections);
 
 cudaError_t proposalRefineBatchClassNMS(cudaStream_t stream, int N,
     int inputCnt, // candidate anchors
@@ -225,36 +225,23 @@ cudaError_t proposalRefineBatchClassNMS(cudaStream_t stream, int N,
 // inDelta: [N, anchorsCnt, 4]
 // outScores: [N, topK, 1]
 // outBbox: [N, topK, 4]
-cudaError_t MultilevelPropose(cudaStream_t stream, int N, 
+cudaError_t MultilevelPropose(cudaStream_t stream, int N,
     int inputCnt, // candidate anchors number among feature map
-    int samples, //pre nms cnt
-    const float* regWeight,
-    const float inputHeight,
-    const float inputWidth,
-    nvinfer1::DataType dtype, const RefineNMSParameters& param, const MultilevelProposeROIWorkSpace& proposalOffset,
-    void* workspace, const void* inScore, const void* inDelta, void* inCountValid, const void* inAnchors,
-    void* outScores, 
+    int samples,  // pre nms cnt
+    const float* regWeight, const float inputHeight, const float inputWidth, nvinfer1::DataType dtype,
+    const RefineNMSParameters& param, const MultilevelProposeROIWorkSpace& proposalOffset, void* workspace,
+    const void* inScore, const void* inDelta, void* inCountValid, const void* inAnchors, void* outScores,
     void* outBbox);
 
-//inScores: [N, topK, 1] * featureCnt
-//inBboxes: [N, topK, 4] * featureCnt
-//outProposals: [N, topK, 4]
-cudaError_t ConcatTopK(cudaStream_t stream, 
-    int N, 
-    int featureCnt, 
-    int topK, 
-    nvinfer1::DataType dtype, 
-    void* workspace, 
-    const ConcatTopKWorkSpace& spaceOffset,
-    void** inScores, 
-    void** inBBox,
-    void* outProposals); 
+// inScores: [N, topK, 1] * featureCnt
+// inBboxes: [N, topK, 4] * featureCnt
+// outProposals: [N, topK, 4]
+cudaError_t ConcatTopK(cudaStream_t stream, int N, int featureCnt, int topK, nvinfer1::DataType dtype, void* workspace,
+    const ConcatTopKWorkSpace& spaceOffset, void** inScores, void** inBBox, void* outProposals);
 
 cudaError_t DecodeBBoxes(cudaStream_t stream, int N,
-    int samples,         // number of anchors per image
-    const float* regWeight, 
-    const float inputHeight,
-    const float inputWidth,
+    int samples, // number of anchors per image
+    const float* regWeight, const float inputHeight, const float inputWidth,
     const void* anchors, // [N, anchors, (y1, x1, y2, x2)]
     const void* delta,   //[N, anchors, (dy, dx, log(dh), log(dw)]
     void* outputBbox);
@@ -293,7 +280,7 @@ cudaError_t roiAlignHalfCenter(cudaStream_t stream, int batchSize, int featureCo
     int inputHeight, int inputWidth, const void* rois, const void* const layers[], const xy_t* layerDims,
 
     void* pooled, const xy_t poolDims);
-    
+
 // RESIZE NEAREST
 void resizeNearest(dim3 grid, dim3 block, cudaStream_t stream, int nbatch, float scale, int2 osize, float const* idata,
     int istride, int ibatchstride, float* odata, int ostride, int obatchstride);
diff --git a/plugin/common/kernels/nmsLayer.cu b/plugin/common/kernels/nmsLayer.cu
index 4bc0e0b9..fba0ee68 100755
--- a/plugin/common/kernels/nmsLayer.cu
+++ b/plugin/common/kernels/nmsLayer.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -14,12 +14,12 @@
  * limitations under the License.
  */
 #include <algorithm>
+#include <array>
 #include "cuda_runtime_api.h"
 #include <cub/cub.cuh>
 #include <functional>
 #include <stdint.h>
 #include <stdio.h>
-#include <vector>
 #include "kernel.h"
 #include "bboxUtils.h"
 
@@ -390,20 +390,9 @@ struct nmsLaunchConfig
     }
 };
 
-static std::vector<nmsLaunchConfig> nmsLCVec;
 #define FLOAT32 nvinfer1::DataType::kFLOAT
-bool initNmsLC()
-{
-    nmsLCVec.reserve(1);
-    nmsLCVec.push_back(nmsLaunchConfig(FLOAT32, NCHW,
-                                       FLOAT32, NC4HW,
-                                       FLOAT32,
-                                       nmsGpu<float, float>));
-    return true;
-}
-
-static bool initializedNmsLC = initNmsLC();
-
+static std::array<nmsLaunchConfig, 1> nmsLCOptions = {
+    nmsLaunchConfig(FLOAT32, NCHW, FLOAT32, NC4HW, FLOAT32, nmsGpu<float, float>)};
 
 // NMS 
 pluginStatus_t nms(cudaStream_t stream,
@@ -422,15 +411,14 @@ pluginStatus_t nms(cudaStream_t stream,
                   const DataType t_rois,
                   void* rois)
 {
-    if (!initializedNmsLC)
-        return STATUS_NOT_INITIALIZED;
+
     nmsLaunchConfig lc(t_fgScores, l_fgScores, t_proposals, l_proposals, t_rois);
-    for (unsigned i = 0; i < nmsLCVec.size(); i++)
+    for (unsigned i = 0; i < nmsLCOptions.size(); i++)
     {
-        if (nmsLCVec[i] == lc)
+        if (nmsLCOptions[i] == lc)
         {
             DEBUG_PRINTF("NMS KERNEL %d\n", i);
-            return nmsLCVec[i].function(stream,
+            return nmsLCOptions[i].function(stream,
                                         N, R,
                                         preNmsTop,
                                         nmsMaxOut,
diff --git a/plugin/common/kernels/normalizeLayer.cu b/plugin/common/kernels/normalizeLayer.cu
index dd2b6bef..5108ea91 100644
--- a/plugin/common/kernels/normalizeLayer.cu
+++ b/plugin/common/kernels/normalizeLayer.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -25,6 +25,19 @@
             printf("%s %d CUBLAS FAIL %s\n", __FILE__, __LINE__, cublasGetErrorString(status)); \
         }                                                                                       \
     } while (0)
+namespace nvinfer1
+{
+namespace plugin
+{
+size_t normalizePluginWorkspaceSize(bool acrossSpatial, int C, int H, int W)
+{
+    if (acrossSpatial)
+        return sizeof(float) * C * H * W;
+    else
+        return (size_t) 0;
+}
+} // namespace plugin
+} // namespace nvinfer1
 
 size_t normalizePluginWorkspaceSize(bool acrossSpatial, int C, int H, int W)
 {
@@ -157,7 +170,10 @@ __global__ void scalChannelKernel(
         outputData[i] = inputData[i] / scale[i / spatialDim];
     }
 }
-
+namespace nvinfer1
+{
+namespace plugin
+{
 pluginStatus_t normalizeInference(
     cudaStream_t stream,
     cublasHandle_t handle,
@@ -217,3 +233,65 @@ pluginStatus_t normalizeInference(
         return normalizeNotAcrossSpatialGpu(stream, channelShared, N, C, H, W, eps, scale, inputData, outputData);
     }
 }
+} // namespace plugin
+} // namespace nvinfer1
+
+pluginStatus_t normalizeInference(
+    cudaStream_t stream,
+    cublasHandle_t handle,
+    const bool acrossSpatial,
+    const bool channelShared,
+    const int N,
+    const int C,
+    const int H,
+    const int W,
+    const float eps,
+    const void* scale,
+    const void* inputData,
+    void* outputData,
+    void* workspace)
+{
+    const int dim = C * H * W;
+    // Normalization is conducted for each sample from the batch indepdently
+    if (acrossSpatial)
+    {
+        float* input = (float*) const_cast<void*>(inputData);
+        float* output = (float*) outputData;
+        float* buffer = (float*) workspace;
+        for (int n = 0; n < N; ++n)
+        {
+            // Take the square of each element in the input
+            squareKernel<<<(dim + 511) / 512, 512, 0, stream>>>(dim, input, buffer);
+            float normsqr = 0.0F;
+            // Sum up all the squared elements
+            CUBLAS_CHECK(cublasSasum(handle, dim, buffer, 1, &normsqr));
+            // Make a copy of the input to the output
+            CUBLAS_CHECK(cublasScopy(handle, dim, input, 1, output, 1));
+            // Calculate the inverse of the square root of the sum
+            // Use eps to prevent being divided by zero
+            normsqr = 1 / sqrt(normsqr + eps);
+            // Scale all the outputs by normsqr
+            CUBLAS_CHECK(cublasSscal(handle, dim, &normsqr, output, 1));
+            // If channel shared is true, scale all the outputs
+            if (channelShared)
+            {
+                CUBLAS_CHECK(cublasSscal(handle, dim, (float*) scale, output, 1));
+            }
+            // Use different scale factors for different channels
+            else
+            {
+                // scale the output according to channels
+                scalChannelKernel<<<(dim + 511) / 512, 512, 0, stream>>>(dim, H * W, output, (float*) scale, output);
+            }
+            // Move cursors
+            input += dim;
+            output += dim;
+        }
+        return STATUS_SUCCESS;
+    }
+    // Normalization ignoring the batch
+    else
+    {
+        return normalizeNotAcrossSpatialGpu(stream, channelShared, N, C, H, W, eps, scale, inputData, outputData);
+    }
+}
\ No newline at end of file
diff --git a/plugin/common/kernels/permuteData.cu b/plugin/common/kernels/permuteData.cu
index b6d4d41b..c9e01749 100644
--- a/plugin/common/kernels/permuteData.cu
+++ b/plugin/common/kernels/permuteData.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -13,7 +13,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include <vector>
+#include <array>
 #include "kernel.h"
 
 template <typename Dtype, unsigned nthds_per_cta>
@@ -95,17 +95,8 @@ struct pdLaunchConfig
     }
 };
 
-static std::vector<pdLaunchConfig> pdFuncVec;
-
-bool permuteDataInit()
-{
-    pdFuncVec.push_back(pdLaunchConfig(DataType::kFLOAT,
-                                       permuteData_gpu<float>));
-    return true;
-}
-
-static bool initialized = permuteDataInit();
-
+static std::array<pdLaunchConfig, 1> pdLCOptions = {
+  pdLaunchConfig(DataType::kFLOAT, permuteData_gpu<float>)};
 
 pluginStatus_t permuteData(cudaStream_t stream,
                         const int nthreads,
@@ -118,12 +109,12 @@ pluginStatus_t permuteData(cudaStream_t stream,
                         void* new_data)
 {
     pdLaunchConfig lc = pdLaunchConfig(DT_DATA);
-    for (unsigned i = 0; i < pdFuncVec.size(); ++i)
+    for (unsigned i = 0; i < pdLCOptions.size(); ++i)
     {
-        if (lc == pdFuncVec[i])
+        if (lc == pdLCOptions[i])
         {
             DEBUG_PRINTF("permuteData kernel %d\n", i);
-            return pdFuncVec[i].function(stream,
+            return pdLCOptions[i].function(stream,
                                          nthreads,
                                          num_classes,
                                          num_data,
diff --git a/plugin/common/kernels/priorBoxLayer.cu b/plugin/common/kernels/priorBoxLayer.cu
index 90a7a6e2..7919fc5b 100644
--- a/plugin/common/kernels/priorBoxLayer.cu
+++ b/plugin/common/kernels/priorBoxLayer.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -14,10 +14,10 @@
  * limitations under the License.
  */
 #include "kernel.h"
-#include "reducedMath.h"
+#include "reducedMathPlugin.h"
 #include <iostream>
 
-using nvinfer1::rt::reduced_divisor;
+using nvinfer1::plugin::reduced_divisor;
 
 template <unsigned nthdsPerCTA>
 __launch_bounds__(nthdsPerCTA)
@@ -195,3 +195,30 @@ pluginStatus_t priorBoxInference(
         return priorBoxGpu(stream, param, H, W, numPriors, numAspectRatios,
                            minSize, nullptr, aspectRatios, outputData);
 }
+
+namespace nvinfer1
+{
+namespace plugin
+{
+pluginStatus_t priorBoxInference(
+    cudaStream_t stream,
+    const PriorBoxParameters param,
+    const int H,
+    const int W,
+    const int numPriors,
+    const int numAspectRatios,
+    const void* minSize,
+    const void* maxSize,
+    const void* aspectRatios,
+    void* outputData)
+{
+    ASSERT(param.numMaxSize >= 0);
+    if (param.numMaxSize)
+        return priorBoxGpu(stream, param, H, W, numPriors, numAspectRatios,
+                           minSize, maxSize, aspectRatios, outputData);
+    else
+        return priorBoxGpu(stream, param, H, W, numPriors, numAspectRatios,
+                           minSize, nullptr, aspectRatios, outputData);
+}
+} // namespace nvinfer1
+} // namespace plugin
diff --git a/plugin/common/kernels/proposalKernel.cu b/plugin/common/kernels/proposalKernel.cu
index 980bccbf..399d8b4d 100755
--- a/plugin/common/kernels/proposalKernel.cu
+++ b/plugin/common/kernels/proposalKernel.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -75,7 +75,6 @@ typedef pluginStatus_t frcnnStatus_t;
 unsigned int hash(const void* array_, size_t size);
 int8_t* alignPtr(int8_t* ptr, uintptr_t to);
 __global__ void setOffset(int stride, int size, int* output);
-bool initNmsLC();
 frcnnStatus_t nms(cudaStream_t stream,
     const int N,
     const int R,
@@ -438,12 +437,6 @@ struct nmsLaunchConfig
 static std::vector<nmsLaunchConfig> nmsLCVec;
 #define FLOAT32 nvinfer1::DataType::kFLOAT
 
-
-static bool initializedNmsLC = initNmsLC();
-
-// }}}
-
-
 __global__ void _inverse_transform_gpu(const float* RPN_prob, const float* RPN_regr, int N,
                                        int INPUT_H, int INPUT_W, int RPN_H, int RPN_W, float RPN_STD_SCALING, int RPN_STRIDE,
                                        float* ANCHOR_SIZES, int anc_size_num, float* ANCHOR_RATIOS, int anc_ratio_num, float bbox_min_size,
diff --git a/plugin/common/kernels/proposalsForward.cu b/plugin/common/kernels/proposalsForward.cu
index bd5dd510..ad4ff9a0 100755
--- a/plugin/common/kernels/proposalsForward.cu
+++ b/plugin/common/kernels/proposalsForward.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/plugin/common/kernels/reducedMath.h b/plugin/common/kernels/reducedMathPlugin.h
similarity index 74%
rename from plugin/common/kernels/reducedMath.h
rename to plugin/common/kernels/reducedMathPlugin.h
index a92c4225..4a98386e 100644
--- a/plugin/common/kernels/reducedMath.h
+++ b/plugin/common/kernels/reducedMathPlugin.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -13,21 +13,19 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#ifndef TRT_REDUCED_MATH_H
-#define TRT_REDUCED_MATH_H
-/*
- * Dynamically strength-reduced div and mod
- * Ideas taken from Sean Baxter's MGPU library.
- * These classes provide for reduced complexity division and modulus
- * on integers, for the case where the same divisor or modulus will
- * be used repeatedly.
- */
+#ifndef _REDUCED_MATH_PLUGIN_H
+#define _REDUCED_MATH_PLUGIN_H
+// Dynamically strength-reduced div and mod
+//
+// Ideas taken from Sean Baxter's MGPU library.
+// These classes provide for reduced complexity division and modulus
+// on integers, for the case where the same divisor or modulus will
+// be used repeatedly.
 
 namespace nvinfer1
 {
-namespace rt
+namespace plugin
 {
-
 namespace detail
 {
 
@@ -43,10 +41,8 @@ __host__ __device__ __forceinline__ unsigned int umulhi(unsigned int x, unsigned
 #endif
 }
 
-/*
- * This is a weird implementation that returns div_up(0,1)=0 but
- * div_up(0,2)=1 (wrong) -- just do not use it with a=0.
- */
+// This is a weird implementation that returns div_up(0,1)=0 but
+// div_up(0,2)=1 (wrong) -- just do not use it with a=0.
 __host__ __device__ inline int div_up(int a, int b)
 {
     return (a - 1) / b + 1;
@@ -57,7 +53,7 @@ __host__ __device__ inline int div_up(int a, int b)
 class reduced_divisor
 {
 public:
-    reduced_divisor() = default;
+    reduced_divisor() {}
     __host__ __forceinline__ reduced_divisor(int _y)
         : y(_y)
     {
@@ -71,11 +67,9 @@ class reduced_divisor
     }
     __host__ __device__ __forceinline__ int div(int x) const
     {
-        /*
-         * if dividing by 1, then find_divisor wouldn't have worked because
-         * mul_coeff would have had to be 2^32, which can't be represented,
-         * so we have to special case that one.
-         */
+        // if dividing by 1, then find_divisor wouldn't have worked because
+        // mul_coeff would have had to be 2^32, which can't be represented,
+        // so we have to special case that one.
         return (y != 1) ? detail::umulhi((unsigned int) x, mul_coeff) >> shift_coeff : x;
     }
     __host__ __device__ __forceinline__ int mod(int x) const
@@ -103,7 +97,7 @@ class reduced_divisor
     int y;
 };
 
-} // namespace rt
-} // namespace nvinfer1
+} // namespace plugin
 
-#endif // TRT_REDUCED_MATH_H
+} // namespace nvinfer1
+#endif /*_REDUCED_MATH_PLUGIN_H*/
diff --git a/plugin/common/kernels/regionForward.cu b/plugin/common/kernels/regionForward.cu
index 552f9c9c..0a2be950 100644
--- a/plugin/common/kernels/regionForward.cu
+++ b/plugin/common/kernels/regionForward.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/plugin/common/kernels/reorgForward.cu b/plugin/common/kernels/reorgForward.cu
index f47b6003..e4b5af88 100644
--- a/plugin/common/kernels/reorgForward.cu
+++ b/plugin/common/kernels/reorgForward.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -13,10 +13,10 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include "reducedMath.h"
+#include "reducedMathPlugin.h"
 #include "kernel.h"
 
-using namespace nvinfer1::rt; // for reduced_divisor
+using namespace nvinfer1::plugin; // for reduced_divisor
 
 template <unsigned nthdsPerCTA>
 __launch_bounds__(nthdsPerCTA)
diff --git a/plugin/common/kernels/roiPooling.cu b/plugin/common/kernels/roiPooling.cu
index 4f0437d4..fb1aaa1d 100755
--- a/plugin/common/kernels/roiPooling.cu
+++ b/plugin/common/kernels/roiPooling.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -15,12 +15,12 @@
  */
 #include "kernel.h"
 #include <algorithm>
+#include <array>
 #include <assert.h>
 #include <cfloat>
 #include <cstdio>
 #include <math.h>
 #include <stdio.h>
-#include <vector>
 
 // ROI POOLING FORWARD KERNEL 
 template <typename DATA_T, typename ROI_T, bool INFER_ONLY>
@@ -230,33 +230,9 @@ struct roiFwdLaunchConfig
 };
 
 #define FLOAT32 nvinfer1::DataType::kFLOAT
-bool roiFwdLCVecInit(std::vector<roiFwdLaunchConfig>& roiFwdLCVec)
-{
-    roiFwdLCVec.push_back(roiFwdLaunchConfig(FLOAT32,
-                                             FLOAT32,
-                                             NCHW,
-                                             FLOAT32,
-                                             NCHW,
-                                             true,
-                                             ROIPoolingForwardKernelAlignedLauncher<float, float, true>));
-    roiFwdLCVec.push_back(roiFwdLaunchConfig(FLOAT32,
-                                             FLOAT32,
-                                             NCHW,
-                                             FLOAT32,
-                                             NCHW,
-                                             false,
-                                             ROIPoolingForwardKernelAlignedLauncher<float, float, false>));
-    return true;
-}
-
-const std::vector<roiFwdLaunchConfig>& getRoiFwdLCVec()
-{
-    static std::vector<roiFwdLaunchConfig> roiFwdLCVec;
-    static bool roiFwdLCVecI = roiFwdLCVecInit(roiFwdLCVec);
-
-    return roiFwdLCVec;
-}
-
+static std::array<roiFwdLaunchConfig, 2> roiFwdLCOptions = {
+    roiFwdLaunchConfig(FLOAT32, FLOAT32, NCHW, FLOAT32, NCHW, true, ROIPoolingForwardKernelAlignedLauncher<float, float, true>),
+    roiFwdLaunchConfig(FLOAT32, FLOAT32, NCHW, FLOAT32, NCHW, false, ROIPoolingForwardKernelAlignedLauncher<float, float, false>)};
 
 // ROI INFERENCE 
 pluginStatus_t roiInference(cudaStream_t stream,
@@ -288,12 +264,12 @@ pluginStatus_t roiInference(cudaStream_t stream,
     roiFwdLaunchConfig rflc = roiFwdLaunchConfig(t_rois, t_featureMap, l_featureMap, t_top, l_top, true);
     ASSERT_PARAM(R > 0);
 
-    for (unsigned i = 0; i < getRoiFwdLCVec().size(); i++)
+    for (unsigned i = 0; i < roiFwdLCOptions.size(); i++)
     {
-        if (rflc == getRoiFwdLCVec()[i])
+        if (rflc == roiFwdLCOptions[i])
         {
             DEBUG_PRINTF("$$$$ ROI KERNEL %d\n", i);
-            return getRoiFwdLCVec()[i].function(stream,
+            return roiFwdLCOptions[i].function(stream,
                                                 R, N, C, H, W, poolingH, poolingW,
                                                 spatialScale, rois, featureMap, top, NULL);
         }
diff --git a/plugin/common/kernels/rproiInferenceFused.cu b/plugin/common/kernels/rproiInferenceFused.cu
index ce695a49..9aafcfa8 100755
--- a/plugin/common/kernels/rproiInferenceFused.cu
+++ b/plugin/common/kernels/rproiInferenceFused.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/plugin/common/kernels/sortScoresPerClass.cu b/plugin/common/kernels/sortScoresPerClass.cu
index 0963f58a..891ec2a7 100644
--- a/plugin/common/kernels/sortScoresPerClass.cu
+++ b/plugin/common/kernels/sortScoresPerClass.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 #include "cub/cub.cuh"
-#include <vector>
+#include <array>
 #include "kernel.h"
 #include "bboxUtils.h"
 #include "cub_helper.h"
@@ -157,15 +157,8 @@ struct sspcLaunchConfig
     }
 };
 
-static std::vector<sspcLaunchConfig> sspcFuncVec;
-bool sspcInit()
-{
-    sspcFuncVec.push_back(sspcLaunchConfig(DataType::kFLOAT,
-                                           sortScoresPerClass_gpu<float>));
-    return true;
-}
-
-static bool initialized = sspcInit();
+static std::array<sspcLaunchConfig, 1> sspcLCOptions = {
+    sspcLaunchConfig(DataType::kFLOAT, sortScoresPerClass_gpu<float>)};
 
 pluginStatus_t sortScoresPerClass(
     cudaStream_t stream,
@@ -180,12 +173,12 @@ pluginStatus_t sortScoresPerClass(
     void* workspace)
 {
     sspcLaunchConfig lc = sspcLaunchConfig(DT_SCORE);
-    for (unsigned i = 0; i < sspcFuncVec.size(); ++i)
+    for (unsigned i = 0; i < sspcLCOptions.size(); ++i)
     {
-        if (lc == sspcFuncVec[i])
+        if (lc == sspcLCOptions[i])
         {
             DEBUG_PRINTF("sortScoresPerClass kernel %d\n", i);
-            return sspcFuncVec[i].function(stream,
+            return sspcLCOptions[i].function(stream,
                                            num,
                                            num_classes,
                                            num_preds_per_class,
diff --git a/plugin/common/kernels/sortScoresPerImage.cu b/plugin/common/kernels/sortScoresPerImage.cu
index 9ea53052..4675ce5a 100644
--- a/plugin/common/kernels/sortScoresPerImage.cu
+++ b/plugin/common/kernels/sortScoresPerImage.cu
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 #include "cub/cub.cuh"
-#include <vector>
+#include <array>
 #include "kernel.h"
 #include "bboxUtils.h"
 #include "cub_helper.h"
@@ -78,15 +78,8 @@ struct sspiLaunchConfig
     }
 };
 
-static std::vector<sspiLaunchConfig> sspiFuncVec;
-bool sspiInit()
-{
-    sspiFuncVec.push_back(sspiLaunchConfig(DataType::kFLOAT,
-                                           sortScoresPerImage_gpu<float>));
-    return true;
-}
-
-static bool initialized = sspiInit();
+static std::array<sspiLaunchConfig, 1> sspiLCOptions = {
+    sspiLaunchConfig(DataType::kFLOAT, sortScoresPerImage_gpu<float>)};
 
 pluginStatus_t sortScoresPerImage(
     cudaStream_t stream,
@@ -100,12 +93,12 @@ pluginStatus_t sortScoresPerImage(
     void* workspace)
 {
     sspiLaunchConfig lc = sspiLaunchConfig(DT_SCORE);
-    for (unsigned i = 0; i < sspiFuncVec.size(); ++i)
+    for (unsigned i = 0; i < sspiLCOptions.size(); ++i)
     {
-        if (lc == sspiFuncVec[i])
+        if (lc == sspiLCOptions[i])
         {
             DEBUG_PRINTF("sortScoresPerImage kernel %d\n", i);
-            return sspiFuncVec[i].function(stream,
+            return sspiLCOptions[i].function(stream,
                                            num_images,
                                            num_items_per_image,
                                            unsorted_scores,
diff --git a/plugin/common/nmsHelper.cpp b/plugin/common/nmsHelper.cpp
index c5abcf80..44dac0c7 100644
--- a/plugin/common/nmsHelper.cpp
+++ b/plugin/common/nmsHelper.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/plugin/common/nmsUtils.h b/plugin/common/nmsUtils.h
index 45807c84..5e38179a 100644
--- a/plugin/common/nmsUtils.h
+++ b/plugin/common/nmsUtils.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/plugin/common/plugin.h b/plugin/common/plugin.h
index b7a85b6c..46c830ed 100644
--- a/plugin/common/plugin.h
+++ b/plugin/common/plugin.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -19,6 +19,7 @@
 #include <cuda_runtime.h>
 #include <iostream>
 #include <memory>
+#include <sstream>
 #include <string>
 
 // Enumerator for status
@@ -74,7 +75,7 @@ T read(const char*& buffer)
 } // namespace nvinfer1
 
 #ifndef DEBUG
-
+#ifndef CHECK_MACROS_PLUGIN_H
 #define ASSERT(assertion)                                                                                              \
     {                                                                                                                  \
         if (!(assertion))                                                                                              \
@@ -83,7 +84,11 @@ T read(const char*& buffer)
             abort();                                                                                                   \
         }                                                                                                              \
     }
-
+#define FAIL(msg)                                                                                                      \
+    {                                                                                                                  \
+        fprintf(stderr, "Failure - " #msg ", %s:%d\n", __FILE__, __LINE__);                                            \
+        abort();                                                                                                       \
+    }
 #define CUASSERT(status_)                                                                                              \
     {                                                                                                                  \
         auto s_ = status_;                                                                                             \
@@ -190,6 +195,7 @@ T read(const char*& buffer)
         printf(__VA_ARGS__);                                                                                           \
     } while (0)
 
+#endif // CHECK_MACROS_H
 #endif
 
 #endif // TRT_PLUGIN_H
diff --git a/plugin/common/pluginLogger.h b/plugin/common/pluginLogger.h
new file mode 100644
index 00000000..09ad8894
--- /dev/null
+++ b/plugin/common/pluginLogger.h
@@ -0,0 +1,32 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef PLUGIN_LOGGER_H
+#define PLUGIN_LOGGER_H
+
+#include "pluginLogging.h"
+
+namespace
+{
+Logger gLogger{Logger::Severity::kINFO};
+LogStreamConsumer gLogVerbose{LOG_VERBOSE(gLogger)};
+LogStreamConsumer gLogInfo{LOG_INFO(gLogger)};
+LogStreamConsumer gLogWarning{LOG_WARN(gLogger)};
+LogStreamConsumer gLogError{LOG_ERROR(gLogger)};
+LogStreamConsumer gLogFatal{LOG_FATAL(gLogger)};
+} // namespace
+
+#endif // PLUGIN_LOGGER_H
diff --git a/plugin/common/pluginLogging.h b/plugin/common/pluginLogging.h
new file mode 100644
index 00000000..aae37de6
--- /dev/null
+++ b/plugin/common/pluginLogging.h
@@ -0,0 +1,503 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef PLUGIN_LOGGING_H
+#define PLUGIN_LOGGING_H
+
+#include "NvInferRuntimeCommon.h"
+#include <cassert>
+#include <ctime>
+#include <iomanip>
+#include <iostream>
+#include <ostream>
+#include <sstream>
+#include <string>
+
+using Severity = nvinfer1::ILogger::Severity;
+
+class LogStreamConsumerBuffer : public std::stringbuf
+{
+public:
+    LogStreamConsumerBuffer(std::ostream& stream, const std::string& prefix, bool shouldLog)
+        : mOutput(stream)
+        , mPrefix(prefix)
+        , mShouldLog(shouldLog)
+    {
+    }
+
+    LogStreamConsumerBuffer(LogStreamConsumerBuffer&& other)
+        : mOutput(other.mOutput)
+    {
+    }
+
+    ~LogStreamConsumerBuffer()
+    {
+        // std::streambuf::pbase() gives a pointer to the beginning of the buffered part of the output sequence
+        // std::streambuf::pptr() gives a pointer to the current position of the output sequence
+        // if the pointer to the beginning is not equal to the pointer to the current position,
+        // call putOutput() to log the output to the stream
+        if (pbase() != pptr())
+        {
+            putOutput();
+        }
+    }
+
+    // synchronizes the stream buffer and returns 0 on success
+    // synchronizing the stream buffer consists of inserting the buffer contents into the stream,
+    // resetting the buffer and flushing the stream
+    virtual int sync()
+    {
+        putOutput();
+        return 0;
+    }
+
+    void putOutput()
+    {
+        if (mShouldLog)
+        {
+            // prepend timestamp
+            std::time_t timestamp = std::time(nullptr);
+            tm* tm_local = std::localtime(&timestamp);
+            std::cout << "[";
+            std::cout << std::setw(2) << std::setfill('0') << 1 + tm_local->tm_mon << "/";
+            std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_mday << "/";
+            std::cout << std::setw(4) << std::setfill('0') << 1900 + tm_local->tm_year << "-";
+            std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_hour << ":";
+            std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_min << ":";
+            std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_sec << "] ";
+            // std::stringbuf::str() gets the string contents of the buffer
+            // insert the buffer contents pre-appended by the appropriate prefix into the stream
+            mOutput << mPrefix << str();
+            // set the buffer to empty
+            str("");
+            // flush the stream
+            mOutput.flush();
+        }
+    }
+
+    void setShouldLog(bool shouldLog)
+    {
+        mShouldLog = shouldLog;
+    }
+
+private:
+    std::ostream& mOutput;
+    std::string mPrefix;
+    bool mShouldLog;
+};
+
+//!
+//! \class LogStreamConsumerBase
+//! \brief Convenience object used to initialize LogStreamConsumerBuffer before std::ostream in LogStreamConsumer
+//!
+class LogStreamConsumerBase
+{
+public:
+    LogStreamConsumerBase(std::ostream& stream, const std::string& prefix, bool shouldLog)
+        : mBuffer(stream, prefix, shouldLog)
+    {
+    }
+
+protected:
+    LogStreamConsumerBuffer mBuffer;
+};
+
+//!
+//! \class LogStreamConsumer
+//! \brief Convenience object used to facilitate use of C++ stream syntax when logging messages.
+//!  Order of base classes is LogStreamConsumerBase and then std::ostream.
+//!  This is because the LogStreamConsumerBase class is used to initialize the LogStreamConsumerBuffer member field
+//!  in LogStreamConsumer and then the address of the buffer is passed to std::ostream.
+//!  This is necessary to prevent the address of an uninitialized buffer from being passed to std::ostream.
+//!  Please do not change the order of the parent classes.
+//!
+class LogStreamConsumer : protected LogStreamConsumerBase, public std::ostream
+{
+public:
+    //! \brief Creates a LogStreamConsumer which logs messages with level severity.
+    //!  Reportable severity determines if the messages are severe enough to be logged.
+    LogStreamConsumer(Severity reportableSeverity, Severity severity)
+        : LogStreamConsumerBase(severityOstream(severity), severityPrefix(severity), severity <= reportableSeverity)
+        , std::ostream(&mBuffer) // links the stream buffer with the stream
+        , mShouldLog(severity <= reportableSeverity)
+        , mSeverity(severity)
+    {
+    }
+
+    LogStreamConsumer(LogStreamConsumer&& other)
+        : LogStreamConsumerBase(severityOstream(other.mSeverity), severityPrefix(other.mSeverity), other.mShouldLog)
+        , std::ostream(&mBuffer) // links the stream buffer with the stream
+        , mShouldLog(other.mShouldLog)
+        , mSeverity(other.mSeverity)
+    {
+    }
+
+    void setReportableSeverity(Severity reportableSeverity)
+    {
+        mShouldLog = mSeverity <= reportableSeverity;
+        mBuffer.setShouldLog(mShouldLog);
+    }
+
+private:
+    static std::ostream& severityOstream(Severity severity)
+    {
+        return severity >= Severity::kINFO ? std::cout : std::cerr;
+    }
+
+    static std::string severityPrefix(Severity severity)
+    {
+        switch (severity)
+        {
+        case Severity::kINTERNAL_ERROR: return "[F] ";
+        case Severity::kERROR: return "[E] ";
+        case Severity::kWARNING: return "[W] ";
+        case Severity::kINFO: return "[I] ";
+        case Severity::kVERBOSE: return "[V] ";
+        default: assert(0); return "";
+        }
+    }
+
+    bool mShouldLog;
+    Severity mSeverity;
+};
+
+//! \class Logger
+//!
+//! \brief Class which manages logging of TensorRT tools and samples
+//!
+//! \details This class provides a common interface for TensorRT tools and samples to log information to the console,
+//! and supports logging two types of messages:
+//!
+//! - Debugging messages with an associated severity (info, warning, error, or internal error/fatal)
+//! - Test pass/fail messages
+//!
+//! The advantage of having all samples use this class for logging as opposed to emitting directly to stdout/stderr is
+//! that the logic for controlling the verbosity and formatting of sample output is centralized in one location.
+//!
+//! In the future, this class could be extended to support dumping test results to a file in some standard format
+//! (for example, JUnit XML), and providing additional metadata (e.g. timing the duration of a test run).
+//!
+//! TODO: For backwards compatibility with existing samples, this class inherits directly from the nvinfer1::ILogger
+//! interface, which is problematic since there isn't a clean separation between messages coming from the TensorRT
+//! library and messages coming from the sample.
+//!
+//! In the future (once all samples are updated to use Logger::getTRTLogger() to access the ILogger) we can refactor the
+//! class to eliminate the inheritance and instead make the nvinfer1::ILogger implementation a member of the Logger
+//! object.
+
+class Logger : public nvinfer1::ILogger
+{
+public:
+    Logger(Severity severity = Severity::kWARNING)
+        : mReportableSeverity(severity)
+    {
+    }
+
+    //!
+    //! \enum TestResult
+    //! \brief Represents the state of a given test
+    //!
+    enum class TestResult
+    {
+        kRUNNING, //!< The test is running
+        kPASSED,  //!< The test passed
+        kFAILED,  //!< The test failed
+        kWAIVED   //!< The test was waived
+    };
+
+    //!
+    //! \brief Forward-compatible method for retrieving the nvinfer::ILogger associated with this Logger
+    //! \return The nvinfer1::ILogger associated with this Logger
+    //!
+    //! TODO Once all samples are updated to use this method to register the logger with TensorRT,
+    //! we can eliminate the inheritance of Logger from ILogger
+    //!
+    nvinfer1::ILogger& getTRTLogger()
+    {
+        return *this;
+    }
+
+    //!
+    //! \brief Implementation of the nvinfer1::ILogger::log() virtual method
+    //!
+    //! Note samples should not be calling this function directly; it will eventually go away once we eliminate the
+    //! inheritance from nvinfer1::ILogger
+    //!
+    void log(Severity severity, const char* msg) override
+    {
+        LogStreamConsumer(mReportableSeverity, severity) << "[TRT] " << std::string(msg) << std::endl;
+    }
+
+    //!
+    //! \brief Method for controlling the verbosity of logging output
+    //!
+    //! \param severity The logger will only emit messages that have severity of this level or higher.
+    //!
+    void setReportableSeverity(Severity severity)
+    {
+        mReportableSeverity = severity;
+    }
+
+    //!
+    //! \brief Opaque handle that holds logging information for a particular test
+    //!
+    //! This object is an opaque handle to information used by the Logger to print test results.
+    //! The sample must call Logger::defineTest() in order to obtain a TestAtom that can be used
+    //! with Logger::reportTest{Start,End}().
+    //!
+    class TestAtom
+    {
+    public:
+        TestAtom(TestAtom&&) = default;
+
+    private:
+        friend class Logger;
+
+        TestAtom(bool started, const std::string& name, const std::string& cmdline)
+            : mStarted(started)
+            , mName(name)
+            , mCmdline(cmdline)
+        {
+        }
+
+        bool mStarted;
+        std::string mName;
+        std::string mCmdline;
+    };
+
+    //!
+    //! \brief Define a test for logging
+    //!
+    //! \param[in] name The name of the test.  This should be a string starting with
+    //!                  "TensorRT" and containing dot-separated strings containing
+    //!                  the characters [A-Za-z0-9_].
+    //!                  For example, "TensorRT.sample_googlenet"
+    //! \param[in] cmdline The command line used to reproduce the test
+    //
+    //! \return a TestAtom that can be used in Logger::reportTest{Start,End}().
+    //!
+    static TestAtom defineTest(const std::string& name, const std::string& cmdline)
+    {
+        return TestAtom(false, name, cmdline);
+    }
+
+    //!
+    //! \brief A convenience overloaded version of defineTest() that accepts an array of command-line arguments
+    //!        as input
+    //!
+    //! \param[in] name The name of the test
+    //! \param[in] argc The number of command-line arguments
+    //! \param[in] argv The array of command-line arguments (given as C strings)
+    //!
+    //! \return a TestAtom that can be used in Logger::reportTest{Start,End}().
+    static TestAtom defineTest(const std::string& name, int argc, char const* const* argv)
+    {
+        auto cmdline = genCmdlineString(argc, argv);
+        return defineTest(name, cmdline);
+    }
+
+    //!
+    //! \brief Report that a test has started.
+    //!
+    //! \pre reportTestStart() has not been called yet for the given testAtom
+    //!
+    //! \param[in] testAtom The handle to the test that has started
+    //!
+    static void reportTestStart(TestAtom& testAtom)
+    {
+        reportTestResult(testAtom, TestResult::kRUNNING);
+        assert(!testAtom.mStarted);
+        testAtom.mStarted = true;
+    }
+
+    //!
+    //! \brief Report that a test has ended.
+    //!
+    //! \pre reportTestStart() has been called for the given testAtom
+    //!
+    //! \param[in] testAtom The handle to the test that has ended
+    //! \param[in] result The result of the test. Should be one of TestResult::kPASSED,
+    //!                   TestResult::kFAILED, TestResult::kWAIVED
+    //!
+    static void reportTestEnd(const TestAtom& testAtom, TestResult result)
+    {
+        assert(result != TestResult::kRUNNING);
+        assert(testAtom.mStarted);
+        reportTestResult(testAtom, result);
+    }
+
+    static int reportPass(const TestAtom& testAtom)
+    {
+        reportTestEnd(testAtom, TestResult::kPASSED);
+        return EXIT_SUCCESS;
+    }
+
+    static int reportFail(const TestAtom& testAtom)
+    {
+        reportTestEnd(testAtom, TestResult::kFAILED);
+        return EXIT_FAILURE;
+    }
+
+    static int reportWaive(const TestAtom& testAtom)
+    {
+        reportTestEnd(testAtom, TestResult::kWAIVED);
+        return EXIT_SUCCESS;
+    }
+
+    static int reportTest(const TestAtom& testAtom, bool pass)
+    {
+        return pass ? reportPass(testAtom) : reportFail(testAtom);
+    }
+
+    Severity getReportableSeverity() const
+    {
+        return mReportableSeverity;
+    }
+
+private:
+    //!
+    //! \brief returns an appropriate string for prefixing a log message with the given severity
+    //!
+    static const char* severityPrefix(Severity severity)
+    {
+        switch (severity)
+        {
+        case Severity::kINTERNAL_ERROR: return "[F] ";
+        case Severity::kERROR: return "[E] ";
+        case Severity::kWARNING: return "[W] ";
+        case Severity::kINFO: return "[I] ";
+        case Severity::kVERBOSE: return "[V] ";
+        default: assert(0); return "";
+        }
+    }
+
+    //!
+    //! \brief returns an appropriate string for prefixing a test result message with the given result
+    //!
+    static const char* testResultString(TestResult result)
+    {
+        switch (result)
+        {
+        case TestResult::kRUNNING: return "RUNNING";
+        case TestResult::kPASSED: return "PASSED";
+        case TestResult::kFAILED: return "FAILED";
+        case TestResult::kWAIVED: return "WAIVED";
+        default: assert(0); return "";
+        }
+    }
+
+    //!
+    //! \brief returns an appropriate output stream (cout or cerr) to use with the given severity
+    //!
+    static std::ostream& severityOstream(Severity severity)
+    {
+        return severity >= Severity::kINFO ? std::cout : std::cerr;
+    }
+
+    //!
+    //! \brief method that implements logging test results
+    //!
+    static void reportTestResult(const TestAtom& testAtom, TestResult result)
+    {
+        severityOstream(Severity::kINFO) << "&&&& " << testResultString(result) << " " << testAtom.mName << " # "
+                                         << testAtom.mCmdline << std::endl;
+    }
+
+    //!
+    //! \brief generate a command line string from the given (argc, argv) values
+    //!
+    static std::string genCmdlineString(int argc, char const* const* argv)
+    {
+        std::stringstream ss;
+        for (int i = 0; i < argc; i++)
+        {
+            if (i > 0)
+                ss << " ";
+            ss << argv[i];
+        }
+        return ss.str();
+    }
+
+    Severity mReportableSeverity;
+};
+
+namespace
+{
+
+//!
+//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kVERBOSE
+//!
+//! Example usage:
+//!
+//!     LOG_VERBOSE(logger) << "hello world" << std::endl;
+//!
+inline LogStreamConsumer LOG_VERBOSE(const Logger& logger)
+{
+    return LogStreamConsumer(logger.getReportableSeverity(), Severity::kVERBOSE);
+}
+
+//!
+//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kINFO
+//!
+//! Example usage:
+//!
+//!     LOG_INFO(logger) << "hello world" << std::endl;
+//!
+inline LogStreamConsumer LOG_INFO(const Logger& logger)
+{
+    return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINFO);
+}
+
+//!
+//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kWARNING
+//!
+//! Example usage:
+//!
+//!     LOG_WARN(logger) << "hello world" << std::endl;
+//!
+inline LogStreamConsumer LOG_WARN(const Logger& logger)
+{
+    return LogStreamConsumer(logger.getReportableSeverity(), Severity::kWARNING);
+}
+
+//!
+//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kERROR
+//!
+//! Example usage:
+//!
+//!     LOG_ERROR(logger) << "hello world" << std::endl;
+//!
+inline LogStreamConsumer LOG_ERROR(const Logger& logger)
+{
+    return LogStreamConsumer(logger.getReportableSeverity(), Severity::kERROR);
+}
+
+//!
+//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kINTERNAL_ERROR
+//         ("fatal" severity)
+//!
+//! Example usage:
+//!
+//!     LOG_FATAL(logger) << "hello world" << std::endl;
+//!
+inline LogStreamConsumer LOG_FATAL(const Logger& logger)
+{
+    return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINTERNAL_ERROR);
+}
+
+} // anonymous namespace
+
+#endif // PLUGIN_LOGGING_H
diff --git a/plugin/common/reducedMathPlugin.cpp b/plugin/common/reducedMathPlugin.cpp
new file mode 100644
index 00000000..878ca0cf
--- /dev/null
+++ b/plugin/common/reducedMathPlugin.cpp
@@ -0,0 +1,86 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+namespace nvinfer1
+{
+namespace plugin
+{
+namespace detail
+{
+
+// Count leading zeros - start from most significant bit.
+int clz(int x)
+{
+    for (int i = 31; i >= 0; --i)
+    {
+        if ((1 << i) & x)
+        {
+            return 31 - i;
+        }
+    }
+    return 32;
+}
+
+#define CUDNN_IS_POW_2(x) (0 == ((x) & ((x) -1)))
+
+int find_log_2(int x, bool round_up = false)
+{
+    int a = 31 - clz(x);
+    if (round_up)
+    {
+        a += !CUDNN_IS_POW_2(x);
+    }
+    return a;
+}
+
+void find_divisor(int denom, unsigned int& mul_coeff, unsigned int& shift_coeff)
+{
+    if (denom == 0)
+    {
+        return;
+    }
+    if (denom == 1)
+    {
+        // if dividing by 1, reduced math doesn't work because mul_coeff would
+        // need to be 2^32, which doesn't fit into unsigned int.  the div()
+        // routine handles this special case separately.
+        mul_coeff = 0;
+        shift_coeff = 0;
+        return;
+    }
+    // To express the division N/D in terms of a multiplication, what we first
+    // imagine is simply N*(1/D).  However, 1/D will always evaluate to 0 (for D>1),
+    // so we need another way.  There's nothing that says we have to use exactly
+    // the fraction 1/D; instead it could be any X/Y that reduces to 1/D (i.e.,
+    // Y=X*D), or at least to "close enough" to it.  If we pick Y that is a power
+    // of two, then the N*(X/Y) can be N*X followed by a right-shift by some amount.
+    // The power of two we should pick should be at least 2^32, because in the
+    // div() routine we'll use umulhi(), which returns only the upper 32 bits --
+    // this being equivalent to a right-shift by 32.  But we might want a higher
+    // power of two for better accuracy depending on the magnitude of the denominator.
+    // Once we've picked Y, then X [our mul_coeff value] is simply Y/D, rounding up,
+    // and we save shift_coeff as whatever further shift we have to do beyond
+    // what the umulhi() implies.
+    unsigned int p = 31 + find_log_2(denom, true);
+    unsigned int m = ((1ull << p) + (unsigned int) denom - 1) / (unsigned int) denom;
+    mul_coeff = m;
+    shift_coeff = p - 32;
+}
+
+} // namespace detail
+
+} // namespace plugin
+
+} // namespace nvinfer1
diff --git a/plugin/common/serialize.hpp b/plugin/common/serialize.hpp
index a98d57bb..f3b3335c 100644
--- a/plugin/common/serialize.hpp
+++ b/plugin/common/serialize.hpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -15,104 +15,118 @@
  */
 #pragma once
 
-#include <cstring>
-#include <vector>
 #include <cassert>
+#include <cstring>
 #include <type_traits>
+#include <vector>
 
 #include <iostream>
-using std::cout;
 using std::cerr;
+using std::cout;
 using std::endl;
 
-template<typename T>
+template <typename T>
 inline void serialize_value(void** buffer, T const& value);
 
-template<typename T>
+template <typename T>
 inline void deserialize_value(void const** buffer, size_t* buffer_size, T* value);
 
-namespace {
+namespace
+{
 
-template<typename T, class Enable=void>
-struct Serializer {};
+template <typename T, class Enable = void>
+struct Serializer
+{
+};
 
-template<typename T>
-struct Serializer<T, typename std::enable_if<
-                       std::is_arithmetic<T>::value ||
-                       std::is_enum<T>::value ||
-                       std::is_pod<T>::value>::type> {
-  static size_t serialized_size(T const& value) {
-    return sizeof(T);
-  }
-  static void serialize(void** buffer, T const& value) {
-    ::memcpy(*buffer, &value, sizeof(T));
-    reinterpret_cast<char*&>(*buffer) += sizeof(T);
-  }
-  static void deserialize(void const** buffer, size_t* buffer_size, T* value) {
-    assert(*buffer_size >= sizeof(T));
-    ::memcpy(value, *buffer, sizeof(T));
-    reinterpret_cast<char const*&>(*buffer) += sizeof(T);
-    *buffer_size -= sizeof(T);
-  }
+template <typename T>
+struct Serializer<T,
+    typename std::enable_if<std::is_arithmetic<T>::value || std::is_enum<T>::value || std::is_pod<T>::value>::type>
+{
+    static size_t serialized_size(T const& value)
+    {
+        return sizeof(T);
+    }
+    static void serialize(void** buffer, T const& value)
+    {
+        ::memcpy(*buffer, &value, sizeof(T));
+        reinterpret_cast<char*&>(*buffer) += sizeof(T);
+    }
+    static void deserialize(void const** buffer, size_t* buffer_size, T* value)
+    {
+        assert(*buffer_size >= sizeof(T));
+        ::memcpy(value, *buffer, sizeof(T));
+        reinterpret_cast<char const*&>(*buffer) += sizeof(T);
+        *buffer_size -= sizeof(T);
+    }
 };
 
-template<>
-struct Serializer<const char*> {
-  static size_t serialized_size(const char* value) {
-    return strlen(value) + 1;
-  }
-  static void serialize(void** buffer, const char* value) {
-    ::strcpy(static_cast<char*>(*buffer), value);
-    reinterpret_cast<char*&>(*buffer) += strlen(value) + 1;
-  }
-  static void deserialize(void const** buffer, size_t* buffer_size, const char** value) {
-    *value = static_cast<char const*>(*buffer);
-    size_t data_size = strnlen(*value, *buffer_size) + 1;
-    assert(*buffer_size >= data_size);
-    reinterpret_cast<char const*&>(*buffer) += data_size;
-    *buffer_size -= data_size;
-  }
+template <>
+struct Serializer<const char*>
+{
+    static size_t serialized_size(const char* value)
+    {
+        return strlen(value) + 1;
+    }
+    static void serialize(void** buffer, const char* value)
+    {
+        ::strcpy(static_cast<char*>(*buffer), value);
+        reinterpret_cast<char*&>(*buffer) += strlen(value) + 1;
+    }
+    static void deserialize(void const** buffer, size_t* buffer_size, const char** value)
+    {
+        *value = static_cast<char const*>(*buffer);
+        size_t data_size = strnlen(*value, *buffer_size) + 1;
+        assert(*buffer_size >= data_size);
+        reinterpret_cast<char const*&>(*buffer) += data_size;
+        *buffer_size -= data_size;
+    }
 };
 
-template<typename T>
-struct Serializer<std::vector<T>, typename std::enable_if<
-                                    std::is_arithmetic<T>::value ||
-                                    std::is_enum<T>::value ||
-                                    std::is_pod<T>::value>::type> {
-  static size_t serialized_size(std::vector<T> const& value) {
-    return sizeof(value.size()) + value.size() * sizeof(T);
-  }
-  static void serialize(void** buffer, std::vector<T> const& value) {
-    serialize_value(buffer, value.size());
-    size_t nbyte = value.size() * sizeof(T);
-    ::memcpy(*buffer, value.data(), nbyte);
-    reinterpret_cast<char*&>(*buffer) += nbyte;
-  }
-  static void deserialize(void const** buffer, size_t* buffer_size, std::vector<T>* value) {
-    size_t size;
-    deserialize_value(buffer, buffer_size, &size);
-    value->resize(size);
-    size_t nbyte = value->size() * sizeof(T);
-    assert(*buffer_size >= nbyte);
-    ::memcpy(value->data(), *buffer, nbyte);
-    reinterpret_cast<char const*&>(*buffer) += nbyte;
-    *buffer_size -= nbyte;
-  }
+template <typename T>
+struct Serializer<std::vector<T>,
+    typename std::enable_if<std::is_arithmetic<T>::value || std::is_enum<T>::value || std::is_pod<T>::value>::type>
+{
+    static size_t serialized_size(std::vector<T> const& value)
+    {
+        return sizeof(value.size()) + value.size() * sizeof(T);
+    }
+    static void serialize(void** buffer, std::vector<T> const& value)
+    {
+        serialize_value(buffer, value.size());
+        size_t nbyte = value.size() * sizeof(T);
+        ::memcpy(*buffer, value.data(), nbyte);
+        reinterpret_cast<char*&>(*buffer) += nbyte;
+    }
+    static void deserialize(void const** buffer, size_t* buffer_size, std::vector<T>* value)
+    {
+        size_t size;
+        deserialize_value(buffer, buffer_size, &size);
+        value->resize(size);
+        size_t nbyte = value->size() * sizeof(T);
+        assert(*buffer_size >= nbyte);
+        ::memcpy(value->data(), *buffer, nbyte);
+        reinterpret_cast<char const*&>(*buffer) += nbyte;
+        *buffer_size -= nbyte;
+    }
 };
 
 } // namespace
 
-template<typename T>
-inline size_t serialized_size(T const& value) {
-  return Serializer<T>::serialized_size(value);
+template <typename T>
+inline size_t serialized_size(T const& value)
+{
+    return Serializer<T>::serialized_size(value);
 }
 
-template<typename T>
-inline void serialize_value(void** buffer, T const& value) {
-  return Serializer<T>::serialize(buffer, value);
+template <typename T>
+inline void serialize_value(void** buffer, T const& value)
+{
+    return Serializer<T>::serialize(buffer, value);
 }
 
-template<typename T>
-inline void deserialize_value(void const** buffer, size_t* buffer_size, T* value) {
-  return Serializer<T>::deserialize(buffer, buffer_size, value);
+template <typename T>
+inline void deserialize_value(void const** buffer, size_t* buffer_size, T* value)
+{
+    return Serializer<T>::deserialize(buffer, buffer_size, value);
 }
diff --git a/plugin/coordConvACPlugin/CMakeLists.txt b/plugin/coordConvACPlugin/CMakeLists.txt
new file mode 100644
index 00000000..40e90bb4
--- /dev/null
+++ b/plugin/coordConvACPlugin/CMakeLists.txt
@@ -0,0 +1,24 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+file(GLOB SRCS *.cpp)
+set(PLUGIN_SOURCES ${PLUGIN_SOURCES} ${SRCS})
+set(PLUGIN_SOURCES ${PLUGIN_SOURCES} PARENT_SCOPE)
+file(GLOB CU_SRCS *.cu)
+set(PLUGIN_CU_SOURCES ${PLUGIN_CU_SOURCES} ${CU_SRCS})
+set(PLUGIN_CU_SOURCES ${PLUGIN_CU_SOURCES} PARENT_SCOPE)
+
+
diff --git a/plugin/coordConvACPlugin/README.md b/plugin/coordConvACPlugin/README.md
new file mode 100644
index 00000000..df686129
--- /dev/null
+++ b/plugin/coordConvACPlugin/README.md
@@ -0,0 +1,80 @@
+# coordConvACPlugin
+
+**Table Of Contents**
+- [coordConvACPlugin](#coordconvacplugin)
+  - [Description](#description)
+    - [Structure](#structure)
+  - [Additional resources](#additional-resources)
+  - [License](#license)
+  - [Changelog](#changelog)
+  - [Known issues](#known-issues)
+
+## Description
+
+The `coordConvACPlugin` implements the `CoordConv` layer. This layer was first introduced by Uber AI Labs in 2018, and improves on regular convolution by adding additional channels containing relative coordinates to the input tensor. These additional channels allows the subsequent convolution to retain information about where it was applied.
+
+Each node with the op name `CoordConvAC` in `ONNX` graph will be mapped to that plugin. `Conv` node should follow after each `CoordConvAC` node into `ONNX` graph. 
+
+For example, say we have an input tensor for a Conv layer named `X` with shape `[N, C, H, W]`, where `N` is the batch size, `C` is the number of channels, `H` is the height, and `W` is the width. In the CoordConv layer for each input the plugin will concatenate two additional channels with shape `[1, H, W]` at the end. The first extra channel contains relative coordinates along the Y axis and the second extra channel contains coordinates along the X axis. As a result we will get a new input tensor with shape `[N, C+2, H, W]` before applying regular convolution.
+
+Relative coordinates have values in the range of `[-1,1]` where -1 is the value of the top row of the first channel (Y axis) and the left column of second channel (X axis), and 1 is the value for the bottom row of the first channel (Y axis) and the right column of second channel (X axis). All other values of the matrix will be filled in with a constant step value. between -1 and 1.
+
+Formula for calculating constant step value is:
+
+`STEP_VALUE_H = 2 / (H - 1)` - step value for the 1st channel
+
+`STEP_VALUE_W = 2 / (W - 1)` - step value for the 2nd channel
+
+Below are examples of 1st and 2nd channels for input data with `H=5, W=5, STEP_VALUE_H=0.5, and STEP_VALUE_W=0.5`
+
+First channel with Y relative coordinates
+
+| | | | | | 
+| ------------- |:-------------:| -----|-------------| -----:|
+| -1	| -1	| -1	| -1	| -1 |
+| -0.5 | 	-0.5 | 	-0.5 | 	-0.5 | 	-0.5 | 
+| 0 | 	0 | 	0 | 	0 | 	0 | 
+| 0.5 | 	0.5 | 	0.5 | 	0.5 | 	0.5 | 
+| 1 | 	1 | 	1 | 	1 | 	1 | 
+
+Second channel with X relative coordinates
+
+|     |     |     |     |     | 
+| ------------- |:-------------:| -----|-------------| -----:|
+| -1	| -0.5	| 0	| 0.5	| 1 |
+|  -1 | 	-0.5 | 	0 | 	0.5 | 	1 | 
+|  -1 | 	-0.5 | 	0 | 	0.5 | 	1 | 
+|  -1 | 	-0.5 | 	0 | 	0.5 | 	1 | 
+|  -1 | 	-0.5 | 	0 | 	0.5 | 	1 | 
+
+These two matrices will be concatenated with the input data with the formula `CONCAT([INPUT_DATA, 1ST_CHANNEL, 2ND_CHANNEL])` on the channel dimension.
+
+  
+### Structure
+
+This plugin takes one input and generates one output. Input shape is `[N, C, H, W]` and the output shape is `[N, C + 2, H, W]`.
+
+## Additional resources
+
+The following resources provide a deeper understanding of the `coordConvACPlugin` plugin:
+
+**Networks**  
+- Paper about Coord Conv layer ["An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution"](https://arxiv.org/abs/1807.03247)    
+- Blog post by Uber AI Labs about [CoordConv layer](https://eng.uber.com/coordconv/)
+- Open-source implementations of the layer in Pytorch [source1](https://github.com/walsvid/CoordConv), [source2](https://github.com/mkocabas/CoordConv-pytorch)
+
+## License
+
+For terms and conditions for use, reproduction, and distribution, see the [TensorRT Software License Agreement](https://docs.nvidia.com/deeplearning/sdk/tensorrt-sla/index.html) 
+documentation.
+
+
+## Changelog
+
+April 2020
+This is the first release of this `README.md` file.
+
+
+## Known issues
+
+There are no known issues in this plugin.
diff --git a/plugin/coordConvACPlugin/coordConvACPlugin.cpp b/plugin/coordConvACPlugin/coordConvACPlugin.cpp
new file mode 100644
index 00000000..7f9d9060
--- /dev/null
+++ b/plugin/coordConvACPlugin/coordConvACPlugin.cpp
@@ -0,0 +1,207 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "coordConvACPlugin.h"
+#include <cstring>
+#include <iostream>
+#include <vector>
+
+using namespace nvinfer1;
+const int NUM_COORDCONV_CHANNELS = 2;
+
+namespace
+{
+const char* COORDCONV_AC_PLUGIN_VERSION{"1"};
+const char* COORDCONV_AC_PLUGIN_NAME{"CoordConvAC"};
+} // namespace
+
+PluginFieldCollection CoordConvACPluginCreator::mFC{};
+std::vector<PluginField> CoordConvACPluginCreator::mPluginAttributes;
+
+CoordConvACPlugin::CoordConvACPlugin() {}
+
+CoordConvACPlugin::CoordConvACPlugin(nvinfer1::DataType iType, int iC, int iH, int iW, int oC, int oH, int oW)
+    : iType(iType)
+    , iC(iC)
+    , iH(iH)
+    , iW(iW)
+    , oC(oC)
+    , oH(oH)
+    , oW(oW)
+{
+}
+
+CoordConvACPlugin::CoordConvACPlugin(const void* data, size_t length)
+{
+    const char *d = reinterpret_cast<const char*>(data), *a = d;
+    iC = read<int>(d);
+    iH = read<int>(d);
+    iW = read<int>(d);
+    oC = read<int>(d);
+    oH = read<int>(d);
+    oW = read<int>(d);
+    ASSERT(d == a + length);
+}
+
+int CoordConvACPlugin::getNbOutputs() const
+{
+    return 1;
+}
+
+int CoordConvACPlugin::initialize()
+{
+    return STATUS_SUCCESS;
+}
+
+void CoordConvACPlugin::terminate() {}
+
+Dims CoordConvACPlugin::getOutputDimensions(int index, const Dims* inputs, int nbInputDims)
+{
+    // CHW
+    nvinfer1::Dims dimsOutput;
+    dimsOutput.nbDims = inputs->nbDims;
+    dimsOutput.d[0] = inputs->d[0] + NUM_COORDCONV_CHANNELS;
+    dimsOutput.d[1] = inputs->d[1];
+    dimsOutput.d[2] = inputs->d[2];
+    dimsOutput.d[3] = inputs->d[3];
+    return dimsOutput;
+}
+
+size_t CoordConvACPlugin::getWorkspaceSize(int maxBatchSize) const
+{
+    return 0;
+}
+
+size_t CoordConvACPlugin::getSerializationSize() const
+{
+    // iC, iH, iW, oC, oH, oW
+    return sizeof(int) * 6;
+}
+
+void CoordConvACPlugin::serialize(void* buffer) const
+{
+    char *d = reinterpret_cast<char*>(buffer), *a = d;
+    write(d, iC);
+    write(d, iH);
+    write(d, iW);
+    write(d, oC);
+    write(d, oH);
+    write(d, oW);
+    ASSERT(d == a + getSerializationSize());
+}
+
+void CoordConvACPlugin::configurePlugin(const Dims* inputDims, int nbInputs, const Dims* outputDims, int nbOutputs,
+    const DataType* inputTypes, const DataType* outputTypes, const bool* inputIsBroadcast,
+    const bool* outputIsBroadcast, nvinfer1::PluginFormat format, int maxBatchSize)
+{
+    ASSERT(nbInputs == 1);
+    ASSERT(nbOutputs == 1);
+
+    iC = inputDims->d[0];
+    iH = inputDims->d[1];
+    iW = inputDims->d[2];
+
+    oC = outputDims->d[0];
+    oH = outputDims->d[1];
+    oW = outputDims->d[2];
+
+    iType = inputTypes[0];
+}
+
+bool CoordConvACPlugin::supportsFormat(DataType type, PluginFormat format) const
+{
+    return ((type == DataType::kFLOAT || type == DataType::kHALF) && format == PluginFormat::kNCHW);
+}
+
+const char* CoordConvACPlugin::getPluginType() const
+{
+    return COORDCONV_AC_PLUGIN_NAME;
+}
+
+const char* CoordConvACPlugin::getPluginVersion() const
+{
+    return COORDCONV_AC_PLUGIN_VERSION;
+}
+
+void CoordConvACPlugin::destroy()
+{
+    delete this;
+}
+
+IPluginV2Ext* CoordConvACPlugin::clone() const
+{
+    auto* plugin = new CoordConvACPlugin(iType, iC, iH, iW, oC, oH, oW);
+    return plugin;
+}
+
+void CoordConvACPlugin::setPluginNamespace(const char* pluginNamespace)
+{
+    mPluginNamespace = pluginNamespace;
+}
+
+const char* CoordConvACPlugin::getPluginNamespace() const
+{
+    return mPluginNamespace;
+}
+
+nvinfer1::DataType CoordConvACPlugin::getOutputDataType(
+    int index, const nvinfer1::DataType* inputTypes, int nbInputs) const
+{
+    return inputTypes[0];
+}
+
+bool CoordConvACPlugin::isOutputBroadcastAcrossBatch(
+    int outputIndex, const bool* inputIsBroadcasted, int nbInputs) const
+{
+    return false;
+}
+
+bool CoordConvACPlugin::canBroadcastInputAcrossBatch(int inputIndex) const
+{
+    return false;
+}
+
+// Plugin creator
+CoordConvACPluginCreator::CoordConvACPluginCreator() {}
+
+const char* CoordConvACPluginCreator::getPluginName() const
+{
+    return COORDCONV_AC_PLUGIN_NAME;
+}
+
+const char* CoordConvACPluginCreator::getPluginVersion() const
+{
+    return COORDCONV_AC_PLUGIN_VERSION;
+}
+
+const PluginFieldCollection* CoordConvACPluginCreator::getFieldNames()
+{
+    return &mFC;
+}
+
+IPluginV2Ext* CoordConvACPluginCreator::createPlugin(const char* name, const PluginFieldCollection* fc)
+{
+    CoordConvACPlugin* plugin = new CoordConvACPlugin();
+    plugin->setPluginNamespace(mNamespace.c_str());
+    return plugin;
+}
+
+IPluginV2Ext* CoordConvACPluginCreator::deserializePlugin(const char* name, const void* serialData, size_t serialLength)
+{
+    CoordConvACPlugin* plugin = new CoordConvACPlugin(serialData, serialLength);
+    plugin->setPluginNamespace(mNamespace.c_str());
+    return plugin;
+}
diff --git a/plugin/coordConvACPlugin/coordConvACPlugin.cu b/plugin/coordConvACPlugin/coordConvACPlugin.cu
new file mode 100644
index 00000000..90fdd3c9
--- /dev/null
+++ b/plugin/coordConvACPlugin/coordConvACPlugin.cu
@@ -0,0 +1,111 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+ #include "coordConvACPlugin.h"
+ #include <cuda_fp16.h>
+ 
+ 
+ template <typename T_DATA>
+     __global__ void kernelCopy(
+         int N,
+         T_DATA* inputs,
+         T_DATA* outputs
+         )
+ {
+     int index = blockIdx.x * blockDim.x + threadIdx.x;
+     if (index < N){
+         outputs[index] = inputs[index];
+     }
+     __syncthreads();
+ }
+ 
+ template <typename T_DATA>
+     __global__ void kernelAC(
+         int N,
+         int iH,
+         int iW,
+         float stepACh,
+         float stepACw,
+         T_DATA* outputs
+         )
+ {
+    int index = blockIdx.x * blockDim.x + threadIdx.x;
+    int channelLength = N/2;
+
+    if (index < channelLength){
+         outputs[index] = -1.0 + (float)(index / iW) * stepACw;
+         outputs[index + channelLength] = -1.0 + (float)((index + channelLength) % iH) * stepACh;
+    }
+    __syncthreads();
+ }
+ 
+ template <typename T>
+ int inferenceAC(
+     int batchSize, 
+     int iC, 
+     int iH, 
+     int iW, 
+     int oC, 
+     int oH, 
+     int oW, 
+     T* inputs, 
+     T* outputs, 
+     cudaStream_t stream){
+         // NCHW
+         const float coordsRange = 2.0;
+         const int nThreads = 512;
+         int lenCopy = iC * iH * iW;
+         int lenAC = (oC * oH * oW) - lenCopy;
+         
+         int nBlocksCopy = (int)((float)lenCopy / nThreads) + 1;
+         int nBlocksAC = (int)((float)lenAC / nThreads) + 1; 
+ 
+         float stepACh = coordsRange / (float)(iH - 1);
+         float stepACw = coordsRange / (float)(iW - 1); 
+        
+         for(int i=0; i<batchSize; ++i){
+             // NOTE: kernelCopy kernel can be replaced with cudaMemcpy function 
+             kernelCopy<<<nBlocksCopy, nThreads, 0, stream>>>(lenCopy, inputs, outputs);
+             outputs += lenCopy;
+
+             kernelAC<<<nBlocksAC, nThreads, 0, stream>>>(lenAC, iH, iW, stepACh, stepACw, outputs);
+             outputs += lenAC;
+             inputs += lenCopy; 
+         }
+     
+     cudaError_t err = cudaGetLastError();
+     if ( cudaSuccess != err )
+     {
+         fprintf( stderr, "cudaCheckError() failed at %s:%i : %s\n",
+                 __FILE__, __LINE__, cudaGetErrorString( err ) );
+         return 1;
+     }
+     return 0;
+ }
+ 
+ int CoordConvACPlugin::enqueue(
+     int batchSize, const void* const* inputs, void** outputs, void* workspace, cudaStream_t stream)
+ {
+     switch(iType){
+         case DataType::kFLOAT:
+             return inferenceAC(batchSize, iC, iH, iW, oC, oH, oW, (float*)inputs[0], (float*)outputs[0], stream);
+         case DataType::kHALF:
+             return inferenceAC(batchSize, iC, iH, iW, oC, oH, oW, (__half*)inputs[0], (__half*)outputs[0], stream);
+     }
+     return 1;
+ }
+ 
\ No newline at end of file
diff --git a/plugin/coordConvACPlugin/coordConvACPlugin.h b/plugin/coordConvACPlugin/coordConvACPlugin.h
new file mode 100644
index 00000000..279837e9
--- /dev/null
+++ b/plugin/coordConvACPlugin/coordConvACPlugin.h
@@ -0,0 +1,119 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef TRT_COORDCONV_PLUGIN_H
+#define TRT_COORDCONV_PLUGIN_H
+
+#include "NvInferPlugin.h"
+#include "kernel.h"
+#include "plugin.h"
+#include <cuda_runtime.h>
+#include <string>
+#include <vector>
+
+namespace nvinfer1
+{
+namespace plugin
+{
+
+class CoordConvACPlugin : public IPluginV2Ext
+{
+public:
+    CoordConvACPlugin();
+
+    CoordConvACPlugin(DataType iType, int iC, int iH, int iW, int oC, int oH, int oW);
+
+    CoordConvACPlugin(const void* data, size_t length);
+
+    ~CoordConvACPlugin() override = default;
+
+    int getNbOutputs() const override;
+
+    Dims getOutputDimensions(int index, const Dims* inputs, int nbInputDims) override;
+
+    int initialize() override;
+
+    void terminate() override;
+
+    size_t getWorkspaceSize(int maxBatchSize) const override;
+
+    int enqueue(
+        int batchSize, const void* const* inputs, void** outputs, void* workspace, cudaStream_t stream) override;
+
+    size_t getSerializationSize() const override;
+
+    void serialize(void* buffer) const override;
+
+    void configurePlugin(const Dims* inputDims, int nbInputs, const Dims* outputDims, int nbOutputs,
+        const DataType* inputTypes, const DataType* outputTypes, const bool* inputIsBroadcast,
+        const bool* outputIsBroadcast, PluginFormat floatFormat, int maxBatchSize) override;
+
+    bool supportsFormat(DataType type, PluginFormat format) const override;
+
+    const char* getPluginType() const override;
+
+    const char* getPluginVersion() const override;
+
+    void destroy() override;
+
+    IPluginV2Ext* clone() const override;
+
+    nvinfer1::DataType getOutputDataType(int index, const nvinfer1::DataType* inputType, int nbInputs) const override;
+
+    void setPluginNamespace(const char* pluginNamespace) override;
+
+    const char* getPluginNamespace() const override;
+
+    bool isOutputBroadcastAcrossBatch(int outputIndex, const bool* inputIsBroadcasted, int nbInputs) const override;
+
+    bool canBroadcastInputAcrossBatch(int inputIndex) const override;
+
+private:
+    DataType iType;
+    int iC, iH, iW;
+    int oC, oH, oW;
+    const char* mPluginNamespace;
+    std::string mNamespace;
+};
+
+class CoordConvACPluginCreator : public BaseCreator
+{
+public:
+    CoordConvACPluginCreator();
+
+    ~CoordConvACPluginCreator() override = default;
+
+    const char* getPluginName() const override;
+
+    const char* getPluginVersion() const override;
+
+    const PluginFieldCollection* getFieldNames() override;
+
+    IPluginV2Ext* createPlugin(const char* name, const PluginFieldCollection* fc) override;
+
+    IPluginV2Ext* deserializePlugin(const char* name, const void* serialData, size_t serialLength) override;
+
+private:
+    static PluginFieldCollection mFC;
+    static std::vector<PluginField> mPluginAttributes;
+
+protected:
+    std::string mNamespace;
+};
+} // namespace plugin
+} // namespace nvinfer1
+
+#endif // TRT_COORDCONV_PLUGIN_H
diff --git a/plugin/cropAndResizePlugin/CMakeLists.txt b/plugin/cropAndResizePlugin/CMakeLists.txt
index de8fff17..85d8ce4e 100644
--- a/plugin/cropAndResizePlugin/CMakeLists.txt
+++ b/plugin/cropAndResizePlugin/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/plugin/cropAndResizePlugin/README.md b/plugin/cropAndResizePlugin/README.md
index e96b92ee..c4bf7c74 100644
--- a/plugin/cropAndResizePlugin/README.md
+++ b/plugin/cropAndResizePlugin/README.md
@@ -77,4 +77,4 @@ This is the first release of this `README.md` file.
 
 ## Known issues
 
-There are no known issues in this plugin.
\ No newline at end of file
+There are no known issues in this plugin.
diff --git a/plugin/cropAndResizePlugin/cropAndResizePlugin.cpp b/plugin/cropAndResizePlugin/cropAndResizePlugin.cpp
index 4e0cb5d5..d75e6f75 100644
--- a/plugin/cropAndResizePlugin/cropAndResizePlugin.cpp
+++ b/plugin/cropAndResizePlugin/cropAndResizePlugin.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -55,15 +55,15 @@ T readFromBuffer(const char*& buffer)
 
 CropAndResizePlugin::CropAndResizePlugin(const std::string name)
     : mLayerName(name)
-    , mCropHeight(7)
     , mCropWidth(7)
+    , mCropHeight(7)
 {
 }
 
 CropAndResizePlugin::CropAndResizePlugin(const std::string name, int crop_width, int crop_height)
     : mLayerName(name)
-    , mCropHeight(crop_height)
     , mCropWidth(crop_width)
+    , mCropHeight(crop_height)
 {
 }
 
@@ -84,11 +84,11 @@ CropAndResizePlugin::CropAndResizePlugin(const std::string name, const void* ser
 CropAndResizePlugin::CropAndResizePlugin(const std::string name, int crop_width, int crop_height, int depth,
     int input_width, int input_height, int max_box_num)
     : mLayerName(name)
-    , mCropHeight(crop_height)
     , mCropWidth(crop_width)
+    , mCropHeight(crop_height)
     , mDepth(depth)
-    , mInputHeight(input_height)
     , mInputWidth(input_width)
+    , mInputHeight(input_height)
     , mNumboxes(max_box_num)
 {
 }
diff --git a/plugin/cropAndResizePlugin/cropAndResizePlugin.h b/plugin/cropAndResizePlugin/cropAndResizePlugin.h
index 1d5af23b..0420fb90 100644
--- a/plugin/cropAndResizePlugin/cropAndResizePlugin.h
+++ b/plugin/cropAndResizePlugin/cropAndResizePlugin.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -98,7 +98,7 @@ class CropAndResizePlugin : public IPluginV2Ext
 
 private:
     const std::string mLayerName;
-    size_t mInputWidth, mInputHeight, mNumboxes, mCropHeight, mCropWidth, mDepth;
+    size_t mCropWidth, mCropHeight, mDepth, mInputWidth, mInputHeight, mNumboxes;
     std::string mNamespace;
 };
 
diff --git a/plugin/detectionLayerPlugin/CMakeLists.txt b/plugin/detectionLayerPlugin/CMakeLists.txt
index de8fff17..85d8ce4e 100644
--- a/plugin/detectionLayerPlugin/CMakeLists.txt
+++ b/plugin/detectionLayerPlugin/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/plugin/detectionLayerPlugin/detectionLayerPlugin.cpp b/plugin/detectionLayerPlugin/detectionLayerPlugin.cpp
index 64a2bec3..d19fde4c 100644
--- a/plugin/detectionLayerPlugin/detectionLayerPlugin.cpp
+++ b/plugin/detectionLayerPlugin/detectionLayerPlugin.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -156,7 +156,9 @@ const char* DetectionLayer::getPluginVersion() const
 
 IPluginV2Ext* DetectionLayer::clone() const
 {
-    return new DetectionLayer(*this);
+    DetectionLayer* plugin = new DetectionLayer(*this);
+    plugin->setPluginNamespace(mNameSpace.c_str());
+    return plugin;
 };
 
 void DetectionLayer::setPluginNamespace(const char* libNamespace)
diff --git a/plugin/detectionLayerPlugin/detectionLayerPlugin.h b/plugin/detectionLayerPlugin/detectionLayerPlugin.h
index e24ff361..5aee1480 100644
--- a/plugin/detectionLayerPlugin/detectionLayerPlugin.h
+++ b/plugin/detectionLayerPlugin/detectionLayerPlugin.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/plugin/embLayerNormPlugin/CMakeLists.txt b/plugin/embLayerNormPlugin/CMakeLists.txt
index ba12f3b3..a6898a83 100644
--- a/plugin/embLayerNormPlugin/CMakeLists.txt
+++ b/plugin/embLayerNormPlugin/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,6 +13,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
+file(GLOB SRCS *.cpp)
+set(PLUGIN_SOURCES ${PLUGIN_SOURCES} ${SRCS})
+set(PLUGIN_SOURCES ${PLUGIN_SOURCES} PARENT_SCOPE)
 file(GLOB CU_SRCS *.cu)
 set(BERT_CU_SOURCES ${BERT_CU_SOURCES} ${CU_SRCS})
 set(BERT_CU_SOURCES ${BERT_CU_SOURCES} PARENT_SCOPE)
diff --git a/plugin/embLayerNormPlugin/embLayerNormKernel.cu b/plugin/embLayerNormPlugin/embLayerNormKernel.cu
new file mode 100644
index 00000000..c0ffab8e
--- /dev/null
+++ b/plugin/embLayerNormPlugin/embLayerNormKernel.cu
@@ -0,0 +1,254 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuda.h>
+#if CUDA_VERSION >= 10010
+
+#include <cassert>
+#include <cstring>
+#include <vector>
+
+#include "NvInfer.h"
+#include "embLayerNormPlugin.h"
+#include "bertCommon.h"
+#include "common.cuh"
+#include "serialize.hpp"
+
+using namespace nvinfer1;
+
+namespace bert
+{
+
+__global__ void fillSBSMaskKernel(
+    const uint32_t warps_m, const uint32_t warps_n, const uint32_t S, const int* inputMaskSB, uint32_t* inputMaskX)
+{
+
+    extern __shared__ int shm_mask[]; // S mask elements of this batch
+
+    const size_t xmmas_n = (S + 16 * warps_n - 1) / (16 * warps_n);
+    const uint32_t threads_per_cta = blockDim.x;
+    const uint32_t xmmas_m = gridDim.x;
+    const uint32_t B = gridDim.y;
+
+    const uint32_t mi = blockIdx.x;
+    const uint32_t bi = blockIdx.y;
+    const uint32_t tidx = threadIdx.x;
+
+    const size_t warp = tidx / 32;
+    const size_t warp_m = warp % warps_m;
+    const size_t warp_n = warp / warps_m;
+    const size_t lane = tidx % 32;
+    const size_t col = warp_n * 16 + lane % 4 * 2;
+
+    //load the mask corresponding to one batch
+    for (uint32_t si = tidx; si < S; si += threads_per_cta)
+    {
+        // not coalesced to conform to current input format: SxB
+        shm_mask[si] = inputMaskSB[si * B + bi];
+    }
+    __syncthreads();
+
+    uint32_t mask = 0u;
+
+    for (size_t ni = 0; ni < xmmas_n; ++ni)
+    {
+        const int offset = ni * 16 * warps_n + col;
+        mask |= (shm_mask[offset + 0] == 1.f ? 1u : 0u) << (8 * ni + 0);
+        mask |= (shm_mask[offset + 1] == 1.f ? 1u : 0u) << (8 * ni + 1);
+        mask |= (shm_mask[offset + 0] == 1.f ? 1u : 0u) << (8 * ni + 2);
+        mask |= (shm_mask[offset + 1] == 1.f ? 1u : 0u) << (8 * ni + 3);
+        mask |= (shm_mask[offset + 8] == 1.f ? 1u : 0u) << (8 * ni + 4);
+        mask |= (shm_mask[offset + 9] == 1.f ? 1u : 0u) << (8 * ni + 5);
+        mask |= (shm_mask[offset + 8] == 1.f ? 1u : 0u) << (8 * ni + 6);
+        mask |= (shm_mask[offset + 9] == 1.f ? 1u : 0u) << (8 * ni + 7);
+    }
+
+    inputMaskX[(bi * xmmas_m + mi) * threads_per_cta + tidx] = mask;
+}
+
+void convertMask(const uint32_t S, const uint32_t B, const uint32_t warps_m, const uint32_t warps_n,
+    const uint32_t warps_k, const int* inputMaskSB, uint32_t* inputMaskX, cudaStream_t stream)
+{
+    const size_t xmmas_m = (S + 16 * warps_m - 1) / (16 * warps_m);
+
+    const size_t threads_per_cta = warps_m * warps_n * warps_k * 32;
+    dim3 grid(xmmas_m, B);
+    fillSBSMaskKernel<<<grid, threads_per_cta, S * sizeof(int), stream>>>(warps_m, warps_n, S, inputMaskSB, inputMaskX);
+    CHECK(cudaPeekAtLastError());
+}
+
+template <unsigned TPB>
+__global__ void maskIdxKernelSmall(int ld, const int* mask, int* maskIdx)
+{
+
+    using BlockReduce = cub::BlockReduce<int, TPB>;
+    __shared__ typename BlockReduce::TempStorage tmpStorage;
+
+    cub::Min min;
+    int threadData(ld); // if the mask admits all values
+
+    if (threadIdx.x < ld)
+    {
+        // mask has input dims {S, B} and gridDims.x is B
+        const int idx = threadIdx.x * gridDim.x + blockIdx.x;
+
+        const int val = mask[idx];
+        if (val == 0) // masked position: report thread idx
+        {
+            threadData = threadIdx.x;
+        }
+    }
+
+    const auto minIdx = BlockReduce(tmpStorage).Reduce(threadData, min);
+
+    if (threadIdx.x == 0)
+    {
+        maskIdx[blockIdx.x] = minIdx;
+    }
+}
+
+template <unsigned TPB>
+__global__ void maskIdxKernel(int ld, const int* mask, int* maskIdx)
+{
+
+    using BlockReduce = cub::BlockReduce<int, TPB>;
+    __shared__ typename BlockReduce::TempStorage tmpStorage;
+
+    cub::Min min;
+    int threadData(ld); // if the mask admits all values
+
+    for (int i = threadIdx.x; i < ld; i += TPB)
+    {
+        // mask has input dims {S, B} and gridDims.x is B
+        const int idx = i * gridDim.x + blockIdx.x;
+
+        const int val = mask[idx];
+        if (val == 0) // masked position: report thread idx
+        {
+            threadData = min(threadData, i);
+        }
+    }
+
+    const auto minIdx = BlockReduce(tmpStorage).Reduce(threadData, min);
+
+    if (threadIdx.x == 0)
+    {
+        maskIdx[blockIdx.x] = minIdx;
+    }
+}
+
+int computeMaskIdx(cudaStream_t stream, const int S, const int B, const int* mask, int* maskIdx)
+{
+    // Mask idx is of length B and assumes the valid region is contiguous starting
+    // from the beginning of the sequence
+
+    // Assume n = BxS
+    if (S <= 32)
+    {
+        maskIdxKernelSmall<32><<<B, 32, 0, stream>>>(S, mask, maskIdx);
+    }
+    else if (S <= 128)
+    {
+        maskIdxKernelSmall<128><<<B, 128, 0, stream>>>(S, mask, maskIdx);
+    }
+    else if (S == 384)
+    {
+        maskIdxKernelSmall<384><<<B, 384, 0, stream>>>(S, mask, maskIdx);
+    }
+    else
+    {
+        maskIdxKernel<256><<<B, 256, 0, stream>>>(S, mask, maskIdx);
+    }
+
+    CHECK(cudaPeekAtLastError());
+
+    return 0;
+}
+
+template <typename T, unsigned TPB>
+__global__ void embLayerNormKernel(int ld, const int* inputIds, const int* tokenIds, const float* beta,
+    const float* gamma, const T* wordEmb, const T* posEmb, const T* tokEmb, T* output)
+{
+
+    cub::Sum pairSum;
+    // 1. lookup word and token of the block
+    // blockIdx.x = position in the sequence
+    // blockIdx.y = batch
+    // gridDim.x = S
+    // gridDim.y = B
+    __shared__ int wordId;
+    __shared__ int tokenId;
+
+    const T rld = T(1.f) / T(ld);
+    const int seqPos = blockIdx.y + blockIdx.x * gridDim.y;
+    if (threadIdx.x == 0)
+    {
+        wordId = inputIds[seqPos];
+        tokenId = tokenIds[seqPos];
+    }
+    __syncthreads();
+
+    // 2. load pos/tok/word embeddings and add them toghether
+    // offset into embeddings is given by wordId * hidden_size
+    const int poffset = blockIdx.x * ld;
+    const int woffset = wordId * ld;
+    const int toffset = tokenId * ld;
+    // the output offset is given by b * (S*hidden_size) + s * hidden_size
+    const int outOffset = seqPos * ld;
+
+    kvp<T> threadData(0, 0);
+
+    for (int it = threadIdx.x; it < ld; it += TPB)
+    {
+        const T w(wordEmb[woffset + it]);
+        const T t(tokEmb[toffset + it]);
+        const T p(posEmb[poffset + it]);
+        const T val = w + t + p;
+
+        output[outOffset + it] = val;
+        const T rldval = rld * val;
+        threadData = pairSum(threadData, kvp<T>(rldval, rldval * val));
+    }
+
+    // 3. layer norm on the sum
+    layerNorm<T, T, float, TPB>(threadData, ld, outOffset, beta, gamma, output);
+}
+
+template <typename T>
+int embSkipLayerNorm(cudaStream_t stream, int ld, int B, int S, const int* inputIds, const int* token_ids,
+    const float* beta, const float* gamma, const T* wordEmb, const T* posEmb, const T* tokEmb, T* output)
+{
+
+    constexpr int tpb = 256;
+    const dim3 grid(S, B, 1);
+    const dim3 block(tpb, 1, 1);
+
+    embLayerNormKernel<T, tpb>
+        <<<grid, block, 0, stream>>>(ld, inputIds, token_ids, beta, gamma, wordEmb, posEmb, tokEmb, output);
+    CHECK(cudaPeekAtLastError());
+
+    return 0;
+}
+
+template int embSkipLayerNorm<float>(cudaStream_t, int, int, int, const int*, const int*, const float*, const float*,
+    const float*, const float*, const float*, float*);
+
+template int embSkipLayerNorm<half>(cudaStream_t, int, int, int, const int*, const int*, const float*, const float*,
+    const half*, const half*, const half*, half*);
+
+} // namespace bert
+
+#endif // CUDA_VERSION >= 10010
diff --git a/plugin/embLayerNormPlugin/embLayerNormPlugin.cu b/plugin/embLayerNormPlugin/embLayerNormPlugin.cpp
similarity index 52%
rename from plugin/embLayerNormPlugin/embLayerNormPlugin.cu
rename to plugin/embLayerNormPlugin/embLayerNormPlugin.cpp
index b895ef0f..8b36965f 100644
--- a/plugin/embLayerNormPlugin/embLayerNormPlugin.cu
+++ b/plugin/embLayerNormPlugin/embLayerNormPlugin.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -14,182 +14,34 @@
  * limitations under the License.
  */
 
+#include <cuda.h>
+#if CUDA_VERSION >= 10010
+
 #include <cassert>
 #include <cstring>
 #include <vector>
 
 #include "NvInfer.h"
 #include "embLayerNormPlugin.h"
-#include "bertCommon.h"
-#include "common.h"
 #include "serialize.hpp"
 
 using namespace nvinfer1;
-using bert::operator+;
 
 namespace bert
 {
+// For full mask mode, we must produce the compressed mask format expected by the fused attention path. Currently, only
+// two sequence lengths are supported. We hard code the sizes here.
+// The number of threads per CTA: warps_m * warps_n * warps_k * 32;
+constexpr size_t threadsPerCta128 = 2 * 2 * 32;
+constexpr size_t threadsPerCta384 = 1 * 8 * 32;
+// The number of xmmas in the M dimension. We use one uint32_t per XMMA in the M dimension: (s + 16*warps_m - 1)
+// / (16*warps_m);
+constexpr size_t xmmasM128 = 4;
+constexpr size_t xmmasM384 = 24;
+// Packed mask size per batch. Layout is XMMAS_M * THREADS_PER_CTA.
+constexpr size_t packedMaskSize128 = xmmasM128 * threadsPerCta128;
+constexpr size_t packedMaskSize384 = xmmasM384 * threadsPerCta384;
 
-template <unsigned TPB>
-__global__ void maskIdxKernelSmall(int ld, const int* mask, int* maskIdx)
-{
-
-    using BlockReduce = cub::BlockReduce<int, TPB>;
-    __shared__ typename BlockReduce::TempStorage tmpStorage;
-
-    // ld is S
-    // blockIdx.x is b
-
-    const int offset = blockIdx.x * ld; // batch strides of S
-
-    cub::Min min;
-    int threadData(ld); // if the mask admits all values
-
-    const int idx = offset + threadIdx.x;
-    if (threadIdx.x < ld)
-    {
-        const int val = mask[idx];
-        if (val == 0) // masked position: report thread idx
-        {
-            threadData = threadIdx.x;
-        }
-    }
-
-    const auto minIdx = BlockReduce(tmpStorage).Reduce(threadData, min);
-
-    if (threadIdx.x == 0)
-    {
-        maskIdx[blockIdx.x] = minIdx;
-    }
-}
-
-template <unsigned TPB>
-__global__ void maskIdxKernel(int ld, const int* mask, int* maskIdx)
-{
-
-    using BlockReduce = cub::BlockReduce<int, TPB>;
-    __shared__ typename BlockReduce::TempStorage tmpStorage;
-
-    // ld is S
-    // blockIdx.x is b
-
-    const int offset = blockIdx.x * ld; // batch strides of S
-
-    cub::Min min;
-    int threadData(ld); // if the mask admits all values
-
-    for (int i = threadIdx.x; i < ld; i += TPB)
-    {
-        const int idx = offset + i;
-        const int val = mask[idx];
-        if (val == 0) // masked position: report thread idx
-        {
-            threadData = min(threadData, i);
-        }
-    }
-
-    const auto minIdx = BlockReduce(tmpStorage).Reduce(threadData, min);
-
-    if (threadIdx.x == 0)
-    {
-        maskIdx[blockIdx.x] = minIdx;
-    }
-}
-
-inline int computeMaskIdx(cudaStream_t stream, const int S, const int B, const int* mask, int* maskIdx)
-{
-    // Mask idx is of length B and assumes the valid region is contiguous starting
-    // from the beginning of the sequence
-
-    // Assume n = BxS
-    if (S <= 32)
-    {
-        maskIdxKernelSmall<32><<<B, 32, 0, stream>>>(S, mask, maskIdx);
-    }
-    else if (S <= 128)
-    {
-        maskIdxKernelSmall<128><<<B, 128, 0, stream>>>(S, mask, maskIdx);
-    }
-    else if (S == 384)
-    {
-        maskIdxKernelSmall<384><<<B, 384, 0, stream>>>(S, mask, maskIdx);
-    }
-    else
-    {
-        maskIdxKernel<256><<<B, 256, 0, stream>>>(S, mask, maskIdx);
-    }
-
-    CHECK(cudaPeekAtLastError());
-
-    return 0;
-}
-
-template <typename T, unsigned TPB>
-__global__ void embLayerNormKernel(int ld, const int* inputIds, const int* tokenIds, const float* beta,
-    const float* gamma, const T* wordEmb, const T* posEmb, const T* tokEmb, T* output)
-{
-
-    cub::Sum pairSum;
-    // 1. lookup word and token of the block
-    // blockIdx.x = position in the sequence
-    // blockIdx.y = batch
-    // gridDim.x = S
-    // gridDim.y = B
-    __shared__ int wordId;
-    __shared__ int tokenId;
-
-    const T rld = T(1.f) / T(ld);
-    const int seqPos = blockIdx.y + blockIdx.x * gridDim.y;
-    if (threadIdx.x == 0)
-    {
-        wordId = inputIds[seqPos];
-        tokenId = tokenIds[seqPos];
-    }
-    __syncthreads();
-
-    // 2. load pos/tok/word embeddings and add them toghether
-    // offset into embeddings is given by wordId * hidden_size
-    const int poffset = blockIdx.x * ld;
-    const int woffset = wordId * ld;
-    const int toffset = tokenId * ld;
-    // the output offset is given by b * (S*hidden_size) + s * hidden_size
-    const int outOffset = seqPos * ld;
-
-    kvp<T> threadData(0, 0);
-
-    for (int it = threadIdx.x; it < ld; it += TPB)
-    {
-        const T w(wordEmb[woffset + it]);
-        const T t(tokEmb[toffset + it]);
-        const T p(posEmb[poffset + it]);
-        const T val = w + t + p;
-
-        output[outOffset + it] = val;
-        const T rldval = rld * val;
-        threadData = pairSum(threadData, kvp<T>(rldval, rldval * val));
-    }
-
-    // 3. layer norm on the sum
-    layerNorm<T, T,float, TPB>(threadData, ld, outOffset, beta, gamma, output);
-}
-
-template <typename T>
-inline int embSkipLayerNorm(cudaStream_t stream, int ld, int B, int S, const int* inputIds, const int* token_ids,
-    const float* beta, const float* gamma, const T* wordEmb, const T* posEmb, const T* tokEmb, T* output)
-{
-
-    constexpr int tpb = 256;
-    const dim3 grid(S, B, 1);
-    const dim3 block(tpb, 1, 1);
-
-    embLayerNormKernel<T, tpb>
-        <<<grid, block, 0, stream>>>(ld, inputIds, token_ids, beta, gamma, wordEmb, posEmb, tokEmb, output);
-    CHECK(cudaPeekAtLastError());
-
-    return 0;
-}
-
-// Clip plugin specific constants
 namespace
 {
 static const char* EMB_LAYER_NORM_VERSION{"1"};
@@ -202,20 +54,12 @@ std::vector<PluginField> EmbLayerNormPluginDynamicCreator::mPluginAttributes;
 
 REGISTER_TENSORRT_PLUGIN(EmbLayerNormPluginDynamicCreator);
 
-EmbLayerNormPluginDynamic::EmbLayerNormPluginDynamic(const std::string& name, const bool outputFp16,
-    const Weights& beta, const Weights& gamma, const Weights& wordEmb, const Weights& posEmb, const Weights& tokEmb)
+EmbLayerNormPluginDynamic::EmbLayerNormPluginDynamic(const std::string& name, const DataType type, const Weights& beta,
+    const Weights& gamma, const Weights& wordEmb, const Weights& posEmb, const Weights& tokEmb, const bool useFullMask)
     : mLayerName(name)
     , mLd(beta.count)
-    , mGamma(gamma)
-    , mBeta(beta)
-    , mWordEmb(wordEmb)
-    , mPosEmb(posEmb)
-    , mTokEmb(tokEmb)
-    , mGammaDev(nullptr)
-    , mBetaDev(nullptr)
-    , mWordEmbDev(nullptr)
-    , mTokEmbDev(nullptr)
-    , mPosEmbDev(nullptr)
+    , mType(type)
+    , mUseFullMask(useFullMask)
 {
     // Assuming Weights.count is the number of elements and not bytes
     assert(beta.count == gamma.count);
@@ -225,14 +69,31 @@ EmbLayerNormPluginDynamic::EmbLayerNormPluginDynamic(const std::string& name, co
     mWordVocabSize = wordEmb.count / mLd;
     mPosVocabSize = posEmb.count / mLd;
     mTokVocabSize = tokEmb.count / mLd;
-    // We set mS in configure
-    mType = outputFp16 ? DataType::kHALF : DataType::kFLOAT;
+    // mS is set during configure
+
+    mBeta.convertAndCopy(beta, nvinfer1::DataType::kFLOAT);
+    mGamma.convertAndCopy(gamma, nvinfer1::DataType::kFLOAT);
+    mWordEmb.convertAndCopy(wordEmb, mType);
+    mTokEmb.convertAndCopy(tokEmb, mType);
+    mPosEmb.convertAndCopy(posEmb, mType);
+
+    copyToDevice(mGamma, sizeof(float) * mGamma.count, mGammaDev);
+    copyToDevice(mBeta, sizeof(float) * mBeta.count, mBetaDev);
+    copyToDevice(mWordEmb, getWeightsSize(mWordEmb, mType), mWordEmbDev);
+    copyToDevice(mPosEmb, getWeightsSize(mPosEmb, mType), mPosEmbDev);
+    copyToDevice(mTokEmb, getWeightsSize(mTokEmb, mType), mTokEmbDev);
 }
 
 EmbLayerNormPluginDynamic::EmbLayerNormPluginDynamic(const std::string& name, const void* data, size_t length)
     : mLayerName(name)
+    , mGammaDev(nullptr)
+    , mBetaDev(nullptr)
+    , mWordEmbDev(nullptr)
+    , mTokEmbDev(nullptr)
+    , mPosEmbDev(nullptr)
 {
-    gLogVerbose << "EMB LN Deser start\n";
+    gLogVerbose << "EmbLayerNormPluginDynamic deserialize\n";
+
     // Deserialize in the same order as serialization
     deserialize_value(&data, &length, &mType);
     deserialize_value(&data, &length, &mLd);
@@ -240,45 +101,32 @@ EmbLayerNormPluginDynamic::EmbLayerNormPluginDynamic(const std::string& name, co
     deserialize_value(&data, &length, &mWordVocabSize);
     deserialize_value(&data, &length, &mPosVocabSize);
     deserialize_value(&data, &length, &mTokVocabSize);
+    deserialize_value(&data, &length, &mUseFullMask);
 
     const char* d = static_cast<const char*>(data);
-    mBetaDev = deserToDev<float>(d, mLd);
-    mGammaDev = deserToDev<float>(d, mLd);
-
-    const size_t wordSize = samplesCommon::getElementSize(mType);
-    mWordEmbDev = deserToDev<char>(d, mLd * mWordVocabSize * wordSize);
-    mPosEmbDev = deserToDev<char>(d, mLd * mPosVocabSize * wordSize);
-    mTokEmbDev = deserToDev<char>(d, mLd * mTokVocabSize * wordSize);
-    // this signals init not to allocate/copy
-    mGamma.count = -1;
-    mBeta.count = -1;
-    mWordEmb.count = -1;
-    mTokEmb.count = -1;
-    mPosEmb.count = -1;
-    mGamma.values = nullptr;
-    mBeta.values = nullptr;
-    mWordEmb.values = nullptr;
-    mTokEmb.values = nullptr;
-    mPosEmb.values = nullptr;
-
-    gLogVerbose << "EMB LN Deser done\n";
+    mBeta.convertAndCopy(d, mLd, nvinfer1::DataType::kFLOAT);
+    mGamma.convertAndCopy(d, mLd, nvinfer1::DataType::kFLOAT);
+    mWordEmb.convertAndCopy(d, mLd * mWordVocabSize, mType);
+    mPosEmb.convertAndCopy(d, mLd * mPosVocabSize, mType);
+    mTokEmb.convertAndCopy(d, mLd * mTokVocabSize, mType);
+
+    copyToDevice(mGamma, sizeof(float) * mGamma.count, mGammaDev);
+    copyToDevice(mBeta, sizeof(float) * mBeta.count, mBetaDev);
+    copyToDevice(mWordEmb, getWeightsSize(mWordEmb, mType), mWordEmbDev);
+    copyToDevice(mPosEmb, getWeightsSize(mPosEmb, mType), mPosEmbDev);
+    copyToDevice(mTokEmb, getWeightsSize(mTokEmb, mType), mTokEmbDev);
 }
 
 // IPluginV2DynamicExt Methods
 IPluginV2DynamicExt* EmbLayerNormPluginDynamic::clone() const
 {
-    gLogVerbose << "EMBLN clone start" << std::endl;
-    auto ret = new EmbLayerNormPluginDynamic(
-        mLayerName, mType == DataType::kHALF, mBeta, mGamma, mWordEmb, mPosEmb, mTokEmb);
-    ret->mS = mS;
-
-    ret->mWordEmbDev = mWordEmbDev;
-    ret->mPosEmbDev = mPosEmbDev;
-    ret->mTokEmbDev = mTokEmbDev;
-    ret->mBetaDev = mBetaDev;
-    ret->mGammaDev = mGammaDev;
-    gLogVerbose << "EMBLN clone done" << std::endl;
-    return ret;
+    gLogVerbose << "EmbLayerNormPluginDynamic clone\n";
+
+    auto p = new EmbLayerNormPluginDynamic(mLayerName, mType, mBeta, mGamma, mWordEmb, mPosEmb, mTokEmb, mUseFullMask);
+    p->mS = mS;
+    p->setPluginNamespace(mNamespace.c_str());
+
+    return p;
 }
 
 DimsExprs EmbLayerNormPluginDynamic::getOutputDimensions(
@@ -306,6 +154,27 @@ DimsExprs EmbLayerNormPluginDynamic::getOutputDimensions(
         return ret;
     }
 
+    if (mUseFullMask)
+    {
+        auto cms128 = exprBuilder.constant(packedMaskSize128);
+        auto cms384 = exprBuilder.constant(packedMaskSize384);
+        auto c128 = exprBuilder.constant(128);
+        auto c384 = exprBuilder.constant(384);
+
+        auto is128 = exprBuilder.operation(DimensionOperation::kEQUAL, *inputs[0].d[SDIM], *c128);
+        auto is384 = exprBuilder.operation(DimensionOperation::kEQUAL, *inputs[0].d[SDIM], *c384);
+        auto sel128 = exprBuilder.operation(DimensionOperation::kPROD, *is128, *cms128);
+        auto sel384 = exprBuilder.operation(DimensionOperation::kPROD, *is384, *cms384);
+        auto maskSize = exprBuilder.operation(DimensionOperation::kSUM, *sel384, *sel128);
+        auto fp16maskSize = exprBuilder.operation(DimensionOperation::kPROD, *maskSize, *exprBuilder.constant(2));
+
+        DimsExprs ret;
+        ret.nbDims = 2;
+        ret.d[0] = inputs[0].d[BDIM];
+        ret.d[1] = fp16maskSize;
+        return ret;
+    }
+
     DimsExprs ret;
     ret.nbDims = 1;
     ret.d[0] = inputs[0].d[BDIM];
@@ -336,20 +205,25 @@ bool EmbLayerNormPluginDynamic::supportsFormatCombination(
             && desc.dims.d[SDIM] == prev.dims.d[SDIM];
     }
 
+    // embedded sequence
     if (pos == 3)
-    { // embedded sequence
-
+    {
         return desc.type == mType && desc.dims.nbDims == 5 && desc.dims.d[BDIM] == prev.dims.d[BDIM]
             && desc.dims.d[SDIM] == prev.dims.d[SDIM] && desc.dims.d[3] == 1 && desc.dims.d[4] == 1;
     }
-
-    // pos == 4: mask
-    return desc.type == DataType::kINT32 && desc.dims.nbDims == 1 && desc.dims.d[0] == prev.dims.d[1];
+    // mask
+    if (mUseFullMask)
+    {
+        return desc.type == DataType::kHALF;
+    }
+    return desc.type == DataType::kINT32;
 }
 
 void EmbLayerNormPluginDynamic::configurePlugin(
     const DynamicPluginTensorDesc* inputs, int nbInputs, const DynamicPluginTensorDesc* outputs, int nbOutputs)
 {
+    gLogVerbose << "EmbLayerNormPluginDynamic configurePlugin\n";
+
     // Validate input arguments
     assert(nbOutputs == 2);
     assert(nbInputs == 3);
@@ -358,26 +232,45 @@ void EmbLayerNormPluginDynamic::configurePlugin(
     mS = inputs[0].desc.dims.d[SDIM];
     const int B = inputs[0].desc.dims.d[BDIM];
     TRT_UNUSED B;
-    assert(mS == inputs[1].desc.dims.d[SDIM]);
+    assert(mS == static_cast<size_t>(inputs[1].desc.dims.d[SDIM]));
     assert(B == inputs[1].desc.dims.d[BDIM]);
-    assert(mS == inputs[2].desc.dims.d[SDIM]);
+    assert(mS == static_cast<size_t>(inputs[2].desc.dims.d[SDIM]));
     assert(B == inputs[2].desc.dims.d[BDIM]);
 
     assert(outputs[0].desc.dims.nbDims == 5);
-    assert(outputs[0].desc.dims.d[SDIM] == mS);
+    assert(static_cast<size_t>(outputs[0].desc.dims.d[SDIM]) == mS);
     assert(outputs[0].desc.dims.d[BDIM] == B);
-    assert(outputs[0].desc.dims.d[2] == mLd);
+    assert(static_cast<size_t>(outputs[0].desc.dims.d[2]) == mLd);
     assert(outputs[0].desc.dims.d[3] == 1);
     assert(outputs[0].desc.dims.d[4] == 1);
 
-    assert(outputs[1].desc.dims.nbDims == 1);
-    assert(outputs[1].desc.dims.d[0] == B);
+    if (mUseFullMask)
+    {
+        assert(outputs[1].desc.dims.nbDims == 2);
+        assert(outputs[1].desc.dims.d[0] == B);
+        assert((outputs[1].desc.dims.d[1] == 2 * packedMaskSize384)
+            || (outputs[1].desc.dims.d[1] == 2 * packedMaskSize128));
+    }
+    else
+    {
+
+        assert(outputs[1].desc.dims.nbDims == 1);
+        assert(outputs[1].desc.dims.d[0] == B);
+    }
 
     assert(inputs[0].desc.type == DataType::kINT32);
     assert(inputs[1].desc.type == DataType::kINT32);
     assert(inputs[2].desc.type == DataType::kINT32);
     assert(outputs[0].desc.type == mType);
-    assert(outputs[1].desc.type == DataType::kINT32);
+    if (mUseFullMask)
+    {
+        assert(outputs[1].desc.type == DataType::kHALF);
+    }
+    else
+    {
+
+        assert(outputs[1].desc.type == DataType::kINT32);
+    }
 }
 
 size_t EmbLayerNormPluginDynamic::getWorkspaceSize(
@@ -394,35 +287,68 @@ int EmbLayerNormPluginDynamic::enqueue(const PluginTensorDesc* inputDesc, const
     int status = -1;
 
     // Our plugin outputs only one tensor
-    const int* inputIds = static_cast<const int*>(inputs[0]);
-    const int* segmentIds = static_cast<const int*>(inputs[1]);
-    const int* inputMask = static_cast<const int*>(inputs[2]);
+    const auto inputIds = static_cast<const int*>(inputs[0]);
+    const auto segmentIds = static_cast<const int*>(inputs[1]);
+    const auto inputMask = static_cast<const int*>(inputs[2]);
 
+    const float* beta = mBetaDev.get();
+    const float* gamma = mGammaDev.get();
     if (mType == DataType::kFLOAT)
     {
-        float* output = static_cast<float*>(outputs[0]);
-        float* wordEmb = static_cast<float*>(mWordEmbDev);
-        float* tokEmb = static_cast<float*>(mTokEmbDev);
-        float* posEmb = static_cast<float*>(mPosEmbDev);
-        embSkipLayerNorm<float>(
-            stream, mLd, batchSize, S, inputIds, segmentIds, mBetaDev, mGammaDev, wordEmb, posEmb, tokEmb, output);
+        auto output = static_cast<float*>(outputs[0]);
+        const auto wordEmb = static_cast<const float*>(mWordEmbDev.get());
+        const auto tokEmb = static_cast<const float*>(mTokEmbDev.get());
+        const auto posEmb = static_cast<const float*>(mPosEmbDev.get());
+        embSkipLayerNorm<float>(stream, static_cast<int>(mLd), batchSize, S, inputIds, segmentIds, beta, gamma, wordEmb,
+            posEmb, tokEmb, output);
     }
     else if (mType == DataType::kHALF)
     {
-        half* output = static_cast<half*>(outputs[0]);
-
-        half* wordEmb = static_cast<half*>(mWordEmbDev);
-        half* tokEmb = static_cast<half*>(mTokEmbDev);
-        half* posEmb = static_cast<half*>(mPosEmbDev);
-        embSkipLayerNorm<half>(
-            stream, mLd, batchSize, S, inputIds, segmentIds, mBetaDev, mGammaDev, wordEmb, posEmb, tokEmb, output);
+        auto output = static_cast<half*>(outputs[0]);
+        const auto wordEmb = static_cast<const half*>(mWordEmbDev.get());
+        const auto tokEmb = static_cast<const half*>(mTokEmbDev.get());
+        const auto posEmb = static_cast<const half*>(mPosEmbDev.get());
+        embSkipLayerNorm<half>(stream, static_cast<int>(mLd), batchSize, S, inputIds, segmentIds, beta, gamma, wordEmb,
+            posEmb, tokEmb, output);
     }
     else
     {
+        gLogError << "Unsupported type error, expected [kHALF,kFLOAT], but received " << static_cast<int>(mType)
+                  << std::endl;
         assert(false);
     }
-    int* maskIdx = static_cast<int*>(outputs[1]);
-    computeMaskIdx(stream, S, batchSize, inputMask, maskIdx);
+
+    CHECK(cudaPeekAtLastError());
+
+    if (mUseFullMask)
+    {
+        assert((S == 128 || S == 384) && "Invalid sequence length for mask type");
+        size_t warps_m = 0, warps_n = 0, warps_k = 1;
+        if (S == 128)
+        {
+            warps_m = 2;
+            warps_n = 2;
+        }
+        else if (S == 384)
+        {
+            warps_m = 1;
+            warps_n = 8;
+        }
+        uint32_t* inputMaskX = static_cast<uint32_t*>(outputs[1]);
+
+        convertMask(S, batchSize, warps_m, warps_n, warps_k, inputMask, inputMaskX, stream);
+    }
+    else
+    {
+
+        int* maskIdx = static_cast<int*>(outputs[1]);
+        computeMaskIdx(stream, S, batchSize, inputMask, maskIdx);
+        // std::vector<int> maskIdx_h(batchSize);
+        // cudaMemcpy(maskIdx_h.data(), maskIdx, batchSize*sizeof(int), cudaMemcpyDeviceToHost);
+        // for(auto x :maskIdx_h ){
+        //    printf("%d\n", x);
+        //}
+    }
 
     return status;
 }
@@ -437,6 +363,10 @@ DataType EmbLayerNormPluginDynamic::getOutputDataType(int index, const DataType*
         assert(mType == DataType::kHALF || mType == DataType::kFLOAT);
         return mType;
     }
+    if (mUseFullMask)
+    {
+        return DataType::kHALF;
+    }
     return DataType::kINT32;
 }
 
@@ -458,105 +388,55 @@ int EmbLayerNormPluginDynamic::getNbOutputs() const
 
 int EmbLayerNormPluginDynamic::initialize()
 {
-    if (mGamma.values)
-    {
-        CHECK(cudaMalloc(&mGammaDev, sizeof(float) * mGamma.count));
-        CHECK(cudaMemcpy(mGammaDev, mGamma.values, sizeof(float) * mGamma.count, cudaMemcpyHostToDevice));
-    }
-    if (mBeta.values)
-    {
-        CHECK(cudaMalloc(&mBetaDev, sizeof(float) * mBeta.count));
-        CHECK(cudaMemcpy(mBetaDev, mBeta.values, sizeof(float) * mBeta.count, cudaMemcpyHostToDevice));
-    }
-    const size_t wordSize = samplesCommon::getElementSize(mType);
-
-    if (mWordEmb.values)
-    {
-        CHECK(cudaMalloc(&mWordEmbDev, wordSize * mWordEmb.count));
-        if (mType == DataType::kFLOAT)
-        {
-            convertAndCopyToDevice(mWordEmb, reinterpret_cast<float*>(mWordEmbDev));
-        }
-        else
-        {
-            convertAndCopyToDevice(mWordEmb, reinterpret_cast<half*>(mWordEmbDev));
-        }
-    }
-    if (mTokEmb.values)
-    {
-        CHECK(cudaMalloc(&mTokEmbDev, wordSize * mTokEmb.count));
-        if (mType == DataType::kFLOAT)
-        {
-            convertAndCopyToDevice(mTokEmb, reinterpret_cast<float*>(mTokEmbDev));
-        }
-        else
-        {
-            convertAndCopyToDevice(mTokEmb, reinterpret_cast<half*>(mTokEmbDev));
-        }
-    }
-
-    if (mPosEmb.values)
-    {
-        CHECK(cudaMalloc(&mPosEmbDev, wordSize * mPosEmb.count));
-        if (mType == DataType::kFLOAT)
-        {
-            convertAndCopyToDevice(mPosEmb, reinterpret_cast<float*>(mPosEmbDev));
-        }
-        else
-        {
-            convertAndCopyToDevice(mPosEmb, reinterpret_cast<half*>(mPosEmbDev));
-        }
-    }
     return 0;
 }
 
 void EmbLayerNormPluginDynamic::terminate()
 {
-    gLogVerbose << "EMBLN terminate start" << std::endl;
-    CHECK(cudaFree(mGammaDev));
-    CHECK(cudaFree(mBetaDev));
-    CHECK(cudaFree(mWordEmbDev));
-    CHECK(cudaFree(mTokEmbDev));
-    CHECK(cudaFree(mPosEmbDev));
-    gLogVerbose << "EMBLN terminate done" << std::endl;
+    gLogVerbose << "EmbLayerNormPluginDynamic terminate\n";
 }
 
 size_t EmbLayerNormPluginDynamic::getSerializationSize() const
 {
-    const size_t wordSize = samplesCommon::getElementSize(mType);
+    const size_t wordSize = getElementSize(mType);
     return 2 * sizeof(float) * mLd        // beta + gamma
-        + sizeof(mType) + sizeof(mLd) * 5 //mLd, mS, m*VocabSize
+        + sizeof(mType) + sizeof(mLd) * 5 // mLd, mS, m*VocabSize
         + wordSize * mLd * mWordVocabSize // word emb
         + wordSize * mLd * mPosVocabSize  // pos emb
         + wordSize * mLd * mTokVocabSize  // tok emb
+        + sizeof(mUseFullMask)            // mask type
         ;
 }
 
 void EmbLayerNormPluginDynamic::serialize(void* buffer) const
 {
-    const size_t wordSize = samplesCommon::getElementSize(mType);
-
     serialize_value(&buffer, mType);
     serialize_value(&buffer, mLd);
     serialize_value(&buffer, mS);
     serialize_value(&buffer, mWordVocabSize);
     serialize_value(&buffer, mPosVocabSize);
     serialize_value(&buffer, mTokVocabSize);
+    serialize_value(&buffer, mUseFullMask);
 
     char* d = static_cast<char*>(buffer);
-    serFromDev(d, mBetaDev, mLd);
-    serFromDev(d, mGammaDev, mLd);
-    serFromDev(d, static_cast<char*>(mWordEmbDev), mLd * mWordVocabSize * wordSize);
-    serFromDev(d, static_cast<char*>(mPosEmbDev), mLd * mPosVocabSize * wordSize);
-    serFromDev(d, static_cast<char*>(mTokEmbDev), mLd * mTokVocabSize * wordSize);
+    serFromDev(d, mBetaDev.get(), mLd);
+    serFromDev(d, mGammaDev.get(), mLd);
+    const size_t wordSize = getElementSize(mType);
+    serFromDev(d, static_cast<char*>(mWordEmbDev.get()), mLd * mWordVocabSize * wordSize);
+    serFromDev(d, static_cast<char*>(mPosEmbDev.get()), mLd * mPosVocabSize * wordSize);
+    serFromDev(d, static_cast<char*>(mTokEmbDev.get()), mLd * mTokVocabSize * wordSize);
 }
 
 void EmbLayerNormPluginDynamic::destroy()
 {
-    gLogVerbose << "EMBLN destroy start" << std::endl;
+    gLogVerbose << "EmbLayerNormPluginDynamic destroy\n";
     // This gets called when the network containing plugin is destroyed
+    mGammaDev.release();
+    mBetaDev.release();
+    mWordEmbDev.release();
+    mPosEmbDev.release();
+    mTokEmbDev.release();
     delete this;
-    gLogVerbose << "EMBLN destroy start" << std::endl;
 }
 
 void EmbLayerNormPluginDynamic::setPluginNamespace(const char* libNamespace)
@@ -594,9 +474,10 @@ const PluginFieldCollection* EmbLayerNormPluginDynamicCreator::getFieldNames()
 
 IPluginV2* EmbLayerNormPluginDynamicCreator::createPlugin(const char* name, const PluginFieldCollection* fc)
 {
-    gLogVerbose << "Creating EmbLayerNormPluginDynamic...\n";
+    gLogVerbose << "EmbLayerNormPluginDynamic createPlugin\n";
 
     bool output_fp16 = false;
+    bool useFullMask = false;
     Weights beta;
     Weights gamma;
     Weights word_emb;
@@ -648,13 +529,19 @@ IPluginV2* EmbLayerNormPluginDynamicCreator::createPlugin(const char* name, cons
         {
             gLogVerbose << "Building output_fp16...\n";
             assert(fc->fields[i].type == PluginFieldType::kINT32);
-            output_fp16 = reinterpret_cast<const int*>(fc->fields[i].data)[0] != 0;
+            output_fp16 = static_cast<const int*>(fc->fields[i].data)[0] != 0;
+        }
+        if (field_name.compare("full_mask") == 0)
+        {
+            gLogVerbose << "Building full_mask...\n";
+            assert(fc->fields[i].type == PluginFieldType::kINT32);
+            useFullMask = static_cast<const int*>(fc->fields[i].data)[0] != 0;
         }
     }
 
     gLogVerbose << "Building the Plugin...\n";
-    EmbLayerNormPluginDynamic* p
-        = new EmbLayerNormPluginDynamic(name, output_fp16, beta, gamma, word_emb, pos_emb, tok_emb);
+    EmbLayerNormPluginDynamic* p = new EmbLayerNormPluginDynamic(
+        name, output_fp16 ? DataType::kHALF : DataType::kFLOAT, beta, gamma, word_emb, pos_emb, tok_emb, useFullMask);
     return p;
 }
 
@@ -675,4 +562,6 @@ const char* EmbLayerNormPluginDynamicCreator::getPluginNamespace() const
 {
     return mNamespace.c_str();
 }
-}
+} // namespace bert
+
+#endif // CUDA_VERSION >= 10010
diff --git a/plugin/embLayerNormPlugin/embLayerNormPlugin.h b/plugin/embLayerNormPlugin/embLayerNormPlugin.h
index 4b843383..8f376483 100644
--- a/plugin/embLayerNormPlugin/embLayerNormPlugin.h
+++ b/plugin/embLayerNormPlugin/embLayerNormPlugin.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -14,26 +14,36 @@
  * limitations under the License.
  */
 
+#include <cuda.h>
+#if CUDA_VERSION >= 10010
+
 #ifndef TRT_EMB_LAYER_NORM_PLUGIN_H
 #define TRT_EMB_LAYER_NORM_PLUGIN_H
 
 #include "NvInferPlugin.h"
 #include "NvInferRuntime.h"
 
+#include "bertCommon.h"
 #include <string>
 #include <vector>
 namespace bert
 {
 
-// One of the preferred ways of making TensorRT to be able to see
-// our custom layer requires extending IPluginV2 and IPluginCreator classes.
-// For requirements for overriden functions, check TensorRT API docs.
+int computeMaskIdx(cudaStream_t stream, const int S, const int B, const int* mask, int* maskIdx);
+
+template <typename T>
+int embSkipLayerNorm(cudaStream_t stream, int ld, int B, int S, const int* inputIds, const int* token_ids,
+    const float* beta, const float* gamma, const T* wordEmb, const T* posEmb, const T* tokEmb, T* output);
+
+void convertMask(const uint32_t S, const uint32_t B, const uint32_t warps_m, const uint32_t warps_n,
+    const uint32_t warps_k, const int* inputMaskSB, uint32_t* inputMaskX, cudaStream_t stream);
 
 class EmbLayerNormPluginDynamic : public nvinfer1::IPluginV2DynamicExt
 {
 public:
-    EmbLayerNormPluginDynamic(const std::string& name, const bool use_fp16, const nvinfer1::Weights& beta, const nvinfer1::Weights& gamma,
-        const nvinfer1::Weights& word_emb, const nvinfer1::Weights& pos_emb, const nvinfer1::Weights& tok_emb);
+    EmbLayerNormPluginDynamic(const std::string& name, const nvinfer1::DataType type, const nvinfer1::Weights& beta,
+        const nvinfer1::Weights& gamma, const nvinfer1::Weights& word_emb, const nvinfer1::Weights& pos_emb,
+        const nvinfer1::Weights& tok_emb, const bool useFullMask);
 
     EmbLayerNormPluginDynamic(const std::string& name, const void* data, size_t length);
 
@@ -73,33 +83,34 @@ class EmbLayerNormPluginDynamic : public nvinfer1::IPluginV2DynamicExt
     const std::string mLayerName;
     std::string mNamespace;
 
-    float* mGammaDev;
-    float* mBetaDev;
-    void* mWordEmbDev;
-    void* mTokEmbDev;
-    void* mPosEmbDev;
+    bert::cuda_unique_ptr<float> mGammaDev;
+    bert::cuda_unique_ptr<float> mBetaDev;
+    bert::cuda_unique_ptr<void> mWordEmbDev;
+    bert::cuda_unique_ptr<void> mTokEmbDev;
+    bert::cuda_unique_ptr<void> mPosEmbDev;
     size_t mLd; // leading dim = hidden size
     size_t mB;  // batch size
     size_t mS;  // sequence length
     size_t mWordVocabSize;
     size_t mPosVocabSize;
     size_t mTokVocabSize;
-    nvinfer1::Weights mBeta;
-    nvinfer1::Weights mGamma;
-    nvinfer1::Weights mWordEmb;
-    nvinfer1::Weights mTokEmb;
-    nvinfer1::Weights mPosEmb;
+    bert::WeightsWithOwnership mBeta;
+    bert::WeightsWithOwnership mGamma;
+    bert::WeightsWithOwnership mWordEmb;
+    bert::WeightsWithOwnership mTokEmb;
+    bert::WeightsWithOwnership mPosEmb;
     nvinfer1::DataType mType;
+    bool mUseFullMask;
 
 protected:
     // To prevent compiler warnings.
-    using nvinfer1::IPluginV2DynamicExt::getOutputDimensions;
-    using nvinfer1::IPluginV2DynamicExt::isOutputBroadcastAcrossBatch;
     using nvinfer1::IPluginV2DynamicExt::canBroadcastInputAcrossBatch;
-    using nvinfer1::IPluginV2DynamicExt::supportsFormat;
     using nvinfer1::IPluginV2DynamicExt::configurePlugin;
-    using nvinfer1::IPluginV2DynamicExt::getWorkspaceSize;
     using nvinfer1::IPluginV2DynamicExt::enqueue;
+    using nvinfer1::IPluginV2DynamicExt::getOutputDimensions;
+    using nvinfer1::IPluginV2DynamicExt::getWorkspaceSize;
+    using nvinfer1::IPluginV2DynamicExt::isOutputBroadcastAcrossBatch;
+    using nvinfer1::IPluginV2DynamicExt::supportsFormat;
 };
 
 class EmbLayerNormPluginDynamicCreator : public nvinfer1::IPluginCreator
@@ -126,5 +137,7 @@ class EmbLayerNormPluginDynamicCreator : public nvinfer1::IPluginCreator
     static std::vector<nvinfer1::PluginField> mPluginAttributes;
     std::string mNamespace;
 };
-}
+} // namespace bert
 #endif // TRT_EMB_LAYER_NORM_PLUGIN_H
+
+#endif // CUDA_VERSION >= 10010
diff --git a/plugin/exports.map b/plugin/exports.map
index a65c632c..d92a7bbb 100644
--- a/plugin/exports.map
+++ b/plugin/exports.map
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/plugin/fcPlugin/CMakeLists.txt b/plugin/fcPlugin/CMakeLists.txt
index 39f2d485..85d8ce4e 100644
--- a/plugin/fcPlugin/CMakeLists.txt
+++ b/plugin/fcPlugin/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,6 +13,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
-file(GLOB CU_SRCS *.cu)
-set(BERT_CU_SOURCES ${BERT_CU_SOURCES} ${CU_SRCS})
-set(BERT_CU_SOURCES ${BERT_CU_SOURCES} PARENT_SCOPE)
+file(GLOB SRCS *.cpp)
+set(PLUGIN_SOURCES ${PLUGIN_SOURCES} ${SRCS})
+set(PLUGIN_SOURCES ${PLUGIN_SOURCES} PARENT_SCOPE)
diff --git a/plugin/fcPlugin/fcPlugin.cu b/plugin/fcPlugin/fcPlugin.cpp
similarity index 78%
rename from plugin/fcPlugin/fcPlugin.cu
rename to plugin/fcPlugin/fcPlugin.cpp
index 821623d1..73ab5070 100644
--- a/plugin/fcPlugin/fcPlugin.cu
+++ b/plugin/fcPlugin/fcPlugin.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -14,10 +14,12 @@
  * limitations under the License.
  */
 
+// cublasLT was introduced in CUDA 10.1
+#include <cuda.h>
+#if CUDA_VERSION >= 10010
+
 #include "NvInfer.h"
 #include "fcPlugin.h"
-#include "bertCommon.h"
-#include "common.h"
 #include "serialize.hpp"
 
 #include <algorithm>
@@ -29,7 +31,6 @@
 #include <vector>
 
 using namespace nvinfer1;
-using bert::operator+;
 
 namespace bert
 {
@@ -58,7 +59,11 @@ static void printPerfStructure(const customMatmulPerf_t& perf, int const& m, int
     double timeAvg = (perf.time * 1e-3) / kernelRepeats; // Convert to seconds, then divide by loops
     double gflop = (2 * static_cast<unsigned long long int>(m * n) * k) * 1e-9; // Real
 
-    gLogVerbose << "Algo=" << p.algoId << " Tile=" << p.tile << " (" << matmulTileName[p.tile] << ") K=" << p.numSplitsK << " Red.Sch.=" << p.reductionScheme << " Swiz=" << p.swizzle << " Cust=" << p.customOption << " Stat=" << perf.status << " Time=" << perf.time << " WSbytes=" << perf.workspaceSize << " math=" << p.mathMode << " waves=" << perf.wavesCount << "GFlops=" << (gflop / timeAvg) << std::endl;
+    gLogVerbose << "Algo=" << p.algoId << " Tile=" << p.tile << " (" << matmulTileName[p.tile] << ") K=" << p.numSplitsK
+                << " Red.Sch.=" << p.reductionScheme << " Swiz=" << p.swizzle << " Cust=" << p.customOption
+                << " Stat=" << perf.status << " Time=" << perf.time << " WSbytes=" << perf.workspaceSize
+                << " math=" << p.mathMode << " waves=" << perf.wavesCount << "GFlops=" << (gflop / timeAvg)
+                << std::endl;
 }
 
 static inline bool time_compare(const customMatmulPerf_t& perf_a, const customMatmulPerf_t& perf_b)
@@ -130,7 +135,12 @@ static cublasStatus_t customMatmulRun(cublasLtHandle_t ltHandle, // to get the c
 void LtGemmSearch(cublasLtHandle_t ltHandle, cublasOperation_t transa, cublasOperation_t transb, int const& m,
     int const& n, int const& k, void const* alpha,                                  /* host pointer */
     void const* A, int const& lda, void const* B, int const& ldb, void const* beta, /* host pointer */
-    void* C, int const& ldc, void* workSpace, size_t workSpaceSize, cudaDataType_t computeType,
+    void* C, int const& ldc, void* workSpace, size_t workSpaceSize,
+#if CUBLAS_VER_MAJOR < 11
+    cudaDataType_t computeType,
+#else
+    cublasComputeType_t computeType,
+#endif
     cudaDataType_t scaleType, cudaDataType_t Atype, cudaDataType_t Btype, cudaDataType_t Ctype,
     std::vector<customMatmulPerf_t>& perfResults)
 {
@@ -154,30 +164,36 @@ void LtGemmSearch(cublasLtHandle_t ltHandle, cublasOperation_t transa, cublasOpe
     int algoIdA[algoIds];
     // customMatmulPerf_t perfResults[algoCombinations];
 
-    CHECK(cublasLtMatmulPreferenceCreate(&preference));
-    CHECK(cublasLtMatmulPreferenceSetAttribute(
+    CUBLASASSERT(cublasLtMatmulPreferenceCreate(&preference));
+    CUBLASASSERT(cublasLtMatmulPreferenceSetAttribute(
         preference, CUBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES, &workSpaceSize, sizeof(workSpaceSize)));
 
     const int mathMode = Ctype == CUDA_R_16F ? 1 : 0;
     cublasLtMatmulPreferenceSetAttribute(preference, CUBLASLT_MATMUL_PREF_MATH_MODE_MASK, &mathMode, sizeof(mathMode));
     // Create operation descriptor; see cublasLtMatmulDescAttributes_t for details
     // about defaults; here we just need to set the transforms for A and B
-    CHECK(cublasLtMatmulDescCreate(&operationDesc, computeType));
-    CHECK(cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSA, &transa, sizeof(transa)));
-    CHECK(cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSB, &transb, sizeof(transa)));
+#if CUBLAS_VER_MAJOR < 11
+    CUBLASASSERT(cublasLtMatmulDescCreate(&operationDesc, computeType));
+#else
+    CUBLASASSERT(cublasLtMatmulDescCreate(&operationDesc, computeType, scaleType));
+#endif
+    CUBLASASSERT(cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSA, &transa, sizeof(transa)));
+    CUBLASASSERT(cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSB, &transb, sizeof(transa)));
 
     // Create matrix descriptors. We are good with the details here so no need to
     // set any extra attributes
-    CHECK(cublasLtMatrixLayoutCreate(&Adesc, Atype, transa == CUBLAS_OP_N ? m : k, transa == CUBLAS_OP_N ? k : m, lda));
-    CHECK(cublasLtMatrixLayoutCreate(&Bdesc, Btype, transb == CUBLAS_OP_N ? k : n, transb == CUBLAS_OP_N ? n : k, ldb));
-    CHECK(cublasLtMatrixLayoutCreate(&Cdesc, Ctype, m, n, ldc));
+    CUBLASASSERT(
+        cublasLtMatrixLayoutCreate(&Adesc, Atype, transa == CUBLAS_OP_N ? m : k, transa == CUBLAS_OP_N ? k : m, lda));
+    CUBLASASSERT(
+        cublasLtMatrixLayoutCreate(&Bdesc, Btype, transb == CUBLAS_OP_N ? k : n, transb == CUBLAS_OP_N ? n : k, ldb));
+    CUBLASASSERT(cublasLtMatrixLayoutCreate(&Cdesc, Ctype, m, n, ldc));
 
     // Request the 4 first AlgoId available for SGEMM ( computeType = scaleType =
     // Atype = Btype = Ctype = Dtype = CUDA_R_32F)
-    CHECK(cublasLtMatmulAlgoGetIds(
+    CUBLASASSERT(cublasLtMatmulAlgoGetIds(
         ltHandle, computeType, scaleType, Atype, Btype, Ctype, Ctype, algoIds, algoIdA, &nbAlgoIds));
 
-    gLogVerbose << "Number of algos" <<  nbAlgoIds << std::endl;
+    gLogVerbose << "Number of algos" << nbAlgoIds << std::endl;
 
     // Create CUDA event to time the execution time of each algo
     CHECK(cudaEventCreate(&startEvent, cudaEventBlockingSync));
@@ -206,7 +222,7 @@ void LtGemmSearch(cublasLtHandle_t ltHandle, cublasOperation_t transa, cublasOpe
         }
 
         // Query the tiles enums supported by that algo
-        CHECK(cublasLtMatmulAlgoCapGetAttribute(&algo, CUBLASLT_ALGO_CAP_TILE_IDS, nullptr, 0, &sizeWritten));
+        CUBLASASSERT(cublasLtMatmulAlgoCapGetAttribute(&algo, CUBLASLT_ALGO_CAP_TILE_IDS, nullptr, 0, &sizeWritten));
         int nbTiles = int(sizeWritten / sizeof(int));
         int* tileA = new int[nbTiles == 0 ? 1 : nbTiles];
         if (nbTiles == 0)
@@ -218,18 +234,18 @@ void LtGemmSearch(cublasLtHandle_t ltHandle, cublasOperation_t transa, cublasOpe
         int splitkSupport, redMask, swizzlingMax, customOptionMax, epilogueMask;
         // Retrieve Algo Capabilities attributes to be able to setup loop over the
         // different combinations
-        CHECK(cublasLtMatmulAlgoCapGetAttribute(
+        CUBLASASSERT(cublasLtMatmulAlgoCapGetAttribute(
             &algo, CUBLASLT_ALGO_CAP_TILE_IDS, tileA, sizeof(int) * nbTiles, &sizeWritten));
-        CHECK(cublasLtMatmulAlgoCapGetAttribute(
+        CUBLASASSERT(cublasLtMatmulAlgoCapGetAttribute(
             &algo, CUBLASLT_ALGO_CAP_SPLITK_SUPPORT, &splitkSupport, sizeof(splitkSupport), &sizeWritten));
-        CHECK(cublasLtMatmulAlgoCapGetAttribute(
+        CUBLASASSERT(cublasLtMatmulAlgoCapGetAttribute(
             &algo, CUBLASLT_ALGO_CAP_REDUCTION_SCHEME_MASK, &redMask, sizeof(redMask), &sizeWritten));
-        CHECK(cublasLtMatmulAlgoCapGetAttribute(
+        CUBLASASSERT(cublasLtMatmulAlgoCapGetAttribute(
             &algo, CUBLASLT_ALGO_CAP_CTA_SWIZZLING_SUPPORT, &swizzlingMax, sizeof(swizzlingMax), &sizeWritten));
-        CHECK(cublasLtMatmulAlgoCapGetAttribute(
+        CUBLASASSERT(cublasLtMatmulAlgoCapGetAttribute(
             &algo, CUBLASLT_ALGO_CAP_CUSTOM_OPTION_MAX, &customOptionMax, sizeof(customOptionMax), &sizeWritten));
 
-        CHECK(cublasLtMatmulAlgoCapGetAttribute(
+        CUBLASASSERT(cublasLtMatmulAlgoCapGetAttribute(
             &algo, CUBLASLT_ALGO_CAP_EPILOGUE_MASK, &epilogueMask, sizeof(epilogueMask), &sizeWritten));
 
         /* Loop over the different tiles */
@@ -238,7 +254,7 @@ void LtGemmSearch(cublasLtHandle_t ltHandle, cublasOperation_t transa, cublasOpe
             /* Loop over the different custom option if any */
             for (int customOption = 0; customOption <= customOptionMax; customOption++)
             {
-                CHECK(cublasLtMatmulAlgoConfigSetAttribute(
+                CUBLASASSERT(cublasLtMatmulAlgoConfigSetAttribute(
                     &algo, CUBLASLT_ALGO_CONFIG_CUSTOM_OPTION, &customOption, sizeof(customOption)));
                 /* Loop over the CTAs swizzling support */
                 for (int k = 0; k <= swizzlingMax; k++)
@@ -253,21 +269,21 @@ void LtGemmSearch(cublasLtHandle_t ltHandle, cublasOperation_t transa, cublasOpe
                     for (int l = 0; (l < (1 + splitK_trial)) && (algoCount < algoCombinations); l++)
                     {
                         /* Setup attribute of the algo to run */
-                        CHECK(cublasLtMatmulAlgoConfigSetAttribute(
+                        CUBLASASSERT(cublasLtMatmulAlgoConfigSetAttribute(
                             &algo, CUBLASLT_ALGO_CONFIG_TILE_ID, &tileA[tileIdx], sizeof(tileA[tileIdx])));
                         int splitK_val = 0;
                         int redScheme = CUBLASLT_REDUCTION_SCHEME_NONE;
-                        CHECK(cublasLtMatmulAlgoConfigSetAttribute(
+                        CUBLASASSERT(cublasLtMatmulAlgoConfigSetAttribute(
                             &algo, CUBLASLT_ALGO_CONFIG_SPLITK_NUM, &splitK_val, sizeof(splitK_val)));
-                        CHECK(cublasLtMatmulAlgoConfigSetAttribute(
+                        CUBLASASSERT(cublasLtMatmulAlgoConfigSetAttribute(
                             &algo, CUBLASLT_ALGO_CONFIG_CTA_SWIZZLING, &k, sizeof(k)));
-                        CHECK(cublasLtMatmulAlgoConfigSetAttribute(
+                        CUBLASASSERT(cublasLtMatmulAlgoConfigSetAttribute(
                             &algo, CUBLASLT_ALGO_CONFIG_REDUCTION_SCHEME, &redScheme, sizeof(int)));
 
                         if (l > 0)
                         { // Split-K case
                             splitK_val = splitKSequenceA[l - 1];
-                            CHECK(cublasLtMatmulAlgoConfigSetAttribute(&algo, CUBLASLT_ALGO_CONFIG_SPLITK_NUM,
+                            CUBLASASSERT(cublasLtMatmulAlgoConfigSetAttribute(&algo, CUBLASLT_ALGO_CONFIG_SPLITK_NUM,
                                 &splitKSequenceA[l - 1], sizeof(splitKSequenceA[l - 1])));
                             /* Going over all the reduction scheme  */
                             for (redScheme = 1; redScheme < static_cast<int>(CUBLASLT_REDUCTION_SCHEME_MASK)
@@ -276,7 +292,7 @@ void LtGemmSearch(cublasLtHandle_t ltHandle, cublasOperation_t transa, cublasOpe
                             {
                                 if (redScheme & redMask)
                                 {
-                                    CHECK(cublasLtMatmulAlgoConfigSetAttribute(
+                                    CUBLASASSERT(cublasLtMatmulAlgoConfigSetAttribute(
                                         &algo, CUBLASLT_ALGO_CONFIG_REDUCTION_SCHEME, &redScheme, sizeof(redScheme)));
 
                                     status
@@ -326,31 +342,36 @@ void LtGemmSearch(cublasLtHandle_t ltHandle, cublasOperation_t transa, cublasOpe
     }
 
     // Descriptors are no longer needed as all GPU work was already enqueued
-    CHECK(cublasLtMatmulPreferenceDestroy(preference));
-    CHECK(cublasLtMatrixLayoutDestroy(Cdesc));
-    CHECK(cublasLtMatrixLayoutDestroy(Bdesc));
-    CHECK(cublasLtMatrixLayoutDestroy(Adesc));
-    CHECK(cublasLtMatmulDescDestroy(operationDesc));
+    CUBLASASSERT(cublasLtMatmulPreferenceDestroy(preference));
+    CUBLASASSERT(cublasLtMatrixLayoutDestroy(Cdesc));
+    CUBLASASSERT(cublasLtMatrixLayoutDestroy(Bdesc));
+    CUBLASASSERT(cublasLtMatrixLayoutDestroy(Adesc));
+    CUBLASASSERT(cublasLtMatmulDescDestroy(operationDesc));
     CHECK(cudaEventDestroy(startEvent));
     CHECK(cudaEventDestroy(stopEvent));
 }
 
 FCPluginDynamic::FCPluginDynamic(const std::string name, const DataType type, const int outDim, const Weights& W)
     : mLayerName(name)
+    , mType(type)
     , mOutDim(outDim)
-    , mW(W)
     , mNumParams(W.count)
-    , mType(type)
+    , mNmax(0)
+    , mK(0)
+    , mWdev(nullptr)
 {
-    mB.count = 0;
-    mB.values = nullptr;
     memset(mAlgo.data, 0, sizeof(mAlgo.data));
+
+    mW.convertAndCopy(W, mType);
+    copyToDevice(mW, getWeightsSize(mW, mType), mWdev);
 }
 
 FCPluginDynamic::FCPluginDynamic(const std::string name, const void* data, size_t length)
     : mLayerName(name)
+    , mWdev(nullptr)
 {
-    gLogVerbose << "FC Deser start\n";
+    gLogVerbose << "FCPluginDynamic deserialize\n";
+
     // Deserialize in the same order as serialization
     deserialize_value(&data, &length, &mType);
     deserialize_value(&data, &length, &mOutDim);
@@ -359,22 +380,33 @@ FCPluginDynamic::FCPluginDynamic(const std::string name, const void* data, size_
     deserialize_value(&data, &length, &mK);
     deserialize_value(&data, &length, &mAlgo);
 
-    // reading this back as bytes, therefore need the element size
     const char* d = static_cast<const char*>(data);
-    size_t wordSize = samplesCommon::getElementSize(mType);
-    mWdev = deserToDev<char>(d, mNumParams * wordSize);
-
-    // this signals init not to allocate/copy
-    mW.count = mNumParams;
-    mW.values = nullptr;
 
-    gLogVerbose << "FC Deser done\n";
+    mW.convertAndCopy(d, mNumParams, mType);
+    copyToDevice(mW, getWeightsSize(mW, mType), mWdev);
 }
 
 // IPluginV2DynamicExt Methods
 IPluginV2DynamicExt* FCPluginDynamic::clone() const
 {
-    return new FCPluginDynamic(mLayerName, mType, mOutDim, mW);
+    gLogVerbose << "FCPluginDynamic clone\n";
+
+    auto p = new FCPluginDynamic(mLayerName, mType, mOutDim, mW);
+    memcpy(p->mAlgo.data, mAlgo.data, sizeof(mAlgo.data));
+    p->setPluginNamespace(mNamespace.c_str());
+
+    return p;
+}
+
+void FCPluginDynamic::attachToContext(
+    cudnnContext* cudnnContext, cublasContext* cublasContext, nvinfer1::IGpuAllocator* gpuAllocator)
+{
+    mLtContext.attach();
+}
+
+void FCPluginDynamic::detachFromContext()
+{
+    mLtContext.detach();
 }
 
 DimsExprs FCPluginDynamic::getOutputDimensions(
@@ -423,28 +455,44 @@ void FCPluginDynamic::configurePlugin(
     assert(inDims0.d[3] == 1);
     assert(inDims0.d[4] == 1);
 
-    // m and k  are mOutDim
+    // m and k are mOutDim
     // n is B*S
     const int S = inputs->max.d[SDIM];
     const int B = inputs->max.d[BDIM];
 
     mNmax = S * B;
 
-    // max workspace size allowed for search
-    size_t actualWorkspace = 0;
+    if (mType == DataType::kFLOAT)
+    {
+        Gemm<float> g(mOutDim, mNmax, mK, false, false);
+        mLtContext.create(g, maxWorkspaceBytes);
+    }
+    else if (mType == DataType::kHALF)
+    {
+        Gemm<half> g(mOutDim, mNmax, mK, false, false);
+        mLtContext.create(g, maxWorkspaceBytes);
+    }
+    else
+    {
+        gLogError << "Unsupported type error, expected [kHALF,kFLOAT], but received " << static_cast<int>(mType)
+                  << std::endl;
+        assert(false);
+    }
+
+    gLogVerbose << "FCPluginDynamic configurePlugin m=" << mOutDim << ", n=" << mNmax << ", k=" << mK << std::endl;
 
-    if (mAlgo.data[0] == 0 && memcmp(mAlgo.data, mAlgo.data+1, sizeof(mAlgo.data)-sizeof(mAlgo.data[0])) == 0)
+    size_t actualWorkspace = 0;
+    if (mAlgo.data[0] == 0 && memcmp(mAlgo.data, mAlgo.data + 1, sizeof(mAlgo.data) - sizeof(mAlgo.data[0])) == 0)
     {
-        gLogVerbose << "Start cuBLAS GEMM search" << std::endl;
+        gLogVerbose << "FCPluginDynamic gemmSearch\n";
         if (mType == DataType::kFLOAT)
         {
             mAlgo = gemmSearch<float>(mOutDim, mNmax, mK, maxWorkspaceBytes, actualWorkspace);
         }
-        else
+        else if (mType == DataType::kHALF)
         {
             mAlgo = gemmSearch<half>(mOutDim, mNmax, mK, maxWorkspaceBytes, actualWorkspace);
         }
-        gLogVerbose << "Done cuBLAS GEMM search" << std::endl;
     }
 
     AlgoProps p;
@@ -460,7 +508,10 @@ void FCPluginDynamic::configurePlugin(
         gLogWarning << "TensorCore support was not selected" << std::endl;
     }
 
-    gLogVerbose << "Algo=" << p.algoId << " Tile=" << p.tile << " (" << matmulTileName[p.tile] << ") K=" << p.numSplitsK << " Red.Sch.=" << p.reductionScheme << " Swiz=" << p.swizzle << " Cust=" << p.customOption << " mathMode=" << p.mathMode << " ws=" << actualWorkspace << std::endl;
+    gLogVerbose << "FCPluginDynamic configuration Algo=" << p.algoId << " Tile=" << p.tile << " ("
+                << matmulTileName[p.tile] << ") K=" << p.numSplitsK << " Red.Sch.=" << p.reductionScheme
+                << " Swiz=" << p.swizzle << " Cust=" << p.customOption << " mathMode=" << p.mathMode
+                << " ws=" << actualWorkspace << std::endl;
 }
 
 size_t FCPluginDynamic::getWorkspaceSize(
@@ -483,31 +534,33 @@ int FCPluginDynamic::enqueue(const PluginTensorDesc* inputDesc, const PluginTens
 
     if (mType == DataType::kFLOAT)
     {
-        const float* input = static_cast<const float*>(inputs[0]);
-        float* output = static_cast<float*>(outputs[0]);
+        const auto input = static_cast<const float*>(inputs[0]);
+        auto output = static_cast<float*>(outputs[0]);
 
         Gemm<float> g(mOutDim, n, mK, false, false);
-        g.A = const_cast<float*>(reinterpret_cast<const float*>(mWdev));
-        g.B = const_cast<float*>(reinterpret_cast<const float*>(input));
+        assert(mWdev != nullptr);
+        g.A = static_cast<float*>(mWdev.get());
+        g.B = const_cast<float*>(input);
         g.C = output;
 
-        CHECK(cublasLtMatmul(mLtContext, g, mAlgo, workSpace, workspaceSize, stream));
+        CUBLASASSERT(cublasLtMatmul(mLtContext, g, mAlgo, workSpace, workspaceSize, stream));
     }
     else if (mType == DataType::kHALF)
     {
-        const half* input = static_cast<const half*>(inputs[0]);
-        half* output = static_cast<half*>(outputs[0]);
+        const auto input = static_cast<const half*>(inputs[0]);
+        auto output = static_cast<half*>(outputs[0]);
 
         Gemm<half> g(mOutDim, n, mK, false, false);
-
-        g.A = const_cast<half*>(reinterpret_cast<const half*>(mWdev));
-        g.B = const_cast<half*>(reinterpret_cast<const half*>(input));
+        assert(mWdev != nullptr);
+        g.A = static_cast<half*>(mWdev.get());
+        g.B = const_cast<half*>(input);
         g.C = output;
-        CHECK(cublasLtMatmul(mLtContext, g, mAlgo, workSpace, workspaceSize, stream));
+        CUBLASASSERT(cublasLtMatmul(mLtContext, g, mAlgo, workSpace, workspaceSize, stream));
     }
     else
     {
-        gLogError << "Unsupported Type\n";
+        gLogError << "Unsupported type error, expected [kHALF,kFLOAT], but received " << static_cast<int>(mType)
+                  << std::endl;
         assert(false);
     }
 
@@ -520,7 +573,6 @@ DataType FCPluginDynamic::getOutputDataType(int index, const DataType* inputType
     assert(index == 0);
     assert(nbInputs == 1);
     assert(inputTypes[0] == DataType::kFLOAT || inputTypes[0] == DataType::kHALF);
-    // assert(inputTypes[0] == DataType::kHALF);
     return inputTypes[0];
 }
 
@@ -542,52 +594,19 @@ int FCPluginDynamic::getNbOutputs() const
 
 int FCPluginDynamic::initialize()
 {
-
-    gLogVerbose << "Initializing FC Plugin with m=" << mOutDim << "n=" << mNmax << "k=" << mK << std::endl;
-    if (mType == DataType::kHALF)
-    {
-        Gemm<half> g(mOutDim, mNmax, mK, false, false);
-        mLtContext.create(g, 4096000);
-    }
-    else
-    {
-        Gemm<float> g(mOutDim, mNmax, mK, false, false);
-        mLtContext.create(g, 4096000);
-    }
-
-    if (mW.values)
-    {
-        // target size
-        size_t wordSize = samplesCommon::getElementSize(mType);
-        size_t nbBytes = mW.count * wordSize;
-        CHECK(cudaMalloc(&mWdev, nbBytes));
-
-        if (mType == DataType::kFLOAT)
-        {
-            convertAndCopyToDevice(mW, reinterpret_cast<float*>(mWdev));
-        }
-        else
-        {
-            convertAndCopyToDevice(mW, reinterpret_cast<half*>(mWdev));
-        }
-    }
-
+    gLogVerbose << "FCPluginDynamic initialize\n";
     return 0;
 }
 
 void FCPluginDynamic::terminate()
 {
-
-    gLogVerbose << "FC Plugin terminate start" << std::endl;
-    cudaFree(mWdev);
-    mLtContext.destroy();
-    gLogVerbose << "FC Plugin terminate done" << std::endl;
+    gLogVerbose << "FCPluginDynamic terminate\n";
 }
 
 size_t FCPluginDynamic::getSerializationSize() const
 {
 
-    size_t wordSize = samplesCommon::getElementSize(mType);
+    size_t wordSize = getElementSize(mType);
     return wordSize * mNumParams + sizeof(mType) + sizeof(mOutDim) + sizeof(mNumParams) + sizeof(mAlgo) + sizeof(mNmax)
         + sizeof(mK);
 }
@@ -601,14 +620,17 @@ void FCPluginDynamic::serialize(void* buffer) const
     serialize_value(&buffer, mK);
     serialize_value(&buffer, mAlgo);
 
-    size_t wordSize = samplesCommon::getElementSize(mType);
+    size_t wordSize = getElementSize(mType);
     char* d = static_cast<char*>(buffer);
-    serFromDev(d, mWdev, mNumParams * wordSize); // in bytes
+    serFromDev(d, static_cast<char*>(mWdev.get()), mNumParams * wordSize);
 }
 
 void FCPluginDynamic::destroy()
 {
+    gLogVerbose << "FCPluginDynamic destroy\n";
     // This gets called when the network containing plugin is destroyed
+    mLtContext.destroy();
+    mWdev.release();
     delete this;
 }
 
@@ -678,14 +700,12 @@ IPluginV2* FCPluginDynamicCreator::createPlugin(const char* name, const PluginFi
             W.type = fieldTypeToDataType(fc->fields[i].type);
             gLogVerbose << "Is W float32: " << (W.type == DataType::kFLOAT) << std::endl;
         }
-
     }
 
     if (outDims <= 0)
     {
         gLogError << "Invalid output dimension" << std::endl;
     }
-
     if (typeId < 0 || typeId > 3)
     {
         gLogError << "Invalid type id" << typeId << std::endl;
@@ -715,4 +735,7 @@ const char* FCPluginDynamicCreator::getPluginNamespace() const
 {
     return mNamespace.c_str();
 }
-}
+
+} // namespace bert
+
+#endif // #if CUDA_VERSION >= 10010
diff --git a/plugin/fcPlugin/fcPlugin.h b/plugin/fcPlugin/fcPlugin.h
index c42c0600..133892bd 100644
--- a/plugin/fcPlugin/fcPlugin.h
+++ b/plugin/fcPlugin/fcPlugin.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -14,12 +14,16 @@
  * limitations under the License.
  */
 
+// cublasLT was introduced in CUDA 10.1
+#include <cuda.h>
+#if CUDA_VERSION >= 10010
+
 #ifndef TRT_FC_PLUGIN_H
 #define TRT_FC_PLUGIN_H
 
-#include "bertCommon.h"
 #include "NvInferPlugin.h"
 
+#include "bertCommon.h"
 #include <cublasLt.h>
 #include <string>
 #include <vector>
@@ -41,7 +45,11 @@ struct GemmTypes<half>
     using dataTypeO = half;
     static const cudaDataType_t cudaTypeS = CUDA_R_16F;
     using dataTypeS = half;
+#if CUBLAS_VER_MAJOR < 11
     static const cudaDataType_t cudaTypeCom = CUDA_R_16F;
+#else
+    static const cublasComputeType_t cudaTypeCom = CUBLAS_COMPUTE_16F;
+#endif
 };
 
 template <>
@@ -53,7 +61,11 @@ struct GemmTypes<float>
     using dataTypeO = float;
     static const cudaDataType_t cudaTypeS = CUDA_R_32F;
     using dataTypeS = float;
+#if CUBLAS_VER_MAJOR < 11
     static const cudaDataType_t cudaTypeCom = CUDA_R_32F;
+#else
+    static const cublasComputeType_t cudaTypeCom = CUBLAS_COMPUTE_32F;
+#endif
 };
 
 template <typename T>
@@ -195,39 +207,67 @@ void LtGemmSearch(cublasLtHandle_t ltHandle, const Gemm<T>& g, void* workSpace,
 
 struct LtContext
 {
-    cublasLtHandle_t cublas;
+    cublasLtHandle_t cublas{nullptr};
     cudaDataType_t typeA;
     cudaDataType_t typeB;
     cudaDataType_t typeC;
+#if CUBLAS_VER_MAJOR < 11
     cudaDataType_t typeComp;
+#else
+    cublasComputeType_t typeComp;
+#endif
+    cudaDataType_t typeS;
     cublasLtMatmulDesc_t operationDesc{nullptr};
     cublasLtMatrixLayout_t Adesc{nullptr};
     cublasLtMatrixLayout_t Bdesc{nullptr};
     cublasLtMatrixLayout_t Cdesc{nullptr};
     cublasLtMatmulHeuristicResult_t heuristicResult = {};
 
-    void destroy()
+    void attach()
     {
+        cublasLtCreate(&cublas);
+    }
 
-        cublasLtMatmulDescDestroy(operationDesc);
-
-        cublasLtMatrixLayoutDestroy(Adesc);
-        cublasLtMatrixLayoutDestroy(Bdesc);
-        cublasLtMatrixLayoutDestroy(Cdesc);
-
+    void detach()
+    {
         cublasLtDestroy(cublas);
     }
+
+    void destroy()
+    {
+        if (operationDesc)
+        {
+            cublasLtMatmulDescDestroy(operationDesc);
+        }
+        if (Adesc)
+        {
+            cublasLtMatrixLayoutDestroy(Adesc);
+        }
+        if (Bdesc)
+        {
+            cublasLtMatrixLayoutDestroy(Bdesc);
+        }
+        if (Cdesc)
+        {
+            cublasLtMatrixLayoutDestroy(Cdesc);
+        }
+    }
+
     template <typename T>
     void create(Gemm<T>& g, size_t workspaceSize)
     {
-        cublasLtCreate(&cublas);
         typeA = Gemm<T>::Types::cudaTypeI;
         typeB = Gemm<T>::Types::cudaTypeI;
         typeC = Gemm<T>::Types::cudaTypeO;
+        typeS = Gemm<T>::Types::cudaTypeS;
         typeComp = Gemm<T>::Types::cudaTypeCom; // compute
 
         // OPERATION
+#if CUBLAS_VER_MAJOR < 11
         cublasLtMatmulDescCreate(&operationDesc, typeComp);
+#else
+        cublasLtMatmulDescCreate(&operationDesc, typeComp, typeS);
+#endif
         cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSA, &g.opA, sizeof(g.opA));
         cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSB, &g.opB, sizeof(g.opB));
 
@@ -336,14 +376,14 @@ inline cublasLtMatmulAlgo_t gemmSearch(
     Gemm<T> g(m, n, k, false, false);
     std::vector<customMatmulPerf_t> perfResults(algoCombinations);
 
-    cudaMalloc(&g.A, g.bytesA);
-    cudaMalloc(&g.B, g.bytesB);
-    cudaMalloc(&g.C, g.bytesC);
+    cudaMalloc(reinterpret_cast<void**>(&g.A), g.bytesA);
+    cudaMalloc(reinterpret_cast<void**>(&g.B), g.bytesB);
+    cudaMalloc(reinterpret_cast<void**>(&g.C), g.bytesC);
 
     void* workspace;
     CHECK(cudaMalloc(&workspace, workspaceSize));
     cublasLtHandle_t lt;
-    CHECK(cublasLtCreate(&lt));
+    CUBLASASSERT(cublasLtCreate(&lt));
     LtGemmSearch(lt, g, workspace, workspaceSize, perfResults);
     cudaDeviceSynchronize();
     cublasLtDestroy(lt);
@@ -370,7 +410,7 @@ inline cublasLtMatmulAlgo_t gemmSearch(Gemm<T>& g, const size_t workspaceSize, s
     void* workspace;
     CHECK(cudaMalloc(&workspace, workspaceSize));
     cublasLtHandle_t lt;
-    CHECK(cublasLtCreate(&lt));
+    CUBLASASSERT(cublasLtCreate(&lt));
     LtGemmSearch(lt, g, workspace, workspaceSize, perfResults);
     cudaDeviceSynchronize();
     cublasLtDestroy(lt);
@@ -426,6 +466,9 @@ class FCPluginDynamic : public nvinfer1::IPluginV2DynamicExt
     void serialize(void* buffer) const override;
     void destroy() override;
     void setPluginNamespace(const char* pluginNamespace) override;
+    void attachToContext(
+        cudnnContext* cudnnContext, cublasContext* cublasContext, nvinfer1::IGpuAllocator* gpuAllocator) override;
+    void detachFromContext() override;
     const char* getPluginNamespace() const override;
 
 private:
@@ -440,22 +483,20 @@ class FCPluginDynamic : public nvinfer1::IPluginV2DynamicExt
 
     cublasLtMatmulAlgo_t mAlgo;
 
-    nvinfer1::Weights mW;
-    nvinfer1::Weights mB;
-
-    char* mWdev; // store weights as bytes: depends on the compute type
+    bert::WeightsWithOwnership mW;
+    bert::cuda_unique_ptr<void> mWdev;
 
     LtContext mLtContext;
 
 protected:
     // To prevent compiler warnings.
-    using nvinfer1::IPluginV2DynamicExt::getOutputDimensions;
-    using nvinfer1::IPluginV2DynamicExt::isOutputBroadcastAcrossBatch;
     using nvinfer1::IPluginV2DynamicExt::canBroadcastInputAcrossBatch;
-    using nvinfer1::IPluginV2DynamicExt::supportsFormat;
     using nvinfer1::IPluginV2DynamicExt::configurePlugin;
-    using nvinfer1::IPluginV2DynamicExt::getWorkspaceSize;
     using nvinfer1::IPluginV2DynamicExt::enqueue;
+    using nvinfer1::IPluginV2DynamicExt::getOutputDimensions;
+    using nvinfer1::IPluginV2DynamicExt::getWorkspaceSize;
+    using nvinfer1::IPluginV2DynamicExt::isOutputBroadcastAcrossBatch;
+    using nvinfer1::IPluginV2DynamicExt::supportsFormat;
 };
 
 class FCPluginDynamicCreator : public nvinfer1::IPluginCreator
@@ -482,5 +523,7 @@ class FCPluginDynamicCreator : public nvinfer1::IPluginCreator
     static std::vector<nvinfer1::PluginField> mPluginAttributes;
     std::string mNamespace;
 };
-}
+} // namespace bert
 #endif // TRT_FC_PLUGIN_H
+
+#endif // #if CUDA_VERSION >= 10010
diff --git a/plugin/flattenConcat/CMakeLists.txt b/plugin/flattenConcat/CMakeLists.txt
index de8fff17..85d8ce4e 100644
--- a/plugin/flattenConcat/CMakeLists.txt
+++ b/plugin/flattenConcat/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/plugin/flattenConcat/flattenConcat.cpp b/plugin/flattenConcat/flattenConcat.cpp
index c80f9a68..58d3840c 100644
--- a/plugin/flattenConcat/flattenConcat.cpp
+++ b/plugin/flattenConcat/flattenConcat.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -23,6 +23,15 @@
 #include <utility>
 #include <vector>
 
+#define ASSERT(assertion)                                                                                              \
+    {                                                                                                                  \
+        if (!(assertion))                                                                                              \
+        {                                                                                                              \
+            std::cerr << "#assertion" << __FILE__ << "," << __LINE__ << std::endl;                                     \
+            abort();                                                                                                   \
+        }                                                                                                              \
+    }
+
 using namespace nvinfer1;
 using nvinfer1::plugin::FlattenConcat;
 using nvinfer1::plugin::FlattenConcatPluginCreator;
@@ -40,68 +49,43 @@ FlattenConcat::FlattenConcat(int concatAxis, bool ignoreBatch)
     ASSERT(mConcatAxisID == 1 || mConcatAxisID == 2 || mConcatAxisID == 3);
 }
 
-FlattenConcat::FlattenConcat(
-    int concatAxis, bool ignoreBatch, int numInputs, int outputConcatAxis, const int* inputConcatAxis, size_t* copySize)
-    : mIgnoreBatch(ignoreBatch)
+FlattenConcat::FlattenConcat(int concatAxis, bool ignoreBatch, int numInputs, int outputConcatAxis,
+    const int* inputConcatAxis, const size_t* copySize)
+    : mCopySize(numInputs)
+    , mInputConcatAxis(numInputs)
+    , mIgnoreBatch(ignoreBatch)
     , mConcatAxisID(concatAxis)
     , mOutputConcatAxis(outputConcatAxis)
     , mNumInputs(numInputs)
 {
-    ASSERT(mConcatAxisID == 1 || mConcatAxisID == 2 || mConcatAxisID == 3);
+    ASSERT(mConcatAxisID >= 1 && mConcatAxisID <= 3);
 
-    // Allocate memory for mInputConcatAxis, mCopySize members
-    LOG_ERROR(cudaMallocHost((void**) &mInputConcatAxis, mNumInputs * sizeof(int)));
-    LOG_ERROR(cudaMallocHost((void**) &mCopySize, mNumInputs * sizeof(size_t)));
-
-    // Perform deep copy
-    if (copySize != nullptr)
-    {
-        for (int i = 0; i < mNumInputs; i++)
-        {
-            mCopySize[i] = static_cast<size_t>(copySize[i]);
-        }
-    }
-
-    for (int i = 0; i < mNumInputs; ++i)
-    {
-        mInputConcatAxis[i] = inputConcatAxis[i];
-    }
-
-    // Create cublas context
-    LOG_ERROR(cublasCreate(&mCublas));
+    std::copy(copySize, copySize + mNumInputs, mCopySize.begin());
+    std::copy(inputConcatAxis, inputConcatAxis + mNumInputs, mInputConcatAxis.begin());
 }
 
 FlattenConcat::FlattenConcat(const void* data, size_t length)
 {
-    const char *d = reinterpret_cast<const char*>(data), *a = d;
+    const char* d = static_cast<const char*>(data);
+    const char* const a = d;
     mIgnoreBatch = read<bool>(d);
     mConcatAxisID = read<int>(d);
-    ASSERT(mConcatAxisID == 1 || mConcatAxisID == 2 || mConcatAxisID == 3);
+    ASSERT(mConcatAxisID >= 1 && mConcatAxisID <= 3);
     mOutputConcatAxis = read<int>(d);
     mNumInputs = read<int>(d);
-    LOG_ERROR(cudaMallocHost((void**) &mInputConcatAxis, mNumInputs * sizeof(int)));
-    LOG_ERROR(cudaMallocHost((void**) &mCopySize, mNumInputs * sizeof(int)));
 
-    std::for_each(mInputConcatAxis, mInputConcatAxis + mNumInputs, [&](int& inp) { inp = read<int>(d); });
+    mInputConcatAxis.resize(mNumInputs);
+    std::for_each(mInputConcatAxis.begin(), mInputConcatAxis.end(), [&](int& inp) { inp = read<int>(d); });
 
     mCHW = read<nvinfer1::DimsCHW>(d);
 
-    std::for_each(mCopySize, mCopySize + mNumInputs, [&](size_t& inp) { inp = read<size_t>(d); });
+    mCopySize.resize(mNumInputs);
+    std::for_each(mCopySize.begin(), mCopySize.end(), [&](size_t& inp) { inp = read<size_t>(d); });
 
     ASSERT(d == a + length);
 }
 
-FlattenConcat::~FlattenConcat()
-{
-    if (mInputConcatAxis)
-    {
-        LOG_ERROR(cudaFreeHost(mInputConcatAxis));
-    }
-    if (mCopySize)
-    {
-        LOG_ERROR(cudaFreeHost(mCopySize));
-    }
-}
+FlattenConcat::~FlattenConcat() {}
 
 int FlattenConcat::getNbOutputs() const
 {
@@ -114,7 +98,8 @@ Dims FlattenConcat::getOutputDimensions(int index, const Dims* inputs, int nbInp
     ASSERT(index == 0);
 
     mNumInputs = nbInputDims;
-    LOG_ERROR(cudaMallocHost((void**) &mInputConcatAxis, nbInputDims * sizeof(int)));
+    mCopySize.resize(mNumInputs);
+    mInputConcatAxis.resize(mNumInputs);
     int outputConcatAxis = 0;
 
     for (int i = 0; i < nbInputDims; ++i)
@@ -143,12 +128,18 @@ Dims FlattenConcat::getOutputDimensions(int index, const Dims* inputs, int nbInp
 
 int FlattenConcat::initialize()
 {
-    return STATUS_SUCCESS;
+    // Create cublas context
+    CUBLASASSERT(cublasCreate(&mCublas));
+    return 0;
 }
 
 void FlattenConcat::terminate()
 {
-    LOG_ERROR(cublasDestroy(mCublas));
+    if (mCublas)
+    {
+        CUBLASASSERT(cublasDestroy(mCublas));
+        mCublas = nullptr;
+    }
 }
 
 size_t FlattenConcat::getWorkspaceSize(int) const
@@ -158,12 +149,11 @@ size_t FlattenConcat::getWorkspaceSize(int) const
 
 int FlattenConcat::enqueue(int batchSize, const void* const* inputs, void** outputs, void*, cudaStream_t stream)
 {
-    int numConcats = 1;
     ASSERT(mConcatAxisID != 0);
     // mCHW is the first input tensor
-    numConcats = std::accumulate(mCHW.d, mCHW.d + mConcatAxisID - 1, 1, std::multiplies<int>());
+    int numConcats = std::accumulate(mCHW.d, mCHW.d + mConcatAxisID - 1, 1, std::multiplies<int>());
 
-    LOG_ERROR(cublasSetStream(mCublas, stream));
+    CUBLASASSERT(cublasSetStream(mCublas, stream));
 
     // Num concats will be proportional to number of samples in a batch
     if (!mIgnoreBatch)
@@ -171,21 +161,16 @@ int FlattenConcat::enqueue(int batchSize, const void* const* inputs, void** outp
         numConcats *= batchSize;
     }
 
-    auto* output = reinterpret_cast<float*>(outputs[0]);
+    auto* output = static_cast<float*>(outputs[0]);
     int offset = 0;
     for (int i = 0; i < mNumInputs; ++i)
     {
-        const auto* input = reinterpret_cast<const float*>(inputs[i]);
-        float* inputTemp;
-        LOG_ERROR(cudaMalloc(&inputTemp, mCopySize[i] * batchSize));
-        LOG_ERROR(cudaMemcpyAsync(inputTemp, input, mCopySize[i] * batchSize, cudaMemcpyDeviceToDevice, stream));
-
+        const auto* input = static_cast<const float*>(inputs[i]);
         for (int n = 0; n < numConcats; ++n)
         {
-            LOG_ERROR(cublasScopy(mCublas, mInputConcatAxis[i], inputTemp + n * mInputConcatAxis[i], 1,
+            CUBLASASSERT(cublasScopy(mCublas, mInputConcatAxis[i], input + n * mInputConcatAxis[i], 1,
                 output + (n * mOutputConcatAxis + offset), 1));
         }
-        LOG_ERROR(cudaFree(inputTemp));
         offset += mInputConcatAxis[i];
     }
 
@@ -194,12 +179,14 @@ int FlattenConcat::enqueue(int batchSize, const void* const* inputs, void** outp
 
 size_t FlattenConcat::getSerializationSize() const
 {
-    return sizeof(bool) + sizeof(int) * (3 + mNumInputs) + sizeof(nvinfer1::Dims) + (sizeof(mCopySize) * mNumInputs);
+    return sizeof(bool) + sizeof(int) * (3 + mNumInputs) + sizeof(nvinfer1::Dims)
+        + (sizeof(decltype(mCopySize)::value_type) * mNumInputs);
 }
 
 void FlattenConcat::serialize(void* buffer) const
 {
-    char *d = reinterpret_cast<char*>(buffer), *a = d;
+    char* d = static_cast<char*>(buffer);
+    const char* const a = d;
     write(d, mIgnoreBatch);
     write(d, mConcatAxisID);
     write(d, mOutputConcatAxis);
@@ -245,7 +232,7 @@ void FlattenConcat::setPluginNamespace(const char* pluginNamespace)
 
 const char* FlattenConcat::getPluginNamespace() const
 {
-    return mPluginNamespace;
+    return mPluginNamespace.c_str();
 }
 
 // Return the DataType of the plugin output at the requested index
@@ -264,11 +251,7 @@ void FlattenConcat::configurePlugin(const Dims* inputDims, int nbInputs, const D
     mNumInputs = nbInputs;
     ASSERT(inputDims[0].nbDims == 3);
 
-    if (mInputConcatAxis == nullptr)
-    {
-        LOG_ERROR(cudaMallocHost((void**) &mInputConcatAxis, mNumInputs * sizeof(int)));
-    }
-
+    mInputConcatAxis.resize(mNumInputs);
     for (int i = 0; i < nbInputs; ++i)
     {
         int flattenInput = 0;
@@ -290,6 +273,7 @@ void FlattenConcat::configurePlugin(const Dims* inputDims, int nbInputs, const D
         mOutputConcatAxis += mInputConcatAxis[i];
     }
 
+    mCopySize.resize(mNumInputs);
     for (int i = 0; i < nbInputs; ++i)
     {
         mCopySize[i] = inputDims[i].d[0] * inputDims[i].d[1] * inputDims[i].d[2] * sizeof(float);
@@ -317,9 +301,10 @@ void FlattenConcat::destroy()
 
 IPluginV2Ext* FlattenConcat::clone() const
 {
-    auto* plugin
-        = new FlattenConcat(mConcatAxisID, mIgnoreBatch, mNumInputs, mOutputConcatAxis, mInputConcatAxis, mCopySize);
-    plugin->setPluginNamespace(mPluginNamespace);
+    auto* plugin = new FlattenConcat(
+        mConcatAxisID, mIgnoreBatch, mNumInputs, mOutputConcatAxis, mInputConcatAxis.data(), mCopySize.data());
+    plugin->setPluginNamespace(mPluginNamespace.c_str());
+    plugin->mCublas = mCublas;
     return plugin;
 }
 
diff --git a/plugin/flattenConcat/flattenConcat.h b/plugin/flattenConcat/flattenConcat.h
index ab2f53e3..0692b8f2 100644
--- a/plugin/flattenConcat/flattenConcat.h
+++ b/plugin/flattenConcat/flattenConcat.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -25,17 +25,6 @@
 #include <string>
 #include <vector>
 
-#define LOG_ERROR(status)                                                                                              \
-    do                                                                                                                 \
-    {                                                                                                                  \
-        auto ret = (status);                                                                                           \
-        if (ret != 0)                                                                                                  \
-        {                                                                                                              \
-            std::cout << "Cuda failure: " << ret << std::endl;                                                         \
-            abort();                                                                                                   \
-        }                                                                                                              \
-    } while (0)
-
 namespace nvinfer1
 {
 namespace plugin
@@ -46,7 +35,7 @@ class FlattenConcat : public IPluginV2Ext
     FlattenConcat(int concatAxis, bool ignoreBatch);
 
     FlattenConcat(int concatAxis, bool ignoreBatch, int numInputs, int outputConcatAxis, const int* inputConcatAxis,
-        size_t* copySize);
+        const size_t* copySize);
 
     FlattenConcat(const void* data, size_t length);
 
@@ -106,13 +95,13 @@ class FlattenConcat : public IPluginV2Ext
 
     Weights deserializeToDevice(const char*& hostBuffer, size_t count);
 
-    size_t* mCopySize = nullptr;
+    std::vector<size_t> mCopySize;
+    std::vector<int> mInputConcatAxis;
     bool mIgnoreBatch{false};
     int mConcatAxisID{0}, mOutputConcatAxis{0}, mNumInputs{0};
-    int* mInputConcatAxis = nullptr;
     nvinfer1::Dims mCHW;
-    const char* mPluginNamespace;
-    cublasHandle_t mCublas;
+    std::string mPluginNamespace;
+    cublasHandle_t mCublas{nullptr};
 };
 
 class FlattenConcatPluginCreator : public BaseCreator
diff --git a/plugin/geluPlugin/CMakeLists.txt b/plugin/geluPlugin/CMakeLists.txt
index 39f2d485..443b76ac 100644
--- a/plugin/geluPlugin/CMakeLists.txt
+++ b/plugin/geluPlugin/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,6 +13,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
+file(GLOB SRCS *.cpp)
+set(PLUGIN_SOURCES ${PLUGIN_SOURCES} ${SRCS})
+set(PLUGIN_SOURCES ${PLUGIN_SOURCES} PARENT_SCOPE)
 file(GLOB CU_SRCS *.cu)
 set(BERT_CU_SOURCES ${BERT_CU_SOURCES} ${CU_SRCS})
 set(BERT_CU_SOURCES ${BERT_CU_SOURCES} PARENT_SCOPE)
diff --git a/plugin/geluPlugin/geluKernel.cu b/plugin/geluPlugin/geluKernel.cu
new file mode 100644
index 00000000..f3f737d1
--- /dev/null
+++ b/plugin/geluPlugin/geluKernel.cu
@@ -0,0 +1,136 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuda.h>
+#if CUDA_VERSION >= 10010
+
+#include <cassert>
+#include <cstring>
+#include <vector>
+
+#include "NvInfer.h"
+#include "geluPlugin.h"
+#include "bertCommon.h"
+#include "common.cuh"
+#include "serialize.hpp"
+
+using namespace nvinfer1;
+
+namespace bert
+{
+
+// constants for approximating the normal cdf
+constexpr float A = 0.5f;
+constexpr float B = 0.7978845608028654f;   // sqrt(2.0/M_PI)
+constexpr float C = 0.035677408136300125f; // 0.044715 * sqrt(2.0/M_PI)
+
+template <typename T, unsigned TPB>
+__global__ void geluKernel(const T a, const T b, const T c, int n, const T* input, T* output)
+{
+    const int idx = blockIdx.x * TPB + threadIdx.x;
+
+    if (idx < n)
+    {
+        const T in = input[idx];
+        const T cdf = a + a * tanh(in * (c * in * in + b));
+        output[idx] = in * cdf;
+    }
+}
+
+int computeGelu(cudaStream_t stream, int n, const float* input, float* output)
+{
+    constexpr int blockSize = 256;
+    const int gridSize = (n + blockSize - 1) / blockSize;
+    geluKernel<float, blockSize><<<gridSize, blockSize, 0, stream>>>(A, B, C, n, input, output);
+
+    CHECK(cudaPeekAtLastError());
+    return 0;
+}
+
+int computeGelu(cudaStream_t stream, int n, const half* input, half* output)
+{
+    constexpr int blockSize = 256;
+
+    if (0 == (n & 1))
+    {
+        const int n2 = n / 2;
+
+        const int gridSize = (n2 + blockSize - 1) / blockSize;
+        const half2 A2 = __floats2half2_rn(A, A);
+        const half2 B2 = __floats2half2_rn(B, B);
+        const half2 C2 = __floats2half2_rn(C, C);
+        const half2* input2 = reinterpret_cast<const half2*>(input);
+        half2* output2 = reinterpret_cast<half2*>(output);
+        geluKernel<half2, blockSize><<<gridSize, blockSize, 0, stream>>>(A2, B2, C2, n2, input2, output2);
+    }
+    else
+    {
+        const int gridSize = (n + blockSize - 1) / blockSize;
+        geluKernel<half, blockSize><<<gridSize, blockSize, 0, stream>>>(A, B, C, n, input, output);
+    }
+
+    CHECK(cudaPeekAtLastError());
+    return 0;
+}
+
+template <typename T, int TPB>
+__global__ void geluBiasKernel(const T a, const T b, const T c, T* output, const T* input, const T* bias, const int ld)
+{
+
+    const int offset = blockIdx.x * ld;
+
+    for (int it = threadIdx.x; it < ld; it += TPB)
+    {
+        const int idx = it + offset;
+        const T in = input[idx] + bias[it];
+        const T cdf = a + a * tanh(in * (c * in * in + b));
+        output[idx] = in * cdf;
+    }
+}
+
+void computeGeluBias(
+    float* output, const float* input, const float* bias, const int ld, const int cols, cudaStream_t stream)
+{
+    geluBiasKernel<float, 256><<<cols, 256, 0, stream>>>(A, B, C, output, input, bias, ld);
+    CHECK(cudaPeekAtLastError());
+}
+
+void computeGeluBias(
+    half* output, const half* input, const half* bias, const int ld, const int cols, cudaStream_t stream)
+{
+    if (ld & 1)
+    {
+        geluBiasKernel<half, 256><<<cols, 256, 0, stream>>>(A, B, C, output, input, bias, ld);
+    }
+    else
+    {
+
+        const half2 A2 = __floats2half2_rn(A, A);
+        const half2 B2 = __floats2half2_rn(B, B);
+        const half2 C2 = __floats2half2_rn(C, C);
+        const int ld2 = ld / 2;
+        const half2* input2 = reinterpret_cast<const half2*>(input);
+        const half2* bias2 = reinterpret_cast<const half2*>(bias);
+        half2* output2 = reinterpret_cast<half2*>(output);
+        geluBiasKernel<half2, 256><<<cols, 256, 0, stream>>>(A2, B2, C2, output2, input2, bias2, ld2);
+    }
+
+    CHECK(cudaPeekAtLastError());
+}
+
+} // namespace bert
+
+#endif // CUDA_VERSION >= 10010
diff --git a/plugin/geluPlugin/geluPlugin.cu b/plugin/geluPlugin/geluPlugin.cpp
similarity index 54%
rename from plugin/geluPlugin/geluPlugin.cu
rename to plugin/geluPlugin/geluPlugin.cpp
index 4ea12224..63892cd5 100644
--- a/plugin/geluPlugin/geluPlugin.cu
+++ b/plugin/geluPlugin/geluPlugin.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -14,14 +14,16 @@
  * limitations under the License.
  */
 
+#include <cuda.h>
+#if CUDA_VERSION >= 10010
+
 #include <cassert>
 #include <cstring>
 #include <vector>
 
 #include "NvInfer.h"
-#include "geluPlugin.h"
 #include "bertCommon.h"
-#include "common.h"
+#include "geluPlugin.h"
 #include "serialize.hpp"
 
 using namespace nvinfer1;
@@ -29,109 +31,6 @@ using namespace nvinfer1;
 namespace bert
 {
 
-// constants for approximating the normal cdf
-constexpr float A = 0.5;
-constexpr float B = 0.7978845608028654;   // sqrt(2.0/M_PI)
-constexpr float C = 0.035677408136300125; // 0.044715 * sqrt(2.0/M_PI)
-
-template <typename T, unsigned TPB>
-__global__ void geluKernel(const T a, const T b, const T c, int n, const T* input, T* output)
-{
-
-    const int idx = blockIdx.x * TPB + threadIdx.x;
-
-    if (idx < n)
-    {
-        const T in = input[idx];
-        const T cdf = a + a * tanh(in * (c * in * in + b));
-        output[idx] = in * cdf;
-    }
-}
-
-inline int computeGelu(cudaStream_t stream, int n, const float* input, float* output)
-{
-
-    constexpr int blockSize = 256;
-    const int gridSize = (n + blockSize - 1) / blockSize;
-    geluKernel<float, blockSize><<<gridSize, blockSize, 0, stream>>>(A, B, C, n, input, output);
-
-    CHECK(cudaPeekAtLastError());
-    return 0;
-}
-
-inline int computeGelu(cudaStream_t stream, int n, const half* input, half* output)
-{
-    const int blockSize = 256;
-
-    if (0 == (n & 1))
-    {
-        const int n2 = n / 2;
-
-        const int gridSize = (n2 + blockSize - 1) / blockSize;
-        const half2 A2 = __floats2half2_rn(A, A);
-        const half2 B2 = __floats2half2_rn(B, B);
-        const half2 C2 = __floats2half2_rn(C, C);
-        const half2* input2 = reinterpret_cast<const half2*>(input);
-        half2* output2 = reinterpret_cast<half2*>(output);
-        geluKernel<half2, blockSize><<<gridSize, blockSize, 0, stream>>>(A2, B2, C2, n2, input2, output2);
-    }
-    else
-    {
-        const int gridSize = (n + blockSize - 1) / blockSize;
-        geluKernel<half, blockSize><<<gridSize, blockSize, 0, stream>>>(A, B, C, n, input, output);
-    }
-
-    CHECK(cudaPeekAtLastError());
-    return 0;
-}
-
-template <typename T, int TPB>
-__global__ void geluBiasKernel(const T a, const T b, const T c, T* output, const T* input, const T* bias, const int ld)
-{
-
-    const int offset = blockIdx.x * ld;
-
-    for (int it = threadIdx.x; it < ld; it += TPB)
-    {
-        const int idx = it + offset;
-        const T in = input[idx] + bias[it];
-        const T cdf = a + a * tanh(in * (c * in * in + b));
-        output[idx] = in * cdf;
-    }
-}
-
-void computeGeluBias(
-    float* output, const float* input, const float* bias, const int ld, const int cols, cudaStream_t stream)
-{
-    geluBiasKernel<float, 256><<<cols, 256, 0, stream>>>(A, B, C, output, input, bias, ld);
-    CHECK(cudaPeekAtLastError());
-}
-
-void computeGeluBias(
-    half* output, const half* input, const half* bias, const int ld, const int cols, cudaStream_t stream)
-{
-    if (ld & 1)
-    {
-        geluBiasKernel<half, 256><<<cols, 256, 0, stream>>>(A, B, C, output, input, bias, ld);
-    }
-    else
-    {
-
-        const half2 A2 = __floats2half2_rn(A, A);
-        const half2 B2 = __floats2half2_rn(B, B);
-        const half2 C2 = __floats2half2_rn(C, C);
-        const int ld2 = ld / 2;
-        const half2* input2 = reinterpret_cast<const half2*>(input);
-        const half2* bias2 = reinterpret_cast<const half2*>(bias);
-        half2* output2 = reinterpret_cast<half2*>(output);
-        geluBiasKernel<half2, 256><<<cols, 256, 0, stream>>>(A2, B2, C2, output2, input2, bias2, ld2);
-    }
-
-    CHECK(cudaPeekAtLastError());
-}
-
-/////////////////////////////////
-
 namespace
 {
 static const char* GELU_PLUGIN_VERSION{"1"};
@@ -144,57 +43,43 @@ std::vector<PluginField> GeluPluginDynamicCreator::mPluginAttributes;
 
 REGISTER_TENSORRT_PLUGIN(GeluPluginDynamicCreator);
 
-GeluPluginDynamic::GeluPluginDynamic(const std::string name, const DataType type)
+GeluPluginDynamic::GeluPluginDynamic(const std::string name, const DataType type, const Weights& bias)
     : mLayerName(name)
     , mType(type)
-    , mHasBias(false)
-    , mLd(0)
-{
-    mBias.values = nullptr;
-    mBias.count = 0;
-}
-
-GeluPluginDynamic::GeluPluginDynamic(const std::string name, const DataType type, const Weights B)
-    : mLayerName(name)
-    , mType(type)
-    , mHasBias(true)
-    , mBias(B)
-    , mLd(B.count)
+    , mLd(bias.count)
 {
+    mHasBias = (bias.values != nullptr);
+    if (mHasBias)
+    {
+        void* cudaMem{nullptr};
+        CHECK(cudaMalloc(&cudaMem, getWeightsSize(bias, mType)));
+        CHECK(cudaMemcpy(cudaMem, bias.values, getWeightsSize(bias, mType), cudaMemcpyHostToDevice));
+        make_cuda_shared(mBiasDev, cudaMem);
+    }
 }
 
 GeluPluginDynamic::GeluPluginDynamic(const std::string name, const void* data, size_t length)
     : mLayerName(name)
 {
-    gLogVerbose << "Starting to deserialize GELU plugin" << std::endl;
+    gLogVerbose << "GeluPluginDynamic deserialize\n";
     deserialize_value(&data, &length, &mType);
     deserialize_value(&data, &length, &mLd);
     deserialize_value(&data, &length, &mHasBias);
 
-    gLogVerbose << "Deserialized parameters: mLd: " << mLd << ", mHasBias: " << mHasBias << std::endl;
     if (mHasBias)
     {
+        assert(mLd > 0);
         const char* d = static_cast<const char*>(data);
-        gLogVerbose << "Deserializing Bias" << std::endl;
-        if (mLd <= 0)
-        {
-            gLogError << "Gelu+bias: deserialization inconsistent. HasBias but mLd is 0" << std::endl;
-        }
-        const size_t wordSize = samplesCommon::getElementSize(mType);
-        mBiasDev = deserToDev<char>(d, mLd * wordSize);
+        make_cuda_shared(mBiasDev, deserToDev<char>(d, mLd * getElementSize(mType)));
     }
-    gLogVerbose << "Finished deserializing GELU plugin" << std::endl;
-    mBias.values = nullptr;
-    mBias.count = mLd;
 }
 // IPluginV2DynamicExt Methods
 nvinfer1::IPluginV2DynamicExt* GeluPluginDynamic::clone() const
 {
-    if (mHasBias)
-    {
-        return new GeluPluginDynamic(mLayerName, mType, mBias);
-    }
-    return new GeluPluginDynamic(mLayerName, mType);
+    gLogVerbose << "GeluPluginDynamic clone\n";
+    auto plugin = new GeluPluginDynamic(*this);
+    plugin->setPluginNamespace(mNamespace.c_str());
+    return plugin;
 }
 
 nvinfer1::DimsExprs GeluPluginDynamic::getOutputDimensions(
@@ -223,6 +108,7 @@ bool GeluPluginDynamic::supportsFormatCombination(
 void GeluPluginDynamic::configurePlugin(const nvinfer1::DynamicPluginTensorDesc* in, int nbInputs,
     const nvinfer1::DynamicPluginTensorDesc* out, int nbOutputs)
 {
+    gLogVerbose << "GeluPluginDynamic configurePlugin\n";
     assert(mType == in[0].desc.type);
 }
 
@@ -247,7 +133,7 @@ int GeluPluginDynamic::enqueue(const nvinfer1::PluginTensorDesc* inputDesc,
         float* output = static_cast<float*>(outputs[0]);
         if (mHasBias)
         {
-            const float* bias = reinterpret_cast<float*>(mBiasDev);
+            const float* bias = static_cast<float*>(mBiasDev.get());
             const int cols = inputVolume / mLd;
             const int rows = mLd;
             computeGeluBias(output, input, bias, rows, cols, stream);
@@ -265,7 +151,7 @@ int GeluPluginDynamic::enqueue(const nvinfer1::PluginTensorDesc* inputDesc,
 
         if (mHasBias)
         {
-            const half* bias = reinterpret_cast<half*>(mBiasDev);
+            const half* bias = static_cast<half*>(mBiasDev.get());
             const int cols = inputVolume / mLd;
             const int rows = mLd;
             computeGeluBias(output, input, bias, rows, cols, stream);
@@ -311,38 +197,18 @@ int GeluPluginDynamic::getNbOutputs() const
 
 int GeluPluginDynamic::initialize()
 {
-    gLogVerbose << "GELU init start" << std::endl;
-    if (mHasBias && mBias.values)
-    {
-        // target size
-        const size_t wordSize = samplesCommon::getElementSize(mType);
-        const size_t nbBytes = mBias.count * wordSize;
-        CHECK(cudaMalloc(&mBiasDev, nbBytes));
-
-        if (mType == DataType::kFLOAT)
-        {
-            convertAndCopyToDevice(mBias, reinterpret_cast<float*>(mBiasDev));
-        }
-        else
-        {
-            convertAndCopyToDevice(mBias, reinterpret_cast<half*>(mBiasDev));
-        }
-    }
-    gLogVerbose << "GELU init done" << std::endl;
+    gLogVerbose << "GeluPluginDynamic initalize\n";
     return 0;
 }
 
 void GeluPluginDynamic::terminate()
 {
-    if (mHasBias)
-    {
-        CHECK(cudaFree(mBiasDev));
-    }
+    gLogVerbose << "GeluPluginDynamic terminate\n";
 }
 
 size_t GeluPluginDynamic::getSerializationSize() const
 {
-    const size_t wordSize = samplesCommon::getElementSize(mType);
+    const size_t wordSize = getElementSize(mType);
     const size_t biasSize = mHasBias ? mLd * wordSize : 0;
     return sizeof(mType) + sizeof(mHasBias) + sizeof(mLd) + biasSize;
 }
@@ -354,20 +220,17 @@ void GeluPluginDynamic::serialize(void* buffer) const
     serialize_value(&buffer, mHasBias);
     if (mHasBias)
     {
-        char *d = static_cast<char*>(buffer);
-        const size_t wordSize = samplesCommon::getElementSize(mType);
-        const size_t biasSize = mHasBias ? mLd * wordSize : 0;
-        if (biasSize <= 0)
-        {
-            gLogError << "Gelu+bias: bias size inconsistent" << std::endl;
-        }
-        serFromDev(d, mBiasDev, mLd * wordSize);
+        assert(mLd > 0);
+        char* d = static_cast<char*>(buffer);
+        serFromDev(d, static_cast<char*>(mBiasDev.get()), mLd * getElementSize(mType));
     }
 }
 
 void GeluPluginDynamic::destroy()
 {
+    gLogVerbose << "GeluPluginDynamic destroy\n";
     // This gets called when the network containing plugin is destroyed
+    mBiasDev.reset();
     delete this;
 }
 
@@ -385,7 +248,6 @@ const char* GeluPluginDynamic::getPluginNamespace() const
 
 GeluPluginDynamicCreator::GeluPluginDynamicCreator()
 {
-
     // Fill PluginFieldCollection with PluginField arguments metadata
     mFC.nbFields = mPluginAttributes.size();
     mFC.fields = mPluginAttributes.data();
@@ -408,6 +270,7 @@ const PluginFieldCollection* GeluPluginDynamicCreator::getFieldNames()
 
 IPluginV2* GeluPluginDynamicCreator::createPlugin(const char* name, const PluginFieldCollection* fc)
 {
+    gLogVerbose << "GeluPluginDynamicCreator createPlugin\n";
 
     Weights bias{DataType::kFLOAT, nullptr, 0};
     int typeId = -1;
@@ -418,12 +281,9 @@ IPluginV2* GeluPluginDynamicCreator::createPlugin(const char* name, const Plugin
         if (field_name.compare("type_id") == 0)
         {
             typeId = *static_cast<const int*>(fc->fields[i].data);
-            gLogVerbose << "Building typeId: " << typeId << std::endl;
         }
-
         if (field_name.compare("bias") == 0)
         {
-            gLogVerbose << "Building bias...\n";
             bias.values = fc->fields[i].data;
             bias.count = fc->fields[i].length;
             bias.type = fieldTypeToDataType(fc->fields[i].type);
@@ -432,17 +292,11 @@ IPluginV2* GeluPluginDynamicCreator::createPlugin(const char* name, const Plugin
 
     if (typeId < 0 || typeId > 3)
     {
-        gLogError << "GELU: invalid typeId " << typeId << std::endl;
+        gLogError << "GeluPluginDynamicCreator: invalid typeId " << typeId << std::endl;
         return nullptr;
     }
-    DataType type = static_cast<DataType>(typeId);
-    gLogVerbose << "Creating GeluPluginDynamic...\n";
-    if (bias.values == nullptr)
-    {
-        return new GeluPluginDynamic(name, type);
-    }
 
-    return new GeluPluginDynamic(name, type, bias);
+    return new GeluPluginDynamic(name, static_cast<DataType>(typeId), bias);
 }
 
 IPluginV2* GeluPluginDynamicCreator::deserializePlugin(const char* name, const void* serialData, size_t serialLength)
@@ -461,4 +315,7 @@ const char* GeluPluginDynamicCreator::getPluginNamespace() const
 {
     return mNamespace.c_str();
 }
-}
+
+} // namespace bert
+
+#endif // CUDA_VERSION >= 10010
diff --git a/plugin/geluPlugin/geluPlugin.h b/plugin/geluPlugin/geluPlugin.h
index d0b4341b..2f0365db 100644
--- a/plugin/geluPlugin/geluPlugin.h
+++ b/plugin/geluPlugin/geluPlugin.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -14,25 +14,34 @@
  * limitations under the License.
  */
 
+#include <cuda.h>
+#if CUDA_VERSION >= 10010
+
 #ifndef TRT_GELU_PLUGIN_H
 #define TRT_GELU_PLUGIN_H
 
 #include "NvInferPlugin.h"
+#include "bertCommon.h"
 #include <string>
 #include <vector>
 
 namespace bert
 {
 
-// One of the preferred ways of making TensorRT to be able to see
-// our custom layer requires extending IPluginV2 and IPluginCreator classes.
-// For requirements for overriden functions, check TensorRT API docs.
+int computeGelu(cudaStream_t stream, int n, const float* input, float* output);
+
+int computeGelu(cudaStream_t stream, int n, const half* input, half* output);
+
+void computeGeluBias(
+    float* output, const float* input, const float* bias, const int ld, const int cols, cudaStream_t stream);
+
+void computeGeluBias(
+    half* output, const half* input, const half* bias, const int ld, const int cols, cudaStream_t stream);
 
 class GeluPluginDynamic : public nvinfer1::IPluginV2DynamicExt
 {
 public:
-    GeluPluginDynamic(const std::string name, const nvinfer1::DataType type);
-    GeluPluginDynamic(const std::string name, const nvinfer1::DataType type, const nvinfer1::Weights B);
+    GeluPluginDynamic(const std::string name, const nvinfer1::DataType type, const nvinfer1::Weights& bias);
 
     GeluPluginDynamic(const std::string name, const void* data, size_t length);
 
@@ -74,19 +83,18 @@ class GeluPluginDynamic : public nvinfer1::IPluginV2DynamicExt
 
     nvinfer1::DataType mType;
     bool mHasBias;
-    nvinfer1::Weights mBias;
-    char* mBiasDev;
+    bert::cuda_shared_ptr<void> mBiasDev;
     size_t mLd;
 
 protected:
     // To prevent compiler warnings.
-    using nvinfer1::IPluginV2DynamicExt::getOutputDimensions;
-    using nvinfer1::IPluginV2DynamicExt::isOutputBroadcastAcrossBatch;
     using nvinfer1::IPluginV2DynamicExt::canBroadcastInputAcrossBatch;
-    using nvinfer1::IPluginV2DynamicExt::supportsFormat;
     using nvinfer1::IPluginV2DynamicExt::configurePlugin;
-    using nvinfer1::IPluginV2DynamicExt::getWorkspaceSize;
     using nvinfer1::IPluginV2DynamicExt::enqueue;
+    using nvinfer1::IPluginV2DynamicExt::getOutputDimensions;
+    using nvinfer1::IPluginV2DynamicExt::getWorkspaceSize;
+    using nvinfer1::IPluginV2DynamicExt::isOutputBroadcastAcrossBatch;
+    using nvinfer1::IPluginV2DynamicExt::supportsFormat;
 };
 
 class GeluPluginDynamicCreator : public nvinfer1::IPluginCreator
@@ -113,5 +121,7 @@ class GeluPluginDynamicCreator : public nvinfer1::IPluginCreator
     static std::vector<nvinfer1::PluginField> mPluginAttributes;
     std::string mNamespace;
 };
-}
+} // namespace bert
 #endif // TRT_GELU_PLUGIN_H
+
+#endif // CUDA_VERSION >= 10010
diff --git a/plugin/generateDetectionPlugin/generateDetectionPlugin.cpp b/plugin/generateDetectionPlugin/generateDetectionPlugin.cpp
index 44d45ccc..3f43bd44 100644
--- a/plugin/generateDetectionPlugin/generateDetectionPlugin.cpp
+++ b/plugin/generateDetectionPlugin/generateDetectionPlugin.cpp
@@ -126,7 +126,7 @@ int GenerateDetection::initialize()
     // Init the regWeight [10, 10, 5, 5]
     mRegWeightDevice = std::make_shared<CudaBind<float>>(4);
     CUASSERT(cudaMemcpy(static_cast<void*>(mRegWeightDevice->mPtr),
-    static_cast<const void*>(TLTMaskRCNNConfig::DETECTION_REG_WEIGHTS), sizeof(float) * 4, cudaMemcpyHostToDevice));
+        static_cast<const void*>(TLTMaskRCNNConfig::DETECTION_REG_WEIGHTS), sizeof(float) * 4, cudaMemcpyHostToDevice));
 
     //@Init the mValidCnt and mDecodedBboxes for max batch size
     std::vector<int> tempValidCnt(mMaxBatchSize, mAnchorsCnt);
@@ -264,17 +264,17 @@ int GenerateDetection::enqueue(
 
     // refine detection
     RefineDetectionWorkSpace refDetcWorkspace(batch_size, mAnchorsCnt, mParam, mType);
-    cudaError_t status = DetectionPostProcess(stream, batch_size, mAnchorsCnt,
-        static_cast<float*>(mRegWeightDevice->mPtr), 
-        static_cast<float>(TLTMaskRCNNConfig::IMAGE_SHAPE.d[1]), // Image Height
-        static_cast<float>(TLTMaskRCNNConfig::IMAGE_SHAPE.d[2]), // Image Width
-        DataType::kFLOAT, // mType,
-        mParam, refDetcWorkspace, workspace,
-        inputs[1],       // inputs[InScore]
-        inputs[0],       // inputs[InDelta],
-        mValidCnt->mPtr, // inputs[InCountValid],
-        inputs[2],       // inputs[ROI]
-        detections);
+    cudaError_t status
+        = DetectionPostProcess(stream, batch_size, mAnchorsCnt, static_cast<float*>(mRegWeightDevice->mPtr),
+            static_cast<float>(TLTMaskRCNNConfig::IMAGE_SHAPE.d[1]), // Image Height
+            static_cast<float>(TLTMaskRCNNConfig::IMAGE_SHAPE.d[2]), // Image Width
+            DataType::kFLOAT,                                        // mType,
+            mParam, refDetcWorkspace, workspace,
+            inputs[1],       // inputs[InScore]
+            inputs[0],       // inputs[InDelta],
+            mValidCnt->mPtr, // inputs[InCountValid],
+            inputs[2],       // inputs[ROI]
+            detections);
 
     assert(status == cudaSuccess);
     return status;
@@ -287,7 +287,8 @@ DataType GenerateDetection::getOutputDataType(int index, const nvinfer1::DataTyp
 }
 
 // Return true if output tensor is broadcast across a batch.
-bool GenerateDetection::isOutputBroadcastAcrossBatch(int outputIndex, const bool* inputIsBroadcasted, int nbInputs) const
+bool GenerateDetection::isOutputBroadcastAcrossBatch(
+    int outputIndex, const bool* inputIsBroadcasted, int nbInputs) const
 {
     return false;
 }
diff --git a/plugin/gridAnchorPlugin/CMakeLists.txt b/plugin/gridAnchorPlugin/CMakeLists.txt
index 3f27c956..85d8ce4e 100644
--- a/plugin/gridAnchorPlugin/CMakeLists.txt
+++ b/plugin/gridAnchorPlugin/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -15,4 +15,4 @@
 #
 file(GLOB SRCS *.cpp)
 set(PLUGIN_SOURCES ${PLUGIN_SOURCES} ${SRCS})
-set(PLUGIN_SOURCES ${PLUGIN_SOURCES} PARENT_SCOPE)
\ No newline at end of file
+set(PLUGIN_SOURCES ${PLUGIN_SOURCES} PARENT_SCOPE)
diff --git a/plugin/gridAnchorPlugin/gridAnchorPlugin.cpp b/plugin/gridAnchorPlugin/gridAnchorPlugin.cpp
index 7f8152dc..635dbbd4 100644
--- a/plugin/gridAnchorPlugin/gridAnchorPlugin.cpp
+++ b/plugin/gridAnchorPlugin/gridAnchorPlugin.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -292,7 +292,7 @@ void GridAnchorGenerator::setPluginNamespace(const char* pluginNamespace)
 
 const char* GridAnchorGenerator::getPluginNamespace() const
 {
-    return mPluginNamespace;
+    return mPluginNamespace.c_str();
 }
 
 #include <iostream>
@@ -342,7 +342,7 @@ void GridAnchorGenerator::destroy()
 IPluginV2Ext* GridAnchorGenerator::clone() const
 {
     IPluginV2Ext* plugin = new GridAnchorGenerator(mParam.data(), mNumLayers);
-    plugin->setPluginNamespace(mPluginNamespace);
+    plugin->setPluginNamespace(mPluginNamespace.c_str());
     return plugin;
 }
 
diff --git a/plugin/gridAnchorPlugin/gridAnchorPlugin.h b/plugin/gridAnchorPlugin/gridAnchorPlugin.h
index 8c412e3e..4ffd903d 100644
--- a/plugin/gridAnchorPlugin/gridAnchorPlugin.h
+++ b/plugin/gridAnchorPlugin/gridAnchorPlugin.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -92,7 +92,7 @@ class GridAnchorGenerator : public IPluginV2Ext
     std::vector<GridAnchorParameters> mParam;
     int* mNumPriors;
     Weights *mDeviceWidths, *mDeviceHeights;
-    const char* mPluginNamespace;
+    std::string mPluginNamespace;
 };
 
 class GridAnchorPluginCreator : public BaseCreator
diff --git a/plugin/groupNormalizationPlugin/CMakeLists.txt b/plugin/groupNormalizationPlugin/CMakeLists.txt
new file mode 100644
index 00000000..16bc6f10
--- /dev/null
+++ b/plugin/groupNormalizationPlugin/CMakeLists.txt
@@ -0,0 +1,18 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+file(GLOB SRCS *.cpp *.cu)
+set(PLUGIN_SOURCES ${PLUGIN_SOURCES} ${SRCS})
+set(PLUGIN_SOURCES ${PLUGIN_SOURCES} PARENT_SCOPE)
diff --git a/plugin/groupNormalizationPlugin/groupNormalizationKernel.cu b/plugin/groupNormalizationPlugin/groupNormalizationKernel.cu
new file mode 100644
index 00000000..ae6bfe08
--- /dev/null
+++ b/plugin/groupNormalizationPlugin/groupNormalizationKernel.cu
@@ -0,0 +1,62 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "groupNormalizationPlugin.h"
+
+namespace nvinfer1
+{
+namespace plugin
+{
+
+template <typename T, unsigned TPB>
+__global__ void scaleShiftChannelsInplaceKernel(T* inOut, const int ld, const float* beta, const float* gamma)
+{
+    // grid is blocks x C x B
+    // ld should be H*W
+    // blockIdx.z = batch
+    // blockIdx.y = channel
+    // blockIdx.x = block per col
+    const T b = beta[blockIdx.y];
+    const T g = gamma[blockIdx.y];
+
+    const int offset = (blockIdx.z * gridDim.y + blockIdx.y) * ld;
+
+    const int tx = blockIdx.x * TPB + threadIdx.x;
+
+    if (tx < ld)
+    {
+        inOut[offset + tx] = g * inOut[offset + tx] + b;
+    }
+}
+
+template <typename T>
+void scaleShiftChannelsInplace(T* inOut, const int B, const int C, const int channelVolume, const float* beta,
+    const float* gamma, cudaStream_t stream)
+{
+
+    constexpr int TPB = 256;
+    const int colBlocks = (channelVolume + TPB - 1) / TPB;
+    const dim3 grid(colBlocks, C, B);
+
+    scaleShiftChannelsInplaceKernel<T, TPB><<<grid, TPB, 0, stream>>>(inOut, channelVolume, beta, gamma);
+
+    CUASSERT(cudaPeekAtLastError());
+}
+
+template void scaleShiftChannelsInplace<float>(float* inOut, const int B, const int C, const int channelVolume, const float* beta,
+    const float* gamma, cudaStream_t stream);
+} /* plugin */
+} /* nvinfer1 */
diff --git a/plugin/groupNormalizationPlugin/groupNormalizationPlugin.cpp b/plugin/groupNormalizationPlugin/groupNormalizationPlugin.cpp
new file mode 100644
index 00000000..379e2540
--- /dev/null
+++ b/plugin/groupNormalizationPlugin/groupNormalizationPlugin.cpp
@@ -0,0 +1,316 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions andcreate
+ * limitations under the License.
+ */
+
+#include "groupNormalizationPlugin.h"
+#include <numeric>
+#include <stdexcept>
+
+using namespace nvinfer1;
+using nvinfer1::plugin::GroupNormalizationPlugin;
+using nvinfer1::plugin::GroupNormalizationPluginCreator;
+
+#define CHECK_CUDNN(call)                                                                                              \
+    do                                                                                                                 \
+    {                                                                                                                  \
+        cudnnStatus_t status = call;                                                                                   \
+        if (status != CUDNN_STATUS_SUCCESS)                                                                            \
+        {                                                                                                              \
+            return status;                                                                                             \
+        }                                                                                                              \
+    } while (0)
+
+namespace
+{
+constexpr const char* GROUP_NORM_VERSION{"1"};
+constexpr const char* GROUP_NORM_NAME{"GroupNormalizationPlugin"};
+} // namespace
+
+// // Static class fields initialization
+PluginFieldCollection GroupNormalizationPluginCreator::mFC{};
+std::vector<nvinfer1::PluginField> GroupNormalizationPluginCreator::mPluginAttributes;
+
+REGISTER_TENSORRT_PLUGIN(GroupNormalizationPluginCreator);
+
+GroupNormalizationPlugin::GroupNormalizationPlugin(float epsilon, int nbGroups)
+    : mEpsilon(epsilon)
+    , mNbGroups(nbGroups)
+{
+    // Number of groups should be positive
+    assert(nbGroups > 0);
+}
+
+int GroupNormalizationPlugin::initialize()
+{
+    return 0;
+}
+
+GroupNormalizationPlugin::GroupNormalizationPlugin(const void* data, size_t length)
+{
+    // Deserialize in the same order as serialization
+    deserialize_value(&data, &length, &mEpsilon);
+    deserialize_value(&data, &length, &mNbGroups);
+}
+
+const char* GroupNormalizationPlugin::getPluginType() const
+{
+    return GROUP_NORM_NAME;
+}
+
+const char* GroupNormalizationPlugin::getPluginVersion() const
+{
+    return GROUP_NORM_VERSION;
+}
+
+int GroupNormalizationPlugin::getNbOutputs() const
+{
+    return 1;
+}
+
+nvinfer1::DimsExprs GroupNormalizationPlugin::getOutputDimensions(
+    int index, const nvinfer1::DimsExprs* inputs, int nbInputs, nvinfer1::IExprBuilder& exprBuilder)
+{
+    // Input (from previous layer), scale and bias are the three inputs to the plugin.
+    assert(nbInputs == 3);
+    assert(index == 0);
+    nvinfer1::DimsExprs output(inputs[0]);
+    return output;
+}
+
+void GroupNormalizationPlugin::attachToContext(
+    cudnnContext* cudnnContext, cublasContext* cublasContext, IGpuAllocator* gpuAllocator)
+{
+    _cudnn_handle = cudnnContext;
+    cudnnCreateTensorDescriptor(&desc);
+    cudnnCreateTensorDescriptor(&bnDesc);
+}
+
+// Detach the plugin object from its execution context.
+void GroupNormalizationPlugin::detachFromContext()
+{
+    cudnnDestroyTensorDescriptor(desc);
+    cudnnDestroyTensorDescriptor(bnDesc);
+}
+
+int GroupNormalizationPlugin::enqueue(const nvinfer1::PluginTensorDesc* inputDesc,
+    const nvinfer1::PluginTensorDesc* outputDesc, const void* const* inputs, void* const* outputs, void* workspace,
+    cudaStream_t stream)
+{
+    // Get the input dimensions
+    nvinfer1::Dims input_dims = inputDesc[0].dims;
+    int batchSize = input_dims.d[0];
+    int nbChannels = input_dims.d[1];
+
+    // Calculate size of each group
+    int groupSize = nbChannels / mNbGroups;
+
+    mChannelVolume
+        = std::accumulate(input_dims.d + 2, input_dims.d + inputDesc[0].dims.nbDims, 1, std::multiplies<int>());
+
+    CHECK_CUDNN(cudnnSetTensor4dDescriptor(desc, // descriptor
+        CUDNN_TENSOR_NCHW,                       // tensor format
+        CUDNN_DATA_FLOAT,                        // type
+        1,                                       // Batchsize
+        batchSize * mNbGroups,                   // Channels
+        groupSize,                               // Height
+        mChannelVolume                           // Width
+        ));
+
+    cudnnDeriveBNTensorDescriptor(bnDesc, desc, CUDNN_BATCHNORM_SPATIAL);
+    CHECK_CUDNN(cudnnSetStream(_cudnn_handle, stream));
+
+    // Reshape the data according in the cudnnSetTensor4dDescriptor.
+    float a = 1.f;
+    float b = 0.f;
+    CHECK_CUDNN(cudnnBatchNormalizationForwardTraining(_cudnn_handle, // handle
+        CUDNN_BATCHNORM_SPATIAL,                                      // BatchNormMode_t, try also non persistent
+        &a,                                                           //
+        &b,                                                           //
+        desc,                                                         // in/out descriptor
+        inputs[0],                                                    // input
+        desc,                                                         // in/out descriptor
+        outputs[0],                                                   // output
+        bnDesc,                                                       //
+        bnScale,                                                      // 1
+        bnBias,                                                       // 0
+        0.0,                                                          // exponential average factor
+        nullptr,                                                      // resultRunningMean
+        nullptr,                                                      // resultRunningVar
+        mEpsilon,                                                     //  eps
+        nullptr,                                                      // resultSaveMean
+        nullptr                                                       // resultSaveInvVar
+        ));
+
+    float* output = static_cast<float*>(outputs[0]);
+    scaleShiftChannelsInplace(output, batchSize, nbChannels, mChannelVolume, static_cast<const float*>(inputs[2]),
+        static_cast<const float*>(inputs[1]), stream); // mBetaDev, mGammaDev,
+    return 0;
+}
+
+size_t GroupNormalizationPlugin::getSerializationSize() const
+{
+    return sizeof(mNbGroups) + sizeof(mEpsilon);
+}
+
+void GroupNormalizationPlugin::serialize(void* buffer) const
+{
+    serialize_value(&buffer, mEpsilon);
+    serialize_value(&buffer, mNbGroups);
+}
+
+bool GroupNormalizationPlugin::supportsFormatCombination(
+    int pos, const nvinfer1::PluginTensorDesc* inOut, int nbInputs, int nbOutputs)
+{
+    assert(inOut && pos < (nbInputs + nbOutputs));
+    return ((inOut[pos].type == nvinfer1::DataType::kFLOAT) && inOut[pos].format == nvinfer1::PluginFormat::kNCHW
+        && inOut[pos].type == inOut[0].type);
+}
+
+void GroupNormalizationPlugin::terminate()
+{
+    cudaFree(bnScale);
+    cudaFree(bnBias);
+}
+
+void GroupNormalizationPlugin::destroy()
+{
+    // This gets called when the network containing plugin is destroyed
+    delete this;
+}
+
+IPluginV2DynamicExt* GroupNormalizationPlugin::clone() const
+{
+    auto* plugin = new GroupNormalizationPlugin(mEpsilon, mNbGroups);
+    plugin->setPluginNamespace(mPluginNamespace);
+    return plugin;
+}
+
+void GroupNormalizationPlugin::configurePlugin(const nvinfer1::DynamicPluginTensorDesc* in, int nbInputs,
+    const nvinfer1::DynamicPluginTensorDesc* out, int nbOutputs)
+{
+
+    for (int i = 0; i < nbInputs; i++)
+    {
+        for (int j = 0; j < in[0].desc.dims.nbDims; j++)
+        {
+            // Do not support dynamic dimensions
+            assert(in[0].desc.dims.d[j] != -1);
+        }
+    }
+
+    int batchSize = in[0].desc.dims.d[0];
+    int nbChannels = in[0].desc.dims.d[1];
+
+    // Allocate device memory and initialize scale and bias values
+    cudaMalloc(&bnScale, batchSize * nbChannels * sizeof(float));
+    cudaMalloc(&bnBias, batchSize * nbChannels * sizeof(float));
+
+    // allot ones and zeros to bn parameters
+    std::vector<float> ones(nbChannels, 1.f);
+    cudaMemcpy(bnScale, ones.data(), nbChannels * sizeof(float), cudaMemcpyHostToDevice);
+
+    std::vector<float> zeroes(nbChannels, 0.f);
+    cudaMemcpy(bnBias, zeroes.data(), nbChannels * sizeof(float), cudaMemcpyHostToDevice);
+}
+
+nvinfer1::DataType GroupNormalizationPlugin::getOutputDataType(
+    int index, const nvinfer1::DataType* inputTypes, int nbInputs) const
+{
+    assert(inputTypes && nbInputs > 0 && index == 0);
+    return inputTypes[0];
+}
+
+size_t GroupNormalizationPlugin::getWorkspaceSize(const nvinfer1::PluginTensorDesc* inputs, int nbInputs,
+    const nvinfer1::PluginTensorDesc* outputs, int nbOutputs) const
+{
+    return 0;
+}
+
+void GroupNormalizationPlugin::setPluginNamespace(const char* libNamespace)
+{
+    mPluginNamespace = libNamespace;
+}
+
+const char* GroupNormalizationPlugin::getPluginNamespace() const
+{
+    return mPluginNamespace;
+}
+
+GroupNormalizationPluginCreator::GroupNormalizationPluginCreator()
+{
+    mPluginAttributes.emplace_back(PluginField("eps", nullptr, PluginFieldType::kFLOAT32, 1));
+    mPluginAttributes.emplace_back(PluginField("num_groups", nullptr, PluginFieldType::kINT32, 1));
+
+    mFC.nbFields = mPluginAttributes.size();
+    mFC.fields = mPluginAttributes.data();
+}
+
+const char* GroupNormalizationPluginCreator::getPluginName() const
+{
+    return GROUP_NORM_NAME;
+}
+
+const char* GroupNormalizationPluginCreator::getPluginVersion() const
+{
+    return GROUP_NORM_VERSION;
+}
+
+const PluginFieldCollection* GroupNormalizationPluginCreator::getFieldNames()
+{
+    return &mFC;
+}
+
+const char* GroupNormalizationPluginCreator::getPluginNamespace() const
+{
+    return mNamespace.c_str();
+}
+
+void GroupNormalizationPluginCreator::setPluginNamespace(const char* libNamespace)
+{
+    mNamespace = libNamespace;
+}
+
+IPluginV2DynamicExt* GroupNormalizationPluginCreator::createPlugin(const char* name, const PluginFieldCollection* fc)
+{
+    // Set default values
+    int nbGroups{1};
+    float epsilon{0.00001f};
+    for (int i = 0; i < fc->nbFields; i++)
+    {
+        std::string field_name(fc->fields[i].name);
+        if (field_name.compare("eps") == 0)
+        {
+            epsilon = *static_cast<const float*>(fc->fields[i].data);
+        }
+        if (field_name.compare("num_groups") == 0)
+        {
+            nbGroups = *static_cast<const int*>(fc->fields[i].data);
+        }
+    }
+
+    GroupNormalizationPlugin* plugin = new GroupNormalizationPlugin(epsilon, nbGroups);
+    plugin->setPluginNamespace(mNamespace.c_str());
+
+    return plugin;
+}
+
+IPluginV2DynamicExt* GroupNormalizationPluginCreator::deserializePlugin(
+    const char* name, const void* serialData, size_t serialLength)
+{
+    GroupNormalizationPlugin* plugin = new GroupNormalizationPlugin(serialData, serialLength);
+    plugin->setPluginNamespace(mNamespace.c_str());
+
+    return plugin;
+}
diff --git a/plugin/groupNormalizationPlugin/groupNormalizationPlugin.h b/plugin/groupNormalizationPlugin/groupNormalizationPlugin.h
new file mode 100644
index 00000000..87891863
--- /dev/null
+++ b/plugin/groupNormalizationPlugin/groupNormalizationPlugin.h
@@ -0,0 +1,148 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef TRT_GROUP_NORM_PLUGIN_H
+#define TRT_GROUP_NORM_PLUGIN_H
+
+#include "plugin.h"
+#include "serialize.hpp"
+#include <cudnn.h>
+#include <iostream>
+#include <string>
+#include <vector>
+
+// One of the preferred ways of making TensorRT to be able to see
+// our custom layer requires extending IPluginV2 and IPluginCreator classes.
+// For requirements for overriden functions, check TensorRT API docs.
+namespace nvinfer1
+{
+namespace plugin
+{
+
+template <typename T>
+void scaleShiftChannelsInplace(T* inOut, const int B, const int C, const int channelVolume, const float* beta,
+    const float* gamma, cudaStream_t stream);
+
+class GroupNormalizationPlugin final : public nvinfer1::IPluginV2DynamicExt
+{
+public:
+    GroupNormalizationPlugin(float epsilon, const int nbGroups);
+
+    GroupNormalizationPlugin(const void* data, size_t length);
+
+    // It doesn't make sense to make GroupNormalizationPlugin without arguments, so we
+    // delete default constructor.
+    GroupNormalizationPlugin() = delete;
+
+    int getNbOutputs() const override;
+
+    // DynamicExt plugins returns DimsExprs class instead of Dims
+    DimsExprs getOutputDimensions(
+        int index, const nvinfer1::DimsExprs* inputs, int nbInputDims, nvinfer1::IExprBuilder& exprBuilder) override;
+
+    int initialize() override;
+
+    void terminate() override;
+
+    size_t getWorkspaceSize(const nvinfer1::PluginTensorDesc* inputs, int nbInputs,
+        const nvinfer1::PluginTensorDesc* outputs, int nbOutputs) const override;
+
+    int enqueue(const nvinfer1::PluginTensorDesc* inputDesc, const nvinfer1::PluginTensorDesc* outputDesc,
+        const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) override;
+
+    size_t getSerializationSize() const override;
+
+    void serialize(void* buffer) const override;
+
+    bool supportsFormatCombination(
+        int pos, const nvinfer1::PluginTensorDesc* inOut, int nbInputs, int nbOutputs) override;
+
+    const char* getPluginType() const override;
+
+    const char* getPluginVersion() const override;
+
+    nvinfer1::IPluginV2DynamicExt* clone() const override;
+
+    void destroy() override;
+
+    DataType getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const override;
+
+    void attachToContext(cudnnContext* cudnn, cublasContext* cublas, nvinfer1::IGpuAllocator* allocator) override;
+
+    void detachFromContext() override;
+
+    void setPluginNamespace(const char* pluginNamespace) override;
+
+    const char* getPluginNamespace() const override;
+
+    void configurePlugin(const nvinfer1::DynamicPluginTensorDesc* in, int nbInputs,
+        const nvinfer1::DynamicPluginTensorDesc* out, int nbOutputs) override;
+
+private:
+    const char* mPluginNamespace;
+    std::string mNamespace;
+
+    float mEpsilon;
+    int mNbGroups;
+    int mChannelVolume;
+
+    cudnnHandle_t _cudnn_handle;
+    cudnnTensorDescriptor_t desc, bnDesc; // describes input and output
+    // These are buffers initialized to 1 and 0 respectively
+    void* bnScale;
+    void* bnBias;
+
+protected:
+    // To prevent compiler warnings.
+    using nvinfer1::IPluginV2DynamicExt::canBroadcastInputAcrossBatch;
+    using nvinfer1::IPluginV2DynamicExt::configurePlugin;
+    using nvinfer1::IPluginV2DynamicExt::enqueue;
+    using nvinfer1::IPluginV2DynamicExt::getOutputDimensions;
+    using nvinfer1::IPluginV2DynamicExt::getWorkspaceSize;
+    using nvinfer1::IPluginV2DynamicExt::isOutputBroadcastAcrossBatch;
+    using nvinfer1::IPluginV2DynamicExt::supportsFormat;
+};
+
+class GroupNormalizationPluginCreator : public IPluginCreator
+{
+public:
+    GroupNormalizationPluginCreator();
+
+    ~GroupNormalizationPluginCreator() override = default;
+
+    const char* getPluginName() const override;
+
+    const char* getPluginVersion() const override;
+
+    const PluginFieldCollection* getFieldNames() override;
+
+    IPluginV2DynamicExt* createPlugin(const char* name, const PluginFieldCollection* fc) override;
+
+    IPluginV2DynamicExt* deserializePlugin(const char* name, const void* serialData, size_t serialLength) override;
+
+    void setPluginNamespace(const char* pluginNamespace) override;
+
+    const char* getPluginNamespace() const override;
+
+private:
+    static PluginFieldCollection mFC;
+    static std::vector<PluginField> mPluginAttributes;
+    std::string mNamespace;
+};
+} // namespace plugin
+} // namespace nvinfer1
+
+#endif // TRT_GROUP_NORM_PLUGIN_H
diff --git a/plugin/instanceNormalizationPlugin/CMakeLists.txt b/plugin/instanceNormalizationPlugin/CMakeLists.txt
index de8fff17..85d8ce4e 100644
--- a/plugin/instanceNormalizationPlugin/CMakeLists.txt
+++ b/plugin/instanceNormalizationPlugin/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/plugin/instanceNormalizationPlugin/instanceNormalizationPlugin.cpp b/plugin/instanceNormalizationPlugin/instanceNormalizationPlugin.cpp
index 14a678f6..740ce02e 100644
--- a/plugin/instanceNormalizationPlugin/instanceNormalizationPlugin.cpp
+++ b/plugin/instanceNormalizationPlugin/instanceNormalizationPlugin.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -13,8 +13,9 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include <stdexcept>
 #include "instanceNormalizationPlugin.h"
+#include <cuda_fp16.h>
+#include <stdexcept>
 
 using namespace nvinfer1;
 using nvinfer1::plugin::InstanceNormalizationPlugin;
@@ -46,27 +47,6 @@ inline bool is_CHW(nvinfer1::Dims const& dims)
         && dims.type[1] == nvinfer1::DimensionType::kSPATIAL && dims.type[2] == nvinfer1::DimensionType::kSPATIAL);
 }
 
-// This is derived from: https://fgiesen.wordpress.com/2012/03/28/half-to-float-done-quic/
-inline float half_to_float_fast(unsigned short value)
-{
-    union F32
-    {
-        unsigned int u;
-        float f;
-    };
-    static const F32 magic = {(254 - 15) << 23};
-    static const F32 was_infnan = {(127 + 16) << 23};
-    F32 result;
-    result.u = (value & 0x7fff) << 13; // exponent/mantissa bits
-    result.f *= magic.f;               // exponent adjust
-    if (result.f >= was_infnan.f)
-    { // make sure Inf/NaN survive
-        result.u |= 255 << 23;
-    }
-    result.u |= (value & 0x8000) << 16; // sign bit
-    return result.f;
-}
-
 cudnnStatus_t convert_trt2cudnn_dtype(nvinfer1::DataType trt_dtype, cudnnDataType_t* cudnn_dtype)
 {
     switch (trt_dtype)
@@ -78,22 +58,24 @@ cudnnStatus_t convert_trt2cudnn_dtype(nvinfer1::DataType trt_dtype, cudnnDataTyp
     return CUDNN_STATUS_SUCCESS;
 }
 
-namespace {
-    constexpr const char* INSTANCE_PLUGIN_VERSION{"001"};
-    constexpr const char* INSTANCE_PLUGIN_NAME{"InstanceNormalization_TRT"};
-}
+namespace
+{
+constexpr const char* INSTANCE_PLUGIN_VERSION{"1"};
+constexpr const char* INSTANCE_PLUGIN_NAME{"InstanceNormalization_TRT"};
+} // namespace
 
 PluginFieldCollection InstanceNormalizationPluginCreator::mFC{};
 std::vector<PluginField> InstanceNormalizationPluginCreator::mPluginAttributes;
 
-
 InstanceNormalizationPlugin::InstanceNormalizationPlugin(
     float epsilon, const std::vector<float>& scale, const std::vector<float>& bias)
     : _epsilon(epsilon)
     , _nchan(scale.size())
     , _h_scale(scale)
     , _h_bias(bias)
-    , _initialized(false)
+    , _d_scale(nullptr)
+    , _d_bias(nullptr)
+    , _d_bytes(0)
 {
     ASSERT(scale.size() == bias.size());
 }
@@ -102,7 +84,9 @@ InstanceNormalizationPlugin::InstanceNormalizationPlugin(
     float epsilon, nvinfer1::Weights const& scale, nvinfer1::Weights const& bias)
     : _epsilon(epsilon)
     , _nchan(scale.count)
-    , _initialized(false)
+    , _d_scale(nullptr)
+    , _d_bias(nullptr)
+    , _d_bytes(0)
 {
     ASSERT(scale.count == bias.count);
     if (scale.type == nvinfer1::DataType::kFLOAT)
@@ -115,7 +99,7 @@ InstanceNormalizationPlugin::InstanceNormalizationPlugin(
         for (int c = 0; c < _nchan; ++c)
         {
             unsigned short value = ((unsigned short*) scale.values)[c];
-            _h_scale.push_back(half_to_float_fast(value));
+            _h_scale.push_back(__internal_half2float(value));
         }
     }
     else
@@ -132,7 +116,7 @@ InstanceNormalizationPlugin::InstanceNormalizationPlugin(
         for (int c = 0; c < _nchan; ++c)
         {
             unsigned short value = ((unsigned short*) bias.values)[c];
-            _h_bias.push_back(half_to_float_fast(value));
+            _h_bias.push_back(__internal_half2float(value));
         }
     }
     else
@@ -141,7 +125,7 @@ InstanceNormalizationPlugin::InstanceNormalizationPlugin(
     }
 }
 
-InstanceNormalizationPlugin::InstanceNormalizationPlugin(void const* serialData, size_t serialLength) : _initialized(false)
+InstanceNormalizationPlugin::InstanceNormalizationPlugin(void const* serialData, size_t serialLength)
 {
     deserialize_value(&serialData, &serialLength, &_epsilon);
     deserialize_value(&serialData, &serialLength, &_nchan);
@@ -169,33 +153,21 @@ DimsExprs InstanceNormalizationPlugin::getOutputDimensions(
 
 int InstanceNormalizationPlugin::initialize()
 {
-    _initialized = true;
-    CHECK_CUDNN(cudnnCreate(&_cudnn_handle));
-    CHECK_CUDNN(cudnnCreateTensorDescriptor(&_b_desc));
-    CHECK_CUDNN(cudnnCreateTensorDescriptor(&_x_desc));
-    CHECK_CUDNN(cudnnCreateTensorDescriptor(&_y_desc));
     return 0;
 }
 
 void InstanceNormalizationPlugin::terminate()
 {
-    if (!_initialized)
-    {
-        return;
-    }
-    cudnnDestroyTensorDescriptor(_y_desc);
-    cudnnDestroyTensorDescriptor(_x_desc);
-    cudnnDestroyTensorDescriptor(_b_desc);
-    cudnnDestroy(_cudnn_handle);
-    _initialized = false;
+    cudaFree(_d_bias);
+    cudaFree(_d_scale);
 }
 
-size_t InstanceNormalizationPlugin::getWorkspaceSize(const nvinfer1::PluginTensorDesc* inputs, int nbInputs, const nvinfer1::PluginTensorDesc* outputs, int nbOutputs) const 
-{ 
-    return 0; 
+size_t InstanceNormalizationPlugin::getWorkspaceSize(const nvinfer1::PluginTensorDesc* inputs, int nbInputs,
+    const nvinfer1::PluginTensorDesc* outputs, int nbOutputs) const
+{
+    return 0;
 }
 
-
 int InstanceNormalizationPlugin::enqueue(const nvinfer1::PluginTensorDesc* inputDesc,
     const nvinfer1::PluginTensorDesc* outputDesc, const void* const* inputs, void* const* outputs, void* workspace,
     cudaStream_t stream)
@@ -209,8 +181,14 @@ int InstanceNormalizationPlugin::enqueue(const nvinfer1::PluginTensorDesc* input
 
     // Note: We repeat the data for each batch entry so that we can do the full
     //       computation in a single CUDNN call in enqueue().
-    CHECK_CUDA(cudaMalloc((void**) &_d_scale, n * nchan_bytes));
-    CHECK_CUDA(cudaMalloc((void**) &_d_bias, n * nchan_bytes));
+    if (_d_bytes < n * nchan_bytes)
+    {
+        cudaFree(_d_bias);
+        cudaFree(_d_scale);
+        _d_bytes = n * nchan_bytes;
+        CHECK_CUDA(cudaMalloc((void**) &_d_scale, _d_bytes));
+        CHECK_CUDA(cudaMalloc((void**) &_d_bias, _d_bytes));
+    }
     for (int i = 0; i < n; ++i)
     {
         CHECK_CUDA(cudaMemcpy(_d_scale + i * c, _h_scale.data(), nchan_bytes, cudaMemcpyHostToDevice));
@@ -218,7 +196,7 @@ int InstanceNormalizationPlugin::enqueue(const nvinfer1::PluginTensorDesc* input
     }
 
     CHECK_CUDNN(cudnnSetTensor4dDescriptor(_b_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, n * c, 1, 1));
-    cudnnDataType_t cudnn_dtype;
+    cudnnDataType_t cudnn_dtype{};
     CHECK_CUDNN(convert_trt2cudnn_dtype(inputDesc[0].type, &cudnn_dtype));
     CHECK_CUDNN(cudnnSetTensor4dDescriptor(_x_desc, CUDNN_TENSOR_NCHW, cudnn_dtype, 1, n * c, h, w));
     CHECK_CUDNN(cudnnSetTensor4dDescriptor(_y_desc, CUDNN_TENSOR_NCHW, cudnn_dtype, 1, n * c, h, w));
@@ -233,20 +211,15 @@ int InstanceNormalizationPlugin::enqueue(const nvinfer1::PluginTensorDesc* input
     //       acceptable.
     CHECK_CUDNN(cudnnBatchNormalizationForwardTraining(_cudnn_handle, CUDNN_BATCHNORM_SPATIAL_PERSISTENT, &alpha, &beta,
         _x_desc, x_ptr, _y_desc, y_ptr, _b_desc, _d_scale, _d_bias, 1., nullptr, nullptr, _epsilon, nullptr, nullptr));
-    cudaFree(_d_bias);
-    cudaFree(_d_scale);
     return 0;
 }
 
 size_t InstanceNormalizationPlugin::getSerializationSize() const
 {
-    return (serialized_size(_epsilon) +
-            serialized_size(_nchan) +
-            serialized_size(_h_scale) +
-            serialized_size(_h_bias));
+    return (serialized_size(_epsilon) + serialized_size(_nchan) + serialized_size(_h_scale) + serialized_size(_h_bias));
 }
 
-void InstanceNormalizationPlugin::serialize(void *buffer) const
+void InstanceNormalizationPlugin::serialize(void* buffer) const
 {
     serialize_value(&buffer, _epsilon);
     serialize_value(&buffer, _nchan);
@@ -273,14 +246,14 @@ const char* InstanceNormalizationPlugin::getPluginVersion() const
 }
 
 void InstanceNormalizationPlugin::destroy()
-{ 
+{
     delete this;
 }
 
 IPluginV2DynamicExt* InstanceNormalizationPlugin::clone() const
-{ 
-    auto plugin = new InstanceNormalizationPlugin{_epsilon, _h_scale, _h_bias};
-    plugin->setPluginNamespace(mPluginNamespace);
+{
+    auto* plugin = new InstanceNormalizationPlugin{_epsilon, _h_scale, _h_bias};
+    plugin->setPluginNamespace(mPluginNamespace.c_str());
     return plugin;
 }
 
@@ -292,7 +265,7 @@ void InstanceNormalizationPlugin::setPluginNamespace(const char* pluginNamespace
 
 const char* InstanceNormalizationPlugin::getPluginNamespace() const
 {
-    return mPluginNamespace;
+    return mPluginNamespace.c_str();
 }
 
 nvinfer1::DataType InstanceNormalizationPlugin::getOutputDataType(
@@ -303,23 +276,47 @@ nvinfer1::DataType InstanceNormalizationPlugin::getOutputDataType(
 }
 
 // Attach the plugin object to an execution context and grant the plugin the access to some context resource.
-void InstanceNormalizationPlugin::attachToContext(cudnnContext* cudnnContext, cublasContext* cublasContext, IGpuAllocator* gpuAllocator)
+void InstanceNormalizationPlugin::attachToContext(
+    cudnnContext* cudnnContext, cublasContext* cublasContext, IGpuAllocator* gpuAllocator)
 {
+    _cudnn_handle = cudnnContext;
+    cudnnCreateTensorDescriptor(&_b_desc);
+    cudnnCreateTensorDescriptor(&_x_desc);
+    cudnnCreateTensorDescriptor(&_y_desc);
 }
 
 // Detach the plugin object from its execution context.
-void InstanceNormalizationPlugin::detachFromContext() {}
+void InstanceNormalizationPlugin::detachFromContext()
+{
+    cudnnDestroyTensorDescriptor(_y_desc);
+    cudnnDestroyTensorDescriptor(_x_desc);
+    cudnnDestroyTensorDescriptor(_b_desc);
+}
 
 void InstanceNormalizationPlugin::configurePlugin(const nvinfer1::DynamicPluginTensorDesc* in, int nbInputs,
     const nvinfer1::DynamicPluginTensorDesc* out, int nbOutputs)
 {
+    auto input_dims = in[0].desc.dims;
     for (int i = 0; i < nbInputs; i++)
     {
-      for (int j = 0; j < in[0].desc.dims.nbDims; j++)
-      {
-        // Do not support dynamic dimensions
-        ASSERT(in[0].desc.dims.d[j] != -1);
-      }
+        for (int j = 0; j < input_dims.nbDims; j++)
+        {
+            // Do not support dynamic dimensions
+            ASSERT(input_dims.d[j] != -1);
+        }
+    }
+
+    int n = input_dims.d[0];
+    int c = input_dims.d[1];
+    size_t nchan_bytes = c * sizeof(float);
+
+    if (_d_bytes < n * nchan_bytes)
+    {
+        cudaFree(_d_bias);
+        cudaFree(_d_scale);
+        _d_bytes = n * nchan_bytes;
+        cudaMalloc((void**) &_d_scale, _d_bytes);
+        cudaMalloc((void**) &_d_bias, _d_bytes);
     }
 }
 
@@ -349,11 +346,12 @@ const PluginFieldCollection* InstanceNormalizationPluginCreator::getFieldNames()
     return &mFC;
 }
 
-IPluginV2DynamicExt* InstanceNormalizationPluginCreator::createPlugin(const char* name, const nvinfer1::PluginFieldCollection* fc)
+IPluginV2DynamicExt* InstanceNormalizationPluginCreator::createPlugin(
+    const char* name, const nvinfer1::PluginFieldCollection* fc)
 {
     std::vector<float> scaleValues;
     std::vector<float> biasValues;
-    float epsilon {};
+    float epsilon{};
     const PluginField* fields = fc->fields;
     for (int i = 0; i < fc->nbFields; ++i)
     {
@@ -361,7 +359,7 @@ IPluginV2DynamicExt* InstanceNormalizationPluginCreator::createPlugin(const char
         if (!strcmp(attrName, "epsilon"))
         {
             ASSERT(fields[i].type == PluginFieldType::kFLOAT32);
-            epsilon= *(static_cast<const float*>(fields[i].data));
+            epsilon = *(static_cast<const float*>(fields[i].data));
         }
         else if (!strcmp(attrName, "scales"))
         {
@@ -397,9 +395,10 @@ IPluginV2DynamicExt* InstanceNormalizationPluginCreator::createPlugin(const char
     return obj;
 }
 
-IPluginV2DynamicExt* InstanceNormalizationPluginCreator::deserializePlugin(const char* name, const void* serialData, size_t serialLength)
+IPluginV2DynamicExt* InstanceNormalizationPluginCreator::deserializePlugin(
+    const char* name, const void* serialData, size_t serialLength)
 {
-    InstanceNormalizationPlugin* obj = new InstanceNormalizationPlugin{serialData, serialLength}; 
+    InstanceNormalizationPlugin* obj = new InstanceNormalizationPlugin{serialData, serialLength};
     obj->setPluginNamespace(mNamespace.c_str());
     return obj;
 }
diff --git a/plugin/instanceNormalizationPlugin/instanceNormalizationPlugin.h b/plugin/instanceNormalizationPlugin/instanceNormalizationPlugin.h
index 9cd48d7c..9df8c0c4 100644
--- a/plugin/instanceNormalizationPlugin/instanceNormalizationPlugin.h
+++ b/plugin/instanceNormalizationPlugin/instanceNormalizationPlugin.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -15,111 +15,112 @@
  */
 #ifndef TRT_INSTANCE_NORMALIZATION_PLUGIN_H
 #define TRT_INSTANCE_NORMALIZATION_PLUGIN_H
-#include "serialize.hpp"
 #include "plugin.h"
+#include "serialize.hpp"
 #include <cudnn.h>
-#include <vector>
 #include <iostream>
 #include <string>
+#include <vector>
 
 typedef unsigned short half_type;
 
-namespace nvinfer1 
+namespace nvinfer1
 {
-namespace plugin 
+namespace plugin
 {
 class InstanceNormalizationPlugin final : public nvinfer1::IPluginV2DynamicExt
 {
 
 public:
-  InstanceNormalizationPlugin(float epsilon, nvinfer1::Weights const& scale, nvinfer1::Weights const& bias);
-  InstanceNormalizationPlugin(float epsilon, const std::vector<float>& scale, const std::vector<float>& bias);
-  InstanceNormalizationPlugin(void const* serialData, size_t serialLength);
+    InstanceNormalizationPlugin(float epsilon, nvinfer1::Weights const& scale, nvinfer1::Weights const& bias);
+    InstanceNormalizationPlugin(float epsilon, const std::vector<float>& scale, const std::vector<float>& bias);
+    InstanceNormalizationPlugin(void const* serialData, size_t serialLength);
 
-  InstanceNormalizationPlugin() = delete;
+    InstanceNormalizationPlugin() = delete;
 
-  ~InstanceNormalizationPlugin() override;
+    ~InstanceNormalizationPlugin() override;
 
-  int getNbOutputs() const override;
+    int getNbOutputs() const override;
 
-  // DynamicExt plugins returns DimsExprs class instead of Dims
-  DimsExprs getOutputDimensions(int outputIndex, const nvinfer1::DimsExprs* inputs, int nbInputs, nvinfer1::IExprBuilder& exprBuilder) override;
+    // DynamicExt plugins returns DimsExprs class instead of Dims
+    DimsExprs getOutputDimensions(
+        int outputIndex, const nvinfer1::DimsExprs* inputs, int nbInputs, nvinfer1::IExprBuilder& exprBuilder) override;
 
-  int initialize() override;
+    int initialize() override;
 
-  void terminate() override;
+    void terminate() override;
 
-  size_t getWorkspaceSize(const nvinfer1::PluginTensorDesc* inputs, int nbInputs, const nvinfer1::PluginTensorDesc* outputs, int nbOutputs) const override;
+    size_t getWorkspaceSize(const nvinfer1::PluginTensorDesc* inputs, int nbInputs,
+        const nvinfer1::PluginTensorDesc* outputs, int nbOutputs) const override;
 
-  int enqueue(const nvinfer1::PluginTensorDesc* inputDesc, const nvinfer1::PluginTensorDesc* outputDesc, 
-              const void* const* inputs, void* const* outputs, 
-              void* workspace, 
-              cudaStream_t stream) override;
+    int enqueue(const nvinfer1::PluginTensorDesc* inputDesc, const nvinfer1::PluginTensorDesc* outputDesc,
+        const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) override;
 
-  size_t getSerializationSize() const override;
+    size_t getSerializationSize() const override;
 
-  void serialize(void* buffer) const override;
+    void serialize(void* buffer) const override;
 
-  // DynamicExt plugin supportsFormat update.
-  bool supportsFormatCombination(int pos, const nvinfer1::PluginTensorDesc* inOut, int nbInputs, int nbOutputs) override;
+    // DynamicExt plugin supportsFormat update.
+    bool supportsFormatCombination(
+        int pos, const nvinfer1::PluginTensorDesc* inOut, int nbInputs, int nbOutputs) override;
 
-  const char* getPluginType() const override;
+    const char* getPluginType() const override;
 
-  const char* getPluginVersion() const override;
+    const char* getPluginVersion() const override;
 
-  void destroy() override;
+    void destroy() override;
 
-  nvinfer1::IPluginV2DynamicExt* clone() const override;
+    nvinfer1::IPluginV2DynamicExt* clone() const override;
 
-  void setPluginNamespace(const char* pluginNamespace) override;
+    void setPluginNamespace(const char* pluginNamespace) override;
 
-  const char* getPluginNamespace() const override;
+    const char* getPluginNamespace() const override;
 
-  DataType getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const override;
+    DataType getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const override;
 
-  void attachToContext(cudnnContext* cudnn, cublasContext* cublas, nvinfer1::IGpuAllocator* allocator) override;
+    void attachToContext(cudnnContext* cudnn, cublasContext* cublas, nvinfer1::IGpuAllocator* allocator) override;
 
-  void detachFromContext() override;
+    void detachFromContext() override;
+
+    void configurePlugin(const nvinfer1::DynamicPluginTensorDesc* in, int nbInputs,
+        const nvinfer1::DynamicPluginTensorDesc* out, int nbOutputs) override;
 
-  void configurePlugin(const nvinfer1::DynamicPluginTensorDesc* in, int nbInputs, 
-                       const nvinfer1::DynamicPluginTensorDesc* out, int nbOutputs) override;
 private:
     float _epsilon;
-    int   _nchan;
+    int _nchan;
     std::vector<float> _h_scale;
     std::vector<float> _h_bias;
     float* _d_scale;
     float* _d_bias;
-    bool _initialized;
+    size_t _d_bytes;
     cudnnHandle_t _cudnn_handle;
     cudnnTensorDescriptor_t _x_desc, _y_desc, _b_desc;
-    const char* mPluginNamespace;
-    std::string mNamespace;
+    std::string mPluginNamespace;
 };
 
 class InstanceNormalizationPluginCreator : public BaseCreator
 {
 public:
-  InstanceNormalizationPluginCreator();
+    InstanceNormalizationPluginCreator();
 
-  ~InstanceNormalizationPluginCreator() override = default;
+    ~InstanceNormalizationPluginCreator() override = default;
 
-  const char* getPluginName() const override;
+    const char* getPluginName() const override;
 
-  const char* getPluginVersion() const override;
+    const char* getPluginVersion() const override;
 
-  const PluginFieldCollection* getFieldNames() override;
+    const PluginFieldCollection* getFieldNames() override;
 
-  IPluginV2DynamicExt* createPlugin(const char* name, const nvinfer1::PluginFieldCollection* fc) override;
+    IPluginV2DynamicExt* createPlugin(const char* name, const nvinfer1::PluginFieldCollection* fc) override;
 
-  IPluginV2DynamicExt* deserializePlugin(const char* name, const void* serialData, size_t serialLength) override;
+    IPluginV2DynamicExt* deserializePlugin(const char* name, const void* serialData, size_t serialLength) override;
 
 private:
-  static PluginFieldCollection mFC;
-  static std::vector<PluginField> mPluginAttributes;
-  std::string mNamespace;
+    static PluginFieldCollection mFC;
+    static std::vector<PluginField> mPluginAttributes;
+    std::string mNamespace;
 };
-} //namespace plugin
-} //namespace nvinfer1
+} // namespace plugin
+} // namespace nvinfer1
 
 #endif // TRT_INSTANCE_NORMALIZATION_PLUGIN_H
diff --git a/plugin/multilevelCropAndResizePlugin/multilevelCropAndResizePlugin.cpp b/plugin/multilevelCropAndResizePlugin/multilevelCropAndResizePlugin.cpp
index c93a17fe..f4673ac2 100644
--- a/plugin/multilevelCropAndResizePlugin/multilevelCropAndResizePlugin.cpp
+++ b/plugin/multilevelCropAndResizePlugin/multilevelCropAndResizePlugin.cpp
@@ -84,8 +84,8 @@ MultilevelCropAndResize::MultilevelCropAndResize(int pooled_size)
     // shape
     mInputHeight = TLTMaskRCNNConfig::IMAGE_SHAPE.d[1];
     mInputWidth = TLTMaskRCNNConfig::IMAGE_SHAPE.d[2];
-    //Threshold to P3: Smaller -> P2
-    mThresh = (224*224) / (4.0f);
+    // Threshold to P3: Smaller -> P2
+    mThresh = (224 * 224) / (4.0f);
 };
 
 int MultilevelCropAndResize::getNbOutputs() const
@@ -188,7 +188,7 @@ int MultilevelCropAndResize::enqueue(
 {
 
     void* pooled = outputs[0];
-    
+
     cudaError_t status = roiAlignHalfCenter(stream, batch_size, mFeatureLength, mROICount, mThresh,
 
         mInputHeight, mInputWidth, inputs[0], &inputs[1], mFeatureSpatialSize,
@@ -214,7 +214,7 @@ void MultilevelCropAndResize::serialize(void* buffer) const
     write(d, mInputHeight);
     write(d, mInputWidth);
     write(d, mThresh);
-    for(int i = 0; i < mFeatureMapCount; i++)
+    for (int i = 0; i < mFeatureMapCount; i++)
     {
         write(d, mFeatureSpatialSize[i].y);
         write(d, mFeatureSpatialSize[i].x);
@@ -231,7 +231,7 @@ MultilevelCropAndResize::MultilevelCropAndResize(const void* data, size_t length
     mInputHeight = read<int>(d);
     mInputWidth = read<int>(d);
     mThresh = read<float>(d);
-    for(int i = 0; i < mFeatureMapCount; i++)
+    for (int i = 0; i < mFeatureMapCount; i++)
     {
         mFeatureSpatialSize[i].y = read<int>(d);
         mFeatureSpatialSize[i].x = read<int>(d);
@@ -248,7 +248,8 @@ DataType MultilevelCropAndResize::getOutputDataType(int index, const nvinfer1::D
 }
 
 // Return true if output tensor is broadcast across a batch.
-bool MultilevelCropAndResize::isOutputBroadcastAcrossBatch(int outputIndex, const bool* inputIsBroadcasted, int nbInputs) const
+bool MultilevelCropAndResize::isOutputBroadcastAcrossBatch(
+    int outputIndex, const bool* inputIsBroadcasted, int nbInputs) const
 {
     return false;
 }
@@ -260,8 +261,8 @@ bool MultilevelCropAndResize::canBroadcastInputAcrossBatch(int inputIndex) const
 }
 
 // Configure the layer with input and output data types.
-void MultilevelCropAndResize::configurePlugin(const Dims* inputDims, int nbInputs, const Dims* outputDims, int nbOutputs,
-    const DataType* inputTypes, const DataType* outputTypes, const bool* inputIsBroadcast,
+void MultilevelCropAndResize::configurePlugin(const Dims* inputDims, int nbInputs, const Dims* outputDims,
+    int nbOutputs, const DataType* inputTypes, const DataType* outputTypes, const bool* inputIsBroadcast,
     const bool* outputIsBroadcast, PluginFormat floatFormat, int maxBatchSize)
 {
     assert(supportsFormat(inputTypes[0], floatFormat));
diff --git a/plugin/multilevelCropAndResizePlugin/multilevelCropAndResizePlugin.h b/plugin/multilevelCropAndResizePlugin/multilevelCropAndResizePlugin.h
index 89ceedca..1c3dfc26 100644
--- a/plugin/multilevelCropAndResizePlugin/multilevelCropAndResizePlugin.h
+++ b/plugin/multilevelCropAndResizePlugin/multilevelCropAndResizePlugin.h
@@ -91,7 +91,7 @@ class MultilevelCropAndResize : public IPluginV2Ext
     void check_valid_inputs(const nvinfer1::Dims* inputs, int nbInputDims);
 
     xy_t mPooledSize;
-    static const int mFeatureMapCount = 5; //p2, p3, p4, p5, p6(Maxpooling)
+    static const int mFeatureMapCount = 5; // p2, p3, p4, p5, p6(Maxpooling)
     int mFeatureLength;
     int mROICount;
     float mThresh;
diff --git a/plugin/multilevelProposeROI/multilevelProposeROIPlugin.cpp b/plugin/multilevelProposeROI/multilevelProposeROIPlugin.cpp
index 2f06fab2..e1072c1d 100644
--- a/plugin/multilevelProposeROI/multilevelProposeROIPlugin.cpp
+++ b/plugin/multilevelProposeROI/multilevelProposeROIPlugin.cpp
@@ -14,8 +14,8 @@
  * limitations under the License.
  */
 #include "multilevelProposeROIPlugin.h"
-#include "tlt_mrcnn_config.h"
 #include "plugin.h"
+#include "tlt_mrcnn_config.h"
 #include <cuda_runtime_api.h>
 #include <iostream>
 #include <math.h>
@@ -134,8 +134,8 @@ int MultilevelProposeROI::initialize()
     // Init the regWeight [1, 1, 1, 1]
     mRegWeightDevice = std::make_shared<CudaBind<float>>(4);
     std::vector<float> reg_weight(4, 1);
-    CUASSERT(cudaMemcpy(static_cast<void*>(mRegWeightDevice->mPtr),
-    static_cast<void*>(reg_weight.data()), sizeof(float) * 4, cudaMemcpyHostToDevice));
+    CUASSERT(cudaMemcpy(static_cast<void*>(mRegWeightDevice->mPtr), static_cast<void*>(reg_weight.data()),
+        sizeof(float) * 4, cudaMemcpyHostToDevice));
 
     // Init the mValidCnt of max batch size
     std::vector<int> tempValidCnt(mMaxBatchSize, mPreNMSTopK);
@@ -146,7 +146,7 @@ int MultilevelProposeROI::initialize()
         mValidCnt->mPtr, static_cast<void*>(tempValidCnt.data()), sizeof(int) * mMaxBatchSize, cudaMemcpyHostToDevice));
 
     // Init the anchors for batch size:
-    for(int i = 0; i < mFeatureCnt; i++)
+    for (int i = 0; i < mFeatureCnt; i++)
     {
         int i_anchors_cnt = mAnchorsCnt[i];
         auto i_anchors_host = mAnchorBoxesHost[i].data();
@@ -155,8 +155,8 @@ int MultilevelProposeROI::initialize()
         uint8_t* device_ptr = static_cast<uint8_t*>(i_anchors_device->mPtr);
         for (int i = 0; i < mMaxBatchSize; i++)
         {
-            CUASSERT(cudaMemcpy(static_cast<void*>(device_ptr + i * batch_offset),
-                static_cast<void*>(i_anchors_host), batch_offset, cudaMemcpyHostToDevice));
+            CUASSERT(cudaMemcpy(static_cast<void*>(device_ptr + i * batch_offset), static_cast<void*>(i_anchors_host),
+                batch_offset, cudaMemcpyHostToDevice));
         }
         mAnchorBoxesDevice.push_back(i_anchors_device);
     }
@@ -164,7 +164,7 @@ int MultilevelProposeROI::initialize()
     // Init the temp storage for proposals from feature maps before concat
     std::vector<float*> score_tp;
     std::vector<float*> box_tp;
-    for(int i = 0; i < mFeatureCnt; i++)
+    for (int i = 0; i < mFeatureCnt; i++)
     {
         auto i_scores_device = std::make_shared<CudaBind<float>>(mKeepTopK * mMaxBatchSize);
         mTempScores.push_back(i_scores_device);
@@ -176,11 +176,11 @@ int MultilevelProposeROI::initialize()
     }
 
     // Init the temp storage for pointer arrays of score and box:
-    CUASSERT(cudaMalloc(&mDeviceScores, sizeof(float*)*mFeatureCnt));
-    CUASSERT(cudaMalloc(&mDeviceBboxes, sizeof(float*)*mFeatureCnt));
+    CUASSERT(cudaMalloc(&mDeviceScores, sizeof(float*) * mFeatureCnt));
+    CUASSERT(cudaMalloc(&mDeviceBboxes, sizeof(float*) * mFeatureCnt));
 
-    CUASSERT(cudaMemcpy(mDeviceScores, score_tp.data(), sizeof(float*)*mFeatureCnt, cudaMemcpyHostToDevice));
-    CUASSERT(cudaMemcpy(mDeviceBboxes, box_tp.data(), sizeof(float*)*mFeatureCnt, cudaMemcpyHostToDevice));
+    CUASSERT(cudaMemcpy(mDeviceScores, score_tp.data(), sizeof(float*) * mFeatureCnt, cudaMemcpyHostToDevice));
+    CUASSERT(cudaMemcpy(mDeviceBboxes, box_tp.data(), sizeof(float*) * mFeatureCnt, cudaMemcpyHostToDevice));
 
     return 0;
 };
@@ -235,7 +235,7 @@ void MultilevelProposeROI::serialize(void* buffer) const
     write(d, mFGThreshold);
     write(d, mIOUThreshold);
     write(d, mMaxBatchSize);
-    for(int i = 0; i < mFeatureCnt; i++)
+    for (int i = 0; i < mFeatureCnt; i++)
     {
         write(d, mAnchorsCnt[i]);
     }
@@ -253,7 +253,7 @@ MultilevelProposeROI::MultilevelProposeROI(const void* data, size_t length)
     float iou_threshold = read<float>(d);
     mMaxBatchSize = read<int>(d);
     assert(mAnchorsCnt.size() == 0);
-    for(int i = 0; i < mFeatureCnt; i++)
+    for (int i = 0; i < mFeatureCnt; i++)
     {
         mAnchorsCnt.push_back(read<int>(d));
     }
@@ -283,29 +283,29 @@ void MultilevelProposeROI::check_valid_inputs(const nvinfer1::Dims* inputs, int
     // foreground_score_px [N, h_x * w_x * anchors_per_location, 1, 1],
     // anchors should be generated inside
     assert(nbInputDims == 2 * mFeatureCnt);
-    for(int i = 0; i < 2 * mFeatureCnt; i += 2)
+    for (int i = 0; i < 2 * mFeatureCnt; i += 2)
     {
         // foreground_delta
         assert(inputs[i].nbDims == 3 && inputs[i].d[1] == 4);
         // foreground_score
-        assert(inputs[i+1].nbDims == 3 && inputs[i+1].d[1] == 1);
+        assert(inputs[i + 1].nbDims == 3 && inputs[i + 1].d[1] == 1);
     }
 };
 
 size_t MultilevelProposeROI::getWorkspaceSize(int batch_size) const
 {
-    size_t total_size = 0; 
+    size_t total_size = 0;
     assert(mAnchorsCnt.size() == mFeatureCnt);
 
-    //workspace for propose on each feature map 
-    for(int i = 0; i < mFeatureCnt; i++)
+    // workspace for propose on each feature map
+    for (int i = 0; i < mFeatureCnt; i++)
     {
-        
+
         MultilevelProposeROIWorkSpace proposal(batch_size, mAnchorsCnt[i], mPreNMSTopK, mParam, mType);
         total_size += proposal.totalSize;
     }
-    
-    //workspace for Concat and TopK
+
+    // workspace for Concat and TopK
     ConcatTopKWorkSpace ct(batch_size, mFeatureCnt, mKeepTopK, mType);
     total_size += ct.totalSize;
 
@@ -335,17 +335,17 @@ void MultilevelProposeROI::generate_pyramid_anchors()
 
     const auto& anchor_scale = TLTMaskRCNNConfig::RPN_ANCHOR_SCALE;
     const auto& min_level = TLTMaskRCNNConfig::MIN_LEVEL;
-    const auto& max_level = TLTMaskRCNNConfig::MAX_LEVEL; 
+    const auto& max_level = TLTMaskRCNNConfig::MAX_LEVEL;
     const auto& aspect_ratios = TLTMaskRCNNConfig::ANCHOR_RATIOS;
-    
-    //Generate anchors strides and scales
+
+    // Generate anchors strides and scales
     std::vector<float> anchor_scales;
     std::vector<int> anchor_strides;
-    for(int i = min_level; i < max_level + 1; i++)
+    for (int i = min_level; i < max_level + 1; i++)
     {
         int stride = static_cast<int>(pow(2.0, i));
         anchor_strides.push_back(stride);
-        anchor_scales.push_back(stride*anchor_scale);
+        anchor_scales.push_back(stride * anchor_scale);
     }
 
     auto& anchors = mAnchorBoxesHost;
@@ -358,16 +358,15 @@ void MultilevelProposeROI::generate_pyramid_anchors()
         int stride = anchor_strides[s];
 
         std::vector<float> s_anchors;
-        for (int y = stride / 2 ; y < image_dims.d[1]; y += stride)
+        for (int y = stride / 2; y < image_dims.d[1]; y += stride)
             for (int x = stride / 2; x < image_dims.d[2]; x += stride)
                 for (auto r : aspect_ratios)
                 {
                     float h = scale * r.second;
                     float w = scale * r.first;
-                    
+
                     // Using y+h/2 instead of y+h/2-1 for alignment with TLT implementation
-                    s_anchors.insert(s_anchors.end(),
-                        {(y - h / 2), (x - w / 2), (y + h / 2 ), (x + w / 2 )});
+                    s_anchors.insert(s_anchors.end(), {(y - h / 2), (x - w / 2), (y + h / 2), (x + w / 2)});
                 }
 
         anchors.push_back(s_anchors);
@@ -381,45 +380,33 @@ int MultilevelProposeROI::enqueue(
 {
 
     void* final_proposals = outputs[0];
-    size_t kernel_workspace_offset = 0; 
+    size_t kernel_workspace_offset = 0;
     cudaError_t status;
 
-    for(int i = 0; i < mFeatureCnt; i++)
+    for (int i = 0; i < mFeatureCnt; i++)
     {
 
         MultilevelProposeROIWorkSpace proposal_ws(batch_size, mAnchorsCnt[i], mPreNMSTopK, mParam, mType);
-        status = MultilevelPropose(stream, 
-                    batch_size, 
-                    mAnchorsCnt[i], 
-                    mPreNMSTopK,
-                    static_cast<float*>(mRegWeightDevice->mPtr), 
-                    static_cast<float>(TLTMaskRCNNConfig::IMAGE_SHAPE.d[1]), //Input Height
-                    static_cast<float>(TLTMaskRCNNConfig::IMAGE_SHAPE.d[2]),
-                    DataType::kFLOAT, // mType,
-                    mParam, 
-                    proposal_ws, 
-                    workspace + kernel_workspace_offset,
-                    inputs[2*i + 1], // inputs[object_score],
-                    inputs[2*i], // inputs[bbox_delta]
-                    mValidCnt->mPtr,
-                    mAnchorBoxesDevice[i]->mPtr, // inputs[anchors]
-                    mTempScores[i]->mPtr, //temp scores [batch_size, topk, 1]
-                    mTempBboxes[i]->mPtr); //temp
+        status = MultilevelPropose(stream, batch_size, mAnchorsCnt[i], mPreNMSTopK,
+            static_cast<float*>(mRegWeightDevice->mPtr),
+            static_cast<float>(TLTMaskRCNNConfig::IMAGE_SHAPE.d[1]), // Input Height
+            static_cast<float>(TLTMaskRCNNConfig::IMAGE_SHAPE.d[2]),
+            DataType::kFLOAT, // mType,
+            mParam, proposal_ws, workspace + kernel_workspace_offset,
+            inputs[2 * i + 1], // inputs[object_score],
+            inputs[2 * i],     // inputs[bbox_delta]
+            mValidCnt->mPtr,
+            mAnchorBoxesDevice[i]->mPtr, // inputs[anchors]
+            mTempScores[i]->mPtr,        // temp scores [batch_size, topk, 1]
+            mTempBboxes[i]->mPtr);       // temp
         assert(status == cudaSuccess);
         kernel_workspace_offset += proposal_ws.totalSize;
     }
 
     ConcatTopKWorkSpace ctopk_ws(batch_size, mFeatureCnt, mKeepTopK, mType);
-    status = ConcatTopK(stream, 
-                batch_size, 
-                mFeatureCnt, 
-                mKeepTopK, 
-                DataType::kFLOAT,
-                workspace + kernel_workspace_offset,
-                ctopk_ws, 
-                reinterpret_cast<void**>(mDeviceScores),
-                reinterpret_cast<void**>(mDeviceBboxes), 
-                final_proposals);
+    status = ConcatTopK(stream, batch_size, mFeatureCnt, mKeepTopK, DataType::kFLOAT,
+        workspace + kernel_workspace_offset, ctopk_ws, reinterpret_cast<void**>(mDeviceScores),
+        reinterpret_cast<void**>(mDeviceBboxes), final_proposals);
 
     assert(status == cudaSuccess);
     return status;
@@ -433,7 +420,8 @@ DataType MultilevelProposeROI::getOutputDataType(int index, const nvinfer1::Data
 }
 
 // Return true if output tensor is broadcast across a batch.
-bool MultilevelProposeROI::isOutputBroadcastAcrossBatch(int outputIndex, const bool* inputIsBroadcasted, int nbInputs) const
+bool MultilevelProposeROI::isOutputBroadcastAcrossBatch(
+    int outputIndex, const bool* inputIsBroadcasted, int nbInputs) const
 {
     return false;
 }
@@ -452,9 +440,9 @@ void MultilevelProposeROI::configurePlugin(const Dims* inputDims, int nbInputs,
     check_valid_inputs(inputDims, nbInputs);
 
     mAnchorsCnt.clear();
-    for(int i = 0; i < mFeatureCnt; i++)
+    for (int i = 0; i < mFeatureCnt; i++)
     {
-        mAnchorsCnt.push_back(inputDims[2*i].d[0]);
+        mAnchorsCnt.push_back(inputDims[2 * i].d[0]);
         assert(mAnchorsCnt[i] == (int) (mAnchorBoxesHost[i].size() / 4));
     }
 
diff --git a/plugin/multilevelProposeROI/multilevelProposeROIPlugin.h b/plugin/multilevelProposeROI/multilevelProposeROIPlugin.h
index 98306435..3212d89b 100644
--- a/plugin/multilevelProposeROI/multilevelProposeROIPlugin.h
+++ b/plugin/multilevelProposeROI/multilevelProposeROIPlugin.h
@@ -103,10 +103,8 @@ class MultilevelProposeROI : public IPluginV2Ext
     std::vector<std::shared_ptr<CudaBind<float>>>
         mAnchorBoxesDevice; // [N, anchors(261888 for resnet101 + 1024*1024), (y1, x1, y2, x2)]
     std::vector<std::vector<float>> mAnchorBoxesHost;
-    std::vector<std::shared_ptr<CudaBind<float>>>
-        mTempScores;
-    std::vector<std::shared_ptr<CudaBind<float>>>
-        mTempBboxes;
+    std::vector<std::shared_ptr<CudaBind<float>>> mTempScores;
+    std::vector<std::shared_ptr<CudaBind<float>>> mTempBboxes;
     float** mDeviceScores;
     float** mDeviceBboxes;
     std::shared_ptr<CudaBind<float>> mRegWeightDevice;
diff --git a/plugin/multilevelProposeROI/tlt_mrcnn_config.h b/plugin/multilevelProposeROI/tlt_mrcnn_config.h
index d21db5fe..dddec2d7 100644
--- a/plugin/multilevelProposeROI/tlt_mrcnn_config.h
+++ b/plugin/multilevelProposeROI/tlt_mrcnn_config.h
@@ -18,8 +18,8 @@
 #define MASKRCNN_CONFIG_HEADER
 #include "NvInfer.h"
 #include <string>
-#include <vector>
 #include <utility>
+#include <vector>
 using namespace nvinfer1;
 
 namespace TLTMaskRCNNConfig
@@ -64,9 +64,8 @@ static const int MAX_LEVEL = 6;
 static const float RPN_ANCHOR_SCALE = 8;
 
 // Ratios of anchors at each cell (width,height)
-static const std::vector<std::pair<float, float>> ANCHOR_RATIOS= {std::make_pair(1.0f, 1.0f), 
-                                                                  std::make_pair(1.4f, 0.7f), 
-                                                                  std::make_pair(0.7f, 1.4f)};
+static const std::vector<std::pair<float, float>> ANCHOR_RATIOS
+    = {std::make_pair(1.0f, 1.0f), std::make_pair(1.4f, 0.7f), std::make_pair(0.7f, 1.4f)};
 
 // Anchor stride
 // If 1 then anchors are created for each cell in the backbone feature map.
@@ -174,5 +173,5 @@ static const Dims3 MODEL_INPUT_SHAPE = IMAGE_SHAPE;
 static const std::vector<std::string> MODEL_OUTPUTS = {"generate_detections", "mask_head/mask_fcn_logits/BiasAdd"};
 static const Dims2 MODEL_DETECTION_SHAPE{DETECTION_MAX_INSTANCES, 6};
 static const Dims4 MODEL_MASK_SHAPE{DETECTION_MAX_INSTANCES, NUM_CLASSES, 28, 28};
-} // namespace MaskRCNNConfig
+} // namespace TLTMaskRCNNConfig
 #endif
diff --git a/plugin/nmsPlugin/CMakeLists.txt b/plugin/nmsPlugin/CMakeLists.txt
index de8fff17..85d8ce4e 100644
--- a/plugin/nmsPlugin/CMakeLists.txt
+++ b/plugin/nmsPlugin/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/plugin/nmsPlugin/README.md b/plugin/nmsPlugin/README.md
index 1566ca8f..d77115be 100644
--- a/plugin/nmsPlugin/README.md
+++ b/plugin/nmsPlugin/README.md
@@ -153,4 +153,4 @@ This is the first release of this `README.md` file.
 
 ## Known issues
 
-There are no known issues in this plugin.
\ No newline at end of file
+There are no known issues in this plugin.
diff --git a/plugin/nmsPlugin/nmsPlugin.cpp b/plugin/nmsPlugin/nmsPlugin.cpp
index f455dfd1..19e0b754 100644
--- a/plugin/nmsPlugin/nmsPlugin.cpp
+++ b/plugin/nmsPlugin/nmsPlugin.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -168,7 +168,7 @@ IPluginV2Ext* DetectionOutput::clone() const
     IPluginV2Ext* plugin = new DetectionOutput(param, C1, C2, numPriors);
 
     // Set the namespace
-    plugin->setPluginNamespace(mPluginNamespace);
+    plugin->setPluginNamespace(mPluginNamespace.c_str());
     return plugin;
 }
 
@@ -180,7 +180,7 @@ void DetectionOutput::setPluginNamespace(const char* pluginNamespace)
 
 const char* DetectionOutput::getPluginNamespace() const
 {
-    return mPluginNamespace;
+    return mPluginNamespace.c_str();
 }
 
 // Return the DataType of the plugin output at the requested index.
diff --git a/plugin/nmsPlugin/nmsPlugin.h b/plugin/nmsPlugin/nmsPlugin.h
index d630f0b7..29d0e21e 100644
--- a/plugin/nmsPlugin/nmsPlugin.h
+++ b/plugin/nmsPlugin/nmsPlugin.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -87,7 +87,7 @@ class DetectionOutput : public IPluginV2Ext
 private:
     DetectionOutputParameters param;
     int C1, C2, numPriors;
-    const char* mPluginNamespace;
+    std::string mPluginNamespace;
 };
 
 class NMSPluginCreator : public BaseCreator
diff --git a/plugin/normalizePlugin/CMakeLists.txt b/plugin/normalizePlugin/CMakeLists.txt
index de8fff17..85d8ce4e 100644
--- a/plugin/normalizePlugin/CMakeLists.txt
+++ b/plugin/normalizePlugin/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/plugin/normalizePlugin/normalizePlugin.cpp b/plugin/normalizePlugin/normalizePlugin.cpp
index d8a7ca18..a0d936ff 100644
--- a/plugin/normalizePlugin/normalizePlugin.cpp
+++ b/plugin/normalizePlugin/normalizePlugin.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -14,6 +14,7 @@
  * limitations under the License.
  */
 #include "normalizePlugin.h"
+#include "half.h"
 #include <cstring>
 #include <cublas_v2.h>
 #include <cudnn.h>
@@ -42,7 +43,6 @@ Normalize::Normalize(const Weights* weights, int nbWeights, bool acrossSpatial,
     ASSERT(nbWeights == 1);
     ASSERT(weights[0].count >= 1);
     mWeights = copyToDevice(weights[0].values, weights[0].count);
-    cublasCreate(&mCublas);
 }
 
 Normalize::Normalize(
@@ -58,12 +58,12 @@ Normalize::Normalize(
     ASSERT(nbWeights == 1);
     ASSERT(weights[0].count >= 1);
     mWeights = copyToDevice(weights[0].values, weights[0].count);
-    cublasCreate(&mCublas);
 }
 
 Normalize::Normalize(const void* buffer, size_t length)
 {
-    const char *d = reinterpret_cast<const char*>(buffer), *a = d;
+    const char* d = static_cast<const char*>(buffer);
+    const char* a = d;
     C = read<int>(d);
     H = read<int>(d);
     W = read<int>(d);
@@ -72,8 +72,8 @@ Normalize::Normalize(const void* buffer, size_t length)
     eps = read<float>(d);
 
     mNbWeights = read<int>(d);
-    mWeights = deserializeToDevice(d, mNbWeights);
-    cublasCreate(&mCublas);
+    int count = read<int>(d);
+    mWeights = deserializeToDevice(d, count);
     ASSERT(d == a + length);
 }
 
@@ -96,10 +96,7 @@ int Normalize::initialize()
     return 0;
 }
 
-void Normalize::terminate()
-{
-    CUBLASASSERT(cublasDestroy(mCublas));
-}
+void Normalize::terminate() {}
 
 size_t Normalize::getWorkspaceSize(int maxBatchSize) const
 {
@@ -111,7 +108,7 @@ int Normalize::enqueue(int batchSize, const void* const* inputs, void** outputs,
     const void* inputData = inputs[0];
     void* outputData = outputs[0];
     pluginStatus_t status = normalizeInference(stream, mCublas, acrossSpatial, channelShared, batchSize, C, H, W, eps,
-        reinterpret_cast<const float*>(mWeights.values), inputData, outputData, workspace);
+        static_cast<const float*>(mWeights.values), inputData, outputData, workspace);
     ASSERT(status == STATUS_SUCCESS);
     return 0;
 }
@@ -119,18 +116,19 @@ int Normalize::enqueue(int batchSize, const void* const* inputs, void** outputs,
 size_t Normalize::getSerializationSize() const
 {
     // C,H,W, acrossSpatial,channelShared, eps, mWeights.count,mWeights.values
-    return sizeof(int) * 3 + sizeof(bool) * 2 + sizeof(float) + sizeof(int) + mWeights.count * sizeof(float);
+    return sizeof(int) * 3 + sizeof(bool) * 2 + sizeof(float) + sizeof(int) * 2 + mWeights.count * sizeof(float);
 }
 
 void Normalize::serialize(void* buffer) const
 {
-    char *d = reinterpret_cast<char*>(buffer), *a = d;
+    char *d = static_cast<char*>(buffer), *a = d;
     write(d, C);
     write(d, H);
     write(d, W);
     write(d, acrossSpatial);
     write(d, channelShared);
     write(d, eps);
+    write(d, (int) mNbWeights);
     write(d, (int) mWeights.count);
     serializeFromDevice(d, mWeights);
 
@@ -171,7 +169,7 @@ void Normalize::setPluginNamespace(const char* pluginNamespace)
 
 const char* Normalize::getPluginNamespace() const
 {
-    return mPluginNamespace;
+    return mPluginNamespace.c_str();
 }
 
 // Return the DataType of the plugin output at the requested index
@@ -219,8 +217,9 @@ void Normalize::configurePlugin(const Dims* inputDims, int nbInputs, const Dims*
 }
 
 // Attach the plugin object to an execution context and grant the plugin the access to some context resource.
-void Normalize::attachToContext(cudnnContext* cudnnContext, cublasContext* cublasContext, IGpuAllocator* gpuAllocator)
+void Normalize::attachToContext(cudnnContext* cudnn, cublasContext* cublas, IGpuAllocator* gpuAllocator)
 {
+    mCublas = cublas;
 }
 
 // Detach the plugin object from its execution context.
@@ -248,7 +247,7 @@ IPluginV2Ext* Normalize::clone() const
     IPluginV2Ext* plugin = new Normalize(&mWeights, mNbWeights, acrossSpatial, channelShared, eps, C, H, W);
 
     // Set the namespace
-    plugin->setPluginNamespace(mPluginNamespace);
+    plugin->setPluginNamespace(mPluginNamespace.c_str());
     return plugin;
 }
 
diff --git a/plugin/normalizePlugin/normalizePlugin.h b/plugin/normalizePlugin/normalizePlugin.h
index b4c39da5..3479ad20 100644
--- a/plugin/normalizePlugin/normalizePlugin.h
+++ b/plugin/normalizePlugin/normalizePlugin.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -90,17 +90,17 @@ class Normalize : public IPluginV2Ext
     void serializeFromDevice(char*& hostBuffer, Weights deviceWeights) const;
     Weights deserializeToDevice(const char*& hostBuffer, size_t count);
 
-    cublasHandle_t mCublas{};
+    cublasHandle_t mCublas;
 
-    int C{};
-    int H{};
-    int W{};
+    Weights mWeights{};
     int mNbWeights{};
     bool acrossSpatial{};
     bool channelShared{};
     float eps{};
-    Weights mWeights{};
-    const char* mPluginNamespace;
+    int C{};
+    int H{};
+    int W{};
+    std::string mPluginNamespace;
 };
 
 class NormalizePluginCreator : public BaseCreator
diff --git a/plugin/nvFasterRCNN/CMakeLists.txt b/plugin/nvFasterRCNN/CMakeLists.txt
index de8fff17..85d8ce4e 100644
--- a/plugin/nvFasterRCNN/CMakeLists.txt
+++ b/plugin/nvFasterRCNN/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/plugin/nvFasterRCNN/nvFasterRCNNPlugin.cpp b/plugin/nvFasterRCNN/nvFasterRCNNPlugin.cpp
index 21fc3458..b91ccc88 100644
--- a/plugin/nvFasterRCNN/nvFasterRCNNPlugin.cpp
+++ b/plugin/nvFasterRCNN/nvFasterRCNNPlugin.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -235,7 +235,7 @@ void RPROIPlugin::destroy()
 IPluginV2Ext* RPROIPlugin::clone() const
 {
     IPluginV2Ext* plugin = new RPROIPlugin(params, anchorsRatiosHost, anchorsScalesHost, A, C, H, W, anchorsDev);
-    plugin->setPluginNamespace(mPluginNamespace);
+    plugin->setPluginNamespace(mPluginNamespace.c_str());
     return plugin;
 }
 
@@ -247,7 +247,7 @@ void RPROIPlugin::setPluginNamespace(const char* pluginNamespace)
 
 const char* RPROIPlugin::getPluginNamespace() const
 {
-    return mPluginNamespace;
+    return mPluginNamespace.c_str();
 }
 
 // Return the DataType of the plugin output at the requested index.
diff --git a/plugin/nvFasterRCNN/nvFasterRCNNPlugin.h b/plugin/nvFasterRCNN/nvFasterRCNNPlugin.h
index 3a68d54f..f38873d5 100644
--- a/plugin/nvFasterRCNN/nvFasterRCNNPlugin.h
+++ b/plugin/nvFasterRCNN/nvFasterRCNNPlugin.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -91,7 +91,7 @@ class RPROIPlugin : public IPluginV2Ext
 
     // These won't be serialized
     float* anchorsDev{nullptr};
-    const char* mPluginNamespace;
+    std::string mPluginNamespace;
 
     // These need to be serialized
     RPROIParams params;
diff --git a/plugin/priorBoxPlugin/CMakeLists.txt b/plugin/priorBoxPlugin/CMakeLists.txt
index de8fff17..85d8ce4e 100644
--- a/plugin/priorBoxPlugin/CMakeLists.txt
+++ b/plugin/priorBoxPlugin/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/plugin/priorBoxPlugin/priorBoxPlugin.cpp b/plugin/priorBoxPlugin/priorBoxPlugin.cpp
index cc3dc1d2..23d25035 100644
--- a/plugin/priorBoxPlugin/priorBoxPlugin.cpp
+++ b/plugin/priorBoxPlugin/priorBoxPlugin.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -38,6 +38,7 @@ std::vector<PluginField> PriorBoxPluginCreator::mPluginAttributes;
 // Constructor
 PriorBox::PriorBox(PriorBoxParameters param)
     : mParam(param)
+    , mOwnsParamMemory(true)
 {
     // minSize is required and needs to be non-negative
     ASSERT(param.numMinSize > 0 && param.minSize != nullptr);
@@ -100,71 +101,55 @@ PriorBox::PriorBox(PriorBoxParameters param)
     }
 }
 
-// Constructor
-PriorBox::PriorBox(PriorBoxParameters param, int H, int W)
+// Constructor used in `clone()`.
+// This constructor does not modify parameters, unlike the previous one.
+PriorBox::PriorBox(
+    PriorBoxParameters param, int numPriors, int H, int W, Weights minSize, Weights maxSize, Weights aspectRatios)
     : mParam(param)
+    , mOwnsParamMemory(false)
+    , numPriors(numPriors)
     , H(H)
     , W(W)
+    , minSize(minSize)
+    , maxSize(maxSize)
+    , aspectRatios(aspectRatios)
 {
-    // minSize is required and needs to be non-negative
-    ASSERT(param.numMinSize > 0 && param.minSize != nullptr);
-    for (int i = 0; i < param.numMinSize; ++i)
-    {
-        ASSERT(param.minSize[i] > 0 && "minSize must be positive");
-    }
-    minSize = copyToDevice(param.minSize, param.numMinSize);
-    ASSERT(param.numAspectRatios >= 0 && param.aspectRatios != nullptr);
-    std::vector<float> tmpAR(1, 1);
-    for (int i = 0; i < param.numAspectRatios; ++i)
-    {
-        float ar = param.aspectRatios[i];
-        bool alreadyExist = false;
-        for (unsigned j = 0; j < tmpAR.size(); ++j)
-        {
-            if (std::fabs(ar - tmpAR[j]) < 1e-6)
-            {
-                alreadyExist = true;
-                break;
-            }
-        }
-        if (!alreadyExist)
-        {
-            tmpAR.push_back(ar);
-            if (param.flip)
-            {
-                tmpAR.push_back(1.0F / ar);
-            }
-        }
-    }
-    aspectRatios = copyToDevice(&tmpAR[0], tmpAR.size());
-    numPriors = tmpAR.size() * param.numMinSize;
-    if (param.numMaxSize > 0)
-    {
-        ASSERT(param.numMinSize == param.numMaxSize && param.maxSize != nullptr);
-        for (int i = 0; i < param.numMaxSize; ++i)
-        {
-            // maxSize should be greater than minSize
-            ASSERT(param.maxSize[i] > param.minSize[i] && "maxSize must be greater than minSize");
-            numPriors++;
-        }
-        maxSize = copyToDevice(param.maxSize, param.numMaxSize);
-    }
 }
 
 PriorBox::PriorBox(const void* data, size_t length)
+    : mOwnsParamMemory(true)
 {
     const char *d = reinterpret_cast<const char*>(data), *a = d;
     mParam = read<PriorBoxParameters>(d);
+    mParam.minSize = new float[mParam.numMinSize];
+    mParam.maxSize = new float[mParam.numMaxSize];
+    mParam.aspectRatios = new float[mParam.numAspectRatios];
+
     numPriors = read<int>(d);
     H = read<int>(d);
     W = read<int>(d);
+    for (auto i = 0; i < mParam.numMinSize; i++)
+    {
+        mParam.minSize[i] = reinterpret_cast<const float*>(d)[i];
+    }
     minSize = deserializeToDevice(d, mParam.numMinSize);
     if (mParam.numMaxSize > 0)
     {
+        for (auto i = 0; i < mParam.numMaxSize; i++)
+        {
+            mParam.maxSize[i] = reinterpret_cast<const float*>(d)[i];
+        }
         maxSize = deserializeToDevice(d, mParam.numMaxSize);
     }
     int numAspectRatios = read<int>(d);
-    aspectRatios = deserializeToDevice(d, numAspectRatios);
+    if (mParam.numAspectRatios > 0)
+    {
+        for (auto i = 0; i < mParam.numAspectRatios; i++)
+        {
+            mParam.aspectRatios[i] = reinterpret_cast<const float*>(d)[i];
+        }
+        aspectRatios = deserializeToDevice(d, numAspectRatios);
+    }
     ASSERT(d == a + length);
 }
 
@@ -196,14 +181,20 @@ int PriorBox::initialize()
 
 void PriorBox::terminate()
 {
-    CUASSERT(cudaFree(const_cast<void*>(minSize.values)));
-    if (mParam.numMaxSize > 0)
-    {
-        CUASSERT(cudaFree(const_cast<void*>(maxSize.values)));
-    }
-    if (mParam.numAspectRatios > 0)
+    if (mOwnsParamMemory)
     {
-        CUASSERT(cudaFree(const_cast<void*>(aspectRatios.values)));
+        CUASSERT(cudaFree(const_cast<void*>(minSize.values)));
+        if (mParam.numMaxSize > 0)
+        {
+            CUASSERT(cudaFree(const_cast<void*>(maxSize.values)));
+        }
+        if (mParam.numAspectRatios > 0)
+        {
+            CUASSERT(cudaFree(const_cast<void*>(aspectRatios.values)));
+        }
+        delete[] mParam.minSize;
+        delete[] mParam.maxSize;
+        delete[] mParam.aspectRatios;
     }
 }
 
@@ -290,8 +281,8 @@ void PriorBox::destroy()
 
 IPluginV2Ext* PriorBox::clone() const
 {
-    IPluginV2Ext* plugin = new PriorBox(mParam, H, W);
-    plugin->setPluginNamespace(mPluginNamespace);
+    IPluginV2Ext* plugin = new PriorBox(mParam, numPriors, H, W, minSize, maxSize, aspectRatios);
+    plugin->setPluginNamespace(mPluginNamespace.c_str());
     return plugin;
 }
 
@@ -303,7 +294,7 @@ void PriorBox::setPluginNamespace(const char* pluginNamespace)
 
 const char* PriorBox::getPluginNamespace() const
 {
-    return mPluginNamespace;
+    return mPluginNamespace.c_str();
 }
 
 // Return the DataType of the plugin output at the requested index.
@@ -379,10 +370,6 @@ PriorBoxPluginCreator::PriorBoxPluginCreator()
 PriorBoxPluginCreator::~PriorBoxPluginCreator()
 {
     // Free allocated memory (if any) here
-    for (auto v : mTmpAllocs)
-    {
-        free(v);
-    }
 }
 
 const char* PriorBoxPluginCreator::getPluginName() const
@@ -412,7 +399,7 @@ IPluginV2Ext* PriorBoxPluginCreator::createPlugin(const char* /*name*/, const Pl
         {
             ASSERT(fields[i].type == PluginFieldType::kFLOAT32);
             int size = fields[i].length;
-            params.minSize = allocMemory<float>(size);
+            params.minSize = new float[size];
             const auto* minS = static_cast<const float*>(fields[i].data);
             for (int j = 0; j < size; j++)
             {
@@ -425,7 +412,7 @@ IPluginV2Ext* PriorBoxPluginCreator::createPlugin(const char* /*name*/, const Pl
         {
             ASSERT(fields[i].type == PluginFieldType::kFLOAT32);
             int size = fields[i].length;
-            params.maxSize = allocMemory<float>(size);
+            params.maxSize = new float[size];
             const auto* maxS = static_cast<const float*>(fields[i].data);
             for (int j = 0; j < size; j++)
             {
@@ -438,7 +425,7 @@ IPluginV2Ext* PriorBoxPluginCreator::createPlugin(const char* /*name*/, const Pl
         {
             ASSERT(fields[i].type == PluginFieldType::kFLOAT32);
             int size = fields[i].length;
-            params.aspectRatios = allocMemory<float>(size);
+            params.aspectRatios = new float[size];
             const auto* aR = static_cast<const float*>(fields[i].data);
             for (int j = 0; j < size; j++)
             {
diff --git a/plugin/priorBoxPlugin/priorBoxPlugin.h b/plugin/priorBoxPlugin/priorBoxPlugin.h
index 975df1ca..e02cbf7c 100644
--- a/plugin/priorBoxPlugin/priorBoxPlugin.h
+++ b/plugin/priorBoxPlugin/priorBoxPlugin.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -33,7 +33,8 @@ class PriorBox : public IPluginV2Ext
 public:
     PriorBox(PriorBoxParameters param);
 
-    PriorBox(PriorBoxParameters param, int H, int W);
+    PriorBox(
+        PriorBoxParameters param, int numPriors, int H, int W, Weights minSize, Weights maxSize, Weights aspectRatios);
 
     PriorBox(const void* buffer, size_t length);
 
@@ -91,9 +92,10 @@ class PriorBox : public IPluginV2Ext
     Weights deserializeToDevice(const char*& hostBuffer, size_t count);
 
     PriorBoxParameters mParam;
+    bool mOwnsParamMemory;
     int numPriors, H, W;
     Weights minSize, maxSize, aspectRatios; // not learnable weights
-    const char* mPluginNamespace;
+    std::string mPluginNamespace;
 };
 
 class PriorBoxPluginCreator : public BaseCreator
@@ -113,20 +115,9 @@ class PriorBoxPluginCreator : public BaseCreator
 
     IPluginV2Ext* deserializePlugin(const char* name, const void* serialData, size_t serialLength) override;
 
-private:
-    template <typename T>
-    T* allocMemory(int size = 1)
-    {
-        mTmpAllocs.reserve(mTmpAllocs.size() + 1);
-        T* tmpMem = static_cast<T*>(malloc(sizeof(T) * size));
-        mTmpAllocs.push_back(tmpMem);
-        return tmpMem;
-    }
-
 private:
     static PluginFieldCollection mFC;
     static std::vector<PluginField> mPluginAttributes;
-    std::vector<void*> mTmpAllocs;
 };
 } // namespace plugin
 } // namespace nvinfer1
diff --git a/plugin/proposalLayerPlugin/CMakeLists.txt b/plugin/proposalLayerPlugin/CMakeLists.txt
index de8fff17..85d8ce4e 100644
--- a/plugin/proposalLayerPlugin/CMakeLists.txt
+++ b/plugin/proposalLayerPlugin/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/plugin/proposalLayerPlugin/mrcnn_config.h b/plugin/proposalLayerPlugin/mrcnn_config.h
index 9cdfae16..04d65211 100644
--- a/plugin/proposalLayerPlugin/mrcnn_config.h
+++ b/plugin/proposalLayerPlugin/mrcnn_config.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -13,6 +13,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
+
 #ifndef MASKRCNN_CONFIG_HEADER
 #define MASKRCNN_CONFIG_HEADER
 #include "NvInfer.h"
@@ -22,28 +23,28 @@ using namespace nvinfer1;
 
 namespace MaskRCNNConfig
 {
-static const nvinfer1::DimsCHW IMAGE_SHAPE{3, 1024, 1024};
+static const nvinfer1::Dims3 IMAGE_SHAPE{3, 1024, 1024};
 
 // Pooled ROIs
 static const int POOL_SIZE = 7;
 static const int MASK_POOL_SIZE = 14;
 
 // Threshold to determine the mask area out of final convolution output
-static const float MASK_THRESHOLD = 0.5;
+static const float MASK_THRESHOLD = 0.5f;
 
 // Bounding box refinement standard deviation for RPN and final detections.
-static const float RPN_BBOX_STD_DEV[] = {0.1, 0.1, 0.2, 0.2};
-static const float BBOX_STD_DEV[] = {0.1, 0.1, 0.2, 0.2};
+static const float RPN_BBOX_STD_DEV[] = {0.1f, 0.1f, 0.2f, 0.2f};
+static const float BBOX_STD_DEV[] = {0.1f, 0.1f, 0.2f, 0.2f};
 
 // Max number of final detections
 static const int DETECTION_MAX_INSTANCES = 100;
 
 // Minimum probability value to accept a detected instance
 // ROIs below this threshold are skipped
-static const float DETECTION_MIN_CONFIDENCE = 0.7;
+static const float DETECTION_MIN_CONFIDENCE = 0.7f;
 
 // Non-maximum suppression threshold for detection
-static const float DETECTION_NMS_THRESHOLD = 0.3;
+static const float DETECTION_NMS_THRESHOLD = 0.3f;
 
 // The strides of each layer of the FPN Pyramid. These values
 // are based on a Resnet101 backbone.
@@ -63,7 +64,7 @@ static const std::vector<float> RPN_ANCHOR_SCALES = {32, 64, 128, 256, 512};
 
 // Ratios of anchors at each cell (width/height)
 // A value of 1 represents a square anchor, and 0.5 is a wide anchor
-static const float RPN_ANCHOR_RATIOS[] = {0.5, 1, 2};
+static const float RPN_ANCHOR_RATIOS[] = {0.5f, 1, 2};
 
 // Anchor stride
 // If 1 then anchors are created for each cell in the backbone feature map.
@@ -76,7 +77,7 @@ static const int MAX_PRE_NMS_RESULTS = 1024; // 3840;
 
 // Non-max suppression threshold to filter RPN proposals.
 // You can increase this during training to generate more propsals.
-static const float RPN_NMS_THRESHOLD = 0.7;
+static const float RPN_NMS_THRESHOLD = 0.7f;
 
 // ROIs kept after non-maximum suppression (training and inference)
 static const int POST_NMS_ROIS_INFERENCE = 1000;
@@ -167,12 +168,8 @@ static const std::vector<std::string> CLASS_NAMES = {
 };
 
 static const std::string MODEL_NAME = "mrcnn_nchw.uff";
-//"input_anchors" is not working as input for .uff model.
-// The anchors are generated in proposallayer plugin.for
-// resnet101 + 1024*1024 input shape: (FP2: 256*256 + FP3: 128*128 + FP4:
-// 64*64 + FP5: 32*32 + FP6(maxpooling) 16*16 = 87296)
 static const std::string MODEL_INPUT = "input_image";
-static const DimsCHW MODEL_INPUT_SHAPE = IMAGE_SHAPE;
+static const Dims3 MODEL_INPUT_SHAPE = IMAGE_SHAPE;
 static const std::vector<std::string> MODEL_OUTPUTS = {"mrcnn_detection", "mrcnn_mask/Sigmoid"};
 static const Dims2 MODEL_DETECTION_SHAPE{DETECTION_MAX_INSTANCES, 6};
 static const Dims4 MODEL_MASK_SHAPE{DETECTION_MAX_INSTANCES, NUM_CLASSES, 28, 28};
diff --git a/plugin/proposalLayerPlugin/proposalLayerPlugin.cpp b/plugin/proposalLayerPlugin/proposalLayerPlugin.cpp
index a5a32bb7..a29379b8 100644
--- a/plugin/proposalLayerPlugin/proposalLayerPlugin.cpp
+++ b/plugin/proposalLayerPlugin/proposalLayerPlugin.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -163,7 +163,9 @@ const char* ProposalLayer::getPluginVersion() const
 
 IPluginV2Ext* ProposalLayer::clone() const
 {
-    return new ProposalLayer(*this);
+    auto plugin = new ProposalLayer(*this);
+    plugin->setPluginNamespace(mNameSpace.c_str());
+    return plugin;
 };
 
 void ProposalLayer::setPluginNamespace(const char* libNamespace)
diff --git a/plugin/proposalLayerPlugin/proposalLayerPlugin.h b/plugin/proposalLayerPlugin/proposalLayerPlugin.h
index 1ddf9b65..a694a325 100644
--- a/plugin/proposalLayerPlugin/proposalLayerPlugin.h
+++ b/plugin/proposalLayerPlugin/proposalLayerPlugin.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/plugin/proposalPlugin/CMakeLists.txt b/plugin/proposalPlugin/CMakeLists.txt
index de8fff17..85d8ce4e 100644
--- a/plugin/proposalPlugin/CMakeLists.txt
+++ b/plugin/proposalPlugin/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/plugin/proposalPlugin/README.md b/plugin/proposalPlugin/README.md
index 4cacd6b6..ab0fb940 100644
--- a/plugin/proposalPlugin/README.md
+++ b/plugin/proposalPlugin/README.md
@@ -82,4 +82,4 @@ This is the first release of this `README.md` file.
 
 ## Known issues
 
-There are no known issues in this plugin.
\ No newline at end of file
+There are no known issues in this plugin.
diff --git a/plugin/proposalPlugin/proposalPlugin.cpp b/plugin/proposalPlugin/proposalPlugin.cpp
index c552abcc..39179635 100644
--- a/plugin/proposalPlugin/proposalPlugin.cpp
+++ b/plugin/proposalPlugin/proposalPlugin.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -73,9 +73,9 @@ ProposalPlugin::ProposalPlugin(const std::string name, int input_height, int inp
     , mBboxMinSize(bbox_min_size)
     , mNmsIouThreshold(nms_iou_threshold)
     , mPreNmsTopN(pre_nms_top_n)
+    , mMaxBoxNum(max_box_num)
     , mAnchorSizeNum(anc_size_num)
     , mAnchorRatioNum(anc_ratio_num)
-    , mMaxBoxNum(max_box_num)
 {
     for (int i = 0; i < anc_size_num; ++i)
     {
@@ -99,9 +99,9 @@ ProposalPlugin::ProposalPlugin(const std::string name, int input_height, int inp
     , mBboxMinSize(bbox_min_size)
     , mNmsIouThreshold(nms_iou_threshold)
     , mPreNmsTopN(pre_nms_top_n)
+    , mMaxBoxNum(max_box_num)
     , mAnchorSizeNum(anc_size_num)
     , mAnchorRatioNum(anc_ratio_num)
-    , mMaxBoxNum(max_box_num)
 {
     for (int i = 0; i < anc_size_num; ++i)
     {
@@ -132,12 +132,12 @@ ProposalPlugin::ProposalPlugin(const std::string name, const void* serial_buf, s
     mBboxMinSize = readFromBuffer<float>(a);
     mNmsIouThreshold = readFromBuffer<float>(a);
 
-    for (int i = 0; i < mAnchorSizeNum; ++i)
+    for (size_t i = 0; i < mAnchorSizeNum; ++i)
     {
         mAnchorSizes.push_back(readFromBuffer<float>(a));
     }
 
-    for (int i = 0; i < mAnchorRatioNum; ++i)
+    for (size_t i = 0; i < mAnchorRatioNum; ++i)
     {
         mAnchorRatios.push_back(readFromBuffer<float>(a));
     }
@@ -219,12 +219,12 @@ void ProposalPlugin::serialize(void* buffer) const
     writeToBuffer<float>(a, mBboxMinSize);
     writeToBuffer<float>(a, mNmsIouThreshold);
 
-    for (int i = 0; i < mAnchorSizeNum; ++i)
+    for (size_t i = 0; i < mAnchorSizeNum; ++i)
     {
         writeToBuffer<float>(a, mAnchorSizes[i]);
     }
 
-    for (int i = 0; i < mAnchorRatioNum; ++i)
+    for (size_t i = 0; i < mAnchorRatioNum; ++i)
     {
         writeToBuffer<float>(a, mAnchorRatios[i]);
     }
diff --git a/plugin/proposalPlugin/proposalPlugin.h b/plugin/proposalPlugin/proposalPlugin.h
index f1aef680..82463189 100644
--- a/plugin/proposalPlugin/proposalPlugin.h
+++ b/plugin/proposalPlugin/proposalPlugin.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -99,6 +99,10 @@ class ProposalPlugin : public IPluginV2Ext
 
     void detachFromContext() override;
 
+    // ProposalPlugin(const std::string name, int input_height, int input_width, int rpn_height, int rpn_width,
+    //     float rpn_std_scaling, int rpn_stride, float bbox_min_size, float nms_iou_threshold, int pre_nms_top_n,
+    //     int max_box_num, const float* anchor_sizes, int anc_size_num, const float* anchor_ratios, int anc_ratio_num);
+
 private:
     const std::string mLayerName;
     std::string mNamespace;
@@ -110,11 +114,12 @@ class ProposalPlugin : public IPluginV2Ext
     size_t mRpnStride;
     float mBboxMinSize;
     float mNmsIouThreshold;
-    size_t mMaxBoxNum;
     size_t mPreNmsTopN;
-    size_t mAnchorSizeNum, mAnchorRatioNum;
+    size_t mMaxBoxNum;
     std::vector<float> mAnchorSizes;
+    size_t mAnchorSizeNum;
     std::vector<float> mAnchorRatios;
+    size_t mAnchorRatioNum;
 };
 
 class ProposalPluginCreator : public BaseCreator
diff --git a/plugin/pyramidROIAlignPlugin/CMakeLists.txt b/plugin/pyramidROIAlignPlugin/CMakeLists.txt
index de8fff17..85d8ce4e 100644
--- a/plugin/pyramidROIAlignPlugin/CMakeLists.txt
+++ b/plugin/pyramidROIAlignPlugin/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/plugin/pyramidROIAlignPlugin/pyramidROIAlignPlugin.cpp b/plugin/pyramidROIAlignPlugin/pyramidROIAlignPlugin.cpp
index accdad50..544581c6 100644
--- a/plugin/pyramidROIAlignPlugin/pyramidROIAlignPlugin.cpp
+++ b/plugin/pyramidROIAlignPlugin/pyramidROIAlignPlugin.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -125,7 +125,9 @@ const char* PyramidROIAlign::getPluginVersion() const
 
 IPluginV2Ext* PyramidROIAlign::clone() const
 {
-    return new PyramidROIAlign(*this);
+    auto plugin = new PyramidROIAlign(*this);
+    plugin->setPluginNamespace(mNameSpace.c_str());
+    return plugin;
 };
 
 void PyramidROIAlign::setPluginNamespace(const char* libNamespace)
diff --git a/plugin/pyramidROIAlignPlugin/pyramidROIAlignPlugin.h b/plugin/pyramidROIAlignPlugin/pyramidROIAlignPlugin.h
index 22ee321f..8034ffda 100644
--- a/plugin/pyramidROIAlignPlugin/pyramidROIAlignPlugin.h
+++ b/plugin/pyramidROIAlignPlugin/pyramidROIAlignPlugin.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/plugin/regionPlugin/CMakeLists.txt b/plugin/regionPlugin/CMakeLists.txt
index de8fff17..85d8ce4e 100644
--- a/plugin/regionPlugin/CMakeLists.txt
+++ b/plugin/regionPlugin/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/plugin/regionPlugin/regionPlugin.cpp b/plugin/regionPlugin/regionPlugin.cpp
index b7a95408..62817c5c 100644
--- a/plugin/regionPlugin/regionPlugin.cpp
+++ b/plugin/regionPlugin/regionPlugin.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -41,6 +41,35 @@ void allocateChunk(T*& ptr, int count)
 {
     ptr = static_cast<T*>(malloc(count * sizeof(T)));
 }
+
+struct SoftmaxTreeDeleter
+{
+    void operator()(softmaxTree* smTree) const
+    {
+        if (smTree)
+        {
+            // free individual elements first
+            safeFree(smTree->leaf);
+            safeFree(smTree->parent);
+            safeFree(smTree->child);
+            safeFree(smTree->group);
+            if (smTree->name)
+            {
+                for (int i = 0; i < smTree->n; i++)
+                {
+                    safeFree(smTree->name[i]);
+                }
+                safeFree(smTree->name);
+            }
+            safeFree(smTree->groupSize);
+            safeFree(smTree->groupOffset);
+
+            // free softmax tree
+            safeFree(smTree);
+        }
+    }
+};
+
 } // namespace
 
 PluginFieldCollection RegionPluginCreator::mFC{};
@@ -50,7 +79,7 @@ Region::Region(RegionParameters params)
     : num(params.num)
     , coords(params.coords)
     , classes(params.classes)
-    , smTree(params.smTree)
+    , smTree(params.smTree, SoftmaxTreeDeleter())
 {
 }
 
@@ -58,7 +87,7 @@ Region::Region(RegionParameters params, int C, int H, int W)
     : num(params.num)
     , coords(params.coords)
     , classes(params.classes)
-    , smTree(params.smTree)
+    , smTree(params.smTree, SoftmaxTreeDeleter())
     , C(C)
     , H(H)
     , W(W)
@@ -84,117 +113,119 @@ Region::Region(const void* buffer, size_t length)
     bool groupOffsetPresent = read<bool>(d);
     if (softmaxTreePresent)
     {
+        softmaxTree* smTreeTemp;
         // need to read each element individually
-        allocateChunk(smTree, 1);
+        allocateChunk(smTreeTemp, 1);
 
-        smTree->n = read<int>(d);
+        smTreeTemp->n = read<int>(d);
 
         if (leafPresent)
         {
-            allocateChunk(smTree->leaf, smTree->n);
+            allocateChunk(smTreeTemp->leaf, smTreeTemp->n);
         }
         else
         {
-            smTree->leaf = nullptr;
+            smTreeTemp->leaf = nullptr;
         }
         if (parentPresent)
         {
-            allocateChunk(smTree->parent, smTree->n);
+            allocateChunk(smTreeTemp->parent, smTreeTemp->n);
         }
         else
         {
-            smTree->parent = nullptr;
+            smTreeTemp->parent = nullptr;
         }
         if (childPresent)
         {
-            allocateChunk(smTree->child, smTree->n);
+            allocateChunk(smTreeTemp->child, smTreeTemp->n);
         }
         else
         {
-            smTree->child = nullptr;
+            smTreeTemp->child = nullptr;
         }
         if (groupPresent)
         {
-            allocateChunk(smTree->group, smTree->n);
+            allocateChunk(smTreeTemp->group, smTreeTemp->n);
         }
         else
         {
-            smTree->group = nullptr;
+            smTreeTemp->group = nullptr;
         }
 
-        for (int i = 0; i < smTree->n; i++)
+        for (int i = 0; i < smTreeTemp->n; i++)
         {
             if (leafPresent)
             {
-                smTree->leaf[i] = read<int>(d);
+                smTreeTemp->leaf[i] = read<int>(d);
             }
             if (parentPresent)
             {
-                smTree->parent[i] = read<int>(d);
+                smTreeTemp->parent[i] = read<int>(d);
             }
             if (childPresent)
             {
-                smTree->child[i] = read<int>(d);
+                smTreeTemp->child[i] = read<int>(d);
             }
             if (groupPresent)
             {
-                smTree->group[i] = read<int>(d);
+                smTreeTemp->group[i] = read<int>(d);
             }
         }
 
         if (namePresent)
         {
-            allocateChunk(smTree->name, smTree->n);
+            allocateChunk(smTreeTemp->name, smTreeTemp->n);
         }
         else
         {
-            smTree->name = nullptr;
+            smTreeTemp->name = nullptr;
         }
 
         if (namePresent)
         {
-            for (int i = 0; i < smTree->n; i++)
+            for (int i = 0; i < smTreeTemp->n; i++)
             {
-                allocateChunk(smTree->name[i], 256);
+                allocateChunk(smTreeTemp->name[i], 256);
                 for (int j = 0; j < 256; j++)
                 {
-                    smTree->name[i][j] = read<char>(d);
+                    smTreeTemp->name[i][j] = read<char>(d);
                 }
             }
         }
 
-        smTree->groups = read<int>(d);
+        smTreeTemp->groups = read<int>(d);
         if (groupSizePresent)
         {
-            allocateChunk(smTree->groupSize, smTree->groups);
+            allocateChunk(smTreeTemp->groupSize, smTreeTemp->groups);
         }
         else
         {
-            smTree->groupSize = nullptr;
+            smTreeTemp->groupSize = nullptr;
         }
         if (groupOffsetPresent)
         {
-            allocateChunk(smTree->groupOffset, smTree->groups);
+            allocateChunk(smTreeTemp->groupOffset, smTreeTemp->groups);
         }
         else
         {
-            smTree->groupOffset = nullptr;
+            smTreeTemp->groupOffset = nullptr;
         }
-        for (int i = 0; i < smTree->groups; i++)
+        for (int i = 0; i < smTreeTemp->groups; i++)
         {
             if (groupSizePresent)
             {
-                smTree->groupSize[i] = read<int>(d);
+                smTreeTemp->groupSize[i] = read<int>(d);
             }
             if (groupOffsetPresent)
             {
-                smTree->groupOffset[i] = read<int>(d);
+                smTreeTemp->groupOffset[i] = read<int>(d);
             }
         }
+        smTree = std::shared_ptr<softmaxTree>(smTreeTemp, SoftmaxTreeDeleter());
     }
     else
     {
-        smTree = nullptr;
+        smTree.reset();
     }
     ASSERT(d == a + length);
 }
@@ -215,7 +246,7 @@ int Region::enqueue(int batchSize, const void* const* inputs, void** outputs, vo
 {
     const void* inputData = inputs[0];
     void* outputData = outputs[0];
-    if (smTree)
+    if (smTree.get())
     {
         hasSoftmaxTree = true;
     }
@@ -224,7 +255,7 @@ int Region::enqueue(int batchSize, const void* const* inputs, void** outputs, vo
         hasSoftmaxTree = false;
     }
     pluginStatus_t status = regionInference(
-        stream, batchSize, C, H, W, num, coords, classes, hasSoftmaxTree, smTree, inputData, outputData);
+        stream, batchSize, C, H, W, num, coords, classes, hasSoftmaxTree, smTree.get(), inputData, outputData);
     ASSERT(status == STATUS_SUCCESS);
     return status;
 }
@@ -233,7 +264,7 @@ size_t Region::getSerializationSize() const
 {
     // C, H, W, num, classes, coords, smTree !nullptr and other array members !nullptr, softmaxTree members
     size_t count = 6 * sizeof(int) + 8 * sizeof(bool);
-    if (smTree)
+    if (smTree.get())
     {
         count += 2 * sizeof(int);
 
@@ -278,14 +309,14 @@ void Region::serialize(void* buffer) const
     write(d, num);
     write(d, classes);
     write(d, coords);
-    write(d, smTree != nullptr);
-    write(d, smTree != nullptr && smTree->leaf != nullptr);
-    write(d, smTree != nullptr && smTree->parent != nullptr);
-    write(d, smTree != nullptr && smTree->child != nullptr);
-    write(d, smTree != nullptr && smTree->group != nullptr);
-    write(d, smTree != nullptr && smTree->name != nullptr);
-    write(d, smTree != nullptr && smTree->groupSize != nullptr);
-    write(d, smTree != nullptr && smTree->groupOffset != nullptr);
+    write(d, smTree.get() != nullptr);
+    write(d, smTree.get() != nullptr && smTree->leaf != nullptr);
+    write(d, smTree.get() != nullptr && smTree->parent != nullptr);
+    write(d, smTree.get() != nullptr && smTree->child != nullptr);
+    write(d, smTree.get() != nullptr && smTree->group != nullptr);
+    write(d, smTree.get() != nullptr && smTree->name != nullptr);
+    write(d, smTree.get() != nullptr && smTree->groupSize != nullptr);
+    write(d, smTree.get() != nullptr && smTree->groupOffset != nullptr);
     // need to do a deep copy
     if (smTree)
     {
@@ -346,31 +377,7 @@ int Region::initialize()
     return STATUS_SUCCESS;
 }
 
-void Region::terminate()
-{
-    // Do this carefully to guard against double frees
-    if (smTree)
-    {
-        // free individual elements first
-        safeFree(smTree->leaf);
-        safeFree(smTree->parent);
-        safeFree(smTree->child);
-        safeFree(smTree->group);
-        if (smTree->name)
-        {
-            for (int i = 0; i < smTree->n; i++)
-            {
-                safeFree(smTree->name[i]);
-            }
-            safeFree(smTree->name);
-        }
-        safeFree(smTree->groupSize);
-        safeFree(smTree->groupOffset);
-
-        // free softmax tree
-        safeFree(smTree);
-    }
-}
+void Region::terminate() {}
 
 const char* Region::getPluginType() const
 {
@@ -394,9 +401,11 @@ void Region::destroy()
 
 IPluginV2Ext* Region::clone() const
 {
-    RegionParameters params{num, coords, classes, smTree};
-    IPluginV2Ext* plugin = new Region(params, C, H, W);
-    plugin->setPluginNamespace(mPluginNamespace);
+    RegionParameters params{num, coords, classes, nullptr};
+    Region* plugin = new Region(params, C, H, W);
+    plugin->setPluginNamespace(mPluginNamespace.c_str());
+    plugin->setSoftmaxTree(smTree);
+
     return plugin;
 }
 
@@ -408,7 +417,7 @@ void Region::setPluginNamespace(const char* pluginNamespace)
 
 const char* Region::getPluginNamespace() const
 {
-    return mPluginNamespace;
+    return mPluginNamespace.c_str();
 }
 
 // Return the DataType of the plugin output at the requested index
diff --git a/plugin/regionPlugin/regionPlugin.h b/plugin/regionPlugin/regionPlugin.h
index a62833de..91790640 100644
--- a/plugin/regionPlugin/regionPlugin.h
+++ b/plugin/regionPlugin/regionPlugin.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -18,6 +18,7 @@
 #include "kernel.h"
 #include "plugin.h"
 #include <iostream>
+#include <memory>
 #include <vector>
 
 namespace nvinfer1
@@ -82,14 +83,19 @@ class Region : public IPluginV2Ext
 
     void detachFromContext() override;
 
+    void setSoftmaxTree(const std::shared_ptr<softmaxTree>& softmaxTree)
+    {
+        smTree = softmaxTree;
+    }
+
 private:
-    int C, H, W;
     int num;
     int coords;
     int classes;
-    softmaxTree* smTree;
+    std::shared_ptr<softmaxTree> smTree;
+    int C, H, W;
     bool hasSoftmaxTree;
-    const char* mPluginNamespace;
+    std::string mPluginNamespace;
 };
 
 class RegionPluginCreator : public BaseCreator
diff --git a/plugin/reorgPlugin/CMakeLists.txt b/plugin/reorgPlugin/CMakeLists.txt
index de8fff17..85d8ce4e 100644
--- a/plugin/reorgPlugin/CMakeLists.txt
+++ b/plugin/reorgPlugin/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/plugin/reorgPlugin/reorgPlugin.cpp b/plugin/reorgPlugin/reorgPlugin.cpp
index 651e86ae..3951824c 100644
--- a/plugin/reorgPlugin/reorgPlugin.cpp
+++ b/plugin/reorgPlugin/reorgPlugin.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -24,6 +24,14 @@ static const char* REORG_PLUGIN_NAME{"Reorg_TRT"};
 PluginFieldCollection ReorgPluginCreator::mFC{};
 std::vector<PluginField> ReorgPluginCreator::mPluginAttributes;
 
+Reorg::Reorg(int C, int H, int W, int stride)
+    : C(C)
+    , H(H)
+    , W(W)
+    , stride(stride)
+{
+}
+
 Reorg::Reorg(int stride)
     : stride(stride)
 {
@@ -116,7 +124,7 @@ void Reorg::setPluginNamespace(const char* pluginNamespace)
 
 const char* Reorg::getPluginNamespace() const
 {
-    return mPluginNamespace;
+    return mPluginNamespace.c_str();
 }
 
 // Return the DataType of the plugin output at the requested index
@@ -165,8 +173,8 @@ void Reorg::detachFromContext() {}
 
 IPluginV2Ext* Reorg::clone() const
 {
-    IPluginV2Ext* plugin = new Reorg(stride);
-    plugin->setPluginNamespace(mPluginNamespace);
+    IPluginV2Ext* plugin = new Reorg(C, H, W, stride);
+    plugin->setPluginNamespace(mPluginNamespace.c_str());
     return plugin;
 }
 
diff --git a/plugin/reorgPlugin/reorgPlugin.h b/plugin/reorgPlugin/reorgPlugin.h
index 2899de26..5d5c2c5b 100644
--- a/plugin/reorgPlugin/reorgPlugin.h
+++ b/plugin/reorgPlugin/reorgPlugin.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -30,6 +30,8 @@ class Reorg : public IPluginV2Ext
 public:
     Reorg(int stride);
 
+    Reorg(int C, int H, int W, int stride);
+
     Reorg(const void* buffer, size_t length);
 
     ~Reorg() override = default;
@@ -81,9 +83,9 @@ class Reorg : public IPluginV2Ext
     void detachFromContext() override;
 
 private:
-    int C, H, W;
-    int stride;
-    const char* mPluginNamespace;
+    int C{}, H{}, W{};
+    int stride{};
+    std::string mPluginNamespace;
 };
 
 class ReorgPluginCreator : public BaseCreator
@@ -105,7 +107,7 @@ class ReorgPluginCreator : public BaseCreator
 
 private:
     static PluginFieldCollection mFC;
-    int stride;
+    int stride{};
     static std::vector<PluginField> mPluginAttributes;
 };
 } // namespace plugin
diff --git a/plugin/resizeNearestPlugin/CMakeLists.txt b/plugin/resizeNearestPlugin/CMakeLists.txt
index de8fff17..85d8ce4e 100644
--- a/plugin/resizeNearestPlugin/CMakeLists.txt
+++ b/plugin/resizeNearestPlugin/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/plugin/resizeNearestPlugin/resizeNearestPlugin.cpp b/plugin/resizeNearestPlugin/resizeNearestPlugin.cpp
index e7733a71..c1295ead 100644
--- a/plugin/resizeNearestPlugin/resizeNearestPlugin.cpp
+++ b/plugin/resizeNearestPlugin/resizeNearestPlugin.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -15,6 +15,7 @@
  */
 #include "resizeNearestPlugin.h"
 #include "plugin.h"
+#include <algorithm>
 #include <cuda_runtime_api.h>
 #include <iostream>
 
@@ -45,17 +46,17 @@ ResizeNearestPluginCreator::ResizeNearestPluginCreator()
 const char* ResizeNearestPluginCreator::getPluginName() const
 {
     return RESIZE_PLUGIN_NAME;
-};
+}
 
 const char* ResizeNearestPluginCreator::getPluginVersion() const
 {
     return RESIZE_PLUGIN_VERSION;
-};
+}
 
 const PluginFieldCollection* ResizeNearestPluginCreator::getFieldNames()
 {
     return &mFC;
-};
+}
 
 IPluginV2Ext* ResizeNearestPluginCreator::createPlugin(const char* name, const PluginFieldCollection* fc)
 {
@@ -70,23 +71,23 @@ IPluginV2Ext* ResizeNearestPluginCreator::createPlugin(const char* name, const P
         }
     }
     return new ResizeNearest(mScale);
-};
+}
 
 IPluginV2Ext* ResizeNearestPluginCreator::deserializePlugin(const char* name, const void* data, size_t length)
 {
     return new ResizeNearest(data, length);
-};
+}
 
 ResizeNearest::ResizeNearest(float scale)
     : mScale(scale)
 {
     assert(mScale > 0);
-};
+}
 
 int ResizeNearest::getNbOutputs() const
 {
     return 1;
-};
+}
 
 Dims ResizeNearest::getOutputDimensions(int index, const Dims* inputDims, int nbInputs)
 {
@@ -107,20 +108,19 @@ Dims ResizeNearest::getOutputDimensions(int index, const Dims* inputDims, int nb
         }
     }
     return output;
-};
+}
 
 int ResizeNearest::initialize()
 {
     return 0;
-};
-
-void ResizeNearest::terminate(){
-
-};
+}
 
-void ResizeNearest::destroy(){
+void ResizeNearest::terminate() {}
 
-};
+void ResizeNearest::destroy()
+{
+    delete this;
+}
 
 size_t ResizeNearest::getWorkspaceSize(int) const
 {
@@ -131,7 +131,7 @@ size_t ResizeNearest::getSerializationSize() const
 {
     // scale, dimensions: 3 * 2
     return sizeof(float) + sizeof(int) * 3 * 2;
-};
+}
 
 void ResizeNearest::serialize(void* buffer) const
 {
@@ -144,7 +144,7 @@ void ResizeNearest::serialize(void* buffer) const
     write(d, mOutputDims.d[1]);
     write(d, mOutputDims.d[2]);
     ASSERT(d == a + getSerializationSize());
-};
+}
 
 ResizeNearest::ResizeNearest(const void* data, size_t length)
 {
@@ -159,27 +159,29 @@ ResizeNearest::ResizeNearest(const void* data, size_t length)
     mOutputDims.d[1] = read<int>(d);
     mOutputDims.d[2] = read<int>(d);
     ASSERT(d == a + length);
-};
+}
 
 const char* ResizeNearest::getPluginType() const
 {
     return "ResizeNearest_TRT";
-};
+}
 
 const char* ResizeNearest::getPluginVersion() const
 {
     return "1";
-};
+}
 
 IPluginV2Ext* ResizeNearest::clone() const
 {
-    return new ResizeNearest(*this);
-};
+    auto plugin = new ResizeNearest(*this);
+    plugin->setPluginNamespace(mNameSpace.c_str());
+    return plugin;
+}
 
 void ResizeNearest::setPluginNamespace(const char* libNamespace)
 {
     mNameSpace = libNamespace;
-};
+}
 
 const char* ResizeNearest::getPluginNamespace() const
 {
@@ -189,7 +191,7 @@ const char* ResizeNearest::getPluginNamespace() const
 bool ResizeNearest::supportsFormat(DataType type, PluginFormat format) const
 {
     return (type == DataType::kFLOAT && format == PluginFormat::kNCHW);
-};
+}
 
 int ResizeNearest::enqueue(
     int batch_size, const void* const* inputs, void** outputs, void* workspace, cudaStream_t stream)
@@ -209,7 +211,7 @@ int ResizeNearest::enqueue(
         ibatchstride, static_cast<float*>(outputs[0]), ostride, obatchstride);
 
     return cudaGetLastError() != cudaSuccess;
-};
+}
 
 // Return the DataType of the plugin output at the requested index
 DataType ResizeNearest::getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const
diff --git a/plugin/resizeNearestPlugin/resizeNearestPlugin.h b/plugin/resizeNearestPlugin/resizeNearestPlugin.h
index 471ce9a4..a34bf7d9 100644
--- a/plugin/resizeNearestPlugin/resizeNearestPlugin.h
+++ b/plugin/resizeNearestPlugin/resizeNearestPlugin.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/plugin/skipLayerNormPlugin/CMakeLists.txt b/plugin/skipLayerNormPlugin/CMakeLists.txt
index 39f2d485..443b76ac 100644
--- a/plugin/skipLayerNormPlugin/CMakeLists.txt
+++ b/plugin/skipLayerNormPlugin/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,6 +13,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
+file(GLOB SRCS *.cpp)
+set(PLUGIN_SOURCES ${PLUGIN_SOURCES} ${SRCS})
+set(PLUGIN_SOURCES ${PLUGIN_SOURCES} PARENT_SCOPE)
 file(GLOB CU_SRCS *.cu)
 set(BERT_CU_SOURCES ${BERT_CU_SOURCES} ${CU_SRCS})
 set(BERT_CU_SOURCES ${BERT_CU_SOURCES} PARENT_SCOPE)
diff --git a/plugin/skipLayerNormPlugin/skipLayerNormKernel.cu b/plugin/skipLayerNormPlugin/skipLayerNormKernel.cu
new file mode 100644
index 00000000..9714503c
--- /dev/null
+++ b/plugin/skipLayerNormPlugin/skipLayerNormKernel.cu
@@ -0,0 +1,296 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuda.h>
+#if CUDA_VERSION >= 10010
+
+#include "NvInfer.h"
+#include "bertCommon.h"
+#include "common.cuh"
+#include "serialize.hpp"
+#include "skipLayerNormPlugin.h"
+
+#include <cassert>
+#include <cstring>
+#include <vector>
+
+using namespace nvinfer1;
+
+namespace bert
+{
+
+template <int TPB, int VPT, bool hasBias>
+__global__ void skiplnDQQ(const int ld, const int8_t* input, const int8_t* skip, int8_t* output, const __half* beta,
+    const __half* gamma, const __half* bias, const float dqScaleIn, const float dqScaleSkip, const float qScale)
+{
+    const int idx = ld * blockIdx.x + threadIdx.x * VPT;
+    // 4 * 1024 * 4 * 2 Bytes = 16KB per block
+    int8_t in_local[VPT];
+    int8_t skip_local[VPT];
+
+    __half in_local_dq[VPT]; // dequantized input + skip + bias
+    __half bias_local[VPT];  // bias and beta
+    __half gamma_local[VPT];
+    copy<sizeof(int8_t) * VPT>(&input[idx], in_local);
+    copy<sizeof(int8_t) * VPT>(&skip[idx], skip_local);
+    copy<sizeof(__half) * VPT>(&bias[threadIdx.x * VPT], bias_local);
+    __half2 loc = __floats2half2_rn(0.f, 0.f); // accumulator
+
+    const __half rld = __half(1) / __half(ld);
+#pragma unroll
+    for (int it = 0; it < VPT; it++)
+    {
+        // DQ input and skip
+        const float tmp_in = in_local[it];
+        const float tmp_skip = skip_local[it];
+        in_local_dq[it] = dqScaleIn * tmp_in + dqScaleSkip * tmp_skip;
+
+        if (hasBias)
+            in_local_dq[it] += bias_local[it];
+        const __half tmp = rld * in_local_dq[it];
+        const __half2 tmp2 = __halves2half2(tmp, tmp * in_local_dq[it]);
+        loc = loc + tmp2;
+    }
+    // load parameters
+    copy<sizeof(__half) * VPT>(&beta[threadIdx.x * VPT], bias_local);
+    copy<sizeof(__half) * VPT>(&gamma[threadIdx.x * VPT], gamma_local);
+
+    using BlockReduce = cub::BlockReduce<__half2, TPB>;
+    __shared__ typename BlockReduce::TempStorage temp_storage;
+    __shared__ __half mu;     // mean
+    __shared__ __half rsigma; // 1 / std.dev.
+
+    const __half2 sum2 = BlockReduce(temp_storage).Reduce(loc, cub::Sum());
+
+    if (threadIdx.x == 0)
+    {
+        mu = __low2half(sum2);
+        rsigma = rsqrt(__high2half(sum2) - mu * mu );
+    }
+    __syncthreads();
+#pragma unroll
+    for (int it = 0; it < VPT; it++)
+    {
+        // apply layernorm
+        const float tmp = gamma_local[it] * (in_local_dq[it] - mu) * rsigma + bias_local[it];
+        // Quantize
+        int tmpq = __float2int_rn(qScale * tmp);
+        tmpq = max(-127, tmpq);
+        tmpq = min(127, tmpq);
+        in_local[it] = tmpq;
+    }
+
+    copy<sizeof(int8_t) * VPT>(in_local, &output[idx]);
+}
+
+template <typename T, int TPB, int VPT, bool hasBias>
+__global__ void skipln_vec(
+    const int ld, const T* input, const T* skip, T* output, const T* beta, const T* gamma, const T* bias)
+{
+    const int idx = ld * blockIdx.x + threadIdx.x * VPT;
+    // 4 * 1024 * 4 * 2 Bytes = 16KB per block
+    T in_local[VPT];
+    T skip_local[VPT];
+    T bias_local[VPT];
+    // T gamma_local[VPT];
+    copy<sizeof(T) * VPT>(&input[idx], in_local);
+    copy<sizeof(T) * VPT>(&skip[idx], skip_local);
+    copy<sizeof(T) * VPT>(&bias[threadIdx.x * VPT], bias_local);
+    T local = 0.f;
+    T local2 = 0.f;
+
+    const T rld = T(1) / T(ld);
+#pragma unroll
+    for (int it = 0; it < VPT; it++)
+    {
+        in_local[it] += skip_local[it];
+        if (hasBias)
+            in_local[it] += bias_local[it];
+        const T tmp = rld * in_local[it];
+        local += tmp;
+        local2 += tmp * in_local[it];
+    }
+
+    copy<sizeof(T) * VPT>(&beta[threadIdx.x * VPT], bias_local);
+    copy<sizeof(T) * VPT>(&gamma[threadIdx.x * VPT], skip_local);
+
+    using BlockReduce = cub::BlockReduce<kvp<T>, TPB>;
+    __shared__ typename BlockReduce::TempStorage temp_storage;
+    __shared__ T mu;     // mean
+    __shared__ T rsigma; // 1 / std.dev.
+
+    const auto sumKV = BlockReduce(temp_storage).Reduce(kvp<T>(local, local2), cub::Sum());
+
+    if (threadIdx.x == 0)
+    {
+        mu = sumKV.key;
+        rsigma = rsqrt(sumKV.value - mu * mu );
+    }
+    __syncthreads();
+    ///*
+#pragma unroll
+    for (int it = 0; it < VPT; it++)
+    {
+        in_local[it] = skip_local[it] * (in_local[it] - mu) * rsigma + bias_local[it];
+    }
+    /* */
+
+    copy<sizeof(T) * VPT>(in_local, &output[idx]);
+}
+
+template <typename T, unsigned TPB, bool hasBias>
+__global__ void skipLayerNormKernelSmall(
+    const int ld, const T* input, const T* skip, const T* beta, const T* gamma, T* output, const T* bias)
+{
+
+    const T rld = T(1) / T(ld);
+    const int offset = blockIdx.x * ld;
+
+    cub::Sum pairSum;
+    // reduce x and x^2
+    kvp<T> threadData(0, 0);
+    const int idx = offset + threadIdx.x;
+    T val = 0;
+
+    if (threadIdx.x < ld)
+    {
+
+        val = input[idx] + skip[idx];
+        if (hasBias)
+        {
+            val += bias[threadIdx.x];
+        }
+
+        const T rldval = rld * val;
+        threadData = pairSum(threadData, kvp<T>(rldval, rldval * val));
+    }
+
+    layerNormSmall<T, T, TPB>(val, threadData, ld, idx, beta, gamma, output);
+}
+
+template <typename T, unsigned TPB, bool hasBias>
+__global__ void skipLayerNormKernel(
+    const int ld, const T* input, const T* skip, const T* beta, const T* gamma, T* output, const T* bias)
+{
+    const T rld = T(1) / T(ld);
+    const int offset = blockIdx.x * ld;
+
+    cub::Sum pairSum;
+    // reduce x and x^2
+    kvp<T> threadData(0, 0);
+
+    for (int i = threadIdx.x; i < ld; i += TPB)
+    {
+        const int idx = offset + i;
+        T val = T(input[idx]) + T(skip[idx]);
+
+        if (hasBias)
+        {
+            val += T(bias[i]);
+        }
+        const T rldval = rld * val;
+        threadData = pairSum(threadData, kvp<T>(rldval, rldval * val));
+        output[idx] = val;
+    }
+
+    layerNorm<T, T, T, TPB>(threadData, ld, offset, beta, gamma, output);
+}
+
+template <bool hasBias>
+int computeSkipLayerNormDQQ(cudaStream_t stream, const int ld, const int n, const int8_t* input, const int8_t* skip,
+    const __half* beta, const __half* gamma, int8_t* output, const __half* bias, const float dqScaleIn,
+    const float dqScaleSkip, const float qScale)
+{
+    // this must be true because n is the total size of the tensor
+    assert(n % ld == 0);
+
+    const int gridSize = n / ld;
+    // we're limited by the size of the parameters, i.e. 8-wide instead of 16
+    constexpr int VPT = 16 / sizeof(__half);
+    if (ld == 768)
+    {
+        constexpr int TPB = 768 / VPT;
+        skiplnDQQ<TPB, VPT, hasBias>
+            <<<gridSize, TPB, 0, stream>>>(ld, input, skip, output, beta, gamma, bias, dqScaleIn, dqScaleSkip, qScale);
+    }
+    else if (ld == 1024)
+    {
+        constexpr int TPB = 1024 / VPT;
+        skiplnDQQ<TPB, VPT, hasBias>
+            <<<gridSize, TPB, 0, stream>>>(ld, input, skip, output, beta, gamma, bias, dqScaleIn, dqScaleSkip, qScale);
+    }
+    else
+    {
+        // TODO need to implement this
+        gLogError << "SkipLayerNormDQQ - FATAL: unsupported hidden layer size: " << ld << std::endl;
+        exit(0);
+    }
+    CHECK(cudaPeekAtLastError());
+
+    return 0;
+}
+
+template <typename T, bool hasBias>
+int computeSkipLayerNorm(cudaStream_t stream, const int ld, const int n, const T* input, const T* skip, const T* beta,
+    const T* gamma, T* output, const T* bias)
+{
+
+    // this must be true because n is the total size of the tensor
+    assert(n % ld == 0);
+    const int gridSize = n / ld;
+    constexpr int VPT = 16 / sizeof(T);
+    if (ld <= 32)
+    {
+        constexpr int blockSize = 32;
+        skipLayerNormKernelSmall<T, blockSize, hasBias>
+            <<<gridSize, blockSize, 0, stream>>>(ld, input, skip, beta, gamma, output, bias);
+    }
+    else if (ld == 768)
+    {
+        constexpr int TPB = 768 / VPT;
+        skipln_vec<T, TPB, VPT, hasBias><<<gridSize, TPB, 0, stream>>>(ld, input, skip, output, beta, gamma, bias);
+    }
+    else if (ld == 1024)
+    {
+        constexpr int TPB = 1024 / VPT;
+        skipln_vec<T, TPB, VPT, hasBias><<<gridSize, TPB, 0, stream>>>(ld, input, skip, output, beta, gamma, bias);
+    }
+    else
+    {
+        constexpr int blockSize = 256;
+        skipLayerNormKernel<T, blockSize, hasBias>
+            <<<gridSize, blockSize, 0, stream>>>(ld, input, skip, beta, gamma, output, bias);
+    }
+    CHECK(cudaPeekAtLastError());
+
+    return 0;
+}
+
+template int computeSkipLayerNormDQQ<true>(cudaStream_t stream, const int ld, const int n, const int8_t* input, const int8_t* skip,
+    const __half* beta, const __half* gamma, int8_t* output, const __half* bias, const float dqScaleIn,
+    const float dqScaleSkip, const float qScale);
+template int computeSkipLayerNormDQQ<false>(cudaStream_t stream, const int ld, const int n, const int8_t* input, const int8_t* skip,
+    const __half* beta, const __half* gamma, int8_t* output, const __half* bias, const float dqScaleIn,
+    const float dqScaleSkip, const float qScale);
+
+template int computeSkipLayerNorm<float, true>(cudaStream_t, const int, const int, const float*, const float*, const float*, const float*, float*, const float*);
+template int computeSkipLayerNorm<float, false>(cudaStream_t, const int, const int, const float*, const float*, const float*, const float*, float*, const float*);
+template int computeSkipLayerNorm<half, true>(cudaStream_t, const int, const int, const half*, const half*, const half*, const half*, half*, const half*);
+template int computeSkipLayerNorm<half, false>(cudaStream_t, const int, const int, const half*, const half*, const half*, const half*, half*, const half*);
+
+} // namespace bert
+
+#endif // CUDA_VERSION >= 10010
diff --git a/plugin/skipLayerNormPlugin/skipLayerNormPlugin.cpp b/plugin/skipLayerNormPlugin/skipLayerNormPlugin.cpp
new file mode 100644
index 00000000..ca1c430e
--- /dev/null
+++ b/plugin/skipLayerNormPlugin/skipLayerNormPlugin.cpp
@@ -0,0 +1,478 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cuda.h>
+#if CUDA_VERSION >= 10010
+
+#include "NvInfer.h"
+#include "serialize.hpp"
+#include "skipLayerNormPlugin.h"
+
+#include <cassert>
+#include <cstring>
+#include <vector>
+
+using namespace nvinfer1;
+
+namespace bert
+{
+
+// Clip plugin specific constants
+namespace
+{
+static const char* SKIP_LAYER_NORM_VERSION{"1"};
+static const char* SKIP_LAYER_NORM_NAME{"CustomSkipLayerNormPluginDynamic"};
+} // namespace
+
+// Static class fields initialization
+PluginFieldCollection SkipLayerNormPluginDynamicCreator::mFC{};
+std::vector<PluginField> SkipLayerNormPluginDynamicCreator::mPluginAttributes;
+
+REGISTER_TENSORRT_PLUGIN(SkipLayerNormPluginDynamicCreator);
+
+static inline DataType getParamWordType(DataType cfgType)
+{
+    if (cfgType == DataType::kINT8)
+    {
+        return DataType::kHALF;
+    }
+
+    return cfgType;
+}
+
+SkipLayerNormPluginDynamic::SkipLayerNormPluginDynamic(const std::string name, const DataType type, const int ld,
+    const Weights& beta, const Weights& gamma, const Weights& bias)
+    : mLayerName(name)
+    , mGammaDev(nullptr)
+    , mBetaDev(nullptr)
+    , mLd(ld)
+    , mType(type)
+    , mBiasDev(nullptr)
+{
+    assert(mType == nvinfer1::DataType::kFLOAT || mType == nvinfer1::DataType::kHALF
+        || mType == nvinfer1::DataType::kINT8);
+    // mCfgType is the dataType for beta, gamma bias weights, always fp16 or fp32
+    // mType is the plugin IO datatype, can be int8
+    mCfgType = mType == DataType::kINT8 ? DataType::kHALF : mType;
+    mParamWordsize = getElementSize(mCfgType);
+
+    mBeta.convertAndCopy(beta, mCfgType);
+    mGamma.convertAndCopy(gamma, mCfgType);
+
+    mHasBias = (bias.values != nullptr);
+    if (mHasBias)
+    {
+        mBias.convertAndCopy(bias, mCfgType);
+    }
+}
+
+SkipLayerNormPluginDynamic::SkipLayerNormPluginDynamic(const std::string name, const void* data, size_t length)
+    : mLayerName(name)
+    , mGammaDev(nullptr)
+    , mBetaDev(nullptr)
+    , mBiasDev(nullptr)
+{
+    gLogVerbose << "SkipLayerNormPluginDynamic deserialize\n";
+
+    // Deserialize in the same order as serialization
+    deserialize_value(&data, &length, &mType);
+    deserialize_value(&data, &length, &mCfgType);
+    deserialize_value(&data, &length, &mLd);
+    deserialize_value(&data, &length, &mHasBias);
+
+    assert(mCfgType == nvinfer1::DataType::kFLOAT || mCfgType == nvinfer1::DataType::kHALF);
+    mParamWordsize = getElementSize(mCfgType);
+
+    const char* d = static_cast<const char*>(data);
+    mBeta.convertAndCopy(d, mLd, mCfgType);
+    mGamma.convertAndCopy(d, mLd, mCfgType);
+    if (mHasBias)
+    {
+        mBias.convertAndCopy(d, mLd, mCfgType);
+    }
+}
+
+// IPluginV2DynamicExt Methods
+IPluginV2DynamicExt* SkipLayerNormPluginDynamic::clone() const
+{
+    gLogVerbose << "SkipLayerNormPluginDynamic clone\n";
+
+    auto p = new SkipLayerNormPluginDynamic(mLayerName, mType, mLd, mBeta, mGamma, mBias);
+    p->initialize();
+    p->setPluginNamespace(mNamespace.c_str());
+    return p;
+}
+
+DimsExprs SkipLayerNormPluginDynamic::getOutputDimensions(
+    int outputIndex, const DimsExprs* inputs, int nbInputs, IExprBuilder& exprBuilder)
+{
+    assert(nbInputs == 2);
+    assert(outputIndex == 0);
+    assert(inputs[0].nbDims == inputs[1].nbDims);
+    return inputs[0];
+}
+
+bool SkipLayerNormPluginDynamic::supportsFormatCombination(
+    int pos, const PluginTensorDesc* inOut, int nbInputs, int nbOutputs)
+{
+    assert(nbInputs == 2);
+    assert(nbOutputs == 1);
+
+    const PluginTensorDesc& in = inOut[pos];
+    if (pos == 0)
+    {
+        // Since H = W = 1, we can report CHWx for any x
+        if (mType == DataType::kINT8)
+        {
+            // won't work for hiddensize too small!
+            TensorFormat myFmt = TensorFormat::kCHW32;
+            if (mLd < 32)
+            {
+                myFmt = TensorFormat::kCHW4;
+                gLogVerbose << "SkipLayerNormDQQ: TensorFormat CHW4"
+                            << " for LD=" << mLd << std::endl;
+            }
+            else
+            {
+                gLogVerbose << "SkipLayerNormDQQ: TensorFormat CHW32"
+                            << " for LD=" << mLd << std::endl;
+            }
+            // TODO do we need to check if the vectorization divides mLd?
+            return ((in.type == mType) && (in.format == myFmt));
+        }
+        return (in.type == mType) && (in.format == TensorFormat::kLINEAR);
+    }
+    const PluginTensorDesc& prev = inOut[pos - 1];
+
+    return in.type == prev.type && in.format == prev.format;
+}
+
+void SkipLayerNormPluginDynamic::configurePlugin(
+    const DynamicPluginTensorDesc* inputs, int nbInputs, const DynamicPluginTensorDesc* outputs, int nbOutputs)
+{
+    gLogVerbose << "SkipLayerNormPluginDynamic configurePlugin\n";
+
+    // Validate input arguments
+    assert(nbOutputs == 1);
+    assert(nbInputs == 2);
+    if (mType == DataType::kFLOAT || mType == DataType::kHALF)
+    {
+        assert(mType == inputs[0].desc.type);
+        assert(mType == inputs[1].desc.type);
+    }
+    else
+    {
+        assert(mType == inputs[0].desc.type || DataType::kFLOAT == inputs[0].desc.type);
+        assert(mType == inputs[1].desc.type || DataType::kFLOAT == inputs[1].desc.type);
+    }
+    const auto& inDims0 = inputs[0].desc.dims;
+    const auto& inDims1 = inputs[1].desc.dims;
+    TRT_UNUSED inDims1;
+    assert(inDims0.nbDims == inDims1.nbDims);
+
+    assert(std::equal(inDims0.d, inDims0.d + inDims0.nbDims, inDims1.d));
+
+    assert(inDims0.nbDims == 5);
+    mLd = inDims0.d[HDIM]; // hiddensize
+    assert(inDims0.d[3] == 1);
+    assert(inDims0.d[4] == 1);
+
+    mCfgType = inputs[0].desc.type == DataType::kINT8 ? DataType::kHALF : inputs[0].desc.type;
+
+    const auto paramType = getParamWordType(mCfgType);
+    mParamWordsize = getElementSize(paramType);
+
+    copyToDevice(mGamma, getWeightsSize(mGamma, paramType), mGammaDev);
+    copyToDevice(mBeta, getWeightsSize(mBeta, paramType), mBetaDev);
+    if (mHasBias)
+    {
+        copyToDevice(mBias, getWeightsSize(mBias, paramType), mBiasDev);
+    }
+}
+
+size_t SkipLayerNormPluginDynamic::getWorkspaceSize(
+    const PluginTensorDesc* inputs, int nbInputs, const PluginTensorDesc* outputs, int nbOutputs) const
+{
+    return 0;
+}
+
+int SkipLayerNormPluginDynamic::enqueue(const PluginTensorDesc* inputDesc, const PluginTensorDesc* outputDesc,
+    const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream)
+{
+    const int inputVolume = volume(inputDesc[0].dims);
+    int status = -1;
+    DataType iType = inputDesc->type;
+
+    // Our plugin outputs only one tensor
+    // Launch CUDA kernel wrapper and save its return value
+    if (iType == DataType::kFLOAT)
+    {
+        const auto input = static_cast<const float*>(inputs[0]);
+        const auto skip = static_cast<const float*>(inputs[1]);
+        auto output = static_cast<float*>(outputs[0]);
+        const auto bias = static_cast<const float*>(mBiasDev.get());
+        const auto beta = static_cast<const float*>(mBetaDev.get());
+        const auto gamma = static_cast<const float*>(mGammaDev.get());
+        if (mHasBias)
+        {
+            status = computeSkipLayerNorm<float, true>(
+                stream, static_cast<int>(mLd), inputVolume, input, skip, beta, gamma, output, bias);
+        }
+        else
+        {
+            status = computeSkipLayerNorm<float, false>(
+                stream, static_cast<int>(mLd), inputVolume, input, skip, beta, gamma, output, bias);
+        }
+    }
+    else if (iType == DataType::kHALF)
+    {
+        const auto input = static_cast<const half*>(inputs[0]);
+        const auto skip = static_cast<const half*>(inputs[1]);
+        auto output = static_cast<half*>(outputs[0]);
+        const auto bias = static_cast<const half*>(mBiasDev.get());
+        const auto beta = static_cast<const half*>(mBetaDev.get());
+        const auto gamma = static_cast<const half*>(mGammaDev.get());
+        if (mHasBias)
+        {
+            status = computeSkipLayerNorm<half, true>(
+                stream, static_cast<int>(mLd), inputVolume, input, skip, beta, gamma, output, bias);
+        }
+        else
+        {
+            status = computeSkipLayerNorm<half, false>(
+                stream, static_cast<int>(mLd), inputVolume, input, skip, beta, gamma, output, bias);
+        }
+    }
+    else if (iType == DataType::kINT8)
+    {
+        const float dqScaleIn = inputDesc[0].scale;
+        const float dqScaleSkip = inputDesc[1].scale;
+        const float qScale = 1.f / outputDesc[0].scale;
+        const auto input = static_cast<const int8_t*>(inputs[0]);
+        const auto skip = static_cast<const int8_t*>(inputs[1]);
+        auto output = static_cast<int8_t*>(outputs[0]);
+        const auto bias = static_cast<const half*>(mBiasDev.get());
+        const auto beta = static_cast<const half*>(mBetaDev.get());
+        const auto gamma = static_cast<const half*>(mGammaDev.get());
+        if (mHasBias)
+        {
+            status = computeSkipLayerNormDQQ<true>(stream, static_cast<int>(mLd), inputVolume, input, skip, beta, gamma,
+                output, bias, dqScaleIn, dqScaleSkip, qScale);
+        }
+        else
+        {
+            status = computeSkipLayerNormDQQ<false>(stream, static_cast<int>(mLd), inputVolume, input, skip, beta,
+                gamma, output, bias, dqScaleIn, dqScaleSkip, qScale);
+        }
+    }
+    else
+    {
+        gLogError << "Unsupported type error, expected [kINT8,kHALF,kFLOAT], but received " << static_cast<int>(iType)
+                  << "." << std::endl;
+        assert(false);
+    }
+    return status;
+}
+
+// IPluginV2Ext Methods
+DataType SkipLayerNormPluginDynamic::getOutputDataType(int index, const DataType* inputTypes, int nbInputs) const
+{
+    assert(index == 0);
+    assert(nbInputs == 2);
+    assert(inputTypes[0] == inputTypes[1]);
+    return inputTypes[0];
+}
+
+// IPluginV2 Methods
+const char* SkipLayerNormPluginDynamic::getPluginType() const
+{
+    return SKIP_LAYER_NORM_NAME;
+}
+
+const char* SkipLayerNormPluginDynamic::getPluginVersion() const
+{
+    return SKIP_LAYER_NORM_VERSION;
+}
+
+int SkipLayerNormPluginDynamic::getNbOutputs() const
+{
+    return 1;
+}
+int SkipLayerNormPluginDynamic::initialize()
+{
+    gLogVerbose << "SkipLayerNormPluginDynamic initialize\n";
+    return 0;
+}
+
+void SkipLayerNormPluginDynamic::terminate()
+{
+    gLogVerbose << "SkipLayerNormPluginDynamic terminate\n";
+}
+
+size_t SkipLayerNormPluginDynamic::getSerializationSize() const
+{
+    const size_t biasSize = mHasBias ? (mLd * mParamWordsize) : 0;
+    return 2 * mParamWordsize * mLd + 2 * sizeof(DataType) + sizeof(mLd) + biasSize + sizeof(mHasBias);
+}
+
+void SkipLayerNormPluginDynamic::serialize(void* buffer) const
+{
+    serialize_value(&buffer, mType);
+    serialize_value(&buffer, mCfgType);
+    serialize_value(&buffer, mLd);
+    serialize_value(&buffer, mHasBias);
+
+    char* d = static_cast<char*>(buffer);
+    serFromDev(d, static_cast<char*>(mBetaDev.get()), mLd * mParamWordsize);
+    serFromDev(d, static_cast<char*>(mGammaDev.get()), mLd * mParamWordsize);
+    if (mHasBias)
+    {
+        serFromDev(d, static_cast<char*>(mBiasDev.get()), mLd * mParamWordsize);
+    }
+}
+
+void SkipLayerNormPluginDynamic::destroy()
+{
+    gLogVerbose << "SkipLayerNormPluginDynamic destroy\n";
+    // This gets called when the network containing plugin is destroyed
+    mGammaDev.release();
+    mBetaDev.release();
+    mBiasDev.release();
+    delete this;
+}
+
+void SkipLayerNormPluginDynamic::setPluginNamespace(const char* libNamespace)
+{
+    mNamespace = libNamespace;
+}
+
+const char* SkipLayerNormPluginDynamic::getPluginNamespace() const
+{
+    return mNamespace.c_str();
+}
+
+/////////////////////////////////////////////////////////
+
+SkipLayerNormPluginDynamicCreator::SkipLayerNormPluginDynamicCreator()
+{
+    mFC.nbFields = mPluginAttributes.size();
+    mFC.fields = mPluginAttributes.data();
+}
+
+const char* SkipLayerNormPluginDynamicCreator::getPluginName() const
+{
+    return SKIP_LAYER_NORM_NAME;
+}
+
+const char* SkipLayerNormPluginDynamicCreator::getPluginVersion() const
+{
+    return SKIP_LAYER_NORM_VERSION;
+}
+
+const PluginFieldCollection* SkipLayerNormPluginDynamicCreator::getFieldNames()
+{
+    return &mFC;
+}
+
+IPluginV2* SkipLayerNormPluginDynamicCreator::createPlugin(const char* name, const PluginFieldCollection* fc)
+{
+    gLogVerbose << "SkipLayerNormPluginDynamicCreator createPlugin\n";
+
+    int ld = 0;
+    Weights beta{DataType::kFLOAT, nullptr, 0};
+    Weights gamma{DataType::kFLOAT, nullptr, 0};
+    Weights bias{DataType::kFLOAT, nullptr, 0};
+    int typeId = -1;
+
+    for (int i = 0; i < fc->nbFields; i++)
+    {
+        std::string field_name(fc->fields[i].name);
+        if (field_name.compare("ld") == 0)
+        {
+            ld = *static_cast<const int*>(fc->fields[i].data);
+            gLogVerbose << "Building ld: " << ld << std::endl;
+        }
+
+        if (field_name.compare("type_id") == 0)
+        {
+            typeId = *static_cast<const int*>(fc->fields[i].data);
+            gLogVerbose << "Building typeId: " << typeId << std::endl;
+        }
+
+        if (field_name.compare("beta") == 0)
+        {
+            gLogVerbose << "Building beta...\n";
+            beta.values = fc->fields[i].data;
+            beta.count = fc->fields[i].length;
+            beta.type = fieldTypeToDataType(fc->fields[i].type);
+        }
+
+        if (field_name.compare("gamma") == 0)
+        {
+            gLogVerbose << "Building gamma...\n";
+            gamma.values = fc->fields[i].data;
+            gamma.count = fc->fields[i].length;
+            gamma.type = fieldTypeToDataType(fc->fields[i].type);
+        }
+
+        if (field_name.compare("bias") == 0)
+        {
+            gLogVerbose << "Building bias...\n";
+            bias.values = fc->fields[i].data;
+            bias.count = fc->fields[i].length;
+            bias.type = fieldTypeToDataType(fc->fields[i].type);
+        }
+    }
+    gLogVerbose << "Type " << typeId << std::endl;
+
+    if (typeId < 0 || typeId > 3)
+    {
+        gLogError << "SkipLayerNorm: Invalid type ID: " << typeId << std::endl;
+    }
+
+    if (beta.count <= 0 || beta.values == nullptr)
+    {
+        gLogError << "SkipLayerNorm: invalid beta" << std::endl;
+    }
+
+    if (gamma.count <= 0 || gamma.values == nullptr)
+    {
+        gLogError << "SkipLayerNorm: invalid gamma" << std::endl;
+    }
+
+    return new SkipLayerNormPluginDynamic(name, static_cast<DataType>(typeId), ld, beta, gamma, bias);
+}
+
+IPluginV2* SkipLayerNormPluginDynamicCreator::deserializePlugin(
+    const char* name, const void* serialData, size_t serialLength)
+{
+    // This object will be deleted when the network is destroyed, which will
+    // call SkipLayerNormPluginDynamic::destroy()
+    return new SkipLayerNormPluginDynamic(name, serialData, serialLength);
+}
+
+void SkipLayerNormPluginDynamicCreator::setPluginNamespace(const char* libNamespace)
+{
+    mNamespace = libNamespace;
+}
+
+const char* SkipLayerNormPluginDynamicCreator::getPluginNamespace() const
+{
+    return mNamespace.c_str();
+}
+} // namespace bert
+
+#endif // CUDA_VERSION >= 10010
diff --git a/plugin/skipLayerNormPlugin/skipLayerNormPlugin.cu b/plugin/skipLayerNormPlugin/skipLayerNormPlugin.cu
deleted file mode 100644
index e8da592c..00000000
--- a/plugin/skipLayerNormPlugin/skipLayerNormPlugin.cu
+++ /dev/null
@@ -1,613 +0,0 @@
-/*
- * Copyright (c) 2019, NVIDIA CORPORATION.  All rights reserved.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "NvInfer.h"
-#include "bertCommon.h"
-#include "common.h"
-#include "serialize.hpp"
-#include "skipLayerNormPlugin.h"
-
-#include <cassert>
-#include <cstring>
-#include <vector>
-
-using namespace nvinfer1;
-using bert::operator+;
-
-namespace bert
-{
-
-template <typename T, int TPB, int VPT, bool hasBias>
-__global__ void skipln_vec(
-    const int ld, const T* input, const T* skip, T* output, const T* beta, const T* gamma, const T* bias)
-{
-    const int idx = ld * blockIdx.x + threadIdx.x * VPT;
-    // 4 * 1024 * 4 * 2 Bytes = 16KB per block
-    T in_local[VPT];
-    T skip_local[VPT];
-    T bias_local[VPT];
-    copy<sizeof(T) * VPT>(&input[idx], in_local);
-    copy<sizeof(T) * VPT>(&skip[idx], skip_local);
-    copy<sizeof(T) * VPT>(&bias[threadIdx.x * VPT], bias_local);
-    T local = 0.f;
-    T local2 = 0.f;
-
-    const T rld = T(1) / T(ld);
-#pragma unroll
-    for (int it = 0; it < VPT; it++)
-    {
-        in_local[it] += skip_local[it];
-        if (hasBias)
-            in_local[it] += bias_local[it];
-        const T tmp = rld * in_local[it];
-        local += tmp;
-        local2 += tmp * in_local[it];
-    }
-
-    copy<sizeof(T) * VPT>(&beta[threadIdx.x * VPT], bias_local);
-    copy<sizeof(T) * VPT>(&gamma[threadIdx.x * VPT], skip_local);
-
-    using BlockReduce = cub::BlockReduce<kvp<T>, TPB>;
-    __shared__ typename BlockReduce::TempStorage temp_storage;
-    __shared__ T mu;     // mean
-    __shared__ T rsigma; // 1 / std.dev.
-
-    const auto sumKV = BlockReduce(temp_storage).Reduce(kvp<T>(local, local2), cub::Sum());
-
-    if (threadIdx.x == 0)
-    {
-        mu = sumKV.key;
-        rsigma = rsqrt(sumKV.value - mu * mu + T(1e-5));
-    }
-    __syncthreads();
-#pragma unroll
-    for (int it = 0; it < VPT; it++)
-    {
-        in_local[it] = skip_local[it] * (in_local[it] - mu) * rsigma + bias_local[it];
-    }
-
-    copy<sizeof(T) * VPT>(in_local, &output[idx]);
-}
-
-template <typename T, unsigned TPB, bool hasBias>
-__global__ void skipLayerNormKernelSmall(
-    const int ld, const T* input, const T* skip, const T* beta, const T* gamma, T* output, const T* bias)
-{
-
-    const T rld = T(1) / T(ld);
-    const int offset = blockIdx.x * ld;
-
-    cub::Sum pairSum;
-    // reduce x and x^2
-    kvp<T> threadData(0, 0);
-    const int idx = offset + threadIdx.x;
-    T val = 0;
-
-    if (threadIdx.x < ld)
-    {
-
-        val = input[idx] + skip[idx];
-        if (hasBias)
-        {
-            val += bias[threadIdx.x];
-        }
-
-        const T rldval = rld * val;
-        threadData = pairSum(threadData, kvp<T>(rldval, rldval * val));
-    }
-
-    layerNormSmall<T, T, TPB>(val, threadData, ld, idx, beta, gamma, output);
-}
-
-template <typename T, unsigned TPB, bool hasBias>
-__global__ void skipLayerNormKernel(
-    const int ld, const T* input, const T* skip, const T* beta, const T* gamma, T* output, const T* bias)
-{
-    const T rld = T(1) / T(ld);
-    const int offset = blockIdx.x * ld;
-
-    cub::Sum pairSum;
-    // reduce x and x^2
-    kvp<T> threadData(0, 0);
-
-    for (int i = threadIdx.x; i < ld; i += TPB)
-    {
-        const int idx = offset + i;
-        T val = T(input[idx]) + T(skip[idx]);
-
-        if (hasBias)
-        {
-            val += T(bias[i]);
-        }
-        const T rldval = rld * val;
-        threadData = pairSum(threadData, kvp<T>(rldval, rldval * val));
-        output[idx] = val;
-    }
-
-    layerNorm<T, T, T, TPB>(threadData, ld, offset, beta, gamma, output);
-}
-
-template <typename T, bool hasBias>
-int computeSkipLayerNorm(cudaStream_t stream, const int ld, const int n, const T* input, const T* skip, const T* beta,
-    const T* gamma, T* output, const T* bias)
-{
-
-    // this must be true because n is the total size of the tensor
-    assert(n % ld == 0);
-    const int gridSize = n / ld;
-    constexpr int VPT = 16 / sizeof(T);
-    if (ld <= 32)
-    {
-        constexpr int blockSize = 32;
-        skipLayerNormKernelSmall<T, blockSize, hasBias>
-            <<<gridSize, blockSize, 0, stream>>>(ld, input, skip, beta, gamma, output, bias);
-    }
-    else if (ld == 768)
-    {
-        constexpr int TPB = 768 / VPT;
-        skipln_vec<T, TPB, VPT, hasBias><<<gridSize, TPB, 0, stream>>>(ld, input, skip, output, beta, gamma, bias);
-    }
-    else if (ld == 1024)
-    {
-        constexpr int TPB = 1024 / VPT;
-        skipln_vec<T, TPB, VPT, hasBias><<<gridSize, TPB, 0, stream>>>(ld, input, skip, output, beta, gamma, bias);
-    }
-    else
-    {
-        constexpr int blockSize = 256;
-        skipLayerNormKernel<T, blockSize, hasBias>
-            <<<gridSize, blockSize, 0, stream>>>(ld, input, skip, beta, gamma, output, bias);
-    }
-    CHECK(cudaPeekAtLastError());
-
-    return 0;
-}
-
-// Clip plugin specific constants
-namespace
-{
-static const char* SKIP_LAYER_NORM_VERSION{"1"};
-static const char* SKIP_LAYER_NORM_NAME{"CustomSkipLayerNormPluginDynamic"};
-} // namespace
-
-// Static class fields initialization
-PluginFieldCollection SkipLayerNormPluginDynamicCreator::mFC{};
-std::vector<PluginField> SkipLayerNormPluginDynamicCreator::mPluginAttributes;
-
-REGISTER_TENSORRT_PLUGIN(SkipLayerNormPluginDynamicCreator);
-
-SkipLayerNormPluginDynamic::SkipLayerNormPluginDynamic(
-    const std::string name, const DataType type, const int ld, const Weights& beta, const Weights& gamma)
-    : mLayerName(name)
-    , mLd(ld)
-    , mGamma(gamma)
-    , mBeta(beta)
-    , mHasBias(false)
-    , mType(type)
-{
-    mBias.values = nullptr;
-    mBias.count = 0;
-}
-
-SkipLayerNormPluginDynamic::SkipLayerNormPluginDynamic(const std::string name, const DataType type, const int ld,
-    const Weights& beta, const Weights& gamma, const Weights& bias)
-    : mLayerName(name)
-    , mLd(ld)
-    , mGamma(gamma)
-    , mBeta(beta)
-    , mHasBias(true)
-    , mBias(bias)
-    , mType(type)
-{
-}
-
-SkipLayerNormPluginDynamic::SkipLayerNormPluginDynamic(const std::string name, const void* data, size_t length)
-    : mLayerName(name)
-{
-    gLogVerbose << "Starting to deserialize SkipLayerNorm plugin" << std::endl;
-    // Deserialize in the same order as serialization
-    deserialize_value(&data, &length, &mType);
-    deserialize_value(&data, &length, &mLd);
-    deserialize_value(&data, &length, &mHasBias);
-
-    const char* d = static_cast<const char*>(data);
-
-    const size_t wordSize = samplesCommon::getElementSize(mType);
-
-    mBetaDev = deserToDev<char>(d, mLd * wordSize);
-    mGammaDev = deserToDev<char>(d, mLd * wordSize);
-    if (mHasBias)
-    {
-        mBiasDev = deserToDev<char>(d, mLd * wordSize);
-    }
-    // this signals init not to allocate/copy
-    mGamma.count = mLd;
-    mGamma.values = nullptr;
-    mBeta.count = mLd;
-    mBeta.values = nullptr;
-    mBias.count = mLd;
-    mBias.values = nullptr;
-
-    gLogVerbose << "Finished deserializing SkipLayerNorm plugin" << std::endl;
-}
-
-// IPluginV2DynamicExt Methods
-IPluginV2DynamicExt* SkipLayerNormPluginDynamic::clone() const
-{
-    if (mHasBias)
-    {
-        return new SkipLayerNormPluginDynamic(mLayerName, mType, mLd, mBeta, mGamma, mBias);
-    }
-    return new SkipLayerNormPluginDynamic(mLayerName, mType, mLd, mBeta, mGamma);
-}
-
-DimsExprs SkipLayerNormPluginDynamic::getOutputDimensions(
-    int outputIndex, const DimsExprs* inputs, int nbInputs, IExprBuilder& exprBuilder)
-{
-    assert(nbInputs == 2);
-    assert(outputIndex == 0);
-    assert(inputs[0].nbDims == inputs[1].nbDims);
-    return inputs[0];
-}
-
-bool SkipLayerNormPluginDynamic::supportsFormatCombination(
-    int pos, const PluginTensorDesc* inOut, int nbInputs, int nbOutputs)
-{
-    assert(nbInputs == 2);
-    assert(nbOutputs == 1);
-
-    const PluginTensorDesc& in = inOut[pos];
-    if (pos == 0)
-    {
-        return (in.type == mType) && (in.format == TensorFormat::kLINEAR);
-    }
-    const PluginTensorDesc& prev = inOut[pos - 1];
-
-    if (pos == 1)
-    {
-        return in.type == prev.type && in.format == prev.format;
-    }
-    // output
-    return in.type == prev.type && in.format == prev.format;
-}
-
-void SkipLayerNormPluginDynamic::configurePlugin(
-    const DynamicPluginTensorDesc* inputs, int nbInputs, const DynamicPluginTensorDesc* outputs, int nbOutputs)
-{
-    // Validate input arguments
-    assert(nbOutputs == 1);
-    assert(nbInputs == 2);
-    assert(mType == inputs[0].desc.type);
-    assert(mType == inputs[1].desc.type);
-    const auto& inDims0 = inputs[0].desc.dims;
-    const auto& inDims1 = inputs[1].desc.dims;
-    TRT_UNUSED inDims1;
-    assert(inDims0.nbDims == inDims1.nbDims);
-
-    assert(std::equal(inDims0.d, inDims0.d + inDims0.nbDims, inDims1.d));
-
-    assert(inDims0.nbDims == 5);
-    mLd = inDims0.d[HDIM]; // hiddensize
-    assert(inDims0.d[3] == 1);
-    assert(inDims0.d[4] == 1);
-}
-
-size_t SkipLayerNormPluginDynamic::getWorkspaceSize(
-    const PluginTensorDesc* inputs, int nbInputs, const PluginTensorDesc* outputs, int nbOutputs) const
-{
-    return 0;
-}
-
-int SkipLayerNormPluginDynamic::enqueue(const PluginTensorDesc* inputDesc, const PluginTensorDesc* outputDesc,
-    const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream)
-{
-    const int inputVolume = volume(inputDesc[0].dims);
-    int status = -1;
-
-    // Our plugin outputs only one tensor
-    // Launch CUDA kernel wrapper and save its return value
-    if (mType == DataType::kFLOAT)
-    {
-        const float* input = static_cast<const float*>(inputs[0]);
-        const float* skip = static_cast<const float*>(inputs[1]);
-        float* output = static_cast<float*>(outputs[0]);
-
-        float* bias = reinterpret_cast<float*>(mBiasDev);
-        const float* beta = static_cast<const float*>(mBetaDev);
-        const float* gamma = static_cast<const float*>(mGammaDev);
-        if (mHasBias)
-        {
-            status
-                = computeSkipLayerNorm<float, true>(stream, mLd, inputVolume, input, skip, beta, gamma, output, bias);
-        }
-        else
-        {
-            status
-                = computeSkipLayerNorm<float, false>(stream, mLd, inputVolume, input, skip, beta, gamma, output, bias);
-        }
-    }
-    else if (mType == DataType::kHALF)
-    {
-        const half* input = static_cast<const half*>(inputs[0]);
-        const half* skip = static_cast<const half*>(inputs[1]);
-        half* output = static_cast<half*>(outputs[0]);
-        half* bias = reinterpret_cast<half*>(mBiasDev);
-
-        const half* beta = static_cast<const half*>(mBetaDev);
-        const half* gamma = static_cast<const half*>(mGammaDev);
-        if (mHasBias)
-        {
-            status = computeSkipLayerNorm<half, true>(stream, mLd, inputVolume, input, skip, beta, gamma, output, bias);
-        }
-        else
-        {
-            status
-                = computeSkipLayerNorm<half, false>(stream, mLd, inputVolume, input, skip, beta, gamma, output, bias);
-        }
-    }
-    else
-    {
-        gLogError << "Unsupported Type\n";
-        assert(false);
-    }
-    return status;
-}
-
-// IPluginV2Ext Methods
-DataType SkipLayerNormPluginDynamic::getOutputDataType(int index, const DataType* inputTypes, int nbInputs) const
-{
-    assert(index == 0);
-    assert(nbInputs == 2);
-    assert(inputTypes[0] == DataType::kFLOAT || inputTypes[0] == DataType::kHALF);
-    assert(inputTypes[0] == inputTypes[1]);
-    return inputTypes[0];
-}
-
-// IPluginV2 Methods
-const char* SkipLayerNormPluginDynamic::getPluginType() const
-{
-    return SKIP_LAYER_NORM_NAME;
-}
-
-const char* SkipLayerNormPluginDynamic::getPluginVersion() const
-{
-    return SKIP_LAYER_NORM_VERSION;
-}
-
-int SkipLayerNormPluginDynamic::getNbOutputs() const
-{
-    return 1;
-}
-int SkipLayerNormPluginDynamic::initialize()
-{
-    const size_t wordSize = samplesCommon::getElementSize(mType);
-    if (mGamma.values)
-    {
-        CHECK(cudaMalloc(&mGammaDev, sizeof(float) * mGamma.count));
-
-        // target size
-        const size_t nbBytes = mGamma.count * wordSize;
-        CHECK(cudaMalloc(&mGammaDev, nbBytes));
-
-        if (mType == DataType::kFLOAT)
-        {
-            convertAndCopyToDevice(mGamma, static_cast<float*>(mGammaDev));
-        }
-        else
-        {
-            convertAndCopyToDevice(mGamma, static_cast<half*>(mGammaDev));
-        }
-    }
-    if (mBeta.values)
-    {
-        CHECK(cudaMalloc(&mBetaDev, sizeof(float) * mBeta.count));
-        const size_t nbBytes = mBeta.count * wordSize;
-        CHECK(cudaMalloc(&mBetaDev, nbBytes));
-
-        if (mType == DataType::kFLOAT)
-        {
-            convertAndCopyToDevice(mBeta, static_cast<float*>(mBetaDev));
-        }
-        else
-        {
-            convertAndCopyToDevice(mBeta, static_cast<half*>(mBetaDev));
-        }
-    }
-
-    if (mHasBias && mBias.values)
-    {
-        // target size
-        const size_t nbBytes = mBias.count * wordSize;
-        CHECK(cudaMalloc(&mBiasDev, nbBytes));
-
-        if (mType == DataType::kFLOAT)
-        {
-            convertAndCopyToDevice(mBias, reinterpret_cast<float*>(mBiasDev));
-        }
-        else
-        {
-            convertAndCopyToDevice(mBias, reinterpret_cast<half*>(mBiasDev));
-        }
-    }
-    return 0;
-}
-
-void SkipLayerNormPluginDynamic::terminate()
-{
-    gLogVerbose << "SKIPLN terminate start" << std::endl;
-    cudaFree(mGammaDev);
-    cudaFree(mBetaDev);
-    if (mHasBias)
-    {
-        cudaFree(mBiasDev);
-    }
-    gLogVerbose << "SKIPLN terminate done" << std::endl;
-}
-
-size_t SkipLayerNormPluginDynamic::getSerializationSize() const
-{
-    const size_t wordSize = samplesCommon::getElementSize(mType);
-    const size_t biasSize = mHasBias ? (mLd * wordSize) : 0;
-    return 2 * wordSize * mLd + sizeof(DataType) + sizeof(mLd) + biasSize + sizeof(mHasBias);
-}
-
-void SkipLayerNormPluginDynamic::serialize(void* buffer) const
-{
-    serialize_value(&buffer, mType);
-    serialize_value(&buffer, mLd);
-    serialize_value(&buffer, mHasBias);
-
-    const size_t wordSize = samplesCommon::getElementSize(mType);
-    char* d = static_cast<char*>(buffer);
-    serFromDev(d, static_cast<char*>(mBetaDev), mLd * wordSize);
-    serFromDev(d, static_cast<char*>(mGammaDev), mLd * wordSize);
-    if (mHasBias)
-    {
-        const size_t wordSize = samplesCommon::getElementSize(mType);
-        serFromDev(d, mBiasDev, mLd * wordSize);
-    }
-}
-
-void SkipLayerNormPluginDynamic::destroy()
-{
-    // This gets called when the network containing plugin is destroyed
-    delete this;
-}
-
-void SkipLayerNormPluginDynamic::setPluginNamespace(const char* libNamespace)
-{
-    mNamespace = libNamespace;
-}
-
-const char* SkipLayerNormPluginDynamic::getPluginNamespace() const
-{
-    return mNamespace.c_str();
-}
-
-/////////////////////////////////////////////////////////
-
-SkipLayerNormPluginDynamicCreator::SkipLayerNormPluginDynamicCreator()
-{
-    mFC.nbFields = mPluginAttributes.size();
-    mFC.fields = mPluginAttributes.data();
-}
-
-const char* SkipLayerNormPluginDynamicCreator::getPluginName() const
-{
-    return SKIP_LAYER_NORM_NAME;
-}
-
-const char* SkipLayerNormPluginDynamicCreator::getPluginVersion() const
-{
-    return SKIP_LAYER_NORM_VERSION;
-}
-
-const PluginFieldCollection* SkipLayerNormPluginDynamicCreator::getFieldNames()
-{
-    return &mFC;
-}
-
-IPluginV2* SkipLayerNormPluginDynamicCreator::createPlugin(const char* name, const PluginFieldCollection* fc)
-{
-    gLogVerbose << "Creating SkipLayerNormPluginDynamicCreator...\n";
-
-    int ld = 0;
-    Weights beta{DataType::kFLOAT, nullptr, 0};
-    Weights gamma{DataType::kFLOAT, nullptr, 0};
-    Weights bias{DataType::kFLOAT, nullptr, 0};
-    int typeId = -1;
-
-    for (int i = 0; i < fc->nbFields; i++)
-    {
-        std::string field_name(fc->fields[i].name);
-        if (field_name.compare("ld") == 0)
-        {
-            ld = *static_cast<const int*>(fc->fields[i].data);
-            gLogVerbose << "Building ld: " << ld << std::endl;
-        }
-
-        if (field_name.compare("type_id") == 0)
-        {
-            typeId = *static_cast<const int*>(fc->fields[i].data);
-            gLogVerbose << "Building typeId: " << typeId << std::endl;
-        }
-
-        if (field_name.compare("beta") == 0)
-        {
-            gLogVerbose << "Building beta...\n";
-            beta.values = fc->fields[i].data;
-            beta.count = fc->fields[i].length;
-            beta.type = fieldTypeToDataType(fc->fields[i].type);
-        }
-
-        if (field_name.compare("gamma") == 0)
-        {
-            gLogVerbose << "Building gamma...\n";
-            gamma.values = fc->fields[i].data;
-            gamma.count = fc->fields[i].length;
-            gamma.type = fieldTypeToDataType(fc->fields[i].type);
-        }
-
-        if (field_name.compare("bias") == 0)
-        {
-            gLogVerbose << "Building bias...\n";
-            bias.values = fc->fields[i].data;
-            bias.count = fc->fields[i].length;
-            bias.type = fieldTypeToDataType(fc->fields[i].type);
-        }
-    }
-
-    if (typeId < 0 || typeId > 3)
-    {
-        gLogError << "SkipLayerNorm: Invalid type ID: " << typeId << std::endl;
-    }
-
-    if (beta.count <= 0 || beta.values == nullptr)
-    {
-        gLogError << "SkipLayerNorm: invalid beta" << std::endl;
-    }
-
-    if (gamma.count <= 0 || gamma.values == nullptr)
-    {
-        gLogError << "SkipLayerNorm: invalid gamma" << std::endl;
-    }
-    DataType type = static_cast<DataType>(typeId);
-    if (bias.values == nullptr)
-    {
-        return new SkipLayerNormPluginDynamic(name, type, ld, beta, gamma);
-    }
-
-    return new SkipLayerNormPluginDynamic(name, type, ld, beta, gamma, bias);
-}
-
-IPluginV2* SkipLayerNormPluginDynamicCreator::deserializePlugin(
-    const char* name, const void* serialData, size_t serialLength)
-{
-    // This object will be deleted when the network is destroyed, which will
-    // call SkipLayerNormPluginDynamic::destroy()
-    return new SkipLayerNormPluginDynamic(name, serialData, serialLength);
-}
-
-void SkipLayerNormPluginDynamicCreator::setPluginNamespace(const char* libNamespace)
-{
-    mNamespace = libNamespace;
-}
-
-const char* SkipLayerNormPluginDynamicCreator::getPluginNamespace() const
-{
-    return mNamespace.c_str();
-}
-} // namespace bert
diff --git a/plugin/skipLayerNormPlugin/skipLayerNormPlugin.h b/plugin/skipLayerNormPlugin/skipLayerNormPlugin.h
index 720c581c..1b5604a4 100644
--- a/plugin/skipLayerNormPlugin/skipLayerNormPlugin.h
+++ b/plugin/skipLayerNormPlugin/skipLayerNormPlugin.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -14,27 +14,35 @@
  * limitations under the License.
  */
 
+#include <cuda.h>
+#if CUDA_VERSION >= 10010
+
 #ifndef TRT_SKIP_LAYER_NORM_PLUGIN_H
 #define TRT_SKIP_LAYER_NORM_PLUGIN_H
 
 #include "NvInferPlugin.h"
+
+#include "bertCommon.h"
+#include <memory>
 #include <string>
 #include <vector>
 
 namespace bert
 {
-// One of the preferred ways of making TensorRT to be able to see
-// our custom layer requires extending IPluginV2 and IPluginCreator classes.
-// For requirements for overriden functions, check TensorRT API docs.
+template <bool hasBias>
+int computeSkipLayerNormDQQ(cudaStream_t stream, const int ld, const int n, const int8_t* input, const int8_t* skip,
+    const __half* beta, const __half* gamma, int8_t* output, const __half* bias, const float dqScaleIn,
+    const float dqScaleSkip, const float qScale);
+
+template <typename T, bool hasBias>
+int computeSkipLayerNorm(cudaStream_t stream, const int ld, const int n, const T* input, const T* skip, const T* beta,
+    const T* gamma, T* output, const T* bias);
 
 class SkipLayerNormPluginDynamic : public nvinfer1::IPluginV2DynamicExt
 {
 public:
-    SkipLayerNormPluginDynamic(const std::string name, const nvinfer1::DataType type, const int ld, const nvinfer1::Weights& beta,
-        const nvinfer1::Weights& gamma);
-
-    SkipLayerNormPluginDynamic(const std::string name, const nvinfer1::DataType type, const int ld, const nvinfer1::Weights& beta,
-        const nvinfer1::Weights& gamma, const nvinfer1::Weights& bias);
+    SkipLayerNormPluginDynamic(const std::string name, const nvinfer1::DataType type, const int ld,
+        const nvinfer1::Weights& beta, const nvinfer1::Weights& gamma, const nvinfer1::Weights& bias);
 
     SkipLayerNormPluginDynamic(const std::string name, const void* data, size_t length);
 
@@ -74,26 +82,29 @@ class SkipLayerNormPluginDynamic : public nvinfer1::IPluginV2DynamicExt
     const std::string mLayerName;
     std::string mNamespace;
 
-    void* mGammaDev;
-    void* mBetaDev;
+    bert::cuda_unique_ptr<void> mGammaDev;
+    bert::cuda_unique_ptr<void> mBetaDev;
     size_t mLd; // leading dim
-    nvinfer1::Weights mBeta;
-    nvinfer1::Weights mGamma;
+    bert::WeightsWithOwnership mGamma;
+    bert::WeightsWithOwnership mBeta;
     nvinfer1::DataType mType;
+    nvinfer1::DataType mCfgType;
 
     bool mHasBias;
-    char* mBiasDev;
-    nvinfer1::Weights mBias;
-    
+    bert::cuda_unique_ptr<void> mBiasDev;
+    bert::WeightsWithOwnership mBias;
+
+    size_t mParamWordsize;
+
 protected:
     // To prevent compiler warnings.
-    using nvinfer1::IPluginV2DynamicExt::getOutputDimensions;
-    using nvinfer1::IPluginV2DynamicExt::isOutputBroadcastAcrossBatch;
     using nvinfer1::IPluginV2DynamicExt::canBroadcastInputAcrossBatch;
-    using nvinfer1::IPluginV2DynamicExt::supportsFormat;
     using nvinfer1::IPluginV2DynamicExt::configurePlugin;
-    using nvinfer1::IPluginV2DynamicExt::getWorkspaceSize;
     using nvinfer1::IPluginV2DynamicExt::enqueue;
+    using nvinfer1::IPluginV2DynamicExt::getOutputDimensions;
+    using nvinfer1::IPluginV2DynamicExt::getWorkspaceSize;
+    using nvinfer1::IPluginV2DynamicExt::isOutputBroadcastAcrossBatch;
+    using nvinfer1::IPluginV2DynamicExt::supportsFormat;
 };
 
 class SkipLayerNormPluginDynamicCreator : public nvinfer1::IPluginCreator
@@ -120,5 +131,7 @@ class SkipLayerNormPluginDynamicCreator : public nvinfer1::IPluginCreator
     static std::vector<nvinfer1::PluginField> mPluginAttributes;
     std::string mNamespace;
 };
-}
+} // namespace bert
 #endif // TRT_SKIP_LAYER_NORM_PLUGIN_H
+
+#endif // CUDA_VERSION >= 10010
diff --git a/plugin/specialSlicePlugin/CMakeLists.txt b/plugin/specialSlicePlugin/CMakeLists.txt
index de8fff17..85d8ce4e 100644
--- a/plugin/specialSlicePlugin/CMakeLists.txt
+++ b/plugin/specialSlicePlugin/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/plugin/specialSlicePlugin/specialSlicePlugin.cpp b/plugin/specialSlicePlugin/specialSlicePlugin.cpp
index 45c61096..9656531c 100644
--- a/plugin/specialSlicePlugin/specialSlicePlugin.cpp
+++ b/plugin/specialSlicePlugin/specialSlicePlugin.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -85,7 +85,9 @@ const char* SpecialSlice::getPluginVersion() const
 
 IPluginV2Ext* SpecialSlice::clone() const
 {
-    return new SpecialSlice(*this);
+    auto plugin = new SpecialSlice(*this);
+    plugin->setPluginNamespace(mNameSpace.c_str());
+    return plugin;
 };
 
 void SpecialSlice::setPluginNamespace(const char* libNamespace)
diff --git a/plugin/specialSlicePlugin/specialSlicePlugin.h b/plugin/specialSlicePlugin/specialSlicePlugin.h
index 0d35ea9d..630b8d79 100644
--- a/plugin/specialSlicePlugin/specialSlicePlugin.h
+++ b/plugin/specialSlicePlugin/specialSlicePlugin.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -49,7 +49,10 @@ class SpecialSlice : public IPluginV2Ext
 
     void terminate() override{};
 
-    void destroy() override{};
+    void destroy() override
+    {
+        delete this;
+    }
 
     size_t getWorkspaceSize(int) const override;
 
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 00000000..e3df40cb
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,6 @@
+numpy
+onnx==1.6.0
+onnxruntime==1.3.0
+pycuda==2019.1.2
+pytest
+tensorflow-gpu>=1.15, <2.0
diff --git a/samples/CMakeLists.txt b/samples/CMakeLists.txt
index eddc7d45..4502ab13 100644
--- a/samples/CMakeLists.txt
+++ b/samples/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -17,7 +17,3 @@
 add_custom_target(samples)
 
 add_subdirectory(opensource)
-
-if(NVINTERNAL)
-    add_subdirectory(proprietary)
-endif()
diff --git a/samples/CMakeSamplesTemplate.txt b/samples/CMakeSamplesTemplate.txt
index 8b0dcec6..7c363aad 100644
--- a/samples/CMakeSamplesTemplate.txt
+++ b/samples/CMakeSamplesTemplate.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -39,7 +39,6 @@ string(REGEX REPLACE "(.)([A-Z][a-z]+)" "\\1_\\2" SAMPLE_NAME_SNAKE_MIXED ${SAMP
 string(REGEX REPLACE "([a-z0-9])([A-Z])" "\\1_\\2" SAMPLE_NAME_SNAKE_MIXED ${SAMPLE_NAME_SNAKE_MIXED})
 string(TOLOWER ${SAMPLE_NAME_SNAKE_MIXED} SAMPLE_NAME_SNAKE)
 
-# WAR bug http://nvbugs/2518092
 # fix a few sample names
 string(REGEX REPLACE "movie_lens" "movielens" SAMPLE_NAME_FIXED ${SAMPLE_NAME_SNAKE})
 string(REGEX REPLACE "google_net" "googlenet" SAMPLE_NAME_FIXED ${SAMPLE_NAME_FIXED})
@@ -70,12 +69,7 @@ if(BUILD_PLUGINS OR BUILD_PARSERS)
     )
 endif()
 
-# TODO: Remove the NVINTERNAL branch once the CMake system is fully integrated into P4
-if (NVINTERNAL)
-    set(ONNX_INCLUDE_DIR ${PROJECT_SOURCE_DIR}/parsers${NVINTERNAL_SUFFIX}/onnx CACHE STRING "Onnx include directory")
-else()
-    set(ONNX_INCLUDE_DIR ${PROJECT_SOURCE_DIR}/parsers${NVINTERNAL_SUFFIX}/onnx CACHE STRING "Onnx include directory")
-endif()
+set(ONNX_INCLUDE_DIR ${PROJECT_SOURCE_DIR}/parsers/onnx CACHE STRING "ONNX include directory")
 
 target_include_directories(${TARGET_NAME}
     PUBLIC ${PROJECT_SOURCE_DIR}/include
@@ -125,9 +119,9 @@ set_target_properties(${TARGET_NAME} PROPERTIES DEBUG_POSTFIX ${TRT_DEBUG_POSTFI
 
 set_target_properties(${TARGET_NAME}
     PROPERTIES
-    ARCHIVE_OUTPUT_DIRECTORY "${TRT_BIN_DIR}"
-    LIBRARY_OUTPUT_DIRECTORY "${TRT_BIN_DIR}"
-    RUNTIME_OUTPUT_DIRECTORY "${TRT_BIN_DIR}"
+    ARCHIVE_OUTPUT_DIRECTORY "${TRT_OUT_DIR}"
+    LIBRARY_OUTPUT_DIRECTORY "${TRT_OUT_DIR}"
+    RUNTIME_OUTPUT_DIRECTORY "${TRT_OUT_DIR}"
 )
 
 add_dependencies(samples ${TARGET_NAME})
diff --git a/samples/README.md b/samples/README.md
deleted file mode 100644
index b0f2f7a9..00000000
--- a/samples/README.md
+++ /dev/null
@@ -1,79 +0,0 @@
-# Cross Compilation Guide
-
-This guide shows how to cross compile TensorRT samples for AArch64 QNX, Linux and Android platform under x86_64 Linux.
-
-### Common Prerequisites
-
-* Install the CUDA cross-platform toolkit for the the corresponding target, and set the environment variable `CUDA_INSTALL_DIR`
-
-  ```shell
-  export CUDA_INSTALL_DIR="your cuda install dir"
-  ```
-
-  `CUDA_INSTALL_DIR` is set to `/usr/local/cuda` by default.
-
-* Install the cuDNN cross-platform libraries for the corresponding target, and set the environment variable `CUDNN_INSTALL_DIR`
-
-  ```shell
-  export CUDNN_INSTALL_DIR="your cudnn install dir"
-  ```
-
-  `CUDNN_INSTALL_DIR` is set to `CUDA_INSTALL_DIR` by default.
-
-* Install the TensorRT cross compilation debian packages for the corresponding target.
-  * QNX AArch64: libnvinfer-dev-cross-qnx, libnvinfer5-cross-qnx
-  * Linux AArch64: libnvinfer-dev-cross-aarch64, libnvinfer5-cross-aarch64
-  * Android AArch64: No debian packages are available.
-
-  If you are using the tar file released by the TensorRT team, you can safely skip this step. The tar file release already includes the cross compile libraries so no additional packages are required.
-
-### Build Samples for QNX AArch64
-
-Download the QNX toolchain and export the following environment variables.
-
-```shell
-export QNX_HOST=/path/to/your/qnx/toolchains/host/linux/x86_64
-export QNX_TARGET=/path/to/your/qnx/toolchain/target/qnx7
-```
-
-Build samples via
-
-```shell
-cd /path/to/TensorRT/samples
-make TARGET=qnx
-```
-
-### Build Samples for Linux AArch64
-
-Sample compilation for Linux aarch64 needs the corresponding g++ compiler, `aarch64-linux-gnu-g++`. In Ubuntu, this can be installed via
-
-```shell
-sudo apt-get install g++-aarch64-linux-gnu
-```
-
-Build samples via
-
-```shell
-cd /path/to/TensorRT/samples
-make TARGET=aarch64
-```
-
-### Build Samples for Android AArch64
-
-Download Android NDK(r16b) from https://developer.android.com/ndk/.  After downloading the NDK, create a standalone toolchain, for example
-
-```shell
-$NDK/build/tools/make_standalone_toolchain.py \
-  --arch arm64 \
-  --api 26 \
-  --install-dir=/path/to/my-toolchain
-```
-
-You can check the details on https://developer.android.com/ndk/guides/standalone_toolchain.
-
-Build samples via
-
-```shell
-cd /path/to/TensorRT/samples
-make TARGET=android64 ANDROID_CC=/path/to/my-toolchain/bin/aarch64-linux-android-clang++
-```
diff --git a/samples/common/BatchStream.h b/samples/common/BatchStream.h
index 306ed591..0fa75785 100644
--- a/samples/common/BatchStream.h
+++ b/samples/common/BatchStream.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -322,7 +322,7 @@ class BatchStream : public IBatchStream
                 return false;
             }
 
-            gLogInfo << "Batch #" << mFileCount << std::endl;
+            sample::gLogInfo << "Batch #" << mFileCount << std::endl;
             file.seekg(((mBatchCount * mBatchSize)) * 7);
 
             for (int i = 1; i <= mBatchSize; i++)
@@ -330,7 +330,7 @@ class BatchStream : public IBatchStream
                 std::string sName;
                 std::getline(file, sName);
                 sName = sName + ".ppm";
-                gLogInfo << "Calibrating with file " << sName << std::endl;
+                sample::gLogInfo << "Calibrating with file " << sName << std::endl;
                 fNames.emplace_back(sName);
             }
 
diff --git a/samples/common/EntropyCalibrator.h b/samples/common/EntropyCalibrator.h
index 433b91e0..833f69b8 100644
--- a/samples/common/EntropyCalibrator.h
+++ b/samples/common/EntropyCalibrator.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/common/ErrorRecorder.h b/samples/common/ErrorRecorder.h
index 16b9e773..26de0153 100644
--- a/samples/common/ErrorRecorder.h
+++ b/samples/common/ErrorRecorder.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/common/argsParser.h b/samples/common/argsParser.h
index b59fe32a..49defa12 100644
--- a/samples/common/argsParser.h
+++ b/samples/common/argsParser.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -83,6 +83,8 @@ struct Args
     int useDLACore{-1};
     int batch{1};
     std::vector<std::string> dataDirs;
+    std::string saveEngine;
+    std::string loadEngine;
     bool useILoop{false};
 };
 
@@ -100,6 +102,7 @@ inline bool parseArgs(Args& args, int argc, char* argv[])
         int arg;
         static struct option long_options[] = {{"help", no_argument, 0, 'h'}, {"datadir", required_argument, 0, 'd'},
             {"int8", no_argument, 0, 'i'}, {"fp16", no_argument, 0, 'f'}, {"useILoop", no_argument, 0, 'l'},
+            {"saveEngine", required_argument, 0, 's'}, {"loadEngine", no_argument, 0, 'o'},
             {"useDLACore", required_argument, 0, 'u'}, {"batch", required_argument, 0, 'b'}, {nullptr, 0, nullptr, 0}};
         int option_index = 0;
         arg = getopt_long(argc, argv, "hd:iu", long_options, &option_index);
@@ -122,6 +125,18 @@ inline bool parseArgs(Args& args, int argc, char* argv[])
                 return false;
             }
             break;
+        case 's':
+            if (optarg)
+            {
+                args.saveEngine = optarg;
+            }
+            break;
+        case 'o':
+            if (optarg)
+            {
+                args.loadEngine = optarg;
+            }
+            break;
         case 'i': args.runInInt8 = true; break;
         case 'f': args.runInFp16 = true; break;
         case 'l': args.useILoop = true; break;
diff --git a/samples/common/buffers.h b/samples/common/buffers.h
index 705ce586..f1a9385f 100644
--- a/samples/common/buffers.h
+++ b/samples/common/buffers.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -29,8 +29,6 @@
 #include <string>
 #include <vector>
 
-using namespace std;
-
 namespace samplesCommon
 {
 
@@ -243,16 +241,18 @@ class BufferManager
     //!
     //! \brief Create a BufferManager for handling buffer interactions with engine.
     //!
-    BufferManager(std::shared_ptr<nvinfer1::ICudaEngine> engine, const int& batchSize,
+    BufferManager(std::shared_ptr<nvinfer1::ICudaEngine> engine, const int batchSize = 0,
         const nvinfer1::IExecutionContext* context = nullptr)
         : mEngine(engine)
         , mBatchSize(batchSize)
     {
+        // Full Dims implies no batch size.
+        assert(engine->hasImplicitBatchDimension() || mBatchSize == 0);
         // Create host and device buffers
         for (int i = 0; i < mEngine->getNbBindings(); i++)
         {
             auto dims = context ? context->getBindingDimensions(i) : mEngine->getBindingDimensions(i);
-            size_t vol = context ? 1 : static_cast<size_t>(mBatchSize);
+            size_t vol = context || !mBatchSize ? 1 : static_cast<size_t>(mBatchSize);
             nvinfer1::DataType type = mEngine->getBindingDataType(i);
             int vecDim = mEngine->getBindingVectorizedDim(i);
             if (-1 != vecDim) // i.e., 0 != lgScalarsPerVector
@@ -332,11 +332,22 @@ class BufferManager
         void* buf = mManagedBuffers[index]->hostBuffer.data();
         size_t bufSize = mManagedBuffers[index]->hostBuffer.nbBytes();
         nvinfer1::Dims bufDims = mEngine->getBindingDimensions(index);
-        size_t rowCount = static_cast<size_t>(bufDims.nbDims >= 1 ? bufDims.d[bufDims.nbDims - 1] : mBatchSize);
+        size_t rowCount = static_cast<size_t>(bufDims.nbDims > 0 ? bufDims.d[bufDims.nbDims - 1] : mBatchSize);
+        int leadDim = mBatchSize;
+        int* trailDims = bufDims.d;
+        int nbDims = bufDims.nbDims;
+
+        // Fix explicit Dimension networks
+        if (!leadDim && nbDims > 0)
+        {
+            leadDim = bufDims.d[0];
+            ++trailDims;
+            --nbDims;
+        }
 
-        os << "[" << mBatchSize;
-        for (int i = 0; i < bufDims.nbDims; i++)
-            os << ", " << bufDims.d[i];
+        os << "[" << leadDim;
+        for (int i = 0; i < nbDims; i++)
+            os << ", " << trailDims[i];
         os << "]" << std::endl;
         switch (mEngine->getBindingDataType(index))
         {
@@ -441,7 +452,7 @@ class BufferManager
     }
 
     std::shared_ptr<nvinfer1::ICudaEngine> mEngine;              //!< The pointer to the engine
-    int mBatchSize;                                              //!< The batch size
+    int mBatchSize;                                              //!< The batch size for legacy networks, 0 otherwise.
     std::vector<std::unique_ptr<ManagedBuffer>> mManagedBuffers; //!< The vector of pointers to managed buffers
     std::vector<void*> mDeviceBindings; //!< The vector of device buffers needed for engine execution
 };
diff --git a/samples/common/common.h b/samples/common/common.h
index 12feca56..6fc247a9 100644
--- a/samples/common/common.h
+++ b/samples/common/common.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -69,7 +69,7 @@ using namespace plugin;
         auto ret = (status);                                                                                           \
         if (ret != 0)                                                                                                  \
         {                                                                                                              \
-            std::cerr << "Cuda failure: " << ret << std::endl;                                                         \
+            sample::gLogError << "Cuda failure: " << ret << std::endl;                                                 \
             abort();                                                                                                   \
         }                                                                                                              \
     } while (0)
@@ -79,12 +79,22 @@ using namespace plugin;
     {                                                                                                                  \
         if (!(status))                                                                                                 \
         {                                                                                                              \
-            std::cerr << errMsg << " Error in " << __FILE__ << ", function " << FN_NAME << "(), line " << __LINE__     \
-                      << std::endl;                                                                                    \
+            sample::gLogError << errMsg << " Error in " << __FILE__ << ", function " << FN_NAME << "(), line "         \
+                              << __LINE__ << std::endl;                                                                \
             return val;                                                                                                \
         }                                                                                                              \
     } while (0)
 
+#define ASSERT(condition)                                                                                              \
+    do                                                                                                                 \
+    {                                                                                                                  \
+        if (!(condition))                                                                                              \
+        {                                                                                                              \
+            sample::gLogError << "Assertion failure: " << #condition << std::endl;                                     \
+            abort();                                                                                                   \
+        }                                                                                                              \
+    } while (0)
+
 #define CHECK_RETURN(status, val) CHECK_RETURN_W_MSG(status, val, "")
 
 #define OBJ_GUARD(A) std::unique_ptr<A, void (*)(A * t)>
@@ -517,7 +527,7 @@ inline void setAllTensorScales(INetworkDefinition* network, float inScales = 2.0
             // Optional inputs are nullptr here and are from RNN layers.
             if (input != nullptr && !input->dynamicRangeIsSet())
             {
-                input->setDynamicRange(-inScales, inScales);
+                ASSERT(input->setDynamicRange(-inScales, inScales));
             }
         }
     }
@@ -537,11 +547,11 @@ inline void setAllTensorScales(INetworkDefinition* network, float inScales = 2.0
                 // Pooling must have the same input and output scales.
                 if (layer->getType() == LayerType::kPOOLING)
                 {
-                    output->setDynamicRange(-inScales, inScales);
+                    ASSERT(output->setDynamicRange(-inScales, inScales));
                 }
                 else
                 {
-                    output->setDynamicRange(-outScales, outScales);
+                    ASSERT(output->setDynamicRange(-outScales, outScales));
                 }
             }
         }
@@ -553,7 +563,7 @@ inline void setDummyInt8Scales(const IBuilderConfig* c, INetworkDefinition* n)
     // Set dummy tensor scales if Int8 mode is requested.
     if (c->getFlag(BuilderFlag::kINT8))
     {
-        gLogWarning
+        sample::gLogWarning
             << "Int8 calibrator not provided. Generating dummy per tensor scales. Int8 accuracy is not guaranteed."
             << std::endl;
         setAllTensorScales(n);
@@ -887,9 +897,9 @@ inline void loadLibrary(const std::string& path)
     if (handle == nullptr)
     {
 #ifdef _MSC_VER
-        gLogError << "Could not load plugin library: " << path << std::endl;
+        sample::gLogError << "Could not load plugin library: " << path << std::endl;
 #else
-        gLogError << "Could not load plugin library: " << path << ", due to: " << dlerror() << std::endl;
+        sample::gLogError << "Could not load plugin library: " << path << ", due to: " << dlerror() << std::endl;
 #endif
     }
 }
diff --git a/samples/common/dumpTFWts.py b/samples/common/dumpTFWts.py
index e11fdf29..91c97741 100644
--- a/samples/common/dumpTFWts.py
+++ b/samples/common/dumpTFWts.py
@@ -1,6 +1,6 @@
 #!/usr/bin/python
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/samples/common/getOptions.cpp b/samples/common/getOptions.cpp
index e35af11b..730d935e 100644
--- a/samples/common/getOptions.cpp
+++ b/samples/common/getOptions.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -170,7 +170,7 @@ TRTParsedArgs parseArgs(int argc, const char* const* argv, const std::vector<TRT
         }
 
         // If only 1 hyphen, char after is the flag.
-        TRTOption opt;
+        TRTOption opt{' ', "", false, ""};
         std::string value;
         if (argStr[1] != '-')
         {
@@ -217,8 +217,8 @@ TRTParsedArgs parseArgs(int argc, const char* const* argv, const std::vector<TRT
             const std::string nextArg(argv[i + 1]);
             if (nextArg.size() >= 1 && nextArg[0] == '-')
             {
-                gLogWarning << "Warning: Using '" << nextArg << "' as a value for '" << argStr
-                            << "', Should this be its own flag?" << std::endl;
+                sample::gLogWarning << "Warning: Using '" << nextArg << "' as a value for '" << argStr
+                                    << "', Should this be its own flag?" << std::endl;
             }
 
             parsedArgs.values[idx].second.push_back(nextArg);
diff --git a/samples/common/getOptions.h b/samples/common/getOptions.h
index a9edb0de..7c3ecb4a 100644
--- a/samples/common/getOptions.h
+++ b/samples/common/getOptions.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/common/half.h b/samples/common/half.h
index 19981369..0a0a7d45 100644
--- a/samples/common/half.h
+++ b/samples/common/half.h
@@ -16,7 +16,7 @@
 // OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -226,7 +226,7 @@
 /// By default this is set to `-1` (`std::round_indeterminate`), which uses truncation (round toward zero, but with
 /// overflows set to infinity) and is the fastest rounding mode possible. It can even be set to
 /// `std::numeric_limits<float>::round_style` to synchronize the rounding mode with that of the underlying
-/// single-precision implementation. For GIE-1275, changing it to 1 (to nearest)
+/// single-precision implementation.
 #ifndef HALF_ROUND_STYLE
 #define HALF_ROUND_STYLE 1 // = std::round_to_nearest
 #endif
diff --git a/samples/common/logger.cpp b/samples/common/logger.cpp
index acbef64c..b0601a30 100644
--- a/samples/common/logger.cpp
+++ b/samples/common/logger.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -17,6 +17,8 @@
 #include "logger.h"
 #include "logging.h"
 
+namespace sample
+{
 Logger gLogger{Logger::Severity::kINFO};
 LogStreamConsumer gLogVerbose{LOG_VERBOSE(gLogger)};
 LogStreamConsumer gLogInfo{LOG_INFO(gLogger)};
@@ -33,3 +35,4 @@ void setReportableSeverity(Logger::Severity severity)
     gLogError.setReportableSeverity(severity);
     gLogFatal.setReportableSeverity(severity);
 }
+} // namespace sample
diff --git a/samples/common/logger.h b/samples/common/logger.h
index e9cafa19..4187f344 100644
--- a/samples/common/logger.h
+++ b/samples/common/logger.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -19,6 +19,8 @@
 
 #include "logging.h"
 
+namespace sample
+{
 extern Logger gLogger;
 extern LogStreamConsumer gLogVerbose;
 extern LogStreamConsumer gLogInfo;
@@ -27,5 +29,6 @@ extern LogStreamConsumer gLogError;
 extern LogStreamConsumer gLogFatal;
 
 void setReportableSeverity(Logger::Severity severity);
+} // namespace sample
 
 #endif // LOGGER_H
diff --git a/samples/common/logging.h b/samples/common/logging.h
index 602b69fb..0fe4db18 100644
--- a/samples/common/logging.h
+++ b/samples/common/logging.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -26,6 +26,9 @@
 #include <sstream>
 #include <string>
 
+namespace sample
+{
+
 using Severity = nvinfer1::ILogger::Severity;
 
 class LogStreamConsumerBuffer : public std::stringbuf
@@ -40,6 +43,8 @@ class LogStreamConsumerBuffer : public std::stringbuf
 
     LogStreamConsumerBuffer(LogStreamConsumerBuffer&& other)
         : mOutput(other.mOutput)
+        , mPrefix(other.mPrefix)
+        , mShouldLog(other.mShouldLog)
     {
     }
 
@@ -425,7 +430,9 @@ class Logger : public nvinfer1::ILogger
         for (int i = 0; i < argc; i++)
         {
             if (i > 0)
+            {
                 ss << " ";
+            }
             ss << argv[i];
         }
         return ss.str();
@@ -500,4 +507,6 @@ inline LogStreamConsumer LOG_FATAL(const Logger& logger)
 
 } // anonymous namespace
 
+} // namespace sample
+
 #endif // TENSORRT_LOGGING_H
diff --git a/samples/common/parserOnnxConfig.h b/samples/common/parserOnnxConfig.h
index acdc293f..c9916619 100644
--- a/samples/common/parserOnnxConfig.h
+++ b/samples/common/parserOnnxConfig.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/common/sampleConfig.h b/samples/common/sampleConfig.h
index d106eb5d..77018010 100644
--- a/samples/common/sampleConfig.h
+++ b/samples/common/sampleConfig.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -49,12 +49,14 @@ class SampleConfig : public nvonnxparser::IOnnxConfig
     int64_t mFirstCalibBatch{0};
     int64_t mUseDLACore{-1};
     nvinfer1::DataType mModelDtype{nvinfer1::DataType::kFLOAT};
+    bool mTF32{true};
     Verbosity mVerbosity{static_cast<int>(nvinfer1::ILogger::Severity::kWARNING)};
     bool mPrintLayercInfo{false};
     bool mDebugBuilder{false};
     InputDataFormat mInputDataFormat{InputDataFormat::kASCII};
     uint64_t mTopK{0};
     float mFailurePercentage{-1.0f};
+    float mTolerance{0.0f};
 
 public:
     SampleConfig()
@@ -89,6 +91,16 @@ class SampleConfig : public nvonnxparser::IOnnxConfig
         return mModelDtype;
     }
 
+    bool getTF32() const
+    {
+        return mTF32;
+    }
+
+    void setTF32(bool enabled)
+    {
+        mTF32 = enabled;
+    }
+
     const char* getModelFileName() const
     {
         return mModelFilename.c_str();
@@ -141,11 +153,11 @@ class SampleConfig : public nvonnxparser::IOnnxConfig
     void setLabel(int64_t label)
     {
         mLabel = label;
-    }//!<  set the Label
+    } //!<  set the Label
     int64_t getLabel() const
     {
         return mLabel;
-    }//!<  get the Label
+    } //!<  get the Label
     bool getPrintLayerInfo() const
     {
         return mPrintLayercInfo;
@@ -281,6 +293,16 @@ class SampleConfig : public nvonnxparser::IOnnxConfig
         mFailurePercentage = f;
     }
 
+    float getTolerance() const
+    {
+        return mTolerance;
+    }
+
+    void setTolerance(float t)
+    {
+        mTolerance = t;
+    }
+
     bool isDebug() const
     {
 #if ONNX_DEBUG
diff --git a/samples/common/sampleDevice.h b/samples/common/sampleDevice.h
index b0efeca6..333f048a 100644
--- a/samples/common/sampleDevice.h
+++ b/samples/common/sampleDevice.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -17,9 +17,10 @@
 #ifndef TRT_SAMPLE_DEVICE_H
 #define TRT_SAMPLE_DEVICE_H
 
+#include <cuda.h>
+#include <cuda_runtime.h>
 #include <iostream>
 #include <thread>
-#include <cuda_runtime.h>
 
 namespace sample
 {
@@ -47,7 +48,7 @@ void cudaSleep(void* sleep)
     std::this_thread::sleep_for(std::chrono::duration<int, std::milli>(*static_cast<int*>(sleep)));
 }
 
-}
+} // namespace
 
 //!
 //! \class TrtCudaStream
@@ -56,7 +57,6 @@ void cudaSleep(void* sleep)
 class TrtCudaStream
 {
 public:
-
     TrtCudaStream()
     {
         cudaCheck(cudaStreamCreate(&mStream));
@@ -80,6 +80,11 @@ class TrtCudaStream
         return mStream;
     }
 
+    void synchronize()
+    {
+        cudaCheck(cudaStreamSynchronize(mStream));
+    }
+
     void wait(TrtCudaEvent& event);
 
     void sleep(int* ms)
@@ -92,7 +97,6 @@ class TrtCudaStream
     }
 
 private:
-
     cudaStream_t mStream{};
 };
 
@@ -103,7 +107,6 @@ class TrtCudaStream
 class TrtCudaEvent
 {
 public:
-
     explicit TrtCudaEvent(bool blocking = true)
     {
         const unsigned int flags = blocking ? cudaEventBlockingSync : cudaEventDefault;
@@ -147,7 +150,6 @@ class TrtCudaEvent
     }
 
 private:
-
     cudaEvent_t mEvent{};
 };
 
@@ -156,6 +158,54 @@ inline void TrtCudaStream::wait(TrtCudaEvent& event)
     cudaCheck(cudaStreamWaitEvent(mStream, event.get(), 0));
 }
 
+//!
+//! \class TrtCudaGraph
+//! \brief Managed CUDA graph
+//!
+class TrtCudaGraph
+{
+public:
+    explicit TrtCudaGraph() = default;
+
+    TrtCudaGraph(const TrtCudaGraph&) = delete;
+
+    TrtCudaGraph& operator=(const TrtCudaGraph&) = delete;
+
+    TrtCudaGraph(TrtCudaGraph&&) = delete;
+
+    TrtCudaGraph& operator=(TrtCudaGraph&&) = delete;
+
+    ~TrtCudaGraph()
+    {
+        if (mGraphExec)
+        {
+            cudaGraphExecDestroy(mGraphExec);
+        }
+    }
+
+    void beginCapture(TrtCudaStream& stream)
+    {
+        cudaCheck(cudaGraphCreate(&mGraph, 0));
+        cudaCheck(cudaStreamBeginCapture(stream.get(), cudaStreamCaptureModeGlobal));
+    }
+
+    void launch(TrtCudaStream& stream)
+    {
+        cudaCheck(cudaGraphLaunch(mGraphExec, stream.get()));
+    }
+
+    void endCapture(TrtCudaStream& stream)
+    {
+        cudaCheck(cudaStreamEndCapture(stream.get(), &mGraph));
+        cudaCheck(cudaGraphInstantiate(&mGraphExec, mGraph, nullptr, nullptr, 0));
+        cudaCheck(cudaGraphDestroy(mGraph));
+    }
+
+private:
+    cudaGraph_t mGraph{};
+    cudaGraphExec_t mGraphExec{};
+};
+
 //!
 //! \class TrtCudaBuffer
 //! \brief Managed buffer for host and device
@@ -164,7 +214,6 @@ template <typename A, typename D>
 class TrtCudaBuffer
 {
 public:
-
     TrtCudaBuffer() = default;
 
     TrtCudaBuffer(const TrtCudaBuffer&) = delete;
@@ -218,28 +267,39 @@ class TrtCudaBuffer
     }
 
 private:
-
     void* mPtr{nullptr};
 };
 
 struct DeviceAllocator
 {
-    void operator()(void** ptr, size_t size) { cudaCheck(cudaMalloc(ptr, size)); }
+    void operator()(void** ptr, size_t size)
+    {
+        cudaCheck(cudaMalloc(ptr, size));
+    }
 };
 
 struct DeviceDeallocator
 {
-    void operator()(void* ptr) { cudaCheck(cudaFree(ptr)); }
+    void operator()(void* ptr)
+    {
+        cudaCheck(cudaFree(ptr));
+    }
 };
 
 struct HostAllocator
 {
-    void operator()(void** ptr, size_t size) { cudaCheck(cudaMallocHost(ptr, size)); }
+    void operator()(void** ptr, size_t size)
+    {
+        cudaCheck(cudaMallocHost(ptr, size));
+    }
 };
 
 struct HostDeallocator
 {
-    void operator()(void* ptr) { cudaCheck(cudaFreeHost(ptr)); }
+    void operator()(void* ptr)
+    {
+        cudaCheck(cudaFreeHost(ptr));
+    }
 };
 
 using TrtDeviceBuffer = TrtCudaBuffer<DeviceAllocator, DeviceDeallocator>;
@@ -253,7 +313,6 @@ using TrtHostBuffer = TrtCudaBuffer<HostAllocator, HostDeallocator>;
 class MirroredBuffer
 {
 public:
-
     void allocate(size_t size)
     {
         mSize = size;
@@ -261,9 +320,15 @@ class MirroredBuffer
         mDeviceBuffer.allocate(size);
     }
 
-    void* getDeviceBuffer() const { return mDeviceBuffer.get(); }
+    void* getDeviceBuffer() const
+    {
+        return mDeviceBuffer.get();
+    }
 
-    void* getHostBuffer() const { return mHostBuffer.get(); }
+    void* getHostBuffer() const
+    {
+        return mHostBuffer.get();
+    }
 
     void hostToDevice(TrtCudaStream& stream)
     {
@@ -275,14 +340,13 @@ class MirroredBuffer
         cudaCheck(cudaMemcpyAsync(mHostBuffer.get(), mDeviceBuffer.get(), mSize, cudaMemcpyDeviceToHost, stream.get()));
     }
 
-    int getSize() const
+    size_t getSize() const
     {
         return mSize;
     }
 
 private:
-
-    int mSize{0};
+    size_t mSize{0};
     TrtHostBuffer mHostBuffer;
     TrtDeviceBuffer mDeviceBuffer;
 };
diff --git a/samples/common/sampleEngines.cpp b/samples/common/sampleEngines.cpp
index 2aa8051b..a6311002 100644
--- a/samples/common/sampleEngines.cpp
+++ b/samples/common/sampleEngines.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -22,15 +22,15 @@
 #include <random>
 #include <string>
 
-#include "NvInfer.h"
 #include "NvCaffeParser.h"
+#include "NvInfer.h"
 #include "NvOnnxParser.h"
 #include "NvUffParser.h"
 
 #include "logger.h"
-#include "sampleUtils.h"
-#include "sampleOptions.h"
 #include "sampleEngines.h"
+#include "sampleOptions.h"
+#include "sampleUtils.h"
 
 using namespace nvinfer1;
 
@@ -58,6 +58,15 @@ struct UffBufferShutter
 
 } // namespace
 
+#define SMP_RETVAL_IF_FALSE(condition, msg, retval, err)                                                               \
+    {                                                                                                                  \
+        if ((condition) == false)                                                                                      \
+        {                                                                                                              \
+            err << msg << std::endl;                                                                                   \
+            return retval;                                                                                             \
+        }                                                                                                              \
+    }
+
 Parser modelToNetwork(const ModelOptions& model, nvinfer1::INetworkDefinition& network, std::ostream& err)
 {
     Parser parser;
@@ -127,17 +136,16 @@ Parser modelToNetwork(const ModelOptions& model, nvinfer1::INetworkDefinition& n
     case ModelFormat::kONNX:
     {
         using namespace nvonnxparser;
-        parser.onnxParser.reset(createParser(network, gLogger.getTRTLogger()));
+        parser.onnxParser.reset(createParser(network, sample::gLogger.getTRTLogger()));
         if (!parser.onnxParser->parseFromFile(
-                model.baseModel.model.c_str(), static_cast<int>(gLogger.getReportableSeverity())))
+                model.baseModel.model.c_str(), static_cast<int>(sample::gLogger.getReportableSeverity())))
         {
             err << "Failed to parse onnx file" << std::endl;
             parser.onnxParser.reset();
         }
         break;
     }
-    case ModelFormat::kANY:
-        break;
+    case ModelFormat::kANY: break;
     }
 
     return parser;
@@ -149,8 +157,8 @@ namespace
 class RndInt8Calibrator : public nvinfer1::IInt8EntropyCalibrator2
 {
 public:
-    RndInt8Calibrator(
-        int batches, const std::string& cacheFile, const nvinfer1::INetworkDefinition& network, std::ostream& err);
+    RndInt8Calibrator(int batches, std::vector<int>& elemCount, const std::string& cacheFile,
+        const nvinfer1::INetworkDefinition& network, std::ostream& err);
 
     ~RndInt8Calibrator()
     {
@@ -180,13 +188,19 @@ class RndInt8Calibrator : public nvinfer1::IInt8EntropyCalibrator2
     std::ostream& mErr;
 };
 
-RndInt8Calibrator::RndInt8Calibrator(
-    int batches, const std::string& cacheFile, const INetworkDefinition& network, std::ostream& err)
+RndInt8Calibrator::RndInt8Calibrator(int batches, std::vector<int>& elemCount, const std::string& cacheFile,
+    const INetworkDefinition& network, std::ostream& err)
     : mBatches(batches)
     , mCurrentBatch(0)
     , mCacheFile(cacheFile)
     , mErr(err)
 {
+    std::ifstream tryCache(cacheFile, std::ios::binary);
+    if (tryCache.good())
+    {
+        return;
+    }
+
     std::default_random_engine generator;
     std::uniform_real_distribution<float> distribution(-1.0F, 1.0F);
     auto gen = [&generator, &distribution]() { return distribution(generator); };
@@ -194,13 +208,12 @@ RndInt8Calibrator::RndInt8Calibrator(
     for (int i = 0; i < network.getNbInputs(); i++)
     {
         auto input = network.getInput(i);
-        int elemCount = volume(input->getDimensions());
-        std::vector<float> rnd_data(elemCount);
-        std::generate_n(rnd_data.begin(), elemCount, gen);
+        std::vector<float> rnd_data(elemCount[i]);
+        std::generate_n(rnd_data.begin(), elemCount[i], gen);
 
         void* data;
-        cudaCheck(cudaMalloc(&data, elemCount * sizeof(float)), mErr);
-        cudaCheck(cudaMemcpy(data, rnd_data.data(), elemCount * sizeof(float), cudaMemcpyHostToDevice), mErr);
+        cudaCheck(cudaMalloc(&data, elemCount[i] * sizeof(float)), mErr);
+        cudaCheck(cudaMemcpy(data, rnd_data.data(), elemCount[i] * sizeof(float), cudaMemcpyHostToDevice), mErr);
 
         mInputDeviceBuffers.insert(std::make_pair(input->getName(), data));
     }
@@ -234,10 +247,11 @@ const void* RndInt8Calibrator::readCalibrationCache(size_t& length)
             std::istream_iterator<char>(input), std::istream_iterator<char>(), std::back_inserter(mCalibrationCache));
     }
 
+    length = mCalibrationCache.size();
     return mCalibrationCache.size() ? mCalibrationCache.data() : nullptr;
 }
 
-void setTensorScales(const INetworkDefinition& network, float inScales = 2.0f, float outScales = 4.0f)
+bool setTensorScales(const INetworkDefinition& network, float inScales = 2.0f, float outScales = 4.0f)
 {
     // Ensure that all layer inputs have a scale.
     for (int l = 0; l < network.getNbLayers(); l++)
@@ -249,7 +263,10 @@ void setTensorScales(const INetworkDefinition& network, float inScales = 2.0f, f
             // Optional inputs are nullptr here and are from RNN layers.
             if (input && !input->dynamicRangeIsSet())
             {
-                input->setDynamicRange(-inScales, inScales);
+                if (!input->setDynamicRange(-inScales, inScales))
+                {
+                    return false;
+                }
             }
         }
         for (int o = 0; o < layer->getNbOutputs(); o++)
@@ -261,15 +278,22 @@ void setTensorScales(const INetworkDefinition& network, float inScales = 2.0f, f
                 // Pooling must have the same input and output scales.
                 if (layer->getType() == LayerType::kPOOLING)
                 {
-                    output->setDynamicRange(-inScales, inScales);
+                    if (!output->setDynamicRange(-inScales, inScales))
+                    {
+                        return false;
+                    }
                 }
                 else
                 {
-                    output->setDynamicRange(-outScales, outScales);
+                    if (!output->setDynamicRange(-outScales, outScales))
+                    {
+                        return false;
+                    }
                 }
             }
         }
     }
+    return true;
 }
 
 } // namespace
@@ -290,6 +314,10 @@ ICudaEngine* networkToEngine(const BuildOptions& build, const SystemOptions& sys
     }
 
     bool hasDynamicShapes{false};
+    if (!build.inputFormats.empty() && build.inputFormats.size() != static_cast<size_t>(network.getNbInputs()))
+    {
+        throw std::invalid_argument("The number of inputIOFormats must match network's inputs.");
+    }
     for (unsigned int i = 0, n = network.getNbInputs(); i < n; i++)
     {
         // Set formats and data types of inputs
@@ -305,11 +333,11 @@ ICudaEngine* networkToEngine(const BuildOptions& build, const SystemOptions& sys
             {
             case DataType::kINT32:
             case DataType::kBOOL:
+            case DataType::kHALF:
                 // Leave these as is.
                 break;
             case DataType::kFLOAT:
             case DataType::kINT8:
-            case DataType::kHALF:
                 // User did not specify a floating-point format.  Default to kFLOAT.
                 input->setType(DataType::kFLOAT);
                 break;
@@ -320,7 +348,9 @@ ICudaEngine* networkToEngine(const BuildOptions& build, const SystemOptions& sys
         if (profile)
         {
             Dims dims = input->getDimensions();
-            const bool isDynamicInput = std::any_of(dims.d, dims.d + dims.nbDims, [](int dim){ return dim == -1; }) || input->isShapeTensor();
+            const bool isScalar = dims.nbDims == 0;
+            const bool isDynamicInput = std::any_of(dims.d, dims.d + dims.nbDims, [](int dim) { return dim == -1; })
+                || input->isShapeTensor();
             if (isDynamicInput)
             {
                 hasDynamicShapes = true;
@@ -331,18 +361,28 @@ ICudaEngine* networkToEngine(const BuildOptions& build, const SystemOptions& sys
                 if (shape == build.shapes.end())
                 {
                     constexpr int DEFAULT_DIMENSION = 1;
-                    Dims staticDims{};
+                    std::vector<int> staticDims;
                     if (input->isShapeTensor())
                     {
-                        staticDims.nbDims = dims.d[0];
-                        std::fill(staticDims.d, staticDims.d + staticDims.nbDims, DEFAULT_DIMENSION);
+                        if (isScalar)
+                        {
+                            staticDims.push_back(1);
+                        }
+                        else
+                        {
+                            staticDims.resize(dims.d[0]);
+                            std::fill(staticDims.begin(), staticDims.end(), DEFAULT_DIMENSION);
+                        }
                     }
                     else
                     {
-                        staticDims.nbDims = dims.nbDims;
-                        std::transform(dims.d, dims.d + dims.nbDims, staticDims.d, [&DEFAULT_DIMENSION](int dim) { return dim > 0 ? dim : DEFAULT_DIMENSION; });
+                        staticDims.resize(dims.nbDims);
+                        std::transform(dims.d, dims.d + dims.nbDims, staticDims.begin(),
+                            [&DEFAULT_DIMENSION](int dim) { return dim > 0 ? dim : DEFAULT_DIMENSION; });
                     }
-                    gLogWarning << "Dynamic dimensions required for input: " << input->getName() << ", but no shapes were provided. Automatically overriding shape to: " << staticDims << std::endl;
+                    sample::gLogWarning << "Dynamic dimensions required for input: " << input->getName()
+                                        << ", but no shapes were provided. Automatically overriding shape to: "
+                                        << staticDims << std::endl;
                     std::fill(shapes.begin(), shapes.end(), staticDims);
                 }
                 else
@@ -350,24 +390,36 @@ ICudaEngine* networkToEngine(const BuildOptions& build, const SystemOptions& sys
                     shapes = shape->second;
                 }
 
-                Dims profileDims{};
+                std::vector<int> profileDims{};
                 if (input->isShapeTensor())
                 {
                     profileDims = shapes[static_cast<size_t>(OptProfileSelector::kMIN)];
-                    profile->setShapeValues(input->getName(), OptProfileSelector::kMIN, profileDims.d, profileDims.nbDims);
+                    SMP_RETVAL_IF_FALSE(profile->setShapeValues(input->getName(), OptProfileSelector::kMIN,
+                                            profileDims.data(), static_cast<int>(profileDims.size())),
+                        "Error in set shape values MIN", nullptr, err);
                     profileDims = shapes[static_cast<size_t>(OptProfileSelector::kOPT)];
-                    profile->setShapeValues(input->getName(), OptProfileSelector::kOPT, profileDims.d, profileDims.nbDims);
+                    SMP_RETVAL_IF_FALSE(profile->setShapeValues(input->getName(), OptProfileSelector::kOPT,
+                                            profileDims.data(), static_cast<int>(profileDims.size())),
+                        "Error in set shape values OPT", nullptr, err);
                     profileDims = shapes[static_cast<size_t>(OptProfileSelector::kMAX)];
-                    profile->setShapeValues(input->getName(), OptProfileSelector::kMAX, profileDims.d, profileDims.nbDims);
+                    SMP_RETVAL_IF_FALSE(profile->setShapeValues(input->getName(), OptProfileSelector::kMAX,
+                                            profileDims.data(), static_cast<int>(profileDims.size())),
+                        "Error in set shape values MAX", nullptr, err);
                 }
                 else
                 {
                     profileDims = shapes[static_cast<size_t>(OptProfileSelector::kMIN)];
-                    profile->setDimensions(input->getName(), OptProfileSelector::kMIN, profileDims);
+                    SMP_RETVAL_IF_FALSE(
+                        profile->setDimensions(input->getName(), OptProfileSelector::kMIN, toDims(profileDims)),
+                        "Error in set dimensions to profile MIN", nullptr, err);
                     profileDims = shapes[static_cast<size_t>(OptProfileSelector::kOPT)];
-                    profile->setDimensions(input->getName(), OptProfileSelector::kOPT, profileDims);
+                    SMP_RETVAL_IF_FALSE(
+                        profile->setDimensions(input->getName(), OptProfileSelector::kOPT, toDims(profileDims)),
+                        "Error in set dimensions to profile OPT", nullptr, err);
                     profileDims = shapes[static_cast<size_t>(OptProfileSelector::kMAX)];
-                    profile->setDimensions(input->getName(), OptProfileSelector::kMAX, profileDims);
+                    SMP_RETVAL_IF_FALSE(
+                        profile->setDimensions(input->getName(), OptProfileSelector::kMAX, toDims(profileDims)),
+                        "Error in set dimensions to profile MAX", nullptr, err);
                 }
             }
         }
@@ -375,14 +427,15 @@ ICudaEngine* networkToEngine(const BuildOptions& build, const SystemOptions& sys
 
     if (profile && hasDynamicShapes)
     {
-        if (!profile->isValid())
-        {
-            err << "Required optimization profile is invalid" << std::endl;
-            return nullptr;
-        }
-        config->addOptimizationProfile(profile);
+        SMP_RETVAL_IF_FALSE(profile->isValid(), "Required optimization profile is invalid", nullptr, err);
+        SMP_RETVAL_IF_FALSE(
+            config->addOptimizationProfile(profile) != -1, "Error in add optimization profile", nullptr, err);
     }
 
+    if (!build.outputFormats.empty() && build.outputFormats.size() != static_cast<size_t>(network.getNbOutputs()))
+    {
+        throw std::invalid_argument("The number of outputIOFormats must match network's outputs.");
+    }
     for (unsigned int i = 0, n = network.getNbOutputs(); i < n; i++)
     {
         // Set formats and data types of outputs
@@ -400,6 +453,20 @@ ICudaEngine* networkToEngine(const BuildOptions& build, const SystemOptions& sys
 
     config->setMaxWorkspaceSize(static_cast<size_t>(build.workspace) << 20);
 
+    if (!build.builderCache)
+    {
+        config->setFlag(BuilderFlag::kDISABLE_TIMING_CACHE);
+    }
+
+    if (!build.tf32)
+    {
+        config->clearFlag(BuilderFlag::kTF32);
+    }
+
+    config->setProfilingVerbosity(build.nvtxMode);
+    config->setMinTimingIterations(build.minTiming);
+    config->setAvgTimingIterations(build.avgTiming);
+
     if (build.fp16)
     {
         config->setFlag(BuilderFlag::kFP16);
@@ -410,6 +477,14 @@ ICudaEngine* networkToEngine(const BuildOptions& build, const SystemOptions& sys
         config->setFlag(BuilderFlag::kINT8);
     }
 
+    if (build.int8 && !build.fp16)
+    {
+        sample::gLogInfo
+            << "FP32 and INT8 precisions have been specified - more performance might be enabled by additionally "
+               "specifying --fp16 or --best"
+            << std::endl;
+    }
+
     auto isInt8 = [](const IOFormat& format) { return format.first == DataType::kINT8; };
     auto int8IO = std::count_if(build.inputFormats.begin(), build.inputFormats.end(), isInt8)
         + std::count_if(build.outputFormats.begin(), build.outputFormats.end(), isInt8);
@@ -418,11 +493,54 @@ ICudaEngine* networkToEngine(const BuildOptions& build, const SystemOptions& sys
     {
         // Explicitly set int8 scales if no calibrator is provided and if I/O tensors use int8,
         // because auto calibration does not support this case.
-        setTensorScales(network);
+        SMP_RETVAL_IF_FALSE(setTensorScales(network), "Error in set tensor scales.", nullptr, err);
     }
     else if (build.int8)
     {
-        config->setInt8Calibrator(new RndInt8Calibrator(1, build.calibration, network, err));
+        IOptimizationProfile* profileCalib{nullptr};
+        if (!build.shapesCalib.empty())
+        {
+            profileCalib = builder.createOptimizationProfile();
+            for (unsigned int i = 0, n = network.getNbInputs(); i < n; i++)
+            {
+                auto input = network.getInput(i);
+                Dims profileDims{};
+                auto shape = build.shapesCalib.find(input->getName());
+                ShapeRange shapesCalib{};
+                shapesCalib = shape->second;
+
+                profileDims = toDims(shapesCalib[static_cast<size_t>(OptProfileSelector::kOPT)]);
+                // Here we check only kMIN as all profileDims are the same.
+                SMP_RETVAL_IF_FALSE(
+                    profileCalib->setDimensions(input->getName(), OptProfileSelector::kMIN, profileDims),
+                    "Error in set dimensions to calibration profile OPT", nullptr, err);
+                profileCalib->setDimensions(input->getName(), OptProfileSelector::kOPT, profileDims);
+                profileCalib->setDimensions(input->getName(), OptProfileSelector::kMAX, profileDims);
+            }
+            SMP_RETVAL_IF_FALSE(profileCalib->isValid(), "Calibration profile is invalid", nullptr, err);
+            SMP_RETVAL_IF_FALSE(
+                config->setCalibrationProfile(profileCalib), "Error in set calibration profile", nullptr, err);
+        }
+
+        std::vector<int> elemCount{};
+        for (int i = 0; i < network.getNbInputs(); i++)
+        {
+            auto input = network.getInput(i);
+            if (profileCalib)
+            {
+                elemCount.push_back(volume(profileCalib->getDimensions(input->getName(), OptProfileSelector::kOPT)));
+            }
+            else if (profile)
+            {
+                elemCount.push_back(volume(profile->getDimensions(input->getName(), OptProfileSelector::kOPT)));
+            }
+            else
+            {
+                elemCount.push_back(volume(input->getDimensions()));
+            }
+        }
+
+        config->setInt8Calibrator(new RndInt8Calibrator(1, elemCount, build.calibration, network, err));
     }
 
     if (build.safe)
@@ -460,15 +578,16 @@ ICudaEngine* networkToEngine(const BuildOptions& build, const SystemOptions& sys
 ICudaEngine* modelToEngine(
     const ModelOptions& model, const BuildOptions& build, const SystemOptions& sys, std::ostream& err)
 {
-    TrtUniquePtr<IBuilder> builder{createInferBuilder(gLogger.getTRTLogger())};
+    TrtUniquePtr<IBuilder> builder{createInferBuilder(sample::gLogger.getTRTLogger())};
     if (builder == nullptr)
     {
         err << "Builder creation failed" << std::endl;
         return nullptr;
     }
     const bool isOnnxModel = model.baseModel.format == ModelFormat::kONNX;
-    auto batchFlag = (build.maxBatch && !isOnnxModel) ? 0U : 1U
-        << static_cast<uint32_t>(nvinfer1::NetworkDefinitionCreationFlag::kEXPLICIT_BATCH);
+    auto batchFlag = (build.maxBatch && !isOnnxModel)
+        ? 0U
+        : 1U << static_cast<uint32_t>(nvinfer1::NetworkDefinitionCreationFlag::kEXPLICIT_BATCH);
     TrtUniquePtr<INetworkDefinition> network{builder->createNetworkV2(batchFlag)};
     if (!network)
     {
@@ -506,7 +625,7 @@ ICudaEngine* loadEngine(const std::string& engine, int DLACore, std::ostream& er
         return nullptr;
     }
 
-    TrtUniquePtr<IRuntime> runtime{createInferRuntime(gLogger.getTRTLogger())};
+    TrtUniquePtr<IRuntime> runtime{createInferRuntime(sample::gLogger.getTRTLogger())};
     if (DLACore != -1)
     {
         runtime->setDLACore(DLACore);
@@ -535,7 +654,8 @@ bool saveEngine(const ICudaEngine& engine, const std::string& fileName, std::ost
     return !engineFile.fail();
 }
 
-TrtUniquePtr<nvinfer1::ICudaEngine> getEngine(const ModelOptions& model, const BuildOptions& build, const SystemOptions& sys, std::ostream& err)
+TrtUniquePtr<nvinfer1::ICudaEngine> getEngine(
+    const ModelOptions& model, const BuildOptions& build, const SystemOptions& sys, std::ostream& err)
 {
     TrtUniquePtr<nvinfer1::ICudaEngine> engine;
     if (build.load)
diff --git a/samples/common/sampleEngines.h b/samples/common/sampleEngines.h
index e2abb8e5..152c8248 100644
--- a/samples/common/sampleEngines.h
+++ b/samples/common/sampleEngines.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -19,11 +19,12 @@
 
 #include <iostream>
 
-#include "NvInfer.h"
 #include "NvCaffeParser.h"
+#include "NvInfer.h"
 #include "NvOnnxParser.h"
 #include "NvUffParser.h"
 
+#include "sampleOptions.h"
 #include "sampleUtils.h"
 
 namespace sample
@@ -86,7 +87,8 @@ bool saveEngine(const nvinfer1::ICudaEngine& engine, const std::string& fileName
 //!
 //! \return Pointer to the engine created or nullptr if the creation failed
 //!
-TrtUniquePtr<nvinfer1::ICudaEngine> getEngine(const ModelOptions& model, const BuildOptions& build, const SystemOptions& sys, std::ostream& err);
+TrtUniquePtr<nvinfer1::ICudaEngine> getEngine(
+    const ModelOptions& model, const BuildOptions& build, const SystemOptions& sys, std::ostream& err);
 
 } // namespace sample
 
diff --git a/samples/common/sampleInference.cpp b/samples/common/sampleInference.cpp
index 0029a479..1d276674 100644
--- a/samples/common/sampleInference.cpp
+++ b/samples/common/sampleInference.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -14,30 +14,31 @@
  * limitations under the License.
  */
 
-#include <array>
-#include <vector>
-#include <numeric>
 #include <algorithm>
-#include <utility>
-#include <thread>
-#include <mutex>
+#include <array>
+#include <chrono>
 #include <functional>
 #include <limits>
 #include <memory>
+#include <mutex>
+#include <numeric>
+#include <thread>
+#include <utility>
+#include <vector>
 
 #include "NvInfer.h"
 
 #include "logger.h"
 #include "sampleDevice.h"
-#include "sampleUtils.h"
+#include "sampleInference.h"
 #include "sampleOptions.h"
 #include "sampleReporting.h"
-#include "sampleInference.h"
+#include "sampleUtils.h"
 
 namespace sample
 {
 
-void setUpInference(InferenceEnvironment& iEnv, const InferenceOptions& inference)
+bool setUpInference(InferenceEnvironment& iEnv, const InferenceOptions& inference)
 {
     for (int s = 0; s < inference.streams; ++s)
     {
@@ -49,33 +50,55 @@ void setUpInference(InferenceEnvironment& iEnv, const InferenceOptions& inferenc
         iEnv.context.front()->setProfiler(iEnv.profiler.get());
     }
 
+    const int nOptProfiles = iEnv.engine->getNbOptimizationProfiles();
+    const int nBindings = iEnv.engine->getNbBindings();
+    const int bindingsInProfile = nOptProfiles > 0 ? nBindings / nOptProfiles : 0;
+    const int endBindingIndex = bindingsInProfile ? bindingsInProfile : iEnv.engine->getNbBindings();
+
+    if (nOptProfiles > 1)
+    {
+        sample::gLogWarning << "Multiple profiles are currently not supported. Running with one profile." << std::endl;
+    }
+
     // Set all input dimensions before all bindings can be allocated
-    for (int b = 0; b < iEnv.engine->getNbBindings(); ++b)
+    for (int b = 0; b < endBindingIndex; ++b)
     {
         if (iEnv.engine->bindingIsInput(b))
         {
             auto dims = iEnv.context.front()->getBindingDimensions(b);
-            const bool isDynamicInput = std::any_of(dims.d, dims.d + dims.nbDims, [](int dim){ return dim == -1; }) || iEnv.engine->isShapeBinding(b);
+            const bool isScalar = dims.nbDims == 0;
+            const bool isDynamicInput = std::any_of(dims.d, dims.d + dims.nbDims, [](int dim) { return dim == -1; })
+                || iEnv.engine->isShapeBinding(b);
             if (isDynamicInput)
             {
                 auto shape = inference.shapes.find(iEnv.engine->getBindingName(b));
 
                 // If no shape is provided, set dynamic dimensions to 1.
-                nvinfer1::Dims staticDims{};
+                std::vector<int> staticDims;
                 if (shape == inference.shapes.end())
                 {
                     constexpr int DEFAULT_DIMENSION = 1;
                     if (iEnv.engine->isShapeBinding(b))
                     {
-                        staticDims.nbDims = dims.d[0];
-                        std::fill(staticDims.d, staticDims.d + staticDims.nbDims, DEFAULT_DIMENSION);
+                        if (isScalar)
+                        {
+                            staticDims.push_back(1);
+                        }
+                        else
+                        {
+                            staticDims.resize(dims.d[0]);
+                            std::fill(staticDims.begin(), staticDims.end(), DEFAULT_DIMENSION);
+                        }
                     }
                     else
                     {
-                        staticDims.nbDims = dims.nbDims;
-                        std::transform(dims.d, dims.d + dims.nbDims, staticDims.d, [&](int dim) { return dim > 0 ? dim : DEFAULT_DIMENSION; });
+                        staticDims.resize(dims.nbDims);
+                        std::transform(dims.d, dims.d + dims.nbDims, staticDims.begin(),
+                            [&](int dim) { return dim >= 0 ? dim : DEFAULT_DIMENSION; });
                     }
-                    gLogWarning << "Dynamic dimensions required for input: " << iEnv.engine->getBindingName(b) << ", but no shapes were provided. Automatically overriding shape to: " << staticDims << std::endl;
+                    sample::gLogWarning << "Dynamic dimensions required for input: " << iEnv.engine->getBindingName(b)
+                                        << ", but no shapes were provided. Automatically overriding shape to: "
+                                        << staticDims << std::endl;
                 }
                 else
                 {
@@ -86,18 +109,24 @@ void setUpInference(InferenceEnvironment& iEnv, const InferenceOptions& inferenc
                 {
                     if (iEnv.engine->isShapeBinding(b))
                     {
-                        c->setInputShapeBinding(b, staticDims.d);
+                        if (!c->setInputShapeBinding(b, staticDims.data()))
+                        {
+                            return false;
+                        }
                     }
                     else
                     {
-                        c->setBindingDimensions(b, staticDims);
+                        if (!c->setBindingDimensions(b, toDims(staticDims)))
+                        {
+                            return false;
+                        }
                     }
                 }
             }
         }
     }
 
-    for (int b = 0; b < iEnv.engine->getNbBindings(); ++b)
+    for (int b = 0; b < endBindingIndex; ++b)
     {
         const auto dims = iEnv.context.front()->getBindingDimensions(b);
         const auto vecDim = iEnv.engine->getBindingVectorizedDim(b);
@@ -119,9 +148,14 @@ void setUpInference(InferenceEnvironment& iEnv, const InferenceOptions& inferenc
             }
         }
     }
+
+    return true;
 }
 
-namespace {
+namespace
+{
+
+using TimePoint = std::chrono::time_point<std::chrono::high_resolution_clock>;
 
 //!
 //! \struct SyncStruct
@@ -131,47 +165,87 @@ struct SyncStruct
 {
     std::mutex mutex;
     TrtCudaStream mainStream;
-    TrtCudaEvent mainStart{cudaEventBlockingSync};
+    TrtCudaEvent gpuStart{cudaEventBlockingSync};
+    TimePoint cpuStart{};
     int sleep{0};
 };
 
+struct Enqueue
+{
+    explicit Enqueue(nvinfer1::IExecutionContext& context, void** buffers)
+        : mContext(context)
+        , mBuffers(buffers)
+    {
+    }
+
+    nvinfer1::IExecutionContext& mContext;
+    void** mBuffers{};
+};
+
 //!
 //! \class EnqueueImplicit
 //! \brief Functor to enqueue inference with implict batch
 //!
-class EnqueueImplicit
+class EnqueueImplicit : private Enqueue
 {
 
 public:
+    explicit EnqueueImplicit(nvinfer1::IExecutionContext& context, void** buffers, int batch)
+        : Enqueue(context, buffers)
+        , mBatch(batch)
+    {
+    }
 
-    explicit EnqueueImplicit(int batch): mBatch(batch) {}
-
-    void operator() (nvinfer1::IExecutionContext& context, void** buffers, TrtCudaStream& stream) const
+    void operator()(TrtCudaStream& stream) const
     {
-        context.enqueue(mBatch, buffers, stream.get(), nullptr);
+        mContext.enqueue(mBatch, mBuffers, stream.get(), nullptr);
     }
 
 private:
-
-    int mBatch{};
+    int mBatch;
 };
 
 //!
 //! \class EnqueueExplicit
 //! \brief Functor to enqueue inference with explict batch
 //!
-class EnqueueExplicit
+class EnqueueExplicit : private Enqueue
 {
 
 public:
+    explicit EnqueueExplicit(nvinfer1::IExecutionContext& context, void** buffers)
+        : Enqueue(context, buffers)
+    {
+    }
 
-    void operator() (nvinfer1::IExecutionContext& context, void** buffers, TrtCudaStream& stream) const
+    void operator()(TrtCudaStream& stream) const
     {
-        context.enqueueV2(buffers, stream.get(), nullptr);
+        mContext.enqueueV2(mBuffers, stream.get(), nullptr);
     }
 };
 
-using EnqueueFunction = std::function<void(nvinfer1::IExecutionContext&, void**, TrtCudaStream&)>;
+//!
+//! \class EnqueueGraph
+//! \brief Functor to enqueue inference from CUDA Graph
+//!
+class EnqueueGraph
+{
+
+public:
+    explicit EnqueueGraph(TrtCudaGraph& graph)
+        : mGraph(graph)
+    {
+    }
+
+    void operator()(TrtCudaStream& stream) const
+    {
+        mGraph.launch(stream);
+    }
+
+    TrtCudaGraph& mGraph;
+};
+
+using EnqueueFunction = std::function<void(TrtCudaStream&)>;
 
 enum class StreamType : int
 {
@@ -196,6 +270,8 @@ using MultiStream = std::array<TrtCudaStream, static_cast<int>(StreamType::kNUM)
 
 using MultiEvent = std::array<std::unique_ptr<TrtCudaEvent>, static_cast<int>(EventType::kNUM)>;
 
+using EnqueueTimes = std::array<TimePoint, 2>;
+
 //!
 //! \class Iteration
 //! \brief Inference iteration and streams management
@@ -204,18 +280,22 @@ class Iteration
 {
 
 public:
-
-    Iteration(int id, bool overlap, bool spin, nvinfer1::IExecutionContext& context, Bindings& bindings,
-               EnqueueFunction enqueue): mContext(context), mBindings(bindings), mEnqueue(enqueue),
-               mStreamId(id), mDepth(1 + overlap), mActive(mDepth), mEvents(mDepth)
+    Iteration(int id, const InferenceOptions& inference, nvinfer1::IExecutionContext& context, Bindings& bindings)
+        : mBindings(bindings)
+        , mStreamId(id)
+        , mDepth(1 + inference.overlap)
+        , mActive(mDepth)
+        , mEvents(mDepth)
+        , mEnqueueTimes(mDepth)
     {
         for (int d = 0; d < mDepth; ++d)
         {
             for (int e = 0; e < static_cast<int>(EventType::kNUM); ++e)
             {
-                mEvents[d][e].reset(new TrtCudaEvent(!spin));
+                mEvents[d][e].reset(new TrtCudaEvent(!inference.spin));
             }
         }
+        createEnqueueFunction(inference, context, bindings);
     }
 
     void query()
@@ -231,7 +311,9 @@ class Iteration
 
         wait(EventType::kINPUT_E, StreamType::kCOMPUTE); // Wait for input DMA before compute
         record(EventType::kCOMPUTE_S, StreamType::kCOMPUTE);
-        mEnqueue(mContext, mBindings.getDeviceBuffers(), getStream(StreamType::kCOMPUTE));
+        recordEnqueueTime();
+        mEnqueue(getStream(StreamType::kCOMPUTE));
+        recordEnqueueTime();
         record(EventType::kCOMPUTE_E, StreamType::kCOMPUTE);
 
         wait(EventType::kCOMPUTE_E, StreamType::kOUTPUT); // Wait for compute before output DMA
@@ -243,34 +325,33 @@ class Iteration
         moveNext();
     }
 
-    float sync(const TrtCudaEvent& start, std::vector<InferenceTrace>& trace)
+    float sync(const TimePoint& cpuStart, const TrtCudaEvent& gpuStart, std::vector<InferenceTrace>& trace)
     {
         if (mActive[mNext])
         {
             getEvent(EventType::kOUTPUT_E).synchronize();
-            trace.emplace_back(getTrace(start));
+            trace.emplace_back(getTrace(cpuStart, gpuStart));
             mActive[mNext] = false;
-            return getEvent(EventType::kCOMPUTE_S) - start;
+            return getEvent(EventType::kCOMPUTE_S) - gpuStart;
         }
         return 0;
     }
 
-    void syncAll(const TrtCudaEvent& start, std::vector<InferenceTrace>& trace)
+    void syncAll(const TimePoint& cpuStart, const TrtCudaEvent& gpuStart, std::vector<InferenceTrace>& trace)
     {
         for (int d = 0; d < mDepth; ++d)
         {
-            sync(start, trace);
+            sync(cpuStart, gpuStart, trace);
             moveNext();
         }
     }
 
-    void wait(TrtCudaEvent& start)
+    void wait(TrtCudaEvent& gpuStart)
     {
-        getStream(StreamType::kINPUT).wait(start);
+        getStream(StreamType::kINPUT).wait(gpuStart);
     }
 
 private:
-
     void moveNext()
     {
         mNext = mDepth - 1 - mNext;
@@ -291,21 +372,58 @@ class Iteration
         getEvent(e).record(getStream(s));
     }
 
+    void recordEnqueueTime()
+    {
+        mEnqueueTimes[mNext][enqueueStart] = std::chrono::high_resolution_clock::now();
+        enqueueStart = 1 - enqueueStart;
+    }
+
+    TimePoint getEnqueueTime(bool start)
+    {
+        return mEnqueueTimes[mNext][start ? 0 : 1];
+    }
+
     void wait(EventType e, StreamType s)
     {
         getStream(s).wait(getEvent(e));
     }
 
-    InferenceTrace getTrace(const TrtCudaEvent& start)
+    InferenceTrace getTrace(const TimePoint& cpuStart, const TrtCudaEvent& gpuStart)
     {
-        return InferenceTrace(mStreamId, getEvent(EventType::kINPUT_S) - start, getEvent(EventType::kINPUT_E) - start,
-                                         getEvent(EventType::kCOMPUTE_S) - start, getEvent(EventType::kCOMPUTE_E) - start,
-                                         getEvent(EventType::kOUTPUT_S)- start, getEvent(EventType::kOUTPUT_E)- start);
+        return InferenceTrace(mStreamId,
+            std::chrono::duration<float, std::milli>(getEnqueueTime(true) - cpuStart).count(),
+            std::chrono::duration<float, std::milli>(getEnqueueTime(false) - cpuStart).count(),
+            getEvent(EventType::kINPUT_S) - gpuStart, getEvent(EventType::kINPUT_E) - gpuStart,
+            getEvent(EventType::kCOMPUTE_S) - gpuStart, getEvent(EventType::kCOMPUTE_E) - gpuStart,
+            getEvent(EventType::kOUTPUT_S) - gpuStart, getEvent(EventType::kOUTPUT_E) - gpuStart);
+    }
+
+    void createEnqueueFunction(
+        const InferenceOptions& inference, nvinfer1::IExecutionContext& context, Bindings& bindings)
+    {
+        if (inference.batch)
+        {
+            mEnqueue = EnqueueFunction(EnqueueImplicit(context, mBindings.getDeviceBuffers(), inference.batch));
+        }
+        else
+        {
+            mEnqueue = EnqueueFunction(EnqueueExplicit(context, mBindings.getDeviceBuffers()));
+        }
+        if (inference.graph)
+        {
+            TrtCudaStream& stream = getStream(StreamType::kCOMPUTE);
+            mEnqueue(stream);
+            stream.synchronize();
+            mGraph.beginCapture(stream);
+            mEnqueue(stream);
+            mGraph.endCapture(stream);
+            mEnqueue = EnqueueFunction(EnqueueGraph(mGraph));
+        }
     }
 
-    nvinfer1::IExecutionContext& mContext;
     Bindings& mBindings;
 
+    TrtCudaGraph mGraph;
     EnqueueFunction mEnqueue;
 
     int mStreamId{0};
@@ -315,11 +433,15 @@ class Iteration
     std::vector<bool> mActive;
     MultiStream mStream;
     std::vector<MultiEvent> mEvents;
+
+    int enqueueStart{0};
+    std::vector<EnqueueTimes> mEnqueueTimes;
 };
 
 using IterationStreams = std::vector<std::unique_ptr<Iteration>>;
 
-void inferenceLoop(IterationStreams& iStreams, const TrtCudaEvent& mainStart, int batch, int iterations, float maxDurationMs, float warmupMs, std::vector<InferenceTrace>& trace)
+void inferenceLoop(IterationStreams& iStreams, const TimePoint& cpuStart, const TrtCudaEvent& gpuStart, int iterations,
+    float maxDurationMs, float warmupMs, std::vector<InferenceTrace>& trace)
 {
     float durationMs = 0;
     int skip = 0;
@@ -332,7 +454,7 @@ void inferenceLoop(IterationStreams& iStreams, const TrtCudaEvent& mainStart, in
         }
         for (auto& s : iStreams)
         {
-            durationMs = std::max(durationMs, s->sync(mainStart, trace));
+            durationMs = std::max(durationMs, s->sync(cpuStart, gpuStart, trace));
         }
         if (durationMs < warmupMs) // Warming up
         {
@@ -345,60 +467,64 @@ void inferenceLoop(IterationStreams& iStreams, const TrtCudaEvent& mainStart, in
     }
     for (auto& s : iStreams)
     {
-        s->syncAll(mainStart, trace);
+        s->syncAll(cpuStart, gpuStart, trace);
     }
 }
 
-void inferenceExecution(const InferenceOptions& inference, InferenceEnvironment& iEnv, SyncStruct& sync, int offset, int streams, std::vector<InferenceTrace>& trace)
+void inferenceExecution(const InferenceOptions& inference, InferenceEnvironment& iEnv, SyncStruct& sync, int offset,
+    int streams, int device, std::vector<InferenceTrace>& trace)
 {
     float warmupMs = static_cast<float>(inference.warmup);
     float durationMs = static_cast<float>(inference.duration) * 1000 + warmupMs;
 
-    auto enqueue = inference.batch ? EnqueueFunction(EnqueueImplicit(inference.batch)) : EnqueueFunction(EnqueueExplicit());
+    cudaCheck(cudaSetDevice(device));
 
     IterationStreams iStreams;
     for (int s = 0; s < streams; ++s)
     {
-        iStreams.emplace_back(new Iteration(offset + s, inference.overlap, inference.spin, *iEnv.context[offset], *iEnv.bindings[offset], enqueue));
+        iStreams.emplace_back(new Iteration(offset + s, inference, *iEnv.context[offset], *iEnv.bindings[offset]));
     }
 
     for (auto& s : iStreams)
     {
-        s->wait(sync.mainStart);
+        s->wait(sync.gpuStart);
     }
 
     std::vector<InferenceTrace> localTrace;
-    inferenceLoop(iStreams, sync.mainStart, inference.batch, inference.iterations, durationMs, warmupMs, localTrace);
+    inferenceLoop(iStreams, sync.cpuStart, sync.gpuStart, inference.iterations, durationMs, warmupMs, localTrace);
 
     sync.mutex.lock();
     trace.insert(trace.end(), localTrace.begin(), localTrace.end());
     sync.mutex.unlock();
 }
 
-inline
-std::thread makeThread(const InferenceOptions& inference, InferenceEnvironment& iEnv, SyncStruct& sync, int thread, int streamsPerThread, std::vector<InferenceTrace>& trace)
+inline std::thread makeThread(const InferenceOptions& inference, InferenceEnvironment& iEnv, SyncStruct& sync,
+    int thread, int streamsPerThread, int device, std::vector<InferenceTrace>& trace)
 {
-    return std::thread(inferenceExecution, std::cref(inference), std::ref(iEnv), std::ref(sync), thread, streamsPerThread, std::ref(trace));
+    return std::thread(inferenceExecution, std::cref(inference), std::ref(iEnv), std::ref(sync), thread,
+        streamsPerThread, device, std::ref(trace));
 }
 
 } // namespace
 
-void runInference(const InferenceOptions& inference, InferenceEnvironment& iEnv, std::vector<InferenceTrace>& trace)
+void runInference(
+    const InferenceOptions& inference, InferenceEnvironment& iEnv, int device, std::vector<InferenceTrace>& trace)
 {
     trace.resize(0);
 
     SyncStruct sync;
     sync.sleep = inference.sleep;
     sync.mainStream.sleep(&sync.sleep);
-    sync.mainStart.record(sync.mainStream);
+    sync.cpuStart = std::chrono::high_resolution_clock::now();
+    sync.gpuStart.record(sync.mainStream);
 
     int threadsNum = inference.threads ? inference.streams : 1;
-    int streamsPerThread  = inference.streams / threadsNum;
+    int streamsPerThread = inference.streams / threadsNum;
 
     std::vector<std::thread> threads;
     for (int t = 0; t < threadsNum; ++t)
     {
-        threads.emplace_back(makeThread(inference, iEnv, sync, t, streamsPerThread, trace));
+        threads.emplace_back(makeThread(inference, iEnv, sync, t, streamsPerThread, device, trace));
     }
     for (auto& th : threads)
     {
diff --git a/samples/common/sampleInference.h b/samples/common/sampleInference.h
index 6a293087..d2b5dd8f 100644
--- a/samples/common/sampleInference.h
+++ b/samples/common/sampleInference.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -17,15 +17,15 @@
 #ifndef TRT_SAMPLE_INFERENCE_H
 #define TRT_SAMPLE_INFERENCE_H
 
-#include <memory>
 #include <iostream>
-#include <vector>
+#include <memory>
 #include <string>
+#include <vector>
 
 #include "NvInfer.h"
 
-#include "sampleUtils.h"
 #include "sampleReporting.h"
+#include "sampleUtils.h"
 
 namespace sample
 {
@@ -41,12 +41,13 @@ struct InferenceEnvironment
 //!
 //! \brief Set up contexts and bindings for inference
 //!
-void setUpInference(InferenceEnvironment& iEnv, const InferenceOptions& inference);
+bool setUpInference(InferenceEnvironment& iEnv, const InferenceOptions& inference);
 
 //!
 //! \brief Run inference and collect timing
 //!
-void runInference(const InferenceOptions& inference, InferenceEnvironment& iEnv, std::vector<InferenceTrace>& trace);
+void runInference(
+    const InferenceOptions& inference, InferenceEnvironment& iEnv, int device, std::vector<InferenceTrace>& trace);
 
 } // namespace sample
 
diff --git a/samples/common/sampleOptions.cpp b/samples/common/sampleOptions.cpp
index 52ae56b0..acf49a06 100644
--- a/samples/common/sampleOptions.cpp
+++ b/samples/common/sampleOptions.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -14,18 +14,18 @@
  * limitations under the License.
  */
 
+#include <algorithm>
 #include <cstring>
-#include <string>
-#include <vector>
 #include <functional>
-#include <algorithm>
-#include <stdexcept>
 #include <iostream>
+#include <stdexcept>
+#include <string>
+#include <vector>
 
 #include "NvInfer.h"
 
-#include "sampleUtils.h"
 #include "sampleOptions.h"
+#include "sampleUtils.h"
 
 namespace sample
 {
@@ -33,11 +33,11 @@ namespace sample
 namespace
 {
 
-inline std::vector<std::string> splitToStringVec(const std::string& option, char separator)
+std::vector<std::string> splitToStringVec(const std::string& option, char separator)
 {
     std::vector<std::string> options;
 
-    for(size_t start = 0; start < option.length(); )
+    for (size_t start = 0; start < option.length();)
     {
         size_t separatorIndex = option.find(separator, start);
         if (separatorIndex == std::string::npos)
@@ -52,52 +52,47 @@ inline std::vector<std::string> splitToStringVec(const std::string& option, char
 }
 
 template <typename T>
-inline T stringToValue(const std::string& option)
+T stringToValue(const std::string& option)
 {
     return T{option};
 }
 
 template <>
-inline int stringToValue<int>(const std::string& option)
+int stringToValue<int>(const std::string& option)
 {
     return std::stoi(option);
 }
 
 template <>
-inline float stringToValue<float>(const std::string& option)
+float stringToValue<float>(const std::string& option)
 {
     return std::stof(option);
 }
 
 template <>
-inline bool stringToValue<bool>(const std::string& option)
+bool stringToValue<bool>(const std::string& option)
 {
     return true;
 }
 
 template <>
-inline nvinfer1::Dims stringToValue<nvinfer1::Dims>(const std::string& option)
+std::vector<int> stringToValue<std::vector<int>>(const std::string& option)
 {
-    nvinfer1::Dims dims;
-    dims.nbDims = 0;
+    std::vector<int> shape;
     std::vector<std::string> dimsStrings = splitToStringVec(option, 'x');
     for (const auto& d : dimsStrings)
     {
-        if (d == "*")
-        {
-            break;
-        }
-        dims.d[dims.nbDims] = stringToValue<int>(d);
-        ++dims.nbDims;
+        shape.push_back(stringToValue<int>(d));
     }
-    return dims;
+    return shape;
 }
 
 template <>
-inline nvinfer1::DataType stringToValue<nvinfer1::DataType>(const std::string& option)
+nvinfer1::DataType stringToValue<nvinfer1::DataType>(const std::string& option)
 {
-    const std::unordered_map<std::string, nvinfer1::DataType> strToDT{{"fp32", nvinfer1::DataType::kFLOAT}, {"fp16", nvinfer1::DataType::kHALF},
-                                                                      {"int8", nvinfer1::DataType::kINT8}, {"int32", nvinfer1::DataType::kINT32}};
+    const std::unordered_map<std::string, nvinfer1::DataType> strToDT{{"fp32", nvinfer1::DataType::kFLOAT},
+        {"fp16", nvinfer1::DataType::kHALF}, {"int8", nvinfer1::DataType::kINT8},
+        {"int32", nvinfer1::DataType::kINT32}};
     auto dt = strToDT.find(option);
     if (dt == strToDT.end())
     {
@@ -107,12 +102,13 @@ inline nvinfer1::DataType stringToValue<nvinfer1::DataType>(const std::string& o
 }
 
 template <>
-inline nvinfer1::TensorFormats stringToValue<nvinfer1::TensorFormats>(const std::string& option)
+nvinfer1::TensorFormats stringToValue<nvinfer1::TensorFormats>(const std::string& option)
 {
     std::vector<std::string> optionStrings = splitToStringVec(option, '+');
-    const std::unordered_map<std::string, nvinfer1::TensorFormat> strToFmt{{"chw", nvinfer1::TensorFormat::kLINEAR}, {"chw2", nvinfer1::TensorFormat::kCHW2},
-                                                                           {"chw4", nvinfer1::TensorFormat::kCHW4}, {"hwc8", nvinfer1::TensorFormat::kHWC8},
-                                                                           {"chw16", nvinfer1::TensorFormat::kCHW16}, {"chw32", nvinfer1::TensorFormat::kCHW32}};
+    const std::unordered_map<std::string, nvinfer1::TensorFormat> strToFmt{{"chw", nvinfer1::TensorFormat::kLINEAR},
+        {"chw2", nvinfer1::TensorFormat::kCHW2}, {"chw4", nvinfer1::TensorFormat::kCHW4},
+        {"hwc8", nvinfer1::TensorFormat::kHWC8}, {"chw16", nvinfer1::TensorFormat::kCHW16},
+        {"chw32", nvinfer1::TensorFormat::kCHW32}};
     nvinfer1::TensorFormats formats{};
     for (auto f : optionStrings)
     {
@@ -128,7 +124,7 @@ inline nvinfer1::TensorFormats stringToValue<nvinfer1::TensorFormats>(const std:
 }
 
 template <>
-inline IOFormat stringToValue<IOFormat>(const std::string& option)
+IOFormat stringToValue<IOFormat>(const std::string& option)
 {
     IOFormat ioFormat{};
     size_t colon = option.find(':');
@@ -138,35 +134,31 @@ inline IOFormat stringToValue<IOFormat>(const std::string& option)
         throw std::invalid_argument(std::string("Invalid IOFormat ") + option);
     }
     ioFormat.first = stringToValue<nvinfer1::DataType>(option.substr(0, colon));
-    ioFormat.second = stringToValue<nvinfer1::TensorFormats>(option.substr(colon+1));
+    ioFormat.second = stringToValue<nvinfer1::TensorFormats>(option.substr(colon + 1));
 
     return ioFormat;
 }
 
 template <typename T>
-inline std::pair<std::string, T> splitNameAndValue(const std::string& s)
+std::pair<std::string, T> splitNameAndValue(const std::string& s)
 {
     std::string tensorName;
     std::string valueString;
-    std::vector<std::string> nameWithQuotes{splitToStringVec(s, '\'')};
-    if (nameWithQuotes.size() == 1)
-    {
-        // Name not wrapped with single quotes
-        std::vector<std::string> nameRange{splitToStringVec(s, ':')};
-        tensorName = nameRange[0];
-        valueString = nameRange[1];
-    }
-    else
+    // Split on the last :
+    std::vector<std::string> nameRange{splitToStringVec(s, ':')};
+    // Everything before the last : is the name
+    tensorName = nameRange[0];
+    for (size_t i = 1; i < nameRange.size() - 1; i++)
     {
-        // Name wrapped with single quotes
-        tensorName = nameWithQuotes[1];
-        valueString = splitToStringVec(nameWithQuotes[2], ':')[1];
+        tensorName += ":" + nameRange[i];
     }
+    // Value is the string element after the last :
+    valueString = nameRange[nameRange.size() - 1];
     return std::pair<std::string, T>(tensorName, stringToValue<T>(valueString));
 }
 
 template <typename T>
-inline void splitInsertKeyValue(const std::vector<std::string>& kvList, T& map)
+void splitInsertKeyValue(const std::vector<std::string>& kvList, T& map)
 {
     for (const auto& kv : kvList)
     {
@@ -174,13 +166,13 @@ inline void splitInsertKeyValue(const std::vector<std::string>& kvList, T& map)
     }
 }
 
-inline const char* boolToEnabled(bool enable)
+const char* boolToEnabled(bool enable)
 {
     return enable ? "Enabled" : "Disabled";
 }
 
 template <typename T>
-inline bool checkEraseOption(Arguments& arguments, const std::string& option, T& value)
+bool checkEraseOption(Arguments& arguments, const std::string& option, T& value)
 {
     auto match = arguments.find(option);
     if (match != arguments.end())
@@ -193,28 +185,125 @@ inline bool checkEraseOption(Arguments& arguments, const std::string& option, T&
     return false;
 }
 
+// Like checkEraseOption, but sets value to false if arguments contain the option.
+// This function should be used for options that default to true.
+bool checkEraseNegativeOption(Arguments& arguments, const std::string& option, bool& value)
+{
+    bool dummy;
+    if (checkEraseOption(arguments, option, dummy))
+    {
+        value = false;
+        return true;
+    }
+    return false;
+}
+
 template <typename T>
-inline bool checkEraseRepeatedOption(Arguments& arguments, const std::string& option, std::vector<T>& values)
+bool checkEraseRepeatedOption(Arguments& arguments, const std::string& option, std::vector<T>& values)
 {
     auto match = arguments.equal_range(option);
     if (match.first == match.second)
     {
         return false;
     }
-    auto addValue = [&values](Arguments::value_type& value) {values.emplace_back(stringToValue<T>(value.second));};
+    auto addValue = [&values](Arguments::value_type& value) { values.emplace_back(stringToValue<T>(value.second)); };
     std::for_each(match.first, match.second, addValue);
     arguments.erase(match.first, match.second);
     return true;
 }
 
-void insertShapes(std::unordered_map<std::string, ShapeRange>& shapes, const std::string& name, const nvinfer1::Dims& dims)
+void insertShapesBuild(std::unordered_map<std::string, ShapeRange>& shapes, nvinfer1::OptProfileSelector selector,
+    const std::string& name, const std::vector<int>& dims)
 {
-    std::pair<std::string, ShapeRange> profile;
-    profile.first = name;
-    profile.second[static_cast<size_t>(nvinfer1::OptProfileSelector::kMIN)] = dims;
-    profile.second[static_cast<size_t>(nvinfer1::OptProfileSelector::kOPT)] = dims;
-    profile.second[static_cast<size_t>(nvinfer1::OptProfileSelector::kMAX)] = dims;
-    shapes.insert(profile);
+    shapes[name][static_cast<size_t>(selector)] = dims;
+}
+
+void insertShapesInference(
+    std::unordered_map<std::string, std::vector<int>>& shapes, const std::string& name, const std::vector<int>& dims)
+{
+    shapes[name] = dims;
+}
+
+std::string removeSingleQuotationMarks(std::string& str)
+{
+    std::vector<std::string> strList{splitToStringVec(str, '\'')};
+    // Remove all the escaped single quotation marks
+    std::string retVal = "";
+    // Do not really care about unterminated sequences
+    for (size_t i = 0; i < strList.size(); i++)
+    {
+        retVal += strList[i];
+    }
+    return retVal;
+}
+
+bool getShapesBuild(Arguments& arguments, std::unordered_map<std::string, ShapeRange>& shapes, const char* argument,
+    nvinfer1::OptProfileSelector selector)
+{
+    std::string list;
+    bool retVal = checkEraseOption(arguments, argument, list);
+    std::vector<std::string> shapeList{splitToStringVec(list, ',')};
+    for (const auto& s : shapeList)
+    {
+        auto nameDimsPair = splitNameAndValue<std::vector<int>>(s);
+        auto tensorName = removeSingleQuotationMarks(nameDimsPair.first);
+        auto dims = nameDimsPair.second;
+        insertShapesBuild(shapes, selector, tensorName, dims);
+    }
+    return retVal;
+}
+
+bool getShapesInference(
+    Arguments& arguments, std::unordered_map<std::string, std::vector<int>>& shapes, const char* argument)
+{
+    std::string list;
+    bool retVal = checkEraseOption(arguments, argument, list);
+    std::vector<std::string> shapeList{splitToStringVec(list, ',')};
+    for (const auto& s : shapeList)
+    {
+        auto nameDimsPair = splitNameAndValue<std::vector<int>>(s);
+        auto tensorName = removeSingleQuotationMarks(nameDimsPair.first);
+        auto dims = nameDimsPair.second;
+        insertShapesInference(shapes, tensorName, dims);
+    }
+    return retVal;
+}
+
+void processShapes(
+    std::unordered_map<std::string, ShapeRange>& shapes, bool minShapes, bool optShapes, bool maxShapes, bool calib)
+{
+    // Only accept optShapes only or all three of minShapes, optShapes, maxShapes
+    if (((minShapes || maxShapes) && !optShapes)   // minShapes only, maxShapes only, both minShapes and maxShapes
+        || (minShapes && !maxShapes && optShapes)  // both minShapes and optShapes
+        || (!minShapes && maxShapes && optShapes)) // both maxShapes and optShapes
+    {
+        if (calib)
+        {
+            throw std::invalid_argument(
+                "Must specify only --optShapesCalib or all of --minShapesCalib, --optShapesCalib, --maxShapesCalib");
+        }
+        else
+        {
+            throw std::invalid_argument(
+                "Must specify only --optShapes or all of --minShapes, --optShapes, --maxShapes");
+        }
+    }
+
+    // If optShapes only, expand optShapes to minShapes and maxShapes
+    if (optShapes && !minShapes && !maxShapes)
+    {
+        std::unordered_map<std::string, ShapeRange> newShapes;
+        for (auto& s : shapes)
+        {
+            insertShapesBuild(newShapes, nvinfer1::OptProfileSelector::kMIN, s.first,
+                s.second[static_cast<size_t>(nvinfer1::OptProfileSelector::kOPT)]);
+            insertShapesBuild(newShapes, nvinfer1::OptProfileSelector::kOPT, s.first,
+                s.second[static_cast<size_t>(nvinfer1::OptProfileSelector::kOPT)]);
+            insertShapesBuild(newShapes, nvinfer1::OptProfileSelector::kMAX, s.first,
+                s.second[static_cast<size_t>(nvinfer1::OptProfileSelector::kOPT)]);
+        }
+        shapes = newShapes;
+    }
 }
 
 template <typename T>
@@ -246,7 +335,20 @@ std::ostream& printBatch(std::ostream& os, int maxBatch)
     return os;
 }
 
+std::ostream& printPrecision(std::ostream& os, const BuildOptions& options)
+{
+    os << "FP32";
+    if (options.fp16)
+    {
+        os << "+FP16";
+    }
+    if (options.int8)
+    {
+        os << "+INT8";
+    }
+    return os;
 }
+} // namespace
 
 Arguments argsToArgumentsMap(int argc, char* argv[])
 {
@@ -325,8 +427,7 @@ void ModelOptions::parse(Arguments& arguments)
         }
         break;
     }
-    case ModelFormat::kONNX:
-        break;
+    case ModelFormat::kONNX: break;
     case ModelFormat::kANY:
     {
         if (checkEraseOption(arguments, "--deploy", prototxt))
@@ -356,11 +457,9 @@ void ModelOptions::parse(Arguments& arguments)
     }
 }
 
-
 void BuildOptions::parse(Arguments& arguments)
 {
-    auto getFormats = [&arguments](std::vector<IOFormat>& formatsVector, const char* argument)
-    {
+    auto getFormats = [&arguments](std::vector<IOFormat>& formatsVector, const char* argument) {
         std::string list;
         checkEraseOption(arguments, argument, list);
         std::vector<std::string> formats{splitToStringVec(list, ',')};
@@ -373,33 +472,19 @@ void BuildOptions::parse(Arguments& arguments)
     getFormats(inputFormats, "--inputIOFormats");
     getFormats(outputFormats, "--outputIOFormats");
 
-    auto getShapes = [&arguments](std::unordered_map<std::string, ShapeRange>& shapes, const char* argument,
-                         nvinfer1::OptProfileSelector selector) {
-        std::string list;
-        checkEraseOption(arguments, argument, list);
-        std::vector<std::string> shapeList{splitToStringVec(list, ',')};
-        for (const auto& s : shapeList)
-        {
-            auto nameDimsPair = splitNameAndValue<nvinfer1::Dims>(s);
-            std::string tensorName = nameDimsPair.first;
-            nvinfer1::Dims dims = nameDimsPair.second;
-
-            if (shapes.find(tensorName) == shapes.end())
-            {
-                insertShapes(shapes, tensorName, dims);
-            }
-            else
-            {
-                shapes[tensorName][static_cast<size_t>(selector)] = dims;
-            }
-        }
-    };
-
     bool explicitBatch{false};
     checkEraseOption(arguments, "--explicitBatch", explicitBatch);
-    getShapes(shapes, "--minShapes", nvinfer1::OptProfileSelector::kMIN);
-    getShapes(shapes, "--optShapes", nvinfer1::OptProfileSelector::kOPT);
-    getShapes(shapes, "--maxShapes", nvinfer1::OptProfileSelector::kMAX);
+    bool minShapes = getShapesBuild(arguments, shapes, "--minShapes", nvinfer1::OptProfileSelector::kMIN);
+    bool optShapes = getShapesBuild(arguments, shapes, "--optShapes", nvinfer1::OptProfileSelector::kOPT);
+    bool maxShapes = getShapesBuild(arguments, shapes, "--maxShapes", nvinfer1::OptProfileSelector::kMAX);
+    processShapes(shapes, minShapes, optShapes, maxShapes, false);
+    bool minShapesCalib
+        = getShapesBuild(arguments, shapesCalib, "--minShapesCalib", nvinfer1::OptProfileSelector::kMIN);
+    bool optShapesCalib
+        = getShapesBuild(arguments, shapesCalib, "--optShapesCalib", nvinfer1::OptProfileSelector::kOPT);
+    bool maxShapesCalib
+        = getShapesBuild(arguments, shapesCalib, "--maxShapesCalib", nvinfer1::OptProfileSelector::kMAX);
+    processShapes(shapesCalib, minShapesCalib, optShapesCalib, maxShapesCalib, true);
     explicitBatch = explicitBatch || !shapes.empty();
 
     int batch{0};
@@ -425,10 +510,45 @@ void BuildOptions::parse(Arguments& arguments)
     checkEraseOption(arguments, "--workspace", workspace);
     checkEraseOption(arguments, "--minTiming", minTiming);
     checkEraseOption(arguments, "--avgTiming", avgTiming);
+
+    bool best{false};
+    checkEraseOption(arguments, "--best", best);
+    if (best)
+    {
+        int8 = true;
+        fp16 = true;
+    }
+
+    checkEraseNegativeOption(arguments, "--noTF32", tf32);
     checkEraseOption(arguments, "--fp16", fp16);
     checkEraseOption(arguments, "--int8", int8);
     checkEraseOption(arguments, "--safe", safe);
-    checkEraseOption(arguments, "--calib", calibration);
+    bool calibCheck = checkEraseOption(arguments, "--calib", calibration);
+    if (int8 && calibCheck && !shapes.empty() && shapesCalib.empty())
+    {
+        shapesCalib = shapes;
+    }
+    checkEraseNegativeOption(arguments, "--noBuilderCache", builderCache);
+
+    std::string nvtxModeString;
+    checkEraseOption(arguments, "--nvtxMode", nvtxModeString);
+    if (nvtxModeString == "default")
+    {
+        nvtxMode = nvinfer1::ProfilingVerbosity::kDEFAULT;
+    }
+    else if (nvtxModeString == "none")
+    {
+        nvtxMode = nvinfer1::ProfilingVerbosity::kNONE;
+    }
+    else if (nvtxModeString == "verbose")
+    {
+        nvtxMode = nvinfer1::ProfilingVerbosity::kVERBOSE;
+    }
+    else if (!nvtxModeString.empty())
+    {
+        throw std::invalid_argument(std::string("Unknown nvtxMode: ") + nvtxModeString);
+    }
+
     if (checkEraseOption(arguments, "--loadEngine", engine))
     {
         load = true;
@@ -477,10 +597,7 @@ void InferenceOptions::parse(Arguments& arguments)
     std::vector<std::string> inputsList{splitToStringVec(list, ',')};
     splitInsertKeyValue(inputsList, inputs);
 
-    list.erase();
-    checkEraseOption(arguments, "--shapes", list);
-    std::vector<std::string> shapeList{splitToStringVec(list, ',')};
-    splitInsertKeyValue(shapeList, shapes);
+    getShapesInference(arguments, shapes, "--shapes");
 
     int batchOpt{0};
     checkEraseOption(arguments, "--batch", batchOpt);
@@ -520,9 +637,11 @@ void ReportingOptions::parse(Arguments& arguments)
 
 bool parseHelp(Arguments& arguments)
 {
-    bool help{false};
-    checkEraseOption(arguments, "--help", help);
-    return help;
+    bool helpLong{false};
+    bool helpShort{false};
+    checkEraseOption(arguments, "--help", helpLong);
+    checkEraseOption(arguments, "-h", helpShort);
+    return helpLong || helpShort;
 }
 
 void AllOptions::parse(Arguments& arguments)
@@ -543,7 +662,9 @@ void AllOptions::parse(Arguments& arguments)
     {
         for (auto& s : inference.shapes)
         {
-            insertShapes(build.shapes, s.first, s.second);
+            insertShapesBuild(build.shapes, nvinfer1::OptProfileSelector::kMIN, s.first, s.second);
+            insertShapesBuild(build.shapes, nvinfer1::OptProfileSelector::kOPT, s.first, s.second);
+            insertShapesBuild(build.shapes, nvinfer1::OptProfileSelector::kMAX, s.first, s.second);
         }
         build.maxBatch = 0;
     }
@@ -553,7 +674,8 @@ void AllOptions::parse(Arguments& arguments)
         {
             for (auto& s : build.shapes)
             {
-                inference.shapes.insert({s.first, s.second[static_cast<size_t>(nvinfer1::OptProfileSelector::kOPT)]});
+                insertShapesInference(
+                    inference.shapes, s.first, s.second[static_cast<size_t>(nvinfer1::OptProfileSelector::kOPT)]);
             }
         }
         if (!build.maxBatch)
@@ -595,15 +717,14 @@ void AllOptions::parse(Arguments& arguments)
         }
         if (build.safe && system.DLACore >= 0)
         {
-            auto checkSafeDLAFormats = [](const std::vector<IOFormat>& fmt)
-            {
-                return fmt.empty() ? false : std::all_of(fmt.begin(), fmt.end(), [](const IOFormat& pair)
-                {
+            auto checkSafeDLAFormats = [](const std::vector<IOFormat>& fmt) {
+                return fmt.empty() ? false : std::all_of(fmt.begin(), fmt.end(), [](const IOFormat& pair) {
                     bool supported{false};
-                    supported |= pair.first == nvinfer1::DataType::kINT8
-                        && pair.second == 1U << static_cast<int>(nvinfer1::TensorFormat::kCHW32);
-                    supported |= pair.first == nvinfer1::DataType::kHALF
-                        && pair.second == 1U << static_cast<int>(nvinfer1::TensorFormat::kCHW16);
+                    const bool isCHW4{pair.second == 1U << static_cast<int>(nvinfer1::TensorFormat::kCHW4)};
+                    const bool isCHW32{pair.second == 1U << static_cast<int>(nvinfer1::TensorFormat::kCHW32)};
+                    const bool isCHW16{pair.second == 1U << static_cast<int>(nvinfer1::TensorFormat::kCHW16)};
+                    supported |= pair.first == nvinfer1::DataType::kINT8 && (isCHW4 || isCHW32);
+                    supported |= pair.first == nvinfer1::DataType::kHALF && (isCHW4 || isCHW16);
                     return supported;
                 });
             };
@@ -642,9 +763,7 @@ std::ostream& operator<<(std::ostream& os, const BaseModelOptions& options)
         os << "UFF";
         break;
     }
-    case ModelFormat::kANY:
-        os << "*";
-        break;
+    case ModelFormat::kANY: os << "*"; break;
     }
     os << std::endl << "Model: " << options.model << std::endl;
 
@@ -678,8 +797,7 @@ std::ostream& operator<<(std::ostream& os, const ModelOptions& options)
         break;
     }
     case ModelFormat::kONNX: // Fallthrough: No options to report for ONNX or the generic case
-    case ModelFormat::kANY:
-        break;
+    case ModelFormat::kANY: break;
     }
 
     os << "Output:";
@@ -774,7 +892,7 @@ std::ostream& operator<<(std::ostream& os, const ShapeRange& dims)
     int i = 0;
     for (const auto& d : dims)
     {
-        if (!d.nbDims)
+        if (!d.size())
         {
             break;
         }
@@ -786,29 +904,30 @@ std::ostream& operator<<(std::ostream& os, const ShapeRange& dims)
 
 std::ostream& operator<<(std::ostream& os, const BuildOptions& options)
 {
-// clang-format off
+    // clang-format off
     os << "=== Build Options ==="                                                                                       << std::endl <<
 
           "Max batch: ";        printBatch(os, options.maxBatch)                                                        << std::endl <<
           "Workspace: "      << options.workspace << " MB"                                                              << std::endl <<
           "minTiming: "      << options.minTiming                                                                       << std::endl <<
           "avgTiming: "      << options.avgTiming                                                                       << std::endl <<
-          "Precision: "      << (options.fp16 ? "FP16" : (options.int8 ? "INT8" : "FP32"))                              << std::endl <<
+          "Precision: ";        printPrecision(os, options)                                                             << std::endl <<
           "Calibration: "    << (options.int8 && options.calibration.empty() ? "Dynamic" : options.calibration.c_str()) << std::endl <<
           "Safe mode: "      << boolToEnabled(options.safe)                                                             << std::endl <<
           "Save engine: "    << (options.save ? options.engine : "")                                                    << std::endl <<
-          "Load engine: "    << (options.load ? options.engine : "")                                                    << std::endl;
-// clang-format on
+          "Load engine: "    << (options.load ? options.engine : "")                                                    << std::endl <<
+          "Builder Cache: "  << boolToEnabled(options.builderCache)                                                     << std::endl <<
+          "NVTX verbosity: " << static_cast<int>(options.nvtxMode)                                                      << std::endl;
+    // clang-format on
 
-    auto printIOFormats = [](std::ostream& os, const char* direction, const std::vector<IOFormat> formats)
-    {
+    auto printIOFormats = [](std::ostream& os, const char* direction, const std::vector<IOFormat> formats) {
         if (formats.empty())
         {
             os << direction << "s format: fp32:CHW" << std::endl;
         }
         else
         {
-            for(const auto& f : formats)
+            for (const auto& f : formats)
             {
                 os << direction << ": " << f << std::endl;
             }
@@ -818,19 +937,20 @@ std::ostream& operator<<(std::ostream& os, const BuildOptions& options)
     printIOFormats(os, "Input", options.inputFormats);
     printIOFormats(os, "Output", options.outputFormats);
     printShapes(os, "build", options.shapes);
+    printShapes(os, "calibration", options.shapesCalib);
 
     return os;
 }
 
 std::ostream& operator<<(std::ostream& os, const SystemOptions& options)
 {
-// clang-format off
+    // clang-format off
     os << "=== System Options ==="                                                                << std::endl <<
 
           "Device: "  << options.device                                                           << std::endl <<
           "DLACore: " << (options.DLACore != -1 ? std::to_string(options.DLACore) : "")           <<
                          (options.DLACore != -1 && options.fallback ? "(With GPU fallback)" : "") << std::endl;
-// clang-format on
+    // clang-format on
     os << "Plugins:";
     for (const auto p : options.plugins)
     {
@@ -843,7 +963,7 @@ std::ostream& operator<<(std::ostream& os, const SystemOptions& options)
 
 std::ostream& operator<<(std::ostream& os, const InferenceOptions& options)
 {
-// clang-format off
+    // clang-format off
     os << "=== Inference Options ==="                                << std::endl <<
 
           "Batch: ";
@@ -855,6 +975,7 @@ std::ostream& operator<<(std::ostream& os, const InferenceOptions& options)
     {
                           os << "Explicit"                           << std::endl;
     }
+    printShapes(os, "inference", options.shapes);
     os << "Iterations: "     << options.iterations                   << std::endl <<
           "Duration: "       << options.duration   << "s (+ "
                              << options.warmup     << "ms warm up)"  << std::endl <<
@@ -865,11 +986,8 @@ std::ostream& operator<<(std::ostream& os, const InferenceOptions& options)
           "Multithreading: " << boolToEnabled(options.threads)       << std::endl <<
           "CUDA Graph: "     << boolToEnabled(options.graph)         << std::endl <<
           "Skip inference: " << boolToEnabled(options.skip)          << std::endl;
-    if (options.batch)
-    {
-        printShapes(os, "inference", options.shapes);
-    }
-// clang-format on
+
+    // clang-format on
     os << "Inputs:" << std::endl;
     for (const auto& input : options.inputs)
     {
@@ -881,7 +999,7 @@ std::ostream& operator<<(std::ostream& os, const InferenceOptions& options)
 
 std::ostream& operator<<(std::ostream& os, const ReportingOptions& options)
 {
-// clang-format off
+    // clang-format off
     os << "=== Reporting Options ==="                                       << std::endl <<
 
           "Verbose: "                     << boolToEnabled(options.verbose) << std::endl <<
@@ -892,7 +1010,7 @@ std::ostream& operator<<(std::ostream& os, const ReportingOptions& options)
           "Export timing to JSON file: "  << options.exportTimes            << std::endl <<
           "Export output to JSON file: "  << options.exportOutput           << std::endl <<
           "Export profile to JSON file: " << options.exportProfile          << std::endl;
-// clang-format on
+    // clang-format on
 
     return os;
 }
@@ -905,38 +1023,38 @@ std::ostream& operator<<(std::ostream& os, const AllOptions& options)
 
 void BaseModelOptions::help(std::ostream& os)
 {
-// clang-format off
+    // clang-format off
     os << "  --uff=<file>                UFF model"                                             << std::endl <<
           "  --onnx=<file>               ONNX model"                                            << std::endl <<
           "  --model=<file>              Caffe model (default = no model, random weights used)" << std::endl;
-// clang-format on
+    // clang-format on
 }
 
 void UffInput::help(std::ostream& os)
 {
-// clang-format off
+    // clang-format off
     os << "  --uffInput=<name>,X,Y,Z     Input blob name and its dimensions (X,Y,Z=C,H,W), it can be specified "
                                                        "multiple times; at least one is required for UFF models" << std::endl <<
           "  --uffNHWC                   Set if inputs are in the NHWC layout instead of NCHW (use "             <<
                                                                     "X,Y,Z=H,W,C order in --uffInput)"           << std::endl;
-// clang-format on
+    // clang-format on
 }
 
 void ModelOptions::help(std::ostream& os)
 {
-// clang-format off
+    // clang-format off
     os << "=== Model Options ==="                                                                                 << std::endl;
     BaseModelOptions::help(os);
     os << "  --deploy=<file>             Caffe prototxt file"                                                     << std::endl <<
           "  --output=<name>[,<name>]*   Output names (it can be specified multiple times); at least one output "
                                                                                   "is required for UFF and Caffe" << std::endl;
     UffInput::help(os);
-// clang-format on
+    // clang-format on
 }
 
 void BuildOptions::help(std::ostream& os)
 {
-// clang-format off
+    // clang-format off
     os << "=== Build Options ==="                                                                                                     << std::endl <<
 
           "  --maxBatch                  Set max batch size and build an implicit batch engine (default = " << defaultMaxBatch << ")" << std::endl <<
@@ -944,56 +1062,72 @@ void BuildOptions::help(std::ostream& os)
           "  --minShapes=spec            Build with dynamic shapes using a profile with the min shapes provided"                      << std::endl <<
           "  --optShapes=spec            Build with dynamic shapes using a profile with the opt shapes provided"                      << std::endl <<
           "  --maxShapes=spec            Build with dynamic shapes using a profile with the max shapes provided"                      << std::endl <<
-          "                              Note: if any of min/max/opt is missing, the profile will be completed using the shapes "     << std::endl <<
-          "                                    provided and assuming that opt will be equal to max unless they are both specified;"   << std::endl <<           
-          "                                    partially specified shapes are applied starting from the batch size;"                  << std::endl <<           
-          "                                    dynamic shapes imply explicit batch"                                                   << std::endl <<           
-          "                                    input names can be wrapped with single quotes (ex: 'Input:0')"                         << std::endl <<
-          "                              Input shapes spec ::= Ishp[\",\"spec]"                                                       << std::endl <<
-          "                                           Ishp ::= name\":\"shape"                                                        << std::endl <<
-          "                                          shape ::= N[[\"x\"N]*\"*\"]"                                                     << std::endl <<
+          "  --minShapesCalib=spec       Calibrate with dynamic shapes using a profile with the min shapes provided"                  << std::endl <<
+          "  --optShapesCalib=spec       Calibrate with dynamic shapes using a profile with the opt shapes provided"                  << std::endl <<
+          "  --maxShapesCalib=spec       Calibrate with dynamic shapes using a profile with the max shapes provided"                  << std::endl <<
+          "                              Note: All three of min, opt and max shapes must be supplied."                                << std::endl <<
+          "                                    However, if only opt shapes is supplied then it will be expanded so"                   << std::endl <<
+          "                                    that min shapes and max shapes are set to the same values as opt shapes."              << std::endl <<
+          "                                    In addition, use of dynamic shapes implies explicit batch."                            << std::endl <<
+          "                                    Input names can be wrapped with escaped single quotes (ex: \\\'Input:0\\\')."          << std::endl <<
+          "                              Example input shapes spec: input0:1x3x256x256,input1:1x3x128x128"                            << std::endl <<
+          "                              Each input shape is supplied as a key-value pair where key is the input name and"            << std::endl <<
+          "                              value is the dimensions (including the batch dimension) to be used for that input."          << std::endl <<
+          "                              Each key-value pair has the key and value separated using a colon (:)."                      << std::endl <<
+          "                              Multiple input shapes can be provided via comma-separated key-value pairs."                  << std::endl <<
           "  --inputIOFormats=spec       Type and formats of the input tensors (default = all inputs in fp32:chw)"                    << std::endl <<
+          "                              Note: If this option is specified, please make sure that all inputs are in the same order "  << std::endl <<
+          "                                     as network inputs ID."                                                                << std::endl <<
           "  --outputIOFormats=spec      Type and formats of the output tensors (default = all outputs in fp32:chw)"                  << std::endl <<
+          "                              Note: If this option is specified, please make sure that all outputs are in the same order " << std::endl <<
+          "                                     as network outputs ID."                                                               << std::endl <<
           "                              IO Formats: spec  ::= IOfmt[\",\"spec]"                                                      << std::endl <<
           "                                          IOfmt ::= type:fmt"                                                              << std::endl <<
           "                                          type  ::= \"fp32\"|\"fp16\"|\"int32\"|\"int8\""                                  << std::endl <<
           "                                          fmt   ::= (\"chw\"|\"chw2\"|\"chw4\"|\"hwc8\"|\"chw16\"|\"chw32\")[\"+\"fmt]"    << std::endl <<
           "  --workspace=N               Set workspace size in megabytes (default = "                      << defaultWorkspace << ")" << std::endl <<
+          "  --noBuilderCache            Disable timing cache in builder (default is to enable timing cache)"                         << std::endl <<
+          "  --nvtxMode=[default|verbose|none] Specify NVTX annotation verbosity"                                                     << std::endl <<
           "  --minTiming=M               Set the minimum number of iterations used in kernel selection (default = "
                                                                                                            << defaultMinTiming << ")" << std::endl <<
           "  --avgTiming=M               Set the number of times averaged in each iteration for kernel selection (default = "
                                                                                                            << defaultAvgTiming << ")" << std::endl <<
-          "  --fp16                      Enable fp16 algorithms, in addition to fp32 (default = disabled)"                            << std::endl <<
-          "  --int8                      Enable int8 algorithms, in addition to fp32 (default = disabled)"                             << std::endl <<
+          "  --noTF32                    Disable tf32 precision (default is to enable tf32, in addition to fp32)"                     << std::endl <<
+          "  --fp16                      Enable fp16 precision, in addition to fp32 (default = disabled)"                             << std::endl <<
+          "  --int8                      Enable int8 precision, in addition to fp32 (default = disabled)"                             << std::endl <<
+          "  --best                      Enable all precisions to achieve the best performance (default = disabled)"                  << std::endl <<
           "  --calib=<file>              Read INT8 calibration cache file"                                                            << std::endl <<
           "  --safe                      Only test the functionality available in safety restricted flows"                            << std::endl <<
           "  --saveEngine=<file>         Save the serialized engine"                                                                  << std::endl <<
           "  --loadEngine=<file>         Load a serialized engine"                                                                    << std::endl;
-// clang-format on
+    // clang-format on
 }
 
 void SystemOptions::help(std::ostream& os)
 {
-// clang-format off
+    // clang-format off
     os << "=== System Options ==="                                                                         << std::endl <<
           "  --device=N                  Select cuda device N (default = "         << defaultDevice << ")" << std::endl <<
           "  --useDLACore=N              Select DLA core N for layers that support DLA (default = none)"   << std::endl <<
           "  --allowGPUFallback          When DLA is enabled, allow GPU fallback for unsupported layers "
                                                                                     "(default = disabled)" << std::endl;
     os << "  --plugins                   Plugin library (.so) to load (can be specified multiple times)"   << std::endl;
-// clang-format on
+    // clang-format on
 }
 
 void InferenceOptions::help(std::ostream& os)
 {
-// clang-format off
+    // clang-format off
     os << "=== Inference Options ==="                                                                                               << std::endl <<
           "  --batch=N                   Set batch size for implicit batch engines (default = "              << defaultBatch << ")" << std::endl <<
-          "  --shapes=spec               Set input shapes for dynamic shapes inputs. Input names can be wrapped with single quotes"
-                                                                                                                  "(ex: 'Input:0')" << std::endl <<
-          "                              Input shapes spec ::= Ishp[\",\"spec]"                                                     << std::endl <<
-          "                                           Ishp ::= name\":\"shape"                                                      << std::endl <<
-          "                                          shape ::= N[[\"x\"N]*\"*\"]"                                                   << std::endl <<
+          "  --shapes=spec               Set input shapes for dynamic shapes inference inputs."                                     << std::endl <<
+          "                              Note: Use of dynamic shapes implies explicit batch."                                       << std::endl <<
+          "                                    Input names can be wrapped with escaped single quotes (ex: \\\'Input:0\\\')."        << std::endl <<
+          "                              Example input shapes spec: input0:1x3x256x256, input1:1x3x128x128"                         << std::endl <<
+          "                              Each input shape is supplied as a key-value pair where key is the input name and"          << std::endl <<
+          "                              value is the dimensions (including the batch dimension) to be used for that input."        << std::endl <<
+          "                              Each key-value pair has the key and value separated using a colon (:)."                    << std::endl <<
+          "                              Multiple input shapes can be provided via comma-separated key-value pairs."                << std::endl <<
           "  --loadInputs=spec           Load input values from files (default = generate random inputs). Input names can be "
                                                                                        "wrapped with single quotes (ex: 'Input:0')" << std::endl <<
           "                              Input values spec ::= Ival[\",\"spec]"                                                     << std::endl <<
@@ -1012,12 +1146,12 @@ void InferenceOptions::help(std::ostream& os)
           "  --threads                   Enable multithreading to drive engines with independent threads (default = disabled)"      << std::endl <<
           "  --useCudaGraph              Use cuda graph to capture engine execution and then launch inference (default = disabled)" << std::endl <<
           "  --buildOnly                 Skip inference perf measurement (default = disabled)"                                      << std::endl;
-// clang-format on
+    // clang-format on
 }
 
 void ReportingOptions::help(std::ostream& os)
 {
-// clang-format off
+    // clang-format off
     os << "=== Reporting Options ==="                                                                    << std::endl <<
           "  --verbose                   Use verbose logging (default = false)"                          << std::endl <<
           "  --avgRuns=N                 Report performance measurements averaged over N consecutive "
@@ -1032,15 +1166,15 @@ void ReportingOptions::help(std::ostream& os)
           "  --exportOutput=<file>       Write the output tensors to a json file (default = disabled)"   << std::endl <<
           "  --exportProfile=<file>      Write the profile information per layer in a json file "
                                                                               "(default = disabled)"     << std::endl;
-// clang-format on
+    // clang-format on
 }
 
 void helpHelp(std::ostream& os)
 {
-// clang-format off
+    // clang-format off
     os << "=== Help ==="                                     << std::endl <<
-          "  --help                      Print this message" << std::endl;
-// clang-format on
+          "  --help, -h                  Print this message" << std::endl;
+    // clang-format on
 }
 
 void AllOptions::help(std::ostream& os)
@@ -1051,7 +1185,7 @@ void AllOptions::help(std::ostream& os)
     os << std::endl;
     InferenceOptions::help(os);
     os << std::endl;
-// clang-format off
+    // clang-format off
     os << "=== Build and Inference Batch Options ==="                                                                   << std::endl <<
           "                              When using implicit batch, the max batch size of the engine, if not given, "   << std::endl <<
           "                              is set to the inference batch size;"                                           << std::endl <<
@@ -1062,7 +1196,7 @@ void AllOptions::help(std::ostream& os)
           "                              enabled but neither is specified, the model must provide complete static"      << std::endl <<
           "                              dimensions, including batch size, for all inputs"                              << std::endl <<
     std::endl;
-// clang-format on
+    // clang-format on
     ReportingOptions::help(os);
     os << std::endl;
     SystemOptions::help(os);
diff --git a/samples/common/sampleOptions.h b/samples/common/sampleOptions.h
index 88fb0e56..dc3fdae0 100644
--- a/samples/common/sampleOptions.h
+++ b/samples/common/sampleOptions.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -17,14 +17,14 @@
 #ifndef TRT_SAMPLE_OPTIONS_H
 #define TRT_SAMPLE_OPTIONS_H
 
-#include <utility>
-#include <stdexcept>
-#include <vector>
+#include <algorithm>
 #include <array>
+#include <iostream>
+#include <stdexcept>
 #include <string>
 #include <unordered_map>
-#include <algorithm>
-#include <iostream>
+#include <utility>
+#include <vector>
 
 #include "NvInfer.h"
 
@@ -64,7 +64,7 @@ using Arguments = std::unordered_multimap<std::string, std::string>;
 
 using IOFormat = std::pair<nvinfer1::DataType, nvinfer1::TensorFormats>;
 
-using ShapeRange = std::array<nvinfer1::Dims, nvinfer1::EnumMax<nvinfer1::OptProfileSelector>()>;
+using ShapeRange = std::array<std::vector<int>, nvinfer1::EnumMax<nvinfer1::OptProfileSelector>()>;
 
 struct Options
 {
@@ -109,14 +109,18 @@ struct BuildOptions : public Options
     int workspace{defaultWorkspace};
     int minTiming{defaultMinTiming};
     int avgTiming{defaultAvgTiming};
+    bool tf32{true};
     bool fp16{false};
     bool int8{false};
     bool safe{false};
     bool save{false};
     bool load{false};
+    bool builderCache{true};
+    nvinfer1::ProfilingVerbosity nvtxMode{nvinfer1::ProfilingVerbosity::kDEFAULT};
     std::string engine;
     std::string calibration;
     std::unordered_map<std::string, ShapeRange> shapes;
+    std::unordered_map<std::string, ShapeRange> shapesCalib;
     std::vector<IOFormat> inputFormats;
     std::vector<IOFormat> outputFormats;
 
@@ -151,7 +155,7 @@ struct InferenceOptions : public Options
     bool graph{false};
     bool skip{false};
     std::unordered_map<std::string, std::string> inputs;
-    std::unordered_map<std::string, nvinfer1::Dims> shapes;
+    std::unordered_map<std::string, std::vector<int>> shapes;
 
     void parse(Arguments& arguments) override;
 
@@ -216,7 +220,6 @@ std::ostream& operator<<(std::ostream& os, const ReportingOptions& options);
 
 std::ostream& operator<<(std::ostream& os, const AllOptions& options);
 
-
 } // namespace sample
 
 #endif // TRT_SAMPLES_OPTIONS_H
diff --git a/samples/common/sampleReporting.cpp b/samples/common/sampleReporting.cpp
index a987ee68..9d8fb749 100644
--- a/samples/common/sampleReporting.cpp
+++ b/samples/common/sampleReporting.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -14,15 +14,15 @@
  * limitations under the License.
  */
 
-#include <iostream>
-#include <iomanip>
-#include <fstream>
-#include <utility>
 #include <algorithm>
+#include <fstream>
+#include <iomanip>
+#include <iostream>
 #include <numeric>
+#include <utility>
 
-#include "sampleOptions.h"
 #include "sampleInference.h"
+#include "sampleOptions.h"
 #include "sampleReporting.h"
 
 using namespace nvinfer1;
@@ -59,18 +59,19 @@ float findMedian(const std::vector<InferenceTime>& timings, const T& toFloat)
         return std::numeric_limits<float>::infinity();
     }
 
-    const int m = timings.size()/2;
+    const int m = timings.size() / 2;
     if (timings.size() % 2)
     {
         return toFloat(timings[m]);
     }
 
-    return (toFloat(timings[m-1]) + toFloat(timings[m])) / 2;
+    return (toFloat(timings[m - 1]) + toFloat(timings[m])) / 2;
 }
 
 inline InferenceTime traceToTiming(const InferenceTrace& a)
 {
-    return InferenceTime((a.inEnd - a.inStart), (a.computeEnd - a.computeStart), (a.outEnd - a.outStart), (a.outEnd - a.inStart));
+    return InferenceTime((a.enqEnd - a.enqStart), (a.inEnd - a.inStart), (a.computeEnd - a.computeStart),
+        (a.outEnd - a.outStart), (a.outEnd - a.inStart));
 };
 
 } // namespace
@@ -93,12 +94,13 @@ void printTiming(const std::vector<InferenceTime>& timings, int runsPerAvg, std:
 
         if (++count == runsPerAvg)
         {
-// clang off
+            // clang off
             os << "Average on " << runsPerAvg << " runs - GPU latency: " << sum.compute / runsPerAvg
-               << " ms - Host latency: " << sum.latency() / runsPerAvg << " ms (end to end "
-               << sum.e2e / runsPerAvg << " ms)" << std::endl;
-// clang on
+               << " ms - Host latency: " << sum.latency() / runsPerAvg << " ms (end to end " << sum.e2e / runsPerAvg
+               << " ms, enqueue " << sum.enq / runsPerAvg << " ms)" << std::endl;
+            // clang on
             count = 0;
+            sum.enq = 0;
             sum.in = 0;
             sum.compute = 0;
             sum.out = 0;
@@ -136,34 +138,62 @@ void printEpilog(std::vector<InferenceTime> timings, float walltimeMs, float per
     const float gpuMedian = findMedian(timings, getCompute);
     const float gpuPercentile = findPercentile(percentile, timings, getCompute);
 
-// clang off
-    os << "Host latency"                                                           << std::endl <<
-          "min: "                << latencyMin                           << " ms "
-          "(end to end "         << endToEndMin                          << " ms)" << std::endl <<
-          "max: "                << latencyMax                           << " ms "
-          "(end to end "         << endToEndMax                          << " ms)" << std::endl <<
-          "mean: "               << totalTime.latency() / timings.size() << " ms "
-          "(end to end "         << totalTime.e2e / timings.size()       << " ms)" << std::endl <<
-          "median: "             << latencyMedian                        << " ms "
-          "(end to end "         << endToEndMedian                       << " ms)" << std::endl <<
-          "percentile: "         << latencyPercentile                    << " ms "
-          "at "                  << percentile                           << "% "
-          "(end to end "         << endToEndPercentile                   << " ms "
-          "at "                  << percentile                           << "%)"   << std::endl <<
-          "throughput: "         << latencyThroughput                    << " qps" << std::endl <<
-          "walltime: "           << walltimeMs / 1000                    << " s"   << std::endl <<
-          "GPU Compute"                                                            << std::endl <<
-          "min: "                << gpuMin                               << " ms"  << std::endl <<
-          "max: "                << gpuMax                               << " ms"  << std::endl <<
-          "mean: "               << totalTime.compute / timings.size()   << " ms"  << std::endl <<
-          "median: "             << gpuMedian                            << " ms"  << std::endl <<
-          "percentile: "         << gpuPercentile                        << " ms "
-          "at "                  << percentile                           << "%"    << std::endl <<
-          "total compute time: " << totalTime.compute / 1000             << " s"   << std::endl;
-// clang on
+    const auto getEnqueue = [](const InferenceTime& t) { return t.enq; };
+    const auto cmpEnqueue = [](const InferenceTime& a, const InferenceTime& b) { return a.enq < b.enq; };
+    std::sort(timings.begin(), timings.end(), cmpEnqueue);
+    const float enqMin = timings.front().enq;
+    const float enqMax = timings.back().enq;
+    const float enqMedian = findMedian(timings, getEnqueue);
+
+    // clang off
+    os << "Host Latency" << std::endl
+       << "min: " << latencyMin
+       << " ms "
+          "(end to end "
+       << endToEndMin << " ms)" << std::endl
+       << "max: " << latencyMax
+       << " ms "
+          "(end to end "
+       << endToEndMax << " ms)" << std::endl
+       << "mean: " << totalTime.latency() / timings.size()
+       << " ms "
+          "(end to end "
+       << totalTime.e2e / timings.size() << " ms)" << std::endl
+       << "median: " << latencyMedian
+       << " ms "
+          "(end to end "
+       << endToEndMedian << " ms)" << std::endl
+       << "percentile: " << latencyPercentile
+       << " ms "
+          "at "
+       << percentile
+       << "% "
+          "(end to end "
+       << endToEndPercentile
+       << " ms "
+          "at "
+       << percentile << "%)" << std::endl
+       << "throughput: " << latencyThroughput << " qps" << std::endl
+       << "walltime: " << walltimeMs / 1000 << " s" << std::endl
+       << "Enqueue Time" << std::endl
+       << "min: " << enqMin << " ms" << std::endl
+       << "max: " << enqMax << " ms" << std::endl
+       << "median: " << enqMedian << " ms" << std::endl
+       << "GPU Compute" << std::endl
+       << "min: " << gpuMin << " ms" << std::endl
+       << "max: " << gpuMax << " ms" << std::endl
+       << "mean: " << totalTime.compute / timings.size() << " ms" << std::endl
+       << "median: " << gpuMedian << " ms" << std::endl
+       << "percentile: " << gpuPercentile
+       << " ms "
+          "at "
+       << percentile << "%" << std::endl
+       << "total compute time: " << totalTime.compute / 1000 << " s" << std::endl;
+    // clang on
 }
 
-void printPerformanceReport(const std::vector<InferenceTrace>& trace, const ReportingOptions& reporting, float warmupMs, int queries, std::ostream& os)
+void printPerformanceReport(const std::vector<InferenceTrace>& trace, const ReportingOptions& reporting, float warmupMs,
+    int queries, std::ostream& os)
 {
     const auto isNotWarmup = [&warmupMs](const InferenceTrace& a) { return a.computeStart >= warmupMs; };
     const auto noWarmup = std::find_if(trace.begin(), trace.end(), isNotWarmup);
@@ -179,8 +209,9 @@ void printPerformanceReport(const std::vector<InferenceTrace>& trace, const Repo
 
 //! Printed format:
 //! [ value, ...]
-//! value ::= { "start in" : time, "end in" : time, "start compute" : time, "end compute" : time, "start out" : time,
-//!             "in" : time, "compute" : time, "out" : time, "latency" : time, "end to end" : time}
+//! value ::= { "start enq : time, "end enq" : time, "start in" : time, "end in" : time, "start compute" : time, "end
+//! compute" : time,
+//!             "start out" : time, "in" : time, "compute" : time, "out" : time, "latency" : time, "end to end" : time}
 //!
 void exportJSONTrace(const std::vector<InferenceTrace>& trace, const std::string& fileName)
 {
@@ -192,14 +223,14 @@ void exportJSONTrace(const std::vector<InferenceTrace>& trace, const std::string
         const InferenceTime it(traceToTiming(t));
         os << sep << "{ ";
         sep = ", ";
-// clang off
-        os << "\"startInMs\" : "      << t.inStart      << sep << "\"endInMs\" : "      << t.inEnd      << sep
+        // clang off
+        os << "\"startEnqMs\" : " << t.inStart << sep << "\"endEnqMs\" : " << t.inEnd << sep
+           << "\"startInMs\" : " << t.enqStart << sep << "\"endInMs\" : " << t.enqEnd << sep
            << "\"startComputeMs\" : " << t.computeStart << sep << "\"endComputeMs\" : " << t.computeEnd << sep
-           << "\"startOutMs\" : "     << t.outStart     << sep << "\"endOutMs\" : "     << t.outEnd     << sep
-           << "\"inMs\" : "           << it.in          << sep << "\"computeMs\" : "    << it.compute   << sep
-           << "\"outMs\" : "          << it.out         << sep << "\"latencyMs\" : "    << it.latency() << sep
-           << "\"endToEndMs\" : "     << it.e2e         << " }"                                         << std::endl;
-// clang on
+           << "\"startOutMs\" : " << t.outStart << sep << "\"endOutMs\" : " << t.outEnd << sep << "\"inMs\" : " << it.in
+           << sep << "\"computeMs\" : " << it.compute << sep << "\"outMs\" : " << it.out << sep
+           << "\"latencyMs\" : " << it.latency() << sep << "\"endToEndMs\" : " << it.e2e << " }" << std::endl;
+        // clang on
     }
     os << "]" << std::endl;
 }
@@ -235,57 +266,51 @@ void Profiler::print(std::ostream& os) const
 
     const float totalTimeMs = getTotalTime();
 
-    const auto cmpLayer = [](const LayerProfile& a, const LayerProfile& b)
-    {
-        return a.name.size() < b.name.size();
-    };
+    const auto cmpLayer = [](const LayerProfile& a, const LayerProfile& b) { return a.name.size() < b.name.size(); };
     const auto longestName = std::max_element(mLayers.begin(), mLayers.end(), cmpLayer);
     const auto nameLength = std::max(longestName->name.size() + 1, nameHdr.size());
     const auto timeLength = timeHdr.size();
     const auto avgLength = avgHdr.size();
     const auto percentageLength = percentageHdr.size();
 
-    os << std::endl << "=== Profile (" << mUpdatesCount << " iterations ) ===" << std::endl
+    os << std::endl
+       << "=== Profile (" << mUpdatesCount << " iterations ) ===" << std::endl
        << std::setw(nameLength) << nameHdr << timeHdr << avgHdr << percentageHdr << std::endl;
 
     for (const auto& p : mLayers)
     {
-// clang off
-        os << std::setw(nameLength)                                             << p.name
-           << std::setw(timeLength)       << std::fixed << std::setprecision(2) << p.timeMs
-           << std::setw(avgLength)        << std::fixed << std::setprecision(2) << p.timeMs / mUpdatesCount
+        // clang off
+        os << std::setw(nameLength) << p.name << std::setw(timeLength) << std::fixed << std::setprecision(2) << p.timeMs
+           << std::setw(avgLength) << std::fixed << std::setprecision(2) << p.timeMs / mUpdatesCount
            << std::setw(percentageLength) << std::fixed << std::setprecision(1) << p.timeMs / totalTimeMs * 100
            << std::endl;
     }
     {
-        os << std::setw(nameLength)                                             << "Total"
-           << std::setw(timeLength)       << std::fixed << std::setprecision(2) << totalTimeMs
-           << std::setw(avgLength)        << std::fixed << std::setprecision(2) << totalTimeMs / mUpdatesCount
-           << std::setw(percentageLength) << std::fixed << std::setprecision(1) << 100.0
-           << std::endl;
-// clang on
+        os << std::setw(nameLength) << "Total" << std::setw(timeLength) << std::fixed << std::setprecision(2)
+           << totalTimeMs << std::setw(avgLength) << std::fixed << std::setprecision(2) << totalTimeMs / mUpdatesCount
+           << std::setw(percentageLength) << std::fixed << std::setprecision(1) << 100.0 << std::endl;
+        // clang on
     }
     os << std::endl;
-
 }
 
 void Profiler::exportJSONProfile(const std::string& fileName) const
 {
     std::ofstream os(fileName, std::ofstream::trunc);
-    os << "[" << std::endl
-       << "  { \"count\" : " << mUpdatesCount << " }" << std::endl;
+    os << "[" << std::endl << "  { \"count\" : " << mUpdatesCount << " }" << std::endl;
 
     const auto totalTimeMs = getTotalTime();
 
     for (const auto& l : mLayers)
     {
-// clang off
-        os << ", {" << " \"name\" : \""      << l.name << "\""
-                       ", \"timeMs\" : "     << l.timeMs
-           <<          ", \"averageMs\" : "  << l.timeMs / mUpdatesCount
-           <<          ", \"percentage\" : " << l.timeMs / totalTimeMs * 100
-           << " }"  << std::endl;
-// clang on
+        // clang off
+        os << ", {"
+           << " \"name\" : \"" << l.name
+           << "\""
+              ", \"timeMs\" : "
+           << l.timeMs << ", \"averageMs\" : " << l.timeMs / mUpdatesCount
+           << ", \"percentage\" : " << l.timeMs / totalTimeMs * 100 << " }" << std::endl;
+        // clang on
     }
     os << "]" << std::endl;
 }
@@ -305,12 +330,12 @@ void dumpOutputs(const nvinfer1::IExecutionContext& context, const Bindings& bin
 void exportJSONOutput(const nvinfer1::IExecutionContext& context, const Bindings& bindings, const std::string& fileName)
 {
     std::ofstream os(fileName, std::ofstream::trunc);
-    std::string sep ="  ";
+    std::string sep = "  ";
     const auto output = bindings.getOutputBindings();
     os << "[" << std::endl;
     for (const auto& binding : output)
     {
-// clang off
+        // clang off
         os << sep << "{ \"name\" : \"" << binding.first << "\"" << std::endl;
         sep = ", ";
         os << "  " << sep << "\"dimensions\" : \"";
@@ -318,8 +343,8 @@ void exportJSONOutput(const nvinfer1::IExecutionContext& context, const Bindings
         os << "\"" << std::endl;
         os << "  " << sep << "\"values\" : [ ";
         bindings.dumpBindingValues(binding.second, os, sep);
-        os << " ]" << std::endl << "  }"  << std::endl;
-// clang on
+        os << " ]" << std::endl << "  }" << std::endl;
+        // clang on
     }
     os << "]" << std::endl;
 }
diff --git a/samples/common/sampleReporting.h b/samples/common/sampleReporting.h
index c30e4b78..9bbc30b7 100644
--- a/samples/common/sampleReporting.h
+++ b/samples/common/sampleReporting.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -33,7 +33,14 @@ namespace sample
 //!
 struct InferenceTime
 {
-    InferenceTime(float i, float c, float o, float e): in(i), compute(c), out(o), e2e(e) {}
+    InferenceTime(float q, float i, float c, float o, float e)
+        : enq(q)
+        , in(i)
+        , compute(c)
+        , out(o)
+        , e2e(e)
+    {
+    }
 
     InferenceTime() = default;
     InferenceTime(const InferenceTime&) = default;
@@ -42,6 +49,7 @@ struct InferenceTime
     InferenceTime& operator=(InferenceTime&&) = default;
     ~InferenceTime() = default;
 
+    float enq{0};     // Enqueue
     float in{0};      // Host to Device
     float compute{0}; // Compute
     float out{0};     // Device to Host
@@ -60,8 +68,18 @@ struct InferenceTime
 //!
 struct InferenceTrace
 {
-    InferenceTrace(int s, float is, float ie, float cs, float ce, float os, float oe):
-        stream(s), inStart(is), inEnd(ie), computeStart(cs), computeEnd(ce), outStart(os), outEnd(oe) {}
+    InferenceTrace(int s, float es, float ee, float is, float ie, float cs, float ce, float os, float oe)
+        : stream(s)
+        , enqStart(es)
+        , enqEnd(ee)
+        , inStart(is)
+        , inEnd(ie)
+        , computeStart(cs)
+        , computeEnd(ce)
+        , outStart(os)
+        , outEnd(oe)
+    {
+    }
 
     InferenceTrace() = default;
     InferenceTrace(const InferenceTrace&) = default;
@@ -71,6 +89,8 @@ struct InferenceTrace
     ~InferenceTrace() = default;
 
     int stream{0};
+    float enqStart{0};
+    float enqEnd{0};
     float inStart{0};
     float inEnd{0};
     float computeStart{0};
@@ -81,12 +101,12 @@ struct InferenceTrace
 
 inline InferenceTime operator+(const InferenceTime& a, const InferenceTime& b)
 {
-    return InferenceTime(a.in + b.in, a.compute + b.compute, a.out + b.out, a.e2e + b.e2e);
+    return InferenceTime(a.enq + b.enq, a.in + b.in, a.compute + b.compute, a.out + b.out, a.e2e + b.e2e);
 }
 
 inline InferenceTime operator+=(InferenceTime& a, const InferenceTime& b)
 {
-    return a = a+b;
+    return a = a + b;
 }
 
 //!
@@ -107,7 +127,8 @@ void printEpilog(std::vector<InferenceTime> timings, float percentile, int queri
 //!
 //! \brief Print and summarize a timing trace
 //!
-void printPerformanceReport(const std::vector<InferenceTrace>& trace, const ReportingOptions& reporting, float warmupMs, int queries, std::ostream& os);
+void printPerformanceReport(const std::vector<InferenceTrace>& trace, const ReportingOptions& reporting, float warmupMs,
+    int queries, std::ostream& os);
 
 //!
 //! \brief Export a timing trace to JSON file
@@ -127,7 +148,8 @@ void dumpOutputs(const nvinfer1::IExecutionContext& context, const Bindings& bin
 //!
 //! \brief Export output tensors to JSON file
 //!
-void exportJSONOutput(const nvinfer1::IExecutionContext& context, const Bindings& bindings, const std::string& fileName);
+void exportJSONOutput(
+    const nvinfer1::IExecutionContext& context, const Bindings& bindings, const std::string& fileName);
 
 //!
 //! \struct LayerProfile
@@ -147,7 +169,6 @@ class Profiler : public nvinfer1::IProfiler
 {
 
 public:
-
     void reportLayerTime(const char* layerName, float timeMs) override;
 
     void print(std::ostream& os) const;
@@ -158,13 +179,9 @@ class Profiler : public nvinfer1::IProfiler
     void exportJSONProfile(const std::string& fileName) const;
 
 private:
-
     float getTotalTime() const
     {
-        const auto plusLayerTime = [](float accumulator, const LayerProfile& lp)
-        {
-            return accumulator + lp.timeMs;
-        };
+        const auto plusLayerTime = [](float accumulator, const LayerProfile& lp) { return accumulator + lp.timeMs; };
         return std::accumulate(mLayers.begin(), mLayers.end(), 0.0, plusLayerTime);
     }
 
diff --git a/samples/common/sampleUtils.h b/samples/common/sampleUtils.h
index c4fff26a..a6b11e43 100644
--- a/samples/common/sampleUtils.h
+++ b/samples/common/sampleUtils.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -17,13 +17,15 @@
 #ifndef TRT_SAMPLE_UTILS_H
 #define TRT_SAMPLE_UTILS_H
 
+#include <fstream>
 #include <iostream>
 #include <memory>
-#include <fstream>
-#include <random>
 #include <numeric>
+#include <random>
 #include <unordered_map>
 #include <vector>
+
+#include <cuda.h>
 #if CUDA_VERSION < 10000
 #include <half.h>
 #else
@@ -32,13 +34,17 @@
 
 #include "NvInfer.h"
 
+#include "logger.h"
 #include "sampleDevice.h"
 
 namespace sample
 {
 
 template <typename T>
-inline T roundUp(T m, T n) { return ((m + n - 1) / n) * n; }
+inline T roundUp(T m, T n)
+{
+    return ((m + n - 1) / n) * n;
+}
 
 inline int volume(const nvinfer1::Dims& d)
 {
@@ -54,8 +60,7 @@ inline int volume(nvinfer1::Dims dims, int vecDim, int comps, int batch)
     return volume(dims) * std::max(batch, 1);
 }
 
-inline
-std::ostream& operator<<(std::ostream& os, const nvinfer1::Dims& dims)
+inline std::ostream& operator<<(std::ostream& os, const nvinfer1::Dims& dims)
 {
     for (int i = 0; i < dims.nbDims; ++i)
     {
@@ -64,6 +69,28 @@ std::ostream& operator<<(std::ostream& os, const nvinfer1::Dims& dims)
     return os;
 }
 
+inline std::ostream& operator<<(std::ostream& os, const std::vector<int>& vec)
+{
+    for (int i = 0, e = static_cast<int>(vec.size()); i < e; ++i)
+    {
+        os << (i ? "x" : "") << vec[i];
+    }
+    return os;
+}
+
+inline nvinfer1::Dims toDims(const std::vector<int>& vec)
+{
+    int limit = static_cast<int>(nvinfer1::Dims::MAX_DIMS);
+    if (static_cast<int>(vec.size()) > limit)
+    {
+        sample::gLogWarning << "Vector too long, only first 8 elements are used in dimension." << std::endl;
+    }
+    // Pick first nvinfer1::Dims::MAX_DIMS elements
+    nvinfer1::Dims dims{std::min(static_cast<int>(vec.size()), limit), {}, {}};
+    std::copy_n(vec.begin(), dims.nbDims, std::begin(dims.d));
+    return dims;
+}
+
 inline int dataTypeSize(nvinfer1::DataType dataType)
 {
     switch (dataType)
@@ -137,7 +164,7 @@ struct Binding
 
     void fill(const std::string& fileName)
     {
-        std::ifstream file(fileName, std::ios::in|std::ios::binary);
+        std::ifstream file(fileName, std::ios::in | std::ios::binary);
         if (file.is_open())
         {
             file.read(static_cast<char*>(buffer.getHostBuffer()), buffer.getSize());
@@ -169,10 +196,10 @@ struct Binding
             fillBuffer<float>(buffer.getHostBuffer(), volume, -1.0, 1.0);
             break;
         }
-        case nvinfer1::DataType::kHALF:
-        {
+        case nvinfer1::DataType::kHALF: {
 #if CUDA_VERSION < 10000
-            fillBuffer<half_float::half>(buffer.getHostBuffer(), volume, static_cast<half_float::half>(-1.0), static_cast<half_float::half>(-1.0));
+            fillBuffer<half_float::half>(buffer.getHostBuffer(), volume, static_cast<half_float::half>(-1.0),
+                static_cast<half_float::half>(-1.0));
 #else
             fillBuffer<__half>(buffer.getHostBuffer(), volume, -1.0, 1.0);
 #endif
@@ -205,8 +232,7 @@ struct Binding
             dumpBuffer<float>(buffer.getHostBuffer(), volume, separator, os);
             break;
         }
-        case nvinfer1::DataType::kHALF:
-        {
+        case nvinfer1::DataType::kHALF: {
 #if CUDA_VERSION < 10000
             dumpBuffer<half_float::half>(buffer.getHostBuffer(), volume, separator, os);
 #else
@@ -216,23 +242,22 @@ struct Binding
         }
         }
     }
-
 };
 
 class Bindings
 {
 public:
-
-    void addBinding(int b, const std::string& name, bool isInput, int volume, nvinfer1::DataType dataType, const std::string& fileName = "")
+    void addBinding(int b, const std::string& name, bool isInput, int volume, nvinfer1::DataType dataType,
+        const std::string& fileName = "")
     {
         while (mBindings.size() <= static_cast<size_t>(b))
         {
-             mBindings.emplace_back();
-             mDevicePointers.emplace_back();
+            mBindings.emplace_back();
+            mDevicePointers.emplace_back();
         }
         mNames[name] = b;
         mBindings[b].isInput = isInput;
-        mBindings[b].buffer.allocate(volume * dataTypeSize(dataType));
+        mBindings[b].buffer.allocate(static_cast<size_t>(volume) * static_cast<size_t>(dataTypeSize(dataType)));
         mBindings[b].volume = volume;
         mBindings[b].dataType = dataType;
         mDevicePointers[b] = mBindings[b].buffer.getDeviceBuffer();
@@ -249,7 +274,10 @@ class Bindings
         }
     }
 
-    void** getDeviceBuffers() { return mDevicePointers.data(); }
+    void** getDeviceBuffers()
+    {
+        return mDevicePointers.data();
+    }
 
     void transferInputToDevice(TrtCudaStream& stream)
     {
@@ -286,6 +314,7 @@ class Bindings
     void dumpBindingDimensions(int binding, const nvinfer1::IExecutionContext& context, std::ostream& os) const
     {
         const auto dims = context.getBindingDimensions(binding);
+        // Do not add a newline terminator, because the caller may be outputting a JSON string.
         os << dims;
     }
 
@@ -312,7 +341,8 @@ class Bindings
         dumpBindings(context, all, os);
     }
 
-    void dumpBindings(const nvinfer1::IExecutionContext& context, bool (*predicate)(const Binding& b), std::ostream& os) const
+    void dumpBindings(
+        const nvinfer1::IExecutionContext& context, bool (*predicate)(const Binding& b), std::ostream& os) const
     {
         for (const auto& n : mNames)
         {
@@ -361,7 +391,6 @@ class Bindings
     }
 
 private:
-
     std::unordered_map<std::string, int> mNames;
     std::vector<Binding> mBindings;
     std::vector<void*> mDevicePointers;
@@ -370,10 +399,14 @@ class Bindings
 template <typename T>
 struct TrtDestroyer
 {
-    void operator()(T* t) { t->destroy(); }
+    void operator()(T* t)
+    {
+        t->destroy();
+    }
 };
 
-template <typename T> using TrtUniquePtr = std::unique_ptr<T, TrtDestroyer<T> >;
+template <typename T>
+using TrtUniquePtr = std::unique_ptr<T, TrtDestroyer<T>>;
 
 } // namespace sample
 
diff --git a/samples/common/windows/getopt.c b/samples/common/windows/getopt.c
index 1976bf6c..515a55bb 100644
--- a/samples/common/windows/getopt.c
+++ b/samples/common/windows/getopt.c
@@ -49,28 +49,12 @@
  * POSSIBILITY OF SUCH DAMAGE.
  */
 
-/*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
 #include <errno.h>
-#include <stdlib.h>
-#include <string.h>
 #include <getopt.h>
 #include <stdarg.h>
 #include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
 #include <windows.h>
 
 #define REPLACE_GETOPT /* use this getopt as the system getopt(3) */
@@ -108,10 +92,8 @@ static char EMSG[] = "";
 #define EMSG ""
 #endif
 
-static int getopt_internal(int, char* const*, const char*,
-                           const struct option*, int*, int);
-static int parse_long_options(char* const*, const char*,
-                              const struct option*, int*, int);
+static int getopt_internal(int, char* const*, const char*, const struct option*, int*, int);
+static int parse_long_options(char* const*, const char*, const struct option*, int*, int);
 static int gcd(int, int);
 static void permute_args(int, int, int, char* const*);
 
@@ -129,8 +111,7 @@ static const char noarg[] = "option doesn't take an argument -- %.*s";
 static const char illoptchar[] = "unknown option -- %c";
 static const char illoptstring[] = "unknown option -- %s";
 
-static void
-_vwarnx(const char* fmt, va_list ap)
+static void _vwarnx(const char* fmt, va_list ap)
 {
     (void) fprintf(stderr, "%s: ", __progname);
     if (fmt != NULL)
@@ -138,8 +119,7 @@ _vwarnx(const char* fmt, va_list ap)
     (void) fprintf(stderr, "\n");
 }
 
-static void
-warnx(const char* fmt, ...)
+static void warnx(const char* fmt, ...)
 {
     va_list ap;
     va_start(ap, fmt);
@@ -150,8 +130,7 @@ warnx(const char* fmt, ...)
 /*
  * Compute the greatest common divisor of a and b.
  */
-static int
-gcd(int a, int b)
+static int gcd(int a, int b)
 {
     int c;
 
@@ -171,16 +150,14 @@ gcd(int a, int b)
  * from nonopt_end to opt_end (keeping the same order of arguments
  * in each block).
  */
-static void
-permute_args(int panonopt_start, int panonopt_end, int opt_end,
-             char* const* nargv)
+static void permute_args(int panonopt_start, int panonopt_end, int opt_end, char* const* nargv)
 {
     int cstart, cyclelen, i, j, ncycle, nnonopts, nopts, pos;
     char* swap;
 
     /*
-	 * compute lengths of blocks and number and size of cycles
-	 */
+     * compute lengths of blocks and number and size of cycles
+     */
     nnonopts = panonopt_end - panonopt_start;
     nopts = opt_end - panonopt_end;
     ncycle = gcd(nnonopts, nopts);
@@ -210,16 +187,16 @@ permute_args(int panonopt_start, int panonopt_end, int opt_end,
  *	Parse long options in argc/argv argument vector.
  * Returns -1 if short_too is set and the option does not match long_options.
  */
-static int
-parse_long_options(char* const* nargv, const char* options,
-                   const struct option* long_options, int* idx, int short_too)
+static int parse_long_options(
+    char* const* nargv, const char* options, const struct option* long_options, int* idx, int short_too)
 {
     char *current_argv, *has_equal;
     size_t current_argv_len;
     int i, ambiguous, match;
 
-#define IDENTICAL_INTERPRETATION(_x, _y) \
-    (long_options[(_x)].has_arg == long_options[(_y)].has_arg && long_options[(_x)].flag == long_options[(_y)].flag && long_options[(_x)].val == long_options[(_y)].val)
+#define IDENTICAL_INTERPRETATION(_x, _y)                                                                               \
+    (long_options[(_x)].has_arg == long_options[(_y)].has_arg && long_options[(_x)].flag == long_options[(_y)].flag    \
+        && long_options[(_x)].val == long_options[(_y)].val)
 
     current_argv = place;
     match = -1;
@@ -239,8 +216,7 @@ parse_long_options(char* const* nargv, const char* options,
     for (i = 0; long_options[i].name; i++)
     {
         /* find matching long option */
-        if (strncmp(current_argv, long_options[i].name,
-                    current_argv_len))
+        if (strncmp(current_argv, long_options[i].name, current_argv_len))
             continue;
 
         if (strlen(long_options[i].name) == current_argv_len)
@@ -251,9 +227,9 @@ parse_long_options(char* const* nargv, const char* options,
             break;
         }
         /*
-		 * If this is a known short option, don't allow
-		 * a partial match of a single character.
-		 */
+         * If this is a known short option, don't allow
+         * a partial match of a single character.
+         */
         if (short_too && current_argv_len == 1)
             continue;
 
@@ -266,22 +242,19 @@ parse_long_options(char* const* nargv, const char* options,
     {
         /* ambiguous abbreviation */
         if (PRINT_ERROR)
-            warnx(ambig, (int) current_argv_len,
-                  current_argv);
+            warnx(ambig, (int) current_argv_len, current_argv);
         optopt = 0;
         return (BADCH);
     }
     if (match != -1)
     { /* option found */
-        if (long_options[match].has_arg == no_argument
-            && has_equal)
+        if (long_options[match].has_arg == no_argument && has_equal)
         {
             if (PRINT_ERROR)
-                warnx(noarg, (int) current_argv_len,
-                      current_argv);
+                warnx(noarg, (int) current_argv_len, current_argv);
             /*
-			 * XXX: GNU sets optopt to val regardless of flag
-			 */
+             * XXX: GNU sets optopt to val regardless of flag
+             */
             if (long_options[match].flag == NULL)
                 optopt = long_options[match].val;
             else
@@ -295,24 +268,22 @@ parse_long_options(char* const* nargv, const char* options,
             else if (long_options[match].has_arg == required_argument)
             {
                 /*
-				 * optional argument doesn't use next nargv
-				 */
+                 * optional argument doesn't use next nargv
+                 */
                 optarg = nargv[optind++];
             }
         }
-        if ((long_options[match].has_arg == required_argument)
-            && (optarg == NULL))
+        if ((long_options[match].has_arg == required_argument) && (optarg == NULL))
         {
             /*
-			 * Missing argument; leading ':' indicates no error
-			 * should be generated.
-			 */
+             * Missing argument; leading ':' indicates no error
+             * should be generated.
+             */
             if (PRINT_ERROR)
-                warnx(recargstring,
-                      current_argv);
+                warnx(recargstring, current_argv);
             /*
-			 * XXX: GNU sets optopt to val regardless of flag
-			 */
+             * XXX: GNU sets optopt to val regardless of flag
+             */
             if (long_options[match].flag == NULL)
                 optopt = long_options[match].val;
             else
@@ -349,9 +320,8 @@ parse_long_options(char* const* nargv, const char* options,
  * getopt_internal --
  *	Parse argc/argv argument vector.  Called by user level routines.
  */
-static int
-getopt_internal(int nargc, char* const* nargv, const char* options,
-                const struct option* long_options, int* idx, int flags)
+static int getopt_internal(
+    int nargc, char* const* nargv, const char* options, const struct option* long_options, int* idx, int flags)
 {
     const char* oli; /* option letter list index */
     int optchar, short_too;
@@ -361,19 +331,19 @@ getopt_internal(int nargc, char* const* nargv, const char* options,
         return (-1);
 
     /*
-	 * XXX Some GNU programs (like cvs) set optind to 0 instead of
-	 * XXX using optreset.  Work around this braindamage.
-	 */
+     * XXX Some GNU programs (like cvs) set optind to 0 instead of
+     * XXX using optreset.  Work around this braindamage.
+     */
     if (optind == 0)
         optind = optreset = 1;
 
     /*
-	 * Disable GNU extensions if POSIXLY_CORRECT is set or options
-	 * string begins with a '+'.
-	 *
-	 * CV, 2009-12-14: Check POSIXLY_CORRECT anew if optind == 0 or
-	 *                 optreset != 0 for GNU compatibility.
-	 */
+     * Disable GNU extensions if POSIXLY_CORRECT is set or options
+     * string begins with a '+'.
+     *
+     * CV, 2009-12-14: Check POSIXLY_CORRECT anew if optind == 0 or
+     *                 optreset != 0 for GNU compatibility.
+     */
     if (posixly_correct == -1 || optreset != 0)
         posixly_correct = (getenv("POSIXLY_CORRECT") != NULL);
     if (*options == '-')
@@ -396,16 +366,15 @@ getopt_internal(int nargc, char* const* nargv, const char* options,
             if (nonopt_end != -1)
             {
                 /* do permutation, if we have to */
-                permute_args(nonopt_start, nonopt_end,
-                             optind, nargv);
+                permute_args(nonopt_start, nonopt_end, optind, nargv);
                 optind -= nonopt_end - nonopt_start;
             }
             else if (nonopt_start != -1)
             {
                 /*
-				 * If we skipped non-options, set optind
-				 * to the first of them.
-				 */
+                 * If we skipped non-options, set optind
+                 * to the first of them.
+                 */
                 optind = nonopt_start;
             }
             nonopt_start = nonopt_end = -1;
@@ -417,18 +386,18 @@ getopt_internal(int nargc, char* const* nargv, const char* options,
             if (flags & FLAG_ALLARGS)
             {
                 /*
-				 * GNU extension:
-				 * return non-option as argument to option 1
-				 */
+                 * GNU extension:
+                 * return non-option as argument to option 1
+                 */
                 optarg = nargv[optind++];
                 return (INORDER);
             }
             if (!(flags & FLAG_PERMUTE))
             {
                 /*
-				 * If no permutation wanted, stop parsing
-				 * at first non-option.
-				 */
+                 * If no permutation wanted, stop parsing
+                 * at first non-option.
+                 */
                 return (-1);
             }
             /* do permutation */
@@ -436,8 +405,7 @@ getopt_internal(int nargc, char* const* nargv, const char* options,
                 nonopt_start = optind;
             else if (nonopt_end != -1)
             {
-                permute_args(nonopt_start, nonopt_end,
-                             optind, nargv);
+                permute_args(nonopt_start, nonopt_end, optind, nargv);
                 nonopt_start = optind - (nonopt_end - nonopt_start);
                 nonopt_end = -1;
             }
@@ -449,20 +417,19 @@ getopt_internal(int nargc, char* const* nargv, const char* options,
             nonopt_end = optind;
 
         /*
-		 * If we have "-" do nothing, if "--" we are done.
-		 */
+         * If we have "-" do nothing, if "--" we are done.
+         */
         if (place[1] != '\0' && *++place == '-' && place[1] == '\0')
         {
             optind++;
             place = EMSG;
             /*
-			 * We found an option (--), so if we skipped
-			 * non-options, we have to permute.
-			 */
+             * We found an option (--), so if we skipped
+             * non-options, we have to permute.
+             */
             if (nonopt_end != -1)
             {
-                permute_args(nonopt_start, nonopt_end,
-                             optind, nargv);
+                permute_args(nonopt_start, nonopt_end, optind, nargv);
                 optind -= nonopt_end - nonopt_start;
             }
             nonopt_start = nonopt_end = -1;
@@ -471,11 +438,11 @@ getopt_internal(int nargc, char* const* nargv, const char* options,
     }
 
     /*
-	 * Check long options if:
-	 *  1) we were passed some
-	 *  2) the arg is not just "-"
-	 *  3) either the arg starts with -- we are getopt_long_only()
-	 */
+     * Check long options if:
+     *  1) we were passed some
+     *  2) the arg is not just "-"
+     *  3) either the arg starts with -- we are getopt_long_only()
+     */
     if (long_options != NULL && place != nargv[optind] && (*place == '-' || (flags & FLAG_LONGONLY)))
     {
         short_too = 0;
@@ -484,8 +451,7 @@ getopt_internal(int nargc, char* const* nargv, const char* options,
         else if (*place != ':' && strchr(options, *place) != NULL)
             short_too = 1; /* could be short option too */
 
-        optchar = parse_long_options(nargv, options, long_options,
-                                     idx, short_too);
+        optchar = parse_long_options(nargv, options, long_options, idx, short_too);
         if (optchar != -1)
         {
             place = EMSG;
@@ -493,13 +459,14 @@ getopt_internal(int nargc, char* const* nargv, const char* options,
         }
     }
 
-    if ((optchar = (int) *place++) == (int) ':' || (optchar == (int) '-' && *place != '\0') || (oli = strchr(options, optchar)) == NULL)
+    if ((optchar = (int) *place++) == (int) ':' || (optchar == (int) '-' && *place != '\0')
+        || (oli = strchr(options, optchar)) == NULL)
     {
         /*
-		 * If the user specified "-" and  '-' isn't listed in
-		 * options, return -1 (non-option) as per POSIX.
-		 * Otherwise, it is an unknown option character (or ':').
-		 */
+         * If the user specified "-" and  '-' isn't listed in
+         * options, return -1 (non-option) as per POSIX.
+         * Otherwise, it is an unknown option character (or ':').
+         */
         if (optchar == (int) '-' && *place == '\0')
             return (-1);
         if (!*place)
@@ -524,8 +491,7 @@ getopt_internal(int nargc, char* const* nargv, const char* options,
         }
         else /* white space */
             place = nargv[optind];
-        optchar = parse_long_options(nargv, options, long_options,
-                                     idx, 0);
+        optchar = parse_long_options(nargv, options, long_options, idx, 0);
         place = EMSG;
         return (optchar);
     }
@@ -570,13 +536,13 @@ int getopt(int nargc, char* const* nargv, const char* options)
 {
 
     /*
-	 * We don't pass FLAG_PERMUTE to getopt_internal() since
-	 * the BSD getopt(3) (unlike GNU) has never done this.
-	 *
-	 * Furthermore, since many privileged programs call getopt()
-	 * before dropping privileges it makes sense to keep things
-	 * as simple (and bug-free) as possible.
-	 */
+     * We don't pass FLAG_PERMUTE to getopt_internal() since
+     * the BSD getopt(3) (unlike GNU) has never done this.
+     *
+     * Furthermore, since many privileged programs call getopt()
+     * before dropping privileges it makes sense to keep things
+     * as simple (and bug-free) as possible.
+     */
     return (getopt_internal(nargc, nargv, options, NULL, NULL, 0));
 }
 #endif /* REPLACE_GETOPT */
@@ -585,22 +551,18 @@ int getopt(int nargc, char* const* nargv, const char* options)
  * getopt_long --
  *	Parse argc/argv argument vector.
  */
-int getopt_long(int nargc, char* const* nargv, const char* options,
-                const struct option* long_options, int* idx)
+int getopt_long(int nargc, char* const* nargv, const char* options, const struct option* long_options, int* idx)
 {
 
-    return (getopt_internal(nargc, nargv, options, long_options, idx,
-                            FLAG_PERMUTE));
+    return (getopt_internal(nargc, nargv, options, long_options, idx, FLAG_PERMUTE));
 }
 
 /*
  * getopt_long_only --
  *	Parse argc/argv argument vector.
  */
-int getopt_long_only(int nargc, char* const* nargv, const char* options,
-                     const struct option* long_options, int* idx)
+int getopt_long_only(int nargc, char* const* nargv, const char* options, const struct option* long_options, int* idx)
 {
 
-    return (getopt_internal(nargc, nargv, options, long_options, idx,
-                            FLAG_PERMUTE | FLAG_LONGONLY));
+    return (getopt_internal(nargc, nargv, options, long_options, idx, FLAG_PERMUTE | FLAG_LONGONLY));
 }
diff --git a/samples/common/windows/getopt.h b/samples/common/windows/getopt.h
index 6023b959..9fc4e24a 100644
--- a/samples/common/windows/getopt.h
+++ b/samples/common/windows/getopt.h
@@ -84,10 +84,9 @@ enum /* permitted values for its `has_arg' field...	*/
     optional_argument  /* option may take an argument		*/
 };
 
-extern int getopt_long(int nargc, char* const* nargv, const char* options,
-                       const struct option* long_options, int* idx);
-extern int getopt_long_only(int nargc, char* const* nargv, const char* options,
-                            const struct option* long_options, int* idx);
+extern int getopt_long(int nargc, char* const* nargv, const char* options, const struct option* long_options, int* idx);
+extern int getopt_long_only(
+    int nargc, char* const* nargv, const char* options, const struct option* long_options, int* idx);
 /*
  * Previous MinGW implementation had...
  */
diff --git a/samples/opensource/CMakeLists.txt b/samples/opensource/CMakeLists.txt
index 8184515a..24df5e7d 100644
--- a/samples/opensource/CMakeLists.txt
+++ b/samples/opensource/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -14,6 +14,7 @@
 # limitations under the License.
 #
 set(OPENSOURCE_SAMPLES_LIST
+    sampleAlgorithmSelector
     sampleCharRNN
     sampleDynamicReshape
     sampleFasterRCNN
@@ -35,6 +36,7 @@ set(OPENSOURCE_SAMPLES_LIST
     sampleUffMNIST
     sampleUffPluginV2Ext
     sampleUffSSD
+    sampleOnnxMnistCoordConvAC
     trtexec
 )
 
diff --git a/samples/opensource/sampleAlgorithmSelector/CMakeLists.txt b/samples/opensource/sampleAlgorithmSelector/CMakeLists.txt
new file mode 100644
index 00000000..cb252f3b
--- /dev/null
+++ b/samples/opensource/sampleAlgorithmSelector/CMakeLists.txt
@@ -0,0 +1,23 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+SET(SAMPLE_SOURCES
+     sampleAlgorithmSelector.cpp
+)
+
+set(SAMPLE_PARSERS "caffe")
+
+include(../../CMakeSamplesTemplate.txt)
diff --git a/samples/opensource/sampleAlgorithmSelector/README.md b/samples/opensource/sampleAlgorithmSelector/README.md
new file mode 100644
index 00000000..922b3051
--- /dev/null
+++ b/samples/opensource/sampleAlgorithmSelector/README.md
@@ -0,0 +1,155 @@
+# Algorithm Selection API usage example based off sampleMNIST in TensorRT
+
+**Table Of Contents**
+- [Description](#description)
+- [How does this sample work?](#how-does-this-sample-work)
+- [Running the sample](#running-the-sample)
+	* [Sample `--help` options](#sample---help-options)
+- [Additional resources](#additional-resources)
+- [License](#license)
+- [Changelog](#changelog)
+- [Known issues](#known-issues)
+
+## Description
+
+This sample, sampleAlgorithmSelector, shows an example of how to use the algorithm selection API based on sampleMNIST.
+[sampleMNIST documentation] (https://docs.nvidia.com/deeplearning/sdk/tensorrt-sample-support-guide/index.html#mnist_sample)
+
+This sample demonstrates the usage of `IAlgorithmSelector` to deterministically build TRT engines.
+It also shows the usage of `IAlgorithmSelector::selectAlgorithms` to define heuristics for selection of algorithms.
+
+## How does this sample work?
+
+This sample uses a Caffe model that was trained on the [MNIST dataset](https://github.com/NVIDIA/DIGITS/blob/master/docs/GettingStarted.md).
+
+Specifically, this sample performs the following steps:
+- Performs the basic setup and initialization of TensorRT using the Caffe parser
+- [Imports a trained Caffe model using Caffe parser](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#import_caffe_c)
+- Preprocesses the input and stores the result in a managed buffer
+- [Sets up three instances of algorithm selector](#setup-the-algorithm-selectors)
+- [Builds three engines using the algorithm selectors](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#build_engine_c)
+- [Serializes and deserializes the engines](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#serial_model_c)
+- [Uses the engines to perform inference on an input image](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#perform_inference_c)
+
+To verify whether the engine is operating correctly, this sample picks a 28x28 image of a digit at random and runs inference on it using the engine it created. The output of the network is a probability distribution on the digit, showing which digit is likely to be that in the image.
+
+### Setup the algorithm selectors
+1. AlgorithmCacheWriter - Uses `IAlgorithmSelector::reportAlgorithms` to write TensorRT's default algorithm choices to a file "AlgorithmChoices.txt".
+2. AlgorithmCacheReader - Uses `IAlgorithmSelector::selectAlgorithms` to replicate algorithm choices from the file "AlgorithmChoices.txt" and verifies the choices using `IAlgorithmSelector::reportAlgorithms`.
+3. MinimumWorkspaceAlgorithmSelector - Uses `IAlgorithmSelector::selectAlgorithms` to select algorithms with minimum workspace requirements.
+ 
+## Running the sample
+
+1. Compile this sample by running `make` in the `<TensorRT root directory>/samples/sampleAlgorithmSelector` directory. The binary named `sample_algorithm_selector` will be created in the `<TensorRT root directory>/bin` directory.
+	```
+	cd <TensorRT root directory>/samples/sampleAlgorithmSelector
+	make
+	```
+	Where `<TensorRT root directory>` is where you installed TensorRT.
+	
+2. Run the sample to perform inference on the digit:
+    ```
+	./sample_algorithm_selector [-h] [--datadir=/path/to/data/dir/] [--useDLA=N] [--fp16 or --int8]
+	```
+	This sample reads three Caffe files to build the network:
+	-   `mnist.prototxt` 
+	The prototxt file that contains the network design.
+
+	-   `mnist.caffemodel`
+	The model file which contains the trained weights for the network.
+
+	-   `mnist_mean.binaryproto`
+	The binaryproto file which contains the means.
+
+	This sample can be run in FP16 and INT8 modes as well.
+
+	**Note:** By default, the sample expects these files to be in either the `data/samples/mnist/` or `data/mnist/` directories. The list of default directories can be changed by adding one or more paths with `--datadir=/new/path/` as a command line argument.
+
+3.  Verify that the sample ran successfully. If the sample runs successfully you should see output similar to the following; ASCII rendering of the input image with digit 3:
+    ```
+	&&&& RUNNING TensorRT.sample_algorithm_selector # ./sample_algorithm_selector
+	[I] Building and running a GPU inference engine for MNIST
+	[I] Input:
+	@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+	@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+	@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+	@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+	@@@@@@@@#-:.-=@@@@@@@@@@@@@@
+	@@@@@%=     . *@@@@@@@@@@@@@
+	@@@@% .:+%%%  *@@@@@@@@@@@@@
+	@@@@+=#@@@@@# @@@@@@@@@@@@@@
+	@@@@@@@@@@@%  @@@@@@@@@@@@@@
+	@@@@@@@@@@@: *@@@@@@@@@@@@@@
+	@@@@@@@@@@- .@@@@@@@@@@@@@@@
+	@@@@@@@@@:  #@@@@@@@@@@@@@@@
+	@@@@@@@@:   +*%#@@@@@@@@@@@@
+	@@@@@@@%         :+*@@@@@@@@
+	@@@@@@@@#*+--.::     +@@@@@@
+	@@@@@@@@@@@@@@@@#=:.  +@@@@@
+	@@@@@@@@@@@@@@@@@@@@  .@@@@@
+	@@@@@@@@@@@@@@@@@@@@#. #@@@@
+	@@@@@@@@@@@@@@@@@@@@#  @@@@@
+	@@@@@@@@@%@@@@@@@@@@- +@@@@@
+	@@@@@@@@#-@@@@@@@@*. =@@@@@@
+	@@@@@@@@ .+%%%%+=.  =@@@@@@@
+	@@@@@@@@           =@@@@@@@@
+	@@@@@@@@*=:   :--*@@@@@@@@@@
+	@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+	@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+	@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+	@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+
+	[I] Output:
+	0:
+	1:
+	2:
+	3: **********
+	4:
+	5:
+	6:
+	7:
+	8:
+	9:
+
+	&&&& PASSED TensorRT.sample_algorithm_selector # ./sample_algorithm_selector
+	```
+
+	This output shows that the sample ran successfully; `PASSED`.
+ 
+
+### Sample --help options
+
+To see the full list of available options and their descriptions, use the `-h` or `--help` command line option. For example:
+```
+Usage: ./sample_algorithm_selector [-h or --help] [-d or --datadir=<path to data directory>] [--useDLACore=<int>]
+--help Display help information
+--datadir Specify path to a data directory, overriding the default. This option can be used multiple times to add multiple directories. If no data directories are given, the default is to use (data/samples/mnist/, data/mnist/)
+--useDLACore=N Specify a DLA engine for layers that support DLA. Value can range from 0 to n-1, where n is the number of DLA engines on the platform.
+--int8 Run in Int8 mode.
+--fp16 Run in FP16 mode.
+```
+
+# Additional resources
+
+The following resources provide a deeper understanding about sampleAlgorithmSelector:
+
+**MNIST**
+- [MNIST dataset](https://github.com/NVIDIA/DIGITS/blob/master/docs/GettingStarted.md)
+
+**Documentation**
+- [Introduction To NVIDIA’s TensorRT Samples](https://docs.nvidia.com/deeplearning/sdk/tensorrt-sample-support-guide/index.html#samples)
+- [Working With TensorRT Using The C++ API](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#c_topics)
+- [NVIDIA’s TensorRT Documentation Library](https://docs.nvidia.com/deeplearning/sdk/tensorrt-archived/index.html)
+
+# License
+
+For terms and conditions for use, reproduction, and distribution, see the [TensorRT Software License Agreement](https://docs.nvidia.com/deeplearning/sdk/tensorrt-sla/index.html) documentation.
+
+# Changelog
+
+November 2019
+This `README.md` file was recreated, updated and reviewed.
+
+# Known issues
+
+There are no known issues in this sample.
\ No newline at end of file
diff --git a/samples/opensource/sampleAlgorithmSelector/sampleAlgorithmSelector.cpp b/samples/opensource/sampleAlgorithmSelector/sampleAlgorithmSelector.cpp
new file mode 100644
index 00000000..e209b020
--- /dev/null
+++ b/samples/opensource/sampleAlgorithmSelector/sampleAlgorithmSelector.cpp
@@ -0,0 +1,752 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+//! \file sampleAlgorithmSelector.cpp
+//! \brief This file contains the implementation of Algorithm Selector sample.
+//!
+//! It demonstrates the usage of IAlgorithmSelector to cache the algorithms used in a network.
+//! It also shows the usage of IAlgorithmSelector::selectAlgorithms to define heuristics for selection of algorithms.
+//! It builds a TensorRT engine by importing a trained MNIST Caffe model and runs inference on an input image of a
+//! digit.
+//! It can be run with the following command line:
+//! Command: ./sample_algorithm_selector [-h or --help] [-d=/path/to/data/dir or --datadir=/path/to/data/dir]
+
+#include "argsParser.h"
+#include "buffers.h"
+#include "common.h"
+#include "logger.h"
+
+#include "NvCaffeParser.h"
+#include "NvInfer.h"
+
+#include <algorithm>
+#include <cassert>
+#include <cmath>
+#include <cuda_runtime_api.h>
+#include <fstream>
+#include <iostream>
+#include <sstream>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+const std::string gSampleName = "TensorRT.sample_algorithm_selector";
+const std::string gCacheFileName = "AlgorithmCache.txt";
+//!
+//! \brief Writes the default algorithm choices made by TensorRT into a file.
+//!
+class AlgorithmCacheWriter : public IAlgorithmSelector
+{
+public:
+    //!
+    //! \brief Return value in [0, nbChoices] for a valid algorithm.
+    //!
+    //! \details Lets TRT use its default tactic selection method.
+    //! Writes all the possible choices to the selection buffer and returns the length of it.
+    //! If BuilderFlag::kSTRICT_TYPES is not set, just returning 0 forces default tactic selection.
+    //!
+    int32_t selectAlgorithms(const nvinfer1::IAlgorithmContext& context, const nvinfer1::IAlgorithm* const* choices,
+        int32_t nbChoices, int32_t* selection) override
+    {
+        // TensorRT always provides more than zero number of algorithms in selectAlgorithms.
+        assert(nbChoices > 0);
+
+        std::iota(selection, selection + nbChoices, 0);
+        return nbChoices;
+    }
+
+    //!
+    //! \brief called by TensorRT to report choices it made.
+    //!
+    //! \details Writes the TensorRT algorithm choices into a file.
+    //!
+    void reportAlgorithms(const nvinfer1::IAlgorithmContext* const* algoContexts,
+        const nvinfer1::IAlgorithm* const* algoChoices, int32_t nbAlgorithms) noexcept override
+    {
+        std::ofstream algorithmFile(mCacheFileName);
+        if (!algorithmFile.good())
+        {
+            sample::gLogError << "Cannot open algorithm cache file: " << mCacheFileName << " to write." << std::endl;
+            abort();
+        }
+
+        for (int32_t i = 0; i < nbAlgorithms; i++)
+        {
+            algorithmFile << algoContexts[i]->getName() << "\n";
+            algorithmFile << algoChoices[i]->getAlgorithmVariant().getImplementation() << "\n";
+            algorithmFile << algoChoices[i]->getAlgorithmVariant().getTactic() << "\n";
+
+            // Write number of inputs and outputs.
+            const int32_t nbInputs = algoContexts[i]->getNbInputs();
+            algorithmFile << nbInputs << "\n";
+            const int32_t nbOutputs = algoContexts[i]->getNbOutputs();
+            algorithmFile << nbOutputs << "\n";
+
+            // Write input and output formats.
+            for (int32_t j = 0; j < nbInputs + nbOutputs; j++)
+            {
+                algorithmFile << static_cast<int32_t>(algoChoices[i]->getAlgorithmIOInfo(j).getTensorFormat()) << "\n";
+                algorithmFile << static_cast<int32_t>(algoChoices[i]->getAlgorithmIOInfo(j).getDataType()) << "\n";
+            }
+        }
+        algorithmFile.close();
+    }
+
+    AlgorithmCacheWriter(const std::string& cacheFileName)
+        : mCacheFileName(cacheFileName)
+    {
+    }
+
+private:
+    std::string mCacheFileName;
+};
+
+//!
+//! \brief Replicates the algorithm selection using a cache file.
+//!
+class AlgorithmCacheReader : public IAlgorithmSelector
+{
+public:
+    //!
+    //! \brief Return value in [0, nbChoices] for a valid algorithm.
+    //!
+    //! \details Use the map created from cache to select algorithms.
+    //!
+    int32_t selectAlgorithms(const nvinfer1::IAlgorithmContext& algoContext,
+        const nvinfer1::IAlgorithm* const* algoChoices, int32_t nbChoices, int32_t* selection) override
+    {
+        // TensorRT always provides more than zero number of algorithms in selectAlgorithms.
+        assert(nbChoices > 0);
+
+        const std::string layerName(algoContext.getName());
+        auto it = choiceMap.find(layerName);
+
+        // The layerName can be used as a unique identifier for a layer.
+        // Since the network and config has not been changed (between the cache and cache read),
+        // This map must contain layerName.
+        assert(it != choiceMap.end());
+        auto& algoItem = it->second;
+
+        assert(algoItem.nbInputs == algoContext.getNbInputs());
+        assert(algoItem.nbOutputs == algoContext.getNbOutputs());
+
+        int32_t nbSelections = 0;
+        for (auto i = 0; i < nbChoices; i++)
+        {
+            // The combination of implementation, tactic and input/output formats is unique to an algorithm,
+            // and can be used to reproduce the same algorithm. Since the network and config has not been changed
+            // (between the cache and cache read), there must be exactly one algorithm match for each layerName.
+            if (areSame(algoItem, *algoChoices[i]))
+            {
+                selection[nbSelections++] = i;
+            }
+        }
+
+        //! There must be only one algorithm selected.
+        assert(nbSelections == 1);
+        return nbSelections;
+    }
+
+    //!
+    //! \brief Called by TensorRT to report choices it made.
+    //!
+    //! \details Verifies that the algorithm used by TensorRT conform to the cache.
+    //!
+    void reportAlgorithms(const nvinfer1::IAlgorithmContext* const* algoContexts,
+        const nvinfer1::IAlgorithm* const* algoChoices, int32_t nbAlgorithms) override
+    {
+        for (auto i = 0; i < nbAlgorithms; i++)
+        {
+            const std::string layerName(algoContexts[i]->getName());
+            assert(choiceMap.find(layerName) != choiceMap.end());
+            const auto& algoItem = choiceMap[layerName];
+            assert(algoItem.nbInputs == algoContexts[i]->getNbInputs());
+            assert(algoItem.nbOutputs == algoContexts[i]->getNbOutputs());
+            assert(algoChoices[i]->getAlgorithmVariant().getImplementation() == algoItem.implementation);
+            assert(algoChoices[i]->getAlgorithmVariant().getTactic() == algoItem.tactic);
+            auto nbFormats = algoItem.nbInputs + algoItem.nbOutputs;
+            for (auto j = 0; j < nbFormats; j++)
+            {
+                assert(algoItem.formats[j].first
+                    == static_cast<int32_t>(algoChoices[i]->getAlgorithmIOInfo(j).getTensorFormat()));
+                assert(algoItem.formats[j].second
+                    == static_cast<int32_t>(algoChoices[i]->getAlgorithmIOInfo(j).getDataType()));
+            }
+        }
+    }
+
+    AlgorithmCacheReader(const std::string& cacheFileName)
+    {
+        //! Use the cache file to create a map of algorithm choices.
+        std::ifstream algorithmFile(cacheFileName);
+        if (!algorithmFile.good())
+        {
+            sample::gLogError << "Cannot open algorithm cache file: " << cacheFileName << " to read." << std::endl;
+            abort();
+        }
+
+        std::string line;
+        while (getline(algorithmFile, line))
+        {
+            std::string layerName;
+            layerName = line;
+
+            AlgorithmCacheItem algoItem;
+            getline(algorithmFile, line);
+            algoItem.implementation = std::stoll(line);
+
+            getline(algorithmFile, line);
+            algoItem.tactic = std::stoll(line);
+
+            getline(algorithmFile, line);
+            algoItem.nbInputs = std::stoi(line);
+
+            getline(algorithmFile, line);
+            algoItem.nbOutputs = std::stoi(line);
+
+            const int32_t nbFormats = algoItem.nbInputs + algoItem.nbOutputs;
+            algoItem.formats.resize(nbFormats);
+            for (int32_t i = 0; i < nbFormats; i++)
+            {
+                getline(algorithmFile, line);
+                algoItem.formats[i].first = std::stoi(line);
+                getline(algorithmFile, line);
+                algoItem.formats[i].second = std::stoi(line);
+            }
+            choiceMap[layerName] = std::move(algoItem);
+        }
+        algorithmFile.close();
+    }
+
+private:
+    struct AlgorithmCacheItem
+    {
+        int64_t implementation;
+        int64_t tactic;
+        int32_t nbInputs;
+        int32_t nbOutputs;
+        std::vector<std::pair<int32_t, int32_t>> formats;
+    };
+    std::unordered_map<std::string, AlgorithmCacheItem> choiceMap;
+
+    //! The combination of implementation, tactic and input/output formats is unique to an algorithm,
+    //! and can be used to check if two algorithms are same.
+    static bool areSame(const AlgorithmCacheItem& algoCacheItem, const IAlgorithm& algoChoice)
+    {
+        if (algoChoice.getAlgorithmVariant().getImplementation() != algoCacheItem.implementation
+            || algoChoice.getAlgorithmVariant().getTactic() != algoCacheItem.tactic)
+        {
+            return false;
+        }
+
+        // Loop over all the AlgorithmIOInfos to see if all of them match to the formats in algo item.
+        const auto nbFormats = algoCacheItem.nbInputs + algoCacheItem.nbOutputs;
+        for (auto j = 0; j < nbFormats; j++)
+        {
+            if (algoCacheItem.formats[j].first
+                    != static_cast<int32_t>(algoChoice.getAlgorithmIOInfo(j).getTensorFormat())
+                || algoCacheItem.formats[j].second
+                    != static_cast<int32_t>(algoChoice.getAlgorithmIOInfo(j).getDataType()))
+            {
+                return false;
+            }
+        }
+
+        return true;
+    }
+};
+
+//!
+//! \brief Selects Algorithms with minimum workspace requirements.
+//!
+class MinimumWorkspaceAlgorithmSelector : public IAlgorithmSelector
+{
+public:
+    //!
+    //! \brief Return value in [0, nbChoices] for a valid algorithm.
+    //!
+    //! \details Use the map created from cache to select algorithms.
+    //!
+    int32_t selectAlgorithms(const nvinfer1::IAlgorithmContext& algoContext,
+        const nvinfer1::IAlgorithm* const* algoChoices, int32_t nbChoices, int32_t* selection) override
+    {
+        // TensorRT always provides more than zero number of algorithms in selectAlgorithms.
+        assert(nbChoices > 0);
+
+        auto it = std::min_element(
+            algoChoices, algoChoices + nbChoices, [](const nvinfer1::IAlgorithm* x, const nvinfer1::IAlgorithm* y) {
+                return x->getWorkspaceSize() < y->getWorkspaceSize();
+            });
+        selection[0] = static_cast<int32_t>(it - algoChoices);
+        return 1;
+    }
+
+    //!
+    //! \brief Called by TensorRT to report choices it made.
+    //!
+    void reportAlgorithms(const nvinfer1::IAlgorithmContext* const* algoContexts,
+        const nvinfer1::IAlgorithm* const* algoChoices, int32_t nbAlgorithms) override
+    {
+        // do nothing
+    }
+};
+
+//!
+//! \brief  The SampleAlgorithmSelector class implements the SampleAlgorithmSelector sample.
+//!
+//! \details It creates the network using a trained Caffe MNIST classification model.
+//!
+class SampleAlgorithmSelector
+{
+    template <typename T>
+    using SampleUniquePtr = std::unique_ptr<T, samplesCommon::InferDeleter>;
+
+public:
+    SampleAlgorithmSelector(const samplesCommon::CaffeSampleParams& params)
+        : mParams(params)
+    {
+    }
+
+    //!
+    //! \brief Builds the network engine.
+    //!
+    bool build(IAlgorithmSelector* selector);
+
+    //!
+    //! \brief Runs the TensorRT inference engine for this sample.
+    //!
+    bool infer();
+
+    //!
+    //! \brief Used to clean up any state created in the sample class.
+    //!
+    bool teardown();
+
+private:
+    //!
+    //! \brief uses a Caffe parser to create the MNIST Network and marks the output layers.
+    //!
+    bool constructNetwork(
+        SampleUniquePtr<nvcaffeparser1::ICaffeParser>& parser, SampleUniquePtr<nvinfer1::INetworkDefinition>& network);
+
+    //!
+    //! \brief Reads the input and mean data, preprocesses, and stores the result in a managed buffer.
+    //!
+    bool processInput(
+        const samplesCommon::BufferManager& buffers, const std::string& inputTensorName, int inputFileIdx) const;
+
+    //!
+    //! \brief Verifies that the output is correct and prints it.
+    //!
+    bool verifyOutput(
+        const samplesCommon::BufferManager& buffers, const std::string& outputTensorName, int groundTruthDigit) const;
+
+    std::shared_ptr<nvinfer1::ICudaEngine> mEngine{nullptr}; //!< The TensorRT engine used to run the network.
+
+    samplesCommon::CaffeSampleParams mParams; //!< The parameters for the sample.
+
+    nvinfer1::Dims mInputDims; //!< The dimensions of the input to the network.
+
+    SampleUniquePtr<nvcaffeparser1::IBinaryProtoBlob>
+        mMeanBlob; //! the mean blob, which we need to keep around until build is done.
+};
+
+//!
+//! \brief Creates the network, configures the builder and creates the network engine.
+//!
+//! \details This function creates the MNIST network by parsing the caffe model and builds
+//!          the engine that will be used to run MNIST (mEngine).
+//!
+//! \return Returns true if the engine was created successfully and false otherwise.
+//!
+bool SampleAlgorithmSelector::build(IAlgorithmSelector* selector)
+{
+    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(sample::gLogger.getTRTLogger()));
+    if (!builder)
+    {
+        return false;
+    }
+
+    auto network = SampleUniquePtr<nvinfer1::INetworkDefinition>(builder->createNetwork());
+    if (!network)
+    {
+        return false;
+    }
+
+    auto config = SampleUniquePtr<nvinfer1::IBuilderConfig>(builder->createBuilderConfig());
+    if (!config)
+    {
+        return false;
+    }
+
+    auto parser = SampleUniquePtr<nvcaffeparser1::ICaffeParser>(nvcaffeparser1::createCaffeParser());
+    if (!parser)
+    {
+        return false;
+    }
+
+    if (!constructNetwork(parser, network))
+    {
+        return false;
+    }
+
+    builder->setMaxBatchSize(mParams.batchSize);
+    config->setMaxWorkspaceSize(16_MiB);
+    config->setAlgorithmSelector(selector);
+    config->setFlag(BuilderFlag::kGPU_FALLBACK);
+
+    if (!mParams.int8)
+    {
+        // The sample fails for Int8 with kSTRICT_TYPES flag set.
+        config->setFlag(BuilderFlag::kSTRICT_TYPES);
+    }
+    if (mParams.fp16)
+    {
+        config->setFlag(BuilderFlag::kFP16);
+    }
+    if (mParams.int8)
+    {
+        config->setFlag(BuilderFlag::kINT8);
+    }
+
+    samplesCommon::enableDLA(builder.get(), config.get(), mParams.dlaCore);
+    mEngine = std::shared_ptr<nvinfer1::ICudaEngine>(
+        builder->buildEngineWithConfig(*network, *config), samplesCommon::InferDeleter());
+
+    if (!mEngine)
+    {
+        return false;
+    }
+
+    assert(network->getNbInputs() == 1);
+    mInputDims = network->getInput(0)->getDimensions();
+    assert(mInputDims.nbDims == 3);
+
+    return true;
+}
+
+//!
+//! \brief Reads the input and mean data, preprocesses, and stores the result in a managed buffer.
+//!
+bool SampleAlgorithmSelector::processInput(
+    const samplesCommon::BufferManager& buffers, const std::string& inputTensorName, int inputFileIdx) const
+{
+    const int inputH = mInputDims.d[1];
+    const int inputW = mInputDims.d[2];
+
+    // Read a random digit file.
+    srand(unsigned(time(nullptr)));
+    std::vector<uint8_t> fileData(inputH * inputW);
+    readPGMFile(locateFile(std::to_string(inputFileIdx) + ".pgm", mParams.dataDirs), fileData.data(), inputH, inputW);
+
+    // Print ASCII representation of digit.
+    sample::gLogInfo << "Input:\n";
+    for (int i = 0; i < inputH * inputW; i++)
+    {
+        sample::gLogInfo << (" .:-=+*#%@"[fileData[i] / 26]) << (((i + 1) % inputW) ? "" : "\n");
+    }
+    sample::gLogInfo << std::endl;
+
+    float* hostInputBuffer = static_cast<float*>(buffers.getHostBuffer(inputTensorName));
+
+    for (int i = 0; i < inputH * inputW; i++)
+    {
+        hostInputBuffer[i] = float(fileData[i]);
+    }
+
+    return true;
+}
+
+//!
+//! \brief Verifies that the output is correct and prints it.
+//!
+bool SampleAlgorithmSelector::verifyOutput(
+    const samplesCommon::BufferManager& buffers, const std::string& outputTensorName, int groundTruthDigit) const
+{
+    const float* prob = static_cast<const float*>(buffers.getHostBuffer(outputTensorName));
+
+    // Print histogram of the output distribution.
+    sample::gLogInfo << "Output:\n";
+    float val{0.0f};
+    int idx{0};
+    const int kDIGITS = 10;
+
+    for (int i = 0; i < kDIGITS; i++)
+    {
+        if (val < prob[i])
+        {
+            val = prob[i];
+            idx = i;
+        }
+
+        sample::gLogInfo << i << ": " << std::string(int(std::floor(prob[i] * 10 + 0.5f)), '*') << "\n";
+    }
+    sample::gLogInfo << std::endl;
+
+    return (idx == groundTruthDigit && val > 0.9f);
+}
+
+//!
+//! \brief Uses a caffe parser to create the MNIST Network and marks the
+//!        output layers.
+//!
+//! \param network Pointer to the network that will be populated with the MNIST network.
+//!
+//! \param builder Pointer to the engine builder.
+//!
+bool SampleAlgorithmSelector::constructNetwork(
+    SampleUniquePtr<nvcaffeparser1::ICaffeParser>& parser, SampleUniquePtr<nvinfer1::INetworkDefinition>& network)
+{
+    const nvcaffeparser1::IBlobNameToTensor* blobNameToTensor = parser->parse(
+        mParams.prototxtFileName.c_str(), mParams.weightsFileName.c_str(), *network, nvinfer1::DataType::kFLOAT);
+
+    for (auto& s : mParams.outputTensorNames)
+    {
+        network->markOutput(*blobNameToTensor->find(s.c_str()));
+    }
+
+    // add mean subtraction to the beginning of the network.
+    nvinfer1::Dims inputDims = network->getInput(0)->getDimensions();
+    mMeanBlob
+        = SampleUniquePtr<nvcaffeparser1::IBinaryProtoBlob>(parser->parseBinaryProto(mParams.meanFileName.c_str()));
+    nvinfer1::Weights meanWeights{nvinfer1::DataType::kFLOAT, mMeanBlob->getData(), inputDims.d[1] * inputDims.d[2]};
+    // For this sample, a large range based on the mean data is chosen and applied to the head of the network.
+    // After the mean subtraction occurs, the range is expected to be between -127 and 127, so the rest of the network
+    // is given a generic range.
+    // The preferred method is use scales computed based on a representative data set
+    // and apply each one individually based on the tensor. The range here is large enough for the
+    // network, but is chosen for example purposes only.
+    float maxMean
+        = samplesCommon::getMaxValue(static_cast<const float*>(meanWeights.values), samplesCommon::volume(inputDims));
+
+    auto mean = network->addConstant(nvinfer1::Dims3(1, inputDims.d[1], inputDims.d[2]), meanWeights);
+    if (!mean->getOutput(0)->setDynamicRange(-maxMean, maxMean))
+    {
+        return false;
+    }
+    if (!network->getInput(0)->setDynamicRange(-maxMean, maxMean))
+    {
+        return false;
+    }
+    auto meanSub = network->addElementWise(*network->getInput(0), *mean->getOutput(0), ElementWiseOperation::kSUB);
+    if (!meanSub->getOutput(0)->setDynamicRange(-maxMean, maxMean))
+    {
+        return false;
+    }
+    network->getLayer(0)->setInput(0, *meanSub->getOutput(0));
+    samplesCommon::setAllTensorScales(network.get(), 127.0f, 127.0f);
+
+    return true;
+}
+
+//!
+//! \brief Runs the TensorRT inference engine for this sample.
+//!
+//! \details This function is the main execution function of the sample. It allocates
+//!          the buffer, sets inputs, executes the engine, and verifies the output.
+//!
+bool SampleAlgorithmSelector::infer()
+{
+    // Create RAII buffer manager object.
+    samplesCommon::BufferManager buffers(mEngine, mParams.batchSize);
+
+    auto context = SampleUniquePtr<nvinfer1::IExecutionContext>(mEngine->createExecutionContext());
+    if (!context)
+    {
+        return false;
+    }
+
+    // Pick a random digit to try to infer.
+    srand(time(NULL));
+    const int digit = rand() % 10;
+
+    // Read the input data into the managed buffers.
+    // There should be just 1 input tensor.
+    assert(mParams.inputTensorNames.size() == 1);
+    if (!processInput(buffers, mParams.inputTensorNames[0], digit))
+    {
+        return false;
+    }
+    // Create CUDA stream for the execution of this inference.
+    cudaStream_t stream;
+    CHECK(cudaStreamCreate(&stream));
+
+    // Asynchronously copy data from host input buffers to device input buffers
+    buffers.copyInputToDeviceAsync(stream);
+
+    // Asynchronously enqueue the inference work
+    if (!context->enqueue(mParams.batchSize, buffers.getDeviceBindings().data(), stream, nullptr))
+    {
+        return false;
+    }
+    // Asynchronously copy data from device output buffers to host output buffers.
+    buffers.copyOutputToHostAsync(stream);
+
+    // Wait for the work in the stream to complete.
+    cudaStreamSynchronize(stream);
+
+    // Release stream.
+    cudaStreamDestroy(stream);
+
+    // Check and print the output of the inference.
+    // There should be just one output tensor.
+    assert(mParams.outputTensorNames.size() == 1);
+    bool outputCorrect = verifyOutput(buffers, mParams.outputTensorNames[0], digit);
+
+    return outputCorrect;
+}
+
+//!
+//! \brief Used to clean up any state created in the sample class.
+//!
+bool SampleAlgorithmSelector::teardown()
+{
+    //! Clean up the libprotobuf files as the parsing is complete.
+    //! \note It is not safe to use any other part of the protocol buffers library after
+    //! ShutdownProtobufLibrary() has been called.
+    nvcaffeparser1::shutdownProtobufLibrary();
+    return true;
+}
+
+//!
+//! \brief Initializes members of the params struct using the command line args
+//!
+samplesCommon::CaffeSampleParams initializeSampleParams(const samplesCommon::Args& args)
+{
+    samplesCommon::CaffeSampleParams params;
+    if (args.dataDirs.empty()) //!< Use default directories if user hasn't provided directory paths.
+    {
+        params.dataDirs.push_back("data/mnist/");
+        params.dataDirs.push_back("data/samples/mnist/");
+    }
+    else //!< Use the data directory provided by the user.
+    {
+        params.dataDirs = args.dataDirs;
+    }
+
+    params.prototxtFileName = locateFile("mnist.prototxt", params.dataDirs);
+    params.weightsFileName = locateFile("mnist.caffemodel", params.dataDirs);
+    params.meanFileName = locateFile("mnist_mean.binaryproto", params.dataDirs);
+    params.inputTensorNames.push_back("data");
+    params.batchSize = 1;
+    params.outputTensorNames.push_back("prob");
+    params.dlaCore = args.useDLACore;
+    params.int8 = args.runInInt8;
+    params.fp16 = args.runInFp16;
+
+    return params;
+}
+
+//!
+//! \brief Prints the help information for running this sample.
+//!
+void printHelpInfo()
+{
+    std::cout << "Usage: ./sample_algorithm_selector [-h or --help] [-d or --datadir=<path to data directory>] "
+                 "[--useDLACore=<int>]\n";
+    std::cout << "--help          Display help information\n";
+    std::cout << "--datadir       Specify path to a data directory, overriding the default. This option can be used "
+                 "multiple times to add multiple directories. If no data directories are given, the default is to use "
+                 "(data/samples/mnist/, data/mnist/)"
+              << std::endl;
+    std::cout << "--useDLACore=N  Specify a DLA engine for layers that support DLA. Value can range from 0 to n-1, "
+                 "where n is the number of DLA engines on the platform."
+              << std::endl;
+    std::cout << "--int8          Run in Int8 mode.\n";
+    std::cout << "--fp16          Run in FP16 mode.\n";
+}
+
+int main(int argc, char** argv)
+{
+    samplesCommon::Args args;
+    bool argsOK = samplesCommon::parseArgs(args, argc, argv);
+    if (!argsOK)
+    {
+        sample::gLogError << "Invalid arguments" << std::endl;
+        printHelpInfo();
+        return EXIT_FAILURE;
+    }
+    if (args.help)
+    {
+        printHelpInfo();
+        return EXIT_SUCCESS;
+    }
+
+    auto sampleTest = sample::gLogger.defineTest(gSampleName, argc, argv);
+
+    sample::gLogger.reportTestStart(sampleTest);
+
+    samplesCommon::CaffeSampleParams params = initializeSampleParams(args);
+
+    // Write Algorithm Cache.
+    SampleAlgorithmSelector sampleAlgorithmSelector(params);
+
+    {
+        sample::gLogInfo << "Building and running a GPU inference engine for MNIST." << std::endl;
+        sample::gLogInfo << "Writing Algorithm Cache for MNIST." << std::endl;
+        AlgorithmCacheWriter algorithmCacheWriter(gCacheFileName);
+
+        if (!sampleAlgorithmSelector.build(&algorithmCacheWriter))
+        {
+            return sample::gLogger.reportFail(sampleTest);
+        }
+
+        if (!sampleAlgorithmSelector.infer())
+        {
+            return sample::gLogger.reportFail(sampleTest);
+        }
+    }
+
+    {
+        // Build network using Cache from previous run.
+        sample::gLogInfo << "Building a GPU inference engine for MNIST using Algorithm Cache." << std::endl;
+        AlgorithmCacheReader algorithmCacheReader(gCacheFileName);
+
+        if (!sampleAlgorithmSelector.build(&algorithmCacheReader))
+        {
+            return sample::gLogger.reportFail(sampleTest);
+        }
+
+        if (!sampleAlgorithmSelector.infer())
+        {
+            return sample::gLogger.reportFail(sampleTest);
+        }
+    }
+
+    {
+        // Build network using MinimumWorkspaceAlgorithmSelector.
+        sample::gLogInfo
+            << "Building a GPU inference engine for MNIST using Algorithms with minimum workspace requirements."
+            << std::endl;
+        MinimumWorkspaceAlgorithmSelector minimumWorkspaceAlgorithmSelector;
+        if (!sampleAlgorithmSelector.build(&minimumWorkspaceAlgorithmSelector))
+        {
+            return sample::gLogger.reportFail(sampleTest);
+        }
+
+        if (!sampleAlgorithmSelector.infer())
+        {
+            return sample::gLogger.reportFail(sampleTest);
+        }
+    }
+
+    if (!sampleAlgorithmSelector.teardown())
+    {
+        return sample::gLogger.reportFail(sampleTest);
+    }
+
+    return sample::gLogger.reportPass(sampleTest);
+}
diff --git a/samples/opensource/sampleCharRNN/CMakeLists.txt b/samples/opensource/sampleCharRNN/CMakeLists.txt
index cc3186bb..ff57a632 100644
--- a/samples/opensource/sampleCharRNN/CMakeLists.txt
+++ b/samples/opensource/sampleCharRNN/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -15,5 +15,9 @@
 #
 SET(SAMPLE_SOURCES
     sampleCharRNN.cpp
+    ../../common/sampleEngines.cpp
 )
+# Required due to inclusion of sampleEnines.h
+SET(SAMPLE_PARSERS "uff" "caffe" "onnx")
+
 include(../../CMakeSamplesTemplate.txt)
diff --git a/samples/opensource/sampleCharRNN/README.md b/samples/opensource/sampleCharRNN/README.md
index daaa5721..8309e7df 100644
--- a/samples/opensource/sampleCharRNN/README.md
+++ b/samples/opensource/sampleCharRNN/README.md
@@ -6,7 +6,7 @@
 	* [TensorRT API layers and ops](#tensorrt-api-layers-and-ops)
 - [Converting TensorFlow weights](#converting-tensorflow-weights)
 - [Running the sample](#running-the-sample)
-	* [Sample `--help` options](#sample---help-options)
+	* [Sample `--help` options](#sample-help-options)
 - [Additional resources](#additional-resources)
 - [License](#license)
 - [Changelog](#changelog)
@@ -85,17 +85,7 @@ If you want to train your own model and not use the pre-trained model included i
 
 ### Sample --help options
 
-To see the full list of available options and their descriptions, use the `-h` or `--help` command line option. The following example output is printed when running the sample:
-```
-Usage: ./sample_char_rnn [-h or --help] [-d or --datadir=<path_to_data_directory>]
-
---help          Display help information
-
---useILoop      Use ILoop LSTM definition
-
---datadir       Specify path to a data directory, overriding the default. This option can be used multiple times to add multiple directories. If no data directories are given, the default is to use data/samples/char-rnn/ and data/char-rnn/
-
-```
+To see the full list of available options and their descriptions, use the `-h` or `--help` command line option.
 
 
 # Additional resources
diff --git a/samples/opensource/sampleCharRNN/sampleCharRNN.cpp b/samples/opensource/sampleCharRNN/sampleCharRNN.cpp
index b7227f8c..9d75f2d5 100644
--- a/samples/opensource/sampleCharRNN/sampleCharRNN.cpp
+++ b/samples/opensource/sampleCharRNN/sampleCharRNN.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -24,6 +24,7 @@
 //!
 
 #include <algorithm>
+#include <array>
 #include <cassert>
 #include <cmath>
 #include <cstdio>
@@ -36,7 +37,6 @@
 #include <sys/stat.h>
 #include <unordered_set>
 #include <vector>
-#include <array>
 
 #include "NvInfer.h"
 #include "NvUtils.h"
@@ -45,6 +45,7 @@
 #include "common.h"
 #include "cuda_runtime_api.h"
 #include "logger.h"
+#include "sampleEngines.h"
 
 const std::string gSampleName = "TensorRT.sample_char_rnn";
 
@@ -56,7 +57,8 @@ static const std::array<int, 4> INDICES{0, 1, 2, 3};
 // The data set used: tensorflow-char-rnn/data/tiny_shakespeare.txt
 //
 // The command used to train:
-// python train.py --data_file=data/tiny_shakespeare.txt --num_epochs=100 --num_layer=2 --hidden_size=512 --embedding_size=512 --dropout=.5
+// python train.py --data_file=data/tiny_shakespeare.txt --num_epochs=100 --num_layer=2 --hidden_size=512
+// --embedding_size=512 --dropout=.5
 //
 // Epochs trained: 100
 // Test perplexity: 4.940
@@ -76,8 +78,8 @@ struct SampleCharRNNWeightNames
     const std::string FCB_NAME{"softmax_softmax_b"};
     const std::string EMBED_NAME{"embedding"};
 
-    std::unordered_set<std::string> names = {{RNNW_L0_NAME, RNNB_L0_NAME, RNNW_L1_NAME,
-                                              RNNB_L1_NAME, FCW_NAME, FCB_NAME, EMBED_NAME}};
+    std::unordered_set<std::string> names
+        = {{RNNW_L0_NAME, RNNB_L0_NAME, RNNW_L1_NAME, RNNB_L1_NAME, FCW_NAME, FCB_NAME, EMBED_NAME}};
 };
 
 struct SampleCharRNNBindingNames
@@ -94,14 +96,19 @@ struct SampleCharRNNBindingNames
 struct SampleCharRNNMaps
 {
     // A mapping from character to index used by the tensorflow model.
-    const std::map<char, int> charToID{{'\n', 0}, {'!', 1}, {' ', 2}, {'$', 3}, {'\'', 4}, {'&', 5}, {'-', 6}, {',', 7}, {'.', 8}, {'3', 9}, {';', 10}, {':', 11}, {'?', 12}, {'A', 13}, {'C', 14}, {'B', 15}, {'E', 16}, {'D', 17}, {'G', 18}, {'F', 19}, {'I', 20}, {'H', 21}, {'K', 22}, {'J', 23}, {'M', 24}, {'L', 25}, {'O', 26}, {'N', 27}, {'Q', 28}, {'P', 29}, {'S', 30}, {'R', 31}, {'U', 32}, {'T', 33}, {'W', 34}, {'V', 35}, {'Y', 36}, {'X', 37}, {'Z', 38}, {'a', 39}, {'c', 40}, {'b', 41}, {'e', 42}, {'d', 43}, {'g', 44}, {'f', 45}, {'i', 46}, {'h', 47}, {'k', 48}, {'j', 49}, {'m', 50}, {'l', 51}, {'o', 52}, {'n', 53}, {'q', 54}, {'p', 55}, {'s', 56}, {'r', 57}, {'u', 58}, {'t', 59}, {'w', 60}, {'v', 61}, {'y', 62}, {'x', 63}, {'z', 64}};
+    const std::map<char, int> charToID{{'\n', 0}, {'!', 1}, {' ', 2}, {'$', 3}, {'\'', 4}, {'&', 5}, {'-', 6}, {',', 7},
+        {'.', 8}, {'3', 9}, {';', 10}, {':', 11}, {'?', 12}, {'A', 13}, {'C', 14}, {'B', 15}, {'E', 16}, {'D', 17},
+        {'G', 18}, {'F', 19}, {'I', 20}, {'H', 21}, {'K', 22}, {'J', 23}, {'M', 24}, {'L', 25}, {'O', 26}, {'N', 27},
+        {'Q', 28}, {'P', 29}, {'S', 30}, {'R', 31}, {'U', 32}, {'T', 33}, {'W', 34}, {'V', 35}, {'Y', 36}, {'X', 37},
+        {'Z', 38}, {'a', 39}, {'c', 40}, {'b', 41}, {'e', 42}, {'d', 43}, {'g', 44}, {'f', 45}, {'i', 46}, {'h', 47},
+        {'k', 48}, {'j', 49}, {'m', 50}, {'l', 51}, {'o', 52}, {'n', 53}, {'q', 54}, {'p', 55}, {'s', 56}, {'r', 57},
+        {'u', 58}, {'t', 59}, {'w', 60}, {'v', 61}, {'y', 62}, {'x', 63}, {'z', 64}};
 
     // A mapping from index to character used by the tensorflow model.
-    const std::vector<char> idToChar{{'\n', '!', ' ', '$', '\'', '&', '-', ',',
-                                      '.', '3', ';', ':', '?', 'A', 'C', 'B', 'E', 'D', 'G', 'F', 'I', 'H', 'K',
-                                      'J', 'M', 'L', 'O', 'N', 'Q', 'P', 'S', 'R', 'U', 'T', 'W', 'V', 'Y', 'X',
-                                      'Z', 'a', 'c', 'b', 'e', 'd', 'g', 'f', 'i', 'h', 'k', 'j', 'm', 'l', 'o',
-                                      'n', 'q', 'p', 's', 'r', 'u', 't', 'w', 'v', 'y', 'x', 'z'}};
+    const std::vector<char> idToChar{{'\n', '!', ' ', '$', '\'', '&', '-', ',', '.', '3', ';', ':', '?', 'A', 'C', 'B',
+        'E', 'D', 'G', 'F', 'I', 'H', 'K', 'J', 'M', 'L', 'O', 'N', 'Q', 'P', 'S', 'R', 'U', 'T', 'W', 'V', 'Y', 'X',
+        'Z', 'a', 'c', 'b', 'e', 'd', 'g', 'f', 'i', 'h', 'k', 'j', 'm', 'l', 'o', 'n', 'q', 'p', 's', 'r', 'u', 't',
+        'w', 'v', 'y', 'x', 'z'}};
 };
 
 struct SampleCharRNNParams : samplesCommon::SampleParams
@@ -114,12 +121,15 @@ struct SampleCharRNNParams : samplesCommon::SampleParams
     int outputSize;
     std::string weightFileName;
 
+    std::string saveEngine;
+    std::string loadEngine;
+
     SampleCharRNNMaps charMaps;
     SampleCharRNNWeightNames weightNames;
     SampleCharRNNBindingNames bindingNames;
 
-    vector<std::string> inputSentences;
-    vector<std::string> outputSentences;
+    std::vector<std::string> inputSentences;
+    std::vector<std::string> outputSentences;
     bool useILoop;
 };
 
@@ -197,8 +207,8 @@ class SampleCharRNNBase
     //!
     //! \brief Perform one time step of inference with the TensorRT execution context
     //!
-    bool stepOnce(samplesCommon::BufferManager& buffers,
-                  SampleUniquePtr<nvinfer1::IExecutionContext>& context, cudaStream_t& stream);
+    bool stepOnce(samplesCommon::BufferManager& buffers, SampleUniquePtr<nvinfer1::IExecutionContext>& context,
+        cudaStream_t& stream);
 
     //!
     //! \brief Copies Ct/Ht output from the RNN to the Ct-1/Ht-1 input buffers for next time step
@@ -271,37 +281,55 @@ class SampleCharRNNLoop : public SampleCharRNNBase
 //!
 bool SampleCharRNNBase::build()
 {
-    NetworkDefinitionCreationFlags flags{
-        mParams.useILoop ? 1U << static_cast<uint32_t>(NetworkDefinitionCreationFlag::kEXPLICIT_BATCH) : 0};
-    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(
-        nvinfer1::createInferBuilder(gLogger.getTRTLogger()));
-    if (!builder)
+    if (mParams.loadEngine.empty())
     {
-        return false;
+        auto builder
+            = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(sample::gLogger.getTRTLogger()));
+        if (!builder)
+        {
+            return false;
+        }
+        NetworkDefinitionCreationFlags flags{
+            1U << static_cast<uint32_t>(NetworkDefinitionCreationFlag::kEXPLICIT_BATCH)};
+        if (!mParams.useILoop)
+        {
+            flags = 0;
+            builder->setMaxBatchSize(mParams.batchSize);
+        }
+        auto network = SampleUniquePtr<nvinfer1::INetworkDefinition>(builder->createNetworkV2(flags));
+        if (!network)
+        {
+            return false;
+        }
+        auto config = SampleUniquePtr<nvinfer1::IBuilderConfig>(builder->createBuilderConfig());
+        if (!config)
+        {
+            return false;
+        }
+
+        mWeightMap = SampleCharRNNBase::loadWeights(mParams.weightFileName);
+
+        config->setMaxWorkspaceSize(32_MiB);
+        config->setFlag(BuilderFlag::kGPU_FALLBACK);
+
+        constructNetwork(builder, network, config);
     }
-    auto network = SampleUniquePtr<nvinfer1::INetworkDefinition>(builder->createNetworkV2(flags));
-    if (!network)
+    else
     {
-        return false;
+        sample::gLogInfo << "Loading engine from: " << mParams.loadEngine << std::endl;
+        mEngine = std::shared_ptr<nvinfer1::ICudaEngine>(
+            sample::loadEngine(mParams.loadEngine, -1, std::cerr), samplesCommon::InferDeleter());
     }
-    auto config = SampleUniquePtr<nvinfer1::IBuilderConfig>(builder->createBuilderConfig());
-    if (!config)
+
+    if (!mEngine)
     {
         return false;
     }
 
-    mWeightMap = SampleCharRNNBase::loadWeights(mParams.weightFileName);
-
-    builder->setMaxBatchSize(
-        flags & static_cast<uint32_t>(NetworkDefinitionCreationFlag::kEXPLICIT_BATCH) ? 0 : mParams.batchSize);
-    config->setMaxWorkspaceSize(32_MiB);
-    config->setFlag(BuilderFlag::kGPU_FALLBACK);
-
-    constructNetwork(builder, network, config);
-
-    if (!mEngine)
+    if (!mParams.saveEngine.empty())
     {
-        return false;
+        sample::gLogInfo << "Saving engine to: " << mParams.saveEngine << std::endl;
+        sample::saveEngine(*mEngine, mParams.saveEngine, std::cerr);
     }
 
     return true;
@@ -326,11 +354,11 @@ std::map<std::string, nvinfer1::Weights> SampleCharRNNBase::loadWeights(const st
     std::map<std::string, nvinfer1::Weights> weightMap;
 
     std::ifstream input(file, std::ios_base::binary);
-    assert(input.is_open() && "Unable to load weight file.");
+    ASSERT(input.is_open() && "Unable to load weight file.");
 
     int32_t count;
     input >> count;
-    assert(count > 0 && "Invalid weight map file.");
+    ASSERT(count > 0 && "Invalid weight map file.");
 
     while (count--)
     {
@@ -380,7 +408,7 @@ std::map<std::string, nvinfer1::Weights> SampleCharRNNBase::loadWeights(const st
     }
 
     input.close();
-    gLogInfo << "Done reading weights from file..." << std::endl;
+    sample::gLogInfo << "Done reading weights from file..." << std::endl;
     return weightMap;
 }
 
@@ -413,8 +441,8 @@ nvinfer1::Weights SampleCharRNNBase::convertRNNWeights(nvinfer1::Weights orig, i
     int dimsW[2]{dataSize, 4 * mParams.hiddenSize};
     int dimsR[2]{mParams.hiddenSize, 4 * mParams.hiddenSize};
     std::copy(data, data + input.count, ptr);
-    utils::transposeSubBuffers(ptr, DataType::kFLOAT, 1, dimsW[0], dimsW[1]);
-    utils::transposeSubBuffers(&ptr[dimsW[0] * dimsW[1]], DataType::kFLOAT, 1, dimsR[0], dimsR[1]);
+    ASSERT(utils::transposeSubBuffers(ptr, DataType::kFLOAT, 1, dimsW[0], dimsW[1]));
+    ASSERT(utils::transposeSubBuffers(&ptr[dimsW[0] * dimsW[1]], DataType::kFLOAT, 1, dimsR[0], dimsR[1]));
     return nvinfer1::Weights{input.type, ptr, input.count};
 }
 
@@ -438,7 +466,7 @@ nvinfer1::Weights SampleCharRNNBase::convertRNNBias(nvinfer1::Weights input)
     float* ptr = new float[input.count * 2];
     const float* iptr = static_cast<const float*>(input.values);
     int64_t count = 4 * mParams.hiddenSize;
-    assert(input.count == count);
+    ASSERT(input.count == count);
     std::copy(iptr, iptr + count, ptr);
     float* shiftedPtr = ptr + count;
     std::fill(shiftedPtr, shiftedPtr + count, 0.0);
@@ -532,19 +560,19 @@ nvinfer1::ILayer* SampleCharRNNLoop::addLSTMLayers(SampleUniquePtr<nvinfer1::INe
 
     nvinfer1::ITensor* data = network->addInput(mParams.bindingNames.INPUT_BLOB_NAME, nvinfer1::DataType::kFLOAT,
         nvinfer1::Dims2(mParams.seqSize, mParams.dataSize));
-    assert(data != nullptr);
+    ASSERT(data != nullptr);
 
     nvinfer1::ITensor* hiddenLayers = network->addInput(mParams.bindingNames.HIDDEN_IN_BLOB_NAME,
         nvinfer1::DataType::kFLOAT, nvinfer1::Dims2(mParams.layerCount, mParams.hiddenSize));
-    assert(hiddenLayers != nullptr);
+    ASSERT(hiddenLayers != nullptr);
 
     nvinfer1::ITensor* cellLayers = network->addInput(mParams.bindingNames.CELL_IN_BLOB_NAME,
         nvinfer1::DataType::kFLOAT, nvinfer1::Dims2(mParams.layerCount, mParams.hiddenSize));
-    assert(cellLayers != nullptr);
+    ASSERT(cellLayers != nullptr);
 
     nvinfer1::ITensor* sequenceSize
         = network->addInput(mParams.bindingNames.SEQ_LEN_IN_BLOB_NAME, nvinfer1::DataType::kINT32, nvinfer1::Dims{});
-    assert(sequenceSize != nullptr);
+    ASSERT(sequenceSize != nullptr);
 
     // convert tensorflow weight format to trt weight format
     std::array<nvinfer1::Weights, 2> rnnw{
@@ -562,7 +590,7 @@ nvinfer1::ILayer* SampleCharRNNLoop::addLSTMLayers(SampleUniquePtr<nvinfer1::INe
 
     nvinfer1::ITensor* maxSequenceSize
         = network->addConstant(nvinfer1::Dims{}, Weights{DataType::kINT32, &mParams.seqSize, 1})->getOutput(0);
-    assert(static_cast<size_t>(mParams.layerCount) <= INDICES.size());
+    ASSERT(static_cast<size_t>(mParams.layerCount) <= INDICES.size());
     LstmIO lstmNext{data, nullptr, nullptr};
     std::vector<nvinfer1::ITensor*> hiddenOutputs;
     std::vector<nvinfer1::ITensor*> cellOutputs;
@@ -575,7 +603,7 @@ nvinfer1::ILayer* SampleCharRNNLoop::addLSTMLayers(SampleUniquePtr<nvinfer1::INe
         int64_t shift = samplesCommon::volume(start);
         const int sizeOfElement = samplesCommon::getElementSize(weights.type);
         int64_t count = samplesCommon::volume(size);
-        assert(shift + count <= weights.count);
+        ASSERT(shift + count <= weights.count);
         return nvinfer1::Weights{weights.type, data + shift * sizeOfElement, count};
     };
     for (int i = 0; i < mParams.layerCount; ++i)
@@ -625,21 +653,26 @@ nvinfer1::ILayer* SampleCharRNNLoop::addLSTMLayers(SampleUniquePtr<nvinfer1::INe
 nvinfer1::ILayer* SampleCharRNNv2::addLSTMLayers(SampleUniquePtr<nvinfer1::INetworkDefinition>& network)
 {
     // Initialize data, hiddenIn, cellIn, and seqLenIn inputs into RNN Layer
-    nvinfer1::ITensor* data = network->addInput(mParams.bindingNames.INPUT_BLOB_NAME, nvinfer1::DataType::kFLOAT, nvinfer1::Dims2(mParams.seqSize, mParams.dataSize));
-    assert(data != nullptr);
+    nvinfer1::ITensor* data = network->addInput(mParams.bindingNames.INPUT_BLOB_NAME, nvinfer1::DataType::kFLOAT,
+        nvinfer1::Dims2(mParams.seqSize, mParams.dataSize));
+    ASSERT(data != nullptr);
 
-    nvinfer1::ITensor* hiddenIn = network->addInput(mParams.bindingNames.HIDDEN_IN_BLOB_NAME, nvinfer1::DataType::kFLOAT, nvinfer1::Dims2(mParams.layerCount, mParams.hiddenSize));
-    assert(hiddenIn != nullptr);
+    nvinfer1::ITensor* hiddenIn = network->addInput(mParams.bindingNames.HIDDEN_IN_BLOB_NAME,
+        nvinfer1::DataType::kFLOAT, nvinfer1::Dims2(mParams.layerCount, mParams.hiddenSize));
+    ASSERT(hiddenIn != nullptr);
 
-    nvinfer1::ITensor* cellIn = network->addInput(mParams.bindingNames.CELL_IN_BLOB_NAME, nvinfer1::DataType::kFLOAT, nvinfer1::Dims2(mParams.layerCount, mParams.hiddenSize));
-    assert(cellIn != nullptr);
+    nvinfer1::ITensor* cellIn = network->addInput(mParams.bindingNames.CELL_IN_BLOB_NAME, nvinfer1::DataType::kFLOAT,
+        nvinfer1::Dims2(mParams.layerCount, mParams.hiddenSize));
+    ASSERT(cellIn != nullptr);
 
-    nvinfer1::ITensor* seqLenIn = network->addInput(mParams.bindingNames.SEQ_LEN_IN_BLOB_NAME, nvinfer1::DataType::kINT32, nvinfer1::Dims{});
-    assert(seqLenIn != nullptr);
+    nvinfer1::ITensor* seqLenIn
+        = network->addInput(mParams.bindingNames.SEQ_LEN_IN_BLOB_NAME, nvinfer1::DataType::kINT32, nvinfer1::Dims{});
+    ASSERT(seqLenIn != nullptr);
 
     // create an RNN layer w/ 2 layers and 512 hidden states
-    nvinfer1::IRNNv2Layer* rnn = network->addRNNv2(*data, mParams.layerCount, mParams.hiddenSize, mParams.seqSize, nvinfer1::RNNOperation::kLSTM);
-    assert(rnn != nullptr);
+    nvinfer1::IRNNv2Layer* rnn = network->addRNNv2(
+        *data, mParams.layerCount, mParams.hiddenSize, mParams.seqSize, nvinfer1::RNNOperation::kLSTM);
+    ASSERT(rnn != nullptr);
 
     // Set RNNv2 optional inputs
     rnn->getOutput(0)->setName("RNN output");
@@ -663,10 +696,8 @@ nvinfer1::ILayer* SampleCharRNNv2::addLSTMLayers(SampleUniquePtr<nvinfer1::INetw
         = SampleCharRNNBase::convertRNNWeights(mWeightMap[mParams.weightNames.RNNW_L1_NAME], mParams.hiddenSize);
     nvinfer1::Weights rnnbL1 = SampleCharRNNBase::convertRNNBias(mWeightMap[mParams.weightNames.RNNB_L1_NAME]);
 
-    std::vector<nvinfer1::RNNGateType> gateOrder({nvinfer1::RNNGateType::kINPUT,
-                                                  nvinfer1::RNNGateType::kCELL,
-                                                  nvinfer1::RNNGateType::kFORGET,
-                                                  nvinfer1::RNNGateType::kOUTPUT});
+    std::vector<nvinfer1::RNNGateType> gateOrder({nvinfer1::RNNGateType::kINPUT, nvinfer1::RNNGateType::kCELL,
+        nvinfer1::RNNGateType::kFORGET, nvinfer1::RNNGateType::kOUTPUT});
     const nvinfer1::DataType dataType = static_cast<nvinfer1::DataType>(rnnwL0.type);
     const float* wtsL0 = static_cast<const float*>(rnnwL0.values);
     const float* biasesL0 = static_cast<const float*>(rnnbL0.values);
@@ -725,33 +756,36 @@ void SampleCharRNNBase::constructNetwork(SampleUniquePtr<nvinfer1::IBuilder>& bu
     auto rnn = addLSTMLayers(network);
 
     // Transpose FC weights since TensorFlow's weights are transposed when compared to TensorRT
-    utils::transposeSubBuffers((void*) mWeightMap[mParams.weightNames.FCW_NAME].values, nvinfer1::DataType::kFLOAT, 1, mParams.hiddenSize, mParams.vocabSize);
+    ASSERT(utils::transposeSubBuffers((void*) mWeightMap[mParams.weightNames.FCW_NAME].values,
+        nvinfer1::DataType::kFLOAT, 1, mParams.hiddenSize, mParams.vocabSize));
 
     // add Constant layers for fully connected weights
-    auto fcwts = network->addConstant(nvinfer1::Dims2(mParams.vocabSize, mParams.hiddenSize), mWeightMap[mParams.weightNames.FCW_NAME]);
+    auto fcwts = network->addConstant(
+        nvinfer1::Dims2(mParams.vocabSize, mParams.hiddenSize), mWeightMap[mParams.weightNames.FCW_NAME]);
 
     // Add matrix multiplication layer for multiplying rnn output with FC weights
     auto matrixMultLayer = network->addMatrixMultiply(*fcwts->getOutput(0), false, *rnn->getOutput(0), true);
-    assert(matrixMultLayer != nullptr);
+    ASSERT(matrixMultLayer != nullptr);
     matrixMultLayer->getOutput(0)->setName("Matrix Multiplicaton output");
 
     // Add elementwise layer for adding bias
     auto fcbias = network->addConstant(nvinfer1::Dims2(mParams.vocabSize, 1), mWeightMap[mParams.weightNames.FCB_NAME]);
-    auto addBiasLayer = network->addElementWise(*matrixMultLayer->getOutput(0), *fcbias->getOutput(0), nvinfer1::ElementWiseOperation::kSUM);
-    assert(addBiasLayer != nullptr);
+    auto addBiasLayer = network->addElementWise(
+        *matrixMultLayer->getOutput(0), *fcbias->getOutput(0), nvinfer1::ElementWiseOperation::kSUM);
+    ASSERT(addBiasLayer != nullptr);
     addBiasLayer->getOutput(0)->setName("Add Bias output");
 
     // Add TopK layer to determine which character has highest probability.
     int reduceAxis = 0x1; // reduce across vocab axis
     auto pred = network->addTopK(*addBiasLayer->getOutput(0), nvinfer1::TopKOperation::kMAX, 1, reduceAxis);
-    assert(pred != nullptr);
+    ASSERT(pred != nullptr);
     pred->getOutput(1)->setName(mParams.bindingNames.OUTPUT_BLOB_NAME);
 
     // Mark the outputs for the network
     network->markOutput(*pred->getOutput(1));
     pred->getOutput(1)->setType(nvinfer1::DataType::kINT32);
 
-    gLogInfo << "Done constructing network..." << std::endl;
+    sample::gLogInfo << "Done constructing network..." << std::endl;
 
     mEngine = std::shared_ptr<nvinfer1::ICudaEngine>(
         builder->buildEngineWithConfig(*network, *config), samplesCommon::InferDeleter());
@@ -766,10 +800,9 @@ void SampleCharRNNBase::constructNetwork(SampleUniquePtr<nvinfer1::IBuilder>& bu
 bool SampleCharRNNBase::infer()
 {
     // Create RAII buffer manager object
-    samplesCommon::BufferManager buffers(mEngine, mParams.batchSize);
+    samplesCommon::BufferManager buffers(mEngine, mParams.useILoop ? 0 : mParams.batchSize);
 
-    auto context = SampleUniquePtr<nvinfer1::IExecutionContext>(
-        mEngine->createExecutionContext());
+    auto context = SampleUniquePtr<nvinfer1::IExecutionContext>(mEngine->createExecutionContext());
 
     if (!context)
     {
@@ -783,15 +816,18 @@ bool SampleCharRNNBase::infer()
     std::string expected = mParams.outputSentences[sentenceIndex];
     std::string genstr;
 
-    gLogInfo << "RNN warmup sentence: " << inputSentence << std::endl;
-    gLogInfo << "Expected output: " << expected << std::endl;
+    sample::gLogInfo << "RNN warmup sentence: " << inputSentence << std::endl;
+    sample::gLogInfo << "Expected output: " << expected << std::endl;
 
     // create stream for trt execution
     cudaStream_t stream;
     CHECK(cudaStreamCreate(&stream));
 
     // Set sequence lengths to maximum
-    std::fill_n(reinterpret_cast<int32_t*>(buffers.getHostBuffer(mParams.bindingNames.SEQ_LEN_IN_BLOB_NAME)), mParams.batchSize, mParams.seqSize);
+    int* sequenceLengthIn
+        = reinterpret_cast<int32_t*>(buffers.getHostBuffer(mParams.bindingNames.SEQ_LEN_IN_BLOB_NAME));
+    auto sequenceLengthTensorSize = buffers.size(mParams.bindingNames.SEQ_LEN_IN_BLOB_NAME);
+    std::fill_n(sequenceLengthIn, sequenceLengthTensorSize / sizeof(mParams.seqSize), mParams.seqSize);
 
     // Initialize hiddenIn and cellIn tensors to zero before seeding
     void* hiddenIn = buffers.getHostBuffer(mParams.bindingNames.HIDDEN_IN_BLOB_NAME);
@@ -836,7 +872,7 @@ bool SampleCharRNNBase::infer()
         genstr.push_back(mParams.charMaps.idToChar.at(predIdx));
     }
 
-    gLogInfo << "Received: " << genstr.substr(inputSentence.size()) << std::endl;
+    sample::gLogInfo << "Received: " << genstr.substr(inputSentence.size()) << std::endl;
 
     // release the stream
     cudaStreamDestroy(stream);
@@ -853,7 +889,8 @@ void SampleCharRNNBase::copyEmbeddingToInput(samplesCommon::BufferManager& buffe
     float* inputBuffer = static_cast<float*>(buffers.getHostBuffer(mParams.bindingNames.INPUT_BLOB_NAME));
     auto index = mParams.charMaps.charToID.at(c);
 
-    std::memcpy(inputBuffer, static_cast<const float*>(embed.values) + index * mParams.dataSize, buffers.size(mParams.bindingNames.INPUT_BLOB_NAME));
+    std::memcpy(inputBuffer, static_cast<const float*>(embed.values) + index * mParams.dataSize,
+        buffers.size(mParams.bindingNames.INPUT_BLOB_NAME));
 }
 
 //!
@@ -937,6 +974,8 @@ SampleCharRNNParams initializeSampleParams(const samplesCommon::Args& args)
     params.outputSize = 1;
     params.weightFileName = locateFile("char-rnn.wts", params.dataDirs);
     params.useILoop = args.useILoop;
+    params.saveEngine = args.saveEngine;
+    params.loadEngine = args.loadEngine;
 
     // Input strings and their respective expected output strings
     const std::vector<std::string> inS{
@@ -978,7 +1017,14 @@ void printHelpInfo()
     std::cout << "Usage: ./sample_char_rnn [-h or --help] [-d or --datadir=<path to data directory>]\n";
     std::cout << "--help          Display help information\n";
     std::cout << "--useILoop      Use ILoop LSTM definition\n";
-    std::cout << "--datadir       Specify path to a data directory, overriding the default. This option can be used multiple times to add multiple directories. If no data directories are given, the default is to use data/samples/char-rnn/ and data/char-rnn/" << std::endl;
+    std::cout << "--datadir       Specify path to a data directory, overriding the default. This option can be used "
+                 "multiple times to add multiple directories. If no data directories are given, the default is to use "
+                 "data/samples/char-rnn/ and data/char-rnn/"
+              << std::endl;
+    std::cout << "--loadEngine    Specify path from which to load the engine. When this option is provided, engine "
+                 "building is skipped."
+              << std::endl;
+    std::cout << "--saveEngine    Specify path at which to save the engine." << std::endl;
 }
 
 //!
@@ -986,12 +1032,12 @@ void printHelpInfo()
 //!
 int main(int argc, char** argv)
 {
-    setReportableSeverity(Logger::Severity::kVERBOSE);
+    sample::setReportableSeverity(sample::Logger::Severity::kVERBOSE);
     samplesCommon::Args args;
     bool argsOK = samplesCommon::parseArgs(args, argc, argv);
     if (!argsOK)
     {
-        gLogError << "Invalid arguments" << std::endl;
+        sample::gLogError << "Invalid arguments" << std::endl;
         printHelpInfo();
         return EXIT_FAILURE;
     }
@@ -1001,9 +1047,9 @@ int main(int argc, char** argv)
         return EXIT_SUCCESS;
     }
 
-    auto sampleTest = gLogger.defineTest(gSampleName, argc, argv);
+    auto sampleTest = sample::gLogger.defineTest(gSampleName, argc, argv);
 
-    gLogger.reportTestStart(sampleTest);
+    sample::gLogger.reportTestStart(sampleTest);
 
     SampleCharRNNParams params = initializeSampleParams(args);
     std::unique_ptr<SampleCharRNNBase> sample;
@@ -1017,21 +1063,20 @@ int main(int argc, char** argv)
         sample.reset(new SampleCharRNNv2(params));
     }
 
-    gLogInfo << "Building and running a GPU inference engine for Char RNN model..."
-             << std::endl;
+    sample::gLogInfo << "Building and running a GPU inference engine for Char RNN model..." << std::endl;
 
     if (!sample->build())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
     if (!sample->infer())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
     if (!sample->teardown())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
 
-    return gLogger.reportPass(sampleTest);
+    return sample::gLogger.reportPass(sampleTest);
 }
diff --git a/samples/opensource/sampleDynamicReshape/CMakeLists.txt b/samples/opensource/sampleDynamicReshape/CMakeLists.txt
index 3651053b..05aacda1 100644
--- a/samples/opensource/sampleDynamicReshape/CMakeLists.txt
+++ b/samples/opensource/sampleDynamicReshape/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleDynamicReshape/README.md b/samples/opensource/sampleDynamicReshape/README.md
index bdeb77bf..f7145421 100644
--- a/samples/opensource/sampleDynamicReshape/README.md
+++ b/samples/opensource/sampleDynamicReshape/README.md
@@ -10,7 +10,7 @@
     * [Running inference](#running-inference)
 	* [TensorRT API layers and ops](#tensorrt-api-layers-and-ops)
 - [Running the sample](#running-the-sample)
-	* [Sample `--help` options](#sample---help-options)
+	* [Sample `--help` options](#sample-help-options)
 - [Additional resources](#additional-resources)
 - [License](#license)
 - [Changelog](#changelog)
@@ -27,7 +27,7 @@ This sample creates an engine for resizing an input with dynamic dimensions to a
 Specifically, this sample:
 -   Creates a network with dynamic input dimensions to act as a preprocessor for the model
 -   Parses an ONNX MNIST model to create a second network
--   Builds engines for both networks
+-   Builds engines for both networks and does calibration if running in int8
 -   Runs inference using both engines
 
 ### Creating the preprocessing network
@@ -37,7 +37,7 @@ First, create a network with full dims support:
 
 Next, add an input layer that accepts an input with a dynamic shape, followed by a resize layer that will reshape the input to the shape the model expects:
 ```
-auto input = preprocessorNetwork->addInput("input", nvinfer1::DataType::kFLOAT, Dims4{1, 1, -1, -1});
+auto input = preprocessorNetwork->addInput("input", nvinfer1::DataType::kFLOAT, Dims4{-1, 1, -1, -1});
 auto resizeLayer = preprocessorNetwork->addResize(*input);
 resizeLayer->setOutputDimensions(mPredictionInputDims);
 preprocessorNetwork->markOutput(*resizeLayer->getOutput(0));
@@ -51,12 +51,12 @@ First, create an empty full-dims network, and parser:
 ```
 const auto explicitBatch = 1U << static_cast<uint32_t>(NetworkDefinitionCreationFlag::kEXPLICIT_BATCH);
 auto network = makeUnique(builder->createNetworkV2(explicitBatch));
-auto parser = nvonnxparser::createParser(*network, gLogger.getTRTLogger());
+auto parser = nvonnxparser::createParser(*network, sample::gLogger.getTRTLogger());
 ```
 
 Next, parse the model file to populate the network:
 ```
-parser->parseFromFile(locateFile(mParams.onnxFileName, mParams.dataDirs).c_str(), static_cast<int>(gLogger.getReportableSeverity()));
+parser->parseFromFile(locateFile(mParams.onnxFileName, mParams.dataDirs).c_str(), static_cast<int>(sample::gLogger.getReportableSeverity()));
 ```
 
 ### Building engines
@@ -73,16 +73,48 @@ profile->setDimensions(input->getName(), OptProfileSelector::kMIN, Dims4{1, 1, 1
 profile->setDimensions(input->getName(), OptProfileSelector::kOPT, Dims4{1, 1, 28, 28});
 profile->setDimensions(input->getName(), OptProfileSelector::kMAX, Dims4{1, 1, 56, 56});
 preprocessorConfig->addOptimizationProfile(profile);
+```
+
+Create an optimization profile for calibration:
+```
+auto profileCalib = builder->createOptimizationProfile();
+const int calibBatchSize{256};
+profileCalib->setDimensions(input->getName(), OptProfileSelector::kMIN, Dims4{calibBatchSize, 1, 28, 28});
+profileCalib->setDimensions(input->getName(), OptProfileSelector::kOPT, Dims4{calibBatchSize, 1, 28, 28});
+profileCalib->setDimensions(input->getName(), OptProfileSelector::kMAX, Dims4{calibBatchSize, 1, 28, 28});
+preprocessorConfig->setCalibrationProfile(profileCalib);
+```
+
+Prepare and set int8 calibrator if running in int8 mode:
+```
+std::unique_ptr<IInt8Calibrator> calibrator;
+if (mParams.int8)
+{
+    preprocessorConfig->setFlag(BuilderFlag::kINT8);    
+    const int nCalibBatches{10}; 
+    MNISTBatchStream calibrationStream(calibBatchSize, nCalibBatches, "train-images-idx3-ubyte",
+        "train-labels-idx1-ubyte", mParams.dataDirs);
+    calibrator.reset(new Int8EntropyCalibrator2<MNISTBatchStream>(
+        calibrationStream, 0, "MNISTPreprocessor", "input"));
+    preprocessorConfig->setInt8Calibrator(calibrator.get());
+}
+```
+
+Run engine build with config: 
+```
 mPreprocessorEngine = makeUnique(builder->buildEngineWithConfig(*preprocessorNetwork, *preprocessorConfig));
 ```
 
-For the MNIST model, attach a Softmax layer to the end of the network and replace the existing network output with the Softmax:
+For the MNIST model, attach a Softmax layer to the end of the network, set softmax axis to 1 since network output has shape [1, 10] in full dims mode and replace the existing network output with the Softmax:
 ```
 auto softmax = network->addSoftMax(*network->getOutput(0));
+softmax->setAxes(1 << 1);
 network->unmarkOutput(*network->getOutput(0));
 network->markOutput(*softmax->getOutput(0));
 ```
 
+A calibrator and a calibration profile are set the same way as above for the preprocessor engine config. `calibBatchSize` is set to 1 for the prediction engine as ONNX model has an explicit batch.
+
 Finally, build as normal:
 `mPredictionEngine = makeUnique(builder->buildEngineWithConfig(*network, *config));`
 
@@ -123,77 +155,84 @@ The IResizeLayer implements the resize operation on an input tensor.
 ## Running the sample
 
 1.  Compile this sample by running `make` in the `<TensorRT root directory>/samples/sampleDynamicReshape` directory. The binary named `sample_dynamic_reshape` will be created in the `<TensorRT root directory>/bin` directory.
-	```
-	cd <TensorRT root directory>/samples/sampleDynamicReshape
-	make
-	```
+    ```
+    cd <TensorRT root directory>/samples/sampleDynamicReshape
+    make
+    ```
 
-	Where `<TensorRT root directory>` is where you installed TensorRT.
+    Where `<TensorRT root directory>` is where you installed TensorRT.
 
 2.  Run the sample.
-	```
-	./sample_dynamic_reshape [-h or --help] [-d or --datadir=<path to data directory>] [--useDLACore=<int>] [--int8 or --fp16]
-	```
+    ```
+    ./sample_dynamic_reshape [-h or --help] [-d or --datadir=<path to data directory>] [--useDLACore=<int>] [--int8 or --fp16]
+    ```
 
 3. Verify that the sample ran successfully. If the sample runs successfully you should see output similar to the following:
-	```
-	&&&& RUNNING TensorRT.sample_dynamic_reshape # ./sample_dynamic_reshape
-	----------------------------------------------------------------
-	Input filename: ../../../../../../data/samples/mnist/mnist.onnx
-	ONNX IR version: 0.0.3
-	Opset version: 1
-	Producer name: CNTK
-	Producer version: 2.4
-	Domain:
-	Model version: 1
-	Doc string:
-	----------------------------------------------------------------
-	[I] Input:
-	@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-	@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-	@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-	@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-	@@@@@@@@@@@*. .*@@@@@@@@@@@
-	@@@@@@@@@@*. +@@@@@@@@@@
-	@@@@@@@@@@. :#+ %@@@@@@@@@
-	@@@@@@@@@@.:@@@+ +@@@@@@@@@
-	@@@@@@@@@@.:@@@@: +@@@@@@@@
-	@@@@@@@@@@=%@@@@: +@@@@@@@@
-	@@@@@@@@@@@@@@@@# +@@@@@@@@
-	@@@@@@@@@@@@@@@@* +@@@@@@@@
-	@@@@@@@@@@@@@@@@: +@@@@@@@@
-	@@@@@@@@@@@@@@@@: +@@@@@@@@
-	@@@@@@@@@@@@@@@* .@@@@@@@@@
-	@@@@@@@@@@%**%@. *@@@@@@@@@
-	@@@@@@@@%+. .: .@@@@@@@@@@
-	@@@@@@@@= .. :@@@@@@@@@@
-	@@@@@@@@: *@@: :@@@@@@@@@@
-	@@@@@@@% %@* *@@@@@@@@@
-	@@@@@@@% ++ ++ .%@@@@@@@@
-	@@@@@@@@- +@@- +@@@@@@@@
-	@@@@@@@@= :*@@@# .%@@@@@@@
-	@@@@@@@@@+*@@@@@%. %@@@@@@
-	@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-	@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-	@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-	@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-
-	[I] Output:
-	Prob 0 0.0000 Class 0:
-	Prob 1 0.0000 Class 1:
-	Prob 2 1.0000 Class 2: **********
-	Prob 3 0.0000 Class 3:
-	Prob 4 0.0000 Class 4:
-	Prob 5 0.0000 Class 5:
-	Prob 6 0.0000 Class 6:
-	Prob 7 0.0000 Class 7:
-	Prob 8 0.0000 Class 8:
-	Prob 9 0.0000 Class 9:
-
-	&&&& PASSED TensorRT.sample_dynamic_reshape # ./sample_dynamic_reshape
-	```
-
-	This output shows that the sample ran successfully; `PASSED`.
+    ```
+  	&&&& RUNNING TensorRT.sample_dynamic_reshape # ./sample_dynamic_reshape
+    ----------------------------------------------------------------
+    Input filename:   ../../../../../data/samples/mnist/mnist.onnx
+    ONNX IR version:  0.0.3
+    Opset version:    8
+    Producer name:    CNTK
+    Producer version: 2.5.1
+    Domain:           ai.cntk
+    Model version:    1
+    Doc string:  
+    ----------------------------------------------------------------
+    [W] [TRT] onnx2trt_utils.cpp:214: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
+    [W] [TRT] onnx2trt_utils.cpp:214: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
+    [I] [TRT] Detected 1 inputs and 1 output network tensors.
+    [I] [TRT] Detected 1 inputs and 1 output network tensors.
+    [I] Profile dimensions in preprocessor engine:
+    [I]     Minimum = (1, 1, 1, 1)
+    [I]     Optimum = (1, 1, 28, 28)
+    [I]     Maximum = (1, 1, 56, 56)
+    [I] Input:
+    @@@@@@@@@@@@@@@@@@@@@@@@@@@@
+    @@@@@@@@@@@@@@@@@@@@@@@@@@@@
+    @@@@@@@@@@@@@@@@@@@@@@@@@@@@
+    @@@@@@@@@@@@@@@@@@@@@@@@@@@@
+    @@@@@@@@@@@*.  .*@@@@@@@@@@@
+    @@@@@@@@@@*.     +@@@@@@@@@@
+    @@@@@@@@@@. :#+   %@@@@@@@@@
+    @@@@@@@@@@.:@@@+  +@@@@@@@@@
+    @@@@@@@@@@.:@@@@: +@@@@@@@@@
+    @@@@@@@@@@=%@@@@: +@@@@@@@@@
+    @@@@@@@@@@@@@@@@# +@@@@@@@@@
+    @@@@@@@@@@@@@@@@* +@@@@@@@@@
+    @@@@@@@@@@@@@@@@: +@@@@@@@@@
+    @@@@@@@@@@@@@@@@: +@@@@@@@@@
+    @@@@@@@@@@@@@@@* .@@@@@@@@@@
+    @@@@@@@@@@%**%@. *@@@@@@@@@@
+    @@@@@@@@%+.  .: .@@@@@@@@@@@
+    @@@@@@@@=  ..   :@@@@@@@@@@@
+    @@@@@@@@: *@@:  :@@@@@@@@@@@
+    @@@@@@@%  %@*    *@@@@@@@@@@
+    @@@@@@@%  ++  ++ .%@@@@@@@@@
+    @@@@@@@@-    +@@- +@@@@@@@@@
+    @@@@@@@@=  :*@@@# .%@@@@@@@@
+    @@@@@@@@@+*@@@@@%.  %@@@@@@@
+    @@@@@@@@@@@@@@@@@@@@@@@@@@@@
+    @@@@@@@@@@@@@@@@@@@@@@@@@@@@
+    @@@@@@@@@@@@@@@@@@@@@@@@@@@@
+    @@@@@@@@@@@@@@@@@@@@@@@@@@@@
+
+    [I] Output:
+    [I]  Prob 0  0.0000 Class 0: 
+    [I]  Prob 1  0.0000 Class 1: 
+    [I]  Prob 2  1.0000 Class 2: **********
+    [I]  Prob 3  0.0000 Class 3: 
+    [I]  Prob 4  0.0000 Class 4: 
+    [I]  Prob 5  0.0000 Class 5: 
+    [I]  Prob 6  0.0000 Class 6: 
+    [I]  Prob 7  0.0000 Class 7: 
+    [I]  Prob 8  0.0000 Class 8: 
+    [I]  Prob 9  0.0000 Class 9: 
+    &&&& PASSED TensorRT.sample_dynamic_reshape # ./sample_dynamic_reshape
+    ```
+
+    This output shows that the sample ran successfully; `PASSED`.
 
 
 ### Sample `--help` options
@@ -225,8 +264,8 @@ For terms and conditions for use, reproduction, and distribution, see the [Tenso
 
 # Changelog
 
-June 2019
-This is the first release of the `README.md` file and sample.
+February 2020
+This is the second release of the `README.md` file and sample.
 
 
 # Known issues
diff --git a/samples/opensource/sampleDynamicReshape/sampleDynamicReshape.cpp b/samples/opensource/sampleDynamicReshape/sampleDynamicReshape.cpp
index 6cd1a689..ae5d6da0 100644
--- a/samples/opensource/sampleDynamicReshape/sampleDynamicReshape.cpp
+++ b/samples/opensource/sampleDynamicReshape/sampleDynamicReshape.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -23,6 +23,8 @@
 //! Command: ./sample_dynamic_reshape [-h or --help [-d=/path/to/data/dir or --datadir=/path/to/data/dir]
 //!
 
+#include "BatchStream.h"
+#include "EntropyCalibrator.h"
 #include "argsParser.h"
 #include "buffers.h"
 #include "common.h"
@@ -54,12 +56,12 @@ class SampleDynamicReshape
     //!
     //! \brief Builds both engines.
     //!
-    void build();
+    bool build();
 
     //!
     //! \brief Prepares the model for inference by creating execution contexts and allocating buffers.
     //!
-    void prepare();
+    bool prepare();
 
     //!
     //! \brief Runs inference using TensorRT on a random image.
@@ -67,15 +69,15 @@ class SampleDynamicReshape
     bool infer();
 
 private:
-    void buildPreprocessorEngine(const SampleUniquePtr<nvinfer1::IBuilder>& builder);
-    void buildPredictionEngine(const SampleUniquePtr<nvinfer1::IBuilder>& builder);
+    bool buildPreprocessorEngine(const SampleUniquePtr<nvinfer1::IBuilder>& builder);
+    bool buildPredictionEngine(const SampleUniquePtr<nvinfer1::IBuilder>& builder);
 
     Dims loadPGMFile(const std::string& fileName);
     bool validateOutput(int digit);
 
     samplesCommon::OnnxSampleParams mParams; //!< The parameters for the sample.
 
-    nvinfer1::Dims mPredictionInputDims;   //!< The dimensions of the input of the MNIST model.
+    nvinfer1::Dims mPredictionInputDims;  //!< The dimensions of the input of the MNIST model.
     nvinfer1::Dims mPredictionOutputDims; //!< The dimensions of the output of the MNIST model.
 
     // Engines used for inference. The first is used for resizing inputs, the second for prediction.
@@ -91,10 +93,6 @@ class SampleDynamicReshape
     template <typename T>
     SampleUniquePtr<T> makeUnique(T* t)
     {
-        if (!t)
-        {
-            throw std::runtime_error{"Failed to create TensorRT object"};
-        }
         return SampleUniquePtr<T>{t};
     }
 };
@@ -106,49 +104,96 @@ class SampleDynamicReshape
 //!          then creates a TensorRT network by parsing the ONNX model and builds
 //!          an engine that will be used to run inference (mPredictionEngine).
 //!
-void SampleDynamicReshape::build()
+//! \return Ruturns false if error in build preprocessor or predict engine.
+//!
+bool SampleDynamicReshape::build()
 {
-    auto builder = makeUnique(nvinfer1::createInferBuilder(gLogger.getTRTLogger()));
-
+    auto builder = makeUnique(nvinfer1::createInferBuilder(sample::gLogger.getTRTLogger()));
+    if (!builder)
+    {
+        sample::gLogError << "Create inference builder failed." << std::endl;
+        return false;
+    }
     // This function will also set mPredictionInputDims and mPredictionOutputDims,
     // so it needs to be called before building the preprocessor.
-    buildPredictionEngine(builder);
-    buildPreprocessorEngine(builder);
+    return buildPredictionEngine(builder) && buildPreprocessorEngine(builder);
 }
 
 //!
 //! \brief Builds an engine for preprocessing (mPreprocessorEngine).
 //!
-void SampleDynamicReshape::buildPreprocessorEngine(const SampleUniquePtr<nvinfer1::IBuilder>& builder)
+//! \return Ruturns false if error in build preprocessor engine.
+//!
+bool SampleDynamicReshape::buildPreprocessorEngine(const SampleUniquePtr<nvinfer1::IBuilder>& builder)
 {
     // Create the preprocessor engine using a network that supports full dimensions (createNetworkV2).
     auto preprocessorNetwork = makeUnique(
         builder->createNetworkV2(1U << static_cast<uint32_t>(NetworkDefinitionCreationFlag::kEXPLICIT_BATCH)));
+    if (!preprocessorNetwork)
+    {
+        sample::gLogError << "Create network failed." << std::endl;
+        return false;
+    }
 
     // Reshape a dynamically shaped input to the size expected by the model, (1, 1, 28, 28).
-    auto input = preprocessorNetwork->addInput("input", nvinfer1::DataType::kFLOAT, Dims4{1, 1, -1, -1});
+    auto input = preprocessorNetwork->addInput("input", nvinfer1::DataType::kFLOAT, Dims4{-1, 1, -1, -1});
     auto resizeLayer = preprocessorNetwork->addResize(*input);
     resizeLayer->setOutputDimensions(mPredictionInputDims);
     preprocessorNetwork->markOutput(*resizeLayer->getOutput(0));
 
     // Finally, configure and build the preprocessor engine.
     auto preprocessorConfig = makeUnique(builder->createBuilderConfig());
+    if (!preprocessorConfig)
+    {
+        sample::gLogError << "Create builder config failed." << std::endl;
+        return false;
+    }
 
     // Create an optimization profile so that we can specify a range of input dimensions.
     auto profile = builder->createOptimizationProfile();
-
     // This profile will be valid for all images whose size falls in the range of [(1, 1, 1, 1), (1, 1, 56, 56)]
     // but TensorRT will optimize for (1, 1, 28, 28)
+    // We do not need to check the return of setDimension and addOptimizationProfile here as all dims are explicitly set
     profile->setDimensions(input->getName(), OptProfileSelector::kMIN, Dims4{1, 1, 1, 1});
     profile->setDimensions(input->getName(), OptProfileSelector::kOPT, Dims4{1, 1, 28, 28});
     profile->setDimensions(input->getName(), OptProfileSelector::kMAX, Dims4{1, 1, 56, 56});
     preprocessorConfig->addOptimizationProfile(profile);
+
+    // Create a calibration profile.
+    auto profileCalib = builder->createOptimizationProfile();
+    const int calibBatchSize{256};
+    // We do not need to check the return of setDimension and setCalibrationProfile here as all dims are explicitly set
+    profileCalib->setDimensions(input->getName(), OptProfileSelector::kMIN, Dims4{calibBatchSize, 1, 28, 28});
+    profileCalib->setDimensions(input->getName(), OptProfileSelector::kOPT, Dims4{calibBatchSize, 1, 28, 28});
+    profileCalib->setDimensions(input->getName(), OptProfileSelector::kMAX, Dims4{calibBatchSize, 1, 28, 28});
+    preprocessorConfig->setCalibrationProfile(profileCalib);
+
+    std::unique_ptr<IInt8Calibrator> calibrator;
+    if (mParams.int8)
+    {
+        preprocessorConfig->setFlag(BuilderFlag::kINT8);
+        const int nCalibBatches{10};
+        MNISTBatchStream calibrationStream(
+            calibBatchSize, nCalibBatches, "train-images-idx3-ubyte", "train-labels-idx1-ubyte", mParams.dataDirs);
+        calibrator.reset(
+            new Int8EntropyCalibrator2<MNISTBatchStream>(calibrationStream, 0, "MNISTPreprocessor", "input"));
+        preprocessorConfig->setInt8Calibrator(calibrator.get());
+    }
+
     mPreprocessorEngine = makeUnique(builder->buildEngineWithConfig(*preprocessorNetwork, *preprocessorConfig));
-    gLogInfo << "Profile dimensions in preprocessor engine:\n";
-    gLogInfo << "    Minimum = " << mPreprocessorEngine->getProfileDimensions(0, 0, OptProfileSelector::kMIN) << '\n';
-    gLogInfo << "    Optimum = " << mPreprocessorEngine->getProfileDimensions(0, 0, OptProfileSelector::kOPT) << '\n';
-    gLogInfo << "    Maximum = " << mPreprocessorEngine->getProfileDimensions(0, 0, OptProfileSelector::kMAX)
-             << std::endl;
+    if (!mPreprocessorEngine)
+    {
+        sample::gLogError << "Preprocessor engine build failed." << std::endl;
+        return false;
+    }
+    sample::gLogInfo << "Profile dimensions in preprocessor engine:" << std::endl;
+    sample::gLogInfo << "    Minimum = " << mPreprocessorEngine->getProfileDimensions(0, 0, OptProfileSelector::kMIN)
+                     << std::endl;
+    sample::gLogInfo << "    Optimum = " << mPreprocessorEngine->getProfileDimensions(0, 0, OptProfileSelector::kOPT)
+                     << std::endl;
+    sample::gLogInfo << "    Maximum = " << mPreprocessorEngine->getProfileDimensions(0, 0, OptProfileSelector::kMAX)
+                     << std::endl;
+    return true;
 }
 
 //!
@@ -158,17 +203,26 @@ void SampleDynamicReshape::buildPreprocessorEngine(const SampleUniquePtr<nvinfer
 //! mPredictionOutputDims according to the dimensions specified by the model. The preprocessor reshapes inputs to
 //! mPredictionInputDims.
 //!
-void SampleDynamicReshape::buildPredictionEngine(const SampleUniquePtr<nvinfer1::IBuilder>& builder)
+//! \return Ruturns false if error in build prediction engine.
+//!
+bool SampleDynamicReshape::buildPredictionEngine(const SampleUniquePtr<nvinfer1::IBuilder>& builder)
 {
     // Create a network using the parser.
     const auto explicitBatch = 1U << static_cast<uint32_t>(NetworkDefinitionCreationFlag::kEXPLICIT_BATCH);
     auto network = makeUnique(builder->createNetworkV2(explicitBatch));
-    auto parser = nvonnxparser::createParser(*network, gLogger.getTRTLogger());
-    bool parsingSuccess = parser->parseFromFile(
-        locateFile(mParams.onnxFileName, mParams.dataDirs).c_str(), static_cast<int>(gLogger.getReportableSeverity()));
+    if (!network)
+    {
+        sample::gLogError << "Create network failed." << std::endl;
+        return false;
+    }
+
+    auto parser = samplesCommon::infer_object(nvonnxparser::createParser(*network, sample::gLogger.getTRTLogger()));
+    bool parsingSuccess = parser->parseFromFile(locateFile(mParams.onnxFileName, mParams.dataDirs).c_str(),
+        static_cast<int>(sample::gLogger.getReportableSeverity()));
     if (!parsingSuccess)
     {
-        throw std::runtime_error{"Failed to parse model"};
+        sample::gLogError << "Failed to parse model." << std::endl;
+        return false;
     }
 
     // Attach a softmax layer to the end of the network.
@@ -184,18 +238,45 @@ void SampleDynamicReshape::buildPredictionEngine(const SampleUniquePtr<nvinfer1:
 
     // Create a builder config
     auto config = makeUnique(builder->createBuilderConfig());
+    if (!config)
+    {
+        sample::gLogError << "Create builder config failed." << std::endl;
+        return false;
+    }
     config->setMaxWorkspaceSize(16_MiB);
     if (mParams.fp16)
     {
         config->setFlag(BuilderFlag::kFP16);
     }
+
+    auto profileCalib = builder->createOptimizationProfile();
+    const auto inputName = mParams.inputTensorNames[0].c_str();
+    const int calibBatchSize{1};
+    // We do not need to check the return of setDimension and setCalibrationProfile here as all dims are explicitly set
+    profileCalib->setDimensions(inputName, OptProfileSelector::kMIN, Dims4{calibBatchSize, 1, 28, 28});
+    profileCalib->setDimensions(inputName, OptProfileSelector::kOPT, Dims4{calibBatchSize, 1, 28, 28});
+    profileCalib->setDimensions(inputName, OptProfileSelector::kMAX, Dims4{calibBatchSize, 1, 28, 28});
+    config->setCalibrationProfile(profileCalib);
+
+    std::unique_ptr<IInt8Calibrator> calibrator;
     if (mParams.int8)
     {
         config->setFlag(BuilderFlag::kINT8);
-        samplesCommon::setAllTensorScales(network.get(), 127.0f, 127.0f);
+        int nCalibBatches{10};
+        MNISTBatchStream calibrationStream(
+            calibBatchSize, nCalibBatches, "train-images-idx3-ubyte", "train-labels-idx1-ubyte", mParams.dataDirs);
+        calibrator.reset(
+            new Int8EntropyCalibrator2<MNISTBatchStream>(calibrationStream, 0, "MNISTPrediction", inputName));
+        config->setInt8Calibrator(calibrator.get());
     }
     // Build the prediciton engine.
     mPredictionEngine = makeUnique(builder->buildEngineWithConfig(*network, *config));
+    if (!mPredictionEngine)
+    {
+        sample::gLogError << "Prediction engine build failed." << std::endl;
+        return false;
+    }
+    return true;
 }
 
 //!
@@ -205,15 +286,30 @@ void SampleDynamicReshape::buildPredictionEngine(const SampleUniquePtr<nvinfer1:
 //! outputs, as well as creating TensorRT execution contexts for both engines. This only needs to be called a single
 //! time.
 //!
-void SampleDynamicReshape::prepare()
+//! \return Ruturns false if error in build preprocessor or predict context.
+//!
+bool SampleDynamicReshape::prepare()
 {
     mPreprocessorContext = makeUnique(mPreprocessorEngine->createExecutionContext());
+    if (!mPreprocessorContext)
+    {
+        sample::gLogError << "Preprocessor context build failed." << std::endl;
+        return false;
+    }
+
     mPredictionContext = makeUnique(mPredictionEngine->createExecutionContext());
+    if (!mPredictionContext)
+    {
+        sample::gLogError << "Prediction context build failed." << std::endl;
+        return false;
+    }
+
     // Since input dimensions are not known ahead of time, we only allocate the output buffer and preprocessor output
     // buffer.
     mPredictionInput.resize(mPredictionInputDims);
     mOutput.hostBuffer.resize(mPredictionOutputDims);
     mOutput.deviceBuffer.resize(mPredictionOutputDims);
+    return true;
 }
 
 //!
@@ -236,7 +332,8 @@ bool SampleDynamicReshape::infer()
         mInput.deviceBuffer.data(), mInput.hostBuffer.data(), mInput.hostBuffer.nbBytes(), cudaMemcpyHostToDevice));
 
     // Set the input size for the preprocessor
-    mPreprocessorContext->setBindingDimensions(0, inputDims);
+    CHECK_RETURN_W_MSG(mPreprocessorContext->setBindingDimensions(0, inputDims), false, "Invalid binding dimensions.");
+
     // We can only run inference once all dynamic input shapes have been specified.
     if (!mPreprocessorContext->allInputDimensionsSpecified())
     {
@@ -287,12 +384,12 @@ Dims SampleDynamicReshape::loadPGMFile(const std::string& fileName)
     infile.read(reinterpret_cast<char*>(fileData.data()), vol);
 
     // Print an ascii representation
-    gLogInfo << "Input:\n";
+    sample::gLogInfo << "Input:\n";
     for (size_t i = 0; i < vol; i++)
     {
-        gLogInfo << (" .:-=+*#%@"[fileData[i] / 26]) << (((i + 1) % w) ? "" : "\n");
+        sample::gLogInfo << (" .:-=+*#%@"[fileData[i] / 26]) << (((i + 1) % w) ? "" : "\n");
     }
-    gLogInfo << std::endl;
+    sample::gLogInfo << std::endl;
 
     // Normalize and copy to the host buffer.
     mInput.hostBuffer.resize(inputDims);
@@ -313,8 +410,10 @@ bool SampleDynamicReshape::validateOutput(int digit)
     int curIndex{0};
     for (const auto& elem : prob)
     {
-        gLogInfo << " Prob " << curIndex << "  " << std::fixed << std::setw(5) << std::setprecision(4) << elem << " "
-                 << "Class " << curIndex << ": " << std::string(int(std::floor(elem * 10 + 0.5f)), '*') << std::endl;
+        sample::gLogInfo << " Prob " << curIndex << "  " << std::fixed << std::setw(5) << std::setprecision(4) << elem
+                         << " "
+                         << "Class " << curIndex << ": " << std::string(int(std::floor(elem * 10 + 0.5f)), '*')
+                         << std::endl;
         ++curIndex;
     }
 
@@ -340,7 +439,6 @@ samplesCommon::OnnxSampleParams initializeSampleParams(const samplesCommon::Args
     params.onnxFileName = "mnist.onnx";
     params.inputTensorNames.push_back("Input3");
     params.outputTensorNames.push_back("Plus214_Output_0");
-    params.batchSize = 1;
     params.int8 = args.runInInt8;
     params.fp16 = args.runInFp16;
     return params;
@@ -353,7 +451,7 @@ void printHelpInfo()
 {
     std::cout << "Usage: ./sample_dynamic_reshape [-h or --help] [-d or --datadir=<path to data directory>]"
               << std::endl;
-    std::cout << "--help          Display help information" << std::endl;
+    std::cout << "--help, -h      Display help information" << std::endl;
     std::cout << "--datadir       Specify path to a data directory, overriding the default. This option can be used "
                  "multiple times to add multiple directories. If no data directories are given, the default is to use "
                  "(data/samples/mnist/, data/mnist/)"
@@ -368,7 +466,7 @@ int main(int argc, char** argv)
     bool argsOK = samplesCommon::parseArgs(args, argc, argv);
     if (!argsOK)
     {
-        gLogError << "Invalid arguments" << std::endl;
+        sample::gLogError << "Invalid arguments" << std::endl;
         printHelpInfo();
         return EXIT_FAILURE;
     }
@@ -378,19 +476,24 @@ int main(int argc, char** argv)
         return EXIT_SUCCESS;
     }
 
-    auto sampleTest = gLogger.defineTest(gSampleName, argc, argv);
+    auto sampleTest = sample::gLogger.defineTest(gSampleName, argc, argv);
 
-    gLogger.reportTestStart(sampleTest);
+    sample::gLogger.reportTestStart(sampleTest);
 
     SampleDynamicReshape sample{initializeSampleParams(args)};
 
-    sample.build();
-    sample.prepare();
-
+    if (!sample.build())
+    {
+        return sample::gLogger.reportFail(sampleTest);
+    }
+    if (!sample.prepare())
+    {
+        return sample::gLogger.reportFail(sampleTest);
+    }
     if (!sample.infer())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
 
-    return gLogger.reportPass(sampleTest);
+    return sample::gLogger.reportPass(sampleTest);
 }
diff --git a/samples/opensource/sampleFasterRCNN/CMakeLists.txt b/samples/opensource/sampleFasterRCNN/CMakeLists.txt
index fd4f5a5a..6cf22945 100644
--- a/samples/opensource/sampleFasterRCNN/CMakeLists.txt
+++ b/samples/opensource/sampleFasterRCNN/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleFasterRCNN/README.md b/samples/opensource/sampleFasterRCNN/README.md
index 6a4cf3db..aac1df49 100644
--- a/samples/opensource/sampleFasterRCNN/README.md
+++ b/samples/opensource/sampleFasterRCNN/README.md
@@ -10,7 +10,7 @@
     * [Verifying the output](#verifying-the-output)
     * [TensorRT API layers and ops](#tensorrt-api-layers-and-ops)
 - [Running the sample](#running-the-sample)
-    * [Sample `--help` options](#sample---help-options)
+    * [Sample `--help` options](#sample-help-options)
 - [Additional resources](#additional-resources)
 - [License](#license)
 - [Changelog](#changelog)
@@ -169,16 +169,10 @@ The SoftMax layer applies the SoftMax function on the input tensor along an inpu
 	```
     This output shows that the sample ran successfully; `PASSED`.
 
-### Sample --help options
 
-To see the full list of available options and their descriptions, use the `-h` or `--help` command line option. The following example output is printed when running the sample:
-```
-./sample_fasterRCNN --help
-Usage: ./build/x86_64-linux/sample_fasterRCNN
-Optional Parameters:
-  -h, --help        Display help information.
-  --useDLACore=N    Specify the DLA engine to run on.
-```
+### Sample `--help` options
+
+To see the full list of available options and their descriptions, use the `-h` or `--help` command line option.
 
 
 # Additional resources
diff --git a/samples/opensource/sampleFasterRCNN/sampleFasterRCNN.cpp b/samples/opensource/sampleFasterRCNN/sampleFasterRCNN.cpp
index 83bd5320..e0b82801 100644
--- a/samples/opensource/sampleFasterRCNN/sampleFasterRCNN.cpp
+++ b/samples/opensource/sampleFasterRCNN/sampleFasterRCNN.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -131,7 +131,7 @@ class SampleFasterRCNN
 //!
 bool SampleFasterRCNN::build()
 {
-    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(gLogger.getTRTLogger()));
+    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(sample::gLogger.getTRTLogger()));
     if (!builder)
     {
         return false;
@@ -364,9 +364,9 @@ bool SampleFasterRCNN::verifyOutput(const samplesCommon::BufferManager& buffers)
                 const int idx = indices[k];
                 const std::string storeName
                     = classes[c] + "-" + std::to_string(scores[idx * outputClsSize + c]) + ".ppm";
-                gLogInfo << "Detected " << classes[c] << " in " << mPPMs[i].fileName << " with confidence "
-                         << scores[idx * outputClsSize + c] * 100.0f << "% "
-                         << " (Result stored in " << storeName << ")." << std::endl;
+                sample::gLogInfo << "Detected " << classes[c] << " in " << mPPMs[i].fileName << " with confidence "
+                                 << scores[idx * outputClsSize + c] * 100.0f << "% "
+                                 << " (Result stored in " << storeName << ")." << std::endl;
 
                 const samplesCommon::BBox b{bbox[idx * outputBBoxSize + c * 4], bbox[idx * outputBBoxSize + c * 4 + 1],
                     bbox[idx * outputBBoxSize + c * 4 + 2], bbox[idx * outputBBoxSize + c * 4 + 3]};
@@ -521,7 +521,7 @@ int main(int argc, char** argv)
     bool argsOK = samplesCommon::parseArgs(args, argc, argv);
     if (!argsOK)
     {
-        gLogError << "Invalid arguments" << std::endl;
+        sample::gLogError << "Invalid arguments" << std::endl;
         printHelpInfo();
         return EXIT_FAILURE;
     }
@@ -531,28 +531,28 @@ int main(int argc, char** argv)
         return EXIT_SUCCESS;
     }
 
-    initLibNvInferPlugins(&gLogger, "");
+    initLibNvInferPlugins(&sample::gLogger, "");
 
-    auto sampleTest = gLogger.defineTest(gSampleName, argc, argv);
+    auto sampleTest = sample::gLogger.defineTest(gSampleName, argc, argv);
 
-    gLogger.reportTestStart(sampleTest);
+    sample::gLogger.reportTestStart(sampleTest);
 
     SampleFasterRCNN sample(initializeSampleParams(args));
 
-    gLogInfo << "Building and running a GPU inference engine for FasterRCNN" << std::endl;
+    sample::gLogInfo << "Building and running a GPU inference engine for FasterRCNN" << std::endl;
 
     if (!sample.build())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
     if (!sample.infer())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
     if (!sample.teardown())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
 
-    return gLogger.reportPass(sampleTest);
+    return sample::gLogger.reportPass(sampleTest);
 }
diff --git a/samples/opensource/sampleGoogleNet/CMakeLists.txt b/samples/opensource/sampleGoogleNet/CMakeLists.txt
index a6a799eb..3b8bc891 100644
--- a/samples/opensource/sampleGoogleNet/CMakeLists.txt
+++ b/samples/opensource/sampleGoogleNet/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleGoogleNet/README.md b/samples/opensource/sampleGoogleNet/README.md
index f32044d6..91f36ad5 100644
--- a/samples/opensource/sampleGoogleNet/README.md
+++ b/samples/opensource/sampleGoogleNet/README.md
@@ -5,7 +5,7 @@
 - [How does this sample work?](#how-does-this-sample-work)
     * [TensorRT API layers and ops](#tensorrt-api-layers-and-ops)
 - [Running the sample](#running-the-sample)
-    * [Sample `--help` options](#sample---help-options)
+    * [Sample `--help` options](#sample-help-options)
 - [Additional resources](#additional-resources)
 - [License](#license)
 - [Changelog](#changelog)
@@ -21,7 +21,7 @@ This sample constructs a network based on a saved Caffe model and network descri
 
 - `googlenet.prototxt` - The prototxt file that contains the network design.    
 - `googlenet.caffemodel` - The model file which contains the trained weights for the network.
-    
+
 For more information, see [Importing A Caffe Model Using The C++ Parser API](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#import_caffe_c).
 
 The sample then builds the TensorRT engine using the constructed network. See [Building an Engine in C++](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#build_engine_c) for more information on this. Finally, the sample runs the engine with the test input (all zeroes) and reports if the sample ran as expected.
@@ -59,12 +59,12 @@ The SoftMax layer applies the SoftMax function on the input tensor along an inpu
 	make
 	```
 	Where `<TensorRT root directory>` is where you installed TensorRT.
-	
+
 2. Run the sample to build and run a GPU inference engine for GoogleNet.
 	`./sample_googlenet --datadir=<path_to_data_directory> --useDLACore=N`
 	Where `<path_to_data_directory>` is the path to your `data` directory.
 	**Note:** By default, this sample assumes both `googlenet.prototxt` and `googlenet.caffemodel` files are located in either the `data/samples/googlenet/` or `data/googlenet/` directories. The default directory can be changed by supplying the `--datadir=<new_path/>` path as a command line argument.
-	
+
 3.  Verify that the sample ran successfully. If the sample runs successfully you should see output similar to the following:
 	```
 	&&&& RUNNING TensorRT.sample_googlenet # ./sample_googlenet
@@ -77,15 +77,11 @@ The SoftMax layer applies the SoftMax function on the input tensor along an inpu
 	```
 	This output shows that the input to the sample is called `data`, the output tensor is called `prob` and the sample ran successfully; `PASSED`.
 
-### Sample --help options
 
-To see the full list of available options and their descriptions, use the `-h` or `--help` command line option. The following example output is printed when running the sample:
-```
-Usage: ./sample_googlenet [-h or --help] [-d or --datadir=<path_to_data_directory>] [--useDLACore=<int>]
---help Display help information
---datadir Specify path to a data directory, overriding the default. This option can be used multiple times to add multiple directories. If no data directories are given, the default is to use data/samples/googlenet/ and data/googlenet/
---useDLACore=N Specify a DLA engine for layers that support DLA. Value can range from 0 to n-1, where n is the number of DLA engines on the platform.
-```
+### Sample `--help` options
+
+To see the full list of available options and their descriptions, use the `-h` or `--help` command line option.
+
 
 # Additional resources
 
@@ -101,14 +97,14 @@ The following resources provide a deeper understanding about GoogleNet:
 
 # License
 
-For terms and conditions for use, reproduction, and distribution, see the [TensorRT Software License Agreement](https://docs.nvidia.com/deeplearning/sdk/tensorrt-sla/index.html) 
+For terms and conditions for use, reproduction, and distribution, see the [TensorRT Software License Agreement](https://docs.nvidia.com/deeplearning/sdk/tensorrt-sla/index.html)
 documentation.
- 
+
 # Changelog
 
 February 2019
 This `README.md` file was recreated, updated and reviewed.
- 
+
 # Known issues
 
 There are no known issues in this sample.
diff --git a/samples/opensource/sampleGoogleNet/sampleGoogleNet.cpp b/samples/opensource/sampleGoogleNet/sampleGoogleNet.cpp
index 0033c874..cbae89ad 100644
--- a/samples/opensource/sampleGoogleNet/sampleGoogleNet.cpp
+++ b/samples/opensource/sampleGoogleNet/sampleGoogleNet.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -91,7 +91,7 @@ class SampleGoogleNet
 //!
 bool SampleGoogleNet::build()
 {
-    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(gLogger.getTRTLogger()));
+    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(sample::gLogger.getTRTLogger()));
     if (!builder)
     {
         return false;
@@ -171,7 +171,7 @@ bool SampleGoogleNet::infer()
         const auto bufferSize = buffers.size(input);
         if (bufferSize == samplesCommon::BufferManager::kINVALID_SIZE_VALUE)
         {
-            gLogError << "input tensor missing: " << input << "\n";
+            sample::gLogError << "input tensor missing: " << input << "\n";
             return EXIT_FAILURE;
         }
         memset(buffers.getHostBuffer(input), 0, bufferSize);
@@ -252,7 +252,7 @@ int main(int argc, char** argv)
     bool argsOK = samplesCommon::parseArgs(args, argc, argv);
     if (!argsOK)
     {
-        gLogError << "Invalid arguments" << std::endl;
+        sample::gLogError << "Invalid arguments" << std::endl;
         printHelpInfo();
         return EXIT_FAILURE;
     }
@@ -263,31 +263,31 @@ int main(int argc, char** argv)
         return EXIT_SUCCESS;
     }
 
-    auto sampleTest = gLogger.defineTest(gSampleName, argc, argv);
+    auto sampleTest = sample::gLogger.defineTest(gSampleName, argc, argv);
 
-    gLogger.reportTestStart(sampleTest);
+    sample::gLogger.reportTestStart(sampleTest);
 
     samplesCommon::CaffeSampleParams params = initializeSampleParams(args);
     SampleGoogleNet sample(params);
 
-    gLogInfo << "Building and running a GPU inference engine for GoogleNet" << std::endl;
+    sample::gLogInfo << "Building and running a GPU inference engine for GoogleNet" << std::endl;
 
     if (!sample.build())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
 
     if (!sample.infer())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
 
     if (!sample.teardown())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
 
-    gLogInfo << "Ran " << argv[0] << " with: " << std::endl;
+    sample::gLogInfo << "Ran " << argv[0] << " with: " << std::endl;
 
     std::stringstream ss;
 
@@ -297,7 +297,7 @@ int main(int argc, char** argv)
         ss << input << " ";
     }
 
-    gLogInfo << ss.str() << std::endl;
+    sample::gLogInfo << ss.str() << std::endl;
 
     ss.str(std::string());
 
@@ -307,7 +307,7 @@ int main(int argc, char** argv)
         ss << output << " ";
     }
 
-    gLogInfo << ss.str() << std::endl;
+    sample::gLogInfo << ss.str() << std::endl;
 
-    return gLogger.reportPass(sampleTest);
+    return sample::gLogger.reportPass(sampleTest);
 }
diff --git a/samples/opensource/sampleINT8/CMakeLists.txt b/samples/opensource/sampleINT8/CMakeLists.txt
index f26ba0a5..b42d745a 100644
--- a/samples/opensource/sampleINT8/CMakeLists.txt
+++ b/samples/opensource/sampleINT8/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleINT8/README.md b/samples/opensource/sampleINT8/README.md
index 8dd7dd34..19bb6f19 100644
--- a/samples/opensource/sampleINT8/README.md
+++ b/samples/opensource/sampleINT8/README.md
@@ -3,21 +3,19 @@
 **Table Of Contents**
 - [Description](#description)
 - [How does this sample work?](#how-does-this-sample-work)
-	* [Defining the network](#defining-the-network)
-	* [Setup the calibrator](#setup-the-calibrator)
-		* [Calibration data](#calibration-data)
-		* [Calibrator interface](#calibrator-interface)
-		* [Calibration file](#calibration-file)
-	* [Configuring the builder](#configuring-the-builder)
-	* [Building the engine](#building-the-engine)
-	* [Running the engine](#running-the-engine)
-	* [Verifying the output](#verifying-the-output)
-	* [TensorRT API layers and ops](#tensorrt-api-layers-and-ops)
+    * [Defining the network](#defining-the-network)
+    * [Setup the calibrator](#setup-the-calibrator)
+        * [Calibration data](#calibration-data)
+        * [Calibrator interface](#calibrator-interface)
+        * [Calibration file](#calibration-file)
+    * [Configuring the builder](#configuring-the-builder)
+    * [Building the engine](#building-the-engine)
+    * [Running the engine](#running-the-engine)
+    * [Verifying the output](#verifying-the-output)
+    * [TensorRT API layers and ops](#tensorrt-api-layers-and-ops)
 - [Batch files for calibration](#batch-files-for-calibration)
-	* [Generating batch files for Caffe users](#generating-batch-files-for-caffe-users)
-	* [Generating batch files for non-Caffe users](#generating-batch-files-for-non-caffe-users)
 - [Running the sample](#running-the-sample)
-	* [Sample `--help` options](#sample---help-options)
+    * [Sample `--help` options](#sample-help-options)
 - [Additional resources](#additional-resources)
 - [License](#license)
 - [Changelog](#changelog)
@@ -34,9 +32,9 @@ Specifically, this sample demonstrates how to perform inference in 8-bit integer
 
 INT8 engines are build from 32-bit network definitions, similarly to 32-bit and 16-bit engines, but with more configuration steps. In particular, the builder and network must be configured to use INT8, which requires per-tensor dynamic ranges. The INT8 calibrator can determine how best to represent weights and activations as 8-bit integers and sets the per tensor dynamic ranges accordingly. Alternatively, you can set custom per tensor dynamic ranges; this is covered in sampleINT8API.
 
-This sample is accompanied by the [MNIST training set](https://github.com/BVLC/caffe/blob/master/data/mnist/get_mnist.sh) located in the `TensorRT-x.x.x.x/data/mnist` directory, where `x.x.x.x` is your installed version of TensorRT. The packaged MNIST model that is shipped with this sample is based on [lenet.prototxt](https://github.com/BVLC/caffe/edit/master/examples/mnist/lenet.prototxt). For more information, see the [MNIST BVLC Caffe example](https://github.com/BVLC/caffe/tree/master/examples/mnist).
+This sample requires the [MNIST training set](https://github.com/BVLC/caffe/blob/master/data/mnist/get_mnist.sh). Download this script into the `TensorRT-x.x.x.x/data/mnist` directory, where `x.x.x.x` is your installed version of TensorRT, and execute the script to download the required data. The packaged MNIST model that is shipped with this sample is based on [lenet.prototxt](https://github.com/BVLC/caffe/edit/master/examples/mnist/lenet.prototxt). For more information, see the [MNIST BVLC Caffe example](https://github.com/BVLC/caffe/tree/master/examples/mnist).
 
-The packaged data set file that is shipped with this sample is based on the [MNIST data set](https://github.com/BVLC/caffe/tree/master/data/mnist). However, the batch file generation from the above data set is described in [Batch files for calibration](#batch-files-for-calibration).
+The data set used by this sample is based on the [MNIST data set](https://github.com/BVLC/caffe/tree/master/data/mnist). The batch file generation from the above data set is described in [Batch files for calibration](#batch-files-for-calibration).
 
 Specifically, this sample performs the following steps:
 
@@ -52,11 +50,11 @@ Specifically, this sample performs the following steps:
 Defining a network for INT8 execution is exactly the same as for any other precision. Weights should be imported as FP32 values, and the builder will calibrate the network to find appropriate quantization factors to reduce the network to INT8 precision. This sample imports the network using the NvCaffeParser:
 
 ```
-const IBlobNameToTensor* blobNameToTensor =
-parser->parse(locateFile(deployFile).c_str(),
-locateFile(modelFile).c_str(),
+const nvcaffeparser1::IBlobNameToTensor* blobNameToTensor =
+parser->parse(locateFile(mParams.prototxtFileName, mParams.dataDirs).c_str(),
+locateFile(mParams.weightsFileName, mParams.dataDirs).c_str(),
 *network,
-DataType::kFLOAT);
+dataType == DataType::kINT8 ? DataType::kFLOAT : dataType);
 ```
 
 ### Setup the calibrator
@@ -65,12 +63,14 @@ Calibration is an additional step required when building networks for INT8. The
 
 #### Calibration data
 
-Calibration must be performed using images representative of those which will be used at runtime. Since the sample is based around Caffe, any image preprocessing that caffe would perform prior to running the network (such as scaling, cropping, or mean subtraction) will be done in Caffe and captured as a set of files. The sample uses a utility class (BatchStream) to read these files and create appropriate input for calibration. Generation of these files is discussed in [Batch files for calibration](#batch-files-for-calibration).
+Calibration must be performed using images representative of those which will be used at runtime. Since the sample is based around Caffe, any image preprocessing that caffe would perform prior to running the network (such as scaling, cropping, or mean subtraction) will be done in Caffe and captured as a set of files. The sample uses a utility class (MNISTBatchStream) to read these files and create appropriate input for calibration. Generation of these files is discussed in [Batch files for calibration](#batch-files-for-calibration).
 
 You can create calibration data stream (calibrationStream), for example:
-`BatchStream calibrationStream(CAL_BATCH_SIZE, NB_CAL_BATCHES);`
+```
+MNISTBatchStream calibrationStream(mParams.calBatchSize, mParams.nbCalBatches, "train-images-idx3-ubyte","train-labels-idx1-ubyte", mParams.dataDirs);
+```
 
-The BatchStream class provides helper methods used to retrieve batch data. Batch stream object is used by the calibrator in order to retrieve batch data while calibrating. In general, the BatchStream class should provide implementation for `getBatch()` and `getBatchSize()` which can be invoked by `IInt8Calibrator::getBatch()` and `IInt8Calibrator::getBatchSize()`. Ideally, you can write your own custom BatchStream class to serve calibration data. For more information, see `BatchStream.h`.
+The MNISTBatchStream class provides helper methods used to retrieve batch data. Batch stream object is used by the calibrator in order to retrieve batch data while calibrating. In general, the BatchStream class should provide implementation for `getBatch()` and `getBatchSize()` which can be invoked by `IInt8Calibrator::getBatch()` and `IInt8Calibrator::getBatchSize()`. Ideally, you can write your own custom BatchStream class to serve calibration data. For more information, see `BatchStream.h`.
 
 **Note:** The calibration data must be representative of the input provided to TensorRT at runtime; for example, for image classification networks, it should not consist of images from just a small subset of categories. For ImageNet networks, around 500 calibration images is adequate.
 
@@ -81,36 +81,31 @@ The application must implement the `IInt8Calibrator` interface to provide calibr
 We can create calibrator object (`calibrator`), for example:
 `std::unique_ptr<IInt8Calibrator> calibrator;`
 
-TensorRT provides 3 implementations for `IInt8Calibrator`:
+TensorRT provides 4 implementations for `IInt8Calibrator`:
 1.  IInt8EntropyCalibrator
 2.  IInt8EntropyCalibrator2
-3.  IInt8LegacyCalibrator
+3.  IInt8MinMaxCalibrator
+4.  IInt8LegacyCalibrator
 
 See `NvInfer.h` for more information on the `IInt8Calibrator` interface variants.
 
 This sample uses `IInt8EntropyCalibrator2` by default. We can set the calibrator interface to use `IInt8EntropyCalibrator2` as shown:
 
 ```
-calibrator.reset(new Int8EntropyCalibrator2(calibrationStream, FIRST_CAL_BATCH, gNetworkName, INPUT_BLOB_NAME));
+calibrator.reset(new Int8EntropyCalibrator2<MNISTBatchStream>(
+calibrationStream, 0, mParams.networkName.c_str(), mParams.inputTensorNames[0].c_str()));
 ```
 
-where `calibrationStream` is a BatchStream object. The calibrator object should be configured to use the calibration batch stream.
+where `calibrationStream` is a MNISTBatchStream object. The calibrator object should be configured to use the calibration batch stream.
 
 In order to perform calibration, the interface must provide implementation for `getBatchSize()` and `getBatch()` to retrieve data from the BatchStream object.
 
 The builder calls the `getBatchSize()` method once, at the start of calibration, to obtain the batch size for the calibration set. The method `getBatch()` is then called repeatedly to obtain batches from the application, until the method returns false. Every calibration batch must include exactly the number of images specified as the batch size.
 
 ```
-bool getBatch(void* bindings[], const char* names[], int nbBindings)
-override
+bool getBatch(void* bindings[], const char* names[], int nbBindings) override
 {
-	if (!mStream.next())
-		return false;
-
-	CHECK(cudaMemcpy(mDeviceInput, mStream.getBatch(), mInputCount * sizeof(float), cudaMemcpyHostToDevice));
-	assert(!strcmp(names[0], INPUT_BLOB_NAME));
-	bindings[0] = mDeviceInput;
-	return true;
+    return mImpl.getBatch(bindings, names, nbBindings);
 }
 ```
 
@@ -131,14 +126,14 @@ The calibration file is called `CalibrationTable<NetworkName>`, where `<NetworkN
 If the `CalibrationTable` file is not found, the builder will run the calibration algorithm again to create it. The `CalibrationTable` contents include:
 
 ```
-TRT-5100-EntropyCalibration2
-data: 3c000889
-conv1: 3c8954be
-pool1: 3c8954be
-conv2: 3dd33169
-pool2: 3dd33169
-ip1: 3daeff07
-ip2: 3e7d50ec
+TRT-7000-EntropyCalibration2
+data: 3c008912
+conv1: 3c88edfc
+pool1: 3c88edfc
+conv2: 3ddc858b
+pool2: 3ddc858b
+ip1: 3db6bd6e
+ip2: 3e691968
 prob: 3c010a14
 ```
 
@@ -148,48 +143,71 @@ Where:
 
 The `CalibrationTable` file is generated during the build phase while running the calibration algorithm. After the calibration file is created, it can be read for subsequent runs without running the calibration again. You can provide implementation for `readCalibrationCache()` to load calibration file from a desired location. If the read calibration file is compatible with calibrator type (which was used to generate the file) and TensorRT version, builder would skip the calibration step and use per tensor scales values from the calibration file instead.
 
-### Configuring the builder
+### Configuring for the builder
+
+1.  Set minimum and average number of timing iterations.
+    `config->setAvgTimingIterations(1);`
+    `config->setMinTimingIterations(1);`
 
-1.  Ensure that INT8 inference is supported on the platform:
-	`if (!builder->platformHasFastInt8()) return false;`
+2.  Set maximum workspace size.
+    `config->setMaxWorkspaceSize(1_GiB);`
 
-2.  Enable INT8 mode. Setting this flag ensures that builder auto-tuner will consider INT8 implementation.
-	`builder->setInt8Mode(true);`
+3.  Set allowed builder precision to INT8 in addition to FP32. Default builder precision is FP32.
+    `config->setFlag(BuilderFlag::kINT8);`
 
-3.  Pass the calibrator object (calibrator) to the builder.
-    `builder->setInt8Calibrator(calibrator);`
+4.  Set maximum batch size.
+    `builder->setMaxBatchSize(mParams.batchSize);`
+
+5.  Pass the calibrator object (calibrator) to the builder.
+    `config->setInt8Calibrator(calibrator.get());`
 
 ### Building the engine
 
-After we configure the builder with INT8 mode and calibrator, we can build the engine similar to any FP32 engine.
-`ICudaEngine* engine = builder->buildCudaEngine(*network);`
+After we configure the builder, we can build the engine similar to any FP32 engine.
+```
+mEngine = std::shared_ptr<nvinfer1::ICudaEngine>(
+builder->buildEngineWithConfig(*network, *config), samplesCommon::InferDeleter());
+````
 
 ### Running the engine
 
 After the engine has been built, it can be used just like an FP32 engine. For example, inputs and outputs remain in 32-bit floating point.
 
-1.  Retrieve the names of the input and output tensors to bind the buffers.
-	```
-	inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME), outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);
-	```
-
-2.  Allocate memory for input and output buffers.
-	```
-	CHECK(cudaMalloc(&buffers[inputIndex], inputSize));
-	CHECK(cudaMalloc(&buffers[outputIndex], outputSize));
-	CHECK(cudaMemcpy(buffers[inputIndex], input, inputSize,
-	cudaMemcpyHostToDevice));
-	```
-
-3.  Create a CUDA stream and run Inference.
-	```
-	cudaStream_t stream;
-	CHECK(cudaStreamCreate(&stream));
-	context.enqueue(batchSize, buffers, stream, nullptr);
-	```
-
-4.  Copy the CUDA buffer output to CPU output buffers for post processing.
-    `CHECK(cudaMemcpy(output, buffers[outputIndex], outputSize, cudaMemcpyDeviceToHost));`
+1.  Allocate memory for input and output buffers.
+    ```
+    samplesCommon::BufferManager buffers(mEngine, mParams.batchSize);
+    ```
+
+2.  Create execution context.
+    ```
+    auto context = SampleUniquePtr<nvinfer1::IExecutionContext>(mEngine->createExecutionContext());
+    ```
+
+3.  Get data dimensions.
+    ```
+    Dims outputDims = context->getEngine().getBindingDimensions(
+    context->getEngine().getBindingIndex(mParams.outputTensorNames[0].c_str()));
+    ```
+
+4.  Read the input data into the managed buffers.
+    ```
+    processInput(buffers, batchStream.getBatch())
+    ```
+
+5.  Copy data from host input buffers to device input buffers
+    ```
+    buffers.copyInputToDevice();
+    ```
+
+6.  Run Inference.
+    ```
+    context->enqueue(mParams.batchSize, buffers.getDeviceBindings().data(), stream, nullptr);
+    ```
+
+7.  Copy the CUDA buffer output to CPU output buffers for post processing.
+    ```
+    buffers.copyOutputToHost();
+    ```
 
 ### Verifying the output
 
@@ -216,57 +234,67 @@ The SoftMax layer applies the SoftMax function on the input tensor along an inpu
 Download the [MNIST dataset](http://yann.lecun.com/exdb/mnist/)
     - This sample requires the [training set](http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz) and [training labels](http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz)
     - Unzip the files obtained above using the `gunzip` utility. For example, `gunzip t10k-labels-idx1-ubyte.gz`.
-	- Lastly, copy these files to the `<TensorRT root directory>/samples/data/int8/mnist/` directory
+    - Lastly, copy these files to the `<TensorRT root directory>/samples/data/int8/mnist/` directory
 
 ## Running the sample
 
 1.  Compile this sample by running make  in the `<TensorRT root directory>/samples/sampleINT8` directory. The binary named `sample_int8` will be created in the `<TensorRT root directory>/bin` directory.
 
-	```
-	cd <TensorRT root directory>/samples/sampleINT8
-	make
-	```
+    ```
+    cd <TensorRT root directory>/samples/sampleINT8
+    make
+    ```
 
-	Where `<TensorRT root directory>` is where you installed TensorRT.
+    Where `<TensorRT root directory>` is where you installed TensorRT.
 
 2.  Run the sample on MNIST.
-    `./sample_int8 mnist`
+    `./sample_int8`
 
 3.  Verify that the sample ran successfully. If the sample runs successfully you should see output similar to the following:
 
-	```
-	&&&& RUNNING TensorRT.sample_int8 # ./sample_int8 mnist
-	[I] FP32 run:400 batches of size 100 starting at 100
-	[I] Processing next set of max 100 batches
-	[I] Processing next set of max 100 batches
-	[I] Processing next set of max 100 batches
-	[I] Processing next set of max 100 batches
-	[I] Top1: 0.9904, Top5: 1
-	[I] Processing 40000 images averaged 0.00170236 ms/image and 0.170236 ms/batch.
-	[I] FP16 run:400 batches of size 100 starting at 100
-	[I] Processing next set of max 100 batches
-	[I] Processing next set of max 100 batches
-	[I] Processing next set of max 100 batches
-	[I] Processing next set of max 100 batches
-	[I] Top1: 0.9904, Top5: 1
-	[I] Processing 40000 images averaged 0.00128872 ms/image and 0.128872 ms/batch.
-
-	INT8 run:400 batches of size 100 starting at 100
-	[I] Processing next set of max 100 batches
-	[I] Processing next set of max 100 batches
-	[I] Processing next set of max 100 batches
-	[I] Processing next set of max 100 batches
-	[I] Top1: 0.9908, Top5: 1
-	[I] Processing 40000 images averaged 0.000946117 ms/image and 0.0946117 ms/batch.
-	&&&& PASSED TensorRT.sample_int8 # ./sample_int8 mnist
-	```
+    ```
+    &&&& RUNNING TensorRT.sample_int8 # ./sample_int8
+    [I] Building and running a GPU inference engine for INT8 sample
+    [I] FP32 run:1800 batches of size 32 starting at 16
+    [I] [TRT] Detected 1 inputs and 1 output network tensors.
+    [I] Processing next set of max 100 batches
+    ...
+    [I] Processing next set of max 100 batches
+    [I] Top1: 0.998646, Top5: 1
+    [I] Processing 57600 images averaged 0.00262977 ms/image and 0.0834112 ms/batch.
+    [I] FP16 run:1800 batches of size 32 starting at 16
+    [I] [TRT] Detected 1 inputs and 1 output network tensors.
+    [I] Processing next set of max 100 batches
+    ...
+    [I] Processing next set of max 100 batches
+    [I] Top1: 0.998646, Top5: 1
+    [I] Processing 57600 images averaged 0.00298152 ms/image and 0.0945682 ms/batch.
+    [I] INT8 run:1800 batches of size 32 starting at 16
+    [I] [TRT] Detected 1 inputs and 1 output network tensors.
+    [I] [TRT] Starting Calibration with batch size 50.
+    [I] [TRT]   Calibrated batch 0 in 0.00211989 seconds.
+    ...
+    [I] [TRT]   Calibrated batch 9 in 0.00207574 seconds.
+    [I] [TRT]   Post Processing Calibration data in 0.180447 seconds.
+    [I] [TRT] Calibration completed in 0.317902 seconds.
+    [I] [TRT] Writing Calibration Cache for calibrator: TRT-7000-EntropyCalibration2
+    [I] [TRT] Detected 1 inputs and 1 output network tensors.
+    [I] Processing next set of max 100 batches
+    ...
+    [I] Processing next set of max 100 batches
+    [I] Top1: 0.998576, Top5: 1
+    [I] Processing 57600 images averaged 0.00227856 ms/image and 0.0722715 ms/batch.
+    &&&& PASSED TensorRT.sample_int8 # ./sample_int8
+    ```
 
 	This output shows that the sample ran successfully; `PASSED`.
 
+
 ### Sample `--help` options
 
 To see the full list of available options and their descriptions, use the `-h` or `--help` command line option.
 
+
 # Additional resources
 
 The following resources provide a deeper understanding how to perform inference in INT8 using custom calibration:
@@ -299,8 +327,8 @@ For terms and conditions for use, reproduction, and distribution, see the [Tenso
 
 # Changelog
 
-March 2019
-This is the first release of this `README.md` file.
+December 2019
+This is the second release of this `README.md` file.
 
 
 # Known issues
diff --git a/samples/opensource/sampleINT8/sampleINT8.cpp b/samples/opensource/sampleINT8/sampleINT8.cpp
index 19853583..54c346c7 100644
--- a/samples/opensource/sampleINT8/sampleINT8.cpp
+++ b/samples/opensource/sampleINT8/sampleINT8.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -65,7 +65,7 @@ class SampleINT8
         : mParams(params)
         , mEngine(nullptr)
     {
-        initLibNvInferPlugins(&gLogger.getTRTLogger(), "");
+        initLibNvInferPlugins(&sample::gLogger.getTRTLogger(), "");
     }
 
     //!
@@ -83,7 +83,7 @@ class SampleINT8
     //!
     //! \brief Runs the TensorRT inference engine for this sample
     //!
-    bool infer(std::pair<float, float>& score, int firstScoreBatch, int nbScoreBatches);
+    bool infer(std::vector<float>& score, int firstScoreBatch, int nbScoreBatches);
 
     //!
     //! \brief Cleans up any state created in the sample class
@@ -127,7 +127,7 @@ class SampleINT8
 bool SampleINT8::build(DataType dataType)
 {
 
-    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(gLogger.getTRTLogger()));
+    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(sample::gLogger.getTRTLogger()));
     if (!builder)
     {
         return false;
@@ -177,7 +177,7 @@ bool SampleINT8::build(DataType dataType)
 //!
 bool SampleINT8::isSupported(DataType dataType)
 {
-    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(gLogger.getTRTLogger()));
+    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(sample::gLogger.getTRTLogger()));
     if (!builder)
     {
         return false;
@@ -221,7 +221,6 @@ bool SampleINT8::constructNetwork(SampleUniquePtr<nvinfer1::IBuilder>& builder,
     config->setAvgTimingIterations(1);
     config->setMinTimingIterations(1);
     config->setMaxWorkspaceSize(1_GiB);
-    config->setFlag(BuilderFlag::kDEBUG);
     if (dataType == DataType::kHALF)
     {
         config->setFlag(BuilderFlag::kFP16);
@@ -246,8 +245,9 @@ bool SampleINT8::constructNetwork(SampleUniquePtr<nvinfer1::IBuilder>& builder,
         samplesCommon::enableDLA(builder.get(), config.get(), mParams.dlaCore);
         if (mParams.batchSize > builder->getMaxDLABatchSize())
         {
-            gLogError << "Requested batch size " << mParams.batchSize << " is greater than the max DLA batch size of "
-                      << builder->getMaxDLABatchSize() << ". Reducing batch size accordingly." << std::endl;
+            sample::gLogError << "Requested batch size " << mParams.batchSize
+                              << " is greater than the max DLA batch size of " << builder->getMaxDLABatchSize()
+                              << ". Reducing batch size accordingly." << std::endl;
             return false;
         }
     }
@@ -268,7 +268,7 @@ bool SampleINT8::constructNetwork(SampleUniquePtr<nvinfer1::IBuilder>& builder,
 //! \details This function is the main execution function of the sample. It allocates the buffer,
 //!          sets inputs and executes the engine.
 //!
-bool SampleINT8::infer(std::pair<float, float>& score, int firstScoreBatch, int nbScoreBatches)
+bool SampleINT8::infer(std::vector<float>& score, int firstScoreBatch, int nbScoreBatches)
 {
     float ms{0.0f};
 
@@ -281,8 +281,8 @@ bool SampleINT8::infer(std::pair<float, float>& score, int firstScoreBatch, int
         return false;
     }
 
-    MNISTBatchStream batchStream(
-        mParams.batchSize, nbScoreBatches + firstScoreBatch, "train-images-idx3-ubyte", "train-labels-idx1-ubyte", mParams.dataDirs);
+    MNISTBatchStream batchStream(mParams.batchSize, nbScoreBatches + firstScoreBatch, "train-images-idx3-ubyte",
+        "train-labels-idx1-ubyte", mParams.dataDirs);
     batchStream.skip(firstScoreBatch);
 
     Dims outputDims = context->getEngine().getBindingDimensions(
@@ -336,17 +336,17 @@ bool SampleINT8::infer(std::pair<float, float>& score, int firstScoreBatch, int
 
         if (batchStream.getBatchesRead() % 100 == 0)
         {
-            gLogInfo << "Processing next set of max 100 batches" << std::endl;
+            sample::gLogInfo << "Processing next set of max 100 batches" << std::endl;
         }
     }
 
     int imagesRead = (batchStream.getBatchesRead() - firstScoreBatch) * mParams.batchSize;
-    score.first = float(top1) / float(imagesRead);
-    score.second = float(top5) / float(imagesRead);
+    score[0] = float(top1) / float(imagesRead);
+    score[1] = float(top5) / float(imagesRead);
 
-    gLogInfo << "Top1: " << score.first << ", Top5: " << score.second << std::endl;
-    gLogInfo << "Processing " << imagesRead << " images averaged " << totalTime / imagesRead << " ms/image and "
-             << totalTime / batchStream.getBatchesRead() << " ms/batch." << std::endl;
+    sample::gLogInfo << "Top1: " << score[0] << ", Top5: " << score[1] << std::endl;
+    sample::gLogInfo << "Processing " << imagesRead << " images averaged " << totalTime / imagesRead << " ms/image and "
+                     << totalTime / batchStream.getBatchesRead() << " ms/batch." << std::endl;
 
     return true;
 }
@@ -438,7 +438,7 @@ void printHelpInfo()
     std::cout << "Usage: ./sample_int8 [-h or --help] [-d or --datadir=<path to data directory>] "
                  "[--useDLACore=<int>]"
               << std::endl;
-    std::cout << "--help          Display help information" << std::endl;
+    std::cout << "--help, -h      Display help information" << std::endl;
     std::cout << "--datadir       Specify path to a data directory, overriding the default. This option can be used "
                  "multiple times to add multiple directories."
               << std::endl;
@@ -446,18 +446,18 @@ void printHelpInfo()
                  "where n is the number of DLA engines on the platform."
               << std::endl;
     std::cout << "batch=N         Set batch size (default = 32)." << std::endl;
-    std::cout << "start=N         Set the first batch to be scored (default = 100). All batches before this batch will "
+    std::cout << "start=N         Set the first batch to be scored (default = 16). All batches before this batch will "
                  "be used for calibration."
               << std::endl;
-    std::cout << "score=N         Set the number of batches to be scored (default = 400)." << std::endl;
+    std::cout << "score=N         Set the number of batches to be scored (default = 1800)." << std::endl;
 }
 
 int main(int argc, char** argv)
 {
-    if (argc >= 2 && (!strncmp(argv[1], "help", 4) || !strncmp(argv[1], "--help", 6) || !strncmp(argv[1], "--h", 3) || !strncmp(argv[1], "-h", 2)))
+    if (argc >= 2 && (!strncmp(argv[1], "--help", 6) || !strncmp(argv[1], "-h", 2)))
     {
         printHelpInfo();
-        return EXIT_FAILURE;
+        return EXIT_SUCCESS;
     }
 
     // By default we score over 57600 images starting at 512, so we don't score those used to search calibration
@@ -484,13 +484,13 @@ int main(int argc, char** argv)
 
     if (batchSize > 128)
     {
-        gLogError << "Please provide batch size <= 128" << std::endl;
+        sample::gLogError << "Please provide batch size <= 128" << std::endl;
         return EXIT_FAILURE;
     }
 
     if ((firstScoreBatch + nbScoreBatches) * batchSize > 60000)
     {
-        gLogError << "Only 60000 images available" << std::endl;
+        sample::gLogError << "Only 60000 images available" << std::endl;
         return EXIT_FAILURE;
     }
 
@@ -499,68 +499,66 @@ int main(int argc, char** argv)
 
     SampleINT8 sample(initializeSampleParams(args, batchSize));
 
-    auto sampleTest = gLogger.defineTest(gSampleName, argc, argv);
+    auto sampleTest = sample::gLogger.defineTest(gSampleName, argc, argv);
 
-    gLogger.reportTestStart(sampleTest);
+    sample::gLogger.reportTestStart(sampleTest);
 
-    gLogInfo << "Building and running a GPU inference engine for INT8 sample" << std::endl;
+    sample::gLogInfo << "Building and running a GPU inference engine for INT8 sample" << std::endl;
 
     std::vector<std::string> dataTypeNames = {"FP32", "FP16", "INT8"};
+    std::vector<std::string> topNames = {"Top1", "Top5"};
     std::vector<DataType> dataTypes = {DataType::kFLOAT, DataType::kHALF, DataType::kINT8};
-    std::vector<std::pair<float, float>> scores(3, std::make_pair(0.0f, 0.0f));
+    std::vector<std::vector<float>> scores(3, std::vector<float>(2, 0.0f));
     for (size_t i = 0; i < dataTypes.size(); i++)
     {
-        gLogInfo << dataTypeNames[i] << " run:" << nbScoreBatches << " batches of size " << batchSize << " starting at "
-                 << firstScoreBatch << std::endl;
+        sample::gLogInfo << dataTypeNames[i] << " run:" << nbScoreBatches << " batches of size " << batchSize
+                         << " starting at " << firstScoreBatch << std::endl;
 
         if (!sample.build(dataTypes[i]))
         {
             if (!sample.isSupported(dataTypes[i]))
             {
-                gLogWarning << "Skipping " << dataTypeNames[i] << " since the platform does not support this data type."
-                            << std::endl;
+                sample::gLogWarning << "Skipping " << dataTypeNames[i]
+                                    << " since the platform does not support this data type." << std::endl;
                 continue;
             }
-            return gLogger.reportFail(sampleTest);
+            return sample::gLogger.reportFail(sampleTest);
         }
         if (!sample.infer(scores[i], firstScoreBatch, nbScoreBatches))
         {
-            return gLogger.reportFail(sampleTest);
+            return sample::gLogger.reportFail(sampleTest);
         }
     }
 
     auto isApproximatelyEqual = [](float a, float b, double tolerance) { return (std::abs(a - b) <= tolerance); };
-    double fp16tolerance{0.5}, int8tolerance{0.01};
+    const double tolerance{0.01};
+    const double goldenMNIST{0.99};
 
-    if (scores[1].first != 0.0f && !isApproximatelyEqual(scores[0].first, scores[1].first, fp16tolerance))
-    {
-        gLogError << "FP32(" << scores[0].first << ") and FP16(" << scores[1].first
-                  << ") Top1 accuracy differ by more than " << fp16tolerance << "." << std::endl;
-        return gLogger.reportFail(sampleTest);
-    }
-    if (scores[2].first != 0.0f && !isApproximatelyEqual(scores[0].first, scores[2].first, int8tolerance))
-    {
-        gLogError << "FP32(" << scores[0].first << ") and Int8(" << scores[2].first
-                  << ") Top1 accuracy differ by more than " << int8tolerance << "." << std::endl;
-        return gLogger.reportFail(sampleTest);
-    }
-    if (scores[1].second != 0.0f && !isApproximatelyEqual(scores[0].second, scores[1].second, fp16tolerance))
+    if ((scores[0][0] < goldenMNIST) || (scores[0][1] < goldenMNIST))
     {
-        gLogError << "FP32(" << scores[0].second << ") and FP16(" << scores[1].second
-                  << ") Top5 accuracy differ by more than " << fp16tolerance << "." << std::endl;
-        return gLogger.reportFail(sampleTest);
+        sample::gLogError << "FP32 accuracy is less than 99%: Top1 = " << scores[0][0] << ", Top5 = " << scores[0][1]
+                          << "." << std::endl;
+        return sample::gLogger.reportFail(sampleTest);
     }
-    if (scores[2].second != 0.0f && !isApproximatelyEqual(scores[0].second, scores[2].second, int8tolerance))
+
+    for (unsigned i = 0; i < topNames.size(); i++)
     {
-        gLogError << "FP32(" << scores[0].second << ") and INT8(" << scores[2].second
-                  << ") Top5 accuracy differ by more than " << int8tolerance << "." << std::endl;
-        return gLogger.reportFail(sampleTest);
+        for (unsigned j = 1; j < dataTypes.size(); j++)
+        {
+            if (scores[j][i] != 0.0f && !isApproximatelyEqual(scores[0][i], scores[j][i], tolerance))
+            {
+                sample::gLogError << "FP32(" << scores[0][i] << ") and " << dataTypeNames[j] << "(" << scores[j][i]
+                                  << ") " << topNames[i] << " accuracy differ by more than " << tolerance << "."
+                                  << std::endl;
+                return sample::gLogger.reportFail(sampleTest);
+            }
+        }
     }
 
     if (!sample.teardown())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
 
-    return gLogger.reportPass(sampleTest);
+    return sample::gLogger.reportPass(sampleTest);
 }
diff --git a/samples/opensource/sampleINT8API/CMakeLists.txt b/samples/opensource/sampleINT8API/CMakeLists.txt
index eefc1dee..db54a8ab 100644
--- a/samples/opensource/sampleINT8API/CMakeLists.txt
+++ b/samples/opensource/sampleINT8API/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleINT8API/README.md b/samples/opensource/sampleINT8API/README.md
index b41e0dad..0d27e3ac 100644
--- a/samples/opensource/sampleINT8API/README.md
+++ b/samples/opensource/sampleINT8API/README.md
@@ -11,7 +11,7 @@
 	* [TensorRT API layers and ops](#tensorrt-api-layers-and-ops)
 - [Prerequisites](#prerequisites)
 - [Running the sample](#running-the-sample)
-	* [Sample `--help` options](#sample---help-options)
+	* [Sample `--help` options](#sample-help-options)
 - [Models other than ResNet-50 with custom configuration](#models-other-than-resnet-50-with-custom-configuration)
 - [Additional resources](#additional-resources)
 - [License](#license)
@@ -160,7 +160,7 @@ The ResNet-50 per tensor dynamic ranges file.
 `airliner.ppm`
 The image to be inferred.
 
-1.  Download the [ONNX ResNet-50 model](https://github.com/onnx/models/tree/master/vision/classification/resnet/resnet50).
+1.  Download the ONNX ResNet-50 model.
     `wget https://s3.amazonaws.com/download.onnx/models/opset_9/resnet50.tar.gz`
 
 2.  Unpackage the model file.
@@ -214,26 +214,11 @@ The image to be inferred.
 
 	This output shows that the sample ran successfully; `PASSED`.
 
-### Sample --help options
-
-To see the full list of available options and their descriptions, use the `-h` or `--help` command line option. For example:
-```
-    Usage:
-		./sample_int8_api [-h or --help] [--model=<model file>] [--ranges=<dynamic range file>] [--image=<image file>] [--reference=<reference file>] [--data=<path to data directory>] [--useDLACore=<int>] [-v or --verbose]
-
-	Help:
-		./sample_int8_api [-h or --help]
-			--help, -h                        Display This help information
-            --model=<model file>              Path to the model file (default = resnet50.onnx)
-			--image=<image file>              Path to the image file to infer (default = airlines.ppm)
-			--reference=<reference file>      Path to the reference labels file (default = reference_labels.txt)
-			--ranges=<dynamic range file>     Path to the custom per tensor dynamic range file for the network (default = resnet50_per_tensor_dynamic_range.txt)
-			--write_tensors                   Option to generate file containing network tensors name. By default writes to network_tensors.txt file. To provide user defined file name use additional option --network_tensors_file.        See --network_tensors_file option usage for more detail.
-			--network_tensors_file=<file>     Path to the network tensors file. This option needs to be used with --write_tensors option. Specify file name (will write to current execution directory) or absolute path to file name to write network tensor names file. Dynamic range corresponding to each network tensor is required to run the sample (default = network_tensors.txt)
-			--data=<path to data dir>         Path to the data directory to search for above files in case absolute paths to files are not provided (default both data/samples/int8_api/ and data/int8_api/)
-			--useDLACore=N                    Specify a DLA engine for layers that support DLA. Value can range from 0 to n-1, where n is the number of DLA engines on the platform.
-			--verbose                         Enable verbose prints
-```
+
+### Sample `--help` options
+
+To see the full list of available options and their descriptions, use the `-h` or `--help` command line option.
+
 
 # Models other than ResNet-50 with custom configuration
 
diff --git a/samples/opensource/sampleINT8API/sampleINT8API.cpp b/samples/opensource/sampleINT8API/sampleINT8API.cpp
index 7a8d3eff..2350654e 100644
--- a/samples/opensource/sampleINT8API/sampleINT8API.cpp
+++ b/samples/opensource/sampleINT8API/sampleINT8API.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -43,8 +43,9 @@ const std::string gSampleName = "TensorRT.sample_int8_api";
 
 struct SampleINT8APIPreprocessing
 {
-    // Preprocessing values are available here: https://github.com/onnx/models/tree/master/models/image_classification/resnet
-    std::vector<int> inputDims{1,3,224,224};
+    // Preprocessing values are available here:
+    // https://github.com/onnx/models/tree/master/models/image_classification/resnet
+    std::vector<int> inputDims{1, 3, 224, 224};
 };
 
 //!
@@ -56,7 +57,6 @@ struct SampleINT8APIParams
     bool verbose{false};
     bool writeNetworkTensors{false};
     int dlaCore{-1};
-    int batchSize{1};
 
     SampleINT8APIPreprocessing mPreproc;
     std::string modelFileName;
@@ -88,17 +88,17 @@ class SampleINT8API
     //!
     //! \brief Builds the network engine
     //!
-    Logger::TestResult build();
+    sample::Logger::TestResult build();
 
     //!
     //! \brief Runs the TensorRT inference engine for this sample
     //!
-    Logger::TestResult infer();
+    sample::Logger::TestResult infer();
 
     //!
     //! \brief Used to clean up any state created in the sample class
     //!
-    Logger::TestResult teardown();
+    sample::Logger::TestResult teardown();
 
     SampleINT8APIParams mParams; //!< Stores Sample Parameter
 
@@ -161,8 +161,8 @@ void SampleINT8API::getInputOutputNames()
         {
             if (mParams.verbose)
             {
-                gLogInfo << "Found input: " << mEngine.get()->getBindingName(b) << " shape=" << dims
-                         << " dtype=" << (int) mEngine.get()->getBindingDataType(b) << std::endl;
+                sample::gLogInfo << "Found input: " << mEngine.get()->getBindingName(b) << " shape=" << dims
+                                 << " dtype=" << (int) mEngine.get()->getBindingDataType(b) << std::endl;
             }
             mInOut["input"] = mEngine.get()->getBindingName(b);
         }
@@ -170,8 +170,8 @@ void SampleINT8API::getInputOutputNames()
         {
             if (mParams.verbose)
             {
-                gLogInfo << "Found output: " << mEngine.get()->getBindingName(b) << " shape=" << dims
-                         << " dtype=" << (int) mEngine.get()->getBindingDataType(b) << std::endl;
+                sample::gLogInfo << "Found output: " << mEngine.get()->getBindingName(b) << " shape=" << dims
+                                 << " dtype=" << (int) mEngine.get()->getBindingDataType(b) << std::endl;
             }
             mInOut["output"] = mEngine.get()->getBindingName(b);
         }
@@ -186,7 +186,7 @@ bool SampleINT8API::readPerTensorDynamicRangeValues()
     std::ifstream iDynamicRangeStream(mParams.dynamicRangeFileName);
     if (!iDynamicRangeStream)
     {
-        gLogError << "Could not find per tensor scales file: " << mParams.dynamicRangeFileName << std::endl;
+        sample::gLogError << "Could not find per tensor scales file: " << mParams.dynamicRangeFileName << std::endl;
         return false;
     }
 
@@ -210,21 +210,20 @@ bool SampleINT8API::readPerTensorDynamicRangeValues()
 //!
 void SampleINT8API::setLayerPrecision(SampleUniquePtr<nvinfer1::INetworkDefinition>& network)
 {
-    gLogInfo << "Setting Per Layer Computation Precision" << std::endl;
+    sample::gLogInfo << "Setting Per Layer Computation Precision" << std::endl;
     for (int i = 0; i < network->getNbLayers(); ++i)
     {
         auto layer = network->getLayer(i);
         if (mParams.verbose)
         {
             std::string layerName = layer->getName();
-            gLogInfo << "Layer: " << layerName << ". Precision: INT8" << std::endl;
+            sample::gLogInfo << "Layer: " << layerName << ". Precision: INT8" << std::endl;
         }
 
         // Don't set the precision on non-computation layers as they don't support
         // int8.
-        if (layer->getType() != LayerType::kCONSTANT
-                && layer->getType() != LayerType::kCONCATENATION
-                && layer->getType() != LayerType::kSHAPE)
+        if (layer->getType() != LayerType::kCONSTANT && layer->getType() != LayerType::kCONCATENATION
+            && layer->getType() != LayerType::kSHAPE)
         {
             // set computation precision of the layer
             layer->setPrecision(nvinfer1::DataType::kINT8);
@@ -236,7 +235,7 @@ void SampleINT8API::setLayerPrecision(SampleUniquePtr<nvinfer1::INetworkDefiniti
             if (mParams.verbose)
             {
                 std::string tensorName = layer->getOutput(j)->getName();
-                gLogInfo << "Tensor: " << tensorName << ". OutputType: INT8" << std::endl;
+                sample::gLogInfo << "Tensor: " << tensorName << ". OutputType: INT8" << std::endl;
             }
             // set output type of execution tensors and not shape tensors.
             if (layer->getOutput(j)->isExecutionTensor())
@@ -252,21 +251,22 @@ void SampleINT8API::setLayerPrecision(SampleUniquePtr<nvinfer1::INetworkDefiniti
 //!
 void SampleINT8API::writeNetworkTensorNames(const SampleUniquePtr<nvinfer1::INetworkDefinition>& network)
 {
-    gLogInfo << "Sample requires to run with per tensor dynamic range." << std::endl;
-    gLogInfo << "In order to run Int8 inference without calibration, user will need to provide dynamic range for all "
-                "the network tensors."
-             << std::endl;
+    sample::gLogInfo << "Sample requires to run with per tensor dynamic range." << std::endl;
+    sample::gLogInfo
+        << "In order to run Int8 inference without calibration, user will need to provide dynamic range for all "
+           "the network tensors."
+        << std::endl;
 
     std::ofstream tensorsFile{mParams.networkTensorsFileName};
 
     // Iterate through network inputs to write names of input tensors.
     for (int i = 0; i < network->getNbInputs(); ++i)
     {
-        string tName = network->getInput(i)->getName();
+        std::string tName = network->getInput(i)->getName();
         tensorsFile << "TensorName: " << tName << std::endl;
         if (mParams.verbose)
         {
-            gLogInfo << "TensorName: " << tName << std::endl;
+            sample::gLogInfo << "TensorName: " << tName << std::endl;
         }
     }
 
@@ -276,19 +276,21 @@ void SampleINT8API::writeNetworkTensorNames(const SampleUniquePtr<nvinfer1::INet
         // Write output tensors of a layer to the file.
         for (int j = 0; j < network->getLayer(i)->getNbOutputs(); ++j)
         {
-            string tName = network->getLayer(i)->getOutput(j)->getName();
+            std::string tName = network->getLayer(i)->getOutput(j)->getName();
             tensorsFile << "TensorName: " << tName << std::endl;
             if (mParams.verbose)
             {
-                gLogInfo << "TensorName: " << tName << std::endl;
+                sample::gLogInfo << "TensorName: " << tName << std::endl;
             }
         }
     }
     tensorsFile.close();
-    gLogInfo << "Successfully generated network tensor names. Writing: " << mParams.networkTensorsFileName << std::endl;
-    gLogInfo << "Use the generated tensor names file to create dynamic range file for Int8 inference. Follow README.md "
-                "for instructions to generate dynamic_ranges.txt file."
-             << std::endl;
+    sample::gLogInfo << "Successfully generated network tensor names. Writing: " << mParams.networkTensorsFileName
+                     << std::endl;
+    sample::gLogInfo
+        << "Use the generated tensor names file to create dynamic range file for Int8 inference. Follow README.md "
+           "for instructions to generate dynamic_ranges.txt file."
+        << std::endl;
 }
 
 //!
@@ -302,59 +304,68 @@ bool SampleINT8API::setDynamicRange(SampleUniquePtr<nvinfer1::INetworkDefinition
         return false;
     }
 
-    gLogInfo << "Setting Per Tensor Dynamic Range" << std::endl;
+    sample::gLogInfo << "Setting Per Tensor Dynamic Range" << std::endl;
     if (mParams.verbose)
     {
-        gLogInfo << "If dynamic range for a tensor is missing, TensorRT will run inference assuming dynamic range for "
-                    "the tensor as optional."
-                 << std::endl;
-        gLogInfo << "If dynamic range for a tensor is required then inference will fail. Follow README.md to generate "
-                    "missing per tensor dynamic range."
-                 << std::endl;
+        sample::gLogInfo
+            << "If dynamic range for a tensor is missing, TensorRT will run inference assuming dynamic range for "
+               "the tensor as optional."
+            << std::endl;
+        sample::gLogInfo
+            << "If dynamic range for a tensor is required then inference will fail. Follow README.md to generate "
+               "missing per tensor dynamic range."
+            << std::endl;
     }
     // set dynamic range for network input tensors
     for (int i = 0; i < network->getNbInputs(); ++i)
     {
-        string tName = network->getInput(i)->getName();
+        std::string tName = network->getInput(i)->getName();
         if (mPerTensorDynamicRangeMap.find(tName) != mPerTensorDynamicRangeMap.end())
         {
-            network->getInput(i)->setDynamicRange(-mPerTensorDynamicRangeMap.at(tName), mPerTensorDynamicRangeMap.at(tName));
+            if (!network->getInput(i)->setDynamicRange(
+                    -mPerTensorDynamicRangeMap.at(tName), mPerTensorDynamicRangeMap.at(tName)))
+            {
+                return false;
+            }
         }
         else
         {
             if (mParams.verbose)
             {
-                gLogWarning << "Missing dynamic range for tensor: " << tName << std::endl;
+                sample::gLogWarning << "Missing dynamic range for tensor: " << tName << std::endl;
             }
         }
     }
 
-     // set dynamic range for layer output tensors
+    // set dynamic range for layer output tensors
     for (int i = 0; i < network->getNbLayers(); ++i)
     {
         auto lyr = network->getLayer(i);
         for (int j = 0, e = lyr->getNbOutputs(); j < e; ++j)
         {
-            string tName = lyr->getOutput(j)->getName();
+            std::string tName = lyr->getOutput(j)->getName();
             if (mPerTensorDynamicRangeMap.find(tName) != mPerTensorDynamicRangeMap.end())
             {
                 // Calibrator generated dynamic range for network tensor can be overriden or set using below API
-                lyr->getOutput(j)->setDynamicRange(
-                    -mPerTensorDynamicRangeMap.at(tName), mPerTensorDynamicRangeMap.at(tName));
+                if (!lyr->getOutput(j)->setDynamicRange(
+                        -mPerTensorDynamicRangeMap.at(tName), mPerTensorDynamicRangeMap.at(tName)))
+                {
+                    return false;
+                }
             }
             else if (lyr->getType() == LayerType::kCONSTANT)
             {
                 IConstantLayer* cLyr = static_cast<IConstantLayer*>(lyr);
                 if (mParams.verbose)
                 {
-                    gLogWarning << "Computing missing dynamic range for tensor, " << tName << ", from weights."
-                                << std::endl;
+                    sample::gLogWarning << "Computing missing dynamic range for tensor, " << tName << ", from weights."
+                                        << std::endl;
                 }
                 auto wts = cLyr->getWeights();
                 double max = std::numeric_limits<double>::min();
                 for (int64_t wb = 0, we = wts.count; wb < we; ++wb)
                 {
-                    double val;
+                    double val{};
                     switch (wts.type)
                     {
                     case DataType::kFLOAT: val = static_cast<const float*>(wts.values)[wb]; break;
@@ -366,13 +377,16 @@ bool SampleINT8API::setDynamicRange(SampleUniquePtr<nvinfer1::INetworkDefinition
                     max = std::max(max, std::abs(val));
                 }
 
-                lyr->getOutput(j)->setDynamicRange(-max, max);
+                if (!lyr->getOutput(j)->setDynamicRange(-max, max))
+                {
+                    return false;
+                }
             }
             else
             {
                 if (mParams.verbose)
                 {
-                    gLogWarning << "Missing dynamic range for tensor: " << tName << std::endl;
+                    sample::gLogWarning << "Missing dynamic range for tensor: " << tName << std::endl;
                 }
             }
         }
@@ -383,18 +397,21 @@ bool SampleINT8API::setDynamicRange(SampleUniquePtr<nvinfer1::INetworkDefinition
     {
         for (int j = 0; j < network->getLayer(i)->getNbOutputs(); ++j)
         {
-            string tName = network->getLayer(i)->getOutput(j)->getName();
+            std::string tName = network->getLayer(i)->getOutput(j)->getName();
             if (mPerTensorDynamicRangeMap.find(tName) != mPerTensorDynamicRangeMap.end())
             {
                 // Calibrator generated dynamic range for network tensor can be overriden or set using below API
-                network->getLayer(i)->getOutput(j)->setDynamicRange(
-                    -mPerTensorDynamicRangeMap.at(tName), mPerTensorDynamicRangeMap.at(tName));
+                if (!network->getLayer(i)->getOutput(j)->setDynamicRange(
+                        -mPerTensorDynamicRangeMap.at(tName), mPerTensorDynamicRangeMap.at(tName)))
+                {
+                    return false;
+                }
             }
             else
             {
                 if (mParams.verbose)
                 {
-                    gLogWarning << "Missing dynamic range for tensor: " << tName << std::endl;
+                    sample::gLogWarning << "Missing dynamic range for tensor: " << tName << std::endl;
                 }
             }
         }
@@ -402,9 +419,10 @@ bool SampleINT8API::setDynamicRange(SampleUniquePtr<nvinfer1::INetworkDefinition
 
     if (mParams.verbose)
     {
-        gLogInfo << "Per Tensor Dynamic Range Values for the Network:" << std::endl;
+        sample::gLogInfo << "Per Tensor Dynamic Range Values for the Network:" << std::endl;
         for (auto iter = mPerTensorDynamicRangeMap.begin(); iter != mPerTensorDynamicRangeMap.end(); ++iter)
-            gLogInfo << "Tensor: " << iter->first << ". Max Absolute Dynamic Range: " << iter->second << std::endl;
+            sample::gLogInfo << "Tensor: " << iter->first << ". Max Absolute Dynamic Range: " << iter->second
+                             << std::endl;
     }
     return true;
 }
@@ -416,7 +434,7 @@ bool SampleINT8API::prepareInput(const samplesCommon::BufferManager& buffers)
 {
     if (samplesCommon::toLower(samplesCommon::getFileType(mParams.imageFileName)).compare("ppm") != 0)
     {
-        gLogError << "Wrong format: " << mParams.imageFileName << " is not a ppm file." << std::endl;
+        sample::gLogError << "Wrong format: " << mParams.imageFileName << " is not a ppm file." << std::endl;
         return false;
     }
 
@@ -426,7 +444,7 @@ bool SampleINT8API::prepareInput(const samplesCommon::BufferManager& buffers)
     int max{0};
     std::string magic{""};
 
-    vector<uint8_t> fileData(channels * height * width);
+    std::vector<uint8_t> fileData(channels * height * width);
     // Prepardde PPM Buffer to read the input image
     // samplesCommon::PPM<channels, height, width> ppm;
     // samplesCommon::readPPMFile(mParams.imageFileName, ppm);
@@ -466,24 +484,24 @@ bool SampleINT8API::verifyOutput(const samplesCommon::BufferManager& buffers) co
 {
     // copy output host buffer data for further processing
     const float* probPtr = static_cast<const float*>(buffers.getHostBuffer(mInOut.at("output")));
-    vector<float> output(probPtr, probPtr + mOutputDims.d[1] * mParams.batchSize);
+    std::vector<float> output(probPtr, probPtr + mOutputDims.d[1]);
 
     auto inds = samplesCommon::argsort(output.cbegin(), output.cend(), true);
 
     // read reference lables to generate prediction lables
-    vector<string> referenceVector;
+    std::vector<std::string> referenceVector;
     if (!samplesCommon::readReferenceFile(mParams.referenceFileName, referenceVector))
     {
-        gLogError << "Unable to read reference file: " << mParams.referenceFileName << std::endl;
+        sample::gLogError << "Unable to read reference file: " << mParams.referenceFileName << std::endl;
         return false;
     }
 
-    vector<string> top5Result = samplesCommon::classify(referenceVector, output, 5);
+    std::vector<std::string> top5Result = samplesCommon::classify(referenceVector, output, 5);
 
-    gLogInfo << "SampleINT8API result: Detected:" << std::endl;
+    sample::gLogInfo << "SampleINT8API result: Detected:" << std::endl;
     for (int i = 1; i <= 5; ++i)
     {
-        gLogInfo << "[" << i << "]  " << top5Result[i - 1] << std::endl;
+        sample::gLogInfo << "[" << i << "]  " << top5Result[i - 1] << std::endl;
     }
 
     return true;
@@ -497,55 +515,57 @@ bool SampleINT8API::verifyOutput(const samplesCommon::BufferManager& buffers) co
 //!
 //! \return Returns true if the engine was created successfully and false otherwise
 //!
-Logger::TestResult SampleINT8API::build()
+sample::Logger::TestResult SampleINT8API::build()
 {
-    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(gLogger.getTRTLogger()));
+    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(sample::gLogger.getTRTLogger()));
     if (!builder)
     {
-        gLogError << "Unable to create builder object." << std::endl;
-        return Logger::TestResult::kFAILED;
+        sample::gLogError << "Unable to create builder object." << std::endl;
+        return sample::Logger::TestResult::kFAILED;
     }
-    
+
     const auto explicitBatch = 1U << static_cast<uint32_t>(NetworkDefinitionCreationFlag::kEXPLICIT_BATCH);
     auto network = SampleUniquePtr<nvinfer1::INetworkDefinition>(builder->createNetworkV2(explicitBatch));
     if (!network)
     {
-        gLogError << "Unable to create network object." << mParams.referenceFileName << std::endl;
-        return Logger::TestResult::kFAILED;
+        sample::gLogError << "Unable to create network object." << mParams.referenceFileName << std::endl;
+        return sample::Logger::TestResult::kFAILED;
     }
 
     auto config = SampleUniquePtr<nvinfer1::IBuilderConfig>(builder->createBuilderConfig());
     if (!config)
     {
-        gLogError << "Unable to create config object." << mParams.referenceFileName << std::endl;
-        return Logger::TestResult::kFAILED;
+        sample::gLogError << "Unable to create config object." << mParams.referenceFileName << std::endl;
+        return sample::Logger::TestResult::kFAILED;
     }
 
-    auto parser = SampleUniquePtr<nvonnxparser::IParser>(nvonnxparser::createParser(*network, gLogger.getTRTLogger()));
+    auto parser
+        = SampleUniquePtr<nvonnxparser::IParser>(nvonnxparser::createParser(*network, sample::gLogger.getTRTLogger()));
     if (!parser)
     {
-        gLogError << "Unable to create parser object." << mParams.referenceFileName << std::endl;
-        return Logger::TestResult::kFAILED;
+        sample::gLogError << "Unable to create parser object." << mParams.referenceFileName << std::endl;
+        return sample::Logger::TestResult::kFAILED;
     }
 
     // Parse ONNX model file to populate TensorRT INetwork
     int verbosity = (int) nvinfer1::ILogger::Severity::kERROR;
     if (!parser->parseFromFile(mParams.modelFileName.c_str(), verbosity))
     {
-        gLogError << "Unable to parse ONNX model file: " << mParams.modelFileName << std::endl;
-        return Logger::TestResult::kFAILED;
+        sample::gLogError << "Unable to parse ONNX model file: " << mParams.modelFileName << std::endl;
+        return sample::Logger::TestResult::kFAILED;
     }
 
     if (mParams.writeNetworkTensors)
     {
         writeNetworkTensorNames(network);
-        return Logger::TestResult::kWAIVED;
+        return sample::Logger::TestResult::kWAIVED;
     }
 
     if (!builder->platformHasFastInt8())
     {
-        gLogError << "Platform does not support INT8 inference. sampleINT8API can only run in INT8 Mode." << std::endl;
-        return Logger::TestResult::kWAIVED;
+        sample::gLogError << "Platform does not support INT8 inference. sampleINT8API can only run in INT8 Mode."
+                          << std::endl;
+        return sample::Logger::TestResult::kWAIVED;
     }
 
     // Configure buider
@@ -557,28 +577,16 @@ Logger::TestResult SampleINT8API::build()
     // Mark calibrator as null. As user provides dynamic range for each tensor, no calibrator is required
     config->setInt8Calibrator(nullptr);
 
-    auto maxBatchSize = mParams.batchSize;
-    if (mParams.dlaCore >= 0)
-    {
-        samplesCommon::enableDLA(builder.get(), config.get(), mParams.dlaCore);
-        if (maxBatchSize > builder->getMaxDLABatchSize())
-        {
-            std::cerr << "Requested batch size " << maxBatchSize << " is greater than the max DLA batch size of "
-                      << builder->getMaxDLABatchSize() << ". Reducing batch size accordingly." << std::endl;
-            maxBatchSize = builder->getMaxDLABatchSize();
-        }
-    }
-    builder->setMaxBatchSize(maxBatchSize);
-
     // force layer to execute with required precision
-    config->setFlag(BuilderFlag::kSTRICT_TYPES);
+    // NVBUG: http://nvbugs/2725746
+    // config->setFlag(BuilderFlag::kSTRICT_TYPES);
     setLayerPrecision(network);
 
     // set INT8 Per Tensor Dynamic range
     if (!setDynamicRange(network))
     {
-        gLogError << "Unable to set per tensor dynamic range." << std::endl;
-        return Logger::TestResult::kFAILED;
+        sample::gLogError << "Unable to set per tensor dynamic range." << std::endl;
+        return sample::Logger::TestResult::kFAILED;
     }
 
     // build TRT engine
@@ -586,8 +594,8 @@ Logger::TestResult SampleINT8API::build()
         builder->buildEngineWithConfig(*network, *config), samplesCommon::InferDeleter());
     if (!mEngine)
     {
-        gLogError << "Unable to build cuda engine." << std::endl;
-        return Logger::TestResult::kFAILED;
+        sample::gLogError << "Unable to build cuda engine." << std::endl;
+        return sample::Logger::TestResult::kFAILED;
     }
 
     // populates input output map structure
@@ -600,7 +608,7 @@ Logger::TestResult SampleINT8API::build()
     const int outputIndex = mEngine.get()->getBindingIndex(mInOut["output"].c_str());
     mOutputDims = mEngine.get()->getBindingDimensions(outputIndex);
 
-    return Logger::TestResult::kRUNNING;
+    return sample::Logger::TestResult::kRUNNING;
 }
 
 //!
@@ -609,15 +617,15 @@ Logger::TestResult SampleINT8API::build()
 //! \details This function is the main execution function of the sample. It allocates
 //!          the buffer, sets inputs, executes the engine, and verifies the output
 //!
-Logger::TestResult SampleINT8API::infer()
+sample::Logger::TestResult SampleINT8API::infer()
 {
     // Create RAII buffer manager object
-    samplesCommon::BufferManager buffers(mEngine, mParams.batchSize);
+    samplesCommon::BufferManager buffers(mEngine);
 
     auto context = SampleUniquePtr<nvinfer1::IExecutionContext>(mEngine->createExecutionContext());
     if (!context)
     {
-        return Logger::TestResult::kFAILED;
+        return sample::Logger::TestResult::kFAILED;
     }
 
     // Read the input data into the managed buffers
@@ -625,7 +633,7 @@ Logger::TestResult SampleINT8API::infer()
 
     if (!prepareInput(buffers))
     {
-        return Logger::TestResult::kFAILED;
+        return sample::Logger::TestResult::kFAILED;
     }
 
     // Create CUDA stream for the execution of this inference
@@ -638,7 +646,7 @@ Logger::TestResult SampleINT8API::infer()
     // Asynchronously enqueue the inference work
     if (!context->enqueueV2(buffers.getDeviceBindings().data(), stream, nullptr))
     {
-        return Logger::TestResult::kFAILED;
+        return sample::Logger::TestResult::kFAILED;
     }
 
     // Asynchronously copy data from device output buffers to host output buffers
@@ -651,15 +659,15 @@ Logger::TestResult SampleINT8API::infer()
     cudaStreamDestroy(stream);
 
     // Check and print the output of the inference
-    return verifyOutput(buffers) ? Logger::TestResult::kRUNNING : Logger::TestResult::kFAILED;
+    return verifyOutput(buffers) ? sample::Logger::TestResult::kRUNNING : sample::Logger::TestResult::kFAILED;
 }
 
 //!
 //! \brief Used to clean up any state created in the sample class
 //!
-Logger::TestResult SampleINT8API::teardown()
+sample::Logger::TestResult SampleINT8API::teardown()
 {
-    return Logger::TestResult::kRUNNING;
+    return sample::Logger::TestResult::kRUNNING;
 }
 
 //!
@@ -738,7 +746,7 @@ bool parseSampleINT8APIArgs(SampleINT8APIArgs& args, int argc, char* argv[])
         }
         else
         {
-            gLogError << "Invalid Argument: " << argv[i] << std::endl;
+            sample::gLogError << "Invalid Argument: " << argv[i] << std::endl;
             return false;
         }
     }
@@ -747,21 +755,21 @@ bool parseSampleINT8APIArgs(SampleINT8APIArgs& args, int argc, char* argv[])
 
 void validateInputParams(SampleINT8APIParams& params)
 {
-    gLogInfo << "Please follow README.md to generate missing input files." << std::endl;
-    gLogInfo << "Validating input parameters. Using following input files for inference." << std::endl;
+    sample::gLogInfo << "Please follow README.md to generate missing input files." << std::endl;
+    sample::gLogInfo << "Validating input parameters. Using following input files for inference." << std::endl;
     params.modelFileName = locateFile(params.modelFileName, params.dataDirs);
-    gLogInfo << "    Model File: " << params.modelFileName << std::endl;
+    sample::gLogInfo << "    Model File: " << params.modelFileName << std::endl;
     if (params.writeNetworkTensors)
     {
-        gLogInfo << "    Writing Network Tensors File to: " << params.networkTensorsFileName << std::endl;
+        sample::gLogInfo << "    Writing Network Tensors File to: " << params.networkTensorsFileName << std::endl;
         return;
     }
     params.imageFileName = locateFile(params.imageFileName, params.dataDirs);
-    gLogInfo << "    Image File: " << params.imageFileName << std::endl;
+    sample::gLogInfo << "    Image File: " << params.imageFileName << std::endl;
     params.referenceFileName = locateFile(params.referenceFileName, params.dataDirs);
-    gLogInfo << "    Reference File: " << params.referenceFileName << std::endl;
+    sample::gLogInfo << "    Reference File: " << params.referenceFileName << std::endl;
     params.dynamicRangeFileName = locateFile(params.dynamicRangeFileName, params.dataDirs);
-    gLogInfo << "    Dynamic Range File: " << params.dynamicRangeFileName << std::endl;
+    sample::gLogInfo << "    Dynamic Range File: " << params.dynamicRangeFileName << std::endl;
     return;
 }
 
@@ -782,7 +790,6 @@ SampleINT8APIParams initializeSampleParams(SampleINT8APIArgs args)
     }
 
     params.dataDirs.push_back(""); //! In case of absolute path search
-    params.batchSize = 1;
     params.verbose = args.verbose;
     params.modelFileName = args.modelFileName;
     params.imageFileName = args.imageFileName;
@@ -800,35 +807,37 @@ SampleINT8APIParams initializeSampleParams(SampleINT8APIArgs args)
 //!
 void printHelpInfo()
 {
-    std::cout << "Usage: ./sample_int8_api [-h or --help] [--model=<model file>] "
-                 "[--ranges=<dynamic range file>] [--image=<image file>] [--reference=<reference file>] "
-                 "[--data=<path to data directory>] [--useDLACore=<int>] [-v or --verbose]\n";
-    std::cout << "--help, -h                        Display This help information" << std::endl;
-    std::cout << "--model=<model file>              Path to the model file (default = resnet50.onnx)"
+    std::cout << "Usage: ./sample_int8_api [-h or --help] [--model=model_file] "
+                 "[--ranges=per_tensor_dynamic_range_file] [--image=image_file] [--reference=reference_file] "
+                 "[--data=/path/to/data/dir] [--useDLACore=<int>] [-v or --verbose]\n";
+    std::cout << "-h or --help. Display This help information" << std::endl;
+    std::cout << "--model=model_file.onnx or /absolute/path/to/model_file.onnx. Generate model file using README.md in "
+                 "case it does not exists. Default to resnet50.onnx"
               << std::endl;
-    std::cout << "--image=<image file>              Path to the image file to infer (default = airlines.ppm)"
+    std::cout << "--image=image.ppm or /absolute/path/to/image.ppm. Image to infer. Defaults to airlines.ppm"
               << std::endl;
-    std::cout << "--reference=<reference file>      Path to the reference labels file (default = reference_labels.txt)"
+    std::cout << "--reference=reference.txt or /absolute/path/to/reference.txt. Reference labels file. Defaults to "
+                 "reference_labels.txt"
               << std::endl;
-    std::cout << "--ranges=<dynamic range file>     Path to the custom per tensor dynamic range file for the network "
-                 "(default = resnet50_per_tensor_dynamic_range.txt)"
+    std::cout << "--ranges=ranges.txt or /absolute/path/to/ranges.txt. Specify custom per tensor dynamic range for the "
+                 "network. Defaults to resnet50_per_tensor_dynamic_range.txt"
               << std::endl;
-    std::cout << "--write_tensors                   Option to generate file containing network tensors name. By default writes to "
+    std::cout << "--write_tensors. Option to generate file containing network tensors name. By default writes to "
                  "network_tensors.txt file. To provide user defined file name use additional option "
                  "--network_tensors_file. See --network_tensors_file option usage for more detail."
               << std::endl;
-    std::cout << "--network_tensors_file=<file>     Path to the network tensors file. This option "
+    std::cout << "--network_tensors_file=network_tensors.txt or /absolute/path/to/network_tensors.txt. This option "
                  "needs to be used with --write_tensors option. Specify file name (will write to current execution "
                  "directory) or absolute path to file name to write network tensor names file. Dynamic range "
-                 "corresponding to each network tensor is required to run the sample (default = network_tensors.txt)"
+                 "corresponding to each network tensor is required to run the sample. Defaults to network_tensors.txt"
               << std::endl;
-    std::cout << "--data=<path to data dir>         Path to the data directory to search for above files in case absolute paths to "
-                 "files are not provided (default both data/samples/int8_api/ and data/int8_api/)"
+    std::cout << "--data=/path/to/data/dir. Specify data directory to search for above files in case absolute paths to "
+                 "files are not provided. Defaults to data/samples/int8_api/ or data/int8_api/"
               << std::endl;
-    std::cout << "--useDLACore=N                    Specify a DLA engine for layers that support DLA. Value can range from 0 to n-1, "
+    std::cout << "--useDLACore=N. Specify a DLA engine for layers that support DLA. Value can range from 0 to n-1, "
                  "where n is the number of DLA engines on the platform."
               << std::endl;
-    std::cout << "--verbose                         Enable verbose prints" << std::endl;
+    std::cout << "--verbose. Outputs per tensor dynamic range and layer precision info for the network" << std::endl;
 }
 
 int main(int argc, char** argv)
@@ -838,7 +847,7 @@ int main(int argc, char** argv)
 
     if (!argsOK)
     {
-        gLogError << "Invalid arguments" << std::endl;
+        sample::gLogError << "Invalid arguments" << std::endl;
         printHelpInfo();
         return EXIT_FAILURE;
     }
@@ -850,38 +859,38 @@ int main(int argc, char** argv)
     }
     if (args.verbose)
     {
-        gLogger.setReportableSeverity(nvinfer1::ILogger::Severity::kVERBOSE);
+        sample::gLogger.setReportableSeverity(nvinfer1::ILogger::Severity::kVERBOSE);
     }
 
-    auto sampleTest = gLogger.defineTest(gSampleName, argc, argv);
+    auto sampleTest = sample::gLogger.defineTest(gSampleName, argc, argv);
 
-    gLogger.reportTestStart(sampleTest);
+    sample::gLogger.reportTestStart(sampleTest);
 
     SampleINT8APIParams params;
     params = initializeSampleParams(args);
 
     SampleINT8API sample(params);
-    gLogInfo << "Building and running a INT8 GPU inference engine for " << params.modelFileName << std::endl;
+    sample::gLogInfo << "Building and running a INT8 GPU inference engine for " << params.modelFileName << std::endl;
 
     auto buildStatus = sample.build();
-    if (buildStatus == Logger::TestResult::kWAIVED)
+    if (buildStatus == sample::Logger::TestResult::kWAIVED)
     {
-        return gLogger.reportWaive(sampleTest);
+        return sample::gLogger.reportWaive(sampleTest);
     }
-    else if (buildStatus == Logger::TestResult::kFAILED)
+    else if (buildStatus == sample::Logger::TestResult::kFAILED)
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
 
-    if (sample.infer() != Logger::TestResult::kRUNNING)
+    if (sample.infer() != sample::Logger::TestResult::kRUNNING)
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
 
-    if (sample.teardown() != Logger::TestResult::kRUNNING)
+    if (sample.teardown() != sample::Logger::TestResult::kRUNNING)
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
 
-    return gLogger.reportPass(sampleTest);
+    return sample::gLogger.reportPass(sampleTest);
 }
diff --git a/samples/opensource/sampleMLP/CMakeLists.txt b/samples/opensource/sampleMLP/CMakeLists.txt
index f25b8927..78b895a2 100644
--- a/samples/opensource/sampleMLP/CMakeLists.txt
+++ b/samples/opensource/sampleMLP/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleMLP/README.md b/samples/opensource/sampleMLP/README.md
index 180bf059..92a2bd5e 100644
--- a/samples/opensource/sampleMLP/README.md
+++ b/samples/opensource/sampleMLP/README.md
@@ -139,19 +139,11 @@ This sample comes with pre-trained weights. However, if you want to train your o
 
 This output shows that the sample ran successfully; `PASSED`.
 
-### Sample --help options
 
-To see the full list of available options and their descriptions, use the `-h` or `--help` command line option. For example:
-```
-./sample_mlp --help
-
-Usage: ./sample_mlp [-h or --help] [-d or --datadir=<path to data directory>] [--useDLACore=<int>]
---help Display help information
---datadir Specify path to a data directory, overriding the default. This option can be used multiple times to add multiple directories. If no data directories are given, the default is to use (data/samples/mnist/, data/mnist/, data/samples/mlp/, data/mlp/)
---useDLACore=N Specify a DLA engine for layers that support DLA. Value can range from 0 to n-1, where n is the number of DLA engines on the platform.
---int8 Run in Int8 mode.
---fp16 Run in FP16 mode.
-```
+### Sample `--help` options
+
+To see the full list of available options and their descriptions, use the `-h` or `--help` command line option.
+
 
 # Additional resources
 
diff --git a/samples/opensource/sampleMLP/convert_weights.py b/samples/opensource/sampleMLP/convert_weights.py
index 7fdce102..e9f46e76 100644
--- a/samples/opensource/sampleMLP/convert_weights.py
+++ b/samples/opensource/sampleMLP/convert_weights.py
@@ -1,6 +1,6 @@
 #!/usr/bin/python
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleMLP/sampleMLP.cpp b/samples/opensource/sampleMLP/sampleMLP.cpp
index 24b61a4e..cb310f37 100644
--- a/samples/opensource/sampleMLP/sampleMLP.cpp
+++ b/samples/opensource/sampleMLP/sampleMLP.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -141,7 +141,7 @@ bool SampleMLP::build()
 {
     mWeightMap = loadWeights(locateFile(mParams.weightsFile, mParams.dataDirs));
 
-    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(gLogger.getTRTLogger()));
+    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(sample::gLogger.getTRTLogger()));
     if (!builder)
     {
         return false;
@@ -303,12 +303,12 @@ bool SampleMLP::processInput(const samplesCommon::BufferManager& buffers)
         mParams.inputW);
 
     // print the ascii representation of the file that was loaded.
-    gLogInfo << "Input:\n";
+    sample::gLogInfo << "Input:\n";
     for (int i = 0; i < (mParams.inputH * mParams.inputW); i++)
     {
-        gLogInfo << (" .:-=+*#%@"[fileData[i] / 26]) << (((i + 1) % mParams.inputW) ? "" : "\n");
+        sample::gLogInfo << (" .:-=+*#%@"[fileData[i] / 26]) << (((i + 1) % mParams.inputW) ? "" : "\n");
     }
-    gLogInfo << std::endl;
+    sample::gLogInfo << std::endl;
 
     float* hostDataBuffer = static_cast<float*>(buffers.getHostBuffer(mParams.inputTensorNames[0]));
     // Normalize the data the same way TensorFlow does.
@@ -331,11 +331,11 @@ bool SampleMLP::verifyOutput(const samplesCommon::BufferManager& buffers)
     bool pass = (idx == mNumber);
     if (pass)
     {
-        gLogInfo << "Algorithm chose " << idx << std::endl;
+        sample::gLogInfo << "Algorithm chose " << idx << std::endl;
     }
     else
     {
-        gLogInfo << "Algorithm chose " << idx << " but expected " << mNumber << "." << std::endl;
+        sample::gLogInfo << "Algorithm chose " << idx << " but expected " << mNumber << "." << std::endl;
     }
 
     return pass;
@@ -565,7 +565,7 @@ int main(int argc, char** argv)
     bool argsOK = samplesCommon::parseArgs(args, argc, argv);
     if (!argsOK)
     {
-        gLogError << "Invalid arguments" << std::endl;
+        sample::gLogError << "Invalid arguments" << std::endl;
         printHelpInfo();
         return EXIT_FAILURE;
     }
@@ -575,26 +575,26 @@ int main(int argc, char** argv)
         return EXIT_SUCCESS;
     }
 
-    auto sampleTest = gLogger.defineTest(gSampleName, argc, argv);
+    auto sampleTest = sample::gLogger.defineTest(gSampleName, argc, argv);
 
-    gLogger.reportTestStart(sampleTest);
+    sample::gLogger.reportTestStart(sampleTest);
 
     SampleMLP sample(initializeSampleParams(args));
 
-    gLogInfo << "Building and running a GPU inference engine for MLP" << std::endl;
+    sample::gLogInfo << "Building and running a GPU inference engine for MLP" << std::endl;
 
     if (!sample.build())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
     if (!sample.infer())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
     if (!sample.teardown())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
 
-    return gLogger.reportPass(sampleTest);
+    return sample::gLogger.reportPass(sampleTest);
 }
diff --git a/samples/opensource/sampleMNIST/CMakeLists.txt b/samples/opensource/sampleMNIST/CMakeLists.txt
index 703d800b..8b93ff33 100644
--- a/samples/opensource/sampleMNIST/CMakeLists.txt
+++ b/samples/opensource/sampleMNIST/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleMNIST/README.md b/samples/opensource/sampleMNIST/README.md
index bc58fcd4..f3f2236e 100644
--- a/samples/opensource/sampleMNIST/README.md
+++ b/samples/opensource/sampleMNIST/README.md
@@ -6,7 +6,7 @@
 - [How does this sample work?](#how-does-this-sample-work)
 	* [TensorRT API layers and ops](#tensorrt-api-layers-and-ops)
 - [Running the sample](#running-the-sample)
-	* [Sample `--help` options](#sample---help-options)
+	* [Sample `--help` options](#sample-help-options)
 - [Additional resources](#additional-resources)
 - [License](#license)
 - [Changelog](#changelog)
@@ -132,17 +132,10 @@ The SoftMax layer applies the SoftMax function on the input tensor along an inpu
 	This output shows that the sample ran successfully; `PASSED`.
  
 
-### Sample --help options
+### Sample `--help` options
+
+To see the full list of available options and their descriptions, use the `-h` or `--help` command line option.
 
-To see the full list of available options and their descriptions, use the `-h` or `--help` command line option. For example:
-```
-Usage: ./sample_mnist [-h or --help] [-d or --datadir=<path to data directory>] [--useDLACore=<int>]
---help Display help information
---datadir Specify path to a data directory, overriding the default. This option can be used multiple times to add multiple directories. If no data directories are given, the default is to use (data/samples/mnist/, data/mnist/)
---useDLACore=N Specify a DLA engine for layers that support DLA. Value can range from 0 to n-1, where n is the number of DLA engines on the platform.
---int8 Run in Int8 mode.
---fp16 Run in FP16 mode.
-```
 
 # Additional resources
 
@@ -169,4 +162,4 @@ This `README.md` file was recreated, updated and reviewed.
 
 # Known issues
 
-There are no known issues in this sample.
\ No newline at end of file
+There are no known issues in this sample.
diff --git a/samples/opensource/sampleMNIST/sampleMNIST.cpp b/samples/opensource/sampleMNIST/sampleMNIST.cpp
index ed31771a..14e0f82a 100644
--- a/samples/opensource/sampleMNIST/sampleMNIST.cpp
+++ b/samples/opensource/sampleMNIST/sampleMNIST.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -76,7 +76,7 @@ class SampleMNIST
     //! \brief uses a Caffe parser to create the MNIST Network and marks the
     //!        output layers
     //!
-    void constructNetwork(
+    bool constructNetwork(
         SampleUniquePtr<nvcaffeparser1::ICaffeParser>& parser, SampleUniquePtr<nvinfer1::INetworkDefinition>& network);
 
     //!
@@ -111,7 +111,7 @@ class SampleMNIST
 //!
 bool SampleMNIST::build()
 {
-    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(gLogger.getTRTLogger()));
+    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(sample::gLogger.getTRTLogger()));
     if (!builder)
     {
         return false;
@@ -135,7 +135,11 @@ bool SampleMNIST::build()
         return false;
     }
 
-    constructNetwork(parser, network);
+    if (!constructNetwork(parser, network))
+    {
+        return false;
+    }
+
     builder->setMaxBatchSize(mParams.batchSize);
     config->setMaxWorkspaceSize(16_MiB);
     config->setFlag(BuilderFlag::kGPU_FALLBACK);
@@ -179,12 +183,12 @@ bool SampleMNIST::processInput(
     readPGMFile(locateFile(std::to_string(inputFileIdx) + ".pgm", mParams.dataDirs), fileData.data(), inputH, inputW);
 
     // Print ASCII representation of digit
-    gLogInfo << "Input:\n";
+    sample::gLogInfo << "Input:\n";
     for (int i = 0; i < inputH * inputW; i++)
     {
-        gLogInfo << (" .:-=+*#%@"[fileData[i] / 26]) << (((i + 1) % inputW) ? "" : "\n");
+        sample::gLogInfo << (" .:-=+*#%@"[fileData[i] / 26]) << (((i + 1) % inputW) ? "" : "\n");
     }
-    gLogInfo << std::endl;
+    sample::gLogInfo << std::endl;
 
     float* hostInputBuffer = static_cast<float*>(buffers.getHostBuffer(inputTensorName));
 
@@ -205,7 +209,7 @@ bool SampleMNIST::verifyOutput(
     const float* prob = static_cast<const float*>(buffers.getHostBuffer(outputTensorName));
 
     // Print histogram of the output distribution
-    gLogInfo << "Output:\n";
+    sample::gLogInfo << "Output:\n";
     float val{0.0f};
     int idx{0};
     const int kDIGITS = 10;
@@ -218,9 +222,9 @@ bool SampleMNIST::verifyOutput(
             idx = i;
         }
 
-        gLogInfo << i << ": " << std::string(int(std::floor(prob[i] * 10 + 0.5f)), '*') << "\n";
+        sample::gLogInfo << i << ": " << std::string(int(std::floor(prob[i] * 10 + 0.5f)), '*') << "\n";
     }
-    gLogInfo << std::endl;
+    sample::gLogInfo << std::endl;
 
     return (idx == groundTruthDigit && val > 0.9f);
 }
@@ -233,7 +237,7 @@ bool SampleMNIST::verifyOutput(
 //!
 //! \param builder Pointer to the engine builder
 //!
-void SampleMNIST::constructNetwork(
+bool SampleMNIST::constructNetwork(
     SampleUniquePtr<nvcaffeparser1::ICaffeParser>& parser, SampleUniquePtr<nvinfer1::INetworkDefinition>& network)
 {
     const nvcaffeparser1::IBlobNameToTensor* blobNameToTensor = parser->parse(
@@ -259,12 +263,23 @@ void SampleMNIST::constructNetwork(
         = samplesCommon::getMaxValue(static_cast<const float*>(meanWeights.values), samplesCommon::volume(inputDims));
 
     auto mean = network->addConstant(nvinfer1::Dims3(1, inputDims.d[1], inputDims.d[2]), meanWeights);
-    mean->getOutput(0)->setDynamicRange(-maxMean, maxMean);
-    network->getInput(0)->setDynamicRange(-maxMean, maxMean);
+    if (!mean->getOutput(0)->setDynamicRange(-maxMean, maxMean))
+    {
+        return false;
+    }
+    if (!network->getInput(0)->setDynamicRange(-maxMean, maxMean))
+    {
+        return false;
+    }
     auto meanSub = network->addElementWise(*network->getInput(0), *mean->getOutput(0), ElementWiseOperation::kSUB);
-    meanSub->getOutput(0)->setDynamicRange(-maxMean, maxMean);
+    if (!meanSub->getOutput(0)->setDynamicRange(-maxMean, maxMean))
+    {
+        return false;
+    }
     network->getLayer(0)->setInput(0, *meanSub->getOutput(0));
     samplesCommon::setAllTensorScales(network.get(), 127.0f, 127.0f);
+
+    return true;
 }
 
 //!
@@ -390,7 +405,7 @@ int main(int argc, char** argv)
     bool argsOK = samplesCommon::parseArgs(args, argc, argv);
     if (!argsOK)
     {
-        gLogError << "Invalid arguments" << std::endl;
+        sample::gLogError << "Invalid arguments" << std::endl;
         printHelpInfo();
         return EXIT_FAILURE;
     }
@@ -400,29 +415,29 @@ int main(int argc, char** argv)
         return EXIT_SUCCESS;
     }
 
-    auto sampleTest = gLogger.defineTest(gSampleName, argc, argv);
+    auto sampleTest = sample::gLogger.defineTest(gSampleName, argc, argv);
 
-    gLogger.reportTestStart(sampleTest);
+    sample::gLogger.reportTestStart(sampleTest);
 
     samplesCommon::CaffeSampleParams params = initializeSampleParams(args);
 
     SampleMNIST sample(params);
-    gLogInfo << "Building and running a GPU inference engine for MNIST" << std::endl;
+    sample::gLogInfo << "Building and running a GPU inference engine for MNIST" << std::endl;
 
     if (!sample.build())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
 
     if (!sample.infer())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
 
     if (!sample.teardown())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
 
-    return gLogger.reportPass(sampleTest);
+    return sample::gLogger.reportPass(sampleTest);
 }
diff --git a/samples/opensource/sampleMNISTAPI/CMakeLists.txt b/samples/opensource/sampleMNISTAPI/CMakeLists.txt
index 5780ddd2..183d9275 100644
--- a/samples/opensource/sampleMNISTAPI/CMakeLists.txt
+++ b/samples/opensource/sampleMNISTAPI/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleMNISTAPI/README.md b/samples/opensource/sampleMNISTAPI/README.md
index 361512d9..fb398972 100644
--- a/samples/opensource/sampleMNISTAPI/README.md
+++ b/samples/opensource/sampleMNISTAPI/README.md
@@ -7,7 +7,7 @@
 	* [TensorRT API layers](#tensorrt-api-layers)
 - [Prerequisites](#prerequisites)
 - [Running the sample](#running-the-sample)
-	* [Sample `--help` options](#sample---help-options)
+	* [Sample `--help` options](#sample-help-options)
 - [Additional resources](#additional-resources)
 - [License](#license)
 - [Changelog](#changelog)
@@ -132,17 +132,11 @@ When you build a network by individually creating every layer, ensure you provid
 
 	This output shows that the sample ran successfully; PASSED.
 
-### Sample --help options
-
-To see the full list of available options and their descriptions, use the `-h` or `--help` command line option. For example:
-```
-Usage: ./sample_mnist_api [-h or --help] [-d or --datadir=<path to data directory>] [--useDLACore=<int>]
--h or --help Display help information
---datadir Specify path to a data directory, overriding the default. This option can be used multiple times to add multiple directories. If no data directories are given, the default is to use (data/samples/mnist/, data/mnist/)
---useDLACore=N Specify a DLA engine for layers that support DLA. Value can range from 0 to n-1, where n is the number of DLA engines on the platform.
---int8 Run in Int8 mode.
---fp16 Run in FP16 mode.
-```
+
+### Sample `--help` options
+
+To see the full list of available options and their descriptions, use the `-h` or `--help` command line option.
+
 
 # Additional resources
 
diff --git a/samples/opensource/sampleMNISTAPI/sampleMNISTAPI.cpp b/samples/opensource/sampleMNISTAPI/sampleMNISTAPI.cpp
index 9d3452d1..ec3edca9 100644
--- a/samples/opensource/sampleMNISTAPI/sampleMNISTAPI.cpp
+++ b/samples/opensource/sampleMNISTAPI/sampleMNISTAPI.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -126,7 +126,7 @@ bool SampleMNISTAPI::build()
 {
     mWeightMap = loadWeights(locateFile(mParams.weightsFile, mParams.dataDirs));
 
-    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(gLogger.getTRTLogger()));
+    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(sample::gLogger.getTRTLogger()));
     if (!builder)
     {
         return false;
@@ -400,7 +400,7 @@ bool SampleMNISTAPI::teardown()
 //!
 std::map<std::string, nvinfer1::Weights> SampleMNISTAPI::loadWeights(const std::string& file)
 {
-    gLogInfo << "Loading weights: " << file << std::endl;
+    sample::gLogInfo << "Loading weights: " << file << std::endl;
 
     // Open weights file
     std::ifstream input(file, std::ios::binary);
@@ -507,7 +507,7 @@ int main(int argc, char** argv)
     bool argsOK = samplesCommon::parseArgs(args, argc, argv);
     if (!argsOK)
     {
-        gLogError << "Invalid arguments" << std::endl;
+        sample::gLogError << "Invalid arguments" << std::endl;
         printHelpInfo();
         return EXIT_FAILURE;
     }
@@ -517,26 +517,26 @@ int main(int argc, char** argv)
         return EXIT_SUCCESS;
     }
 
-    auto sampleTest = gLogger.defineTest(gSampleName, argc, argv);
+    auto sampleTest = sample::gLogger.defineTest(gSampleName, argc, argv);
 
-    gLogger.reportTestStart(sampleTest);
+    sample::gLogger.reportTestStart(sampleTest);
 
     SampleMNISTAPI sample(initializeSampleParams(args));
 
-    gLogInfo << "Building and running a GPU inference engine for MNIST API" << std::endl;
+    sample::gLogInfo << "Building and running a GPU inference engine for MNIST API" << std::endl;
 
     if (!sample.build())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
     if (!sample.infer())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
     if (!sample.teardown())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
 
-    return gLogger.reportPass(sampleTest);
+    return sample::gLogger.reportPass(sampleTest);
 }
diff --git a/samples/opensource/sampleMovieLens/CMakeLists.txt b/samples/opensource/sampleMovieLens/CMakeLists.txt
index c25b846f..981f16e0 100644
--- a/samples/opensource/sampleMovieLens/CMakeLists.txt
+++ b/samples/opensource/sampleMovieLens/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleMovieLens/README.md b/samples/opensource/sampleMovieLens/README.md
index cef6c719..d5ffd574 100644
--- a/samples/opensource/sampleMovieLens/README.md
+++ b/samples/opensource/sampleMovieLens/README.md
@@ -10,7 +10,7 @@
     * [TensorRT API layers and ops](#tensorrt-api-layers-and-ops)
 - [Training an NCF network](#training-an-ncf-network)
 - [Running the sample](#running-the-sample)
-    * [Sample `--help` options](#sample---help-options)
+    * [Sample `--help` options](#sample-help-options)
 - [Additional resources](#additional-resources)
 - [License](#license)
 - [Changelog](#changelog)
@@ -144,18 +144,10 @@ This sample comes with a pre-trained model. However, if you want to train your o
     This output shows that the sample ran successfully; `PASSED`.
 
 
-### Sample --help options
+### Sample `--help` options
+
+To see the full list of available options and their descriptions, use the `-h` or `--help` command line option.
 
-To see the full list of available options and their descriptions, use the `-h` or `--help` command line option. For example:
-```
-Usage: ./sample_movielens [-h or --help] [-b NUM_USERS] [--useDLACore=<int>] [--verbose]
---help          Display help information.
---verbose       Enable verbose prints.
--b NUM_USERS    Number of Users i.e. Batch Size (default numUsers==32).
---useDLACore=N  Specify a DLA engine for layers that support DLA. Value can range from 0 to n-1, where n is the number of DLA engines on the platform.
---fp16          Run in FP16 mode.
---strict        Run with strict type constraints.
-```
 
 # Additional resources
 
diff --git a/samples/opensource/sampleMovieLens/preprocess.py b/samples/opensource/sampleMovieLens/preprocess.py
index d113cbc0..1d13423d 100644
--- a/samples/opensource/sampleMovieLens/preprocess.py
+++ b/samples/opensource/sampleMovieLens/preprocess.py
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleMovieLens/sampleMovieLens.cpp b/samples/opensource/sampleMovieLens/sampleMovieLens.cpp
index 2062d769..7bdb7a16 100644
--- a/samples/opensource/sampleMovieLens/sampleMovieLens.cpp
+++ b/samples/opensource/sampleMovieLens/sampleMovieLens.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -163,7 +163,7 @@ class SampleMovieLens
 //!
 bool SampleMovieLens::build()
 {
-    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(gLogger.getTRTLogger()));
+    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(sample::gLogger.getTRTLogger()));
     if (!builder)
     {
         return false;
@@ -227,16 +227,16 @@ void SampleMovieLens::constructNetwork(SampleUniquePtr<nvinfer1::IBuilder>& buil
     parser->registerOutput(mParams.outputTensorNames[0].c_str());
 
     auto dType = mParams.fp16 ? nvinfer1::DataType::kHALF : nvinfer1::DataType::kFLOAT;
-    gLogInfo << "Begin parsing model..." << std::endl;
+    sample::gLogInfo << "Begin parsing model..." << std::endl;
 
     // Parse the uff model to populate the network
     if (!parser->parse(mParams.uffFileName.c_str(), *network, dType))
     {
-        gLogError << "Failure while parsing UFF file" << std::endl;
+        sample::gLogError << "Failure while parsing UFF file" << std::endl;
         return;
     }
 
-    gLogInfo << "End parsing model..." << std::endl;
+    sample::gLogInfo << "End parsing model..." << std::endl;
 
     // Add postprocessing i.e. topk layer to the UFF Network
     // Retrieve last layer of UFF Network
@@ -262,7 +262,7 @@ void SampleMovieLens::constructNetwork(SampleUniquePtr<nvinfer1::IBuilder>& buil
     // Set the topK indices tensor as INT32 type
     topK->getOutput(1)->setType(nvinfer1::DataType::kINT32);
 
-    gLogInfo << "Done constructing network..." << std::endl;
+    sample::gLogInfo << "Done constructing network..." << std::endl;
 
     mEngine = std::shared_ptr<nvinfer1::ICudaEngine>(
         builder->buildEngineWithConfig(*network, *config), samplesCommon::InferDeleter());
@@ -313,7 +313,7 @@ bool SampleMovieLens::infer()
     // Wait for the work in the stream to complete
     cudaStreamSynchronize(stream);
     timer.stop();
-    gLogInfo << "Done execution. Duration : " << timer.microseconds() << " microseconds." << std::endl;
+    sample::gLogInfo << "Done execution. Duration : " << timer.microseconds() << " microseconds." << std::endl;
 
     // Release stream
     cudaStreamDestroy(stream);
@@ -423,7 +423,7 @@ void SampleMovieLens::readInputSample(std::ifstream& file, OutputParams& outPara
 void SampleMovieLens::parseMovieLensData()
 {
     std::ifstream file;
-    file.open(mParams.ratingInputFile, ios::binary);
+    file.open(mParams.ratingInputFile, std::ios::binary);
     std::string line;
     int userIdx = 0;
     while (std::getline(file, line) && userIdx < mParams.batchSize)
@@ -457,15 +457,16 @@ bool SampleMovieLens::teardown()
 //!
 void SampleMovieLens::printOutputParams(OutputParams& outParams)
 {
-    gLogVerbose << "User Id                            :   " << outParams.userId << std::endl;
-    gLogVerbose << "Expected Predicted Max Rating Item :   " << outParams.expectedPredictedMaxRatingItem << std::endl;
-    gLogVerbose << "Expected Predicted Max Rating Prob :   " << outParams.expectedPredictedMaxRatingItemProb
-                << std::endl;
-    gLogVerbose << "Total TopK Items : " << outParams.itemProbPairVec.size() << std::endl;
+    sample::gLogVerbose << "User Id                            :   " << outParams.userId << std::endl;
+    sample::gLogVerbose << "Expected Predicted Max Rating Item :   " << outParams.expectedPredictedMaxRatingItem
+                        << std::endl;
+    sample::gLogVerbose << "Expected Predicted Max Rating Prob :   " << outParams.expectedPredictedMaxRatingItemProb
+                        << std::endl;
+    sample::gLogVerbose << "Total TopK Items : " << outParams.itemProbPairVec.size() << std::endl;
     for (unsigned int i = 0; i < outParams.itemProbPairVec.size(); ++i)
     {
-        gLogVerbose << outParams.itemProbPairVec.at(i).first << " : " << outParams.itemProbPairVec.at(i).second
-                    << std::endl;
+        sample::gLogVerbose << outParams.itemProbPairVec.at(i).first << " : " << outParams.itemProbPairVec.at(i).second
+                            << std::endl;
     }
 }
 
@@ -477,12 +478,12 @@ bool SampleMovieLens::verifyOutput(
 {
     bool pass{true};
 
-    gLogInfo << "Num of users : " << mParams.batchSize << std::endl;
-    gLogInfo << "Num of Movies : " << mParams.numMoviesPerUser << std::endl;
+    sample::gLogInfo << "Num of users : " << mParams.batchSize << std::endl;
+    sample::gLogInfo << "Num of Movies : " << mParams.numMoviesPerUser << std::endl;
 
-    gLogVerbose << "|-----------|------------|-----------------|-----------------|" << std::endl;
-    gLogVerbose << "|   User    |   Item     |  Expected Prob  |  Predicted Prob |" << std::endl;
-    gLogVerbose << "|-----------|------------|-----------------|-----------------|" << std::endl;
+    sample::gLogVerbose << "|-----------|------------|-----------------|-----------------|" << std::endl;
+    sample::gLogVerbose << "|   User    |   Item     |  Expected Prob  |  Predicted Prob |" << std::endl;
+    sample::gLogVerbose << "|-----------|------------|-----------------|-----------------|" << std::endl;
 
     for (int i = 0; i < mParams.batchSize; ++i)
     {
@@ -498,9 +499,9 @@ bool SampleMovieLens::verifyOutput(
             float predictedProb = topKItemProb[i * mParams.topKMovies + k];
             float expectedProb = mParams.userToExpectedItemProbMap.at(userIdx).at(k).second;
             int predictedItem = mParams.userToItemsMap.at(userIdx).at(predictedIdx);
-            gLogVerbose << "|" << std::setw(10) << userIdx << " | " << std::setw(10) << predictedItem << " | "
-                        << std::setw(15) << expectedProb << " | " << std::setw(15) << predictedProb << " | "
-                        << std::endl;
+            sample::gLogVerbose << "|" << std::setw(10) << userIdx << " | " << std::setw(10) << predictedItem << " | "
+                                << std::setw(15) << expectedProb << " | " << std::setw(15) << predictedProb << " | "
+                                << std::endl;
         }
     }
 
@@ -510,8 +511,9 @@ bool SampleMovieLens::verifyOutput(
         int maxPredictedIdx = topKItemNumber[i * mParams.topKMovies];
         int maxExpectedItem = mParams.userToExpectedItemProbMap.at(userIdx).at(0).first;
         int maxPredictedItem = mParams.userToItemsMap.at(userIdx).at(maxPredictedIdx);
-        gLogInfo << "| User :" << std::setw(4) << userIdx << "  |  Expected Item :" << std::setw(5) << maxExpectedItem
-                 << "  |  Predicted Item :" << std::setw(5) << maxPredictedItem << " | " << std::endl;
+        sample::gLogInfo << "| User :" << std::setw(4) << userIdx << "  |  Expected Item :" << std::setw(5)
+                         << maxExpectedItem << "  |  Predicted Item :" << std::setw(5) << maxPredictedItem << " | "
+                         << std::endl;
     }
 
     return pass;
@@ -558,7 +560,7 @@ bool parseSampleMovieLensArgs(SampleMovieLensArgs& args, int argc, char* argv[])
         else if (argStr == "--verbose")
         {
             args.verbose = true;
-            setReportableSeverity(Logger::Severity::kVERBOSE);
+            sample::setReportableSeverity(sample::Logger::Severity::kVERBOSE);
         }
         else if (argStr.substr(0, 13) == "--useDLACore=" && argStr.size() > 13)
         {
@@ -626,7 +628,7 @@ int main(int argc, char** argv)
     bool argsOK = parseSampleMovieLensArgs(args, argc, argv);
     if (!argsOK)
     {
-        gLogError << "Invalid arguments" << std::endl;
+        sample::gLogError << "Invalid arguments" << std::endl;
         printHelpInfo();
         return EXIT_FAILURE;
     }
@@ -636,27 +638,27 @@ int main(int argc, char** argv)
         return EXIT_SUCCESS;
     }
 
-    auto sampleTest = gLogger.defineTest(gSampleName, argc, argv);
+    auto sampleTest = sample::gLogger.defineTest(gSampleName, argc, argv);
 
-    gLogger.reportTestStart(sampleTest);
+    sample::gLogger.reportTestStart(sampleTest);
 
     SampleMovieLensParams params = initializeSampleParams(args);
     SampleMovieLens sample(params);
 
-    gLogInfo << "Building and running a GPU inference engine for MLP NCF model..." << std::endl;
+    sample::gLogInfo << "Building and running a GPU inference engine for MLP NCF model..." << std::endl;
 
     if (!sample.build())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
     if (!sample.infer())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
     if (!sample.teardown())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
 
-    return gLogger.reportPass(sampleTest);
+    return sample::gLogger.reportPass(sampleTest);
 }
diff --git a/samples/opensource/sampleMovieLens/sampleMovieLensTraining.patch b/samples/opensource/sampleMovieLens/sampleMovieLensTraining.patch
index b52edd47..e2d36161 100644
--- a/samples/opensource/sampleMovieLens/sampleMovieLensTraining.patch
+++ b/samples/opensource/sampleMovieLens/sampleMovieLensTraining.patch
@@ -1,4 +1,4 @@
-Copyright 1993-2019 NVIDIA Corporation.  All rights reserved.
+Copyright 1993-2020 NVIDIA Corporation.  All rights reserved.
 
 NOTICE TO LICENSEE:
 
diff --git a/samples/opensource/sampleMovieLensMPS/CMakeLists.txt b/samples/opensource/sampleMovieLensMPS/CMakeLists.txt
index 29a4b428..5e2159b8 100644
--- a/samples/opensource/sampleMovieLensMPS/CMakeLists.txt
+++ b/samples/opensource/sampleMovieLensMPS/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleMovieLensMPS/README.md b/samples/opensource/sampleMovieLensMPS/README.md
index e6f4fd69..268ff29d 100644
--- a/samples/opensource/sampleMovieLensMPS/README.md
+++ b/samples/opensource/sampleMovieLensMPS/README.md
@@ -10,7 +10,7 @@
 	* [TensorRT API layers and ops](#tensorrt-api-layers-and-ops)
 - [Training an NCF network](#training-an-ncf-network)
 - [Running the sample](#running-the-sample)
-	* [Sample `--help` options](#sample---help-options)
+	* [Sample `--help` options](#sample-help-options)
 - [Additional resources](#additional-resources)
 - [License](#license)
 - [Changelog](#changelog)
@@ -65,22 +65,6 @@ The Scale layer implements a per-tensor, per-channel, or per-element affine tran
 [Shuffle layer](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#shuffle-layer)
 The Shuffle layer implements a reshape and transpose operator for tensors.
 
-### TensorRT API layers and ops
-
-In this sample, the following layers are used.  For more information about these layers, see the [TensorRT Developer Guide: Layers](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#layers) documentation.
-
-[Activation layer](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#activation-layer)
-The Activation layer implements element-wise activation functions.
-
-[MatrixMultiply layer](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#matrixmultiply-layer)
-The MatrixMultiply layer implements matrix multiplication for a collection of matrices.
-
-[Scale layer](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#scale-layer)
-The Scale layer implements a per-tensor, per-channel, or per-element affine transformation and/or exponentiation by constant values.
-
-[Shuffle layer](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#shuffle-layer)
-The Shuffle layer implements a reshape and transpose operator for tensors.
-
 [TopK layer](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#topk-layer)
 The TopK layer finds the top `K` maximum (or minimum) elements along a dimension, returning a reduced tensor and a tensor of index positions.
 
@@ -101,7 +85,8 @@ This sample comes with a pre-trained model. However, if you want to train your o
 2. Set-up an MPS server.
 	```
 	export CUDA_VISIBLE_DEVICES=<GPU_ID>
-	nvidia-smi -i <GPU_ID> -c EXCLUSIVE_PROCESSexport CUDA_VISIBLE_DEVICES=0
+	nvidia-smi -i <GPU_ID> -c EXCLUSIVE_PROCESS
+	export CUDA_VISIBLE_DEVICES=0
 	export CUDA_MPS_PIPE_DIRECTORY=/tmp/nvidia-mps # Select a location that's accessible to the given $UID
 	export CUDA_MPS_LOG_DIRECTORY=/tmp/nvidia-log # Select a location that's accessible to the given $UID
 	nvidia-cuda-mps-control -d # Start the daemon.
@@ -149,20 +134,11 @@ This sample comes with a pre-trained model. However, if you want to train your o
 6. To restore the system to its original state, shutdown MPS, if needed.
 	`echo quit | nvidia-cuda-mps-control`
 
-### Sample --help options
-
-To see the full list of available options and their descriptions, use the `-h` or `--help` command line option. For example:
-```
-Usage:
-         ./sample_movielens_mps [-h] [-b NUM_USERS] [-p NUM_PROCESSES] [--useDLACore=<int>] [--verbose]
-        -h             Display help information. All single dash options enable perf mode.
-        -b             Number of Users i.e. Batch Size (default numUsers=32).
-        -p             Number of child processes to launch (default nbProcesses=1. Using MPS with this option is strongly recommended).
-        --useDLACore=N Specify a DLA engine for layers that support DLA. Value can range from 0 to n-1, where n is the number of DLA engines on the platform.
-        --verbose      Enable verbose prints.
-        --int8         Run in Int8 mode.
-        --fp16         Run in FP16 mode.
-```
+
+### Sample `--help` options
+
+To see the full list of available options and their descriptions, use the `-h` or `--help` command line option.
+
 
 # Additional resources
 
diff --git a/samples/opensource/sampleMovieLensMPS/preprocess.py b/samples/opensource/sampleMovieLensMPS/preprocess.py
index ca641ad1..d16cfe99 100644
--- a/samples/opensource/sampleMovieLensMPS/preprocess.py
+++ b/samples/opensource/sampleMovieLensMPS/preprocess.py
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleMovieLensMPS/sampleMovieLensMPS.cpp b/samples/opensource/sampleMovieLensMPS/sampleMovieLensMPS.cpp
index 8250123f..ffa3e962 100644
--- a/samples/opensource/sampleMovieLensMPS/sampleMovieLensMPS.cpp
+++ b/samples/opensource/sampleMovieLensMPS/sampleMovieLensMPS.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -292,7 +292,7 @@ bool parseArgs(Args& args, int argc, char* argv[])
         else if (argStr == "--verbose")
         {
             args.enableVerbose = true;
-            setReportableSeverity(Severity::kVERBOSE);
+            sample::setReportableSeverity(ILogger::Severity::kVERBOSE);
         }
         else if (argStr.compare(0, 13, "--useDLACore=") == 0 && argStr.size() > 13)
         {
@@ -316,12 +316,15 @@ bool parseArgs(Args& args, int argc, char* argv[])
 
 void printOutputArgs(OutputArgs& pargs)
 {
-    gLogVerbose << "User Id                            :   " << pargs.userId << std::endl;
-    gLogVerbose << "Expected Predicted Max Rating Item :   " << pargs.expectedPredictedMaxRatingItem << std::endl;
-    gLogVerbose << "Expected Predicted Max Rating Prob :   " << pargs.expectedPredictedMaxRatingItemProb << std::endl;
-    gLogVerbose << "Total TopK Items : " << pargs.itemProbPairVec.size() << std::endl;
+    sample::gLogVerbose << "User Id                            :   " << pargs.userId << std::endl;
+    sample::gLogVerbose << "Expected Predicted Max Rating Item :   " << pargs.expectedPredictedMaxRatingItem
+                        << std::endl;
+    sample::gLogVerbose << "Expected Predicted Max Rating Prob :   " << pargs.expectedPredictedMaxRatingItemProb
+                        << std::endl;
+    sample::gLogVerbose << "Total TopK Items : " << pargs.itemProbPairVec.size() << std::endl;
     for (unsigned i = 0; i < pargs.itemProbPairVec.size(); ++i)
-        gLogVerbose << pargs.itemProbPairVec.at(i).first << " : " << pargs.itemProbPairVec.at(i).second << std::endl;
+        sample::gLogVerbose << pargs.itemProbPairVec.at(i).first << " : " << pargs.itemProbPairVec.at(i).second
+                            << std::endl;
 }
 
 std::string readNextLine(std::ifstream& file, char delim)
@@ -413,12 +416,12 @@ bool printInferenceOutput(
     T1* topKItemNumber{static_cast<T1*>(topKItemNumberPtr)};
     T2* topKItemProb{static_cast<T2*>(topKItemProbPtr)};
 
-    gLogInfo << "Num of users : " << args.numUsers << std::endl;
-    gLogInfo << "Num of Movies : " << args.numMoviesPerUser << std::endl;
+    sample::gLogInfo << "Num of users : " << args.numUsers << std::endl;
+    sample::gLogInfo << "Num of Movies : " << args.numMoviesPerUser << std::endl;
 
-    gLogVerbose << "|-----------|------------|-----------------|-----------------|" << std::endl;
-    gLogVerbose << "|   User    |   Item     |  Expected Prob  |  Predicted Prob |" << std::endl;
-    gLogVerbose << "|-----------|------------|-----------------|-----------------|" << std::endl;
+    sample::gLogVerbose << "|-----------|------------|-----------------|-----------------|" << std::endl;
+    sample::gLogVerbose << "|   User    |   Item     |  Expected Prob  |  Predicted Prob |" << std::endl;
+    sample::gLogVerbose << "|-----------|------------|-----------------|-----------------|" << std::endl;
 
     for (int i = 0; i < args.numUsers; ++i)
     {
@@ -434,9 +437,9 @@ bool printInferenceOutput(
             float predictedProb = topKItemProb[i * args.topKMovies + k];
             float expectedProb = args.userToExpectedItemProbMap.at(userIdx).at(k).second;
             int predictedItem = args.userToItemsMap.at(userIdx).at(predictedIdx);
-            gLogVerbose << "|" << std::setw(10) << userIdx << " | " << std::setw(10) << predictedItem << " | "
-                        << std::setw(15) << expectedProb << " | " << std::setw(15) << predictedProb << " | "
-                        << std::endl;
+            sample::gLogVerbose << "|" << std::setw(10) << userIdx << " | " << std::setw(10) << predictedItem << " | "
+                                << std::setw(15) << expectedProb << " | " << std::setw(15) << predictedProb << " | "
+                                << std::endl;
         }
     }
 
@@ -446,15 +449,15 @@ bool printInferenceOutput(
         int maxPredictedIdx = topKItemNumber[i * args.topKMovies];
         int maxExpectedItem = args.userToExpectedItemProbMap.at(userIdx).at(0).first;
         int maxPredictedItem = args.userToItemsMap.at(userIdx).at(maxPredictedIdx);
-        gLogInfo << "| PID : " << std::setw(4) << getpid() << " | User :" << std::setw(4) << userIdx
-                 << "  |  Expected Item :" << std::setw(5) << maxExpectedItem << "  |  Predicted Item :" << std::setw(5)
-                 << maxPredictedItem << " | " << std::endl;
+        sample::gLogInfo << "| PID : " << std::setw(4) << getpid() << " | User :" << std::setw(4) << userIdx
+                         << "  |  Expected Item :" << std::setw(5) << maxExpectedItem
+                         << "  |  Predicted Item :" << std::setw(5) << maxPredictedItem << " | " << std::endl;
     }
 
     return pass;
 }
 
-void submitWork(Batch& b, const Args& args)
+bool submitWork(Batch& b, const Args& args)
 {
     int userInputIndex = b.mEngine->getBindingIndex(USER_BLOB_NAME);
     int itemInputIndex = b.mEngine->getBindingIndex(ITEM_BLOB_NAME);
@@ -468,7 +471,10 @@ void submitWork(Batch& b, const Args& args)
     CHECK(cudaMemcpyAsync(b.mDeviceMemory[itemInputIndex], b.mHostMemory[itemInputIndex], b.mMemSizes[itemInputIndex],
         cudaMemcpyHostToDevice, b.mStream));
 
-    b.mContext->enqueue(args.numUsers, b.mDeviceMemory, b.mStream, nullptr);
+    if (!b.mContext->enqueue(args.numUsers, b.mDeviceMemory, b.mStream, nullptr))
+    {
+        return false;
+    }
 
     // copy output from device to host
     CHECK(cudaMemcpyAsync(b.mHostMemory[outputPredictionIndex], b.mDeviceMemory[outputPredictionIndex],
@@ -477,12 +483,14 @@ void submitWork(Batch& b, const Args& args)
         b.mMemSizes[outputItemProbIndex], cudaMemcpyDeviceToHost, b.mStream));
     CHECK(cudaMemcpyAsync(b.mHostMemory[outputItemNameIndex], b.mDeviceMemory[outputItemNameIndex],
         b.mMemSizes[outputItemNameIndex], cudaMemcpyDeviceToHost, b.mStream));
+
+    return true;
 }
 
 std::shared_ptr<ICudaEngine> loadModelAndCreateEngine(const char* uffFile, IUffParser* parser, const Args& args)
 {
     // Create the builder
-    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(gLogger.getTRTLogger()));
+    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(sample::gLogger.getTRTLogger()));
     if (builder == nullptr)
     {
         throw std::runtime_error("Could not create builder.");
@@ -500,18 +508,18 @@ std::shared_ptr<ICudaEngine> loadModelAndCreateEngine(const char* uffFile, IUffP
         throw std::runtime_error("Could not create network.");
     }
 
-    gLogInfo << "Begin parsing model..." << std::endl;
+    sample::gLogInfo << "Begin parsing model..." << std::endl;
 
     auto dType = args.enableFP16 ? nvinfer1::DataType::kHALF : nvinfer1::DataType::kFLOAT;
 
     // Parse the uff model to populate the network
     if (!parser->parse(uffFile, *network, dType))
     {
-        gLogError << "Failure while parsing UFF file" << std::endl;
+        sample::gLogError << "Failure while parsing UFF file" << std::endl;
         return nullptr;
     }
 
-    gLogInfo << "End parsing model..." << std::endl;
+    sample::gLogInfo << "End parsing model..." << std::endl;
 
     // Add postprocessing i.e. topk layer to the UFF Network
     // Retrieve last layer of UFF Network
@@ -561,17 +569,17 @@ std::shared_ptr<ICudaEngine> loadModelAndCreateEngine(const char* uffFile, IUffP
     auto engine = samplesCommon::infer_object(builder->buildEngineWithConfig(*network, *config));
     if (!engine)
     {
-        gLogError << "Unable to create engine" << std::endl;
+        sample::gLogError << "Unable to create engine" << std::endl;
         return nullptr;
     }
 
-    gLogInfo << "End building engine..." << std::endl;
+    sample::gLogInfo << "End building engine..." << std::endl;
     return engine;
 }
 
 bool doInference(void* modelStreamData, int modelStreamSize, void* userInputPtr, void* itemInputPtr, Args& args)
 {
-    auto runtime = SampleUniquePtr<nvinfer1::IRuntime>(nvinfer1::createInferRuntime(gLogger.getTRTLogger()));
+    auto runtime = SampleUniquePtr<nvinfer1::IRuntime>(nvinfer1::createInferRuntime(sample::gLogger.getTRTLogger()));
     if (args.useDLACore >= 0)
     {
         runtime->setDLACore(args.useDLACore);
@@ -586,11 +594,15 @@ bool doInference(void* modelStreamData, int modelStreamSize, void* userInputPtr,
         samplesCommon::GpuTimer timer{b.mStream};
         timer.start();
         // Run inference for all the nbProcesses
-        submitWork(b, args);
+        if (!submitWork(b, args))
+        {
+            sample::gLogError << "Error in submit work." << std::endl;
+            return false;
+        }
         cudaStreamSynchronize(b.mStream);
         timer.stop();
-        gLogInfo << "Done execution in process: " << getpid() << " . Duration : " << timer.microseconds()
-                 << " microseconds." << std::endl;
+        sample::gLogInfo << "Done execution in process: " << getpid() << " . Duration : " << timer.microseconds()
+                         << " microseconds." << std::endl;
     }
 
     int outputItemProbIndex = b.mEngine->getBindingIndex(TOPK_ITEM_PROB);
@@ -607,7 +619,7 @@ int mainMovieLensMPS(Args& args, OutputArgs& pargs)
 {
     // Parse the ratings file and populate ground truth data
     args.ratingInputFile = locateFile(args.ratingInputFile, directories);
-    gLogInfo << args.ratingInputFile << std::endl;
+    sample::gLogInfo << args.ratingInputFile << std::endl;
 
     // Parse ground truth data and inputs, common to all processes (if using MPS)
     parseMovieLensData(args);
@@ -732,8 +744,8 @@ int mainMovieLensMPS(Args& args, OutputArgs& pargs)
             wait(&status);
         }
         timer.stop();
-        gLogInfo << "Number of processes executed : " << args.nbProcesses
-                 << ". Total MPS Run Duration : " << timer.milliseconds() << " milliseconds." << std::endl;
+        sample::gLogInfo << "Number of processes executed : " << args.nbProcesses
+                         << ". Total MPS Run Duration : " << timer.milliseconds() << " milliseconds." << std::endl;
     }
 
     bool pass = !args.failCount;
@@ -758,12 +770,12 @@ int main(int argc, char* argv[])
     if (!argsOK)
     {
         printHelpInfo();
-        gLogError << "Invalid arguments" << std::endl;
+        sample::gLogError << "Invalid arguments" << std::endl;
         return EXIT_FAILURE;
     }
 
-    auto sampleTest = gLogger.defineTest(gSampleName, argc, argv);
-    gLogger.reportTestStart(sampleTest);
+    auto sampleTest = sample::gLogger.defineTest(gSampleName, argc, argv);
+    sample::gLogger.reportTestStart(sampleTest);
     bool pass = false;
 
     try
@@ -772,7 +784,7 @@ int main(int argc, char* argv[])
     }
     catch (const std::exception& e)
     {
-        gLogError << e.what() << std::endl;
+        sample::gLogError << e.what() << std::endl;
     }
-    return gLogger.reportTest(sampleTest, pass);
+    return sample::gLogger.reportTest(sampleTest, pass);
 }
diff --git a/samples/opensource/sampleMovieLensMPS/sampleMovieLensTraining.patch b/samples/opensource/sampleMovieLensMPS/sampleMovieLensTraining.patch
index b52edd47..e2d36161 100644
--- a/samples/opensource/sampleMovieLensMPS/sampleMovieLensTraining.patch
+++ b/samples/opensource/sampleMovieLensMPS/sampleMovieLensTraining.patch
@@ -1,4 +1,4 @@
-Copyright 1993-2019 NVIDIA Corporation.  All rights reserved.
+Copyright 1993-2020 NVIDIA Corporation.  All rights reserved.
 
 NOTICE TO LICENSEE:
 
diff --git a/samples/opensource/sampleNMT/CMakeLists.txt b/samples/opensource/sampleNMT/CMakeLists.txt
index 63248530..bd40be0c 100644
--- a/samples/opensource/sampleNMT/CMakeLists.txt
+++ b/samples/opensource/sampleNMT/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/README.md b/samples/opensource/sampleNMT/README.md
index 44990836..7aae855c 100644
--- a/samples/opensource/sampleNMT/README.md
+++ b/samples/opensource/sampleNMT/README.md
@@ -10,7 +10,7 @@
 	* [TensorRT API layers and ops](#tensorrt-api-layers-and-ops)
 - [Prerequisites](#prerequisites)
 - [Running the sample](#running-the-sample)
-	* [Sample `--help` options](#sample---help-options)
+	* [Sample `--help` options](#sample-help-options)
 - [Additional resources](#additional-resources)
 - [License](#license)
 - [Changelog](#changelog)
@@ -122,25 +122,8 @@ Now that you have trained weights, downloaded the text and vocabulary data, and
 
 ### Sample `--help` options
 
-To see the full list of available options and their descriptions, use the `-h` or `--help` command line option.  For example:
-```
-data_dir: /workspace/tensorrt/samples/sampleNMT/data/deen
-data_writer: text
-Component Info:
-– Data Reader: Text Reader, vocabulary size = 36548
-– Input Embedder: SLP Embedder, num inputs = 36548, num outputs = 512
-– Output Embedder: SLP Embedder, num inputs = 36548, num outputs = 512
-– Encoder: LSTM Encoder, num layers = 2, num units = 512
-– Decoder: LSTM Decoder, num layers = 2, num units = 512
-– Alignment: Multiplicative Alignment, source states size = 512, attention keys size = 512
-– Context: Ragged softmax + Batch GEMM
-– Attention: SLP Attention, num inputs = 1024, num outputs = 512
-– Projection: SLP Projection, num inputs = 512, num outputs = 36548
-– Likelihood: Softmax Likelihood
-– Search Policy: Beam Search Policy, beam = 5
-– Data Writer: Text Writer, vocabulary size = 36548
-End of Component Info
-```
+To see the full list of available options and their descriptions, use the `-h` or `--help` command line option.
+
 
 ## Additional resources
 
@@ -156,7 +139,7 @@ The following resources provide a deeper understanding about Neural Machine Tran
 - [NMT (seq2seq) Tutorial](https://github.com/tensorflow/nmt)
 
 **Blogs**
-- [Neural Machine Translation Inference in TensorRT](https://devblogs.nvidia.com/neural-machine-translation-inference-tensorrt-4/)
+- [Neural Machine Translation Inference in TensorRT](https://news.developer.nvidia.com/neural-machine-translation-now-available-with-tensorrt/)
 - [Introduction to NMT](https://devblogs.nvidia.com/introduction-neural-machine-translation-with-gpus/)
 
 **Videos**
diff --git a/samples/opensource/sampleNMT/chptToBin.py b/samples/opensource/sampleNMT/chptToBin.py
index fd4122c0..b93ea15e 100644
--- a/samples/opensource/sampleNMT/chptToBin.py
+++ b/samples/opensource/sampleNMT/chptToBin.py
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/component.h b/samples/opensource/sampleNMT/component.h
index 9222a538..5194749a 100644
--- a/samples/opensource/sampleNMT/component.h
+++ b/samples/opensource/sampleNMT/component.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/cudaError.h b/samples/opensource/sampleNMT/cudaError.h
index 99aee487..fc90e8e5 100644
--- a/samples/opensource/sampleNMT/cudaError.h
+++ b/samples/opensource/sampleNMT/cudaError.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/data/benchmarkWriter.cpp b/samples/opensource/sampleNMT/data/benchmarkWriter.cpp
index 463324e4..845d70ec 100644
--- a/samples/opensource/sampleNMT/data/benchmarkWriter.cpp
+++ b/samples/opensource/sampleNMT/data/benchmarkWriter.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -45,10 +45,10 @@ void BenchmarkWriter::finalize()
 {
     std::chrono::duration<float> sec = std::chrono::high_resolution_clock::now() - mStartTS;
     int totalTokenCount = mInputTokenCount + mOutputTokenCount;
-    gLogInfo << mSampleCount << " sequences generated in " << sec.count() << " seconds, "
-             << (mSampleCount / sec.count()) << " samples/sec" << std::endl;
-    gLogInfo << totalTokenCount << " tokens processed (source and destination), " << (totalTokenCount / sec.count())
-             << " tokens/sec" << std::endl;
+    sample::gLogInfo << mSampleCount << " sequences generated in " << sec.count() << " seconds, "
+                     << (mSampleCount / sec.count()) << " samples/sec" << std::endl;
+    sample::gLogInfo << totalTokenCount << " tokens processed (source and destination), "
+                     << (totalTokenCount / sec.count()) << " tokens/sec" << std::endl;
 }
 
 std::string BenchmarkWriter::getInfo()
diff --git a/samples/opensource/sampleNMT/data/benchmarkWriter.h b/samples/opensource/sampleNMT/data/benchmarkWriter.h
index baa01d91..4b18e562 100644
--- a/samples/opensource/sampleNMT/data/benchmarkWriter.h
+++ b/samples/opensource/sampleNMT/data/benchmarkWriter.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/data/bleuScoreWriter.cpp b/samples/opensource/sampleNMT/data/bleuScoreWriter.cpp
index da816725..b7a92501 100644
--- a/samples/opensource/sampleNMT/data/bleuScoreWriter.cpp
+++ b/samples/opensource/sampleNMT/data/bleuScoreWriter.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -184,7 +184,7 @@ void BLEUScoreWriter::initialize() {}
 
 void BLEUScoreWriter::finalize()
 {
-    gLogInfo << "BLEU score = " << getScore() << std::endl;
+    sample::gLogInfo << "BLEU score = " << getScore() << std::endl;
 }
 
 float BLEUScoreWriter::getScore() const
diff --git a/samples/opensource/sampleNMT/data/bleuScoreWriter.h b/samples/opensource/sampleNMT/data/bleuScoreWriter.h
index 954d2107..53c47d27 100644
--- a/samples/opensource/sampleNMT/data/bleuScoreWriter.h
+++ b/samples/opensource/sampleNMT/data/bleuScoreWriter.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/data/dataReader.h b/samples/opensource/sampleNMT/data/dataReader.h
index d5293d02..b7e5638c 100644
--- a/samples/opensource/sampleNMT/data/dataReader.h
+++ b/samples/opensource/sampleNMT/data/dataReader.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/data/dataWriter.cpp b/samples/opensource/sampleNMT/data/dataWriter.cpp
index 0a74e2a4..c5e4062f 100644
--- a/samples/opensource/sampleNMT/data/dataWriter.cpp
+++ b/samples/opensource/sampleNMT/data/dataWriter.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/data/dataWriter.h b/samples/opensource/sampleNMT/data/dataWriter.h
index 09bae426..a5226798 100644
--- a/samples/opensource/sampleNMT/data/dataWriter.h
+++ b/samples/opensource/sampleNMT/data/dataWriter.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/data/limitedSamplesDataReader.cpp b/samples/opensource/sampleNMT/data/limitedSamplesDataReader.cpp
index d0fd58f1..a8924997 100644
--- a/samples/opensource/sampleNMT/data/limitedSamplesDataReader.cpp
+++ b/samples/opensource/sampleNMT/data/limitedSamplesDataReader.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/data/limitedSamplesDataReader.h b/samples/opensource/sampleNMT/data/limitedSamplesDataReader.h
index 75a1fdba..a9444d47 100644
--- a/samples/opensource/sampleNMT/data/limitedSamplesDataReader.h
+++ b/samples/opensource/sampleNMT/data/limitedSamplesDataReader.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/data/sequenceProperties.h b/samples/opensource/sampleNMT/data/sequenceProperties.h
index 5c73f639..e0928d68 100644
--- a/samples/opensource/sampleNMT/data/sequenceProperties.h
+++ b/samples/opensource/sampleNMT/data/sequenceProperties.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/data/textReader.cpp b/samples/opensource/sampleNMT/data/textReader.cpp
index 8e0c539d..403de10e 100644
--- a/samples/opensource/sampleNMT/data/textReader.cpp
+++ b/samples/opensource/sampleNMT/data/textReader.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/data/textReader.h b/samples/opensource/sampleNMT/data/textReader.h
index 15e28f88..4d01bb57 100644
--- a/samples/opensource/sampleNMT/data/textReader.h
+++ b/samples/opensource/sampleNMT/data/textReader.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/data/textWriter.cpp b/samples/opensource/sampleNMT/data/textWriter.cpp
index ed2a556b..87431bd6 100644
--- a/samples/opensource/sampleNMT/data/textWriter.cpp
+++ b/samples/opensource/sampleNMT/data/textWriter.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/data/textWriter.h b/samples/opensource/sampleNMT/data/textWriter.h
index 645defe7..e2300ff5 100644
--- a/samples/opensource/sampleNMT/data/textWriter.h
+++ b/samples/opensource/sampleNMT/data/textWriter.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/data/vocabulary.cpp b/samples/opensource/sampleNMT/data/vocabulary.cpp
index ebaab836..abe21309 100644
--- a/samples/opensource/sampleNMT/data/vocabulary.cpp
+++ b/samples/opensource/sampleNMT/data/vocabulary.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/data/vocabulary.h b/samples/opensource/sampleNMT/data/vocabulary.h
index 595cadb4..36cae7e3 100644
--- a/samples/opensource/sampleNMT/data/vocabulary.h
+++ b/samples/opensource/sampleNMT/data/vocabulary.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/deviceBuffer.h b/samples/opensource/sampleNMT/deviceBuffer.h
index bb549cfd..1bb3034e 100644
--- a/samples/opensource/sampleNMT/deviceBuffer.h
+++ b/samples/opensource/sampleNMT/deviceBuffer.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/get_newstest2015.sh b/samples/opensource/sampleNMT/get_newstest2015.sh
index a2ff70ef..96a6cb70 100755
--- a/samples/opensource/sampleNMT/get_newstest2015.sh
+++ b/samples/opensource/sampleNMT/get_newstest2015.sh
@@ -1,7 +1,7 @@
 #!/usr/bin/env bash
 
 # Copyright 2017 Google Inc.
-# Modifications copyright (C) 2019 Nvidia
+# Modifications Copyright (c) 2020 Nvidia
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/model/alignment.h b/samples/opensource/sampleNMT/model/alignment.h
index b2e87fb4..f6d117ec 100644
--- a/samples/opensource/sampleNMT/model/alignment.h
+++ b/samples/opensource/sampleNMT/model/alignment.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/model/attention.h b/samples/opensource/sampleNMT/model/attention.h
index 969f801a..9133956d 100644
--- a/samples/opensource/sampleNMT/model/attention.h
+++ b/samples/opensource/sampleNMT/model/attention.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/model/beamSearchPolicy.cpp b/samples/opensource/sampleNMT/model/beamSearchPolicy.cpp
index 1c2aed8e..a1b36fc7 100644
--- a/samples/opensource/sampleNMT/model/beamSearchPolicy.cpp
+++ b/samples/opensource/sampleNMT/model/beamSearchPolicy.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/model/beamSearchPolicy.h b/samples/opensource/sampleNMT/model/beamSearchPolicy.h
index 4ca778a4..c532c325 100644
--- a/samples/opensource/sampleNMT/model/beamSearchPolicy.h
+++ b/samples/opensource/sampleNMT/model/beamSearchPolicy.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/model/componentWeights.cpp b/samples/opensource/sampleNMT/model/componentWeights.cpp
index 6f5274b6..af8f5722 100644
--- a/samples/opensource/sampleNMT/model/componentWeights.cpp
+++ b/samples/opensource/sampleNMT/model/componentWeights.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/model/componentWeights.h b/samples/opensource/sampleNMT/model/componentWeights.h
index 4d1ea959..6b20d56b 100644
--- a/samples/opensource/sampleNMT/model/componentWeights.h
+++ b/samples/opensource/sampleNMT/model/componentWeights.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/model/contextNMT.cpp b/samples/opensource/sampleNMT/model/contextNMT.cpp
index cfb0eb7f..bc769348 100644
--- a/samples/opensource/sampleNMT/model/contextNMT.cpp
+++ b/samples/opensource/sampleNMT/model/contextNMT.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/model/contextNMT.h b/samples/opensource/sampleNMT/model/contextNMT.h
index 47f9baa4..73a3e087 100644
--- a/samples/opensource/sampleNMT/model/contextNMT.h
+++ b/samples/opensource/sampleNMT/model/contextNMT.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/model/debugUtil.cpp b/samples/opensource/sampleNMT/model/debugUtil.cpp
index bd878ab1..2f0ef7e7 100644
--- a/samples/opensource/sampleNMT/model/debugUtil.cpp
+++ b/samples/opensource/sampleNMT/model/debugUtil.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/model/debugUtil.h b/samples/opensource/sampleNMT/model/debugUtil.h
index 3c7efc49..4aed07e4 100644
--- a/samples/opensource/sampleNMT/model/debugUtil.h
+++ b/samples/opensource/sampleNMT/model/debugUtil.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/model/decoder.h b/samples/opensource/sampleNMT/model/decoder.h
index 52075080..34b41d04 100644
--- a/samples/opensource/sampleNMT/model/decoder.h
+++ b/samples/opensource/sampleNMT/model/decoder.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/model/embedder.h b/samples/opensource/sampleNMT/model/embedder.h
index 4a963e08..c5a67406 100644
--- a/samples/opensource/sampleNMT/model/embedder.h
+++ b/samples/opensource/sampleNMT/model/embedder.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/model/encoder.h b/samples/opensource/sampleNMT/model/encoder.h
index a5fe09db..9505f38e 100644
--- a/samples/opensource/sampleNMT/model/encoder.h
+++ b/samples/opensource/sampleNMT/model/encoder.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/model/likelihood.h b/samples/opensource/sampleNMT/model/likelihood.h
index fa6dbf10..bd33431e 100644
--- a/samples/opensource/sampleNMT/model/likelihood.h
+++ b/samples/opensource/sampleNMT/model/likelihood.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/model/likelihoodCombinationOperator.h b/samples/opensource/sampleNMT/model/likelihoodCombinationOperator.h
index 651c1f3e..85e6fdf6 100644
--- a/samples/opensource/sampleNMT/model/likelihoodCombinationOperator.h
+++ b/samples/opensource/sampleNMT/model/likelihoodCombinationOperator.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/model/lstmDecoder.cpp b/samples/opensource/sampleNMT/model/lstmDecoder.cpp
index 8ec7face..3a0c0a54 100644
--- a/samples/opensource/sampleNMT/model/lstmDecoder.cpp
+++ b/samples/opensource/sampleNMT/model/lstmDecoder.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/model/lstmDecoder.h b/samples/opensource/sampleNMT/model/lstmDecoder.h
index 5248ddff..5b3e2182 100644
--- a/samples/opensource/sampleNMT/model/lstmDecoder.h
+++ b/samples/opensource/sampleNMT/model/lstmDecoder.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/model/lstmEncoder.cpp b/samples/opensource/sampleNMT/model/lstmEncoder.cpp
index f156dec3..733e5c2f 100644
--- a/samples/opensource/sampleNMT/model/lstmEncoder.cpp
+++ b/samples/opensource/sampleNMT/model/lstmEncoder.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/model/lstmEncoder.h b/samples/opensource/sampleNMT/model/lstmEncoder.h
index 26ee2e97..048217bd 100644
--- a/samples/opensource/sampleNMT/model/lstmEncoder.h
+++ b/samples/opensource/sampleNMT/model/lstmEncoder.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/model/multiplicativeAlignment.cpp b/samples/opensource/sampleNMT/model/multiplicativeAlignment.cpp
index 607d5837..f015cc9b 100644
--- a/samples/opensource/sampleNMT/model/multiplicativeAlignment.cpp
+++ b/samples/opensource/sampleNMT/model/multiplicativeAlignment.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/model/multiplicativeAlignment.h b/samples/opensource/sampleNMT/model/multiplicativeAlignment.h
index 44fd0017..5d02c9cd 100644
--- a/samples/opensource/sampleNMT/model/multiplicativeAlignment.h
+++ b/samples/opensource/sampleNMT/model/multiplicativeAlignment.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/model/projection.h b/samples/opensource/sampleNMT/model/projection.h
index 8c2d766b..63c7119d 100644
--- a/samples/opensource/sampleNMT/model/projection.h
+++ b/samples/opensource/sampleNMT/model/projection.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/model/slpAttention.cpp b/samples/opensource/sampleNMT/model/slpAttention.cpp
index 7440e599..6636dd6e 100644
--- a/samples/opensource/sampleNMT/model/slpAttention.cpp
+++ b/samples/opensource/sampleNMT/model/slpAttention.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/model/slpAttention.h b/samples/opensource/sampleNMT/model/slpAttention.h
index 078d8828..7084b401 100644
--- a/samples/opensource/sampleNMT/model/slpAttention.h
+++ b/samples/opensource/sampleNMT/model/slpAttention.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/model/slpEmbedder.cpp b/samples/opensource/sampleNMT/model/slpEmbedder.cpp
index 5f3224da..8cabaa55 100644
--- a/samples/opensource/sampleNMT/model/slpEmbedder.cpp
+++ b/samples/opensource/sampleNMT/model/slpEmbedder.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/model/slpEmbedder.h b/samples/opensource/sampleNMT/model/slpEmbedder.h
index 700d1f2b..56cac206 100644
--- a/samples/opensource/sampleNMT/model/slpEmbedder.h
+++ b/samples/opensource/sampleNMT/model/slpEmbedder.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/model/slpProjection.cpp b/samples/opensource/sampleNMT/model/slpProjection.cpp
index 25ed1e71..fde723cf 100644
--- a/samples/opensource/sampleNMT/model/slpProjection.cpp
+++ b/samples/opensource/sampleNMT/model/slpProjection.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/model/slpProjection.h b/samples/opensource/sampleNMT/model/slpProjection.h
index 27c7586b..cbd15138 100644
--- a/samples/opensource/sampleNMT/model/slpProjection.h
+++ b/samples/opensource/sampleNMT/model/slpProjection.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/model/softmaxLikelihood.cpp b/samples/opensource/sampleNMT/model/softmaxLikelihood.cpp
index 6d16fe11..56926f2c 100644
--- a/samples/opensource/sampleNMT/model/softmaxLikelihood.cpp
+++ b/samples/opensource/sampleNMT/model/softmaxLikelihood.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/model/softmaxLikelihood.h b/samples/opensource/sampleNMT/model/softmaxLikelihood.h
index 75dd73dd..6161d353 100644
--- a/samples/opensource/sampleNMT/model/softmaxLikelihood.h
+++ b/samples/opensource/sampleNMT/model/softmaxLikelihood.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/pinnedHostBuffer.h b/samples/opensource/sampleNMT/pinnedHostBuffer.h
index 8f4f1045..bfd79669 100644
--- a/samples/opensource/sampleNMT/pinnedHostBuffer.h
+++ b/samples/opensource/sampleNMT/pinnedHostBuffer.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/sampleNMT.cpp b/samples/opensource/sampleNMT/sampleNMT.cpp
index d7de1c76..74f4ae74 100644
--- a/samples/opensource/sampleNMT/sampleNMT.cpp
+++ b/samples/opensource/sampleNMT/sampleNMT.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -215,7 +215,7 @@ nmtSample::DataWriter::ptr getDataWriter()
     }
     else
     {
-        gLogError << "Invalid data writer specified: " << gDataWriterStr << std::endl;
+        sample::gLogError << "Invalid data writer specified: " << gDataWriterStr << std::endl;
         assert(0);
         return nmtSample::DataWriter::ptr();
     }
@@ -228,7 +228,7 @@ bool parseString(const char* arg, const char* name, std::string& value)
     if (match)
     {
         value = arg + n + 3;
-        gLogInfo << name << ": " << value << std::endl;
+        sample::gLogInfo << name << ": " << value << std::endl;
     }
     return match;
 }
@@ -240,7 +240,7 @@ bool parseInt(const char* arg, const char* name, int& value)
     if (match)
     {
         value = atoi(arg + n + 3);
-        gLogInfo << name << ": " << value << std::endl;
+        sample::gLogInfo << name << ": " << value << std::endl;
     }
     return match;
 }
@@ -260,7 +260,7 @@ bool parseBool(const char* arg, const char* longName, bool& value, char shortNam
     }
     if (match)
     {
-        gLogInfo << longName << ": true" << std::endl;
+        sample::gLogInfo << longName << ": true" << std::endl;
         value = true;
     }
     return match;
@@ -365,7 +365,7 @@ bool parseNMTArgs(samplesCommon::Args& args, int argc, char* argv[])
 nvinfer1::ICudaEngine* getEncoderEngine(
     nmtSample::Embedder::ptr inputEmbedder, nmtSample::Encoder::ptr encoder, nmtSample::Alignment::ptr alignment)
 {
-    nvinfer1::IBuilder* encoderBuilder = nvinfer1::createInferBuilder(gLogger.getTRTLogger());
+    nvinfer1::IBuilder* encoderBuilder = nvinfer1::createInferBuilder(sample::gLogger.getTRTLogger());
     assert(encoderBuilder != nullptr);
     nvinfer1::IBuilderConfig* encoderConfig = encoderBuilder->createBuilderConfig();
     encoderBuilder->setMaxBatchSize(gMaxBatchSize);
@@ -512,7 +512,7 @@ nvinfer1::ICudaEngine* getGeneratorEngine(nmtSample::Embedder::ptr outputEmbedde
     nmtSample::Alignment::ptr alignment, nmtSample::Context::ptr context, nmtSample::Attention::ptr attention,
     nmtSample::Projection::ptr projection, nmtSample::Likelihood::ptr likelihood)
 {
-    nvinfer1::IBuilder* generatorBuilder = nvinfer1::createInferBuilder(gLogger.getTRTLogger());
+    nvinfer1::IBuilder* generatorBuilder = nvinfer1::createInferBuilder(sample::gLogger.getTRTLogger());
     assert(generatorBuilder != nullptr);
     nvinfer1::IBuilderConfig* generatorConfig = generatorBuilder->createBuilderConfig();
     generatorBuilder->setMaxBatchSize(gMaxBatchSize);
@@ -684,7 +684,7 @@ nvinfer1::ICudaEngine* getGeneratorEngine(nmtSample::Embedder::ptr outputEmbedde
 nvinfer1::ICudaEngine* getGeneratorShuffleEngine(
     const std::vector<nvinfer1::Dims>& decoderStateSizes, int attentionSize)
 {
-    nvinfer1::IBuilder* shuffleBuilder = nvinfer1::createInferBuilder(gLogger.getTRTLogger());
+    nvinfer1::IBuilder* shuffleBuilder = nvinfer1::createInferBuilder(sample::gLogger.getTRTLogger());
     assert(shuffleBuilder != nullptr);
     nvinfer1::IBuilderConfig* shuffleConfig = shuffleBuilder->createBuilderConfig();
     shuffleBuilder->setMaxBatchSize(gMaxBatchSize);
@@ -794,9 +794,9 @@ void processBindings(
 
 int main(int argc, char** argv)
 {
-    auto sampleTest = gLogger.defineTest(gSampleName, argc, argv);
+    auto sampleTest = sample::gLogger.defineTest(gSampleName, argc, argv);
 
-    gLogger.reportTestStart(sampleTest);
+    sample::gLogger.reportTestStart(sampleTest);
 
     samplesCommon::Args args;
     bool argsOK = parseNMTArgs(args, argc, argv);
@@ -806,11 +806,11 @@ int main(int argc, char** argv)
     }
     if (!argsOK)
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
     if (gVerbose)
     {
-        setReportableSeverity(Severity::kVERBOSE);
+        sample::setReportableSeverity(ILogger::Severity::kVERBOSE);
     }
 
     // Set up output vocabulary
@@ -819,8 +819,8 @@ int main(int argc, char** argv)
         std::ifstream vocabStream(locateNMTFile(vocabularyFilePath));
         if (!vocabStream.good())
         {
-            gLogError << "Cannot open file " << vocabularyFilePath << std::endl;
-            return gLogger.reportFail(sampleTest);
+            sample::gLogError << "Cannot open file " << vocabularyFilePath << std::endl;
+            return sample::gLogger.reportFail(sampleTest);
         }
         vocabStream >> *gOutputVocabulary;
     }
@@ -845,20 +845,20 @@ int main(int argc, char** argv)
 
     if (gPrintComponentInfo)
     {
-        gLogInfo << "Component Info:" << std::endl;
-        gLogInfo << "- Data Reader: " << dataReader->getInfo() << std::endl;
-        gLogInfo << "- Input Embedder: " << inputEmbedder->getInfo() << std::endl;
-        gLogInfo << "- Output Embedder: " << outputEmbedder->getInfo() << std::endl;
-        gLogInfo << "- Encoder: " << encoder->getInfo() << std::endl;
-        gLogInfo << "- Decoder: " << decoder->getInfo() << std::endl;
-        gLogInfo << "- Alignment: " << alignment->getInfo() << std::endl;
-        gLogInfo << "- Context: " << context->getInfo() << std::endl;
-        gLogInfo << "- Attention: " << attention->getInfo() << std::endl;
-        gLogInfo << "- Projection: " << projection->getInfo() << std::endl;
-        gLogInfo << "- Likelihood: " << likelihood->getInfo() << std::endl;
-        gLogInfo << "- Search Policy: " << searchPolicy->getInfo() << std::endl;
-        gLogInfo << "- Data Writer: " << dataWriter->getInfo() << std::endl;
-        gLogInfo << "End of Component Info" << std::endl;
+        sample::gLogInfo << "Component Info:" << std::endl;
+        sample::gLogInfo << "- Data Reader: " << dataReader->getInfo() << std::endl;
+        sample::gLogInfo << "- Input Embedder: " << inputEmbedder->getInfo() << std::endl;
+        sample::gLogInfo << "- Output Embedder: " << outputEmbedder->getInfo() << std::endl;
+        sample::gLogInfo << "- Encoder: " << encoder->getInfo() << std::endl;
+        sample::gLogInfo << "- Decoder: " << decoder->getInfo() << std::endl;
+        sample::gLogInfo << "- Alignment: " << alignment->getInfo() << std::endl;
+        sample::gLogInfo << "- Context: " << context->getInfo() << std::endl;
+        sample::gLogInfo << "- Attention: " << attention->getInfo() << std::endl;
+        sample::gLogInfo << "- Projection: " << projection->getInfo() << std::endl;
+        sample::gLogInfo << "- Likelihood: " << likelihood->getInfo() << std::endl;
+        sample::gLogInfo << "- Search Policy: " << searchPolicy->getInfo() << std::endl;
+        sample::gLogInfo << "- Data Writer: " << dataWriter->getInfo() << std::endl;
+        sample::gLogInfo << "End of Component Info" << std::endl;
     }
 
     std::vector<nvinfer1::Dims> stateSizes = decoder->getStateSizes();
@@ -1172,7 +1172,11 @@ int main(int argc, char** argv)
                 *inputOriginalSequenceLengthsHostBuffer);
         });
 
-        encoderContext->enqueue(inputSamplesRead, &encoderBindings[0], stream, nullptr);
+        if (!encoderContext->enqueue(inputSamplesRead, &encoderBindings[0], stream, nullptr))
+        {
+            sample::gLogError << "Error in encoder context enqueue" << std::endl;
+            return sample::gLogger.reportTest(sampleTest, false);
+        }
 
         // Limit output sequences length to input_sequence_length * 2
         std::transform((const int*) *inputSequenceLengthsHostBuffer,
@@ -1196,12 +1200,25 @@ int main(int argc, char** argv)
             // Generator initialization and beam shuffling
             if (outputTimestep == 0)
             {
-                generatorContext->enqueue(validSampleCount, &generatorBindingsFirstStep[0], stream, nullptr);
+                if (!generatorContext->enqueue(validSampleCount, &generatorBindingsFirstStep[0], stream, nullptr))
+                {
+                    sample::gLogError << "Error in generator context enqueue step" << outputTimestep << std::endl;
+                    return sample::gLogger.reportTest(sampleTest, false);
+                }
             }
             else
             {
-                generatorShuffleContext->enqueue(validSampleCount, &generatorShuffleBindings[0], stream, nullptr);
-                generatorContext->enqueue(validSampleCount, &generatorBindings[0], stream, nullptr);
+                if (!generatorShuffleContext->enqueue(validSampleCount, &generatorShuffleBindings[0], stream, nullptr))
+                {
+                    sample::gLogError << "Error in generator shuffle context enqueue step " << outputTimestep
+                                      << std::endl;
+                    return sample::gLogger.reportTest(sampleTest, false);
+                }
+                if (!generatorContext->enqueue(validSampleCount, &generatorBindings[0], stream, nullptr))
+                {
+                    sample::gLogError << "Error in generator context enqueue step" << outputTimestep << std::endl;
+                    return sample::gLogger.reportTest(sampleTest, false);
+                }
             }
 
             CUDA_CHECK(cudaMemcpyAsync(*outputCombinedLikelihoodHostBuffer, *outputCombinedLikelihoodDeviceBuffer,
@@ -1268,8 +1285,8 @@ int main(int argc, char** argv)
 
     if (gDataWriterStr == "benchmark")
     {
-        gLogInfo << "Average latency (without data read) = " << totalLatency / static_cast<float>(batchCount) << " ms"
-                 << std::endl;
+        sample::gLogInfo << "Average latency (without data read) = " << totalLatency / static_cast<float>(batchCount)
+                         << " ms" << std::endl;
     }
 
     if (gEnableProfiling)
@@ -1277,12 +1294,12 @@ int main(int argc, char** argv)
         if (gAggregateProfiling)
         {
             SimpleProfiler aggregateProfiler("Aggregate", profilers);
-            gLogInfo << aggregateProfiler << std::endl;
+            sample::gLogInfo << aggregateProfiler << std::endl;
         }
         else
         {
             for (const auto& profiler : profilers)
-                gLogInfo << profiler << std::endl;
+                sample::gLogInfo << profiler << std::endl;
         }
     }
 
@@ -1298,5 +1315,5 @@ int main(int argc, char** argv)
 
     bool pass = gDataWriterStr != "bleu" || score >= 25.0f;
 
-    return gLogger.reportTest(sampleTest, pass);
+    return sample::gLogger.reportTest(sampleTest, pass);
 }
diff --git a/samples/opensource/sampleNMT/trtUtil.cpp b/samples/opensource/sampleNMT/trtUtil.cpp
index d51c3a3f..224e77ef 100644
--- a/samples/opensource/sampleNMT/trtUtil.cpp
+++ b/samples/opensource/sampleNMT/trtUtil.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleNMT/trtUtil.h b/samples/opensource/sampleNMT/trtUtil.h
index b96da3cc..4a76aad4 100644
--- a/samples/opensource/sampleNMT/trtUtil.h
+++ b/samples/opensource/sampleNMT/trtUtil.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleOnnxMNIST/CMakeLists.txt b/samples/opensource/sampleOnnxMNIST/CMakeLists.txt
index 4c9396ad..d70743d4 100644
--- a/samples/opensource/sampleOnnxMNIST/CMakeLists.txt
+++ b/samples/opensource/sampleOnnxMNIST/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleOnnxMNIST/README.md b/samples/opensource/sampleOnnxMNIST/README.md
index 56f176b3..ddd27a7e 100644
--- a/samples/opensource/sampleOnnxMNIST/README.md
+++ b/samples/opensource/sampleOnnxMNIST/README.md
@@ -9,7 +9,7 @@
 	* [Running inference](#running-inference)
 	* [TensorRT API layers and ops](#tensorrt-api-layers-and-ops)
 - [Running the sample](#running-the-sample)
-	* [Sample `--help` options](#sample---help-options)
+	* [Sample `--help` options](#sample-help-options)
 - [Additional resources](#additional-resources)
 - [License](#license)
 - [Changelog](#changelog)
@@ -17,7 +17,7 @@
 
 ## Description
 
-This sample, sampleOnnxMNIST, converts a model trained on the [MNIST dataset](https://github.com/onnx/models/tree/master/mnist) in Open Neural Network Exchange (ONNX) format to a TensorRT network and runs inference on the network.
+This sample, sampleOnnxMNIST, converts a model trained on the [MNIST dataset](https://github.com/onnx/models/tree/master/vision/classification/mnist) in Open Neural Network Exchange (ONNX) format to a TensorRT network and runs inference on the network.
 
 ONNX is a standard for representing deep learning models that enables models to be transferred between frameworks.
 
@@ -35,15 +35,15 @@ Specifically, this sample:
 The model file can be converted to a TensorRT network using the ONNX parser. The parser can be initialized with the
 network definition that the parser will write to and the logger object.
 
-`auto parser = nvonnxparser::createParser(*network, gLogger.getTRTLogger());`
+`auto parser = nvonnxparser::createParser(*network, sample::gLogger.getTRTLogger());`
 
 The ONNX model file is then passed onto the parser along with the logging level
 
 ```
-if (!parser->parseFromFile(model_file, static_cast<int>(gLogger.getReportableSeverity())))
+if (!parser->parseFromFile(model_file, static_cast<int>(sample::gLogger.getReportableSeverity())))
 {
 	  string msg("failed to parse onnx file");
-	  gLogger->log(nvinfer1::ILogger::Severity::kERROR, msg.c_str());
+	  sample::gLogger->log(nvinfer1::ILogger::Severity::kERROR, msg.c_str());
 	  exit(EXIT_FAILURE);
 }
 ```
@@ -58,7 +58,7 @@ After the TensorRT network is constructed by parsing the model, the TensorRT eng
 ### Building the engine
 
 To build the engine, create the builder and pass a logger created for TensorRT which is used for reporting errors, warnings and informational messages in the network:
-`IBuilder* builder = createInferBuilder(gLogger);`
+`IBuilder* builder = createInferBuilder(sample::gLogger);`
 
 To build the engine from the generated TensorRT network, issue the following call:
 `nvinfer1::ICudaEngine* engine = builder->buildCudaEngine(*network);`
@@ -169,17 +169,11 @@ The Shuffle layer implements a reshape and transpose operator for tensors.
 
 	This output shows that the sample ran successfully; PASSED.
 
-### Sample --help options
 
-To see the full list of available options and their descriptions, use the `-h` or `--help` command line option. For example:
-```
-Usage: ./sample_onnx_mnist [-h or --help] [-d or --datadir=<path to data directory>] [--useDLACore=<int>]
---help Display help information
---datadir Specify path to a data directory, overriding the default. This option can be used multiple times to add multiple directories. If no data directories are given, the default is to use (data/samples/mnist/, data/mnist/)
---useDLACore=N Specify a DLA engine for layers that support DLA. Value can range from 0 to n-1, where n is the number of DLA engines on the platform.
---int8 Run in Int8 mode.
---fp16 Run in FP16 mode.
-```
+### Sample `--help` options
+
+To see the full list of available options and their descriptions, use the `-h` or `--help` command line option.
+
 
 # Additional resources
 
diff --git a/samples/opensource/sampleOnnxMNIST/sampleOnnxMNIST.cpp b/samples/opensource/sampleOnnxMNIST/sampleOnnxMNIST.cpp
index e19c867b..b8ccf069 100644
--- a/samples/opensource/sampleOnnxMNIST/sampleOnnxMNIST.cpp
+++ b/samples/opensource/sampleOnnxMNIST/sampleOnnxMNIST.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -102,13 +102,13 @@ class SampleOnnxMNIST
 //!
 bool SampleOnnxMNIST::build()
 {
-    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(gLogger.getTRTLogger()));
+    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(sample::gLogger.getTRTLogger()));
     if (!builder)
     {
         return false;
     }
 
-    const auto explicitBatch = 1U << static_cast<uint32_t>(NetworkDefinitionCreationFlag::kEXPLICIT_BATCH);     
+    const auto explicitBatch = 1U << static_cast<uint32_t>(NetworkDefinitionCreationFlag::kEXPLICIT_BATCH);
     auto network = SampleUniquePtr<nvinfer1::INetworkDefinition>(builder->createNetworkV2(explicitBatch));
     if (!network)
     {
@@ -121,7 +121,8 @@ bool SampleOnnxMNIST::build()
         return false;
     }
 
-    auto parser = SampleUniquePtr<nvonnxparser::IParser>(nvonnxparser::createParser(*network, gLogger.getTRTLogger()));
+    auto parser
+        = SampleUniquePtr<nvonnxparser::IParser>(nvonnxparser::createParser(*network, sample::gLogger.getTRTLogger()));
     if (!parser)
     {
         return false;
@@ -163,14 +164,13 @@ bool SampleOnnxMNIST::constructNetwork(SampleUniquePtr<nvinfer1::IBuilder>& buil
     SampleUniquePtr<nvinfer1::INetworkDefinition>& network, SampleUniquePtr<nvinfer1::IBuilderConfig>& config,
     SampleUniquePtr<nvonnxparser::IParser>& parser)
 {
-    auto parsed = parser->parseFromFile(
-        locateFile(mParams.onnxFileName, mParams.dataDirs).c_str(), static_cast<int>(gLogger.getReportableSeverity()));
+    auto parsed = parser->parseFromFile(locateFile(mParams.onnxFileName, mParams.dataDirs).c_str(),
+        static_cast<int>(sample::gLogger.getReportableSeverity()));
     if (!parsed)
     {
         return false;
     }
 
-    builder->setMaxBatchSize(mParams.batchSize);
     config->setMaxWorkspaceSize(16_MiB);
     if (mParams.fp16)
     {
@@ -196,7 +196,7 @@ bool SampleOnnxMNIST::constructNetwork(SampleUniquePtr<nvinfer1::IBuilder>& buil
 bool SampleOnnxMNIST::infer()
 {
     // Create RAII buffer manager object
-    samplesCommon::BufferManager buffers(mEngine, mParams.batchSize);
+    samplesCommon::BufferManager buffers(mEngine);
 
     auto context = SampleUniquePtr<nvinfer1::IExecutionContext>(mEngine->createExecutionContext());
     if (!context)
@@ -247,12 +247,12 @@ bool SampleOnnxMNIST::processInput(const samplesCommon::BufferManager& buffers)
     readPGMFile(locateFile(std::to_string(mNumber) + ".pgm", mParams.dataDirs), fileData.data(), inputH, inputW);
 
     // Print an ascii representation
-    gLogInfo << "Input:" << std::endl;
+    sample::gLogInfo << "Input:" << std::endl;
     for (int i = 0; i < inputH * inputW; i++)
     {
-        gLogInfo << (" .:-=+*#%@"[fileData[i] / 26]) << (((i + 1) % inputW) ? "" : "\n");
+        sample::gLogInfo << (" .:-=+*#%@"[fileData[i] / 26]) << (((i + 1) % inputW) ? "" : "\n");
     }
-    gLogInfo << std::endl;
+    sample::gLogInfo << std::endl;
 
     float* hostDataBuffer = static_cast<float*>(buffers.getHostBuffer(mParams.inputTensorNames[0]));
     for (int i = 0; i < inputH * inputW; i++)
@@ -283,7 +283,7 @@ bool SampleOnnxMNIST::verifyOutput(const samplesCommon::BufferManager& buffers)
         sum += output[i];
     }
 
-    gLogInfo << "Output:" << std::endl;
+    sample::gLogInfo << "Output:" << std::endl;
     for (int i = 0; i < outputSize; i++)
     {
         output[i] /= sum;
@@ -293,10 +293,12 @@ bool SampleOnnxMNIST::verifyOutput(const samplesCommon::BufferManager& buffers)
             idx = i;
         }
 
-        gLogInfo << " Prob " << i << "  " << std::fixed << std::setw(5) << std::setprecision(4) << output[i] << " "
-                 << "Class " << i << ": " << std::string(int(std::floor(output[i] * 10 + 0.5f)), '*') << std::endl;
+        sample::gLogInfo << " Prob " << i << "  " << std::fixed << std::setw(5) << std::setprecision(4) << output[i]
+                         << " "
+                         << "Class " << i << ": " << std::string(int(std::floor(output[i] * 10 + 0.5f)), '*')
+                         << std::endl;
     }
-    gLogInfo << std::endl;
+    sample::gLogInfo << std::endl;
 
     return idx == mNumber && val > 0.9f;
 }
@@ -318,7 +320,6 @@ samplesCommon::OnnxSampleParams initializeSampleParams(const samplesCommon::Args
     }
     params.onnxFileName = "mnist.onnx";
     params.inputTensorNames.push_back("Input3");
-    params.batchSize = 1;
     params.outputTensorNames.push_back("Plus214_Output_0");
     params.dlaCore = args.useDLACore;
     params.int8 = args.runInInt8;
@@ -353,7 +354,7 @@ int main(int argc, char** argv)
     bool argsOK = samplesCommon::parseArgs(args, argc, argv);
     if (!argsOK)
     {
-        gLogError << "Invalid arguments" << std::endl;
+        sample::gLogError << "Invalid arguments" << std::endl;
         printHelpInfo();
         return EXIT_FAILURE;
     }
@@ -363,22 +364,22 @@ int main(int argc, char** argv)
         return EXIT_SUCCESS;
     }
 
-    auto sampleTest = gLogger.defineTest(gSampleName, argc, argv);
+    auto sampleTest = sample::gLogger.defineTest(gSampleName, argc, argv);
 
-    gLogger.reportTestStart(sampleTest);
+    sample::gLogger.reportTestStart(sampleTest);
 
     SampleOnnxMNIST sample(initializeSampleParams(args));
 
-    gLogInfo << "Building and running a GPU inference engine for Onnx MNIST" << std::endl;
+    sample::gLogInfo << "Building and running a GPU inference engine for Onnx MNIST" << std::endl;
 
     if (!sample.build())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
     if (!sample.infer())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
 
-    return gLogger.reportPass(sampleTest);
+    return sample::gLogger.reportPass(sampleTest);
 }
diff --git a/samples/opensource/sampleOnnxMnistCoordConvAC/CMakeLists.txt b/samples/opensource/sampleOnnxMnistCoordConvAC/CMakeLists.txt
new file mode 100644
index 00000000..8b388d0f
--- /dev/null
+++ b/samples/opensource/sampleOnnxMnistCoordConvAC/CMakeLists.txt
@@ -0,0 +1,23 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+SET(SAMPLE_SOURCES
+    sampleOnnxMnistCoordConvAC.cpp
+)
+
+set(SAMPLE_PARSERS "onnx")
+set(PLUGINS_NEEDED ON)
+
+include(../../CMakeSamplesTemplate.txt)
\ No newline at end of file
diff --git a/samples/opensource/sampleOnnxMnistCoordConvAC/README.md b/samples/opensource/sampleOnnxMnistCoordConvAC/README.md
new file mode 100644
index 00000000..2049cd0e
--- /dev/null
+++ b/samples/opensource/sampleOnnxMnistCoordConvAC/README.md
@@ -0,0 +1,245 @@
+# Implementing CoordConv in TensorRT with a custom plugin
+
+
+**Table Of Contents**
+- [Implementing CoordConv in TensorRT with a custom plugin](#implementing-coordconv-in-tensorrt-with-a-custom-plugin)
+	- [Description](#description)
+	- [How does this sample work?](#how-does-this-sample-work)
+		- [Converting the ONNX model to a TensorRT network](#converting-the-onnx-model-to-a-tensorrt-network)
+		- [Building the engine](#building-the-engine)
+		- [Running inference](#running-inference)
+		- [TensorRT API layers and ops](#tensorrt-api-layers-and-ops)
+	- [Running the sample](#running-the-sample)
+		- [Sample --help options](#sample---help-options)
+- [Additional resources](#additional-resources)
+- [License](#license)
+- [Changelog](#changelog)
+- [Known issues](#known-issues)
+
+## Description
+
+This sample, sampleOnnxMnistCoordConvAC, converts a model trained on the `MNIST dataset` in Open Neural Network Exchange (ONNX) format to a TensorRT network and runs inference on the network.
+This model was trained in PyTorch and it contains custom CoordConv layers instead of Conv layers.<br/>
+Model with CoordConvAC layers training script and code of CoordConv layers in PyTorch: [link](https://github.com/denti/mnist-coordconv-pytorch/blob/master/main_coord_conv.py)<br/>
+Original model with usual Conv layers: [link](https://github.com/pytorch/examples/tree/master/mnist)
+
+[CoordConv](https://arxiv.org/abs/1807.03247)  layer is a layer proposed by Uber AI Labs at 2018.
+It improves the quality of regular Conv layers by adding additional channels with relative coordinates to the input data.
+This layer is used in classification, detection, segmentation and other NN architectures.
+The CoordConv layer maps to the `CoordConvAC_TRT` custom plugin implemented in TensorRT for fast inference.
+This plugin can be found at `TensorRT/plugin/coordConvACPlugin`. Additional information about the layer and plugin implementation can be found at `TensorRT/plugin/coordConvACPlugin/README.md`
+
+ONNX is a standard for representing deep learning models that enables models to be transferred between frameworks.
+
+## How does this sample work?
+
+This sample creates and runs a TensorRT engine on an ONNX model of MNIST trained with CoordConv layers. It demonstrates how TensorRT can parse and import ONNX models, as well as use plugins to run custom layers in neural networks.
+
+Specifically, this sample:
+- [Converts the ONNX model with custom layer to a TensorRT network](#converting-the-onnx-model-to-a-tensorrt-network)
+- [Builds an engine with custom layer](#building-an-engine)
+- [Runs inference using the generated TensorRT network](#running-inference)
+
+### Converting the ONNX model to a TensorRT network
+
+The model file can be converted to a TensorRT network using the ONNX parser. The parser can be initialized with the
+network definition that the parser will write to and the logger object.
+
+`auto parser = nvonnxparser::createParser(*network, sample::gLogger.getTRTLogger());`
+
+Plugins library needs to be added to the code to parse custom layers implemented as Plugins
+
+`initLibNvInferPlugins(&sample::gLogger, "ONNXTRT_NAMESPACE");`
+
+The ONNX model file is then passed onto the parser along with the logging level
+
+```
+if (!parser->parseFromFile(model_file, static_cast<int>(sample::gLogger.getReportableSeverity())))
+{
+	  string msg("failed to parse onnx file");
+	  sample::gLogger->log(nvinfer1::ILogger::Severity::kERROR, msg.c_str());
+	  exit(EXIT_FAILURE);
+}
+```
+
+To view additional information about the network, including layer information and individual layer dimensions, issue the following call:
+```
+parser->reportParsingInfo();
+```
+
+After the TensorRT network is constructed by parsing the model, the TensorRT engine can be built to run inference.
+
+### Building the engine
+
+To build the engine, create the builder and pass a logger created for TensorRT which is used for reporting errors, warnings and informational messages in the network:
+`IBuilder* builder = createInferBuilder(sample::gLogger);`
+
+To build the engine from the generated TensorRT network, issue the following call:
+`nvinfer1::ICudaEngine* engine = builder->buildCudaEngine(*network);`
+
+After you build the engine, verify that the engine is running properly by confirming the output is what you expected. The output format of this sample should be the same as the output of sampleMNIST.
+
+### Running inference
+
+To run inference using the created engine, see [Performing Inference In C++](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#perform_inference_c).
+
+**Note:** It’s important to preprocess the data and convert it to the format accepted by the network. In this example, the sample input is in PGM (portable graymap) format. The model expects an input of image `1x28x28` scaled to between `[0,1]`. 
+
+**Note2:** Additional preprocessing needs to be applied to the data before putting it to the NN input due to the same normalization preprocessing were used when model was trained [transforms.Normalize((0.1307,), (0.3081,))](https://github.com/pytorch/examples/tree/master/mnist):
+
+```
+const float PYTORCH_NORMALIZE_MEAN = 0.1307;
+const float PYTORCH_NORMALIZE_STD = 0.3081;
+hostDataBuffer[i] = ((1.0 - float(fileData[i] / 255.0)) - PYTORCH_NORMALIZE_MEAN) / PYTORCH_NORMALIZE_STD;
+```
+
+### TensorRT API layers and ops
+
+In this sample, the following layers and plugins are used. For more information about these layers, see the [TensorRT Developer Guide: Layers](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#layers) documentation.
+
+[CoordConvAC layer](https://github.com/NVIDIA/TensorRT/tree/master/plugin/coordConvACPlugin)
+Custom layer implemented with CUDA API that implements operation AddChannels. This layer expands the input data by adding additional channels with relative coordinates.
+
+[Activation layer](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#activation-layer)
+The Activation layer implements element-wise activation functions. Specifically, this sample uses the Activation layer with the type `kRELU`.
+
+[Convolution layer](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#convolution-layer)
+The Convolution layer computes a 2D (channel, height, and width) convolution, with or without bias.
+
+[FullyConnected layer](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#fullyconnected-layer)
+The FullyConnected layer implements a matrix-vector product, with or without bias.
+
+[Pooling layer](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#pooling-layer)
+The Pooling layer implements pooling within a channel. Supported pooling types are `maximum`, `average` and `maximum-average blend`.
+
+[Scale layer](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#scale-layer)
+The Scale layer implements a per-tensor, per-channel, or per-element affine transformation and/or exponentiation by constant values.
+
+[Shuffle layer](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#shuffle-layer)
+The Shuffle layer implements a reshape and transpose operator for tensors.
+
+
+
+## Running the sample
+
+1.  Compile this sample by running `make` in the `<TensorRT root directory>/samples/sampleOnnxMnistCoordConvAC` directory. The binary named `sample_onnx_mnist_coord_conv_ac` will be created in the `<TensorRT root directory>/bin` directory.
+	```
+	cd <TensorRT root directory>/samples/sampleOnnxMnistCoordConvAC
+	make
+	```
+
+	Where `<TensorRT root directory>` is where you installed TensorRT.
+
+2.  Run the sample to build and run the MNIST engine from the ONNX model.
+	```
+	./sample_onnx_mnist_coord_conv_ac [-h or --help] [-d or --datadir=<path to data directory>] [--useDLACore=<int>] [--int8 or --fp16]
+	```
+
+3.  Verify that the sample ran successfully. If the sample runs successfully you should see output similar to the following:
+	```
+	&&&& RUNNING TensorRT.sample_coord_conv_ac_onnx_mnist # ./sample_onnx_mnist_coord_conv_ac
+	----------------------------------------------------------------
+	Input filename:   data/mnist/mnist_with_coordconv.onnx
+    ONNX IR version:  0.0.6
+    Opset version:    11
+    Producer name:
+    Producer version:
+    Domain:
+    Model version:    0
+    Doc string:
+	----------------------------------------------------------------
+	[I] Input:
+	@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+	@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+	@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+	@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+	@@@@@@@@@@@*.  .*@@@@@@@@@@@
+	@@@@@@@@@@*.     +@@@@@@@@@@
+	@@@@@@@@@@. :#+   %@@@@@@@@@
+	@@@@@@@@@@.:@@@+  +@@@@@@@@@
+	@@@@@@@@@@.:@@@@:  +@@@@@@@@
+	@@@@@@@@@@=%@@@@:  +@@@@@@@@
+	@@@@@@@@@@@@@@@@#  +@@@@@@@@
+	@@@@@@@@@@@@@@@@*  +@@@@@@@@
+	@@@@@@@@@@@@@@@@:  +@@@@@@@@
+	@@@@@@@@@@@@@@@@:  +@@@@@@@@
+	@@@@@@@@@@@@@@@*  .@@@@@@@@@
+	@@@@@@@@@@%**%@.  *@@@@@@@@@
+	@@@@@@@@%+.  .:  .@@@@@@@@@@
+	@@@@@@@@=  ..    :@@@@@@@@@@
+	@@@@@@@@:  *@@:  :@@@@@@@@@@
+	@@@@@@@%   %@*    *@@@@@@@@@
+	@@@@@@@%   ++ ++  .%@@@@@@@@
+	@@@@@@@@-    +@@-  +@@@@@@@@
+	@@@@@@@@=  :*@@@#  .%@@@@@@@
+	@@@@@@@@@+*@@@@@%.   %@@@@@@
+	@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+	@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+	@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+	@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+
+	[I] Output:
+    Prob 0  0.0001 Class 0:
+    Prob 1  0.0003 Class 1:
+    Prob 2  0.9975 Class 2: **********
+    Prob 3  0.0009 Class 3:
+    Prob 4  0.0000 Class 4:
+    Prob 5  0.0001 Class 5:
+    Prob 6  0.0001 Class 6:
+    Prob 7  0.0000 Class 7:
+    Prob 8  0.0009 Class 8:
+    Prob 9  0.0000 Class 9:
+
+    &&&& PASSED TensorRT.sample_coord_conv_ac_onnx_mnist # ./sample_onnx_mnist_coord_conv_ac
+	```
+
+	This output shows that the sample ran successfully; PASSED.
+
+### Sample --help options
+
+To see the full list of available options and their descriptions, use the `-h` or `--help` command line option. For example:
+```
+Usage: ./sample_onnx_mnist_coord_conv_ac [-h or --help] [-d or --datadir=<path to data directory>] [--useDLACore=<int>]
+--help Display help information
+--datadir Specify path to a data directory, overriding the default. This option can be used multiple times to add multiple directories. If no data directories are given, the default is to use (data/samples/mnist/, data/mnist/)
+--useDLACore=N Specify a DLA engine for layers that support DLA. Value can range from 0 to n-1, where n is the number of DLA engines on the platform.
+--int8 Run in Int8 mode.
+--fp16 Run in FP16 mode.
+```
+
+# Additional resources
+
+The following resources provide a deeper understanding about the ONNX project and MNIST model:
+
+**CoordConv Layer**
+- [Arxiv paper by Uber AI Labs](https://arxiv.org/abs/1807.03247)
+- [Blog post about the CoordConv layer](https://eng.uber.com/coordconv/)
+- [Path to the layer's plugin in repository](https://github.com/NVIDIA/TensorRT/tree/master/plugin/coordConvACPlugin)
+
+**ONNX**
+- [GitHub: ONNX](https://github.com/onnx/onnx)
+- [Github: ONNX-TensorRT Open source parser](https://github.com/onnx/onnx-tensorrt)
+
+**Models**
+- [MNIST - Handwritten Digit Recognition](https://github.com/onnx/models/tree/master/mnist)
+- [GitHub: ONNX Models](https://github.com/onnx/models)
+
+**Documentation**
+- [Introduction To NVIDIA’s TensorRT Samples](https://docs.nvidia.com/deeplearning/sdk/tensorrt-sample-support-guide/index.html#samples)
+- [Working With TensorRT Using The C++ API](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#c_topics)
+- [NVIDIA’s TensorRT Documentation Library](https://docs.nvidia.com/deeplearning/sdk/tensorrt-archived/index.html)
+
+# License
+
+For terms and conditions for use, reproduction, and distribution, see the [TensorRT Software License Agreement](https://docs.nvidia.com/deeplearning/sdk/tensorrt-sla/index.html) documentation.
+
+
+# Changelog
+
+April 2020
+This `README.md` file was recreated, updated and reviewed.
+
+
+# Known issues
+
+There are no known issues in this sample.
diff --git a/samples/opensource/sampleOnnxMnistCoordConvAC/sampleOnnxMnistCoordConvAC.cpp b/samples/opensource/sampleOnnxMnistCoordConvAC/sampleOnnxMnistCoordConvAC.cpp
new file mode 100644
index 00000000..210bfa6f
--- /dev/null
+++ b/samples/opensource/sampleOnnxMnistCoordConvAC/sampleOnnxMnistCoordConvAC.cpp
@@ -0,0 +1,393 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+//!
+//! sampleOnnxMnistCoordConvAC.cpp
+//! This file contains the implementation of the ONNX MNIST sample. It creates the network using
+//! the MNIST onnx model.
+//! It can be run with the following command line:
+//! Command: ./sample_onnx_mnist_coord_conv_ac [-h or --help] [-d=/path/to/data/dir or --datadir=/path/to/data/dir]
+//! [--useDLACore=<int>]
+//!
+
+#include "argsParser.h"
+#include "buffers.h"
+#include "common.h"
+#include "logger.h"
+#include "parserOnnxConfig.h"
+
+#include "NvInfer.h"
+#include <cuda_runtime_api.h>
+
+#include <cstdlib>
+#include <fstream>
+#include <iostream>
+#include <sstream>
+
+const std::string gSampleName = "TensorRT.sample_onnx_mnist_coord_conv_ac";
+
+// Normalization constants from Pytorch transform.Normalize().
+// They are needed to preprocess the data:
+// https://discuss.pytorch.org/t/understanding-transform-normalize/21730
+const float PYTORCH_NORMALIZE_MEAN = 0.1307;
+const float PYTORCH_NORMALIZE_STD = 0.3081;
+
+//! \brief  The SampleOnnxMnistCoordConvAC class implements the ONNX MNIST sample
+//!
+//! \details It creates the network using an ONNX model
+//!
+class SampleOnnxMnistCoordConvAC
+{
+    template <typename T>
+    using SampleUniquePtr = std::unique_ptr<T, samplesCommon::InferDeleter>;
+
+public:
+    SampleOnnxMnistCoordConvAC(const samplesCommon::OnnxSampleParams& params)
+        : mParams(params)
+        , mEngine(nullptr)
+    {
+    }
+
+    //!
+    //! \brief Function builds the network engine
+    //!
+    bool build();
+
+    //!
+    //! \brief Runs the TensorRT inference engine for this sample
+    //!
+    bool infer();
+
+private:
+    samplesCommon::OnnxSampleParams mParams; //!< The parameters for the sample.
+
+    nvinfer1::Dims mInputDims;  //!< The dimensions of the input to the network.
+    nvinfer1::Dims mOutputDims; //!< The dimensions of the output to the network.
+    int mNumber{0};             //!< The number to classify
+
+    std::shared_ptr<nvinfer1::ICudaEngine> mEngine; //!< The TensorRT engine used to run the network
+
+    //!
+    //! \brief Parses an ONNX model for MNIST and creates a TensorRT network
+    //!
+    bool constructNetwork(SampleUniquePtr<nvinfer1::IBuilder>& builder,
+        SampleUniquePtr<nvinfer1::INetworkDefinition>& network, SampleUniquePtr<nvinfer1::IBuilderConfig>& config,
+        SampleUniquePtr<nvonnxparser::IParser>& parser);
+
+    //!
+    //! \brief Reads the input  and stores the result in a managed buffer
+    //!
+    bool processInput(const samplesCommon::BufferManager& buffers);
+
+    //!
+    //! \brief Classifies digits and verify result
+    //!
+    bool verifyOutput(const samplesCommon::BufferManager& buffers);
+};
+
+//!
+//! \brief Creates the network, configures the builder and creates the network engine
+//!
+//! \details This function creates the Onnx MNIST network by parsing the Onnx model and builds
+//!          the engine that will be used to run MNIST (mEngine)
+//!
+//! \return Returns true if the engine was created successfully and false otherwise
+//!
+bool SampleOnnxMnistCoordConvAC::build()
+{
+
+    initLibNvInferPlugins(&sample::gLogger, "");
+    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(sample::gLogger.getTRTLogger()));
+    if (!builder)
+    {
+        return false;
+    }
+
+    const auto explicitBatch = 1U << static_cast<uint32_t>(NetworkDefinitionCreationFlag::kEXPLICIT_BATCH);
+    auto network = SampleUniquePtr<nvinfer1::INetworkDefinition>(builder->createNetworkV2(explicitBatch));
+    if (!network)
+    {
+        return false;
+    }
+
+    auto config = SampleUniquePtr<nvinfer1::IBuilderConfig>(builder->createBuilderConfig());
+    if (!config)
+    {
+        return false;
+    }
+
+    auto parser
+        = SampleUniquePtr<nvonnxparser::IParser>(nvonnxparser::createParser(*network, sample::gLogger.getTRTLogger()));
+    if (!parser)
+    {
+        return false;
+    }
+
+    auto constructed = constructNetwork(builder, network, config, parser);
+    if (!constructed)
+    {
+        return false;
+    }
+
+    mEngine = std::shared_ptr<nvinfer1::ICudaEngine>(
+        builder->buildEngineWithConfig(*network, *config), samplesCommon::InferDeleter());
+    if (!mEngine)
+    {
+        return false;
+    }
+
+    assert(network->getNbInputs() == 1);
+    mInputDims = network->getInput(0)->getDimensions();
+    assert(mInputDims.nbDims == 4);
+
+    assert(network->getNbOutputs() == 1);
+    mOutputDims = network->getOutput(0)->getDimensions();
+    assert(mOutputDims.nbDims == 2);
+
+    return true;
+}
+
+//!
+//! \brief Uses a ONNX parser to create the Onnx MNIST Network and marks the
+//!        output layers
+//!
+//! \param network Pointer to the network that will be populated with the Onnx MNIST network
+//!
+//! \param builder Pointer to the engine builder
+//!
+bool SampleOnnxMnistCoordConvAC::constructNetwork(SampleUniquePtr<nvinfer1::IBuilder>& builder,
+    SampleUniquePtr<nvinfer1::INetworkDefinition>& network, SampleUniquePtr<nvinfer1::IBuilderConfig>& config,
+    SampleUniquePtr<nvonnxparser::IParser>& parser)
+{
+    auto parsed = parser->parseFromFile(locateFile(mParams.onnxFileName, mParams.dataDirs).c_str(),
+        static_cast<int>(sample::gLogger.getReportableSeverity()));
+    if (!parsed)
+    {
+        return false;
+    }
+
+    config->setMaxWorkspaceSize(16_MiB);
+    if (mParams.fp16)
+    {
+        config->setFlag(BuilderFlag::kFP16);
+    }
+    if (mParams.int8)
+    {
+        config->setFlag(BuilderFlag::kINT8);
+        samplesCommon::setAllTensorScales(network.get(), 127.0f, 127.0f);
+    }
+
+    samplesCommon::enableDLA(builder.get(), config.get(), mParams.dlaCore);
+
+    return true;
+}
+
+//!
+//! \brief Runs the TensorRT inference engine for this sample
+//!
+//! \details This function is the main execution function of the sample. It allocates the buffer,
+//!          sets inputs and executes the engine.
+//!
+bool SampleOnnxMnistCoordConvAC::infer()
+{
+    // Create RAII buffer manager object
+    samplesCommon::BufferManager buffers(mEngine);
+
+    auto context = SampleUniquePtr<nvinfer1::IExecutionContext>(mEngine->createExecutionContext());
+    if (!context)
+    {
+        return false;
+    }
+
+    // Read the input data into the managed buffers
+    assert(mParams.inputTensorNames.size() == 1);
+    if (!processInput(buffers))
+    {
+        return false;
+    }
+
+    // Memcpy from host input buffers to device input buffers
+    buffers.copyInputToDevice();
+
+    bool status = context->executeV2(buffers.getDeviceBindings().data());
+    if (!status)
+    {
+        return false;
+    }
+
+    // Memcpy from device output buffers to host output buffers
+    buffers.copyOutputToHost();
+
+    // Verify results
+    if (!verifyOutput(buffers))
+    {
+        return false;
+    }
+
+    return true;
+}
+
+//!
+//! \brief Reads the input and stores the result in a managed buffer
+//!
+bool SampleOnnxMnistCoordConvAC::processInput(const samplesCommon::BufferManager& buffers)
+{
+    const int inputH = mInputDims.d[2];
+    const int inputW = mInputDims.d[3];
+
+    // Read a random digit file
+    srand(unsigned(time(nullptr)));
+    std::vector<uint8_t> fileData(inputH * inputW);
+    mNumber = 2;
+    readPGMFile(locateFile("2.pgm", mParams.dataDirs), fileData.data(), inputH, inputW);
+
+    // Print an ascii representation
+    sample::gLogInfo << "Input:" << std::endl;
+    for (int i = 0; i < inputH * inputW; i++)
+    {
+        sample::gLogInfo << (" .:-=+*#%@"[fileData[i] / 26]) << (((i + 1) % inputW) ? "" : "\n");
+    }
+    sample::gLogInfo << std::endl;
+
+    float* hostDataBuffer = static_cast<float*>(buffers.getHostBuffer(mParams.inputTensorNames[0]));
+    for (int i = 0; i < inputH * inputW; i++)
+    {
+        hostDataBuffer[i] = ((1.0 - float(fileData[i] / 255.0)) - PYTORCH_NORMALIZE_MEAN) / PYTORCH_NORMALIZE_STD;
+    }
+
+    return true;
+}
+
+//!
+//! \brief Classifies digits and verify result
+//!
+//! \return whether the classification output matches expectations
+//!
+bool SampleOnnxMnistCoordConvAC::verifyOutput(const samplesCommon::BufferManager& buffers)
+{
+    const int outputSize = mOutputDims.d[1];
+    float* output = static_cast<float*>(buffers.getHostBuffer(mParams.outputTensorNames[0]));
+    float val{0.0f};
+    int idx{0};
+
+    // Calculate Softmax
+    float sum{0.0f};
+    for (int i = 0; i < outputSize; i++)
+    {
+        output[i] = exp(output[i]);
+        sum += output[i];
+    }
+
+    sample::gLogInfo << "Output:" << std::endl;
+    for (int i = 0; i < outputSize; i++)
+    {
+        output[i] /= sum;
+        val = std::max(val, output[i]);
+        if (val == output[i])
+        {
+            idx = i;
+        }
+
+        sample::gLogInfo << " Prob " << i << "  " << std::fixed << std::setw(5) << std::setprecision(4) << output[i]
+                         << " "
+                         << "Class " << i << ": " << std::string(int(std::floor(output[i] * 10 + 0.5f)), '*')
+                         << std::endl;
+    }
+    sample::gLogInfo << std::endl;
+
+    return idx == mNumber && val > 0.9f;
+}
+
+//!
+//! \brief Initializes members of the params struct using the command line args
+//!
+samplesCommon::OnnxSampleParams initializeSampleParams(const samplesCommon::Args& args)
+{
+    samplesCommon::OnnxSampleParams params;
+    if (args.dataDirs.empty()) //!< Use default directories if user hasn't provided directory paths
+    {
+        params.dataDirs.push_back("data/mnist/");
+        params.dataDirs.push_back("data/samples/mnist/");
+    }
+    else //!< Use the data directory provided by the user
+    {
+        params.dataDirs = args.dataDirs;
+    }
+    params.onnxFileName = "mnist_with_coordconv.onnx";
+    params.inputTensorNames.push_back("conv1");
+    params.outputTensorNames.push_back("fc2");
+    params.dlaCore = args.useDLACore;
+    params.int8 = args.runInInt8;
+    params.fp16 = args.runInFp16;
+
+    return params;
+}
+
+//!
+//! \brief Prints the help information for running this sample
+//!
+void printHelpInfo()
+{
+    std::cout << "Usage: ./sample_onnx_mnist_coord_conv_ac [-h or --help] [-d or --datadir=<path to data directory>] "
+                 "[--useDLACore=<int>]"
+              << std::endl;
+    std::cout << "--help          Display help information" << std::endl;
+    std::cout << "--datadir       Specify path to a data directory, overriding the default. This option can be used "
+                 "multiple times to add multiple directories. If no data directories are given, the default is to use "
+                 "(data/samples/mnist/, data/mnist/)"
+              << std::endl;
+    std::cout << "--useDLACore=N  Specify a DLA engine for layers that support DLA. Value can range from 0 to n-1, "
+                 "where n is the number of DLA engines on the platform."
+              << std::endl;
+    std::cout << "--int8          Run in Int8 mode." << std::endl;
+    std::cout << "--fp16          Run in FP16 mode." << std::endl;
+}
+
+int main(int argc, char** argv)
+{
+    samplesCommon::Args args;
+    bool argsOK = samplesCommon::parseArgs(args, argc, argv);
+    if (!argsOK)
+    {
+        sample::gLogError << "Invalid arguments" << std::endl;
+        printHelpInfo();
+        return EXIT_FAILURE;
+    }
+    if (args.help)
+    {
+        printHelpInfo();
+        return EXIT_SUCCESS;
+    }
+
+    auto sampleTest = sample::gLogger.defineTest(gSampleName, argc, argv);
+
+    sample::gLogger.reportTestStart(sampleTest);
+
+    SampleOnnxMnistCoordConvAC sample(initializeSampleParams(args));
+
+    sample::gLogInfo << "Building and running a GPU inference engine for Onnx MNIST" << std::endl;
+
+    if (!sample.build())
+    {
+        return sample::gLogger.reportFail(sampleTest);
+    }
+    if (!sample.infer())
+    {
+        return sample::gLogger.reportFail(sampleTest);
+    }
+
+    return sample::gLogger.reportPass(sampleTest);
+}
diff --git a/samples/opensource/samplePlugin/CMakeLists.txt b/samples/opensource/samplePlugin/CMakeLists.txt
index 728710a1..6438c9f1 100644
--- a/samples/opensource/samplePlugin/CMakeLists.txt
+++ b/samples/opensource/samplePlugin/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/samples/opensource/samplePlugin/README.md b/samples/opensource/samplePlugin/README.md
index 87d1849b..08e14907 100644
--- a/samples/opensource/samplePlugin/README.md
+++ b/samples/opensource/samplePlugin/README.md
@@ -1,16 +1,17 @@
 # Adding A Custom Layer To Your Network In TensorRT
 
 **Table Of Contents**
+
 - [Description](#description)
 - [How does this sample work?](#how-does-this-sample-work)
-	* [Defining the network](#defining-the-network)
-	* [Enabling custom layers in NvCaffeParser](#enabling-custom-layers-in-nvcaffeparser)
-	* [Building the engine](#building-the-engine)
-	* [Serializing and deserializing](#serializing-and-deserializing)
-	* [Resource management and execution](#resource-management-and-execution)
-	* [TensorRT API layers and ops](#tensorrt-api-layers-and-ops)
+    * [Defining the network](#defining-the-network)
+    * [Enabling custom layers in NvCaffeParser](#enabling-custom-layers-in-nvcaffeparser)
+    * [Building the engine](#building-the-engine)
+    * [Serializing and deserializing](#serializing-and-deserializing)
+    * [Resource management and execution](#resource-management-and-execution)
+    * [TensorRT API layers and ops](#tensorrt-api-layers-and-ops)
 - [Running the sample](#running-the-sample)
-	* [Sample `--help` options](#sample---help-options)
+    * [Sample `--help` options](#sample-help-options)
 - [Additional resources](#additional-resources)
 - [License](#license)
 - [Changelog](#changelog)
@@ -25,25 +26,26 @@ This sample, samplePlugin, defines a custom layer that supports multiple data fo
 This sample implements the MNIST model (`data/samples/mnist/mnist.prototxt`) with the difference that the custom layer implements the Caffe InnerProduct layer using gemm routines (Matrix Multiplication) in cuBLAS and tensor addition in cuDNN (bias offset). Normally, the Caffe InnerProduct layer can be implemented in TensorRT using the IFullyConnected layer. However, in this sample, we use `FCPlugin` for this layer as an example of how to use plugins. The sample demonstrates plugin usage through the `IPluginExt` interface and uses the `nvcaffeparser1::IPluginFactoryExt` to add the plugin object to the network.
 
 Specifically, this sample:
--  [Defines the network](#defining-the-network)    
+-  [Defines the network](#defining-the-network)
 -  [Enables custom layers](#enabling-custom-layers-in-nvcaffeparser)
 -  [Builds the engine](#building-the-engine)
--  [Serialize and deserialize](#serializing-and-deserializing4)
--  [Initializes the plugin and executes the custom layer](#resource-management-and-execution)
+-  [Serialize and deserialize](#serializing-and-deserializing)
+-  [Manages resources and executes the engine](#resource-management-and-execution)
 
 ### Defining the network
 
 The `FCPlugin` redefines the InnerProduct layer, which has a single output. Accordingly, `getNbOutputs` returns `1` and `getOutputDimensions` includes validation checks and returns the dimensions of the output:
-```
+
+```c++
 Dims getOutputDimensions(int index, const Dims* inputDims,
-						 int nbInputDims) override  
-{  
-	assert(index == 0 && nbInputDims == 1 &&
-		   inputDims[0].nbDims == 3);  
-	assert(mNbInputChannels == inputDims[0].d[0] *
-							   inputDims[0].d[1] *
-							   inputDims[0].d[2]);  
-	return DimsCHW(mNbOutputChannels, 1, 1);  
+                         int nbInputDims) override
+{
+    assert(index == 0 && nbInputDims == 1 &&
+           inputDims[0].nbDims == 3);
+    assert(mNbInputChannels == inputDims[0].d[0] *
+                               inputDims[0].d[1] *
+                               inputDims[0].d[2]);
+    return DimsCHW(mNbOutputChannels, 1, 1);
 }
 ```
 
@@ -51,20 +53,21 @@ Dims getOutputDimensions(int index, const Dims* inputDims,
 
 The model is imported using the Caffe parser (see [Importing A Caffe Model Using The C++ Parser API](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#import_caffe_c)  and [Using Custom Layers When Importing A Model From a Framework](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#using_custom_layer)). To use the `FCPlugin` implementation for the InnerProduct layer, a plugin factory is defined which recognizes the name of the InnerProduct layer (inner product `ip2`  in Caffe).
 
+```c++
+bool isPlugin(const char* name) override
+{    return !strcmp(name, "ip2"); }
 ```
-bool isPlugin(const char* name) override  
-{ 	 return !strcmp(name, "ip2"); }  
-```  
 
 The factory can then instantiate `FCPlugin` objects as directed by the parser. The `createPlugin` method receives the layer name, and a set of weights extracted from the Caffe model file, which are then passed to the plugin constructor. Since the lifetime of the weights and that of the newly created plugin are decoupled, the plugin makes a copy of the weights in the constructor.
-```
-virtual nvinfer1::IPlugin* createPlugin(const char* layerName, const nvinfer1::Weights* weights, int nbWeights) override  
-{  
-    ...  
-	mPlugin =
-	  std::unique_ptr<FCPlugin>(new FCPlugin(weights,nbWeights));
-  
-	return mPlugin.get();  
+
+```c++
+virtual nvinfer1::IPlugin* createPlugin(const char* layerName, const nvinfer1::Weights* weights, int nbWeights) override
+{
+    ...
+    mPlugin =
+      std::unique_ptr<FCPlugin>(new FCPlugin(weights,nbWeights));
+
+    return mPlugin.get();
 }
 ```
 
@@ -72,22 +75,22 @@ virtual nvinfer1::IPlugin* createPlugin(const char* layerName, const nvinfer1::W
 
 `FCPlugin` does not need any scratch space, therefore, for building the engine, the most important methods deal with the formats supported and the configuration. `FCPlugin` supports two formats: NCHW in both single and half precision as defined in the `supportsFormat` method.
 
-```
+```c++
 bool supportsFormat(DataType type, PluginFormat format) const override
 {
-	return (type == DataType::kFLOAT || type == DataType::kHALF) &&
-		   format == PluginFormat::kNCHW;
+    return (type == DataType::kFLOAT || type == DataType::kHALF) &&
+           format == PluginFormat::kNCHW;
 }
 ```
 
 Supported configurations are selected in the building phase. The builder selects a configuration with the networks `configureWithFormat()` method, to give it a chance to select an algorithm based on its inputs. In this example, the inputs are checked to ensure they are in a supported format, and the selected format is recorded in a member variable. No other information needs to be stored in this simple case; in more complex cases, you may need to do so or even choose an ad-hoc algorithm for the given configuration.
 
-```
+```c++
 void configureWithFormat(..., DataType type, PluginFormat format, ...) override
 {
-	assert((type == DataType::kFLOAT || type == DataType::kHALF) &&
-			format == PluginFormat::kNCHW);
-	mDataType = type;
+    assert((type == DataType::kFLOAT || type == DataType::kHALF) &&
+            format == PluginFormat::kNCHW);
+    mDataType = type;
 
 }
 ```
@@ -98,53 +101,53 @@ The configuration takes place at build time, therefore, any information or state
 
 Fully compliant plugins support serialization and deserialization, as described in [Serializing A Model In C++](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#serial_model_c). In the example, `FCPlugin` stores the number of channels and weights, the format selected, and the actual weights. The size of these variables makes up for the size of the serialized image; the size is returned by `getSerializationSize`:
 
-```
+```c++
 virtual size_t getSerializationSize() override
 {
-	return sizeof(mNbInputChannels) + sizeof(mNbOutputChannels) +
-		   sizeof(mBiasWeights.count) + sizeof(mDataType) +
-		   (mKernelWeights.count + mBiasWeights.count) *
-		   type2size(mDataType);
+    return sizeof(mNbInputChannels) + sizeof(mNbOutputChannels) +
+           sizeof(mBiasWeights.count) + sizeof(mDataType) +
+           (mKernelWeights.count + mBiasWeights.count) *
+           type2size(mDataType);
 }
 ```
 
 Eventually, when the engine is serialized, these variables are serialized, the weights converted is needed, and written on a buffer:
 
-```
+```c++
 virtual void serialize(void* buffer) override
 {
-	char* d = static_cast<char*>(buffer), *a = d;
-	write(d, mNbInputChannels);
-	...
-	convertAndCopyToBuffer(d, mKernelWeights);
-	convertAndCopyToBuffer(d, mBiasWeights);
-	assert(d == a + getSerializationSize());
+    char* d = static_cast<char*>(buffer), *a = d;
+    write(d, mNbInputChannels);
+    ...
+    convertAndCopyToBuffer(d, mKernelWeights);
+    convertAndCopyToBuffer(d, mBiasWeights);
+    assert(d == a + getSerializationSize());
 }
 ```
-  
+
 Then, when the engine is deployed, it is deserialized. As the runtime scans the serialized image, when a plugin image is encountered, it create a new plugin instance via the factory. The plugin object created during deserialization (shown below using new) is destroyed when the engine is destroyed by calling `FCPlugin::destroy()`.
 
-```
-IPlugin* createPlugin(...) override  
-{  
+```c++
+IPlugin* createPlugin(...) override
+{
     ...
-	return new FCPlugin(serialData, serialLength);  
-}  
-```  
+    return new FCPlugin(serialData, serialLength);
+}
+```
 
 In the same order as in the serialization, the variables are read and their values restored. In addition, at this point the weights have been converted to selected format and can be stored directly on the device.
 
-```
+```c++
 FCPlugin(const void* data, size_t length)
 {
-	const char* d = static_cast<const char*>(data), *a = d;
-	read(d, mNbInputChannels);
-	...
-	deserializeToDevice(d, mDeviceKernel,
-						mKernelWeights.count*type2size(mDataType));
-	deserializeToDevice(d, mDeviceBias,
-						mBiasWeights.count*type2size(mDataType));
-	assert(d == a + length);
+    const char* d = static_cast<const char*>(data), *a = d;
+    read(d, mNbInputChannels);
+    ...
+    deserializeToDevice(d, mDeviceKernel,
+                        mKernelWeights.count*type2size(mDataType));
+    deserializeToDevice(d, mDeviceBias,
+                        mBiasWeights.count*type2size(mDataType));
+    assert(d == a + length);
 }
 ```
 
@@ -152,56 +155,56 @@ FCPlugin(const void* data, size_t length)
 
 Before a custom layer is executed, the plugin is initialized. This is where resources are held for the lifetime of the plugin and can be acquired and initialized. In this example, weights are kept in CPU memory at first, so that during the build phase, for each configuration tested, weights can be converted to the desired format and then copied to the device in the initialization of the plugin. The method `initialize` creates the required cuBLAS and cuDNN handles, sets up tensor descriptors, allocates device memory, and copies the weights to device memory. Conversely, terminate destroys the handles and frees the memory allocated on the device.
 
-```
-int initialize() override  
-{  
-	CHECK(cudnnCreate(&mCudnn));  
-	CHECK(cublasCreate(&mCublas));  
+```c++
+int initialize() override
+{
+    CHECK(cudnnCreate(&mCudnn));
+    CHECK(cublasCreate(&mCublas));
     ...
-	if (mKernelWeights.values != nullptr)
-		convertAndCopyToDevice(mDeviceKernel, mKernelWeights);
+    if (mKernelWeights.values != nullptr)
+        convertAndCopyToDevice(mDeviceKernel, mKernelWeights);
     ...
-}  
-```  
+}
+```
 
 The core of the plugin is `enqueue`, which is used to execute the custom layer at runtime. The `call` parameters include the actual batch size, inputs, and outputs. The handles for cuBLAS and cuDNN operations are placed on the given stream; then, according to the data type and format configured, the plugin executes in single or half precision.
 
 **Note:** The two handles are part of the plugin object, therefore, the same engine cannot be executed concurrently on multiple streams. In order to enable multiple streams of execution, plugins must be re-entrant and handle stream-specific data accordingly.
 
-```
+```c++
 virtual int enqueue(int batchSize, const void*const * inputs, void** outputs, ...) override
 {
-	...
-	cublasSetStream(mCublas, stream);
-	cudnnSetStream(mCudnn, stream);
-	if (mDataType == DataType::kFLOAT)
-	{...}
-	else
-	{
-		CHECK(cublasHgemm(mCublas, CUBLAS_OP_T, CUBLAS_OP_N,
-						  mNbOutputChannels, batchSize,
-						  mNbInputChannels, &oneh,
-						  mDeviceKernel), mNbInputChannels,
-						  inputs[0], mNbInputChannels, &zeroh,
-						  outputs[0], mNbOutputChannels));
-	}
-	if (mBiasWeights.count)
-	{
-		cudnnDataType_t cudnnDT = mDataType == DataType::kFLOAT ?
-								  CUDNN_DATA_FLOAT : CUDNN_DATA_HALF;
-		...
-	}
-	return 0;
+    ...
+    cublasSetStream(mCublas, stream);
+    cudnnSetStream(mCudnn, stream);
+    if (mDataType == DataType::kFLOAT)
+    {...}
+    else
+    {
+        CHECK(cublasHgemm(mCublas, CUBLAS_OP_T, CUBLAS_OP_N,
+                          mNbOutputChannels, batchSize,
+                          mNbInputChannels, &oneh,
+                          mDeviceKernel), mNbInputChannels,
+                          inputs[0], mNbInputChannels, &zeroh,
+                          outputs[0], mNbOutputChannels));
+    }
+    if (mBiasWeights.count)
+    {
+        cudnnDataType_t cudnnDT = mDataType == DataType::kFLOAT ?
+                                  CUDNN_DATA_FLOAT : CUDNN_DATA_HALF;
+        ...
+    }
+    return 0;
 }
 ```
-  
+
 The plugin object created in the sample is cloned by each of the network, builder, and engine by calling the `FCPlugin::clone()` method. The `clone()` method calls the plugin constructor and can also clone plugin parameters, if necessary.
 
-```
-IPluginExt* clone()  
-{  
-		return new FCPlugin(&mKernelWeights, mNbWeights, mNbOutputChannels);  
-}  
+```c++
+IPluginExt* clone()
+{
+        return new FCPlugin(&mKernelWeights, mNbWeights, mNbOutputChannels);
+}
 ```
 
 The cloned plugin objects are deleted when the network, builder, or engine are destroyed. This is done by invoking the `FCPlugin::destroy()` method.
@@ -213,7 +216,7 @@ The cloned plugin objects are deleted when the network, builder, or engine are d
 In this sample, the following layers are used.  For more information about these layers, see the [TensorRT Developer Guide: Layers](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#layers) documentation.
 
 [Activation layer](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#activation-layer)
-The Activation layer implements element-wise activation functions. Specifically, this sample uses the Activation layer with the type `kRELU`. 
+The Activation layer implements element-wise activation functions. Specifically, this sample uses the Activation layer with the type `kRELU`.
 
 [Convolution layer](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#convolution-layer)
 The Convolution layer computes a 2D (channel, height, and width) convolution, with or without bias.
@@ -230,83 +233,76 @@ The Scale layer implements a per-tensor, per-channel, or per-element affine tran
 [SoftMax layer](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#softmax-layer)
 The SoftMax layer applies the SoftMax function on the input tensor along an input dimension specified by the user.
 
-	
+
 ## Running the sample
 
 1. Compile this sample by running `make` in the `<TensorRT root directory>/samples/samplePlugin` directory. The binary named `sample_plugin` will be created in the `<TensorRT root directory>/bin` directory.
-	```
-	cd <TensorRT root directory>/samples/samplePlugin
-	make
-	```
-	Where `<TensorRT root directory>` is where you installed TensorRT.
-	
+    ```sh
+    cd <TensorRT root directory>/samples/samplePlugin
+    make
+    ```
+    Where `<TensorRT root directory>` is where you installed TensorRT.
+
 2. Run the sample to perform inference on the digit:
     ```
-	./sample_plugin
-	```
+    ./sample_plugin
+    ```
 3.  Verify that the sample ran successfully. If the sample runs successfully you should see output similar to the following:
     ```
-	&&&& RUNNING TensorRT.sample_plugin # ./build/x86_64-linux/sample_plugin  
-	[I] [TRT] Detected 1 input and 1 output network tensors.  
-	[I] Input:  
-	@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-	@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-	@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-	@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-	@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-	@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-	@@@@@@@@@@@@@@%.-@@@@@@@@@@@
-	@@@@@@@@@@@*-    %@@@@@@@@@@
-	@@@@@@@@@@= .-.  *@@@@@@@@@@
-	@@@@@@@@@= +@@@  *@@@@@@@@@@
-	@@@@@@@@* =@@@@  %@@@@@@@@@@
-	@@@@@@@@..@@@@%  @@@@@@@@@@@
-	@@@@@@@# *@@@@-  @@@@@@@@@@@
-	@@@@@@@: @@@@%   @@@@@@@@@@@
-	@@@@@@@: @@@@-   @@@@@@@@@@@
-	@@@@@@@: =+*= +: *@@@@@@@@@@
-	@@@@@@@*.    +@: *@@@@@@@@@@
-	@@@@@@@@%#**#@@: *@@@@@@@@@@
-	@@@@@@@@@@@@@@@: -@@@@@@@@@@
-	@@@@@@@@@@@@@@@+ :@@@@@@@@@@
-	@@@@@@@@@@@@@@@*  @@@@@@@@@@
-	@@@@@@@@@@@@@@@@  %@@@@@@@@@
-	@@@@@@@@@@@@@@@@  #@@@@@@@@@
-	@@@@@@@@@@@@@@@@: +@@@@@@@@@
-	@@@@@@@@@@@@@@@@- +@@@@@@@@@
-	@@@@@@@@@@@@@@@@*:%@@@@@@@@@
-	@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-	@@@@@@@@@@@@@@@@@@@@@@@@@@@@  
-	  
-	[I] Output:  
-	0:  
-	1:  
-	2:  
-	3:  
-	4:  
-	5:  
-	6:  
-	7:  
-	8:  
-	9: **********  
-  
-	&&&& PASSED TensorRT.sample_plugin # ./build/x86_64-linux/sample_plugin
-	```
-
-	This output shows that the sample ran successfully; `PASSED`.
- 
-
-### Sample --help options
-
-To see the full list of available options and their descriptions, use the `-h` or `--help` command line option. For example:
-```
-Usage: ./sample_plugin [-h or --help] [-d or --datadir=<path to data directory>] [--useDLACore=<int>]  
--h or --help Display help information  
---datadir Specify path to a data directory, overriding the default. This option can be used multiple times to add multiple directories. If no data directories are given, the default is to use (data/samples/mnist/, data/mnist/)  
---useDLACore=N Specify a DLA engine for layers that support DLA. Value can range from 0 to n-1, where n is the number of DLA engines on the platform.  
---int8 Run in Int8 mode.  
---fp16 Run in FP16 mode.
-```
+    &&&& RUNNING TensorRT.sample_plugin # ./build/x86_64-linux/sample_plugin
+    [I] [TRT] Detected 1 input and 1 output network tensors.
+    [I] Input:
+    @@@@@@@@@@@@@@@@@@@@@@@@@@@@
+    @@@@@@@@@@@@@@@@@@@@@@@@@@@@
+    @@@@@@@@@@@@@@@@@@@@@@@@@@@@
+    @@@@@@@@@@@@@@@@@@@@@@@@@@@@
+    @@@@@@@@@@@@@@@@@@@@@@@@@@@@
+    @@@@@@@@@@@@@@@@@@@@@@@@@@@@
+    @@@@@@@@@@@@@@%.-@@@@@@@@@@@
+    @@@@@@@@@@@*-    %@@@@@@@@@@
+    @@@@@@@@@@= .-.  *@@@@@@@@@@
+    @@@@@@@@@= +@@@  *@@@@@@@@@@
+    @@@@@@@@* =@@@@  %@@@@@@@@@@
+    @@@@@@@@..@@@@%  @@@@@@@@@@@
+    @@@@@@@# *@@@@-  @@@@@@@@@@@
+    @@@@@@@: @@@@%   @@@@@@@@@@@
+    @@@@@@@: @@@@-   @@@@@@@@@@@
+    @@@@@@@: =+*= +: *@@@@@@@@@@
+    @@@@@@@*.    +@: *@@@@@@@@@@
+    @@@@@@@@%#**#@@: *@@@@@@@@@@
+    @@@@@@@@@@@@@@@: -@@@@@@@@@@
+    @@@@@@@@@@@@@@@+ :@@@@@@@@@@
+    @@@@@@@@@@@@@@@*  @@@@@@@@@@
+    @@@@@@@@@@@@@@@@  %@@@@@@@@@
+    @@@@@@@@@@@@@@@@  #@@@@@@@@@
+    @@@@@@@@@@@@@@@@: +@@@@@@@@@
+    @@@@@@@@@@@@@@@@- +@@@@@@@@@
+    @@@@@@@@@@@@@@@@*:%@@@@@@@@@
+    @@@@@@@@@@@@@@@@@@@@@@@@@@@@
+    @@@@@@@@@@@@@@@@@@@@@@@@@@@@
+
+    [I] Output:
+    0:
+    1:
+    2:
+    3:
+    4:
+    5:
+    6:
+    7:
+    8:
+    9: **********
+
+    &&&& PASSED TensorRT.sample_plugin # ./build/x86_64-linux/sample_plugin
+    ```
+
+    This output shows that the sample ran successfully; `PASSED`.
+
+
+### Sample `--help` options
+
+To see the full list of available options and their descriptions, use the `-h` or `--help` command line option.
+
 
 # Additional resources
 
diff --git a/samples/opensource/samplePlugin/fcPlugin.h b/samples/opensource/samplePlugin/fcPlugin.h
index 4a4818d2..17641838 100644
--- a/samples/opensource/samplePlugin/fcPlugin.h
+++ b/samples/opensource/samplePlugin/fcPlugin.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -345,7 +345,7 @@ class PluginFactory : public nvinfer1::IPluginFactory, public nvcaffeparser1::IP
         }
         catch (std::exception& e)
         {
-            gLogError << e.what() << std::endl;
+            sample::gLogError << e.what() << std::endl;
         }
 
         return nullptr;
@@ -357,13 +357,14 @@ class PluginFactory : public nvinfer1::IPluginFactory, public nvcaffeparser1::IP
         try
         {
             assert(isPlugin(layerName));
-            // This plugin object is destroyed when engine is destroyed by calling
-            // IPluginExt::destroy()
-            return new FCPlugin(serialData, serialLength);
+            // IPlugin resource will not be released when engine destroy.
+            // Use this unique ptr in factory to release the data.
+            mPlugin = std::unique_ptr<FCPlugin>(new FCPlugin(serialData, serialLength));
+            return mPlugin.get();
         }
         catch (std::exception& e)
         {
-            gLogError << e.what() << std::endl;
+            sample::gLogError << e.what() << std::endl;
         }
 
         return nullptr;
diff --git a/samples/opensource/samplePlugin/fp16.h b/samples/opensource/samplePlugin/fp16.h
index 06418bf1..e016d6b3 100644
--- a/samples/opensource/samplePlugin/fp16.h
+++ b/samples/opensource/samplePlugin/fp16.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/samplePlugin/samplePlugin.cpp b/samples/opensource/samplePlugin/samplePlugin.cpp
index c8d7a450..2385e8e3 100644
--- a/samples/opensource/samplePlugin/samplePlugin.cpp
+++ b/samples/opensource/samplePlugin/samplePlugin.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -63,6 +63,12 @@ class SamplePlugin
     {
     }
 
+    ~SamplePlugin()
+    {
+        // Release the engine first before the plugin released.
+        mEngine.reset();
+    }
+
     //!
     //! \brief Builds the network engine
     //!
@@ -106,6 +112,8 @@ class SamplePlugin
         mMeanBlob; //!< The mean blob, which need to keep around until build time
 
     nvinfer1::Dims mInputDims; //!< The dimensions of the input to the network.
+
+    PluginFactory runtimePluginFactory;
 };
 
 //!
@@ -118,7 +126,7 @@ class SamplePlugin
 //!
 bool SamplePlugin::build()
 {
-    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(gLogger.getTRTLogger()));
+    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(sample::gLogger.getTRTLogger()));
     if (!builder)
     {
         return false;
@@ -166,24 +174,21 @@ bool SamplePlugin::build()
     // serialize it to mModelStream object (which can be written to a file), then
     // deserialize mModelStream with a IRuntime object to recreate the original engine.
     // Note for this sample we could have simply used the original engine produced by builder->buildEngineWithConfig()
-    auto modelStream
-        = SampleUniquePtr<nvinfer1::IHostMemory>(builder->buildEngineWithConfig(*network, *config)->serialize());
+    auto builtEngine = SampleUniquePtr<nvinfer1::ICudaEngine>(builder->buildEngineWithConfig(*network, *config));
+    auto modelStream = SampleUniquePtr<nvinfer1::IHostMemory>(builtEngine->serialize());
     assert(modelStream != nullptr);
 
-    auto runtime = SampleUniquePtr<nvinfer1::IRuntime>(nvinfer1::createInferRuntime(gLogger.getTRTLogger()));
+    auto runtime = SampleUniquePtr<nvinfer1::IRuntime>(nvinfer1::createInferRuntime(sample::gLogger.getTRTLogger()));
     if (mParams.dlaCore >= 0)
     {
         runtime->setDLACore(mParams.dlaCore);
     }
 
-    // The PluginFactory object also contains the methods needed to deserialize
-    // our engine that was built with the FC plugin layer
-    PluginFactory pluginFactory;
     mEngine = std::shared_ptr<nvinfer1::ICudaEngine>(
-        runtime->deserializeCudaEngine(modelStream->data(), modelStream->size(), &pluginFactory),
+        runtime->deserializeCudaEngine(modelStream->data(), modelStream->size(), &runtimePluginFactory),
         samplesCommon::InferDeleter());
 
-    gLogInfo << "Done preparing engine..." << std::endl;
+    sample::gLogInfo << "Done preparing engine..." << std::endl;
 
     assert(network->getNbInputs() == 1);
     mInputDims = network->getInput(0)->getDimensions();
@@ -216,7 +221,7 @@ void SamplePlugin::constructNetwork(SampleUniquePtr<nvinfer1::IBuilder>& builder
     // Parse mean blob for preprocessing input later
     mMeanBlob
         = SampleUniquePtr<nvcaffeparser1::IBinaryProtoBlob>(parser->parseBinaryProto(mParams.meanFileName.c_str()));
-    gLogInfo << "Done constructing network..." << std::endl;
+    sample::gLogInfo << "Done constructing network..." << std::endl;
 }
 
 //!
@@ -276,7 +281,8 @@ bool SamplePlugin::infer()
     // The output correctness is not used to determine the test result.
     if (!outputCorrect && mParams.dlaCore != -1)
     {
-        gLogInfo << "Warning: infer result is not correct. It maybe caused by dummy scales in INT8 mode." << std::endl;
+        sample::gLogInfo << "Warning: infer result is not correct. It maybe caused by dummy scales in INT8 mode."
+                         << std::endl;
     }
 
     return true;
@@ -297,12 +303,12 @@ bool SamplePlugin::processInput(
     readPGMFile(locateFile(std::to_string(inputFileIdx) + ".pgm", mParams.dataDirs), fileData.data(), inputH, inputW);
 
     // Print ASCII representation of digit
-    gLogInfo << "Input:\n";
+    sample::gLogInfo << "Input:\n";
     for (int i = 0; i < inputH * inputW; i++)
     {
-        gLogInfo << (" .:-=+*#%@"[fileData[i] / 26]) << (((i + 1) % inputW) ? "" : "\n");
+        sample::gLogInfo << (" .:-=+*#%@"[fileData[i] / 26]) << (((i + 1) % inputW) ? "" : "\n");
     }
-    gLogInfo << std::endl;
+    sample::gLogInfo << std::endl;
 
     float* hostInputBuffer = static_cast<float*>(buffers.getHostBuffer(inputTensorName));
     const float* meanData = reinterpret_cast<const float*>(mMeanBlob->getData());
@@ -324,7 +330,7 @@ bool SamplePlugin::verifyOutput(
     const float* prob = static_cast<const float*>(buffers.getHostBuffer(outputTensorName));
 
     // Print histogram of the output distribution
-    gLogInfo << "Output:\n";
+    sample::gLogInfo << "Output:\n";
     float val{0.0f};
     int idx{0};
     const int kDIGITS = 10;
@@ -337,9 +343,9 @@ bool SamplePlugin::verifyOutput(
             idx = i;
         }
 
-        gLogInfo << i << ": " << std::string(int(std::floor(prob[i] * 10 + 0.5f)), '*') << "\n";
+        sample::gLogInfo << i << ": " << std::string(int(std::floor(prob[i] * 10 + 0.5f)), '*') << "\n";
     }
-    gLogInfo << std::endl;
+    sample::gLogInfo << std::endl;
 
     return (idx == groundTruthDigit && val > 0.9f);
 }
@@ -410,7 +416,7 @@ int main(int argc, char** argv)
     bool argsOK = samplesCommon::parseArgs(args, argc, argv);
     if (!argsOK)
     {
-        gLogError << "Invalid arguments" << std::endl;
+        sample::gLogError << "Invalid arguments" << std::endl;
         printHelpInfo();
         return EXIT_FAILURE;
     }
@@ -420,29 +426,29 @@ int main(int argc, char** argv)
         return EXIT_SUCCESS;
     }
 
-    auto sampleTest = gLogger.defineTest(gSampleName, argc, argv);
+    auto sampleTest = sample::gLogger.defineTest(gSampleName, argc, argv);
 
-    gLogger.reportTestStart(sampleTest);
+    sample::gLogger.reportTestStart(sampleTest);
 
     samplesCommon::CaffeSampleParams params = initializeSampleParams(args);
 
     SamplePlugin sample(params);
-    gLogInfo << "Building and running a GPU inference engine for MNIST" << std::endl;
+    sample::gLogInfo << "Building and running a GPU inference engine for MNIST" << std::endl;
 
     if (!sample.build())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
 
     if (!sample.infer())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
 
     if (!sample.teardown())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
 
-    return gLogger.reportPass(sampleTest);
+    return sample::gLogger.reportPass(sampleTest);
 }
diff --git a/samples/opensource/sampleReformatFreeIO/CMakeLists.txt b/samples/opensource/sampleReformatFreeIO/CMakeLists.txt
index c9d1ee83..0d8058b4 100755
--- a/samples/opensource/sampleReformatFreeIO/CMakeLists.txt
+++ b/samples/opensource/sampleReformatFreeIO/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleReformatFreeIO/README.md b/samples/opensource/sampleReformatFreeIO/README.md
index 46674974..de6e8677 100755
--- a/samples/opensource/sampleReformatFreeIO/README.md
+++ b/samples/opensource/sampleReformatFreeIO/README.md
@@ -5,7 +5,7 @@
 - [Description](#description)
 - [How does this sample work?](#how-does-this-sample-work)
 - [Running the sample](#running-the-sample)
-	* [Sample `--help` options](#sample---help-options)
+	* [Sample `--help` options](#sample-help-options)
 - [Additional resources](#additional-resources)
 - [License](#license)
 - [Changelog](#changelog)
@@ -62,9 +62,11 @@ This sample, sampleReformatFreeIO, uses a Caffe model that was trained on the [M
 	```
 	This output shows that the sample ran successfully; `PASSED`.
 
+
 ### Sample `--help` options
 
-To see the full list of available options and their descriptions, use the `./sample_reformat_free_io --help` command.
+To see the full list of available options and their descriptions, use the `-h` or `--help` command line option.
+
 
 ## Additional resources
 
diff --git a/samples/opensource/sampleReformatFreeIO/sampleReformatFreeIO.cpp b/samples/opensource/sampleReformatFreeIO/sampleReformatFreeIO.cpp
index 1348af99..a6dd78a4 100644
--- a/samples/opensource/sampleReformatFreeIO/sampleReformatFreeIO.cpp
+++ b/samples/opensource/sampleReformatFreeIO/sampleReformatFreeIO.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -282,7 +282,7 @@ class SampleReformatFreeIO
 //!
 bool SampleReformatFreeIO::build(int dataWidth)
 {
-    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(gLogger.getTRTLogger()));
+    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(sample::gLogger.getTRTLogger()));
     if (!builder)
     {
         return false;
@@ -336,7 +336,9 @@ bool SampleReformatFreeIO::build(int dataWidth)
         builder->buildEngineWithConfig(*network, *config), samplesCommon::InferDeleter());
 
     if (!mEngine)
+    {
         return false;
+    }
 
     assert(network->getNbInputs() == 1);
     mInputDims = network->getInput(0)->getDimensions();
@@ -450,12 +452,12 @@ bool SampleReformatFreeIO::readDigits(SampleBuffer& buffer, int groundTruthDigit
         locateFile(std::to_string(groundTruthDigit) + ".pgm", mParams.dataDirs), fileData.data(), inputH, inputW);
 
     // Print ASCII representation of digit
-    gLogInfo << "Input:\n";
+    sample::gLogInfo << "Input:\n";
     for (int i = 0; i < inputH * inputW; i++)
     {
-        gLogInfo << (" .:-=+*#%@"[fileData[i] / 26]) << (((i + 1) % inputW) ? "" : "\n");
+        sample::gLogInfo << (" .:-=+*#%@"[fileData[i] / 26]) << (((i + 1) % inputW) ? "" : "\n");
     }
-    gLogInfo << std::endl;
+    sample::gLogInfo << std::endl;
 
     float* inputBuf = reinterpret_cast<float*>(buffer.buffer);
 
@@ -476,7 +478,7 @@ bool SampleReformatFreeIO::verifyOutput(SampleBuffer& outputBuf, int groundTruth
     const T* prob = reinterpret_cast<const T*>(outputBuf.buffer);
 
     // Print histogram of the output distribution
-    gLogInfo << "Output:\n";
+    sample::gLogInfo << "Output:\n";
     float val{0.0f};
     float elem{0.0f};
     int idx{0};
@@ -491,9 +493,9 @@ bool SampleReformatFreeIO::verifyOutput(SampleBuffer& outputBuf, int groundTruth
             idx = i;
         }
 
-        gLogInfo << i << ": " << std::string(int(std::floor(elem * 10 + 0.5f)), '*') << "\n";
+        sample::gLogInfo << i << ": " << std::string(int(std::floor(elem * 10 + 0.5f)), '*') << "\n";
     }
-    gLogInfo << std::endl;
+    sample::gLogInfo << std::endl;
 
     return (idx == groundTruthDigit && val > 0.9f);
 }
@@ -632,24 +634,24 @@ void printHelpInfo()
 //! \brief Used to run the engine build and inference/reference functions
 //!
 template <typename T>
-int process(SampleReformatFreeIO& sample, const Logger::TestAtom& sampleTest, SampleBuffer& inputBuf,
+bool process(SampleReformatFreeIO& sample, const sample::Logger::TestAtom& sampleTest, SampleBuffer& inputBuf,
     SampleBuffer& outputBuf, SampleBuffer& goldenInput, SampleBuffer& goldenOutput)
 {
-    gLogInfo << "Building and running a GPU inference engine for reformat free I/O" << std::endl;
+    sample::gLogInfo << "Building and running a GPU inference engine for reformat free I/O" << std::endl;
 
     inputBuf = SampleBuffer(sample.mInputDims, sizeof(T), sample.mTensorFormat);
     outputBuf = SampleBuffer(sample.mOutputDims, sizeof(T), sample.mTensorFormat);
 
     if (!sample.build(sizeof(T)))
     {
-        return gLogger.reportFail(sampleTest);
+        return false;
     }
 
     convertGoldenData<T>(goldenInput, inputBuf);
 
     if (!sample.infer(inputBuf, outputBuf))
     {
-        return gLogger.reportFail(sampleTest);
+        return false;
     }
 
     SampleBuffer linearOutputBuf(sample.mOutputDims, sizeof(T), TensorFormat::kLINEAR);
@@ -658,20 +660,20 @@ int process(SampleReformatFreeIO& sample, const Logger::TestAtom& sampleTest, Sa
 
     if (!sample.verifyOutput<T>(linearOutputBuf, sample.mDigit))
     {
-        return gLogger.reportFail(sampleTest);
+        return false;
     }
 
-    return 0;
+    return true;
 }
 
-int runFP32Reference(SampleReformatFreeIO& sample, const Logger::TestAtom& sampleTest, SampleBuffer& goldenInput,
-    SampleBuffer& goldenOutput)
+bool runFP32Reference(SampleReformatFreeIO& sample, const sample::Logger::TestAtom& sampleTest,
+    SampleBuffer& goldenInput, SampleBuffer& goldenOutput)
 {
-    gLogInfo << "Building and running a FP32 GPU inference to get golden input/output" << std::endl;
+    sample::gLogInfo << "Building and running a FP32 GPU inference to get golden input/output" << std::endl;
 
     if (!sample.build(sizeof(float)))
     {
-        return gLogger.reportFail(sampleTest);
+        return false;
     }
 
     goldenInput = SampleBuffer(sample.mInputDims, sizeof(float), TensorFormat::kLINEAR);
@@ -682,15 +684,15 @@ int runFP32Reference(SampleReformatFreeIO& sample, const Logger::TestAtom& sampl
 
     if (!sample.infer(goldenInput, goldenOutput))
     {
-        return gLogger.reportFail(sampleTest);
+        return false;
     }
 
     if (!sample.verifyOutput<float>(goldenOutput, sample.mDigit))
     {
-        return gLogger.reportFail(sampleTest);
+        return false;
     }
 
-    return 0;
+    return true;
 }
 
 int main(int argc, char** argv)
@@ -699,7 +701,7 @@ int main(int argc, char** argv)
     bool argsOK = samplesCommon::parseArgs(args, argc, argv);
     if (!argsOK)
     {
-        gLogError << "Invalid arguments" << std::endl;
+        sample::gLogError << "Invalid arguments" << std::endl;
         printHelpInfo();
         return EXIT_FAILURE;
     }
@@ -709,9 +711,9 @@ int main(int argc, char** argv)
         return EXIT_SUCCESS;
     }
 
-    auto sampleTest = gLogger.defineTest(gSampleName, argc, argv);
+    auto sampleTest = sample::gLogger.defineTest(gSampleName, argc, argv);
 
-    gLogger.reportTestStart(sampleTest);
+    sample::gLogger.reportTestStart(sampleTest);
 
     samplesCommon::CaffeSampleParams params = initializeSampleParams(args);
 
@@ -733,37 +735,47 @@ int main(int argc, char** argv)
     srand(unsigned(time(nullptr)));
     sample.mDigit = rand() % 10;
 
-    gLogInfo << "The test chooses MNIST as the network and recognizes a randomly generated digit" << std::endl;
-    gLogInfo << "Firstly it runs the FP32 as the golden data, then INT8/FP16 with different formats will be tested"
-             << std::endl
-             << std::endl;
+    sample::gLogInfo << "The test chooses MNIST as the network and recognizes a randomly generated digit" << std::endl;
+    sample::gLogInfo
+        << "Firstly it runs the FP32 as the golden data, then INT8/FP16 with different formats will be tested"
+        << std::endl
+        << std::endl;
 
-    runFP32Reference(sample, sampleTest, goldenInput, goldenOutput);
+    if (!runFP32Reference(sample, sampleTest, goldenInput, goldenOutput))
+    {
+        return sample::gLogger.reportFail(sampleTest);
+    }
 
     // Test INT8 formats
     for (auto elem : vecINT8TensorFmt)
     {
-        gLogInfo << "Testing datatype INT8 with format " << elem.second << std::endl;
+        sample::gLogInfo << "Testing datatype INT8 with format " << elem.second << std::endl;
         sample.mTensorFormat = elem.first;
         SampleBuffer inputBuf, outputBuf;
 
-        process<int8_t>(sample, sampleTest, inputBuf, outputBuf, goldenInput, goldenOutput);
+        if (!process<int8_t>(sample, sampleTest, inputBuf, outputBuf, goldenInput, goldenOutput))
+        {
+            return sample::gLogger.reportFail(sampleTest);
+        }
     }
 
     // Test FP16 formats
     for (auto elem : vecFP16TensorFmt)
     {
-        gLogInfo << "Testing datatype FP16 with format " << elem.second << std::endl;
+        sample::gLogInfo << "Testing datatype FP16 with format " << elem.second << std::endl;
         sample.mTensorFormat = elem.first;
         SampleBuffer inputBuf, outputBuf;
 
-        process<half_float::half>(sample, sampleTest, inputBuf, outputBuf, goldenInput, goldenOutput);
+        if (!process<half_float::half>(sample, sampleTest, inputBuf, outputBuf, goldenInput, goldenOutput))
+        {
+            return sample::gLogger.reportFail(sampleTest);
+        }
     }
 
     if (!sample.teardown())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
 
-    return gLogger.reportPass(sampleTest);
+    return sample::gLogger.reportPass(sampleTest);
 }
diff --git a/samples/opensource/sampleSSD/CMakeLists.txt b/samples/opensource/sampleSSD/CMakeLists.txt
index b6f778ea..1fd6286e 100644
--- a/samples/opensource/sampleSSD/CMakeLists.txt
+++ b/samples/opensource/sampleSSD/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleSSD/PrepareINT8CalibrationBatches.sh b/samples/opensource/sampleSSD/PrepareINT8CalibrationBatches.sh
index 66aa25fd..07fa2723 100755
--- a/samples/opensource/sampleSSD/PrepareINT8CalibrationBatches.sh
+++ b/samples/opensource/sampleSSD/PrepareINT8CalibrationBatches.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleSSD/README.md b/samples/opensource/sampleSSD/README.md
index 9bedc383..2ca14521 100644
--- a/samples/opensource/sampleSSD/README.md
+++ b/samples/opensource/sampleSSD/README.md
@@ -11,7 +11,7 @@
     * [TensorRT plugin layers in SSD](#tensorrt-plugin-layers-in-ssd)
 - [Prerequisites](#prerequisites)
 - [Running the sample](#running-the-sample)
-    * [Sample `--help` options](#sample---help-options)
+    * [Sample `--help` options](#sample-help-options)
 - [Additional resources](#additional-resources)
 - [License](#license)
 - [Changelog](#changelog)
@@ -39,20 +39,20 @@ The input to the SSD network in this sample is a RGB 300x300 image. The image fo
 
 The authors of SSD have trained the network such that the first Convolution layer sees the image data in `B`, `G`, and `R` order. Therefore, the channel order needs to be changed when the PPM image is being put into the network’s input buffer.
 
-```
+```c++
 float pixelMean[3]{ 104.0f, 117.0f, 123.0f }; // also in BGR order
 float* data = new float[N * kINPUT_C * kINPUT_H * kINPUT_W];
-     for (int i = 0, volImg = kINPUT_C * kINPUT_H * kINPUT_W; i < N; ++i)
+for (int i = 0, volImg = kINPUT_C * kINPUT_H * kINPUT_W; i < N; ++i)
+{
+    for (int c = 0; c < kINPUT_C; ++c)
     {
-           for (int c = 0; c < kINPUT_C; ++c)
-           {
-                  // the color image to input should be in BGR order
-                  for (unsigned j = 0, volChl = kINPUT_H * kINPUT_W; j < volChl; ++j)
-                  {
-                        data[i * volImg + c * volChl + j] = float(ppms[i].buffer[j * kINPUT_C + 2 - c]) - pixelMean[c];
-                  }
-           }
+        // the color image to input should be in BGR order
+        for (unsigned j = 0, volChl = kINPUT_H * kINPUT_W; j < volChl; ++j)
+        {
+            data[i * volImg + c * volChl + j] = float(ppms[i].buffer[j * kINPUT_C + 2 - c]) - pixelMean[c];
+        }
     }
+}
 ```
 
 The `readPPMFile` and `writePPMFileWithBBox` functions read a PPM image and produce output images with red colored bounding boxes respectively.
@@ -80,19 +80,19 @@ For details on how to build the TensorRT engine, see [Building An Engine In C++]
 After deserializing the engine, you can perform inference. To perform inference, see [Performing Inference In C++](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#perform_inference_c).
 
 In sampleSSD, there is a single input:
--  `data`, namely the image input  
+-  `data`, namely the image input
 
 And 2 outputs:
 -  `detectionOut` is the detection array, containing the image ID, label, confidence, 4 coordinates
 -  `keepCount` is the number of valid detections
- 
+
 The outputs of the SSD network are directly human interpretable. The results are organized as tuples of 7. In each tuple, the 7 elements are:
 -   image ID
 -   object label
 -   confidence score
 -   (x,y) coordinates of the lower left corner of the bounding box
 -   (x,y) coordinates of the upper right corner of the bounding box
-  
+
 This information can be drawn in the output PPM image using the `writePPMFileWithBBox` function. The `kVISUAL_THRESHOLD` parameter can be used to control the visualization of objects in the image. It is currently set to 0.6, therefore, the output will display all objects with confidence score of 60% and above.
 
 ### TensorRT API layers and ops
@@ -100,7 +100,7 @@ This information can be drawn in the output PPM image using the `writePPMFileWit
 In this sample, the following layers are used.  For more information about these layers, see the [TensorRT Developer Guide: Layers](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#layers) documentation.
 
 [Activation layer](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#activation-layer)
-The Activation layer implements element-wise activation functions. Specifically, this sample uses the Activation layer with the type `kRELU`. 
+The Activation layer implements element-wise activation functions. Specifically, this sample uses the Activation layer with the type `kRELU`.
 
 [Concatenation layer](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#concatenation-layer)
 The Concatenation layer links together multiple tensors of the same non-channel sizes along the channel dimension.
@@ -226,18 +226,23 @@ message DetectionOutputParameter {
 
 Due to the size of the SSD Caffe model, it is not included in the product bundle. Before you can run the sample, you’ll need to download the model, perform some configuration, and generate INT8 calibration batches.
 
-1.  Download [models_VGGNet_VOC0712_SSD_300x300.tar.gz](https://drive.google.com/file/d/0BzKzrI_SkD1_WVVTSmQxU0dVRzA/view).
-
-2.  Extract the contents.
-    `tar xvf models_VGGNet_VOC0712_SSD_300x300.tar.gz`
-    
-    1. Generate MD5 hash and compare against the reference below:
-        `md5sum models_VGGNet_VOC0712_SSD_300x300.tar.gz`
-
-        If the model is correct, you should see the following MD5 hash output:
-        `9a795fc161fff2e8f3aed07f4d488faf models_VGGNet_VOC0712_SSD_300x300.tar.gz`
+1.  Install Pillow.
+        -   For Python 2 users, run: `python2 -m pip install Pillow`
+        -   For Python 3 users, run: `python3 -m pip install Pillow`
 
-    2. Edit the `deploy.prototxt` file and change all the Flatten layers to Reshape operations with the following parameters:
+2.  Preparing models:
+    1.  Download [models_VGGNet_VOC0712_SSD_300x300.tar.gz](https://drive.google.com/file/d/0BzKzrI_SkD1_WVVTSmQxU0dVRzA/view).
+    2.  Check the [MD5 hash](https://en.wikipedia.org/wiki/Md5sum) of it is `9a795fc161fff2e8f3aed07f4d488faf`.
+        ```sh
+        md5sum models_VGGNet_VOC0712_SSD_300x300.tar.gz
+        ```
+    3.  Extract the archive, and copy the model file to the TensorRT `data` directory.
+        ```sh
+        tar xvf models_VGGNet_VOC0712_SSD_300x300.tar.gz
+        cp models/VGGNet/VOC0712/SSD_300x300/VGG_VOC0712_SSD_300x300_iter_120000.caffemodel <TensorRT root directory>/data/ssd
+        cp models/VGGNet/VOC0712/SSD_300x300/deploy.prototxt <TensorRT root directory>/data/ssd/ssd.prototxt
+        ```
+    4.  In `ssd.prototxt`, change all `Flatten` layers to `Reshape` operations (e.g. `type:Reshape`) as TensorRT enables `Flatten` by `Reshape`, and add `reshape_param` (like below) to each of them:
         ```
         reshape_param {
             shape {
@@ -248,46 +253,28 @@ Due to the size of the SSD Caffe model, it is not included in the product bundle
             }
         }
         ```
+    5.  In `ssd.prototxt`, add `top: "keep_count"` in `detection_out` layer as TensorRT DetectionOutput Plugin requires this output.
 
-    3. Update the `detection_out` layer to add the `keep_count` output as expected by the TensorRT DetectionOutput Plugin.
-    `top: "keep_count"`
-
-    4. Rename the updated `deploy.prototxt` file to `ssd.prototxt` and move the file to the `data` directory.
-    `mv ssd.prototxt <TensorRT_Install_Directory>/data/ssd`
-
-    5. Move the `caffemodel` file to the `data` directory.
-        ```
-        mv VGG_VOC0712_SSD_300x300_iter_120000.caffemodel <TensorRT_Install_Directory>/data/ssd
-        ```
-
-3.  Generate the INT8 calibration batches.
-    1.  Install Pillow.
-        -   For Python 2 users, run:
-             `python2 -m pip install Pillow`
-
-        -   For Python 3 users, run:
-            `python3 -m pip install Pillow`
-
-    2.  Generate the INT8 batches.
-        `prepareINT8CalibrationBatches.sh`
-
-        The script selects 500 random JPEG images from the PASCAL VOC dataset and converts them to PPM images. These 500 PPM images are used to generate INT8 calibration batches.
+4.  Generate the INT8 calibration batches. The script selects 500 random JPEG images from the PASCAL VOC dataset and converts them to PPM images. These 500 PPM images are used to generate INT8 calibration batches.
+    ```sh
+    <TensorRT root directory>/samples/sampleSSD/PrepareINT8CalibrationBatches.sh
+    ```
+    **Note:** Do not move the batch files from the `<TensorRT root directory>/data/ssd/batches` directory.
 
-        **Note:** Do not move the batch files from the `<TensorRT_Install_Directory>/data/ssd/batches` directory.
+    If you want to use a different dataset to generate INT8 batches, use the `batchPrepare.py` script and place the batch files in the `<TensorRT root directory>/data/ssd/batches` directory.
 
-        If you want to use a different dataset to generate INT8 batches, use the `batchPrepare.py` script and place the batch files in the `<TensorRT_Install_Directory>/data/ssd/batches` directory.
 
 ## Running the sample
 
 1. Compile this sample by running `make` in the `<TensorRT root directory>/samples/sampleSSD` directory. The binary named `sample_ssd` will be created in the `<TensorRT root directory>/bin` directory.
-    ```
+    ```sh
     cd <TensorRT root directory>/samples/sampleSSD
     make
     ```
     Where `<TensorRT root directory>` is where you installed TensorRT.
-    
+
 2. Run the sample to perform inference on the digit:
-    ```
+    ```sh
     ./sample_ssd [-h] [--fp16] [--int8]
     ```
 3.  Verify that the sample ran successfully. If the sample runs successfully you should see output similar to the following:
@@ -305,19 +292,12 @@ Due to the size of the SSD Caffe model, it is not included in the product bundle
     ```
 
     This output shows that the sample ran successfully; `PASSED`.
- 
 
-### Sample --help options
 
-To see the full list of available options and their descriptions, use the `-h` or `--help` command line option. For example:
-```
-Usage: ./build/x86_64-linux/sample_ssd
-Optional Parameters:
-    -h, --help      Display help information.
-    --useDLACore=N  Specify the DLA engine to run on.
-    --fp16          Specify to run in fp16 mode.
-    --int8          Specify to run in int8 mode.
-```
+### Sample `--help` options
+
+To see the full list of available options and their descriptions, use the `-h` or `--help` command line option.
+
 
 # Additional resources
 
diff --git a/samples/opensource/sampleSSD/batchPrepare.py b/samples/opensource/sampleSSD/batchPrepare.py
index e1fd827d..185075b2 100644
--- a/samples/opensource/sampleSSD/batchPrepare.py
+++ b/samples/opensource/sampleSSD/batchPrepare.py
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleSSD/requirements.txt b/samples/opensource/sampleSSD/requirements.txt
index 7e2fba5e..a97c2f26 100644
--- a/samples/opensource/sampleSSD/requirements.txt
+++ b/samples/opensource/sampleSSD/requirements.txt
@@ -1 +1 @@
-Pillow
+Pillow==6.2.2
diff --git a/samples/opensource/sampleSSD/sampleSSD.cpp b/samples/opensource/sampleSSD/sampleSSD.cpp
index 108d6c40..50522ca4 100644
--- a/samples/opensource/sampleSSD/sampleSSD.cpp
+++ b/samples/opensource/sampleSSD/sampleSSD.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -121,9 +121,9 @@ class SampleSSD
 //!
 bool SampleSSD::build()
 {
-    initLibNvInferPlugins(&gLogger.getTRTLogger(), "");
+    initLibNvInferPlugins(&sample::gLogger.getTRTLogger(), "");
 
-    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(gLogger.getTRTLogger()));
+    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(sample::gLogger.getTRTLogger()));
     if (!builder)
     {
         return false;
@@ -193,7 +193,7 @@ bool SampleSSD::constructNetwork(SampleUniquePtr<nvinfer1::IBuilder>& builder,
 
     if (mParams.int8)
     {
-        gLogInfo << "Using Entropy Calibrator 2" << std::endl;
+        sample::gLogInfo << "Using Entropy Calibrator 2" << std::endl;
         BatchStream calibrationStream(
             mParams.batchSize, mParams.nbCalBatches, mParams.calibrationBatches, mParams.dataDirs);
         calibrator.reset(
@@ -352,11 +352,11 @@ bool SampleSSD::verifyOutput(const samplesCommon::BufferManager& buffers)
                 correctDetection = true;
             }
 
-            gLogInfo << " Image name:" << mPPMs[p].fileName.c_str() << ", Label: " << classes[(int) det[1]].c_str()
-                     << ","
-                     << " confidence: " << det[2] * 100.f << " xmin: " << det[3] * inputW
-                     << " ymin: " << det[4] * inputH << " xmax: " << det[5] * inputW << " ymax: " << det[6] * inputH
-                     << std::endl;
+            sample::gLogInfo << " Image name:" << mPPMs[p].fileName.c_str()
+                             << ", Label: " << classes[(int) det[1]].c_str() << ","
+                             << " confidence: " << det[2] * 100.f << " xmin: " << det[3] * inputW
+                             << " ymin: " << det[4] * inputH << " xmax: " << det[5] * inputW
+                             << " ymax: " << det[6] * inputH << std::endl;
 
             samplesCommon::writePPMFileWithBBox(
                 storeName, mPPMs[p], {det[3] * inputW, det[4] * inputH, det[5] * inputW, det[6] * inputH});
@@ -430,7 +430,7 @@ int main(int argc, char** argv)
     bool argsOK = samplesCommon::parseArgs(args, argc, argv);
     if (!argsOK)
     {
-        gLogError << "Invalid arguments" << std::endl;
+        sample::gLogError << "Invalid arguments" << std::endl;
         printHelpInfo();
         return EXIT_FAILURE;
     }
@@ -440,26 +440,26 @@ int main(int argc, char** argv)
         return EXIT_SUCCESS;
     }
 
-    auto sampleTest = gLogger.defineTest(gSampleName, argc, argv);
+    auto sampleTest = sample::gLogger.defineTest(gSampleName, argc, argv);
 
-    gLogger.reportTestStart(sampleTest);
+    sample::gLogger.reportTestStart(sampleTest);
 
     SampleSSD sample(initializeSampleParams(args));
 
-    gLogInfo << "Building and running a GPU inference engine for SSD" << std::endl;
+    sample::gLogInfo << "Building and running a GPU inference engine for SSD" << std::endl;
 
     if (!sample.build())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
     if (!sample.infer())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
     if (!sample.teardown())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
 
-    return gLogger.reportPass(sampleTest);
+    return sample::gLogger.reportPass(sampleTest);
 }
diff --git a/samples/opensource/sampleUffFasterRCNN/CMakeLists.txt b/samples/opensource/sampleUffFasterRCNN/CMakeLists.txt
index 897b9947..611d7fb3 100644
--- a/samples/opensource/sampleUffFasterRCNN/CMakeLists.txt
+++ b/samples/opensource/sampleUffFasterRCNN/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleUffFasterRCNN/README.md b/samples/opensource/sampleUffFasterRCNN/README.md
index a19e1c16..14ac2b21 100644
--- a/samples/opensource/sampleUffFasterRCNN/README.md
+++ b/samples/opensource/sampleUffFasterRCNN/README.md
@@ -7,10 +7,10 @@
     * [Preparing the data](#preparing-the-data)
     * [sampleUffFasterRCNN plugins](#sampleufffasterrcnn-plugins)
     * [Verifying the output](#verifying-the-output)
-	* [TensorRT API layers and ops](#tensorrt-api-layers-and-ops)
+    * [TensorRT API layers and ops](#tensorrt-api-layers-and-ops)
 - [Prerequisites](#prerequisites)
 - [Running the sample](#running-the-sample)
-	* [Sample `--help` options](#sample---help-options)
+    * [Sample `--help` options](#sample-help-options)
 - [Additional resources](#additional-resources)
 - [License](#license)
 - [Changelog](#changelog)
@@ -64,7 +64,7 @@ Specifically, this sample performs the following steps:
 ### Processing the input graph
 
 The TensorFlow FasterRCNN graph has some operations that are currently not supported in TensorRT. Using a preprocessor on the graph, we can combine multiple operations in the graph into a single custom operation which can be implemented as a plugin layer in TensorRT. Currently, the preprocessor provides the ability to stitch all nodes within a namespace into one custom node.
-  
+
 To use the preprocessor, the `convert-to-uff` utility should be called with a `-p` flag and a config file. The config script should also include attributes for all custom plugins which will be embedded in the generated `.uff` file. Current sample script for UFF Faster R-CNN is located in `config.py` in this sample.
 
 ### Preparing the data
@@ -74,7 +74,7 @@ The generated network has an input node called `input_1`, and the output nodes's
 The input to the UFF Faster R-CNN network in this sample is 3 channel 480x272 images. In the sample, we subtract the per-channel mean values for the input images.
 
 Since TensorRT does not depend on any computer vision libraries, the images are represented in binary R, G, and B values for each pixel. The format is Portable PixMap (PPM), which is a netpbm color image format. In this format, the R, G, and B values for each pixel are represented by a byte of integer (0-255) and they are stored together, pixel by pixel. The channel order of the input image is actually BGR instead of RGB due to implementation.
-  
+
 There is a simple PPM reading function called `readPPMFile`.
 
 ### sampleUffFasterRCNN plugins
@@ -126,85 +126,70 @@ The Scale layer implements a per-tensor, per-channel, or per-element affine tran
 The SoftMax layer applies the SoftMax function on the input tensor along an input dimension specified by the user.
 
 ## Prerequisites
-1. Install the UFF toolkit and graph surgeon; depending on your TensorRT installation method, to install the toolkit and graph surgeon, choose the method you used to install TensorRT for instructions (see [TensorRT Installation Guide: Installing TensorRT](https://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html#installing)).  
-    
+1. Install the UFF toolkit and graph surgeon; depending on your TensorRT installation method, to install the toolkit and graph surgeon, choose the method you used to install TensorRT for instructions (see [TensorRT Installation Guide: Installing TensorRT](https://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html#installing)).
+
 2. We provide a bash script to download the model as well as other data required for this sample: `./download_model.sh`.
 
    The model is downloaded and unzipped in the directory `uff_faster_rcnn` and the `pb` model is `uff_faster_rcnn/faster_rcnn.pb`.
 
    Along with the `pb` mode there are some PPM images and a `list.txt` in the directory. These PPM images are the test images used in this sample. The `list.txt` is used in the INT8 mode for listing the image names used in INT8 calibration step in TensorRT.
 
-3. Perform preprocessing on the TensorFlow model using the UFF converter.  
-	1.  Copy the TensorFlow protobuf file (`faster_rcnn.pb`) from the downloaded directory in the previous step to the working directory (for example `/usr/src/tensorrt/data/faster-rcnn-uff`).
+3. Perform preprocessing on the TensorFlow model using the UFF converter.
+    1.  Copy the TensorFlow protobuf file (`faster_rcnn.pb`) from the downloaded directory in the previous step to the working directory (for example `/usr/src/tensorrt/data/faster-rcnn-uff`).
 
-	2.  Patch the UFF converter.
+    2.  Patch the UFF converter.
 
-		Apply a patch to the UFF converter to fix an issue with the Softmax layer in the UFF package. Let `UFF_ROOT` denotes the root directory of the Python UFF package, for example, `/usr/lib/python2.7/dist-packages/uff`
+        Apply a patch to the UFF converter to fix an issue with the Softmax layer in the UFF package. Let `UFF_ROOT` denotes the root directory of the Python UFF package, for example, `/usr/lib/python2.7/dist-packages/uff`
 
-		Then, apply the patch with the following command:
-		`patch UFF_ROOT/converters/tensorflow/converter_functions.py < fix_sofmax.patch` 
+        Then, apply the patch with the following command:
+        `patch UFF_ROOT/converters/tensorflow/converter_functions.py < fix_softmax.patch`
 
-		The patch file `fix_softmax.patch` is generated using the UFF package version 0.6.3 in TensorRT 5.1 GA. Ensure your UFF package version is also 0.6.3 before applying the patch. For TensorRT 6.0, feel free to ignore this since it should already be fixed.
+        The patch file `fix_softmax.patch` is generated using the UFF package version 0.6.3 in TensorRT 5.1 GA. Ensure your UFF package version is also 0.6.3 before applying the patch. For TensorRT 6.0, feel free to ignore this since it should already be fixed.
 
-	3.  Run the following command for the conversion.
-		```
-		convert-to-uff -p config.py -O dense_class/Softmax -O dense_regress/BiasAdd -O proposal faster_rcnn.pb  
-		```  
-		This saves the converted `.uff` file in the same directory as the input with the name `faster_rcnn.uff`.  
-  
-		The `config.py` script specifies the preprocessing operations necessary for the UFF Faster R-CNN TensorFlow graph. The plugin nodes and plugin parameters used in the `config.py` script should match the registered plugins in TensorRT.  
+    3.  Run the following command for the conversion.
+        ```
+        convert-to-uff -p config.py -O dense_class/Softmax -O dense_regress/BiasAdd -O proposal faster_rcnn.pb
+        ```
+        This saves the converted `.uff` file in the same directory as the input with the name `faster_rcnn.uff`.
 
-4. The sample also requires a `list.txt` file with a list of all the calibration images (basename, without suffix) when running in INT8 mode. Copy the `list.txt` to the same directory that contains the `pb` model.  
+        The `config.py` script specifies the preprocessing operations necessary for the UFF Faster R-CNN TensorFlow graph. The plugin nodes and plugin parameters used in the `config.py` script should match the registered plugins in TensorRT.
+
+4. The sample also requires a `list.txt` file with a list of all the calibration images (basename, without suffix) when running in INT8 mode. Copy the `list.txt` to the same directory that contains the `pb` model.
 
 5. Copy the PPM images in the data directory the same directory that contains the `pb` model.
 
 
 ## Running the sample
 
-1. Following the [top level guide](../../../README.md) to build the OSS samples(including this sample, of course). The binary named `sample_uff_fasterRCNN` will be created in the `build/cmake/out` directory.
+1. Following the [top level guide](../../../README.md) to build the OSS samples(including this sample, of course). The binary named `sample_uff_faster_rcnn` will be created in the `build/cmake/out` directory.
 
 2. Run the sample to perform object detection and localization.
 
    To run the sample in FP32 mode:
-	```
-	./sample_uff_fasterRCNN --datadir /data/uff_faster_rcnn -W 480 -H 272 -I 2016_1111_185016_003_00001_night_000441.ppm
-	```
+    ```
+    ./sample_uff_faster_rcnn --datadir /data/uff_faster_rcnn -W 480 -H 272 -I 2016_1111_185016_003_00001_night_000441.ppm
+    ```
 
    To run the sample in INT8 mode:
-	```
-	./sample_uff_fasterRCNN --datadir /data/uff_faster_rcnn -i -W 480 -H 272 -I 2016_1111_185016_003_00001_night_000441.ppm
-	```
-	
+    ```
+    ./sample_uff_faster_rcnn --datadir /data/uff_faster_rcnn -i -W 480 -H 272 -I 2016_1111_185016_003_00001_night_000441.ppm
+    ```
+
 3. Verify that the sample ran successfully. If the sample runs successfully you should see output similar to the following:
-	```
+    ```
     Detected Automobile in 2016_1111_185016_003_00001_night_000441.ppm with confidence 99.9734%
     Detected Automobile in 2016_1111_185016_003_00001_night_000441.ppm with confidence 99.9259%
     Detected Automobile in 2016_1111_185016_003_00001_night_000441.ppm with confidence 98.7359%
     Detected Automobile in 2016_1111_185016_003_00001_night_000441.ppm with confidence 92.4371%
     Detected Automobile in 2016_1111_185016_003_00001_night_000441.ppm with confidence 89.7888%
-	```
+    ```
    This output shows that the sample ran successfully; `PASSED`.
 
 
 ### Sample `--help` options
 
-To see the full list of available options and their descriptions, use the `-h` or `--help` command line option.  
-```
-Usage: ./sample_uff_fasterRCNN --datadir /data/uff_faster_rcnn -h
---help[-h] Display help information
---datadir[-d] Specify path to a data directory, overriding the default. This option can be repeated to add multiple directories. If the option is unspecified, the default is to search data/faster-rcnn/ and data/samples/faster-rcnn/.
---useDLACore[-u] Specify a DLA engine for layers that support DLA. Value can range from 0 to n-1, where n is the number of DLA engines on the platform.
---fp16[-f] Specify to run in fp16 mode.
---int8[-i] Specify to run in int8 mode.
---inputWidth[-W] Specify the input width of the model.
---inputHeight[-H] Specify the input height of the model.
---batchSize[-B] Specify the batch size for inference.
---profile[-p] Whether to do per-layer profiling.
---repeat[-r] Specify the repeat number to execute the TRT context, used to smooth the profiling time.
---inputImages[-I] Specify the input images for inference.
---saveEngine[-s] Path to save engine.
---loadEngine[-l] Path to load engine.
-```
+To see the full list of available options and their descriptions, use the `-h` or `--help` command line option.
+
 
 # Additional resources
 
diff --git a/samples/opensource/sampleUffFasterRCNN/config.py b/samples/opensource/sampleUffFasterRCNN/config.py
index b4471222..257de1a4 100644
--- a/samples/opensource/sampleUffFasterRCNN/config.py
+++ b/samples/opensource/sampleUffFasterRCNN/config.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -31,6 +31,5 @@
 def preprocess(dynamic_graph):
     # Now create a new graph by collapsing namespaces
     dynamic_graph.append(Proposal)
-    dynamic_graph.remove('input_2')
+    dynamic_graph.remove(dynamic_graph.find_nodes_by_name('input_2'))
     dynamic_graph.collapse_namespaces(namespace_plugin_map)
-
diff --git a/samples/opensource/sampleUffFasterRCNN/download_model.sh b/samples/opensource/sampleUffFasterRCNN/download_model.sh
index 331a5313..82bc84b5 100755
--- a/samples/opensource/sampleUffFasterRCNN/download_model.sh
+++ b/samples/opensource/sampleUffFasterRCNN/download_model.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleUffFasterRCNN/frcnnUtils.h b/samples/opensource/sampleUffFasterRCNN/frcnnUtils.h
index 8db87017..8c06c9c9 100644
--- a/samples/opensource/sampleUffFasterRCNN/frcnnUtils.h
+++ b/samples/opensource/sampleUffFasterRCNN/frcnnUtils.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -191,8 +191,8 @@ void resizePPM(vPPM& ppm, int target_width, int target_height)
 class BatchStream
 {
 public:
-    BatchStream(
-        int batchSize, int maxBatches, std::string prefix, std::string suffix, std::vector<std::string> directories)
+    BatchStream(int batchSize, int maxBatches, const std::string& prefix, const std::string& suffix,
+        const std::vector<std::string>& directories)
         : mBatchSize(batchSize)
         , mMaxBatches(maxBatches)
         , mPrefix(prefix)
@@ -219,13 +219,13 @@ class BatchStream
         reset(0);
     }
 
-    BatchStream(int batchSize, int maxBatches, std::string prefix, std::vector<std::string> directories)
+    BatchStream(int batchSize, int maxBatches, const std::string& prefix, const std::vector<std::string>& directories)
         : BatchStream(batchSize, maxBatches, prefix, ".batch", directories)
     {
     }
 
-    BatchStream(
-        int batchSize, int maxBatches, nvinfer1::Dims dims, std::string listFile, std::vector<std::string> directories)
+    BatchStream(int batchSize, int maxBatches, const nvinfer1::Dims& dims, const std::string& listFile,
+        const std::vector<std::string>& directories)
         : mBatchSize(batchSize)
         , mMaxBatches(maxBatches)
         , mDims(dims)
@@ -364,7 +364,7 @@ class BatchStream
                 return false;
             }
 
-            gLogInfo << "Batch #" << mFileCount << std::endl;
+            sample::gLogInfo << "Batch #" << mFileCount << std::endl;
             file.seekg(((mBatchCount * mBatchSize)) * 7);
 
             for (int i = 1; i <= mBatchSize; i++)
@@ -372,7 +372,7 @@ class BatchStream
                 std::string sName;
                 std::getline(file, sName);
                 sName = sName + ".ppm";
-                gLogInfo << "Calibrating with file " << sName << std::endl;
+                sample::gLogInfo << "Calibrating with file " << sName << std::endl;
                 fNames.emplace_back(sName);
             }
 
@@ -431,8 +431,8 @@ class BatchStream
 class EntropyCalibratorImpl
 {
 public:
-    EntropyCalibratorImpl(
-        BatchStream& stream, int firstBatch, std::string networkName, const char* inputBlobName, bool readCache = true)
+    EntropyCalibratorImpl(BatchStream& stream, int firstBatch, const std::string& networkName,
+        const char* inputBlobName, bool readCache = true)
         : mStream(stream)
         , mCalibrationTableName("CalibrationTable" + networkName)
         , mInputBlobName(inputBlobName)
diff --git a/samples/opensource/sampleUffFasterRCNN/sampleUffFasterRCNN.cpp b/samples/opensource/sampleUffFasterRCNN/sampleUffFasterRCNN.cpp
index 0a769b21..f05e8442 100644
--- a/samples/opensource/sampleUffFasterRCNN/sampleUffFasterRCNN.cpp
+++ b/samples/opensource/sampleUffFasterRCNN/sampleUffFasterRCNN.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -162,7 +162,7 @@ class SampleUffFasterRcnn
 
 bool SampleUffFasterRcnn::build()
 {
-    initLibNvInferPlugins(&gLogger.getTRTLogger(), "");
+    initLibNvInferPlugins(&sample::gLogger.getTRTLogger(), "");
 
     if (mParams.loadEngine.size() > 0)
     {
@@ -179,7 +179,7 @@ bool SampleUffFasterRcnn::build()
             file.close();
         }
 
-        IRuntime* infer = nvinfer1::createInferRuntime(gLogger);
+        IRuntime* infer = nvinfer1::createInferRuntime(sample::gLogger);
         if (mParams.dlaCore >= 0)
         {
             infer->setDLACore(mParams.dlaCore);
@@ -188,7 +188,7 @@ bool SampleUffFasterRcnn::build()
             infer->deserializeCudaEngine(trtModelStream.data(), size, nullptr), samplesCommon::InferDeleter());
 
         infer->destroy();
-        gLogInfo << "TRT Engine loaded from: " << mParams.loadEngine << endl;
+        sample::gLogInfo << "TRT Engine loaded from: " << mParams.loadEngine << std::endl;
         if (!mEngine)
         {
             return false;
@@ -199,7 +199,7 @@ bool SampleUffFasterRcnn::build()
         }
     }
 
-    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(gLogger.getTRTLogger()));
+    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(sample::gLogger.getTRTLogger()));
 
     if (mParams.dlaCore >= 0)
     {
@@ -266,12 +266,18 @@ bool SampleUffFasterRcnn::constructNetwork(SampleUniquePtr<nvinfer1::IBuilder>&
 
     if (mParams.int8)
     {
-        gLogInfo << "Using Entropy Calibrator 2" << std::endl;
+        sample::gLogInfo << "Using Entropy Calibrator 2" << std::endl;
         const std::string listFileName = "list.txt";
         const int imageC = 3;
         const int imageH = mParams.inputHeight;
         const int imageW = mParams.inputWidth;
-        const nvinfer1::DimsNCHW imageDims{mParams.calBatchSize, imageC, imageH, imageW};
+        nvinfer1::DimsNCHW imageDims{mParams.calBatchSize, imageC, imageH, imageW};
+        // To prevent compiler initialization warning with some versions of gcc
+        for (int i = imageDims.nbDims; i < Dims::MAX_DIMS; ++i)
+        {
+            imageDims.d[i] = 0;
+            imageDims.type[i] = DimensionType::kSPATIAL;
+        }
         BatchStream calibrationStream(
             mParams.calBatchSize, mParams.nbCalBatches, imageDims, listFileName, mParams.dataDirs);
         calibrator.reset(
@@ -301,7 +307,7 @@ bool SampleUffFasterRcnn::constructNetwork(SampleUniquePtr<nvinfer1::IBuilder>&
         p.write(reinterpret_cast<const char*>(ptr->data()), ptr->size());
         ptr->destroy();
         p.close();
-        gLogInfo << "TRT Engine file saved to: " << mParams.saveEngine << endl;
+        sample::gLogInfo << "TRT Engine file saved to: " << mParams.saveEngine << std::endl;
     }
 
     return true;
@@ -337,11 +343,10 @@ bool SampleUffFasterRcnn::infer()
     for (int i = 0; i < mParams.repeat; ++i)
     {
         status = context->execute(mParams.batchSize, buffers.getDeviceBindings().data());
-    }
-
-    if (!status)
-    {
-        return false;
+        if (!status)
+        {
+            return false;
+        }
     }
 
     if (mParams.profile)
@@ -738,7 +743,7 @@ int main(int argc, char** argv)
 
     if (!argsOK)
     {
-        gLogError << "Invalid arguments" << std::endl;
+        sample::gLogError << "Invalid arguments" << std::endl;
         printHelpInfo();
         return EXIT_FAILURE;
     }
@@ -749,25 +754,25 @@ int main(int argc, char** argv)
         return EXIT_SUCCESS;
     }
 
-    auto sampleTest = gLogger.defineTest(gSampleName, argc, const_cast<const char**>(argv));
-    gLogger.reportTestStart(sampleTest);
+    auto sampleTest = sample::gLogger.defineTest(gSampleName, argc, const_cast<const char**>(argv));
+    sample::gLogger.reportTestStart(sampleTest);
     SampleUffFasterRcnn sample(initializeSampleParams(args));
-    gLogInfo << "Building and running a GPU inference engine for FasterRCNN" << std::endl;
+    sample::gLogInfo << "Building and running a GPU inference engine for FasterRCNN" << std::endl;
 
     if (!sample.build())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
 
     if (!sample.infer())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
 
     if (!sample.teardown())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
 
-    return gLogger.reportPass(sampleTest);
+    return sample::gLogger.reportPass(sampleTest);
 }
diff --git a/samples/opensource/sampleUffMNIST/CMakeLists.txt b/samples/opensource/sampleUffMNIST/CMakeLists.txt
index 89a8599b..9dc0a8f8 100644
--- a/samples/opensource/sampleUffMNIST/CMakeLists.txt
+++ b/samples/opensource/sampleUffMNIST/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleUffMNIST/README.md b/samples/opensource/sampleUffMNIST/README.md
index 32e0e8b2..95505839 100644
--- a/samples/opensource/sampleUffMNIST/README.md
+++ b/samples/opensource/sampleUffMNIST/README.md
@@ -5,7 +5,7 @@
 - [How does this sample work?](#how-does-this-sample-work)
 	* [TensorRT API layers and ops](#tensorrt-api-layers-and-ops)
 - [Running the sample](#running-the-sample)
-	* [Sample `--help` options](#sample---help-options)
+	* [Sample `--help` options](#sample-help-options)
 - [Additional resources](#additional-resources)
 - [License](#license)
 - [Changelog](#changelog)
@@ -124,17 +124,11 @@ The Shuffle layer implements a reshape and transpose operator for tensors.
   
 	This output shows that the sample ran successfully; PASSED.
 
-### Sample --help options
-
-To see the full list of available options and their descriptions, use the `-h` or `--help` command line option. For example:
-```
-Usage: ./sample_uff_mnist [-h or --help] [-d or --datadir=<path to data directory>] [--useDLACore=<int>]
---help Display help information
---datadir Specify path to a data directory, overriding the default. This option can be used multiple times to add multiple directories. If no data directories are given, the default is to use (data/samples/mnist/, data/mnist/)
---useDLACore=N Specify a DLA engine for layers that support DLA. Value can range from 0 to n-1, where n is the number of DLA engines on the platform.
---int8 Run in Int8 mode.
---fp16 Run in FP16 mode.
-```
+
+### Sample `--help` options
+
+To see the full list of available options and their descriptions, use the `-h` or `--help` command line option.
+
 
 # Additional resources
 
diff --git a/samples/opensource/sampleUffMNIST/sampleUffMNIST.cpp b/samples/opensource/sampleUffMNIST/sampleUffMNIST.cpp
index 44e7967c..565967e2 100644
--- a/samples/opensource/sampleUffMNIST/sampleUffMNIST.cpp
+++ b/samples/opensource/sampleUffMNIST/sampleUffMNIST.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -114,7 +114,7 @@ class SampleUffMNIST
 //!
 bool SampleUffMNIST::build()
 {
-    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(gLogger.getTRTLogger()));
+    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(sample::gLogger.getTRTLogger()));
     if (!builder)
     {
         return false;
@@ -203,12 +203,12 @@ bool SampleUffMNIST::processInput(
     readPGMFile(locateFile(std::to_string(inputFileIdx) + ".pgm", mParams.dataDirs), fileData.data(), inputH, inputW);
 
     // Print ASCII representation of digit
-    gLogInfo << "Input:\n";
+    sample::gLogInfo << "Input:\n";
     for (int i = 0; i < inputH * inputW; i++)
     {
-        gLogInfo << (" .:-=+*#%@"[fileData[i] / 26]) << (((i + 1) % inputW) ? "" : "\n");
+        sample::gLogInfo << (" .:-=+*#%@"[fileData[i] / 26]) << (((i + 1) % inputW) ? "" : "\n");
     }
-    gLogInfo << std::endl;
+    sample::gLogInfo << std::endl;
 
     float* hostInputBuffer = static_cast<float*>(buffers.getHostBuffer(inputTensorName));
 
@@ -227,7 +227,7 @@ bool SampleUffMNIST::verifyOutput(
 {
     const float* prob = static_cast<const float*>(buffers.getHostBuffer(outputTensorName));
 
-    gLogInfo << "Output:\n";
+    sample::gLogInfo << "Output:\n";
 
     float val{0.0f};
     int idx{0};
@@ -245,17 +245,17 @@ bool SampleUffMNIST::verifyOutput(
     // Print output values for each index
     for (int j = 0; j < kDIGITS; j++)
     {
-        gLogInfo << j << "=> " << setw(10) << prob[j] << "\t : ";
+        sample::gLogInfo << j << "=> " << std::setw(10) << prob[j] << "\t : ";
 
         // Emphasize index with highest output value
         if (j == idx)
         {
-            gLogInfo << "***";
+            sample::gLogInfo << "***";
         }
-        gLogInfo << "\n";
+        sample::gLogInfo << "\n";
     }
 
-    gLogInfo << std::endl;
+    sample::gLogInfo << std::endl;
     return (idx == groundTruthDigit);
 }
 
@@ -310,7 +310,7 @@ bool SampleUffMNIST::infer()
 
     total /= kDIGITS;
 
-    gLogInfo << "Average over " << kDIGITS << " runs is " << total << " ms." << std::endl;
+    sample::gLogInfo << "Average over " << kDIGITS << " runs is " << total << " ms." << std::endl;
 
     return outputCorrect;
 }
@@ -379,7 +379,7 @@ int main(int argc, char** argv)
     bool argsOK = samplesCommon::parseArgs(args, argc, argv);
     if (!argsOK)
     {
-        gLogError << "Invalid arguments" << std::endl;
+        sample::gLogError << "Invalid arguments" << std::endl;
         printHelpInfo();
         return EXIT_FAILURE;
     }
@@ -388,27 +388,27 @@ int main(int argc, char** argv)
         printHelpInfo();
         return EXIT_SUCCESS;
     }
-    auto sampleTest = gLogger.defineTest(gSampleName, argc, argv);
+    auto sampleTest = sample::gLogger.defineTest(gSampleName, argc, argv);
 
-    gLogger.reportTestStart(sampleTest);
+    sample::gLogger.reportTestStart(sampleTest);
 
     samplesCommon::UffSampleParams params = initializeSampleParams(args);
 
     SampleUffMNIST sample(params);
-    gLogInfo << "Building and running a GPU inference engine for Uff MNIST" << std::endl;
+    sample::gLogInfo << "Building and running a GPU inference engine for Uff MNIST" << std::endl;
 
     if (!sample.build())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
     if (!sample.infer())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
     if (!sample.teardown())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
 
-    return gLogger.reportPass(sampleTest);
+    return sample::gLogger.reportPass(sampleTest);
 }
diff --git a/samples/opensource/sampleUffMaskRCNN/CMakeLists.txt b/samples/opensource/sampleUffMaskRCNN/CMakeLists.txt
index 0a17a97b..5a2eaef9 100644
--- a/samples/opensource/sampleUffMaskRCNN/CMakeLists.txt
+++ b/samples/opensource/sampleUffMaskRCNN/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleUffMaskRCNN/README.md b/samples/opensource/sampleUffMaskRCNN/README.md
index 4075245c..f2b9d9d7 100644
--- a/samples/opensource/sampleUffMaskRCNN/README.md
+++ b/samples/opensource/sampleUffMaskRCNN/README.md
@@ -6,7 +6,7 @@
     * [TensorRT API layers and ops](#tensorrt-api-layers-and-ops)
 - [Prerequisites](#prerequisites)
 - [Running the sample](#running-the-sample)
-	* [Sample `--help` options](#sample---help-options)
+    * [Sample `--help` options](#sample-help-options)
 - [Additional resources](#additional-resources)
 - [License](#license)
 - [Changelog](#changelog)
@@ -31,7 +31,7 @@ The main components of this network are the `ResizeNearest`, `ProposalLayer`, `P
 - `PyramidROIAlign` - Crop and resize the feature of ROIs (first stage's proposals) from the corresponding feature layer.
 
 - `DetectionLayer` - Refine the first stage's proposals to produce final detections.
-  
+
 - `SpecialSlice` - A workaround plugin to slice detection output [y1, x1, y2, x2, class_id, score] to [y1, x1, y2 , x2] for data with more than one index dimensions (batch_idx, proposal_idx, detections(y1, x1, y2, x2)).
 
 
@@ -61,98 +61,94 @@ The Pooling layer implements pooling within a channel. Supported pooling types a
 ## Prerequisites
 
 1.  Install the dependent Python packages.
-	```
-	pip install -r $TRT_SOURCE/samples/opensource/sampleUffMaskRCNN/converted/requirements.txt
-	```
+    ```
+    pip install -r $TRT_SOURCE/samples/opensource/sampleUffMaskRCNN/converted/requirements.txt
+    ```
 
 2.  Install the UFF toolkit and graph surgeon; depending on your TensorRT installation method. To install the toolkit and graph surgeon, choose the method you used to install TensorRT for instructions. See [TensorRT Installation Guide: Installing TensorRT](https://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html#installing).
-  
+
 3.  Modify the `conv2d_transpose` conversion function in UFF, for example `/usr/local/lib/python3.5/dist-packages/uff/converters/tensorflow/converter_functions.py` or `/usr/lib/python3.6/dist-packages/uff/converters/tensorflow/converter_functions.py`.
-	```    
-	uff_graph.conv_transpose(
-	    inputs[0], inputs[2], inputs[1],
-	    strides, padding,
-	    dilation=None, number_groups=number_groups,
-	    left_format=lhs_fmt, right_format=rhs_fmt,
-	    name=name, fields=fields
-	    )
-	```
+    ```
+    uff_graph.conv_transpose(
+        inputs[0], inputs[2], inputs[1],
+        strides, padding,
+        dilation=None, number_groups=number_groups,
+        left_format=lhs_fmt, right_format=rhs_fmt,
+        name=name, fields=fields
+        )
+    ```
 
 4.  Download the Mask R-CNN repo and export to `PYTHONPATH`.
-	```
-	git clone https://github.com/matterport/Mask_RCNN.git
-	export PYTHONPATH=$PYTHONPATH:$PWD/Mask_RCNN
-	```
-  
+    ```
+    git clone https://github.com/matterport/Mask_RCNN.git
+    export PYTHONPATH=$PYTHONPATH:$PWD/Mask_RCNN
+    ```
+
 5.  Apply the patch into Mask R-CNN repo to update the model from NHWC to NCHW.
-	```    
-	cd Mask_RCNN
-	git checkout 3deaec5
-	git am $TRT_SOURCE/samples/opensource/sampleUffMaskRCNN/converted/0001-Update-the-Mask_RCNN-model-from-NHWC-to-NCHW.patch
-	```
+    ```
+    cd Mask_RCNN
+    git checkout 3deaec5
+    git am $TRT_SOURCE/samples/opensource/sampleUffMaskRCNN/converted/0001-Update-the-Mask_RCNN-model-from-NHWC-to-NCHW.patch
+    ```
 
 6.  Download the pre-trained Keras model and place it into your `/data` folder
-	```
-	wget https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5
-	```
+    ```
+    wget https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5
+    ```
 
-	**Note:** The md5sum of model file is e98aaff6f99e307b5e2a8a3ff741a518.
+    **Note:** The md5sum of model file is e98aaff6f99e307b5e2a8a3ff741a518.
 
 7.  Convert the h5 model to the UFF model and place it into your `/data` folder
-	```
-	cd $TRT_SOURCE/samples/opensource/sampleUffMaskRCNN/converted/
-	python mrcnn_to_trt_single.py -w /path/to/data/mask_rcnn_coco.h5 -o /path/to/data/mrcnn_nchw.uff -p ./config.py
-	```
- 
+    ```
+    cd $TRT_SOURCE/samples/opensource/sampleUffMaskRCNN/converted/
+    python mrcnn_to_trt_single.py -w /path/to/data/mask_rcnn_coco.h5 -o /path/to/data/mrcnn_nchw.uff -p ./config.py
+    ```
+
 8.  Populate your `/data` folder with the following test images.
-	```
-	/usr/src/tensorrt/data/faster-rcnn/001763.ppm
-	/usr/src/tensorrt/data/faster-rcnn/004545.ppm
-	```
+    ```
+    /usr/src/tensorrt/data/faster-rcnn/001763.ppm
+    /usr/src/tensorrt/data/faster-rcnn/004545.ppm
+    ```
 
 ## Running the sample
 
-1.  Compile this sample by running `make` in the `<TensorRT root directory>/samples/sample_uff_maskRCNN` directory. The binary named `sample_uff_maskRCNN` will be created in the `<TensorRT root directory>/bin` directory.
-	```
-	cd <TensorRT root directory>/samples/sample_uff_maskRCNN
-	make
-	```
- 
-	Where `<TensorRT root directory>` is where you installed TensorRT.  
+1.  Compile this sample by running `make` in the `<TensorRT root directory>/samples/sampleUffMaskRCNN` directory. The binary named `sample_uff_mask_rcnn` will be created in the `<TensorRT root directory>/bin` directory.
+    ```
+    cd <TensorRT root directory>/samples/sampleUffMaskRCNN
+    make
+    ```
+
+    Where `<TensorRT root directory>` is where you installed TensorRT.
 
 2.  Run the sample to perform object detection and object mask prediction.
 
-	To run the sample in FP32 mode:
-	```
-	./sample_uff_maskRCNN -d path/to/data
-	```
+    To run the sample in FP32 mode:
+    ```
+    ./sample_uff_mask_rcnn -d path/to/data
+    ```
+
+    To run the sample in FP16 mode:
+    ```
+    ./sample_uff_mask_rcnn -d path/to/data --fp16
+    ```
 
-	To run the sample in FP16 mode:
-	```
-	./sample_uff_maskRCNN -d path/to/data --fp16
-	```
-	
 3.  Verify that the sample ran successfully. If the sample runs successfully you should see output similar to the following:
-	```
-	[I] Detected dog in../../data/001763.ppm with confidence 99.9064 and coordinates (257.351, 14.2238, 489.272, 364.817)
-	[I] Detected dog in../../data/001763.ppm with confidence 99.8484 and coordinates (14.3269, 52.0974, 320.913, 363.364)
-	[I] The results are stored in current directory: 0.ppm
-	[I] Detected horse in../../data/004545.ppm with confidence 99.9796 and coordinates (164.81, 22.6816, 386.512, 308.955)
-	[I] Detected bottle in../../data/004545.ppm with confidence 98.5529 and coordinates (218.719, 237.04, 229.382, 261.205)
-	[I] The results are stored in current directory: 1.ppm
-	&&&& PASSED TensorRT.sample_maskrcnn # ../build/cmake/out/sample_uff_maskRCNN -d ../../data/
-	```
-	This output shows that the sample ran successfully; `PASSED`.
+    ```
+    [I] Detected dog in../../data/001763.ppm with confidence 99.9064 and coordinates (257.351, 14.2238, 489.272, 364.817)
+    [I] Detected dog in../../data/001763.ppm with confidence 99.8484 and coordinates (14.3269, 52.0974, 320.913, 363.364)
+    [I] The results are stored in current directory: 0.ppm
+    [I] Detected horse in../../data/004545.ppm with confidence 99.9796 and coordinates (164.81, 22.6816, 386.512, 308.955)
+    [I] Detected bottle in../../data/004545.ppm with confidence 98.5529 and coordinates (218.719, 237.04, 229.382, 261.205)
+    [I] The results are stored in current directory: 1.ppm
+    &&&& PASSED TensorRT.sample_maskrcnn # ../build/cmake/out/sample_uff_mask_rcnn -d ../../data/
+    ```
+    This output shows that the sample ran successfully; `PASSED`.
+
 
 ### Sample `--help` options
 
-To see the full list of available options and their descriptions, use the `-h` or `--help` command line option.  
-```
-Usage: ./sample_maskRCNN [-h or --help] [-d or --datadir=<path to data directory>]
---help Display help information
---datadir Specify path to a data directory, overriding the default. This option can be used multiple times to add multiple directories. If no data directories are given, the default is to use data/samples/maskrcnn/ and data/maskrcnn/
---fp16 Specify to run in fp16 mode.
-```
+To see the full list of available options and their descriptions, use the `-h` or `--help` command line option.
+
 
 ## Additional resources
 
diff --git a/samples/opensource/sampleUffMaskRCNN/converted/README.md b/samples/opensource/sampleUffMaskRCNN/converted/README.md
new file mode 100644
index 00000000..d7589477
--- /dev/null
+++ b/samples/opensource/sampleUffMaskRCNN/converted/README.md
@@ -0,0 +1,128 @@
+# **Model Conversion Guideline**
+This is the general guideline to convert a keras model to .uff.
+
+## **General Conversion steps and tips** 
+
+- Build up the NCHW inference architecture:
+    - If the model is trained in NHWC, we should make sure NCHW architecture can consume the pretrained weights. Generally, most layers could work well directly in NHWC -> NCHW conversion except **Reshape**, **Flatten**, **Dense** and **Softmax** applied to feature map
+    - Input shape of model should be set.
+- Using tensorflow's graph_utils, graph_io API to convert keras model to .pb.
+- Converting .pb to .uff using uff converter:
+    - We should map all the tf nodes which are not supported by TensorRT directly to plugin node.
+    - graphsurgeon cannot handle situation like:
+
+    ```
+    input: "tf_node"
+    input: "tf_node:1"
+    input: "tf_node:2"
+    ```
+    although above inputs come from a same node "tf_node" but the graphsurgeon would regard them as output from 3 different nodes. So to get rid of this situation, we need to add "tf_node:1" and "tf_node:2" to plugin map dict (even they are not really nodes)
+    - the convolution, deconvolution and dense layer's input orders matter. Generally, the inputs[0] should be the feature map input and inputs[1] of node should be the kernel weights. So in most cases, we should reverse the input list when connect the broken graph using `node.input.append(other_node.name)`(The example code is in `config.py`):
+    - the **CPU** version tensorflow's NCHW convolution would introduce the nodes graph like:
+    ```
+    transpose_NCHW2NHWC -> Convolution_NHWC -> transpose_NHWC2NCHW
+    ```
+    So Try to install tensorflow like:
+    ```
+    pip install tensorflow-gpu==1.9.0
+    ```
+    - The UFF Parser cannot handle the inputs with more than one index dimension for convolution, deconvolution and softmax correctly. So a patched lib is needed to parse .uff with
+      nodes whose input tensor has more than one index dimension (this will be the next TensorRT release).
+
+## **Usage for MaskRCNN conversion scripts**
+
+### Dependency:
+ - [Matterport's Mask_RCNN v2.0 Release](https://github.com/matterport/Mask_RCNN/releases/tag/v2.0)
+ - graphsurgeon (provided with TensorRT binary release package)
+ - uff (provided with TensorRT binary release package)
+
+### Steps
+
+- Update the Mask_RCNN model from NHWC to NCHW:
+  
+    1) Set default image data format :
+    ```python
+    import keras.backend as K
+    K.set_image_data_format('channels_first')
+    ```
+    2) change all BN layers from NHWC to NCHW:
+    ```python
+    # In function: indentity_block, conv_block, fpn_classifier_graph, build_fpn_mask_graph 
+    x = BatchNorm(name=bn_name_base + '2a', axis=1)(x, training=train_bn)
+    x = KL.TimeDistributed(BatchNorm(axis=1), name='mrcnn_class_bn1')(x, training=train_bn)
+    ```
+    3) Modify class `PyramidROIAlign` to be compatible with NCHW format:
+    
+    - wrap `permutation` with `crop_and_resize` because `tf.crop_and_resize` only supports `NHWC` format:
+    ```python
+    def NCHW_crop_and_resize(feature_map, level_boxes, box_indices, crop_size, method="bilinear"):
+        # NCHW(0,1,2,3) -> NHWC(0,2,3,1):
+        feature_map = tf.transpose(feature_map, [0, 2, 3, 1])
+
+        # crop_and_resize:
+        box_feature = tf.image.crop_and_resize(feature_map, level_boxes,
+                    box_indices, crop_size, method=method)
+    
+        # NHWC(0,1,2,3) -> NCHW(0,3,1,2)
+        box_feature = tf.transpose(box_feature, [0, 3, 1, 2])
+    
+        return box_feature
+    
+    pooled.append(NCHW_crop_and_resize(feature_maps[i], level_boxes,
+        box_indices, self.pool_shape, method="bilinear"))
+    ```
+    - Change the `compute_output_shape` to return `NCHW` shape:
+    ```python
+    return input_shape[0][:2]  + (input_shape[2][1], ) + self.pool_shape
+    ```
+    4) Change the input format in function `build_rpn_model`:
+    ```python
+    input_feature_map = KL.Input(shape=[depth, None, None],
+                                 name="input_rpn_feature_map")
+    ```
+    5) Permute the feature in function `rpn_graph` and change 'lambda' to 'Reshape': 
+    ```python
+    x = KL.Permute((2,3,1))(x)
+    rpn_class_logits = KL.Reshape((-1, 2))(x)
+    
+    x = KL.Permute((2,3,1))(x)
+rpn_bbox = KL.Reshape((-1, 4))(x)
+    ```
+    6) Change squeeze axis in function `fpn_classifier_graph`:
+    ```python
+    shared = KL.Lambda(lambda x: K.squeeze(K.squeeze(x, 4), 3),
+                       name="pool_squeeze")(x)
+    ```
+    7) Change the input format in function `build` of class `MaskRCNN:`
+    ```python
+shape=[config.IMAGE_SHAPE[2], 1024, 1024 ], name="input_image")
+    ```
+    8) (Optional) Change the input blob for prediction in function `detect` of class `MaskRCNN:`
+    ```python
+    molded_input_images = np.transpose(molded_images, (0, 3, 1, 2))
+    detections, _, _, mrcnn_mask, _, _, _ =\
+        self.keras_model.predict([molded_input_images, image_metas, anchors], verbose=0)
+    mrcnn_mask = np.transpose(mrcnn_mask, (0, 1, 3, 4, 2))
+    ```
+- Download the pretrained Keras model:
+```
+wget https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5
+```
+- change the conv2d_transpose conversion function in uff (*/usr/local/lib/python3.5/dist-packages/uff/converters/tensorflow/converter_functions.py*) as follows:
+```python
+uff_graph.conv_transpose(
+    inputs[0], inputs[2], inputs[1],
+    strides, padding,
+    dilation=None, number_groups=number_groups,
+    left_format=lhs_fmt, right_format=rhs_fmt,
+    name=name, fields=fields
+    )
+
+```
+- use the script as:
+```
+python mrcnn_to_trt_single.py -w /path/to/weights_file.h5 -o /path/to/output_model.uff -p ./config.py
+```
+
+>  NOTE: For reference, the successful converted model should contain 3049 nodes.
+
diff --git a/samples/opensource/sampleUffMaskRCNN/converted/config.py b/samples/opensource/sampleUffMaskRCNN/converted/config.py
index d3c4d2fa..73d44ab9 100644
--- a/samples/opensource/sampleUffMaskRCNN/converted/config.py
+++ b/samples/opensource/sampleUffMaskRCNN/converted/config.py
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -113,12 +113,16 @@ def connect(dynamic_graph, connections_list):
         if node_a_name not in dynamic_graph.node_map[node_b_name].input:
             dynamic_graph.node_map[node_b_name].input.insert(0, node_a_name)
 
+def remove(dynamic_graph, remove_list):
+    for node_name in remove_list:
+        dynamic_graph.remove(dynamic_graph.node_map[node_name])
+
 def preprocess(dynamic_graph):
     # Now create a new graph by collapsing namespaces
     dynamic_graph.collapse_namespaces(namespace_plugin_map, unique_inputs=True)
-    dynamic_graph.remove(timedistributed_remove_list)
-    dynamic_graph.remove(dense_compatible_patch)
-    dynamic_graph.remove(['input_anchors', 'input_image_meta'])
+    remove(dynamic_graph, timedistributed_remove_list)
+    remove(dynamic_graph, dense_compatible_patch)
+    remove(dynamic_graph, ['input_anchors', 'input_image_meta'])
 
     connect(dynamic_graph, timedistributed_connect_pairs)
     connect(dynamic_graph, dense_compatible_connect_pairs)
diff --git a/samples/opensource/sampleUffMaskRCNN/converted/mrcnn_to_trt_single.py b/samples/opensource/sampleUffMaskRCNN/converted/mrcnn_to_trt_single.py
index 71450a19..cf78817b 100644
--- a/samples/opensource/sampleUffMaskRCNN/converted/mrcnn_to_trt_single.py
+++ b/samples/opensource/sampleUffMaskRCNN/converted/mrcnn_to_trt_single.py
@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleUffMaskRCNN/mrcnn_config.h b/samples/opensource/sampleUffMaskRCNN/mrcnn_config.h
index 6fa3b056..06732220 100644
--- a/samples/opensource/sampleUffMaskRCNN/mrcnn_config.h
+++ b/samples/opensource/sampleUffMaskRCNN/mrcnn_config.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleUffMaskRCNN/sampleUffMaskRCNN.cpp b/samples/opensource/sampleUffMaskRCNN/sampleUffMaskRCNN.cpp
index eda6a0f9..941775b8 100644
--- a/samples/opensource/sampleUffMaskRCNN/sampleUffMaskRCNN.cpp
+++ b/samples/opensource/sampleUffMaskRCNN/sampleUffMaskRCNN.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -15,8 +15,8 @@
  */
 
 #ifndef _MSC_VER
-#include <unistd.h>
 #include <sys/time.h>
+#include <unistd.h>
 #endif
 
 #include <assert.h>
@@ -203,6 +203,7 @@ PPM<uint8_t> resizeMask(const BBoxInfo& box, const float mask_threshold)
     raw_mask.h = MaskRCNNConfig::MASK_POOL_SIZE * 2;
     raw_mask.w = MaskRCNNConfig::MASK_POOL_SIZE * 2;
     raw_mask.buffer.resize(raw_mask.h * raw_mask.w, 0);
+    raw_mask.max = std::numeric_limits<int>::lowest();
     for (int i = 0; i < raw_mask.h * raw_mask.w; i++)
         raw_mask.buffer[i] = box.mask->raw[i];
 
@@ -345,13 +346,13 @@ class SampleMaskRCNN
 
     bool verifyOutput(const samplesCommon::BufferManager& buffers);
 
-    vector<MaskRCNNUtils::BBoxInfo> decodeOutput(const int imageIdx, void* detectionsHost, void* masksHost);
+    std::vector<MaskRCNNUtils::BBoxInfo> decodeOutput(const int imageIdx, void* detectionsHost, void* masksHost);
 };
 
 bool SampleMaskRCNN::build()
 {
-    initLibNvInferPlugins(&gLogger.getTRTLogger(), "");
-    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(gLogger.getTRTLogger()));
+    initLibNvInferPlugins(&sample::gLogger.getTRTLogger(), "");
+    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(sample::gLogger.getTRTLogger()));
     if (!builder)
     {
         return false;
@@ -447,8 +448,9 @@ bool SampleMaskRCNN::infer()
     }
     auto tEnd = std::chrono::high_resolution_clock::now();
     float totalHost = std::chrono::duration<float, std::milli>(tEnd - tStart).count();
-    gLogInfo << "Run for 10 times with Batch Size " << mParams.batchSize << std::endl;
-    gLogInfo << "Average inference time is " << (totalHost / 10) / mParams.batchSize << " ms/frame" << std::endl;
+    sample::gLogInfo << "Run for 10 times with Batch Size " << mParams.batchSize << std::endl;
+    sample::gLogInfo << "Average inference time is " << (totalHost / 10) / mParams.batchSize << " ms/frame"
+                     << std::endl;
 
     if (!status)
     {
@@ -518,7 +520,8 @@ bool SampleMaskRCNN::processInput(const samplesCommon::BufferManager& buffers)
     return true;
 }
 
-vector<MaskRCNNUtils::BBoxInfo> SampleMaskRCNN::decodeOutput(const int imageIdx, void* detectionsHost, void* masksHost)
+std::vector<MaskRCNNUtils::BBoxInfo> SampleMaskRCNN::decodeOutput(
+    const int imageIdx, void* detectionsHost, void* masksHost)
 {
     int input_dim_h = MaskRCNNConfig::IMAGE_SHAPE.d[1], input_dim_w = MaskRCNNConfig::IMAGE_SHAPE.d[2];
     assert(input_dim_h == input_dim_w);
@@ -581,18 +584,18 @@ bool SampleMaskRCNN::verifyOutput(const samplesCommon::BufferManager& buffers)
 
     for (int p = 0; p < mParams.batchSize; ++p)
     {
-        vector<MaskRCNNUtils::BBoxInfo> binfo = decodeOutput(p, detectionsHost, masksHost);
+        std::vector<MaskRCNNUtils::BBoxInfo> binfo = decodeOutput(p, detectionsHost, masksHost);
         for (size_t roi_id = 0; roi_id < binfo.size(); roi_id++)
         {
             const auto resized_mask = MaskRCNNUtils::resizeMask(binfo[roi_id], mParams.maskThreshold); // mask threshold
             MaskRCNNUtils::addBBoxPPM(mOriginalPPMs[p], binfo[roi_id], resized_mask);
 
-            gLogInfo << "Detected " << MaskRCNNConfig::CLASS_NAMES[binfo[roi_id].label] << " in"
-                     << mOriginalPPMs[p].fileName << " with confidence " << binfo[roi_id].prob * 100.f
-                     << " and coordinates (" << binfo[roi_id].box.x1 << ", " << binfo[roi_id].box.y1 << ", "
-                     << binfo[roi_id].box.x2 << ", " << binfo[roi_id].box.y2 << ")" << std::endl;
+            sample::gLogInfo << "Detected " << MaskRCNNConfig::CLASS_NAMES[binfo[roi_id].label] << " in"
+                             << mOriginalPPMs[p].fileName << " with confidence " << binfo[roi_id].prob * 100.f
+                             << " and coordinates (" << binfo[roi_id].box.x1 << ", " << binfo[roi_id].box.y1 << ", "
+                             << binfo[roi_id].box.x2 << ", " << binfo[roi_id].box.y2 << ")" << std::endl;
         }
-        gLogInfo << "The results are stored in current directory: " << std::to_string(p) + ".ppm" << std::endl;
+        sample::gLogInfo << "The results are stored in current directory: " << std::to_string(p) + ".ppm" << std::endl;
         MaskRCNNUtils::writePPMFile(std::to_string(p) + ".ppm", mOriginalPPMs[p]);
     }
 
@@ -646,7 +649,7 @@ int main(int argc, char** argv)
     bool argsOK = samplesCommon::parseArgs(args, argc, argv);
     if (!argsOK)
     {
-        gLogError << "Invalid arguments" << std::endl;
+        sample::gLogError << "Invalid arguments" << std::endl;
         printHelpInfo();
         return EXIT_FAILURE;
     }
@@ -656,26 +659,26 @@ int main(int argc, char** argv)
         return EXIT_SUCCESS;
     }
 
-    auto sampleTest = gLogger.defineTest(gSampleName, argc, argv);
+    auto sampleTest = sample::gLogger.defineTest(gSampleName, argc, argv);
 
-    gLogger.reportTestStart(sampleTest);
+    sample::gLogger.reportTestStart(sampleTest);
 
     SampleMaskRCNN sample(initializeSampleParams(args));
 
-    gLogInfo << "Building and running a GPU inference engine for Mask RCNN" << std::endl;
+    sample::gLogInfo << "Building and running a GPU inference engine for Mask RCNN" << std::endl;
 
     if (!sample.build())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
     if (!sample.infer())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
     if (!sample.teardown())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
 
-    return gLogger.reportPass(sampleTest);
+    return sample::gLogger.reportPass(sampleTest);
 }
diff --git a/samples/opensource/sampleUffPluginV2Ext/CMakeLists.txt b/samples/opensource/sampleUffPluginV2Ext/CMakeLists.txt
index 23e4c591..93bdfd26 100755
--- a/samples/opensource/sampleUffPluginV2Ext/CMakeLists.txt
+++ b/samples/opensource/sampleUffPluginV2Ext/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleUffPluginV2Ext/README.md b/samples/opensource/sampleUffPluginV2Ext/README.md
index 1e21bc5d..0a02d6bc 100755
--- a/samples/opensource/sampleUffPluginV2Ext/README.md
+++ b/samples/opensource/sampleUffPluginV2Ext/README.md
@@ -10,7 +10,7 @@
     * [Implement execution](#implement-execution)
     * [Manage resources](#manage-resources)
 -  [Running the sample](#running-the-sample)
-    * [Sample `--help` options](#sample---help-options)
+    * [Sample `--help` options](#sample-help-options)
 -  [Additional resources](#additional-resources)
 -  [License](#license)
 -  [Changelog](#changelog)
@@ -259,21 +259,11 @@ following.
 ```
 This output shows that the sample ran successfully; `PASSED`.
 
+
 ### Sample `--help` options
 
 To see the full list of available options and their descriptions, use the `-h` or `--help` command line option.
 
-```
-    ./sample_uff_plugin_v2_ext --help
-    Usage: ./sample_uff_plugin_v2_ext [-h or --help] [-d or --datadir=<path to data directory>] [--useDLACore=<int>]
-    --help Display help information
-    --datadir Specify path to a data directory, overriding the default. This option can be used multiple times to add multiple
-    directories. If no data directories are given, the default is to use (data/samples/mnist/, data/mnist/)
-    --useDLACore=N Specify a DLA engine for layers that support DLA. Value can range from 0 to n-1, where n is the number of DLA
-    engines on the platform.
-    --int8 Run in Int8 mode.
-    --fp16 Run in FP16 mode.
-```
 
 ## Additional resources
 
diff --git a/samples/opensource/sampleUffPluginV2Ext/sampleUffPluginV2Ext.cpp b/samples/opensource/sampleUffPluginV2Ext/sampleUffPluginV2Ext.cpp
old mode 100644
new mode 100755
index 96301dd6..c717e3b4
--- a/samples/opensource/sampleUffPluginV2Ext/sampleUffPluginV2Ext.cpp
+++ b/samples/opensource/sampleUffPluginV2Ext/sampleUffPluginV2Ext.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -119,12 +119,12 @@ void* createMnistCudaBuffer(int64_t eltCount, DataType dtype, int num)
     readPGMFile(std::to_string(num) + ".pgm", fileData);
 
     // display the number in an ascii representation
-    gLogInfo << "Input:\n";
+    sample::gLogInfo << "Input:\n";
     for (int i = 0; i < eltCount; i++)
     {
-        gLogInfo << (" .:-=+*#%@"[fileData[i] / 26]) << (((i + 1) % INPUT_W) ? "" : "\n");
+        sample::gLogInfo << (" .:-=+*#%@"[fileData[i] / 26]) << (((i + 1) % INPUT_W) ? "" : "\n");
     }
-    gLogInfo << std::endl;
+    sample::gLogInfo << std::endl;
 
     // initialize the inputs buffer
     for (int i = 0; i < eltCount; i++)
@@ -150,22 +150,22 @@ bool verifyOutput(int64_t eltCount, DataType dtype, void* buffer, int num)
 
     int maxIdx = std::distance(outputs.begin(), std::max_element(outputs.begin(), outputs.end()));
 
-    std::ios::fmtflags prevSettings = gLogInfo.flags();
-    gLogInfo.setf(std::ios::fixed, std::ios::floatfield);
-    gLogInfo.precision(6);
-    gLogInfo << "Output:\n";
+    std::ios::fmtflags prevSettings = sample::gLogInfo.flags();
+    sample::gLogInfo.setf(std::ios::fixed, std::ios::floatfield);
+    sample::gLogInfo.precision(6);
+    sample::gLogInfo << "Output:\n";
     for (int64_t eltIdx = 0; eltIdx < eltCount; ++eltIdx)
     {
-        gLogInfo << eltIdx << " => " << std::setw(10) << outputs[eltIdx] << "\t : ";
+        sample::gLogInfo << eltIdx << " => " << std::setw(10) << outputs[eltIdx] << "\t : ";
         if (eltIdx == maxIdx)
         {
-            gLogInfo << "***";
+            sample::gLogInfo << "***";
             pass = eltIdx == num ? true : false;
         }
-        gLogInfo << "\n";
+        sample::gLogInfo << "\n";
     }
-    gLogInfo.flags(prevSettings);
-    gLogInfo << std::endl;
+    sample::gLogInfo.flags(prevSettings);
+    sample::gLogInfo << std::endl;
     return pass;
 }
 
@@ -205,29 +205,29 @@ class SampleUffPluginV2Ext
         parser->registerInput("in", Dims3(1, 28, 28), UffInputOrder::kNCHW);
         parser->registerOutput("out");
 
-        SampleUniquePtr<IBuilder> builder{createInferBuilder(gLogger.getTRTLogger())};
+        SampleUniquePtr<IBuilder> builder{createInferBuilder(sample::gLogger.getTRTLogger())};
         if (!builder.get())
         {
-            gLogError << "Failed to create infer builder. " << std::endl;
+            sample::gLogError << "Failed to create infer builder. " << std::endl;
             return false;
         }
 
         SampleUniquePtr<INetworkDefinition> network{builder->createNetwork()};
         if (!network.get())
         {
-            gLogError << "Failed to create network. " << std::endl;
+            sample::gLogError << "Failed to create network. " << std::endl;
             return false;
         }
 
         if (!parser->parse(mParams.uffFileName.data(), *network, nvinfer1::DataType::kFLOAT))
         {
-            gLogError << "Failure while parsing UFF file" << std::endl;
+            sample::gLogError << "Failure while parsing UFF file" << std::endl;
             return false;
         }
 
         if (gArgs.runInInt8)
         {
-            samplesCommon::setAllTensorScales(network.get(), 5.0f, 5.0f);
+            samplesCommon::setAllTensorScales(network.get(), 25.0f, 25.0f);
         }
 
         SampleUniquePtr<IBuilderConfig> networkConfig{builder->createBuilderConfig()};
@@ -253,7 +253,7 @@ class SampleUffPluginV2Ext
         mEngine.reset(builder->buildEngineWithConfig(*network, *networkConfig));
         if (!mEngine.get())
         {
-            gLogError << "Unable to create engine. " << std::endl;
+            sample::gLogError << "Unable to create engine. " << std::endl;
             return false;
         }
         return true;
@@ -290,7 +290,7 @@ class SampleUffPluginV2Ext
             {
                 buffers[bindingIdxInput] = createMnistCudaBuffer(bufferSizesInput.first, bufferSizesInput.second, num);
                 auto t_start = std::chrono::high_resolution_clock::now();
-                context->execute(batchSize, &buffers[0]);
+                ASSERT(context->execute(batchSize, &buffers[0]));
                 auto t_end = std::chrono::high_resolution_clock::now();
                 ms = std::chrono::duration<float, std::milli>(t_end - t_start).count();
                 total += ms;
@@ -307,7 +307,7 @@ class SampleUffPluginV2Ext
                 CHECK(cudaFree(buffers[bindingIdxInput]));
             }
             total /= numberRun;
-            gLogInfo << "Average over " << numberRun << " runs is " << total << " ms." << std::endl;
+            sample::gLogInfo << "Average over " << numberRun << " runs is " << total << " ms." << std::endl;
         }
 
         for (int bindingIdx = 0; bindingIdx < nbBindings; ++bindingIdx)
@@ -720,7 +720,7 @@ int main(int argc, char** argv)
     }
     if (!argsOK)
     {
-        gLogError << "Invalid arguments" << std::endl;
+        sample::gLogError << "Invalid arguments" << std::endl;
         printHelpInfo();
         return EXIT_FAILURE;
     }
@@ -728,30 +728,29 @@ int main(int argc, char** argv)
     {
         gArgs.dataDirs = std::vector<std::string>{"data/samples/mnist/", "data/mnist/"};
     }
+    auto sampleTest = sample::gLogger.defineTest(gSampleName, argc, argv);
 
-    auto sampleTest = gLogger.defineTest(gSampleName, argc, argv);
-
-    gLogger.reportTestStart(sampleTest);
+    sample::gLogger.reportTestStart(sampleTest);
 
     samplesCommon::UffSampleParams params;
     params.uffFileName = locateFile("lenet5_custom_pool.uff", gArgs.dataDirs);
-    gLogInfo << params.uffFileName << std::endl;
+    sample::gLogInfo << params.uffFileName << std::endl;
     SampleUffPluginV2Ext sample(params);
 
     if (!sample.build())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
 
     if (!sample.infer())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
 
     if (!sample.teardown())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
 
-    return gLogger.reportPass(sampleTest);
+    return sample::gLogger.reportPass(sampleTest);
 }
diff --git a/samples/opensource/sampleUffSSD/CMakeLists.txt b/samples/opensource/sampleUffSSD/CMakeLists.txt
index c7e29d28..3bb317bd 100644
--- a/samples/opensource/sampleUffSSD/CMakeLists.txt
+++ b/samples/opensource/sampleUffSSD/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleUffSSD/README.md b/samples/opensource/sampleUffSSD/README.md
index 15ec7cdc..dffe738f 100644
--- a/samples/opensource/sampleUffSSD/README.md
+++ b/samples/opensource/sampleUffSSD/README.md
@@ -11,7 +11,7 @@
 	* [TensorRT API layers and ops](#tensorrt-api-layers-and-ops)
 - [Prerequisites](#prerequisites)
 - [Running the sample](#running-the-sample)
-	* [Sample `--help` options](#sample---help-options)
+	* [Sample `--help` options](#sample-help-options)
 - [Additional resources](#additional-resources)
 - [License](#license)
 - [Changelog](#changelog)
@@ -267,17 +267,11 @@ calibration can be performed. Currently, we require a file called `list.txt`, wi
 
 	This output shows that the sample ran successfully; `PASSED`.
 
-### Sample --help options
 
-To see the full list of available options and their descriptions, use the `-h` or `--help` command line option. For example:
-```
-Usage: ./sample_uff_ssd
-Optional Parameters:
-  -h, --help Display help information.
-  --useDLACore=N    Specify the DLA engine to run on.
-  --fp16            Specify to run in fp16 mode.
-  --int8            Specify to run in int8 mode.
-```  
+### Sample `--help` options
+
+To see the full list of available options and their descriptions, use the `-h` or `--help` command line option.
+
 
 # Additional resources
 
diff --git a/samples/opensource/sampleUffSSD/config.py b/samples/opensource/sampleUffSSD/config.py
index b7c43c10..c60c81d3 100644
--- a/samples/opensource/sampleUffSSD/config.py
+++ b/samples/opensource/sampleUffSSD/config.py
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/samples/opensource/sampleUffSSD/sampleUffSSD.cpp b/samples/opensource/sampleUffSSD/sampleUffSSD.cpp
index 5594f036..0a07d332 100644
--- a/samples/opensource/sampleUffSSD/sampleUffSSD.cpp
+++ b/samples/opensource/sampleUffSSD/sampleUffSSD.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -123,9 +123,9 @@ class SampleUffSSD
 //!
 bool SampleUffSSD::build()
 {
-    initLibNvInferPlugins(&gLogger.getTRTLogger(), "");
+    initLibNvInferPlugins(&sample::gLogger.getTRTLogger(), "");
 
-    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(gLogger.getTRTLogger()));
+    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(sample::gLogger.getTRTLogger()));
     if (!builder)
     {
         return false;
@@ -197,7 +197,7 @@ bool SampleUffSSD::constructNetwork(SampleUniquePtr<nvinfer1::IBuilder>& builder
 
     if (mParams.int8)
     {
-        gLogInfo << "Using Entropy Calibrator 2" << std::endl;
+        sample::gLogInfo << "Using Entropy Calibrator 2" << std::endl;
         const std::string listFileName = "list.txt";
         const int imageC = 3;
         const int imageH = 300;
@@ -337,7 +337,7 @@ bool SampleUffSSD::verifyOutput(const samplesCommon::BufferManager& buffers)
 
     // Gather class labels
     std::ifstream labelFile(locateFile(mParams.labelsFileName, mParams.dataDirs));
-    string line;
+    std::string line;
     int id = 0;
     while (getline(labelFile, line))
     {
@@ -373,13 +373,13 @@ bool SampleUffSSD::verifyOutput(const samplesCommon::BufferManager& buffers)
                 correctDetection = true;
             }
 
-            gLogInfo << "Detected " << classes[detection].c_str() << " in the image " << int(det[0]) << " ("
-                     << mPPMs[p].fileName.c_str() << ")"
-                     << " with confidence " << det[2] * 100.f << " and coordinates (" << det[3] * inputW << ","
-                     << det[4] * inputH << ")"
-                     << ",(" << det[5] * inputW << "," << det[6] * inputH << ")." << std::endl;
+            sample::gLogInfo << "Detected " << classes[detection].c_str() << " in the image " << int(det[0]) << " ("
+                             << mPPMs[p].fileName.c_str() << ")"
+                             << " with confidence " << det[2] * 100.f << " and coordinates (" << det[3] * inputW << ","
+                             << det[4] * inputH << ")"
+                             << ",(" << det[5] * inputW << "," << det[6] * inputH << ")." << std::endl;
 
-            gLogInfo << "Result stored in " << storeName.c_str() << "." << std::endl;
+            sample::gLogInfo << "Result stored in " << storeName.c_str() << "." << std::endl;
 
             samplesCommon::writePPMFileWithBBox(
                 storeName, mPPMs[p], {det[3] * inputW, det[4] * inputH, det[5] * inputW, det[6] * inputH});
@@ -455,7 +455,7 @@ int main(int argc, char** argv)
     bool argsOK = samplesCommon::parseArgs(args, argc, argv);
     if (!argsOK)
     {
-        gLogError << "Invalid arguments" << std::endl;
+        sample::gLogError << "Invalid arguments" << std::endl;
         printHelpInfo();
         return EXIT_FAILURE;
     }
@@ -465,26 +465,26 @@ int main(int argc, char** argv)
         return EXIT_SUCCESS;
     }
 
-    auto sampleTest = gLogger.defineTest(gSampleName, argc, argv);
+    auto sampleTest = sample::gLogger.defineTest(gSampleName, argc, argv);
 
-    gLogger.reportTestStart(sampleTest);
+    sample::gLogger.reportTestStart(sampleTest);
 
     SampleUffSSD sample(initializeSampleParams(args));
 
-    gLogInfo << "Building and running a GPU inference engine for SSD" << std::endl;
+    sample::gLogInfo << "Building and running a GPU inference engine for SSD" << std::endl;
 
     if (!sample.build())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
     if (!sample.infer())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
     if (!sample.teardown())
     {
-        return gLogger.reportFail(sampleTest);
+        return sample::gLogger.reportFail(sampleTest);
     }
 
-    return gLogger.reportPass(sampleTest);
+    return sample::gLogger.reportPass(sampleTest);
 }
diff --git a/samples/opensource/trtexec/CMakeLists.txt b/samples/opensource/trtexec/CMakeLists.txt
index 1ea7b229..6eaeda12 100644
--- a/samples/opensource/trtexec/CMakeLists.txt
+++ b/samples/opensource/trtexec/CMakeLists.txt
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -20,7 +20,6 @@ SET(SAMPLE_SOURCES
     ../../common/sampleReporting.cpp
     trtexec.cpp
 )
-
 set(SAMPLE_PARSERS "caffe" "uff" "onnx")
-set(PLUGINS_NEEDED ON)
+
 include(../../CMakeSamplesTemplate.txt)
diff --git a/samples/opensource/trtexec/README.md b/samples/opensource/trtexec/README.md
index f8978ef5..d79a3c95 100644
--- a/samples/opensource/trtexec/README.md
+++ b/samples/opensource/trtexec/README.md
@@ -8,8 +8,8 @@
     * [Example 2: Profiling a custom layer](#example-2-profiling-a-custom-layer)
     * [Example 3: Running a network on DLA](#example-3-running-a-network-on-dla)
     * [Example 4: Running an ONNX model with full dimensions and dynamic shapes](#example-4-running-an-onnx-model-with-full-dimensions-and-dynamic-shapes)
-    * [Example 5: Collecting and printing a timing trace](#example-5-collecting-and-printing-a-timing-trace)
-    * [Example 6: Tune throughput with multi-streaming](#example-6-tune-throughput-with-multi-streaming)
+    * [Example 5: Collecting and printing a timing trace](#example-4-collecting-and-printing-a-timing-trace)
+    * [Example 6: Tune throughput with multi-streaming](#example-5-tune-throughput-with-multi-streaming)
 - [Tool command line arguments](#tool-command-line-arguments)
 - [Additional resources](#additional-resources)
 - [License](#license)
@@ -72,6 +72,7 @@ To run the MNIST network on DLA using `trtexec`, issue:
 ```
 ./trtexec --deploy=data/mnist/mnist.prototxt --output=prob --useDLACore=0 --fp16 --allowGPUFallback
 ```
+
 For more information about DLA, see [Working With DLA](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#dla_topic).
 
 ### Example 4: Running an ONNX model with full dimensions and dynamic shapes
@@ -93,11 +94,9 @@ To run an ONNX model in full-dimensions mode with an given input shape:
 To benchmark your ONNX model with a range of possible input shapes:
 
 ```
-./trtexec --onnx=model.onnx --minShapes=input:1x3x244x244 --optShapes=input:16x3x244x244 --maxShapes=<inputName1>input:32x3x244x244 --shapes=input:5x3x244x244
+./trtexec --onnx=model.onnx --minShapes=input:1x3x244x244 --optShapes=input:16x3x244x244 --maxShapes=input:32x3x244x244 --shapes=input:5x3x244x244
 ```
 
-For more information about using dynamic shapes, see [Working With Dynamic Shapes](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#work_dynamic_shapes)
-
 ### Example 5: Collecting and printing a timing trace
 
 When running, `trtexec` prints the measured performance, but can also export the measurement trace to a json file:
diff --git a/samples/opensource/trtexec/prn_utils.py b/samples/opensource/trtexec/prn_utils.py
index 8998d554..4283dc31 100644
--- a/samples/opensource/trtexec/prn_utils.py
+++ b/samples/opensource/trtexec/prn_utils.py
@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -24,20 +24,22 @@
 
 from __future__ import print_function
 
-def combine_descriptions(prolog, features, descriptions):
+
+
+def combineDescriptions(prolog, features, descriptions):
     ''' Combine features with their descriptions '''
 
-    full_description = prolog
+    fullDescription = prolog
     sep = ' '
     for feature, description in zip(features, descriptions):
-        full_description += sep + feature + ' (' + description + ')'
+        fullDescription += sep + feature + ' (' + description + ')'
         sep = ', '
 
-    return full_description
+    return fullDescription
 
 
 
-def print_header(allFeatures, features, gp, count):
+def printHeader(allFeatures, selection, gp = False, count = False):
     ''' Print table header '''
 
     if gp:
@@ -48,7 +50,7 @@ def print_header(allFeatures, features, gp, count):
         sep = ''
 
     for feature in allFeatures:
-       if feature in features:
+       if feature in selection:
            print(sep + feature, end = '')
            sep = ', '
 
@@ -56,7 +58,7 @@ def print_header(allFeatures, features, gp, count):
 
 
 
-def print_csv(data, count):
+def printCsv(data, count = False):
     ''' Print trace in CSV format '''
 
     c = 0
@@ -68,22 +70,27 @@ def print_csv(data, count):
         else:
             sep = ''
         for r in row:
-            print('{}{:.6}'.format(sep, float(r)), end = '')
+            if isinstance(r, str):
+                print(sep + r, end = '')
+            else:
+                print('{}{:.6}'.format(sep, float(r)), end = '')
             sep = ', '
         print('')
 
 
 
-def filter_data(data, all_features, feature_set):
+def filterData(data, allFeatures, selection):
     ''' Drop features not in the given set '''
 
     filteredData = []
-
     for d in data:
         row = []
-        for f in all_features:
-            if f in feature_set:
-                row.append(d[f])
+        for f in allFeatures:
+            if f in selection:
+                if f in d:
+                    row.append(d[f])
+                else:
+                    row.append('')
         filteredData.append(row)
 
     return filteredData
diff --git a/samples/opensource/trtexec/profiler.py b/samples/opensource/trtexec/profiler.py
index 72fa2b04..2149ba09 100755
--- a/samples/opensource/trtexec/profiler.py
+++ b/samples/opensource/trtexec/profiler.py
@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -29,75 +29,188 @@
 import sys
 import json
 import argparse
-import prn_utils
+import prn_utils as pu
 
 
-all_features = ['name', 'timeMs', 'averageMs', 'percentage']
 
-default_features = ",".join(all_features)
+allFeatures = ['name', 'timeMs', 'averageMs', 'percentage']
+
+defaultFeatures = ",".join(allFeatures)
 
 descriptions = ['layer name', 'total layer time', 'average layer time', 'percentage of total time']
 
-features_description = prn_utils.combine_descriptions('Features are (times in ms):',
-                                                      all_features, descriptions)
+featuresDescription = pu.combineDescriptions('Features are (times in ms):', allFeatures, descriptions)
 
 
 
-def hasNames(feature_set):
+def hasNames(features):
     ''' Check if the name is included in the set '''
 
-    return 'name' in feature_set
+    return 'name' in features
 
 
 
-def total_data(data, names):
+def totalData(features, profile):
     ''' Add row at the bottom with the total '''
 
-    accumulator = []
+    accumulator = {}
+    for f in features:
+        accumulator[f] = 0
+    accumulator['name'] = 'total'
+
+    for row in profile:
+        for f in features:
+            if f in row and not f == 'name':
+                accumulator[f] += row[f]
+
+    return accumulator
+
+
+
+def findAndRemove(profile, name):
+    ''' Find named row in profile and remove '''
+
+    for r in range(len(profile)):
+        if profile[r]['name'] == name:
+            row = profile[r]
+            del profile[r]
+            return row
+
+    return None
+
+
+
+def refName(name):
+    ''' Add prefix ref to name '''
+
+    return 'ref' + name[0].capitalize() + name[1:]
+
+
+def refFeatures(names):
+    ''' Add prefix ref to features names '''
+
+    refNames = []
+    for name in names:
+        refNames.append(refName(name))
+    return refNames
+
+
+
+def mergeHeaders(features, skipFirst = True):
+    ''' Duplicate feature names for reference and target profile '''
+
+    if skipFirst:
+        return [features[0]] + refFeatures(features[1:]) + features[1:] + ['% difference']
+    return refFeatures(features) + features + ['% difference']
 
-    if names:
-        start = 1
-        accumulator.append('total')
-    else:
-        start = 0
-    for f in range(start, len(data[0])):
-        accumulator.append(0)
 
-    for row in data:
-        for f in range(start, len(row)):
-             accumulator[f] += row[f]
 
-    data.append(accumulator)
+def addReference(row, reference):
+    ''' Add reference results to results dictionary '''
 
-    return data
+    for k,v in reference.items():
+        if k == 'name':
+            if k in row:
+                continue
+        else:
+            k = refName(k)
+        row[k] = v
+
+
+
+def mergeRow(reference, profile, diff):
+    ''' Merge reference and target profile results into a single row '''
+
+    row = {}
+    if profile:
+        row = profile
+    if reference:
+        addReference(row, reference)
+    if diff:
+        row['% difference'] = diff;
+
+    return row
+
+
+
+def alignData(reference, profile, threshold):
+    ''' Align and merge reference and target profiles '''
+
+    alignedData = []
+    for ref in reference:
+        prof = findAndRemove(profile, ref['name'])
+
+        if prof:
+            diff = (prof['averageMs'] / ref['averageMs'] - 1)*100
+            if abs(diff) >= threshold:
+                alignedData.append(mergeRow(ref, prof, diff))
+        else:
+            alignedData.append(mergeRow(ref, None, None))
+
+    for prof in profile:
+        alignedData.append(mergeRow(None, prof, None))
+
+    return alignedData
 
 
 
 def main():
     parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument('--features', metavar='F[,F]*', default='name,timeMs,averageMs,percentage',
-                        help='Comma separated list of features to print. ' + features_description)
+    parser.add_argument('--features', metavar='F[,F]*', default=defaultFeatures,
+                        help='Comma separated list of features to print. ' + featuresDescription)
     parser.add_argument('--total', action='store_true', help='Add total time row.')
     parser.add_argument('--gp', action='store_true', help='Print GNUPlot format.')
     parser.add_argument('--no-header', action='store_true', help='Omit the header row.')
+    parser.add_argument('--threshold', metavar='T', default=0.0, type=float,
+                        help='Threshold of percentage difference.')
+    parser.add_argument('--reference', metavar='R', help='Reference profile file name.')
     parser.add_argument('name', metavar='filename', help='Profile file.')
     args = parser.parse_args()
 
-    feature_set = args.features.split(',')
-    count = args.gp and not hasNames(feature_set)
+    global allFeatures
+    features = args.features.split(',')
+    for f in features:
+        if not f in allFeatures:
+            print('Feature {} not recognized'.format(f))
+            return
 
-    if not args.no_header:
-        prn_utils.print_header(all_features, feature_set, args.gp, count)
+    count = args.gp and not hasNames(features)
+
+    profile = None
+    reference = None
 
     with open(args.name) as f:
         profile = json.load(f)
+        profileCount = profile[0]['count']
+        profile = profile[1:]
+
+    if args.reference:
+        with open(args.reference) as f:
+            reference = json.load(f)
+            referenceCount = reference[0]['count']
+            reference = reference[1:]
+        allFeatures = mergeHeaders(allFeatures)
+        features = mergeHeaders(features, hasNames(features))
+
+    if not args.no_header:
+        if reference:
+            comment = '#' if args.gp else ''
+            print(comment + 'reference count: {} - profile count: {}'.format(referenceCount, profileCount))
+        pu.printHeader(allFeatures, features, args.gp, count)
+
+    if reference:
+        profile = alignData(reference, profile, args.threshold)
+
+    if args.total:
+        profile.append(totalData(allFeatures, profile))
+        if reference:
+            total = profile[len(profile) - 1]
+            total['% difference'] = (total['averageMs'] / total['refAverageMs'] - 1)*100
 
-        data = prn_utils.filter_data(profile[1:], all_features, feature_set)
+    profile = pu.filterData(profile, allFeatures, features)
 
-        if args.total:
-            data = total_data(data, hasNames(feature_set))
+    pu.printCsv(profile, count)
 
-        prn_utils.print_csv(data, count)
 
 
 if __name__ == '__main__':
diff --git a/samples/opensource/trtexec/tracer.py b/samples/opensource/trtexec/tracer.py
index 2d3c6046..e913d518 100755
--- a/samples/opensource/trtexec/tracer.py
+++ b/samples/opensource/trtexec/tracer.py
@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -31,49 +31,47 @@
 import sys
 import json
 import argparse
-import prn_utils
+import prn_utils as pu
 
 
 timestamps = ['startInMs', 'endInMs', 'startComputeMs', 'endComputeMs', 'startOutMs', 'endOutMs']
 
 intervals = ['inMs', 'computeMs', 'outMs', 'latencyMs', 'endToEndMs']
 
-all_metrics = timestamps + intervals
+allMetrics = timestamps + intervals
 
-default_metrics = ",".join(all_metrics)
+defaultMetrics = ",".join(allMetrics)
 
 descriptions = ['start input', 'end input', 'start compute', 'end compute', 'start output',
                 'end output', 'input', 'compute', 'output', 'latency', 'end to end latency']
 
-metrics_description = prn_utils.combine_descriptions('Possible metrics (all in ms) are:',
-                                                     all_metrics, descriptions)
+metricsDescription = pu.combine_descriptions('Possible metrics (all in ms) are:',
+                                             allMetrics, descriptions)
 
 
 
-def skip_trace(trace, start):
+def skipTrace(trace, start):
     ''' Skip trace entries until start time '''
 
-    trailing = []
+    for t in range(len(trace)):
+        if trace[t]['startComputeMs'] >= start:
+            return trace[t:]
 
-    for t in trace:
-        if t['start compute'] >= start:
-            trailing.append(t)
+    return []
 
-    return trailing
 
 
-
-def hasTimestamp(metric_set):
+def hasTimestamp(metrics):
     ''' Check if features have at least one timestamp '''
 
     for timestamp in timestamps:
-        if timestamp in metric_set:
+        if timestamp in metrics:
             return True
     return False;
 
 
 
-def avg_data(data, avg, times):
+def avgData(data, avg, times):
     ''' Average trace entries (every avg entries) '''
 
     averaged = []
@@ -82,11 +80,11 @@ def avg_data(data, avg, times):
 
     for row in data:
         if r == 0:
-            for t in range(len(row)):
-                accumulator.append(row[t])
+            for m in row:
+                accumulator.append(m)
         else:
-            for t in range(times, len(row)):
-                accumulator[t] += row[t]
+            for m in row[times:]:
+                accumulator[t] += m
 
         r += 1
         if r == avg:
@@ -102,33 +100,35 @@ def avg_data(data, avg, times):
 
 def main():
     parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument('--metrics', metavar='M[,M]*', default=default_metrics,
-                        help='Comma separated list of metrics to print. ' + metrics_description)
-    parser.add_argument('--avg', metavar='N', type=int, default=1, help='Print average every N records.')
-    parser.add_argument('--start', metavar='T', type=float, default=0, help='Start trace at time T (drop records with compute start before T).')
+    parser.add_argument('--metrics', metavar='M[,M]*', default=defaultMetrics,
+                        help='Comma separated list of metrics to print. ' + metricsDescription)
+    parser.add_argument('--avg', metavar='N', type=int, default=1,
+                        help='Print average every N records.')
+    parser.add_argument('--start', metavar='T', type=float, default=0,
+                        help='Start trace at time T (drop records with compute start before T ms).')
     parser.add_argument('--gp', action='store_true', help='Print GNUPlot format.')
     parser.add_argument('--no-header', action='store_true', help='Omit the header row.')
     parser.add_argument('name', metavar='filename', help='Trace file.')
     args = parser.parse_args()
 
-    metric_set = args.metrics.split(',')
-    count = args.gp and ( not hasTimestamp(metric_set) or len(metric_set) == 1)
+    metrics = args.metrics.split(',')
+    count = args.gp and (not hasTimestamp(metricts) or len(metrics) == 1)
 
     if not args.no_header:
-        prn_utils.print_header(all_metrics, metric_set, args.gp, count)
+        pu.printHeader(allMetrics, metrics, args.gp, count)
 
     with open(args.name) as f:
         trace = json.load(f)
 
-        if args.start > 0:
-            trace = skip_trace(trace, args.start)
+    if args.start > 0:
+        trace = skipTrace(trace, args.start)
 
-        data = prn_utils.filter_data(trace, all_metrics, metric_set)
+    trace = pu.filterData(trace, allMetrics, metrics)
 
-        if args.avg > 1:
-            data = avg_data(data, args.avg, hasTimestamp(metric_set))
+    if args.avg > 1:
+        trace = avgData(trace, args.avg, hasTimestamp(metrics))
 
-        prn_utils.print_csv(data, count)
+    pu.printCsv(trace, count)
 
 
 if __name__ == '__main__':
diff --git a/samples/opensource/trtexec/trtexec.cpp b/samples/opensource/trtexec/trtexec.cpp
index 809dc88f..ab6d9a13 100644
--- a/samples/opensource/trtexec/trtexec.cpp
+++ b/samples/opensource/trtexec/trtexec.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+ * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -23,12 +23,12 @@
 #include <functional>
 #include <iostream>
 #include <iterator>
+#include <memory>
 #include <sstream>
 #include <string.h>
 #include <sys/stat.h>
 #include <time.h>
 #include <vector>
-#include <memory>
 
 #include "NvInfer.h"
 #include "NvInferPlugin.h"
@@ -36,9 +36,10 @@
 #include "buffers.h"
 #include "common.h"
 #include "logger.h"
-#include "sampleOptions.h"
+#include "sampleDevice.h"
 #include "sampleEngines.h"
 #include "sampleInference.h"
+#include "sampleOptions.h"
 #include "sampleReporting.h"
 
 using namespace nvinfer1;
@@ -47,15 +48,20 @@ using namespace sample;
 int main(int argc, char** argv)
 {
     const std::string sampleName = "TensorRT.trtexec";
-    const std::string supportNote{"Note: CUDA graphs is not supported in this version."};
 
-    auto sampleTest = gLogger.defineTest(sampleName, argc, argv);
+    auto sampleTest = sample::gLogger.defineTest(sampleName, argc, argv);
 
-    gLogger.reportTestStart(sampleTest);
+    sample::gLogger.reportTestStart(sampleTest);
 
     Arguments args = argsToArgumentsMap(argc, argv);
     AllOptions options;
 
+    if (parseHelp(args))
+    {
+        AllOptions::help(std::cout);
+        return EXIT_SUCCESS;
+    }
+
     if (!args.empty())
     {
         bool failed{false};
@@ -67,22 +73,21 @@ int main(int argc, char** argv)
             {
                 for (const auto& arg : args)
                 {
-                    gLogError << "Unknown option: " << arg.first << " " << arg.second << std::endl;
+                    sample::gLogError << "Unknown option: " << arg.first << " " << arg.second << std::endl;
                 }
                 failed = true;
             }
         }
         catch (const std::invalid_argument& arg)
         {
-            gLogError << arg.what() << std::endl;
+            sample::gLogError << arg.what() << std::endl;
             failed = true;
         }
 
         if (failed)
         {
             AllOptions::help(std::cout);
-            std::cout << supportNote << std::endl;
-            return gLogger.reportFail(sampleTest);
+            return sample::gLogger.reportFail(sampleTest);
         }
     }
     else
@@ -93,44 +98,44 @@ int main(int argc, char** argv)
     if (options.helps)
     {
         AllOptions::help(std::cout);
-        std::cout << supportNote << std::endl;
-        return gLogger.reportPass(sampleTest);
+        return sample::gLogger.reportPass(sampleTest);
     }
 
-    gLogInfo << options;
+    sample::gLogInfo << options;
     if (options.reporting.verbose)
     {
-        setReportableSeverity(Severity::kVERBOSE);
+        sample::setReportableSeverity(ILogger::Severity::kVERBOSE);
     }
 
-    cudaSetDevice(options.system.device);
+    cudaCheck(cudaSetDevice(options.system.device));
 
-    initLibNvInferPlugins(&gLogger.getTRTLogger(), "");
+    initLibNvInferPlugins(&sample::gLogger.getTRTLogger(), "");
 
     for (const auto& pluginPath : options.system.plugins)
     {
-        gLogInfo << "Loading supplied plugin library: " << pluginPath << std::endl;
+        sample::gLogInfo << "Loading supplied plugin library: " << pluginPath << std::endl;
         samplesCommon::loadLibrary(pluginPath);
     }
 
     InferenceEnvironment iEnv;
-    iEnv.engine = getEngine(options.model, options.build, options.system, gLogError);
+    iEnv.engine = getEngine(options.model, options.build, options.system, sample::gLogError);
     if (!iEnv.engine)
     {
-        gLogError << "Engine set up failed" << std::endl;
-        return gLogger.reportFail(sampleTest);
+        sample::gLogError << "Engine set up failed" << std::endl;
+        return sample::gLogger.reportFail(sampleTest);
     }
     if (options.inference.skip)
     {
-        return gLogger.reportPass(sampleTest);
+        return sample::gLogger.reportPass(sampleTest);
     }
 
     if (options.build.safe && options.system.DLACore >= 0)
     {
-        gLogInfo << "Safe DLA capability is detected. Please save DLA loadable with --saveEngine option, "
-                    "then use dla_safety_runtime to run inference with saved DLA loadable, "
-                    "or alternatively run with your own application" << std::endl;
-        return gLogger.reportFail(sampleTest);
+        sample::gLogInfo << "Safe DLA capability is detected. Please save DLA loadable with --saveEngine option, "
+                            "then use dla_safety_runtime to run inference with saved DLA loadable, "
+                            "or alternatively run with your own application"
+                         << std::endl;
+        return sample::gLogger.reportFail(sampleTest);
     }
 
     if (options.reporting.profile || !options.reporting.exportTimes.empty())
@@ -138,15 +143,21 @@ int main(int argc, char** argv)
         iEnv.profiler.reset(new Profiler);
     }
 
-    setUpInference(iEnv, options.inference);
+    if (!setUpInference(iEnv, options.inference))
+    {
+        sample::gLogError << "Inference set up failed" << std::endl;
+        return sample::gLogger.reportFail(sampleTest);
+    }
     std::vector<InferenceTrace> trace;
-    runInference(options.inference, iEnv, trace);
+    sample::gLogInfo << "Starting inference threads" << std::endl;
+    runInference(options.inference, iEnv, options.system.device, trace);
 
-    printPerformanceReport(trace, options.reporting, static_cast<float>(options.inference.warmup), options.inference.batch, gLogInfo);
+    printPerformanceReport(trace, options.reporting, static_cast<float>(options.inference.warmup),
+        options.inference.batch, sample::gLogInfo);
 
     if (options.reporting.output)
     {
-        dumpOutputs(*iEnv.context.front(), *iEnv.bindings.front(), gLogInfo);
+        dumpOutputs(*iEnv.context.front(), *iEnv.bindings.front(), sample::gLogInfo);
     }
     if (!options.reporting.exportOutput.empty())
     {
@@ -158,12 +169,12 @@ int main(int argc, char** argv)
     }
     if (options.reporting.profile)
     {
-        iEnv.profiler->print(gLogInfo);
+        iEnv.profiler->print(sample::gLogInfo);
     }
     if (!options.reporting.exportProfile.empty())
     {
         iEnv.profiler->exportJSONProfile(options.reporting.exportProfile);
     }
 
-    return gLogger.reportPass(sampleTest);
+    return sample::gLogger.reportPass(sampleTest);
 }
diff --git a/scripts/stubify.sh b/scripts/stubify.sh
index 036d6587..ee114d00 100755
--- a/scripts/stubify.sh
+++ b/scripts/stubify.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/third_party/protobuf.cmake b/third_party/protobuf.cmake
index 8a2c7c0c..f45e0e33 100644
--- a/third_party/protobuf.cmake
+++ b/third_party/protobuf.cmake
@@ -1,6 +1,5 @@
-
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/third_party/zlib.cmake b/third_party/zlib.cmake
index 13e5da32..0c2ce5e6 100644
--- a/third_party/zlib.cmake
+++ b/third_party/zlib.cmake
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/tools/onnx-graphsurgeon/.gitignore b/tools/onnx-graphsurgeon/.gitignore
new file mode 100644
index 00000000..a7db94fa
--- /dev/null
+++ b/tools/onnx-graphsurgeon/.gitignore
@@ -0,0 +1,118 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# ONNX GS
+examples/*/*.onnx
diff --git a/tools/onnx-graphsurgeon/CHANGELOG.md b/tools/onnx-graphsurgeon/CHANGELOG.md
new file mode 100644
index 00000000..61c486d4
--- /dev/null
+++ b/tools/onnx-graphsurgeon/CHANGELOG.md
@@ -0,0 +1,75 @@
+# ONNX GraphSurgeon changelog history
+
+Dates are in YYYY-MM-DD format.
+
+## vNext
+
+### Fixed
+- `fold_constants()` will no longer fail if there is nothing to fold in the graph
+
+
+## v0.2.3
+### Added
+- `Graph.register()` now accepts an `opsets` argument so that functions can be registered for specific opsets.
+
+### Removed
+- `has_metadata` has been removed from `Tensor`, since the function is no longer used.
+
+
+## v0.2.2 (2020-06-17)
+### Fixed
+- ONNX GraphSurgeon now enforces the constraint that graph inputs/outputs must include type information.
+- Fixed a bug where `opset` was not being considering when running inference for constant folding.
+
+
+## v0.2.1 (2020-06-10)
+### Added
+- Added `layer()` function to `Graph` to make it easier to generate models from scratch
+- Added `i()` and `o()` convenience functions to `Tensor`, which are similar to the functions for `Node`, but return `Tensor`s instead of `Node`s
+
+
+## v0.2.0 (2020-04-15)
+### Added
+- Added an `examples` directory
+- Added `has_metadata()` to `Tensor` classes to determine if dtype/shape are known.
+- Added a `check_duplicates` parameter to `Graph.tensors()` to make it easy to check for duplicate tensors in the graph.
+
+### Changed
+- Various improvements to the logger
+- Updated `OnnxImporter` so that it can correctly import shapes and types from an ONNX graph after shape inference.
+- Made `Tensor` an abstract class - all tensors in a graph are now either `Variable` or `Constant`
+- Renames `generate_tensor_map()` to `tensors()` in `Graph`
+- Removed `Tensor` suffix from Tensor classes.
+
+
+## v0.1.3 (2020-02-26)
+### Fixed
+- The `import_onnx` and `export_onnx` functions will now preserve opset information and `dim_param` values in shapes.
+
+
+## v0.1.2 (2020-02-19)
+### Added
+- Added `i()` and `o()` convenience functions to `Node` for retrieving input/output nodes.
+- Added `fold_constants()` to `Graph` to allow for folding constants in the graph.
+- Added `__deepcopy__()` to `Graph`.
+- Added `to_constant()` and `to_variable()` functions to `Variable` and `Constant` respectively to transmute them in-place.
+
+
+## v0.1.1 (2020-02-11)
+### Changed
+- Removed some type annotations to allow compatibility with Python 3.5.
+
+
+## v0.1.0 (2020-02-11)
+### Added
+- Added `Node`, `Tensor` and `Graph` classes.
+- Added `BaseImporter` and `OnnxImporter` classes.
+- Added support for importing initializers in the `OnnxImporter`
+- Added `Variable` and `Constant`
+- Consolidates inputs/outputs of Nodes/Tensors. Now, inputs/outputs should generally only be added to `Node`s.
+- Added `OnnxExporter` to export `Graph` to `onnx.GraphProto`
+- Added `OnnxExporter` and `OnnxImporter` to public imports
+- Added `toposort` function to `Graph`, which will topologically sort it.
+- Added `cleanup` function to `Graph`, which will remove unused nodes and tensors.
+- Added high-level API for importing/exporting `Graph`s from/to ONNX models.
+- `Graph`s are now generated with a default name of `onnx_graphsurgeon`
diff --git a/samples/opensource/trtexec/giexec b/tools/onnx-graphsurgeon/Makefile
old mode 100755
new mode 100644
similarity index 57%
rename from samples/opensource/trtexec/giexec
rename to tools/onnx-graphsurgeon/Makefile
index a4d2c2d6..1e3e461b
--- a/samples/opensource/trtexec/giexec
+++ b/tools/onnx-graphsurgeon/Makefile
@@ -1,6 +1,5 @@
-#!/bin/bash
 #
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,23 +12,19 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+#
 
-# Stub to call trtexec
+test:
+	python3 -m pytest tests/ -v -x --pdb
 
-CUR_DPATH="$(dirname "$(readlink -f "${BASH_SOURCE}")")"
-TRTEXEC_FPATH=$CUR_DPATH/trtexec
+clean:
+	rm -rf dist/ build/ onnx_graphsurgeon.egg-info/
 
-if [[ ! -f "$TRTEXEC_FPATH" ]]
-then
-    echo "Could not find trtexec"
-    exit 1
-fi
+build: clean
+	python3 setup.py bdist_wheel
 
-if [[ ! -x "$TRTEXEC_FPATH" ]]
-then
-    echo "trtexec is not executable"
-    exit 1
-fi
+install_venv: build
+	python3 -m pip install --force-reinstall $(CURDIR)/dist/*.whl
 
-# Forward input args to trtexec
-$TRTEXEC_FPATH "$@"
+install: build
+	python3 -m pip install --force-reinstall $(CURDIR)/dist/*.whl --user
diff --git a/tools/onnx-graphsurgeon/README.md b/tools/onnx-graphsurgeon/README.md
new file mode 100644
index 00000000..a602cb90
--- /dev/null
+++ b/tools/onnx-graphsurgeon/README.md
@@ -0,0 +1,190 @@
+# ONNX GraphSurgeon
+
+
+## Table of Contents
+
+- [Introduction](#introduction)
+- [Installation](#installation)
+    - [Building From Source](#building-from-source)
+        - [Using Make Targets](#using-make-targets)
+        - [Building Manually](#building-manually)
+- [Understanding The Basics](#understanding-the-basics)
+    - [Importers](#importers)
+    - [IR](#ir)
+        - [Tensor](#tensor)
+        - [Node](#node)
+        - [A Note On Modifying Inputs And Outputs](#a-note-on-modifying-inputs-and-outputs)
+        - [Graph](#graph)
+    - [Exporters](#exporters)
+- [Examples](#examples)
+
+## Introduction
+
+ONNX GraphSurgeon is a tool that allows you to easily generate new ONNX graphs, or modify existing ones.
+
+
+## Installation
+
+### Building From Source
+
+#### Using Make Targets
+```
+make install
+```
+Or, if installing inside a virtual environment:
+```
+make install_venv
+```
+
+#### Building Manually
+
+1. Build a wheel:
+```
+make build
+```
+
+2. Install the wheel manually from **outside** the repository:
+```
+python3 -m pip install onnx_graphsurgeon/dist/onnx_graphsurgeon-X.Y.Z-py2.py3-none-any.whl --user
+```
+where `X.Y.Z` is the version number.
+
+
+## Understanding The Basics
+
+ONNX GraphSurgeon is composed of three major components: Importers, the IR, and Exporters.
+
+### Importers
+
+Importers are used to import a graph into the ONNX GraphSurgeon IR.
+The importer interface is defined in [base_importer.py](./onnx_graphsurgeon/importers/base_importer.py).
+
+ONNX GraphSurgeon also provides [high-level importer APIs](./onnx_graphsurgeon/api/api.py) for ease of use.
+
+### IR
+
+The Intermediate Representation (IR) is where all modifications to the graph are made. It can also be used to
+create new graphs from scratch. The IR involves three components: [Tensor](./onnx_graphsurgeon/ir/tensor.py)s,
+[Node](./onnx_graphsurgeon/ir/node.py)s, and [Graph](./onnx_graphsurgeon/ir/graph.py)s.
+
+Nearly all of the member variables of each component can be freely modified. For details on the various
+attributes of these classes, you can view the help output using `help(<class_or_instance>)` in an
+interactive shell, or using `print(help(<class_or_instance>))` in a script, where `<class_or_instance>`
+is an ONNX GraphSurgeon type, or an instance of that type.
+
+#### Tensor
+
+Tensors are divided into two subclasses: `Variable` and `Constant`.
+
+- A `Constant` is a tensor whose values are known upfront, and can be retrieved as a NumPy array and modified.
+- A `Variable` is a tensor whose values are unknown until inference-time, but may contain information about data type and shape.
+
+The inputs and outputs of Tensors are always Nodes.
+
+**An example constant tensor from ResNet50:**
+```
+>>> print(tensor)
+Constant (gpu_0/res_conv1_bn_s_0)
+[0.85369843 1.1515082  0.9152944  0.9577646  1.0663182  0.55629414
+ 1.2009839  1.1912311  2.2619808  0.62263143 1.1149117  1.4921428
+ 0.89566356 1.0358194  1.431092   1.5360111  1.25086    0.8706703
+ 1.2564877  0.8524589  0.9436758  0.7507614  0.8945271  0.93587166
+ 1.8422242  3.0609846  1.3124607  1.2158023  1.3937513  0.7857263
+ 0.8928106  1.3042281  1.0153942  0.89356416 1.0052011  1.2964457
+ 1.1117343  1.0669073  0.91343874 0.92906713 1.0465593  1.1261675
+ 1.4551278  1.8252873  1.9678202  1.1031747  2.3236883  0.8831993
+ 1.1133649  1.1654979  1.2705412  2.5578163  0.9504889  1.0441847
+ 1.0620039  0.92997414 1.2119316  1.3101407  0.7091761  0.99814713
+ 1.3404484  0.96389204 1.3435135  0.9236031 ]
+```
+
+**An example variable tensor from ResNet50:**
+```
+>>> print(tensor)
+Variable (gpu_0/data_0): (shape=[1, 3, 224, 224], dtype=float32)
+```
+
+
+#### Node
+
+A `Node` defines an operation in the graph. A node may specify attributes; attribute values can be any
+Python primitive types, as well as ONNX GraphSurgeon `Graph`s or `Tensor`s
+
+The inputs and outputs of Nodes are always Tensors
+
+**An example ReLU node from ResNet50:**
+```
+>>> print(node)
+ (Relu)
+    Inputs: [Tensor (gpu_0/res_conv1_bn_1)]
+    Outputs: [Tensor (gpu_0/res_conv1_bn_2)]
+```
+
+In this case, the node has no attributes. Otherwise, attributes are displayed as an `OrderedDict`.
+
+
+#### A Note On Modifying Inputs And Outputs
+
+The `inputs`/`outputs` members of nodes and tensors have special logic that will update the inputs/outputs of all
+affected nodes/tensors when you make a change. This means, for example, that you do **not** need to update the `inputs`
+of a Node when you make a change to the `outputs` of its input tensor.
+
+Consider the following node:
+```
+>>> print(node)
+ (Relu).
+    Inputs: [Tensor (gpu_0/res_conv1_bn_1)]
+    Outputs: [Tensor (gpu_0/res_conv1_bn_2)]
+```
+
+The input tensor can be accessed like so:
+```
+>>> tensor = node.inputs[0]
+>>> print(tensor)
+Tensor (gpu_0/res_conv1_bn_1)
+>>> print(tensor.outputs)
+[ (Relu).
+	Inputs: [Tensor (gpu_0/res_conv1_bn_1)]
+	Outputs: [Tensor (gpu_0/res_conv1_bn_2)]
+```
+
+If we remove the node from the outputs of the tensor, this is reflected in the node inputs as well:
+```
+>>> del tensor.outputs[0]
+>>> print(tensor.outputs)
+[]
+>>> print(node)
+ (Relu).
+    Inputs: []
+    Outputs: [Tensor (gpu_0/res_conv1_bn_2)]
+```
+
+
+#### Graph
+
+A `Graph` contains zero or more `Node`s and input/output `Tensor`s.
+
+Intermediate tensors are not explicitly tracked, but are instead retrieved from the nodes contained within the graph.
+
+The `Graph` class exposes several functions. A small subset is listed here:
+
+- `cleanup()`: Removes unused nodes and tensors in the graph
+- `toposort()`: Topologically sorts the graph.
+- `tensors()`: Returns a `Dict[str, Tensor]` mapping tensor names to tensors, by walking over all the tensors in the graph.
+    This is an `O(N)` operation, and so may be slow for large graphs.
+
+To see the full Graph API, you can see `help(onnx_graphsurgeon.Graph)` in an interactive Python shell.
+
+### Exporters
+
+Exporters are used to export the ONNX GraphSurgeon IR to ONNX or other types of graphs.
+The exporter interface is defined in [base_exporter.py](./onnx_graphsurgeon/exporters/base_exporter.py).
+
+ONNX GraphSurgeon also provides [high-level exporter APIs](./onnx_graphsurgeon/api/api.py) for ease of use.
+
+
+## Examples
+
+The [examples](./examples) directory contains several examples of common use-cases of ONNX GraphSurgeon.
+
+The visualizations provided were generated using [Netron](https://github.com/lutzroeder/netron).
diff --git a/tools/onnx-graphsurgeon/examples/01_creating_a_model/README.md b/tools/onnx-graphsurgeon/examples/01_creating_a_model/README.md
new file mode 100644
index 00000000..22484602
--- /dev/null
+++ b/tools/onnx-graphsurgeon/examples/01_creating_a_model/README.md
@@ -0,0 +1,19 @@
+# Creating An ONNX Model
+
+ONNX GraphSurgeon includes an [IR (intermediate representation)](../../README.md#ir) 
+that can be exported to ONNX. Additionally, the IR exposes a simple API so that it is 
+possible to build graphs manually.
+
+This example creates an ONNX model containing a single GlobalLpPool node.
+
+## Running the example
+
+Generate the model and save it to `test_globallppool.onnx` by running:
+
+```bash
+python3 example.py
+```
+
+The generated model will look like this:
+
+![../resources/01_test_globallppool.onnx.png](../resources/01_test_globallppool.onnx.png)
diff --git a/tools/onnx-graphsurgeon/examples/01_creating_a_model/example.py b/tools/onnx-graphsurgeon/examples/01_creating_a_model/example.py
new file mode 100644
index 00000000..fcc898df
--- /dev/null
+++ b/tools/onnx-graphsurgeon/examples/01_creating_a_model/example.py
@@ -0,0 +1,27 @@
+#!/usr/bin/env python3
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import onnx_graphsurgeon as gs
+import numpy as np
+import onnx
+
+X = gs.Variable(name="X", dtype=np.float32, shape=(1, 3, 5, 5))
+Y = gs.Variable(name="Y", dtype=np.float32, shape=(1, 3, 1, 1))
+node = gs.Node(op="GlobalLpPool", attrs={"p": 2}, inputs=[X], outputs=[Y])
+
+graph = gs.Graph(nodes=[node], inputs=[X], outputs=[Y])
+onnx.save(gs.export_onnx(graph), "test_globallppool.onnx")
diff --git a/tools/onnx-graphsurgeon/examples/02_creating_a_model_with_initializer/README.md b/tools/onnx-graphsurgeon/examples/02_creating_a_model_with_initializer/README.md
new file mode 100644
index 00000000..43242ce8
--- /dev/null
+++ b/tools/onnx-graphsurgeon/examples/02_creating_a_model_with_initializer/README.md
@@ -0,0 +1,16 @@
+# Creating An ONNX Model With An Initializer
+
+This example creates an ONNX model containing a single Convolution node with weights.
+
+`Constant`s in ONNX GraphSurgeon are automatically exported as initializers in the ONNX graph.
+
+## Running the example
+
+Generate the model and save it to `test_conv.onnx` by running:
+```bash
+python3 example.py
+```
+
+The generated model will look like this:
+
+![../resources/02_test_conv.onnx.png](../resources/02_test_conv.onnx.png)
diff --git a/tools/onnx-graphsurgeon/examples/02_creating_a_model_with_initializer/example.py b/tools/onnx-graphsurgeon/examples/02_creating_a_model_with_initializer/example.py
new file mode 100644
index 00000000..fe740600
--- /dev/null
+++ b/tools/onnx-graphsurgeon/examples/02_creating_a_model_with_initializer/example.py
@@ -0,0 +1,32 @@
+#!/usr/bin/env python3
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import onnx_graphsurgeon as gs
+import numpy as np
+import onnx
+
+X = gs.Variable(name="X", dtype=np.float32, shape=(1, 3, 224, 224))
+# Since W is a Constant, it will automatically be exported as an initializer
+W = gs.Constant(name="W", values=np.ones(shape=(5, 3, 3, 3), dtype=np.float32))
+
+Y = gs.Variable(name="Y", dtype=np.float32, shape=(1, 5, 222, 222))
+
+node = gs.Node(op="Conv", inputs=[X, W], outputs=[Y])
+
+# Note that initializers do not necessarily have to be graph inputs
+graph = gs.Graph(nodes=[node], inputs=[X], outputs=[Y])
+onnx.save(gs.export_onnx(graph), "test_conv.onnx")
diff --git a/tools/onnx-graphsurgeon/examples/03_isolating_a_subgraph/README.md b/tools/onnx-graphsurgeon/examples/03_isolating_a_subgraph/README.md
new file mode 100644
index 00000000..1dc29060
--- /dev/null
+++ b/tools/onnx-graphsurgeon/examples/03_isolating_a_subgraph/README.md
@@ -0,0 +1,31 @@
+# Isolating A Subgraph
+
+This example first generates a very basic model,
+then extracts a subgraph from this model.
+
+Isolating a subgraph from a model is as simple as modifying the inputs and
+outputs of the graph, running `graph.cleanup()`, and then re-exporting the graph.
+
+We do **not** need to know which nodes, initializers, or intermediate tensors we
+want - marking the inputs and outputs is sufficient for ONNX GraphSurgeon to be able
+to deduce the other information automatically.
+
+## Running the example
+
+1. Generate a model with several nodes and save it to `model.onnx` by running:
+    ```bash
+    python3 generate.py
+    ```
+
+    The generated model computes `Y = x0 + (a * x1 + b)`:
+
+    ![../resources/03_model.onnx.png](../resources/03_model.onnx.png)
+
+2. Isolate the subgraph that computes `(a * x1 + b)` and save it to `subgraph.onnx` by running:
+    ```bash
+    python3 isolate.py
+    ```
+
+    The resulting model computes `add_out = (a * x1 + b)`:
+
+    ![../resources/03_subgraph.onnx.png](../resources/03_subgraph.onnx.png)
diff --git a/tools/onnx-graphsurgeon/examples/03_isolating_a_subgraph/generate.py b/tools/onnx-graphsurgeon/examples/03_isolating_a_subgraph/generate.py
new file mode 100644
index 00000000..ac89acb9
--- /dev/null
+++ b/tools/onnx-graphsurgeon/examples/03_isolating_a_subgraph/generate.py
@@ -0,0 +1,48 @@
+#!/usr/bin/env python3
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import onnx_graphsurgeon as gs
+import numpy as np
+import onnx
+
+# Computes Y = x0 + (a * x1 + b)
+
+shape = (1, 3, 224, 224)
+# Inputs
+x0 = gs.Variable(name="x0", dtype=np.float32, shape=shape)
+x1 = gs.Variable(name="x1", dtype=np.float32, shape=shape)
+
+# Intermediate tensors
+a = gs.Constant("a", values=np.ones(shape=shape, dtype=np.float32))
+b = gs.Constant("b", values=np.ones(shape=shape, dtype=np.float32))
+mul_out = gs.Variable(name="mul_out")
+add_out = gs.Variable(name="add_out")
+
+# Outputs
+Y = gs.Variable(name="Y", dtype=np.float32, shape=shape)
+
+nodes = [
+    # mul_out = a * x1
+    gs.Node(op="Mul", inputs=[a, x1], outputs=[mul_out]),
+    # add_out = mul_out + b
+    gs.Node(op="Add", inputs=[mul_out, b], outputs=[add_out]),
+    # Y = x0 + add
+    gs.Node(op="Add", inputs=[x0, add_out], outputs=[Y]),
+]
+
+graph = gs.Graph(nodes=nodes, inputs=[x0, x1], outputs=[Y])
+onnx.save(gs.export_onnx(graph), "model.onnx")
diff --git a/tools/onnx-graphsurgeon/examples/03_isolating_a_subgraph/isolate.py b/tools/onnx-graphsurgeon/examples/03_isolating_a_subgraph/isolate.py
new file mode 100644
index 00000000..31867201
--- /dev/null
+++ b/tools/onnx-graphsurgeon/examples/03_isolating_a_subgraph/isolate.py
@@ -0,0 +1,61 @@
+#!/usr/bin/env python3
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import onnx_graphsurgeon as gs
+import numpy as np
+import onnx
+
+# Though omitted in this example, in some cases, it may be useful to embed
+# shape information in the graph. We can use ONNX shape inference to do this:
+#
+# from onnx import shape_inference
+# model = shape_inference.infer_shapes(onnx.load("model.onnx"))
+#
+# IMPORTANT: In some cases, ONNX shape inference may not correctly infer shapes,
+# which will result in an invalid subgraph. To avoid this, you can instead modify
+# the tensors to include the shape information yourself.
+
+model = onnx.load("model.onnx")
+graph = gs.import_onnx(model)
+
+# Since we already know the names of the tensors we're interested in, we can
+# grab them directly from the tensor map.
+#
+# NOTE: If you do not know the tensor names you want, you can view the graph in
+# Netron to determine them, or use ONNX GraphSurgeon in an interactive shell
+# to print the graph.
+tensors = graph.tensors()
+
+# If you want to embed shape information, but cannot use ONNX shape inference,
+# you can manually modify the tensors at this point:
+#
+# graph.inputs = [tensors["x1"].to_variable(dtype=np.float32, shape=(1, 3, 224, 224))]
+# graph.outputs = [tensors["add_out"].to_variable(dtype=np.float32, shape=(1, 3, 224, 224))]
+#
+# IMPORTANT: You must include type information for input and output tensors if it is not already
+# present in the graph.
+#
+# NOTE: ONNX GraphSurgeon will also accept dynamic shapes - simply set the corresponding
+# dimension(s) to `gs.Tensor.DYNAMIC`, e.g. `shape=(gs.Tensor.DYNAMIC, 3, 224, 224)`
+graph.inputs = [tensors["x1"].to_variable(dtype=np.float32)]
+graph.outputs = [tensors["add_out"].to_variable(dtype=np.float32)]
+
+# Notice that we do not need to manually modify the rest of the graph. ONNX GraphSurgeon will
+# take care of removing any unnecessary nodes or tensors, so that we are left with only the subgraph.
+graph.cleanup()
+
+onnx.save(gs.export_onnx(graph), "subgraph.onnx")
diff --git a/tools/onnx-graphsurgeon/examples/04_modifying_a_model/README.md b/tools/onnx-graphsurgeon/examples/04_modifying_a_model/README.md
new file mode 100644
index 00000000..2a77b1de
--- /dev/null
+++ b/tools/onnx-graphsurgeon/examples/04_modifying_a_model/README.md
@@ -0,0 +1,35 @@
+# Modiyfing A Model
+
+This example first generates a very basic model,
+then modifies the resulting model in various ways.
+
+By importing an ONNX graph into the [ONNX GraphSurgeon IR](../../README.md#ir), it is
+possible to modify virtually every aspect of the graph.
+We can then export the modified IR back to ONNX.
+
+## Running the example
+
+1. Generate a model with several nodes and save it to `model.onnx` by running:
+    ```bash
+    python3 generate.py
+    ```
+
+    The generated model computes `Y = x0 + (a * x1 + b)`:
+
+    ![../resources/04_model.onnx.png](../resources/04_model.onnx.png)
+
+2. Modify the model in various ways, and save it to `modified.onnx` by running:
+    ```bash
+    python3 modify.py
+    ```
+
+    This script does the following:
+    - Removes the `b` input of the first `Add` node
+    - Changes the first `Add` to a `LeakyRelu`
+    - Adds an `Identity` node after the first `Add`
+    - Changes the output of the graph to be the output of the `Identity` node
+    - Runs `cleanup()` which removes the `x0` tensor and second `Add` node due to the previous change to the graph outputs.
+
+    The resulting graph computes `identity_out = leaky_relu(a * x1)`:
+
+    ![../resources/04_modified.onnx.png](../resources/04_modified.onnx.png)
diff --git a/tools/onnx-graphsurgeon/examples/04_modifying_a_model/generate.py b/tools/onnx-graphsurgeon/examples/04_modifying_a_model/generate.py
new file mode 100644
index 00000000..ac89acb9
--- /dev/null
+++ b/tools/onnx-graphsurgeon/examples/04_modifying_a_model/generate.py
@@ -0,0 +1,48 @@
+#!/usr/bin/env python3
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import onnx_graphsurgeon as gs
+import numpy as np
+import onnx
+
+# Computes Y = x0 + (a * x1 + b)
+
+shape = (1, 3, 224, 224)
+# Inputs
+x0 = gs.Variable(name="x0", dtype=np.float32, shape=shape)
+x1 = gs.Variable(name="x1", dtype=np.float32, shape=shape)
+
+# Intermediate tensors
+a = gs.Constant("a", values=np.ones(shape=shape, dtype=np.float32))
+b = gs.Constant("b", values=np.ones(shape=shape, dtype=np.float32))
+mul_out = gs.Variable(name="mul_out")
+add_out = gs.Variable(name="add_out")
+
+# Outputs
+Y = gs.Variable(name="Y", dtype=np.float32, shape=shape)
+
+nodes = [
+    # mul_out = a * x1
+    gs.Node(op="Mul", inputs=[a, x1], outputs=[mul_out]),
+    # add_out = mul_out + b
+    gs.Node(op="Add", inputs=[mul_out, b], outputs=[add_out]),
+    # Y = x0 + add
+    gs.Node(op="Add", inputs=[x0, add_out], outputs=[Y]),
+]
+
+graph = gs.Graph(nodes=nodes, inputs=[x0, x1], outputs=[Y])
+onnx.save(gs.export_onnx(graph), "model.onnx")
diff --git a/tools/onnx-graphsurgeon/examples/04_modifying_a_model/modify.py b/tools/onnx-graphsurgeon/examples/04_modifying_a_model/modify.py
new file mode 100644
index 00000000..f2ba7503
--- /dev/null
+++ b/tools/onnx-graphsurgeon/examples/04_modifying_a_model/modify.py
@@ -0,0 +1,47 @@
+#!/usr/bin/env python3
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import onnx_graphsurgeon as gs
+import numpy as np
+import onnx
+
+graph = gs.import_onnx(onnx.load("model.onnx"))
+
+# 1. Remove the `b` input of the add node
+first_add = [node for node in graph.nodes if node.op == "Add"][0]
+first_add.inputs = [inp for inp in first_add.inputs if inp.name != "b"]
+
+# 2. Change the Add to a LeakyRelu
+first_add.op = "LeakyRelu"
+first_add.attrs["alpha"] = 0.02
+
+# 3. Add an identity after the add node
+identity_out = gs.Variable("identity_out", dtype=np.float32)
+identity = gs.Node(op="Identity", inputs=first_add.outputs, outputs=[identity_out])
+graph.nodes.append(identity)
+
+# 4. Modify the graph output to be the identity output
+graph.outputs = [identity_out]
+
+# 5. Remove unused nodes/tensors, and topologically sort the graph
+# ONNX requires nodes to be topologically sorted to be considered valid.
+# Therefore, you should only need to sort the graph when you have added new nodes out-of-order.
+# In this case, the identity node is already in the correct spot (it is the last node,
+# and was appended to the end of the list), but to be on the safer side, we can sort anyway.
+graph.cleanup().toposort()
+
+onnx.save(gs.export_onnx(graph), "modified.onnx")
diff --git a/tools/onnx-graphsurgeon/examples/05_folding_constants/README.md b/tools/onnx-graphsurgeon/examples/05_folding_constants/README.md
new file mode 100644
index 00000000..ba68bf9c
--- /dev/null
+++ b/tools/onnx-graphsurgeon/examples/05_folding_constants/README.md
@@ -0,0 +1,47 @@
+# Folding Constants
+
+This example first generates a a model with several operations that can be evaluated
+prior to inference time, then folds these operations and exports a new model.
+
+Constant folding involves pre-computing expressions that do not depend on runtime
+information. Practically, this would mean that any nodes that are dependent only on
+`Constant`s in an ONNX GraphSurgeon graph can be folded.
+
+One limitation of ONNX GraphSurgeon's built-in constant folding is that it will not
+rotate nodes. So, assuming `x` is a graph input and `c0`, `c1` and `c2` are constants in
+the graph:
+- `x + (c0 + (c1 + c2))` **will** be folded
+- `((x + c0) + c1) + c2` will **not** be folded, even though this is mathematically equivalent
+        (when not considering floating point rounding error) to the previous expression.
+
+## Prerequisites
+
+1. ONNX GraphSurgeon uses [ONNX Runtime](https://github.com/microsoft/onnxruntime) to
+    evaluate the constant expressions in the graph. This can be installed with:
+    ```bash
+    python3 -m pip install onnxruntime
+    ```
+
+## Running the example
+
+1. Generate a model with several nodes and save it to `model.onnx` by running:
+    ```bash
+    python3 generate.py
+    ```
+
+    The generated model computes `output = input + ((a + b) + d)` where `a`,`b`, and `d` are constants
+    all set to `1`:
+
+    ![../resources/05_model.onnx.png](../resources/05_model.onnx.png)
+
+2. Fold constants in the graph, and save it to `folded.onnx` by running:
+    ```bash
+    python3 fold.py
+    ```
+
+    This will replace the expression: `((a + b) + d)` with a single constant tensor (which will be all `3`s).
+    The resulting graph will compute `output = input + e` where `e = ((a + b) + d)`:
+
+    This script will also display the help output for `Graph.fold_constants()`
+
+    ![../resources/05_folded.onnx.png](../resources/05_folded.onnx.png)
diff --git a/tools/onnx-graphsurgeon/examples/05_folding_constants/fold.py b/tools/onnx-graphsurgeon/examples/05_folding_constants/fold.py
new file mode 100644
index 00000000..d3443358
--- /dev/null
+++ b/tools/onnx-graphsurgeon/examples/05_folding_constants/fold.py
@@ -0,0 +1,32 @@
+#!/usr/bin/env python3
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import onnx_graphsurgeon as gs
+import onnx
+
+print("Graph.fold_constants Help:\n{}".format(gs.Graph.fold_constants.__doc__))
+
+graph = gs.import_onnx(onnx.load("model.onnx"))
+
+# Fold constants in the graph using ONNX Runtime. This will replace
+# expressions that can be evaluated prior to runtime with constant tensors.
+# The `fold_constants()` function will not, however, remove the nodes that
+# it replaced - it simply changes the inputs of subsequent nodes.
+# To remove these unused nodes, we can follow up `fold_constants()` with `cleanup()`
+graph.fold_constants().cleanup()
+
+onnx.save(gs.export_onnx(graph), "folded.onnx")
diff --git a/tools/onnx-graphsurgeon/examples/05_folding_constants/generate.py b/tools/onnx-graphsurgeon/examples/05_folding_constants/generate.py
new file mode 100644
index 00000000..642dab57
--- /dev/null
+++ b/tools/onnx-graphsurgeon/examples/05_folding_constants/generate.py
@@ -0,0 +1,48 @@
+#!/usr/bin/env python3
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import onnx_graphsurgeon as gs
+import numpy as np
+import onnx
+
+# Computes outputs = input + ((a + b) + d)
+
+shape = (1, 3)
+# Inputs
+input = gs.Variable("input", shape=shape, dtype=np.float32)
+
+# Intermediate tensors
+a = gs.Constant("a", values=np.ones(shape=shape, dtype=np.float32))
+b = gs.Constant("b", values=np.ones(shape=shape, dtype=np.float32))
+c = gs.Variable("c")
+d = gs.Constant("d", values=np.ones(shape=shape, dtype=np.float32))
+e = gs.Variable("e")
+
+# Outputs
+output = gs.Variable("output", shape=shape, dtype=np.float32)
+
+nodes = [
+    # c = (a + b)
+    gs.Node("Add", inputs=[a, b], outputs=[c]),
+    # e = (c + d)
+    gs.Node("Add", inputs=[c, d], outputs=[e]),
+    # output = input + e
+    gs.Node("Add", inputs=[input, e], outputs=[output]),
+]
+
+graph = gs.Graph(nodes=nodes, inputs=[input], outputs=[output])
+onnx.save(gs.export_onnx(graph), "model.onnx")
diff --git a/tools/onnx-graphsurgeon/examples/06_removing_nodes/README.md b/tools/onnx-graphsurgeon/examples/06_removing_nodes/README.md
new file mode 100644
index 00000000..bdd38ffa
--- /dev/null
+++ b/tools/onnx-graphsurgeon/examples/06_removing_nodes/README.md
@@ -0,0 +1,56 @@
+# Removing A Node
+
+This example first generates a very basic model, and then removes a node in the
+middle of the model, and reconnects the remaining nodes in the graph.
+
+In order to remove a node, we replace the output tensors of its input node
+with its own outputs, and then remove its own connection to its outputs.
+
+For example, for a graph with the following structure:
+
+     Node0
+       |
+    Tensor0
+       |
+     Node1
+       |
+    Tensor1
+       |
+     Node2
+       |
+    Tensor2
+
+In order to remove `Node1`,
+1. Change `Node0`'s outputs to `[Tensor1]` instead of `[Tensor0]`.
+2. Change `Node1`'s outputs to `[]` instead of `[Tensor1]`
+
+The resulting graph looks like this:
+
+     Node0     
+       |    
+    Tensor1       Tensor0
+       |             |
+     Node2         Node1
+       |
+    Tensor2
+
+Note that `Node1` remains in the graph.
+This is easily remedied by running the `cleanup()` function.
+
+## Running the example
+
+1. Generate a model with several nodes and save it to `model.onnx` by running:
+    ```bash
+    python3 generate.py
+    ```
+
+    The generated model includes a couple identity layers and a fake node that will be removed.
+
+    ![../resources/06_model.onnx.png](../resources/06_model.onnx.png)
+
+2. Remove the fake node, and save it to `removed.onnx` by running:
+    ```bash
+    python3 remove.py
+    ```
+
+    ![../resources/06_removed.onnx.png](../resources/06_removed.onnx.png)
diff --git a/tools/onnx-graphsurgeon/examples/06_removing_nodes/generate.py b/tools/onnx-graphsurgeon/examples/06_removing_nodes/generate.py
new file mode 100644
index 00000000..cc50c1e6
--- /dev/null
+++ b/tools/onnx-graphsurgeon/examples/06_removing_nodes/generate.py
@@ -0,0 +1,39 @@
+#!/usr/bin/env python3
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import onnx_graphsurgeon as gs
+import numpy as np
+import onnx
+
+# Inputs
+x = gs.Variable(name="x", dtype=np.float32, shape=(1, 3, 224, 224))
+
+# Intermediate tensors
+i0 = gs.Variable(name="i0")
+i1 = gs.Variable(name="i1")
+
+# Outputs
+y = gs.Variable(name="y", dtype=np.float32)
+
+nodes = [
+    gs.Node(op="Identity", inputs=[x], outputs=[i0]),
+    gs.Node(op="FakeNodeToRemove", inputs=[i0], outputs=[i1]),
+    gs.Node(op="Identity", inputs=[i1], outputs=[y]),
+]
+
+graph = gs.Graph(nodes=nodes, inputs=[x], outputs=[y])
+onnx.save(gs.export_onnx(graph), "model.onnx")
diff --git a/tools/onnx-graphsurgeon/examples/06_removing_nodes/remove.py b/tools/onnx-graphsurgeon/examples/06_removing_nodes/remove.py
new file mode 100644
index 00000000..1099d89b
--- /dev/null
+++ b/tools/onnx-graphsurgeon/examples/06_removing_nodes/remove.py
@@ -0,0 +1,39 @@
+#!/usr/bin/env python3
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import onnx_graphsurgeon as gs
+import onnx
+
+graph = gs.import_onnx(onnx.load("model.onnx"))
+
+fake_node = [node for node in graph.nodes if node.op == "FakeNodeToRemove"][0]
+
+# Get the input node of the fake node
+# Node provides i() and o() functions that can optionally be provided an index (default is 0)
+# These serve as convenience functions for the alternative, which would be to fetch the input/output
+# tensor first, then fetch the input/output node of the tensor.
+# For example, node.i() is equivalent to node.inputs[0].inputs[0]
+inp_node = fake_node.i()
+
+# Reconnect the input node to the output tensors of the fake node, so that the first identity
+# node in the example graph now skips over the fake node.
+inp_node.outputs = fake_node.outputs
+fake_node.outputs.clear()
+
+# Remove the fake node from the graph completely
+graph.cleanup()
+onnx.save(gs.export_onnx(graph), "removed.onnx")
diff --git a/tools/onnx-graphsurgeon/examples/07_creating_a_model_with_the_layer_api/README.md b/tools/onnx-graphsurgeon/examples/07_creating_a_model_with_the_layer_api/README.md
new file mode 100644
index 00000000..87fa6471
--- /dev/null
+++ b/tools/onnx-graphsurgeon/examples/07_creating_a_model_with_the_layer_api/README.md
@@ -0,0 +1,47 @@
+# Creating A Model Using The Graph Layer API
+
+This example uses the `Graph.layer()` function in conjunction with `Graph.register()` to
+demonstrate how to construct complicated ONNX models more easily.
+
+The `Graph.layer()` API allows you to more easily add `Node`s to a `Graph`. In addition to
+creating nodes, this function can also create the input and output tensors, and automatically
+insert the node in the graph. For details, see the `help` output for `Graph.layer()`.
+
+**Note**: You still need to set `Graph` inputs and outputs yourself!
+
+
+`Graph.layer()` can be used to implement your own functions that can be registered with `Graph.register()`.
+For example, to implement a `graph.add` function that inserts an "Add" operation into the graph, you could write:
+
+```python
+@gs.Graph.register()
+def add(self, a, b):
+    return self.layer(op="Add", inputs=[a, b], outputs=["add_out"])
+```
+
+and invoke it like so:
+
+```python
+X = gs.Variable(name="X", shape=(64, 64), dtype=np.float32)
+
+[Y] = graph.add(graph.add(X, B), C)
+
+graph.inputs = [X]
+graph.outputs = [Y]
+```
+
+This would generate a graph which computes `Y = (X + B) + C` (here B and C are assumed to be constants),
+without requiring you to manually create the intermediate tensors involved.
+
+## Running the example
+
+1. Generate a moderately complex model and save it to `model.onnx` by running:
+    ```bash
+    python3 generate.py
+    ```
+
+    This script will also display the help output for `Graph.layer()`
+
+    The generated model will look like this:
+
+    ![../resources/07_model.onnx.png](../resources/07_model.onnx.png)
diff --git a/tools/onnx-graphsurgeon/examples/07_creating_a_model_with_the_layer_api/generate.py b/tools/onnx-graphsurgeon/examples/07_creating_a_model_with_the_layer_api/generate.py
new file mode 100644
index 00000000..60e6061d
--- /dev/null
+++ b/tools/onnx-graphsurgeon/examples/07_creating_a_model_with_the_layer_api/generate.py
@@ -0,0 +1,99 @@
+#!/usr/bin/env python3
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import onnx_graphsurgeon as gs
+import numpy as np
+import onnx
+
+print("Graph.layer Help:\n{}".format(gs.Graph.layer.__doc__))
+
+# For automatically propagating data types
+def propagate_dtype(outputs, dtype):
+    for output in outputs:
+        output.dtype = dtype
+    return outputs
+
+
+# We can use `Graph.register()` to add a function to the Graph class. Later, we can invoke the function
+# directly on instances of the graph, e.g., `graph.add(...)`
+@gs.Graph.register()
+def add(self, a, b):
+    # The Graph.layer function creates a node, adds inputs and outputs to it, and finally adds it to the graph.
+    # It returns the output tensors of the node to make it easy to chain.
+    # The function will append an index to any strings provided for inputs/outputs prior
+    # to using them to construct tensors. This will ensure that multiple calls to the layer() function
+    # will generate distinct tensors. However, this does NOT guarantee that there will be no overlap with
+    # other tensors in the graph. Hence, you should choose the prefixes to minimize the possibility of
+    # collisions.
+    return propagate_dtype(self.layer(op="Add", inputs=[a, b], outputs=["add_out_gs"]), a.dtype or b.dtype)
+
+
+@gs.Graph.register()
+def mul(self, a, b):
+    return propagate_dtype(self.layer(op="Mul", inputs=[a, b], outputs=["mul_out_gs"]), a.dtype or b.dtype)
+
+
+@gs.Graph.register()
+def gemm(self, a, b, trans_a=False, trans_b=False):
+    attrs = {"transA": int(trans_a), "transB": int(trans_b)}
+    return propagate_dtype(self.layer(op="Gemm", inputs=[a, b], outputs=["gemm_out_gs"], attrs=attrs), a.dtype or b.dtype)
+
+
+# You can also specify a set of opsets when regsitering a function.
+# By default, the function is registered for all opsets lower than Graph.DEFAULT_OPSET
+@gs.Graph.register(opsets=[11])
+def relu(self, a):
+    return propagate_dtype(self.layer(op="Relu", inputs=[a], outputs=["act_out_gs"]), a.dtype)
+
+
+# Note that the same function can be defined in different ways for different opsets.
+# It will only be called if the Graph's opset matches one of the opsets for which the function is registered.
+# Hence, for the opset 11 graph used in this example, the following function will never be used.
+@gs.Graph.register(opsets=[1])
+def relu(self, a):
+    raise NotImplementedError("This function has not been implemented!")
+
+
+##########################################################################################################
+# The functions registered above greatly simplify the process of building the graph itself.
+
+graph = gs.Graph(opset=11)
+
+# Generates a graph which computes:
+# output = ReLU((A * X^T) + B) (.) C + D
+X = gs.Variable(name="X", shape=(64, 64), dtype=np.float32)
+graph.inputs = [X]
+
+# axt = (A * X^T)
+# Note that we can use NumPy arrays directly (e.g. Tensor A),
+# instead of Constants. These will automatically be converted to Constants.
+A = np.ones(shape=(64, 64), dtype=np.float32)
+axt = graph.gemm(A, X, trans_b=True)
+
+# dense = ReLU(axt + B)
+B = np.ones((64, 64), dtype=np.float32) * 0.5
+dense = graph.relu(*graph.add(*axt, B))
+
+# output = dense (.) C + D
+# If a Tensor instance is provided (e.g. Tensor C), it will not be modified at all.
+# If you prefer to set the exact names of tensors in the graph, you should
+# construct tensors manually instead of passing strings or NumPy arrays.
+C = gs.Constant(name="C", values=np.ones(shape=(64, 64), dtype=np.float32))
+D = np.ones(shape=(64, 64), dtype=np.float32)
+graph.outputs = graph.add(*graph.mul(*dense, C), D)
+
+onnx.save(gs.export_onnx(graph), "model.onnx")
diff --git a/tools/onnx-graphsurgeon/examples/resources/01_test_globallppool.onnx.png b/tools/onnx-graphsurgeon/examples/resources/01_test_globallppool.onnx.png
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diff --git a/tools/onnx-graphsurgeon/onnx_graphsurgeon/__init__.py b/tools/onnx-graphsurgeon/onnx_graphsurgeon/__init__.py
new file mode 100644
index 00000000..217eadfc
--- /dev/null
+++ b/tools/onnx-graphsurgeon/onnx_graphsurgeon/__init__.py
@@ -0,0 +1,7 @@
+from onnx_graphsurgeon.util.exception import OnnxGraphSurgeonException
+from onnx_graphsurgeon.ir.tensor import Tensor, Constant, Variable
+from onnx_graphsurgeon.api.api import import_onnx, export_onnx
+from onnx_graphsurgeon.ir.graph import Graph
+from onnx_graphsurgeon.ir.node import Node
+
+__version__ = "0.2.3"
diff --git a/tools/onnx-graphsurgeon/onnx_graphsurgeon/api/__init__.py b/tools/onnx-graphsurgeon/onnx_graphsurgeon/api/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tools/onnx-graphsurgeon/onnx_graphsurgeon/api/api.py b/tools/onnx-graphsurgeon/onnx_graphsurgeon/api/api.py
new file mode 100644
index 00000000..5af19a4d
--- /dev/null
+++ b/tools/onnx-graphsurgeon/onnx_graphsurgeon/api/api.py
@@ -0,0 +1,59 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# Contains high-level API functions.
+from onnx_graphsurgeon.logger.logger import G_LOGGER
+from onnx_graphsurgeon.ir.graph import Graph
+
+
+def import_onnx(onnx_model: "onnx.ModelProto") -> Graph:
+    """
+    Import an onnx-graphsurgeon Graph from the provided ONNX model.
+
+    Args:
+        onnx_model (onnx.ModelProto): The ONNX model.
+
+    Returns:
+        Graph: A corresponding onnx-graphsurgeon Graph.
+    """
+    from onnx_graphsurgeon.importers.onnx_importer import OnnxImporter
+
+    return OnnxImporter.import_graph(onnx_model.graph, opset=OnnxImporter.get_opset(onnx_model))
+
+
+def export_onnx(graph: Graph, do_type_check=True, **kwargs) -> "onnx.ModelProto":
+    """
+    Exports an onnx-graphsurgeon Graph to an ONNX model.
+
+    Args:
+        graph (Graph): The graph to export
+
+    Optional Args:
+        do_type_check (bool): Whether to check that input and output tensors have data types defined, and fail if not.
+        **kwargs: Additional arguments to onnx.helper.make_model
+
+    Returns:
+        onnx.ModelProto: A corresponding ONNX model.
+    """
+    from onnx_graphsurgeon.exporters.onnx_exporter import OnnxExporter
+    import onnx
+
+    onnx_graph = OnnxExporter.export_graph(graph, do_type_check=do_type_check)
+
+    if "opset_imports" not in kwargs:
+        kwargs["opset_imports"] = [onnx.helper.make_opsetid("", graph.opset)]
+
+    return onnx.helper.make_model(onnx_graph, **kwargs)
diff --git a/tools/onnx-graphsurgeon/onnx_graphsurgeon/exporters/__init__.py b/tools/onnx-graphsurgeon/onnx_graphsurgeon/exporters/__init__.py
new file mode 100644
index 00000000..99c1c2a6
--- /dev/null
+++ b/tools/onnx-graphsurgeon/onnx_graphsurgeon/exporters/__init__.py
@@ -0,0 +1 @@
+from onnx_graphsurgeon.exporters.base_exporter import BaseExporter
diff --git a/tools/onnx-graphsurgeon/onnx_graphsurgeon/exporters/base_exporter.py b/tools/onnx-graphsurgeon/onnx_graphsurgeon/exporters/base_exporter.py
new file mode 100644
index 00000000..d6b6353d
--- /dev/null
+++ b/tools/onnx-graphsurgeon/onnx_graphsurgeon/exporters/base_exporter.py
@@ -0,0 +1,31 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from onnx_graphsurgeon.ir.graph import Graph
+
+class BaseExporter(object):
+    @staticmethod
+    def export_graph(graph: Graph):
+        """
+        Export a graph to some destination graph.
+
+        Args:
+            graph (Graph): The source graph to export.
+
+        Returns:
+            object: The exported graph. For example, this might be an onnx.GraphProto
+        """
+        raise NotImplementedError("BaseExporter is an abstract class")
diff --git a/tools/onnx-graphsurgeon/onnx_graphsurgeon/exporters/onnx_exporter.py b/tools/onnx-graphsurgeon/onnx_graphsurgeon/exporters/onnx_exporter.py
new file mode 100644
index 00000000..da3ec34d
--- /dev/null
+++ b/tools/onnx-graphsurgeon/onnx_graphsurgeon/exporters/onnx_exporter.py
@@ -0,0 +1,95 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from onnx_graphsurgeon.exporters.base_exporter import BaseExporter
+from onnx_graphsurgeon.logger.logger import G_LOGGER
+from onnx_graphsurgeon.ir.tensor import Tensor, Constant, Variable
+from onnx_graphsurgeon.ir.graph import Graph
+from onnx_graphsurgeon.ir.node import Node
+
+from collections import OrderedDict
+from typing import Union
+import onnx.numpy_helper
+import numpy as np
+import onnx
+
+
+def dtype_to_onnx(dtype: np.dtype) -> int:
+    return onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(dtype)]
+
+
+class OnnxExporter(BaseExporter):
+    @staticmethod
+    def export_tensor_proto(tensor: Constant) -> onnx.TensorProto:
+        onnx_tensor = onnx.numpy_helper.from_array(tensor.values)
+        onnx_tensor.name = tensor.name
+        return onnx_tensor
+
+
+    @staticmethod
+    def export_value_info_proto(tensor: Tensor, do_type_check: bool) -> onnx.ValueInfoProto:
+        if isinstance(tensor, Constant):
+            onnx_tensor = onnx.helper.make_tensor_value_info(tensor.name, dtype_to_onnx(tensor.values.dtype), tensor.values.shape)
+        elif isinstance(tensor, Variable):
+            if do_type_check and tensor.dtype is None:
+                G_LOGGER.critical("Graph input and output tensors must include dtype information. Please set the dtype attribute for: {:}".format(tensor))
+
+            if tensor.dtype is not None:
+                onnx_tensor = onnx.helper.make_tensor_value_info(tensor.name, dtype_to_onnx(tensor.dtype), tensor.shape)
+            else:
+                onnx_tensor = onnx.helper.make_empty_tensor_value_info(tensor.name)
+        return onnx_tensor
+
+
+    @staticmethod
+    def export_node(node: Node) -> onnx.NodeProto:
+        # Cannot pass in attrs directly as make_node will change the order
+        onnx_node = onnx.helper.make_node(node.op, inputs=[t.name for t in node.inputs], outputs=[t.name for t in node.outputs], name=node.name)
+        # Convert Tensors and Graphs to TensorProtos and GraphProtos respectively
+        for key, val in node.attrs.items():
+            if isinstance(val, Tensor):
+                val = OnnxExporter.export_tensor_proto(val)
+            elif isinstance(val, Graph):
+                val = OnnxExporter.export_graph(val)
+            onnx_node.attribute.extend([onnx.helper.make_attribute(key, val)])
+        return onnx_node
+
+
+    @staticmethod
+    def export_graph(graph: Graph, do_type_check=True) -> onnx.GraphProto:
+        """
+        Export an onnx-graphsurgeon Graph to an ONNX GraphProto.
+
+        Args:
+            graph (Graph): The graph to export.
+
+        Optional Args:
+            do_type_check (bool): Whether to check that input and output tensors have data types defined, and fail if not.
+        """
+        nodes = [OnnxExporter.export_node(node) for node in graph.nodes]
+        inputs = [OnnxExporter.export_value_info_proto(inp, do_type_check) for inp in graph.inputs]
+        outputs = [OnnxExporter.export_value_info_proto(out, do_type_check) for out in graph.outputs]
+        tensor_map = graph.tensors()
+        initializer = [OnnxExporter.export_tensor_proto(tensor) for tensor in tensor_map.values() if isinstance(tensor, Constant)]
+
+        # Remove inputs and outputs to export ValueInfoProtos
+        for tensor in graph.inputs + graph.outputs:
+            if tensor.name in tensor_map:
+                del tensor_map[tensor.name]
+
+        # Omit tensors if we don't know their shape/type
+        value_info = [OnnxExporter.export_value_info_proto(tensor, do_type_check) for tensor in tensor_map.values() if isinstance(tensor, Variable) and tensor.dtype is not None]
+        return onnx.helper.make_graph(nodes=nodes, name=graph.name, inputs=inputs, outputs=outputs, initializer=initializer, doc_string=graph.doc_string, value_info=value_info)
diff --git a/tools/onnx-graphsurgeon/onnx_graphsurgeon/importers/__init__.py b/tools/onnx-graphsurgeon/onnx_graphsurgeon/importers/__init__.py
new file mode 100644
index 00000000..4a0f61e2
--- /dev/null
+++ b/tools/onnx-graphsurgeon/onnx_graphsurgeon/importers/__init__.py
@@ -0,0 +1 @@
+from onnx_graphsurgeon.importers.base_importer import BaseImporter
diff --git a/tools/onnx-graphsurgeon/onnx_graphsurgeon/importers/base_importer.py b/tools/onnx-graphsurgeon/onnx_graphsurgeon/importers/base_importer.py
new file mode 100644
index 00000000..d154289d
--- /dev/null
+++ b/tools/onnx-graphsurgeon/onnx_graphsurgeon/importers/base_importer.py
@@ -0,0 +1,31 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from onnx_graphsurgeon.ir.graph import Graph
+
+class BaseImporter(object):
+    @staticmethod
+    def import_graph(graph) -> Graph:
+        """
+        Import a graph from some source graph.
+
+        Args:
+            graph (object): The source graph to import. For example, this might be an onnx.GraphProto.
+
+        Returns:
+            Graph: The equivalent onnx-graphsurgeon graph.
+        """
+        raise NotImplementedError("BaseImporter is an abstract class")
diff --git a/tools/onnx-graphsurgeon/onnx_graphsurgeon/importers/onnx_importer.py b/tools/onnx-graphsurgeon/onnx_graphsurgeon/importers/onnx_importer.py
new file mode 100644
index 00000000..b2a1122e
--- /dev/null
+++ b/tools/onnx-graphsurgeon/onnx_graphsurgeon/importers/onnx_importer.py
@@ -0,0 +1,207 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from onnx_graphsurgeon.ir.tensor import Tensor, Constant, Variable
+from onnx_graphsurgeon.importers.base_importer import BaseImporter
+from onnx_graphsurgeon.logger.logger import G_LOGGER
+from onnx_graphsurgeon.ir.graph import Graph
+from onnx_graphsurgeon.ir.node import Node
+from onnx_graphsurgeon.util import misc
+
+from typing import List, Union
+from collections import OrderedDict
+import onnx.numpy_helper
+import numpy as np
+import onnx
+import copy
+
+# Maps values from the AttributeType enum to their string representations, e.g., {1: "FLOAT"}
+ATTR_TYPE_MAPPING = dict(zip(onnx.AttributeProto.AttributeType.values(), onnx.AttributeProto.AttributeType.keys()))
+
+# Maps an ONNX attribute to the corresponding Python property
+ONNX_PYTHON_ATTR_MAPPING = {
+    "FLOAT": "f",
+    "INT": "i",
+    "STRING": "s",
+    "TENSOR": "t",
+    "GRAPH": "g",
+    "FLOATS": "floats",
+    "INTS": "ints",
+    "STRINGS": "strings",
+}
+
+def get_onnx_tensor_shape(onnx_tensor: Union[onnx.ValueInfoProto, onnx.TensorProto]) -> List[int]:
+    shape = []
+    if isinstance(onnx_tensor, onnx.TensorProto):
+        shape = onnx_tensor.dims
+    else:
+        for dim in onnx_tensor.type.tensor_type.shape.dim:
+            if dim.dim_param:
+                shape.append(dim.dim_param)
+            else:
+                shape.append(dim.dim_value)
+    return shape
+
+
+def get_onnx_tensor_dtype(onnx_tensor: Union[onnx.ValueInfoProto, onnx.TensorProto]) -> np.dtype:
+    if isinstance(onnx_tensor, onnx.TensorProto):
+        onnx_type = onnx_tensor.data_type
+    else:
+        onnx_type = onnx_tensor.type.tensor_type.elem_type
+    if onnx_type in onnx.mapping.TENSOR_TYPE_TO_NP_TYPE:
+        return onnx.mapping.TENSOR_TYPE_TO_NP_TYPE[onnx_type]
+    return None
+
+
+class OnnxImporter(BaseImporter):
+    @staticmethod
+    def get_opset(model: onnx.ModelProto):
+        try:
+            return model.opset_import[0].version
+        except:
+            G_LOGGER.warning("Model does not contain opset information! Using default opset.")
+            return None
+
+
+    @staticmethod
+    def import_tensor(onnx_tensor: Union[onnx.ValueInfoProto, onnx.TensorProto]) -> Tensor:
+        try:
+            values = onnx.numpy_helper.to_array(onnx_tensor)
+            return Constant(name=onnx_tensor.name, values=values)
+        except ValueError:
+            return Variable(name=onnx_tensor.name, dtype=get_onnx_tensor_dtype(onnx_tensor), shape=get_onnx_tensor_shape(onnx_tensor))
+
+
+    @staticmethod
+    def import_node(onnx_node: onnx.NodeProto, tensor_map: "OrderedDict[str, Tensor]") -> Node:
+        def attrs_to_dict(attrs):
+            attr_dict = OrderedDict()
+            for attr in attrs:
+                def process_attr(attr_str: str):
+                    processed = getattr(attr, ONNX_PYTHON_ATTR_MAPPING[attr_str])
+                    if attr_str == "STRING":
+                        processed = processed.decode()
+                    elif attr_str == "TENSOR":
+                        processed = OnnxImporter.import_tensor(processed)
+                    elif attr_str == "GRAPH":
+                        processed = OnnxImporter.import_graph(processed, tensor_map)
+                    elif attr_str == "FLOATS" or attr_str == "INTS":
+                        # Proto hacky list to normal Python list
+                        processed = [p for p in processed]
+                    elif attr_str == "STRINGS":
+                        processed = [p.decode() for p in processed]
+                    return processed
+
+                if attr.type in ATTR_TYPE_MAPPING:
+                    attr_str = ATTR_TYPE_MAPPING[attr.type]
+                    if attr_str in ONNX_PYTHON_ATTR_MAPPING:
+                        attr_dict[attr.name] = process_attr(attr_str)
+                    else:
+                        G_LOGGER.warning("Attribute of type {:} is currently unsupported. Skipping attribute.".format(attr_str))
+                else:
+                    G_LOGGER.warning("Attribute type: {:} was not recognized. Was the graph generated with a newer IR version than the installed `onnx` package? Skipping attribute.".format(attr.type))
+            return attr_dict
+
+        # Optional inputs/outputs are represented by empty tensors. All other tensors should already have been populated during shape inference.
+        def check_tensor(name: str):
+            if name not in tensor_map:
+                if name:
+                    G_LOGGER.debug("Tensor: {:} was not generated during shape inference, or shape inference was not run on this model. Creating a new Tensor.".format(name))
+                    tensor_map[name] = Variable(name)
+                else:
+                    # Empty tensors are not tracked by the graph, as these represent optional inputs/outputs that have been omitted.
+                    G_LOGGER.verbose("Generating empty tensor")
+                    return Variable.empty()
+            return tensor_map[name]
+
+        # Retrieve Tensors for node inputs/outputs. Only empty tensors should need to be newly added.
+        def retrieve_node_inputs() -> List[Tensor]:
+            inputs = [] # List[Tensor]
+            for input_name in onnx_node.input:
+                inputs.append(check_tensor(input_name))
+            return inputs
+
+        def retrieve_node_outputs() -> List[Tensor]:
+            outputs = [] # List[Tensor]
+            for output_name in onnx_node.output:
+                outputs.append(check_tensor(output_name))
+            return outputs
+
+        return Node(op=onnx_node.op_type, name=onnx_node.name, attrs=attrs_to_dict(onnx_node.attribute), inputs=retrieve_node_inputs(), outputs=retrieve_node_outputs())
+
+
+    @staticmethod
+    def import_graph(onnx_graph: onnx.GraphProto, tensor_map: "OrderedDict[str, Tensor]"=None, opset=None) -> Graph:
+        """
+        Imports a Graph from an ONNX Graph.
+
+        Args:
+            onnx_graph (onnx.GraphProto): The ONNX graph to import.
+
+        Optional Args:
+            tensor_map (OrderedDict[str, Tensor]): A mapping of tensor names to Tensors. This is generally only useful for subgraph import.
+            opset (int): The ONNX opset to use for this graph.
+        """
+        # Tensor map should not be modified - may be from outer graph
+        tensor_map = copy.copy(misc.default_value(tensor_map, OrderedDict()))
+
+        # Retrieves a Tensor from tensor_map if present, otherwise imports the tensor
+        # If overwrite=True, this function will overwrite previously imported tensors
+        # if the new tensor has more information available.
+        def get_tensor(onnx_tensor: Union[onnx.ValueInfoProto, onnx.TensorProto], overwrite=False) -> Tensor:
+            if onnx_tensor.name not in tensor_map:
+                tensor_map[onnx_tensor.name] = OnnxImporter.import_tensor(onnx_tensor)
+            elif overwrite:
+                tensor = OnnxImporter.import_tensor(onnx_tensor)
+                if isinstance(tensor_map[onnx_tensor.name], Variable):
+                    tensor_map[onnx_tensor.name].dtype = tensor_map[onnx_tensor.name].dtype or tensor.dtype
+                    tensor_map[onnx_tensor.name].shape = tensor_map[onnx_tensor.name].shape or tensor.shape
+            return tensor_map[onnx_tensor.name]
+
+        # Import initializers contents into Constants.
+        G_LOGGER.debug("Importing initializers")
+        for initializer in onnx_graph.initializer:
+            get_tensor(initializer)
+
+        # Import all tensors whose shapes are known. Tensors may be repeated, and some of these
+        # duplicates may not include shape/dtype information, so overwrite is set to True
+        # so that we can capture all the information available about the tensor
+        G_LOGGER.debug("Importing tensors with known shapes")
+        for tensor in onnx_graph.value_info:
+            get_tensor(tensor, overwrite=True)
+
+        # Import graph inputs and outputs. Initializers are not considered to be inputs.
+        initializer_names = set([tensor.name for tensor in onnx_graph.initializer])
+        G_LOGGER.debug("Importing graph inputs")
+        graph_inputs = [] # List[Tensor]
+        for inp in onnx_graph.input:
+            if inp.name not in initializer_names:
+                tensor = get_tensor(inp)
+                graph_inputs.append(tensor)
+
+        G_LOGGER.debug("Importing graph outputs")
+        graph_outputs = [] # List[Tensor]
+        for out in onnx_graph.output:
+            tensor = get_tensor(out)
+            graph_outputs.append(tensor)
+
+        G_LOGGER.debug("Importing nodes")
+        nodes = [] # List[Node]
+        for onnx_node in onnx_graph.node:
+            node = OnnxImporter.import_node(onnx_node, tensor_map)
+            nodes.append(node)
+
+        return Graph(nodes=nodes, inputs=graph_inputs, outputs=graph_outputs, name=onnx_graph.name, doc_string=onnx_graph.doc_string, opset=opset)
diff --git a/tools/onnx-graphsurgeon/onnx_graphsurgeon/ir/__init__.py b/tools/onnx-graphsurgeon/onnx_graphsurgeon/ir/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tools/onnx-graphsurgeon/onnx_graphsurgeon/ir/graph.py b/tools/onnx-graphsurgeon/onnx_graphsurgeon/ir/graph.py
new file mode 100644
index 00000000..58063e2a
--- /dev/null
+++ b/tools/onnx-graphsurgeon/onnx_graphsurgeon/ir/graph.py
@@ -0,0 +1,416 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from onnx_graphsurgeon.logger.logger import G_LOGGER
+from onnx_graphsurgeon.ir.tensor import Tensor, Constant, Variable
+from onnx_graphsurgeon.ir.node import Node
+from onnx_graphsurgeon.util import misc
+
+from collections import OrderedDict, defaultdict
+from typing import Sequence, Set, Dict, Tuple
+import numpy as np
+import copy
+
+
+# Functor that returns whether a Tensor has never been seen before
+class UnseenTensor(object):
+    def __init__(self, initial_tensors=None):
+        tensors = misc.default_value(initial_tensors, [])
+        self.seen_tensors = set([tensor.name for tensor in tensors])
+
+    def __call__(self, tensor):
+        # Empty tensors are never "seen"
+        if tensor.is_empty():
+            return True
+        elif tensor.name not in self.seen_tensors:
+            self.seen_tensors.add(tensor.name)
+            return True
+        return False
+
+
+class NodeIDAdder(object):
+    def __init__(self, graph):
+        self.graph = graph
+
+    def __enter__(self):
+        # To get unique ids for each node, add an `id` attribute. This will be removed before the function returns.
+        # Using the index in the node list allows the same object to count as different nodes.
+        for index, node in enumerate(self.graph.nodes):
+            node.id = index
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        for node in self.graph.nodes:
+            del node.id
+
+
+class Graph(object):
+    DEFAULT_OPSET = 11
+    OPSET_FUNC_MAP = defaultdict(dict)
+
+    @staticmethod
+    def register(opsets=list(range(DEFAULT_OPSET + 1))):
+        """
+        Registers a function with the Graph class for the specified group of opsets.
+        After registering the function, it can be accessed like a normal member function.
+
+        For example:
+        ::
+            @Graph.register()
+            def add(self, a, b):
+                return self.layer(op="Add", inputs=[a, b], outputs=["add_out_gs"])
+
+            graph.add(a, b)
+
+        Optional Args:
+            opsets (Sequence[int]): A group of opsets for which to register the function. By default, the function is registered for all opsets up to and including Graph.DEFAULT_OPSET. Multiple functions with the same name may be registered simultaneously if they are registered for different opsets. Registering a function with a duplicate name for the same opsets will overwrite any function previously registered for those opsets.
+        """
+        def register_func(func):
+            if hasattr(Graph, func.__name__):
+                G_LOGGER.warning("Registered function: {:} is hidden by a Graph attribute or function with the same name. This function will never be called!".format(func.__name__))
+
+            for opset in opsets:
+                Graph.OPSET_FUNC_MAP[opset][func.__name__] = func
+            return func
+        return register_func
+
+
+    def __init__(self, nodes: Sequence[Node]=None, inputs: Sequence[Tensor]=None, outputs: Sequence[Tensor]=None, name=None, doc_string=None, opset=None):
+        """
+        Represents a graph containing nodes and tensors.
+
+        Optional Args:
+            nodes (Sequence[Node]): A list of the nodes in this graph.
+            inputs (Sequence[Tensor]): A list of graph input Tensors.
+            outputs (Sequence[Tensor]): A list of graph output Tensors.
+            name (str): The name of the graph. Defaults to "onnx_graphsurgeon".
+            doc_string (str): A doc_string for the graph. Defaults to "".
+        """
+        self.nodes = misc.default_value(nodes, [])
+        self.inputs = misc.default_value(inputs, [])
+        self.outputs = misc.default_value(outputs, [])
+
+        self.name = misc.default_value(name, "onnx_graphsurgeon")
+        self.__name__ = self.name
+
+        self.doc_string = misc.default_value(doc_string, "")
+        self.opset = misc.default_value(opset, Graph.DEFAULT_OPSET)
+        # Printing graphs can be very expensive
+        G_LOGGER.ultra_verbose(lambda: "Created Graph: {:}".format(self))
+        # For layer() function
+        self.name_idx = 0
+
+
+    def __getattr__(self, name):
+        try:
+            return super().__getattribute__(name)
+        except AttributeError as err:
+            if self.opset not in Graph.OPSET_FUNC_MAP or name not in Graph.OPSET_FUNC_MAP[self.opset]:
+                G_LOGGER.error("No function: {:} registered for opset: {:}".format(name, self.opset))
+                raise err
+            return lambda *args, **kwargs: Graph.OPSET_FUNC_MAP[self.opset][name](self, *args, **kwargs)
+
+
+    def __eq__(self, other: "Graph"):
+        nodes_match = len(self.nodes) == len(other.nodes) and all([node == other_node for node, other_node in zip(self.nodes, other.nodes)])
+        inputs_match = len(self.inputs) == len(other.inputs) and all([inp == other_inp for inp, other_inp in zip(self.inputs, other.inputs)])
+        outputs_match = len(self.outputs) == len(other.outputs) and all([out == other_out for out, other_out in zip(self.outputs, other.outputs)])
+        return nodes_match and inputs_match and outputs_match
+
+
+    def node_ids(self):
+        """
+        Returns a context manager that supplies unique integer IDs for Nodes in the Graph.
+
+        Example:
+            with graph.node_ids():
+                assert graph.nodes[0].id != graph.nodes[1].id
+
+        Returns:
+            NodeIDAdder: A context manager that supplies unique integer IDs for Nodes.
+        """
+        return NodeIDAdder(self)
+
+
+    def _get_node_id(self, node):
+        try:
+            return node.id
+        except AttributeError:
+            G_LOGGER.critical("Encountered a node not in the graph:\n{:}.\n\nTo fix this, please append the node to this graph's `nodes` attribute.".format(node))
+
+
+    # Returns a list of node ids of used nodes, and a list of used tensors.
+    def _get_used_node_ids(self):
+        used_node_ids = set()
+        # Traverse backwards from outputs to find all used nodes.
+        ignore_seen = UnseenTensor()
+        used_tensors = list(filter(ignore_seen, self.outputs))
+
+        index = 0
+        while index < len(used_tensors):
+            used_tensor = used_tensors[index]
+            index += 1
+            for node in used_tensor.inputs:
+                used_node_ids.add(self._get_node_id(node))
+                used_tensors.extend(filter(ignore_seen, node.inputs))
+        return used_node_ids, used_tensors
+
+
+    def cleanup(self, remove_unused_node_outputs=True):
+        """
+        Removes unused nodes and tensors from the graph.
+        A node or tensor is considered unused if it does not contribute to any of the graph outputs.
+
+        Note: This function will never modify graph output tensors.
+
+        Optional Args:
+            remove_unused_node_outputs (bool): Whether to remove unused output tensors of nodes. This will never remove
+                empty tensor outputs. If this is set to False, outputs of nodes kept in the graph will not be modified.
+
+        Returns:
+            self
+        """
+        with self.node_ids():
+            used_node_ids, used_tensors = self._get_used_node_ids()
+
+            inputs = []
+            for inp in self.inputs:
+                if inp in used_tensors:
+                    inputs.append(inp)
+                else:
+                    G_LOGGER.debug("Removing unused input: {:}".format(inp))
+            self.inputs = inputs
+
+            nodes = []
+            for node in self.nodes:
+                if self._get_node_id(node) in used_node_ids:
+                    nodes.append(node)
+                else:
+                    node.inputs.clear()
+                    node.outputs.clear()
+                    G_LOGGER.verbose("Removing unused node: {:}".format(node))
+
+            # Last pass to remove any hanging tensors - tensors without outputs
+            if remove_unused_node_outputs:
+                graph_output_names = set([tensor.name for tensor in self.outputs])
+                for node in nodes:
+                    def is_hanging_tensor(tensor):
+                        return not tensor.is_empty() and len(tensor.outputs) == 0 and tensor.name not in graph_output_names
+
+                    [node.outputs.remove(out) for out in node.outputs if is_hanging_tensor(out)]
+
+            self.nodes = nodes
+            return self
+
+
+    def toposort(self):
+        """
+        Topologically sort the graph in place.
+
+        Returns:
+            self
+        """
+        # Keeps track of a node and it's level in the graph hierarchy. 0 corresponds to an input node, N corresponds to a node with N layers of inputs.
+        class HierarchyDescriptor(object):
+            def __init__(self, node=None, level=None):
+                self.node = node
+                self.level = level
+
+
+            def __lt__(self, other):
+                return self.level < other.level
+
+        hierarchy_levels = {} # Dict[int, HierarchyDescriptor]
+
+        def get_hierarchy_level(node):
+            # Return all nodes that contribute to this node.
+            def get_input_nodes(node):
+                inputs = {}
+                for tensor in node.inputs:
+                    for node in tensor.inputs:
+                        inputs[self._get_node_id(node)] = node
+                return inputs.values()
+
+            if self._get_node_id(node) in hierarchy_levels:
+                return hierarchy_levels[self._get_node_id(node)].level
+
+            # The level of a node is the level of it's highest input + 1.
+            try:
+                max_input_level = max([get_hierarchy_level(input_node) for input_node in get_input_nodes(node)] + [-1])
+            except RecursionError:
+                G_LOGGER.critical("Cycle detected in graph! Are there tensors with duplicate names in the graph?")
+
+            return max_input_level + 1
+
+        with self.node_ids():
+            for node in self.nodes:
+                hierarchy_levels[self._get_node_id(node)] = HierarchyDescriptor(node, level=get_hierarchy_level(node))
+
+        self.nodes = [hd.node for hd in sorted(hierarchy_levels.values())]
+        return self
+
+
+    def tensors(self, check_duplicates=False):
+        """
+        Creates a tensor map of all the tensors in this graph by walking over all nodes. Empty tensors are omitted from this map. The graph must not contain tensors with duplicate names.
+
+        Tensors are guaranteed to be in order of the nodes in the graph. Hence, if the graph is topologically sorted, the tensor map will be too.
+
+        Optional Args:
+            check_duplicates (bool): Whether to fail if multiple tensors with the same name are encountered.
+
+        Raises:
+            OnnxGraphSurgeonException: If check_duplicates is True, and multiple distinct tensors in the graph share the same name.
+
+        Returns:
+            OrderedDict[str, Tensor]: A mapping of tensor names to tensors.
+        """
+        tensor_map = OrderedDict()
+
+        def add_to_tensor_map(tensor):
+            if not tensor.is_empty():
+                if check_duplicates and tensor.name in tensor_map and not (tensor_map[tensor.name] is tensor):
+                    G_LOGGER.critical("Found distinct tensors that share the same name:\n[id: {:}] {:}\n[id: {:}] {:}"
+                        .format(id(tensor_map[tensor.name]), tensor_map[tensor.name], id(tensor), tensor))
+                tensor_map[tensor.name] = tensor
+
+        for node in self.nodes:
+            for tensor in node.inputs + node.outputs:
+                add_to_tensor_map(tensor)
+        return tensor_map
+
+
+    def fold_constants(self):
+        """
+        Folds constants in-place in the graph. The graph must be topologically sorted prior to calling this function (see `toposort()`).
+
+        NOTE: This function will not remove constants after folding them. In order to get rid of these hanging nodes, you can run the `cleanup()` function.
+
+        NOTE: Due to how this is implemented, the graph must be exportable to ONNX, and evaluable in ONNX Runtime.
+
+        Returns:
+            self
+        """
+        from onnx_graphsurgeon.api.api import export_onnx
+        import onnxruntime
+        import onnx
+
+        temp_graph = copy.deepcopy(self)
+
+        # Since the graph is topologically sorted, this should find all constant nodes in the graph.
+        graph_constants = {tensor.name: tensor for tensor in temp_graph.tensors().values() if isinstance(tensor, Constant)}
+        for node in temp_graph.nodes:
+            if all([inp.name in graph_constants for inp in node.inputs]):
+                graph_constants.update({out.name: out for out in node.outputs})
+
+        # Next build a graph with just the constants, and evaluate - no need to evaluate constants
+        outputs_to_evaluate = [tensor for tensor in graph_constants.values() if isinstance(tensor, Variable)]
+
+        if not outputs_to_evaluate:
+            G_LOGGER.warning("Could not find any operations in this graph that can be folded. This could mean that constant folding has already been run on this graph. Skipping.")
+            return self
+
+        output_names = [out.name for out in outputs_to_evaluate]
+
+        temp_graph.outputs = outputs_to_evaluate
+        temp_graph.cleanup()
+
+        # Determining types is not trivial, and ONNX-RT does its own type inference.
+        sess = onnxruntime.InferenceSession(export_onnx(temp_graph, do_type_check=False).SerializeToString())
+        constant_values = sess.run(output_names, {})
+
+        # Finally, replace the Variables in the original graph with constants.
+        graph_tensors = self.tensors()
+        for name, values in zip(output_names, constant_values):
+            graph_tensors[name].to_constant(values)
+            graph_tensors[name].inputs.clear() # Constants do not need inputs
+
+        return self
+
+
+    def _generate_name(self, prefix):
+        name = "{}_{}".format(prefix, self.name_idx)
+        self.name_idx += 1
+        return name
+
+
+    def layer(self, inputs=[], outputs=[], *args, **kwargs):
+        """
+        Creates a node, adds it to this graph, and optionally creates its input and output tensors.
+
+        The input and output lists can include various different types:
+            - Tensor: Any Tensors provided will be used as-is in the inputs/outputs of the node created
+            - str: If a string is provided, this function will generate a new tensor, using the string to generate a name. It will append an index to the end of the provided string to attempt to avoid duplicates, but since this doesn't guarantee that the name is unique, you should try to ensure that the string provided is as specific as possible.
+            - np.ndarray: If a NumPy array is provided, this function will generate a Constant tensor using the prefix: "onnx_graphsurgeon_constant"
+
+        Optional Args:
+            inputs (List[Union[Tensor, str, np.array]]): The list of inputs
+            outputs (List[Union[Tensor, str, np.array]]): The list of outputs
+            *args and **kwargs: These are passed directly to the constructor of Node
+
+        Returns:
+            List[Tensor]: The output tensors of the node
+        """
+        def process_io(io):
+            new_io = []
+            for elem in io:
+                if isinstance(elem, Tensor):
+                    new_io.append(elem)
+                elif isinstance(elem, str):
+                    tensor = Variable(name=self._generate_name(elem))
+                    new_io.append(tensor)
+                elif isinstance(elem, np.ndarray):
+                    new_io.append(Constant(name=self._generate_name("onnx_graphsurgeon_constant"), values=elem))
+                else:
+                    G_LOGGER.critical("Unrecognized type passed to Graph.layer: {:}.\n\tHint: Did you forget to unpack a list with `*`?\n\tPlease use Tensors, strings, or NumPy arrays.".format(elem))
+            return new_io
+
+        inputs = process_io(inputs)
+        outputs = process_io(outputs)
+
+        if "name" not in kwargs:
+            kwargs["name"] = self._generate_name("onnx_graphsurgeon_node")
+
+        node = Node(*args, **kwargs, inputs=inputs, outputs=outputs)
+        self.nodes.append(node)
+        return node.outputs
+
+
+    def __deepcopy__(self, memo):
+        """
+        Makes a deep copy of this graph.
+        """
+        # First, reconstruct each tensor in the graph, but with no inputs or outputs
+        tensor_map = self.tensors()
+        new_tensors = {name: tensor.copy() for name, tensor in tensor_map.items()}
+
+        # Next, copy nodes, and update inputs/outputs
+        new_nodes = []
+        for node in self.nodes:
+            new_node = node.copy(inputs=[new_tensors[inp.name] for inp in node.inputs], outputs=[new_tensors[out.name] for out in node.outputs])
+            new_nodes.append(new_node)
+
+        new_graph_inputs = [new_tensors[inp.name] for inp in self.inputs]
+        new_graph_outputs = [new_tensors[out.name] for out in self.outputs]
+        return Graph(nodes=new_nodes, inputs=new_graph_inputs, outputs=new_graph_outputs, name=copy.deepcopy(self.name, memo), doc_string=copy.deepcopy(self.doc_string, memo), opset=copy.deepcopy(self.opset, memo))
+
+
+    def __str__(self):
+        nodes_str = "\n".join([str(node) for node in self.nodes])
+        return "Graph {:} (Opset: {:})\nInputs: {:}\nNodes: {:}\nOutputs: {:}".format(self.name, self.opset, self.inputs, nodes_str, self.outputs)
+
+
+    def __repr__(self):
+        return self.__str__()
diff --git a/tools/onnx-graphsurgeon/onnx_graphsurgeon/ir/node.py b/tools/onnx-graphsurgeon/onnx_graphsurgeon/ir/node.py
new file mode 100644
index 00000000..183f9a0d
--- /dev/null
+++ b/tools/onnx-graphsurgeon/onnx_graphsurgeon/ir/node.py
@@ -0,0 +1,121 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from onnx_graphsurgeon.logger.logger import G_LOGGER
+from onnx_graphsurgeon.ir.tensor import Tensor
+from onnx_graphsurgeon.util import misc
+
+from collections import OrderedDict
+from typing import List, Dict
+
+class Node(object):
+    def __init__(self, op: str, name: str=None, attrs: Dict[str, object]=None, inputs: List["Tensor"]=None, outputs: List["Tensor"]=None):
+        """
+        A node represents an operation in a graph, and consumes zero or more Tensors, and produces zero or more Tensors.
+
+        Args:
+            op (str): The operation this node performs.
+
+
+        Optional Args:
+            name (str): The name of this node.
+            attrs (Dict[str, object]): A dictionary that maps attribute names to their values.
+            inputs (List[Tensor]): A list of zero or more input Tensors.
+            outputs (List[Tensor]): A list of zero or more output Tensors.
+        """
+        self.op = op
+        self.name = misc.default_value(name, "")
+        self.attrs = misc.default_value(attrs, OrderedDict())
+        self.inputs = misc.SynchronizedList(self, field_name="outputs", initial=misc.default_value(inputs, []))
+        self.outputs = misc.SynchronizedList(self, field_name="inputs", initial=misc.default_value(outputs, []))
+
+
+    def i(self, tensor_idx=0, producer_idx=0):
+        """
+        Convenience function to get a producer node of one of this node's input tensors.
+        Note that the parameters are swapped compared to the o() function; this is because tensors are likely to have only a single producer
+
+        Args:
+            tensor_idx (int): The index of the input tensor of this node. Defaults to 0.
+            producer_idx (int): The index of the producer of the input tensor, if the tensor has multiple producers. Defaults to 0
+
+        Example:
+            assert node.i() == node.inputs[0].inputs[0]
+            assert node.i(1, 2) == node.inputs[1].inputs[2]
+
+        Returns:
+            Node: The specified producer (input) node.
+        """
+        return self.inputs[tensor_idx].inputs[producer_idx]
+
+
+    def o(self, consumer_idx=0, tensor_idx=0):
+        """
+        Convenience function to get a consumer node of one of this node's output tensors.
+
+        Args:
+            consumer_idx (int): The index of the consumer of the input tensor. Defaults to 0.
+            tensor_idx (int): The index of the output tensor of this node, if the node has multiple outputs. Defaults to 0.
+
+        Example:
+            assert node.o() == node.outputs[0].outputs[0]
+            assert node.o(2, 1) == node.outputs[1].outputs[2]
+
+        Returns:
+            Node: The specified consumer (output) node
+        """
+        return self.outputs[tensor_idx].outputs[consumer_idx]
+
+
+    def __setattr__(self, name, value):
+        if name in ["inputs", "outputs"]:
+            try:
+                getattr(self, name).clear()
+                getattr(self, name).extend(value)
+            except AttributeError:
+                super().__setattr__(name, value)
+        else:
+            super().__setattr__(name, value)
+
+
+    def copy(self, inputs: List["Tensor"]=None, outputs: List["Tensor"]=None):
+        """
+        Makes a shallow copy of this node, overriding input and output information.
+
+        Note: Generally, you should only ever make a deep copy of a Graph.
+        """
+        return Node(self.op, self.name, self.attrs, inputs=inputs, outputs=outputs)
+
+
+    def __str__(self):
+        ret = "{:} ({:})\n\tInputs: {:}\n\tOutputs: {:}".format(self.name, self.op, self.inputs, self.outputs)
+        if self.attrs:
+            ret += "\nAttributes: {:}".format(self.attrs)
+        return ret
+
+    def __repr__(self):
+        return self.__str__()
+
+
+    def __eq__(self, other):
+        """
+        Check whether two nodes are equal by comparing name, attributes, op, inputs, and outputs.
+        """
+        G_LOGGER.verbose("Comparing node: {:} with {:}".format(self.name, other.name))
+        attrs_match = self.name == other.name and self.op == other.op and self.attrs == other.attrs
+        inputs_match = len(self.inputs) == len(other.inputs) and all([inp == other_inp for inp, other_inp in zip(self.inputs, other.inputs)])
+        outputs_match = len(self.outputs) == len(other.outputs) and all([out == other_out for out, other_out in zip(self.outputs, other.outputs)])
+        return attrs_match and inputs_match and outputs_match
diff --git a/tools/onnx-graphsurgeon/onnx_graphsurgeon/ir/tensor.py b/tools/onnx-graphsurgeon/onnx_graphsurgeon/ir/tensor.py
new file mode 100644
index 00000000..d77dc8b3
--- /dev/null
+++ b/tools/onnx-graphsurgeon/onnx_graphsurgeon/ir/tensor.py
@@ -0,0 +1,219 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from onnx_graphsurgeon.logger.logger import G_LOGGER
+from onnx_graphsurgeon.util import misc
+
+from typing import Set, Sequence, Union
+import numpy as np
+
+
+class Tensor(object):
+    DYNAMIC = -1
+
+    def __init__(self):
+        raise NotImplementedError("Tensor is an abstract class")
+
+
+    def __setattr__(self, name, value):
+        if name in ["inputs", "outputs"]:
+            try:
+                getattr(self, name).clear()
+                getattr(self, name).extend(value)
+            except AttributeError:
+                super().__setattr__(name, value)
+        else:
+            super().__setattr__(name, value)
+
+
+    def is_empty(self):
+        """
+        Returns whether this tensor is empty.
+
+        Returns:
+            bool: Whether the tensor is empty.
+        """
+        return self.name == ""
+
+
+    def to_constant(self, values: np.ndarray):
+        """
+        Modifies this tensor in-place to convert it to a Constant. This means that all consumers/producers of the tensor will see the update.
+
+        Args:
+            values (np.ndarray): The values in this tensor
+
+        Returns:
+            self
+        """
+        self.__class__ = Constant
+        self.values = values
+        return self
+
+
+    def to_variable(self, dtype: np.dtype=None, shape: Sequence[Union[int, str]]=[]):
+        """
+        Modifies this tensor in-place to convert it to a Variable. This means that all consumers/producers of the tensor will see the update.
+
+        Optional Args:
+            dtype (np.dtype): The data type of the tensor.
+            shape (Sequence[int]): The shape of the tensor.
+
+        Returns:
+            self
+        """
+        self.__class__ = Variable
+        self.dtype = dtype
+        self.shape = shape
+        return self
+
+
+    def i(self, tensor_idx=0, producer_idx=0):
+        """
+        Convenience function to get an input tensor of one of this tensor's input nodes.
+        Note that the parameters are swapped compared to the o() function; this is because tensors are likely to have only a single producer
+
+        Args:
+            tensor_idx (int): The index of the input tensor of the input node. Defaults to 0.
+            producer_idx (int): The index of the producer node of the input tensor, if the tensor has multiple producers. Defaults to 0.
+
+        Example:
+            assert tensor.i() == tensor.inputs[0].inputs[0]
+            assert tensor.i(1, 2) == tensor.inputs[2].inputs[1]
+
+        Returns:
+            Node: The specified producer (input) tensor.
+        """
+        return self.inputs[producer_idx].inputs[tensor_idx]
+
+
+    def o(self, consumer_idx=0, tensor_idx=0):
+        """
+        Convenience function to get an output tensor of one of this tensor's output nodes.
+
+        Args:
+            consumer_idx (int): The index of the consumer of the input tensor. Defaults to 0.
+            tensor_idx (int): The index of the output tensor of the node, if the node has multiple outputs. Defaults to 0.
+
+        Example:
+            assert tensor.o() == tensor.outputs[0].outputs[0]
+            assert tensor.o(2, 1) == tensor.outputs[2].outputs[1]
+
+        Returns:
+            Node: The specified consumer (output) tensor
+        """
+        return self.outputs[consumer_idx].outputs[tensor_idx]
+
+
+    def __str__(self):
+        return "{:} ({:}): (shape={:}, dtype={:})".format(type(self).__name__, self.name, self.shape, self.dtype)
+
+
+    def __repr__(self):
+        return self.__str__()
+
+
+    def __eq__(self, other):
+        """
+        Perform a check to see if two tensors are equal.
+
+        Tensors are considered equal if they share the same name. A Graph must not include Tensors with duplicate names.
+        """
+        return self.name == other.name
+
+
+class Variable(Tensor):
+    @staticmethod
+    def empty():
+        return Variable(name="")
+
+
+    def __init__(self, name: str, dtype: np.dtype=None, shape: Sequence[Union[int, str]]=None):
+        """
+        Represents a Tensor whose value is not known until inference-time.
+
+        Args:
+            name (str): The name of the tensor.
+            dtype (np.dtype): The data type of the tensor.
+            shape (Sequence[Union[int, str]]): The shape of the tensor. This may contain strings if the model uses dimension parameters.
+        """
+        self.name = name
+        self.inputs = misc.SynchronizedList(self, field_name="outputs", initial=[])
+        self.outputs = misc.SynchronizedList(self, field_name="inputs", initial=[])
+        self.dtype = dtype
+        self.shape = misc.default_value(shape, None)
+
+
+    def to_constant(self, values: np.ndarray):
+        del self.dtype
+        del self.shape
+        return super().to_constant(values)
+
+
+    def copy(self):
+        """
+        Makes a shallow copy of this tensor, omitting input and output information.
+
+        Note: Generally, you should only ever make a deep copy of a Graph.
+        """
+        return Variable(self.name, self.dtype, self.shape)
+
+
+class Constant(Tensor):
+    def __init__(self, name: str, values: np.ndarray):
+        """
+        Represents a Tensor whose value is known.
+
+        Args:
+            name (str): The name of the tensor.
+            values (np.ndarray): The values in this tensor, in the form of a NumPy array.
+            dtype (np.dtype): The data type of the tensor.
+            shape (Sequence[Union[int, str]]): The shape of the tensor.
+        """
+        self.name = name
+        self.inputs = misc.SynchronizedList(self, field_name="outputs", initial=[])
+        self.outputs = misc.SynchronizedList(self, field_name="inputs", initial=[])
+        if not isinstance(values, np.ndarray):
+            G_LOGGER.critical("Provided `values` argument is not a NumPy array (please provide a NumPy array to construct a Constant): {:}".format(values))
+        self.values = np.array(values)
+
+
+    def to_variable(self, dtype: np.dtype=None, shape: Sequence[Union[int, str]]=[]):
+        del self.values
+        return super().to_variable(dtype, shape)
+
+
+    def copy(self):
+        """
+        Makes a shallow copy of this tensor, omitting input and output information.
+
+        Note: Generally, you should only ever make a deep copy of a Graph.
+        """
+        return Constant(self.name, self.values)
+
+
+    @property
+    def shape(self):
+        return self.values.shape
+
+    @property
+    def dtype(self):
+        return self.values.dtype.type
+
+    def __repr__(self):
+        ret = self.__str__()
+        ret += "\n{:}".format(self.values)
+        return ret
diff --git a/tools/onnx-graphsurgeon/onnx_graphsurgeon/logger/__init__.py b/tools/onnx-graphsurgeon/onnx_graphsurgeon/logger/__init__.py
new file mode 100644
index 00000000..3dbdd691
--- /dev/null
+++ b/tools/onnx-graphsurgeon/onnx_graphsurgeon/logger/__init__.py
@@ -0,0 +1 @@
+from onnx_graphsurgeon.logger.logger import G_LOGGER
diff --git a/tools/onnx-graphsurgeon/onnx_graphsurgeon/logger/logger.py b/tools/onnx-graphsurgeon/onnx_graphsurgeon/logger/logger.py
new file mode 100644
index 00000000..346a8600
--- /dev/null
+++ b/tools/onnx-graphsurgeon/onnx_graphsurgeon/logger/logger.py
@@ -0,0 +1,250 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from onnx_graphsurgeon.util.exception import OnnxGraphSurgeonException
+
+import inspect
+import enum
+import time
+import sys
+import os
+
+# Context manager to apply indentation to messages
+class LoggerIndent(object):
+    def __init__(self, logger, indent):
+        self.logger = logger
+        self.old_indent = self.logger.logging_indent
+        self.indent = indent
+
+    def __enter__(self):
+        self.logger.logging_indent = self.indent
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.logger.logging_indent = self.old_indent
+
+
+# Context manager to suppress messages
+class LoggerSuppress(object):
+    def __init__(self, logger, severity):
+        self.logger = logger
+        self.old_severity = self.logger.severity
+        self.severity = severity
+
+    def __enter__(self):
+        self.logger.severity = self.severity
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.logger.severity = self.old_severity
+
+
+class LogMode(enum.IntEnum):
+    EACH = 0 # Log the message each time
+    ONCE = 1 # Log the message only once. The same message will not be logged again.
+
+
+class Logger(object):
+    ULTRA_VERBOSE = -10
+    VERBOSE = 0
+    DEBUG = 10
+    INFO = 20
+    WARNING = 30
+    ERROR = 40
+    CRITICAL = 50
+
+    SEVERITY_LETTER_MAPPING = {
+        ULTRA_VERBOSE: "[UV]",
+        VERBOSE: "[V]",
+        DEBUG: "[D]",
+        INFO: "[I]",
+        WARNING: "[W]",
+        ERROR: "[E]",
+        CRITICAL: "[C]",
+    }
+
+    SEVERITY_COLOR_MAPPING = {
+        ULTRA_VERBOSE: "cyan",
+        VERBOSE: "dark_gray",
+        DEBUG: "light_gray",
+        INFO: "light_green",
+        WARNING: "light_yellow",
+        ERROR: "red_1",
+        CRITICAL: "red_1",
+    }
+
+    def __init__(self, severity=INFO, colors=True, letter=True, timestamp=False, line_info=False):
+        """
+        Logger.
+
+        Optional Args:
+            severity (Logger.Severity): Messages below this severity are ignored.
+            colors (bool): Whether to use colored output.
+            letter (bool): Whether to prepend each logging message with a letter indicating it's severity. Defaults to True.
+            timestamp (bool): Whether to include a timestamp in the logging output. Defaults to False.
+            line_info (bool): Whether to include file and line number information in the logging output. Defaults to False.
+        """
+        self._severity = severity
+        self.logging_indent = 0
+        self.root_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir,  os.pardir))
+        self.once_logged = set()
+        self.colors = colors
+        self.letter = letter
+        self.timestamp = timestamp
+        self.line_info = line_info
+        self.logger_callbacks = []
+
+
+    @property
+    def severity(self):
+        return self._severity
+
+
+    @severity.setter
+    def severity(self, value):
+        self._severity = value
+        for callback in self.logger_callbacks:
+            callback(self._severity)
+
+
+    def register_callback(self, callback):
+        """
+        Registers a callback with the logger, which will be invoked when the logging severity is modified.
+        The callback is guaranteed to be called at least once in the register_callback function.
+
+        Args:
+            callback (Callable(Logger.Severity)): A callback that accepts the current logger severity.
+        """
+        callback(self._severity)
+        self.logger_callbacks.append(callback)
+
+
+    def indent(self, level=1):
+        """
+        Returns a context manager that indents all strings logged by the specified amount.
+        """
+        return LoggerIndent(self, level + self.logging_indent)
+
+
+    def suppress(self, severity=CRITICAL):
+        """
+        Returns a context manager that temporarily changes the severity of the logger for its duration.
+
+        Args:
+            severity (Logger.Severity): The severity to set the logger to. Defaults to Logger.CRITICAL, which will suppress all messages.
+        """
+        return LoggerSuppress(self, severity)
+
+
+
+    # If once is True, the logger will only log this message a single time. Useful in loops.
+    # message may be a callable which returns a message. This way, only if the message needs to be logged is it ever generated.
+    def log(self, message, severity, mode=LogMode.EACH, stack_depth=2):
+        def process_message(message, stack_depth):
+            def get_prefix():
+                def get_line_info():
+                    module = inspect.getmodule(sys._getframe(stack_depth + 3))
+                    # Handle logging from the top-level of a module.
+                    if not module:
+                        module = inspect.getmodule(sys._getframe(stack_depth + 2))
+                    filename = module.__file__
+                    filename = os.path.relpath(filename, self.root_dir)
+                    # If the file is not located in trt_smeagol, use its basename instead.
+                    if os.pardir in filename:
+                        filename = os.path.basename(filename)
+                    return "[{:}:{:}] ".format(filename, sys._getframe(stack_depth).f_lineno)
+
+                prefix = ""
+                if self.letter:
+                    prefix += Logger.SEVERITY_LETTER_MAPPING[severity] + " "
+                if self.timestamp:
+                    prefix += "({:}) ".format(time.strftime("%X"))
+                if self.line_info:
+                    prefix += get_line_info()
+                return prefix
+
+            def apply_indentation(message):
+                message_lines = str(message).splitlines()
+                return "\n".join(["\t" * self.logging_indent + line for line in message_lines])
+
+            def apply_color(message):
+                if self.colors:
+                    try:
+                        import colored
+                        color = Logger.SEVERITY_COLOR_MAPPING[severity]
+                        return colored.stylize(message, [colored.fg(color)])
+                    except (ImportError, ModuleNotFoundError):
+                        self.colors = False
+                        self.warning("colored module is not installed, will not use colors when logging. To enable colors, please install the colored module: python3 -m pip install colored")
+                        self.colors = True
+                return message
+
+
+            prefix = get_prefix()
+            message = apply_indentation(message)
+            return apply_color("{:}{:}".format(prefix, message))
+
+
+        def should_log(message):
+            should = severity >= self._severity
+            if mode == LogMode.ONCE:
+                message_hash = hash(message)
+                should &= message_hash not in self.once_logged
+                self.once_logged.add(message_hash)
+            return should
+
+
+        if not should_log(message):
+            return
+
+        if callable(message):
+            message = message()
+        message = str(message)
+        print(process_message(message, stack_depth=stack_depth))
+
+
+    def ultra_verbose(self, message, mode=LogMode.EACH):
+        self.log(message, Logger.ULTRA_VERBOSE, mode=mode, stack_depth=3)
+
+
+    def verbose(self, message, mode=LogMode.EACH):
+        self.log(message, Logger.VERBOSE, mode=mode, stack_depth=3)
+
+
+    def debug(self, message, mode=LogMode.EACH):
+        self.log(message, Logger.DEBUG, mode=mode, stack_depth=3)
+
+
+    def info(self, message, mode=LogMode.EACH):
+        self.log(message, Logger.INFO, mode=mode, stack_depth=3)
+
+
+    def warning(self, message, mode=LogMode.EACH):
+        self.log(message, Logger.WARNING, mode=mode, stack_depth=3)
+
+
+    def error(self, message, mode=LogMode.EACH):
+        self.log(message, Logger.ERROR, mode=mode, stack_depth=3)
+
+
+    # Like error, but immediately exits.
+    def critical(self, message):
+        self.log(message, Logger.CRITICAL, stack_depth=3)
+        raise OnnxGraphSurgeonException("Error encountered - see logging output for details") from None # Erase exception chain
+
+
+global G_LOGGER
+G_LOGGER = Logger()
diff --git a/tools/onnx-graphsurgeon/onnx_graphsurgeon/util/__init__.py b/tools/onnx-graphsurgeon/onnx_graphsurgeon/util/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tools/onnx-graphsurgeon/onnx_graphsurgeon/util/exception.py b/tools/onnx-graphsurgeon/onnx_graphsurgeon/util/exception.py
new file mode 100644
index 00000000..d31a9fd2
--- /dev/null
+++ b/tools/onnx-graphsurgeon/onnx_graphsurgeon/util/exception.py
@@ -0,0 +1,18 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+class OnnxGraphSurgeonException(Exception):
+    pass
diff --git a/tools/onnx-graphsurgeon/onnx_graphsurgeon/util/misc.py b/tools/onnx-graphsurgeon/onnx_graphsurgeon/util/misc.py
new file mode 100644
index 00000000..029c3475
--- /dev/null
+++ b/tools/onnx-graphsurgeon/onnx_graphsurgeon/util/misc.py
@@ -0,0 +1,129 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from onnx_graphsurgeon.logger.logger import G_LOGGER
+
+from collections import OrderedDict
+from typing import List, Sequence
+
+
+# default_value exists to solve issues that might result from Python's normal default argument behavior.
+# Specifically, consider the following class:
+#
+# class MyClass(object):
+#     def __init__(self, value=[]):
+#         self.value = value
+#
+# This leads to unwanted behavior when the default value is used:
+#
+# >>> x = MyClass()
+# >>> x.value.append("SHOULD NOT BE IN Y")
+# >>> y = MyClass()
+# >>> y.value
+# ['SHOULD NOT BE IN Y']
+#
+# If we rewrite the class using default value:
+#
+# class MyClass(object):
+#     def __init__(self, value=None):
+#         self.value = default_value(value, [])
+#
+# Then we get the desired behavior:
+#
+# >>> x = MyClass()
+# >>> x.value.append("SHOULD NOT BE IN Y")
+# >>> y = MyClass()
+# >>> y.value
+# []
+def default_value(value, default):
+    return value if value is not None else default
+
+
+# Special type of list that synchronizes contents with another list.
+# Concrete example: Assume some node, n, contains an input tensor, t. If we remove t from n.inputs,
+# we also need to remove n from t.outputs. To avoid having to do this manually, we use SynchronizedList,
+# which takes an attribute name as a parameter, and then synchronizes to that attribute of each of its elements.
+# So, in the example above, we can make n.inputs a synchronized list whose field_name is set to "outputs".
+# See test_ir.TestNodeIO for functional tests
+class SynchronizedList(list):
+    def __init__(self, parent_obj, field_name, initial):
+        self.parent_obj = parent_obj
+        self.field_name = field_name
+        self.extend(initial)
+
+
+    def _add_to_elem(self, elem):
+        # Explicitly avoid SynchronizedList overrides to prevent infinite recursion
+        list.append(getattr(elem, self.field_name), self.parent_obj)
+
+
+    def _remove_from_elem(self, elem):
+        # Explicitly avoid SynchronizedList overrides to prevent infinite recursion
+        list.remove(getattr(elem, self.field_name), self.parent_obj)
+
+
+    def __delitem__(self, index):
+        self._remove_from_elem(self[index])
+        super().__delitem__(index)
+
+
+    def __setitem__(self, index, elem):
+        self._remove_from_elem(self[index])
+        super().__setitem__(index, elem)
+        self._add_to_elem(elem)
+
+
+    def append(self, x):
+        super().append(x)
+        self._add_to_elem(x)
+
+
+    def extend(self, iterable: Sequence[object]):
+        super().extend(iterable)
+        for elem in iterable:
+            self._add_to_elem(elem)
+
+    def insert(self, i, x):
+        super().insert(i, x)
+        self._add_to_elem(x)
+
+
+    def remove(self, x):
+        super().remove(x)
+        self._remove_from_elem(x)
+
+
+    def pop(self, i=-1):
+        elem = super().pop(i)
+        self._remove_from_elem(elem)
+        return elem
+
+
+    def clear(self):
+        for elem in self:
+            self._remove_from_elem(elem)
+        super().clear()
+
+
+    def __add__(self, other_list: List[object]):
+        new_list = SynchronizedList(self.parent_obj, self.field_name, self)
+        new_list += other_list
+        return new_list
+
+
+    def __iadd__(self, other_list: List[object]):
+        self.extend(other_list)
+        return self
diff --git a/tools/onnx-graphsurgeon/setup.cfg b/tools/onnx-graphsurgeon/setup.cfg
new file mode 100644
index 00000000..2a9acf13
--- /dev/null
+++ b/tools/onnx-graphsurgeon/setup.cfg
@@ -0,0 +1,2 @@
+[bdist_wheel]
+universal = 1
diff --git a/tools/onnx-graphsurgeon/setup.py b/tools/onnx-graphsurgeon/setup.py
new file mode 100644
index 00000000..951326f3
--- /dev/null
+++ b/tools/onnx-graphsurgeon/setup.py
@@ -0,0 +1,53 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import sys
+import onnx_graphsurgeon
+from setuptools import setup, find_packages
+
+def no_publish():
+    blacklist = ['register']
+    for cmd in blacklist:
+        if cmd in sys.argv:
+            raise RuntimeError("Command \"{}\" blacklisted".format(cmd))
+
+
+REQUIRED_PACKAGES = [
+    "numpy",
+    "onnx",
+]
+
+def main():
+    no_publish()
+    setup(
+        name="onnx_graphsurgeon",
+        version=onnx_graphsurgeon.__version__,
+        description="ONNX GraphSurgeon",
+        long_description=open("README.md", "r", encoding="utf-8").read(),
+        url="https://github.com/nvidia/tensorrt/tools/onnx-graphsurgeon",
+        author="Pranav Marathe",
+        author_email="pranavm@nvidia.com",
+        classifiers=[
+            'Intended Audience :: Developers',
+            'Programming Language :: Python :: 3',
+        ],
+        install_requires=REQUIRED_PACKAGES,
+        packages=find_packages(),
+        zip_safe=True,
+    )
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/onnx-graphsurgeon/tests/models/dim_param.onnx b/tools/onnx-graphsurgeon/tests/models/dim_param.onnx
new file mode 100644
index 00000000..8a5a26d8
Binary files /dev/null and b/tools/onnx-graphsurgeon/tests/models/dim_param.onnx differ
diff --git a/tools/onnx-graphsurgeon/tests/models/identity.onnx b/tools/onnx-graphsurgeon/tests/models/identity.onnx
new file mode 100644
index 00000000..b7302044
--- /dev/null
+++ b/tools/onnx-graphsurgeon/tests/models/identity.onnx
@@ -0,0 +1,15 @@
+backend-test:[
+
+
x
y"Identity
test_identityZ
+
x
+

+

+

+
+b
+
y
+

+

+

+
+B
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diff --git a/tools/onnx-graphsurgeon/tests/models/lstm.onnx b/tools/onnx-graphsurgeon/tests/models/lstm.onnx
new file mode 100644
index 00000000..9bbf0f9b
Binary files /dev/null and b/tools/onnx-graphsurgeon/tests/models/lstm.onnx differ
diff --git a/tools/onnx-graphsurgeon/tests/models/scan.onnx b/tools/onnx-graphsurgeon/tests/models/scan.onnx
new file mode 100644
index 00000000..f2ec1423
--- /dev/null
+++ b/tools/onnx-graphsurgeon/tests/models/scan.onnx
@@ -0,0 +1,43 @@
+:¼
+à

+initial
+
x
y
z"Scan*­

+body2¡

+
+sum_in
+nextsum_out"Add
+
+sum_outscan_out"Identity	scan_bodyZ
+sum_in
+
+

+Z
+next
+
+

+b
+sum_out
+
+

+b
+scan_out
+
+

+ 
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+num_scan_inputs
 
graphZ
+initial
+
+

+Z
+
x
+

+
+b
+
y
+
+

+b
+
z
+

+
+B
diff --git a/tools/onnx-graphsurgeon/tests/onnx_models.py b/tools/onnx-graphsurgeon/tests/onnx_models.py
new file mode 100644
index 00000000..7cf8d69c
--- /dev/null
+++ b/tools/onnx-graphsurgeon/tests/onnx_models.py
@@ -0,0 +1,148 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from onnx_graphsurgeon.logger.logger import G_LOGGER
+from onnx_graphsurgeon.ir.tensor import Tensor, Constant, Variable
+from onnx_graphsurgeon.ir.graph import Graph
+from onnx_graphsurgeon.ir.node import Node
+from onnx_graphsurgeon.importers.onnx_importer import OnnxImporter
+
+G_LOGGER.severity = G_LOGGER.ULTRA_VERBOSE
+
+from collections import OrderedDict
+import onnx.numpy_helper
+from typing import List
+import numpy as np
+import onnx
+import os
+
+TEST_ROOT = os.path.realpath(os.path.dirname(__file__))
+
+class Model(object):
+    def __init__(self, path: str, inputs: List[Tensor], outputs: List[Tensor], nodes: List[Node], opset: int):
+        self.path = path
+        self.inputs = inputs
+        self.outputs = outputs
+        self.nodes = nodes
+        self.opset = opset
+
+    def load(self):
+        return onnx.load(self.path)
+
+    def assert_equal(self, graph: Graph):
+
+
+        assert graph.inputs == self.inputs
+        G_LOGGER.debug("Graph inputs matched")
+
+        for actual, expected in zip(graph.nodes, self.nodes):
+            G_LOGGER.debug("Actual Node: {:}.\n\nExpected Node: {:}".format(actual, expected))
+            # Break down fields to make debugging failures easier.
+            assert actual.op == expected.op
+            assert actual.inputs == expected.inputs
+            assert actual.outputs == expected.outputs
+            assert actual.name == expected.name
+            for (akey, aval), (ekey, eval) in zip(actual.attrs.items(), expected.attrs.items()):
+                assert akey == ekey
+                assert aval == eval
+            assert actual == expected
+        G_LOGGER.debug("Graph nodes matched")
+
+        assert graph.outputs == self.outputs
+        G_LOGGER.debug("Graph outputs matched")
+
+
+def identity_model():
+    path = os.path.join(TEST_ROOT, "models", "identity.onnx")
+    model = onnx.load(path)
+
+    x = Variable(name="x", dtype=np.float32, shape=(1, 1, 2, 2))
+    y = Variable(name="y", dtype=np.float32, shape=(1, 1, 2, 2))
+    node = Node(op="Identity", inputs=[x], outputs=[y])
+
+    return Model(path, inputs=[x], outputs=[y], nodes=[node], opset=OnnxImporter.get_opset(model))
+
+
+def dim_param_model():
+    path = os.path.join(TEST_ROOT, "models", "dim_param.onnx")
+    model = onnx.load(path)
+
+    x = Variable(name="Input:0", dtype=np.float32, shape=("dim0", 16, 128))
+    y = Variable(name="Output:0", dtype=np.float32, shape=("dim0", 16, 128))
+    node = Node(op="Identity", inputs=[x], outputs=[y])
+
+    return Model(path, inputs=[x], outputs=[y], nodes=[node], opset=OnnxImporter.get_opset(model))
+
+
+def lstm_model():
+    path = os.path.join(TEST_ROOT, "models", "lstm.onnx")
+    model = onnx.load(path)
+    onnx_graph = model.graph
+
+    def load_initializer(index: int) -> np.ndarray:
+        return onnx.numpy_helper.to_array(onnx_graph.initializer[index])
+
+    # Optional inputs are represented by empty tensors
+    X = Variable(name="X", dtype=np.float32, shape=(4, 3, 6))
+    W = Constant(name="W", values=load_initializer(0))
+    R = Constant(name="R", values=load_initializer(1))
+    B = Constant(name="B", values=load_initializer(2))
+    initial_c = Constant(name="initial_c", values=load_initializer(3))
+
+    Y = Variable(name="Y", dtype=np.float32, shape=(4, 1, 3, 5))
+    Y_h = Variable(name="Y_h", dtype=np.float32, shape=(1, 3, 5))
+    Y_c = Variable(name="Y_c", dtype=np.float32, shape=(1, 3, 5))
+
+    attrs = OrderedDict()
+    attrs["direction"] = "forward"
+    attrs["hidden_size"] = 5
+    node = Node(op="LSTM", attrs=attrs, inputs=[X, W, R, B, Variable.empty(), Variable.empty(), initial_c], outputs=[Y, Y_h, Y_c])
+
+    # Initializers will not be included in the graph inputs.
+    return Model(path, inputs=[X], outputs=[Y, Y_h, Y_c], nodes=[node], opset=OnnxImporter.get_opset(model))
+
+
+def scan_model():
+    path = os.path.join(TEST_ROOT, "models", "scan.onnx")
+    model = onnx.load(path)
+
+    # Body graph
+    sum_in = Variable(name="sum_in", dtype=np.float32, shape=(2, ))
+    next = Variable(name="next", dtype=np.float32, shape=(2, ))
+    sum_out = Variable(name="sum_out", dtype=np.float32, shape=(2, ))
+    scan_out = Variable(name="scan_out", dtype=np.float32, shape=(2, ))
+
+    body_nodes = [
+        Node(op="Add", inputs=[sum_in, next], outputs=[sum_out]),
+        Node(op="Identity", inputs=[sum_out], outputs=[scan_out]),
+    ]
+    body_graph = Graph(nodes=body_nodes, inputs=[sum_in, next], outputs=[sum_out, scan_out], name="scan_body")
+
+    # Outer graph
+    inputs = [
+        Variable(name="initial", dtype=np.float32, shape=(2, )),
+        Variable(name="x", dtype=np.float32, shape=(3, 2)),
+    ]
+    outputs = [
+        Variable(name="y", dtype=np.float32, shape=(2, )),
+        Variable(name="z", dtype=np.float32, shape=(3, 2)),
+    ]
+
+    attrs = OrderedDict()
+    attrs["body"] = body_graph
+    attrs["num_scan_inputs"] = 1
+    scan_node = Node(op="Scan", inputs=inputs, outputs=outputs, attrs=attrs)
+    return Model(path, inputs=inputs, outputs=outputs, nodes=[scan_node], opset=OnnxImporter.get_opset(model))
diff --git a/tools/onnx-graphsurgeon/tests/requirements.txt b/tools/onnx-graphsurgeon/tests/requirements.txt
new file mode 100644
index 00000000..6fc4cc53
--- /dev/null
+++ b/tools/onnx-graphsurgeon/tests/requirements.txt
@@ -0,0 +1,4 @@
+pytest
+onnx
+onnxruntime
+numpy
diff --git a/tools/onnx-graphsurgeon/tests/test_api.py b/tools/onnx-graphsurgeon/tests/test_api.py
new file mode 100644
index 00000000..f4489c69
--- /dev/null
+++ b/tools/onnx-graphsurgeon/tests/test_api.py
@@ -0,0 +1,41 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from onnx_graphsurgeon.importers.onnx_importer import OnnxImporter
+import onnx_graphsurgeon
+
+from onnx_models import identity_model
+
+import tempfile
+import pytest
+import onnx
+import os
+
+class TestApi(object):
+    def setup_method(self):
+        self.imported_graph = OnnxImporter.import_graph(identity_model().load().graph)
+
+
+    def test_import(self):
+        graph = onnx_graphsurgeon.import_onnx(onnx.load(identity_model().path))
+        assert graph == self.imported_graph
+
+
+    def test_export(self):
+        with tempfile.NamedTemporaryFile() as f:
+            onnx_model = onnx_graphsurgeon.export_onnx(self.imported_graph)
+            assert onnx_model
+            assert OnnxImporter.import_graph(onnx_model.graph) == self.imported_graph
diff --git a/tools/onnx-graphsurgeon/tests/test_examples.py b/tools/onnx-graphsurgeon/tests/test_examples.py
new file mode 100644
index 00000000..54a2ade3
--- /dev/null
+++ b/tools/onnx-graphsurgeon/tests/test_examples.py
@@ -0,0 +1,98 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from onnx_graphsurgeon.logger.logger import G_LOGGER
+import onnx_graphsurgeon as gs
+
+import subprocess as sp
+import numpy as np
+import onnxruntime
+import tempfile
+import pytest
+import onnx
+import sys
+import os
+
+
+ROOT_DIR = os.path.realpath(os.path.join(os.path.dirname(__file__), os.path.pardir))
+EXAMPLES_ROOT = os.path.join(ROOT_DIR, "examples")
+
+class Artifact(object):
+    def __init__(self, name, infer=True):
+        self.name = name
+        self.infer = infer
+
+
+EXAMPLES = [
+    ("01_creating_a_model", [Artifact("test_globallppool.onnx")]),
+    ("02_creating_a_model_with_initializer", [Artifact("test_conv.onnx")]),
+    ("03_isolating_a_subgraph", [Artifact("model.onnx"), Artifact("subgraph.onnx")]),
+    ("04_modifying_a_model", [Artifact("model.onnx"), Artifact("modified.onnx")]),
+    ("05_folding_constants", [Artifact("model.onnx"), Artifact("folded.onnx")]),
+    ("06_removing_nodes", [Artifact("model.onnx", infer=False), Artifact("removed.onnx")]),
+    ("07_creating_a_model_with_the_layer_api", [Artifact("model.onnx")]),
+]
+
+# Extract any ``` blocks from the README
+def load_commands_from_readme(readme):
+    def ignore_command(cmd):
+        return "pip" in cmd
+
+    commands = []
+    with open(readme, 'r') as f:
+        in_command_block = False
+        for line in f.readlines():
+            if not in_command_block and "```bash" in line:
+                in_command_block = True
+            elif in_command_block:
+                if "```" in line:
+                    in_command_block = False
+                elif not ignore_command(line):
+                    commands.append(line.strip())
+    return commands
+
+
+def infer_model(path):
+    model = onnx.load(path)
+    graph = gs.import_onnx(model)
+
+    feed_dict = {}
+    for tensor in graph.inputs:
+        feed_dict[tensor.name] = np.random.random_sample(size=tensor.shape).astype(tensor.dtype)
+
+    output_names = [out.name for out in graph.outputs]
+
+    sess = onnxruntime.InferenceSession(model.SerializeToString())
+    outputs = sess.run(output_names, feed_dict)
+    G_LOGGER.info("Inference outputs: {:}".format(outputs))
+    return outputs
+
+
+@pytest.mark.parametrize("example_dir,artifacts", EXAMPLES)
+def test_examples(example_dir, artifacts):
+    example_dir = os.path.join(EXAMPLES_ROOT, example_dir)
+    readme = os.path.join(example_dir, "README.md")
+    commands = load_commands_from_readme(readme)
+    for command in commands:
+        G_LOGGER.info(command)
+        assert sp.run(["bash", "-c", command], cwd=example_dir, env={"PYTHONPATH": ROOT_DIR}).returncode == 0
+
+    for artifact in artifacts:
+        artifact_path = os.path.join(example_dir, artifact.name)
+        assert os.path.exists(artifact_path)
+        if artifact.infer:
+            assert infer_model(artifact_path)
+        os.remove(artifact_path)
diff --git a/tools/onnx-graphsurgeon/tests/test_exporters.py b/tools/onnx-graphsurgeon/tests/test_exporters.py
new file mode 100644
index 00000000..80d5cbf9
--- /dev/null
+++ b/tools/onnx-graphsurgeon/tests/test_exporters.py
@@ -0,0 +1,136 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from onnx_graphsurgeon.exporters.onnx_exporter import OnnxExporter
+from onnx_graphsurgeon.importers.onnx_importer import OnnxImporter
+from onnx_graphsurgeon.logger.logger import G_LOGGER
+
+from onnx_models import identity_model, lstm_model, scan_model, dim_param_model
+
+from onnx_graphsurgeon.ir.tensor import Tensor, Constant, Variable
+from onnx_graphsurgeon.ir.graph import Graph
+from onnx_graphsurgeon.ir.node import Node
+
+from collections import OrderedDict
+import onnx.numpy_helper
+import numpy as np
+import pytest
+import onnx
+
+class TestOnnxExporter(object):
+    def test_export_constant_tensor_to_tensor_proto(self):
+        name = "constant_tensor"
+        shape = (3, 224, 224)
+        values = np.random.random_sample(size=shape).astype(np.float32)
+
+        tensor = Constant(name=name, values=values)
+        onnx_tensor = OnnxExporter.export_tensor_proto(tensor)
+        assert onnx_tensor.name == name
+        assert np.all(onnx.numpy_helper.to_array(onnx_tensor) == values)
+        assert onnx_tensor.data_type == onnx.TensorProto.FLOAT
+        assert tuple(onnx_tensor.dims) == shape
+
+
+    def test_export_constant_tensor_to_value_info_proto(self):
+        name = "constant_tensor"
+        shape = (3, 224, 224)
+        values = np.random.random_sample(size=shape).astype(np.float32)
+
+        tensor = Constant(name=name, values=values)
+        onnx_tensor = OnnxExporter.export_value_info_proto(tensor, do_type_check=True)
+        assert onnx_tensor.name == name
+        assert onnx_tensor.type.tensor_type.elem_type == onnx.TensorProto.FLOAT
+
+        onnx_shape = []
+        for dim in onnx_tensor.type.tensor_type.shape.dim:
+            onnx_shape.append(dim.dim_value)
+        assert tuple(onnx_shape) == shape
+
+
+    def test_export_variable_tensor(self):
+        name = "variable_tensor"
+        shape = (3, 224, 224)
+        dtype = np.float32
+
+        tensor = Variable(dtype=dtype, shape=shape, name=name)
+        onnx_tensor = OnnxExporter.export_value_info_proto(tensor, do_type_check=True)
+        assert onnx_tensor.name == name
+        assert onnx_tensor.type.tensor_type.elem_type == onnx.TensorProto.FLOAT
+
+        onnx_shape = []
+        for dim in onnx_tensor.type.tensor_type.shape.dim:
+            onnx_shape.append(dim.dim_value)
+        assert tuple(onnx_shape) == shape
+
+
+    # TODO: Test subgraph export.
+    def test_export_node(self):
+        name = "TestNode"
+        op = "Test"
+        inputs = [Variable(name="input")]
+        outputs = [Variable(name="output")]
+        attrs = OrderedDict()
+        attrs["float_attr"] = 4.0
+        attrs["int_attr"] = 10
+        attrs["str_attr"] = "constant"
+        attrs["tensor_attr"] = Constant("testTensor", np.ones(shape=(1, 2, 3, 4), dtype=np.float32))
+        attrs["floats_attr"] = [1.0, 2.0, 3.0, 4.0]
+        attrs["ints_attr"] = [4, 3, 2, 1]
+        attrs["strings_attr"] = ["constant", "and", "variable"]
+        node = Node(op=op, name=name, inputs=inputs, outputs=outputs, attrs=attrs)
+
+        onnx_node = OnnxExporter.export_node(node)
+        assert onnx_node.name == name
+        assert onnx_node.op_type == op
+        assert onnx_node.input == ["input"]
+        assert onnx_node.output == ["output"]
+        for onnx_attr, (name, attr) in zip(onnx_node.attribute, attrs.items()):
+            assert onnx_attr.name == name
+            if isinstance(attr, float):
+                assert onnx_attr.f == attr
+            elif isinstance(attr, int):
+                assert onnx_attr.i == attr
+            elif isinstance(attr, str):
+                assert onnx_attr.s.decode() == attr
+            elif isinstance(attr, Tensor):
+                assert onnx_attr.t.SerializeToString() == OnnxExporter.export_tensor_proto(attr).SerializeToString()
+            elif isinstance(attr, list):
+                if isinstance(attr[0], float):
+                    assert onnx_attr.floats == attr
+                elif isinstance(attr[0], int):
+                    assert onnx_attr.ints == attr
+                elif isinstance(attr[0], str):
+                    assert [s.decode() for s in onnx_attr.strings] == attr
+                else:
+                    raise AssertionError("Unrecognized list attribute: ({:}: {:}) of type: {:}".format(name, attr, type(attr)))
+            else:
+                raise AssertionError("Unrecognized attribute: ({:}: {:}) of type: {:}".format(name, attr, type(attr)))
+
+
+    # See test_importers for import correctness checks
+    # This function first imports an ONNX graph, and then re-exports it with no changes.
+    # The exported ONNX graph should exactly match the original.
+    @pytest.mark.parametrize("model", [identity_model(), lstm_model(), scan_model(), dim_param_model()])
+    def test_export_graph(self, model):
+        onnx_graph = model.load().graph
+        graph = OnnxImporter.import_graph(onnx_graph)
+        exported_onnx_graph = OnnxExporter.export_graph(graph)
+        imported_graph = OnnxImporter.import_graph(exported_onnx_graph)
+        assert graph == imported_graph
+        assert graph.opset == imported_graph.opset
+        # ONNX exports the initializers in this model differently after importing - ONNX GS can't do much about this.
+        if model.path != lstm_model().path:
+            assert onnx_graph == exported_onnx_graph
diff --git a/tools/onnx-graphsurgeon/tests/test_importers.py b/tools/onnx-graphsurgeon/tests/test_importers.py
new file mode 100644
index 00000000..dca0217c
--- /dev/null
+++ b/tools/onnx-graphsurgeon/tests/test_importers.py
@@ -0,0 +1,119 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from onnx_graphsurgeon.importers.onnx_importer import OnnxImporter
+from onnx_graphsurgeon.logger.logger import G_LOGGER
+from onnx_graphsurgeon.ir.tensor import Tensor, Variable, Constant
+
+from onnx_models import identity_model, lstm_model, scan_model, dim_param_model
+
+from collections import OrderedDict
+import onnx.shape_inference
+import onnx.numpy_helper
+import numpy as np
+import pytest
+import onnx
+
+G_LOGGER.severity = G_LOGGER.ULTRA_VERBOSE
+
+class TestOnnxImporter(object):
+    def test_import_variable_tensor(self):
+        name = "test0"
+        shape = [1, 2, 3, 4]
+        onnx_tensor = onnx.helper.make_tensor_value_info(name, onnx.TensorProto.FLOAT, shape)
+        tensor = OnnxImporter.import_tensor(onnx_tensor)
+        assert type(tensor) == Variable
+        assert tensor.name == name
+        assert tensor.dtype == np.float32
+        assert tensor.shape == shape
+
+
+    def test_import_constant_tensor(self):
+        shape = (3, 3, 3)
+        dtype = np.float32
+        onnx_tensor = onnx.numpy_helper.from_array(np.ones(shape=shape, dtype=dtype))
+        tensor = OnnxImporter.import_tensor(onnx_tensor)
+        assert type(tensor) == Constant
+        assert tensor.dtype == dtype
+        assert tensor.shape == shape
+
+
+    def test_import_tensor_unknown_metadata(self):
+        name = "test0"
+        onnx_tensor = onnx.helper.make_empty_tensor_value_info(name)
+        tensor = OnnxImporter.import_tensor(onnx_tensor)
+        assert type(tensor) == Variable
+        assert tensor.name == name
+
+
+    # TODO: Test all attribute types - missing graph
+    def test_import_node(self):
+        op = "Test"
+        inputs = ["x"]
+        outputs = ["y"]
+        float_attr = 4.0
+        int_attr = 10
+        str_attr = "constant"
+        tensor_vals = np.ones(shape=(1, 2, 3, 4), dtype=np.float32)
+        tensor_attr = onnx.numpy_helper.from_array(tensor_vals)
+        floats_attr = [1.0, 2.0, 3.0, 4.0]
+        ints_attr = [4, 3, 2, 1]
+        strings_attr = ["constant", "and", "variable"]
+
+        onnx_node = onnx.helper.make_node(op, inputs, outputs, float_attr=float_attr, int_attr=int_attr, str_attr=str_attr, tensor_attr=tensor_attr, floats_attr=floats_attr, ints_attr=ints_attr, strings_attr=strings_attr)
+        node = OnnxImporter.import_node(onnx_node, OrderedDict())
+        assert node.op == op
+        assert node.attrs["float_attr"] == float_attr
+        assert node.attrs["int_attr"] == int_attr
+        assert node.attrs["str_attr"] == str_attr
+        # Tensor should turn into a Constant
+        assert np.all(node.attrs["tensor_attr"].values == tensor_vals)
+        assert node.attrs["floats_attr"] == floats_attr
+        assert node.attrs["ints_attr"] == ints_attr
+        assert node.attrs["strings_attr"] == strings_attr
+
+
+    @pytest.mark.parametrize("model", [identity_model(), lstm_model(), scan_model()])
+    def test_import_graph(self, model):
+        graph = OnnxImporter.import_graph(model.load().graph)
+        model.assert_equal(graph)
+
+
+    def test_import_graph_value_info(self):
+        model = onnx.shape_inference.infer_shapes(identity_model().load())
+        graph = OnnxImporter.import_graph(model.graph)
+        tensors = graph.tensors()
+        assert all([type(tensor) == Variable and tensor.dtype is not None and tensor.shape for tensor in tensors.values()])
+
+
+    def test_import_graph_tensor_map_preserved(self):
+        model = identity_model()
+        tensor_map = OrderedDict()
+        graph = OnnxImporter.import_graph(model.load().graph, tensor_map=tensor_map)
+        assert len(tensor_map) == 0
+        model.assert_equal(graph)
+
+
+    def test_import_graph_with_initializer(self):
+        model = lstm_model()
+        graph = OnnxImporter.import_graph(model.load().graph)
+        model.assert_equal(graph)
+
+
+    def test_import_graph_with_dim_param(self):
+        model = dim_param_model()
+        graph = OnnxImporter.import_graph(model.load().graph)
+        model.assert_equal(graph)
diff --git a/tools/onnx-graphsurgeon/tests/test_ir.py b/tools/onnx-graphsurgeon/tests/test_ir.py
new file mode 100644
index 00000000..9d7bd5e6
--- /dev/null
+++ b/tools/onnx-graphsurgeon/tests/test_ir.py
@@ -0,0 +1,729 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from onnx_graphsurgeon.util.exception import OnnxGraphSurgeonException
+from onnx_graphsurgeon.ir.tensor import Constant, Variable
+from onnx_graphsurgeon.logger.logger import G_LOGGER
+from onnx_graphsurgeon.ir.graph import Graph
+from onnx_graphsurgeon.ir.node import Node
+
+import numpy as np
+import pytest
+import onnx
+import copy
+
+G_LOGGER.severity = G_LOGGER.ULTRA_VERBOSE
+
+class TensorBaseTests(object):
+    def test_can_convert_in_place_to_constant(self):
+        tensor = self.tensor.to_constant(values=np.ones((1, 3, 5, 5), dtype=np.float64))
+        assert tensor is self.tensor
+        assert isinstance(tensor, Constant)
+        assert isinstance(self.input_node.outputs[0], Constant)
+        assert isinstance(self.output_node.inputs[0], Constant)
+        assert tensor.shape == (1, 3, 5, 5)
+        assert tensor.dtype == np.float64
+        assert np.all(self.input_node.outputs[0].values == tensor.values)
+        assert np.all(self.output_node.inputs[0].values == tensor.values)
+
+    def test_can_convert_in_place_to_variable(self):
+        tensor = self.tensor.to_variable(dtype=np.float32, shape=(1, 3, 224, 224))
+        assert tensor is self.tensor
+        assert isinstance(tensor, Variable)
+        assert isinstance(self.input_node.outputs[0], Variable)
+        assert tensor.dtype == np.float32
+        assert tensor.shape == (1, 3, 224, 224)
+        assert self.input_node.outputs[0].dtype == tensor.dtype
+        assert self.input_node.outputs[0].shape == tensor.shape
+
+    def test_equals(self):
+        assert self.tensor == self.tensor
+
+    def test_set_inputs_updates_old_inputs(self):
+        dummy = Node(op="dummy")
+        self.tensor.inputs = [dummy]
+        assert len(self.input_node.outputs) == 0
+        assert dummy.outputs[0] == self.tensor
+
+    def test_set_outputs_updates_old_outputs(self):
+        dummy = Node(op="dummy")
+        self.tensor.outputs = [dummy]
+        assert len(self.output_node.inputs) == 0
+        assert dummy.inputs[0] == self.tensor
+
+    def test_can_copy_inputs_from_other_node(self):
+        tensor = Variable(name="other_test_tensor")
+        tensor.inputs = self.tensor.inputs
+        assert tensor.inputs == self.tensor.inputs
+
+    def test_can_copy_outputs_from_other_node(self):
+        tensor = Variable(name="other_test_tensor")
+        tensor.outputs = self.tensor.outputs
+        assert tensor.outputs == self.tensor.outputs
+
+    def test_i(self):
+        x = Variable(name="x")
+        y = Variable(name="y")
+        node = Node(op="Add", name="Input", inputs=[x], outputs=[y])
+        assert y.i() == x
+
+    def test_i_multiple_inputs(self):
+        x = Variable(name="x")
+        x2 = Variable(name="x2")
+        y = Variable(name="y")
+        node = Node(op="Add", name="Input", inputs=[x, x2], outputs=[y])
+        assert y.i() == x
+        assert y.i(1) == x2
+
+    def test_o(self):
+        x = Variable(name="x")
+        y = Variable(name="y")
+        node = Node(op="Add", name="Input", inputs=[x], outputs=[y])
+        assert x.o() == y
+
+
+    def test_o_multiple_outputs(self):
+        x = Variable(name="x")
+        y = Variable(name="y")
+        y2 = Variable(name="y2")
+        node = Node(op="Add", name="Input", inputs=[x], outputs=[y])
+        node2 = Node(op="Add", name="Input", inputs=[x], outputs=[y2])
+        assert x.o() == y
+        assert x.o(1) == y2
+
+
+class TestVariable(TensorBaseTests):
+    def setup_method(self):
+        self.tensor = Variable(name="test_tensor", dtype=np.float32, shape=(1, 3, 224, 224))
+        self.input_node = Node(op="Add", outputs=[self.tensor])
+        self.output_node = Node(op="Add", inputs=[self.tensor])
+
+    def test_equals_name_mismatch(self):
+        tensor = Variable(name="test_tensor0", dtype=np.float32, shape=(1, 3, 224, 224))
+        assert not self.tensor == tensor
+
+
+class TestConstant(TensorBaseTests):
+    def setup_method(self):
+        self.tensor = Constant(name="test_tensor", values=np.ones((1, 3, 5, 5), dtype=np.float64))
+        self.input_node = Node(op="Add", outputs=[self.tensor]) # Doesn't make sense for Constants, but needed to make base tests happy.
+        self.output_node = Node(op="Add", inputs=[self.tensor])
+
+    def test_can_get_shape(self):
+        assert self.tensor.shape == (1, 3, 5, 5)
+
+    def test_can_get_dtype(self):
+        assert self.tensor.dtype == np.float64
+
+
+class TestNode(object):
+    def setup_method(self):
+        self.input_tensor = Variable(name="x")
+        self.output_tensor = Variable(name="y")
+        self.node = Node(op="Add", name="Test", inputs=[self.input_tensor], outputs=[self.output_tensor])
+
+    def test_equals(self):
+        assert self.node == self.node
+
+    def test_equals_name_mismatch(self):
+        node = Node(op="Add", name="OtherTest")
+        assert not self.node == node
+
+    def test_equals_op_mismatch(self):
+        node = Node(op="Subtract", name="Test")
+        assert not self.node == node
+
+    def test_equals_num_inputs_mismatch(self):
+        node = Node(op="Subtract", name="Test")
+        assert not self.node == node
+
+    def test_equals(self):
+        assert self.node == self.node
+
+    def test_equals_inputs_mismatch(self):
+        tensor = Variable(name="other_tensor")
+        assert not self.input_tensor == tensor
+
+        node = Node(op="Add", name="Test", inputs=[tensor])
+        assert not self.node == node
+
+    def test_set_inputs_updates_old_inputs(self):
+        dummy = Variable(name="dummy")
+        self.node.inputs = [dummy]
+        assert len(self.input_tensor.outputs) == 0
+        assert dummy.outputs[0] == self.node
+
+    def test_set_outputs_updates_old_outputs(self):
+        dummy = Variable(name="dummy")
+        self.node.outputs = [dummy]
+        assert len(self.output_tensor.inputs) == 0
+        assert dummy.inputs[0] == self.node
+
+    def test_can_copy_inputs_from_other_node(self):
+        node = Node(op="Subtract")
+        node.inputs = self.node.inputs
+        assert node.inputs == self.node.inputs
+
+    def test_can_copy_outputs_from_other_node(self):
+        node = Node(op="Subtract")
+        node.outputs = self.node.outputs
+        assert node.outputs == self.node.outputs
+
+    def test_i(self):
+        intermediate_tensor = Variable(name="intermediate")
+        input_node = Node(op="Add", name="Input", inputs=[self.input_tensor], outputs=[intermediate_tensor])
+        output_node = Node(op="Add", name="Out", inputs=[intermediate_tensor], outputs=[self.output_tensor])
+        assert output_node.i() == input_node
+
+    def test_i_multiple_inputs(self):
+        intermediate_tensor = Variable(name="intermediate")
+        intermediate_tensor2 = Variable(name="intermediate2")
+        input_node = Node(op="Add", name="Input", inputs=[self.input_tensor], outputs=[intermediate_tensor])
+        input_node2 = Node(op="Add", name="Input2", inputs=[self.input_tensor], outputs=[intermediate_tensor2])
+        output_node = Node(op="Add", name="Out", inputs=[intermediate_tensor, intermediate_tensor2], outputs=[self.output_tensor])
+        assert output_node.i() == input_node
+        assert output_node.i(1) == input_node2
+
+    def test_o(self):
+        intermediate_tensor = Variable(name="intermediate")
+        input_node = Node(op="Add", name="Input", inputs=[self.input_tensor], outputs=[intermediate_tensor])
+        output_node = Node(op="Add", name="Out", inputs=[intermediate_tensor], outputs=[self.output_tensor])
+        assert input_node.o() == output_node
+
+    def test_o_multiple_outputs(self):
+        intermediate_tensor = Variable(name="intermediate")
+        intermediate_tensor2 = Variable(name="intermediate2")
+        input_node = Node(op="Add", name="Input", inputs=[self.input_tensor], outputs=[intermediate_tensor])
+        output_node = Node(op="Add", name="Out", inputs=[intermediate_tensor], outputs=[self.output_tensor])
+        output_node2 = Node(op="Add", name="Input2", inputs=[intermediate_tensor], outputs=[intermediate_tensor2])
+        assert input_node.o() == output_node
+        assert input_node.o(1) == output_node2
+
+
+class TestNodeIO(object):
+    def setup_method(self, field_names):
+        self.tensors = [Variable(name="test_tensor_{:}".format(i), dtype=np.float32, shape=(1, 3, 224, 224)) for i in range(10)]
+        self.node = Node(op="Dummy")
+
+    def get_lists(self, field_names):
+        return getattr(self.node, field_names[0]), field_names[1]
+
+    @pytest.mark.parametrize("field_names", [("inputs", "outputs"), ("outputs", "inputs")])
+    def test_append(self, field_names):
+        nlist, tensor_field = self.get_lists(field_names)
+        nlist.append(self.tensors[0])
+        assert nlist[0] == self.tensors[0]
+        assert getattr(self.tensors[0], tensor_field)[0] == self.node
+
+    @pytest.mark.parametrize("field_names", [("inputs", "outputs"), ("outputs", "inputs")])
+    def test_extend(self, field_names):
+        nlist, tensor_field = self.get_lists(field_names)
+        nlist.extend(self.tensors)
+        for tensor in self.tensors:
+            assert tensor in nlist
+            assert getattr(tensor, tensor_field)[0] == self.node
+
+    @pytest.mark.parametrize("field_names", [("inputs", "outputs"), ("outputs", "inputs")])
+    def test_insert(self, field_names):
+        nlist, tensor_field = self.get_lists(field_names)
+        nlist.append(self.tensors[1])
+        nlist.insert(0, self.tensors[0])
+        assert nlist[0] == self.tensors[0]
+        assert getattr(self.tensors[0], tensor_field)[0] == self.node
+
+    @pytest.mark.parametrize("field_names", [("inputs", "outputs"), ("outputs", "inputs")])
+    def test_remove(self, field_names):
+        nlist, tensor_field = self.get_lists(field_names)
+        nlist.append(self.tensors[0])
+        nlist.remove(self.tensors[0])
+        assert len(nlist) == 0
+        assert len(getattr(self.tensors[0], tensor_field)) == 0
+
+    @pytest.mark.parametrize("field_names", [("inputs", "outputs"), ("outputs", "inputs")])
+    def test_pop(self, field_names):
+        nlist, tensor_field = self.get_lists(field_names)
+        nlist.append(self.tensors[0])
+        tensor = nlist.pop()
+        assert len(nlist) == 0
+        assert len(getattr(tensor, tensor_field)) == 0
+
+    @pytest.mark.parametrize("field_names", [("inputs", "outputs"), ("outputs", "inputs")])
+    def test_pop_index(self, field_names):
+        nlist, tensor_field = self.get_lists(field_names)
+        nlist.extend(self.tensors)
+        tensor = nlist.pop(1)
+        assert self.tensors[1] not in nlist
+        assert len(getattr(tensor, tensor_field)) == 0
+
+    @pytest.mark.parametrize("field_names", [("inputs", "outputs"), ("outputs", "inputs")])
+    def test_del_index(self, field_names):
+        nlist, tensor_field = self.get_lists(field_names)
+        nlist.extend(self.tensors)
+        tensor = nlist[1]
+        del nlist[1]
+        assert self.tensors[1] not in nlist
+        assert len(getattr(tensor, tensor_field)) == 0
+
+    @pytest.mark.parametrize("field_names", [("inputs", "outputs"), ("outputs", "inputs")])
+    def test_clear(self, field_names):
+        nlist, tensor_field = self.get_lists(field_names)
+        nlist.extend(self.tensors)
+        nlist.clear()
+        assert len(nlist) == 0
+        assert all([len(getattr(tensor, tensor_field)) == 0 for tensor in self.tensors])
+
+    @pytest.mark.parametrize("field_names", [("inputs", "outputs"), ("outputs", "inputs")])
+    def test_add(self, field_names):
+        nlist, tensor_field = self.get_lists(field_names)
+        nlist = nlist + self.tensors
+        for tensor in self.tensors:
+            assert tensor in nlist
+            assert getattr(tensor, tensor_field)[0] == self.node
+
+    @pytest.mark.parametrize("field_names", [("inputs", "outputs"), ("outputs", "inputs")])
+    def test_iadd(self, field_names):
+        nlist, tensor_field = self.get_lists(field_names)
+        nlist += self.tensors
+        for tensor in self.tensors:
+            assert tensor in nlist
+            assert getattr(tensor, tensor_field)[0] == self.node
+
+    @pytest.mark.parametrize("field_names", [("inputs", "outputs"), ("outputs", "inputs")])
+    def test_setitem(self, field_names):
+        nlist, tensor_field = self.get_lists(field_names)
+        nlist.append(self.tensors[0])
+        new_tensor = Variable("new_tensor")
+        nlist[0] = new_tensor
+        assert nlist[0] == new_tensor
+        assert len(getattr(self.tensors[0], tensor_field)) == 0
+        assert getattr(new_tensor, tensor_field)[0] == self.node
+
+
+def tensors_linear_graph():
+    inputs = [Variable(name="x")]
+    intermediate0 = Variable(name="intermediate0")
+    intermediate1 = Variable(name="intermediate1")
+    intermediate2 = Variable(name="intermediate2")
+    outputs = [Variable(name="y")]
+
+    tensors = inputs + [intermediate0, intermediate1, intermediate2] + outputs
+    tensors = {tensor.name: tensor for tensor in tensors}
+    # Nodes are NOT in topo order.
+    nodes = [
+        Node(op="Add", name="Test0", inputs=inputs, outputs=[intermediate0]),
+        Node(op="Add", name="Test1", inputs=[intermediate0], outputs=[intermediate1]),
+        Node(op="Add", name="Test2", inputs=[intermediate1], outputs=[intermediate2]),
+        Node(op="Add", name="Test3", inputs=[intermediate2], outputs=outputs),
+    ]
+    return Graph(nodes=nodes, inputs=inputs, outputs=outputs), nodes, tensors
+
+
+def build_basic_graph():
+    inputs = [Variable(name="x")]
+    outputs = [Variable(name="y")]
+    nodes = [
+        Node(op="Add", name="Test", inputs=inputs, outputs=outputs),
+    ]
+    return Graph(nodes=nodes, inputs=inputs, outputs=outputs)
+
+
+def build_two_layer_graph():
+    inputs = [Variable(name="x")]
+    intermediate_tensor = Variable(name="intermediate")
+    outputs = [Variable(name="y")]
+    nodes = [
+        Node(op="Add", name="Test0", inputs=inputs, outputs=[intermediate_tensor]),
+        Node(op="Add", name="Test1", inputs=[intermediate_tensor], outputs=outputs),
+    ]
+    return Graph(nodes=nodes, inputs=inputs, outputs=outputs)
+
+
+def build_two_layer_graph_multiple_io():
+    inputs = [Variable(name="x0"), Variable(name="x1")]
+    intermediate_tensor = Variable(name="intermediate")
+    outputs = [Variable(name="y0"), Variable(name="y1")]
+    nodes = [
+        Node(op="Add", name="Test0", inputs=inputs, outputs=[intermediate_tensor]),
+        Node(op="Add", name="Test1", inputs=[intermediate_tensor], outputs=outputs),
+    ]
+    return Graph(nodes=nodes, inputs=inputs, outputs=outputs)
+
+
+GRAPH_TEST_CASES = [
+    build_basic_graph(),
+    build_two_layer_graph(),
+    build_two_layer_graph_multiple_io(),
+]
+
+
+def toposort_linear_graph():
+    inputs = [Variable(name="x")]
+    intermediate0 = Variable(name="intermediate0")
+    intermediate1 = Variable(name="intermediate1")
+    intermediate2 = Variable(name="intermediate2")
+    outputs = [Variable(name="y")]
+    # Nodes are NOT in topo order.
+    nodes = [
+        Node(op="Add", name="Test0", inputs=inputs, outputs=[intermediate0]),
+        Node(op="Add", name="Test2", inputs=[intermediate1], outputs=[intermediate2]),
+        Node(op="Add", name="Test3", inputs=[intermediate2], outputs=outputs),
+        Node(op="Add", name="Test1", inputs=[intermediate0], outputs=[intermediate1]),
+    ]
+    expected_node_order = [nodes[0], nodes[3], nodes[1], nodes[2]]
+    return Graph(nodes=nodes, inputs=inputs, outputs=outputs), expected_node_order
+
+
+# Graph structure:
+# x
+# |
+# Test0 -> out0 (graph output)
+# |
+# out0
+# |
+# Test1 -> out1 (graph output)
+# |
+# out1
+# |
+# Test2 -> out2 (graph_output)
+def toposort_multi_tier_output_graph():
+    inputs = [Variable(name="x")]
+    outputs = [Variable(name="out0"), Variable(name="out1"), Variable(name="out2")]
+    out0, out1, out2 = outputs
+    nodes = [
+        Node(op="Add", name="Test2", inputs=[out1], outputs=[out2]),
+        Node(op="Add", name="Test0", inputs=inputs, outputs=[out0]),
+        Node(op="Add", name="Test1", inputs=[out0], outputs=[out1]),
+    ]
+    expected_node_order = [nodes[1], nodes[2], nodes[0]]
+    return Graph(nodes=nodes, inputs=inputs, outputs=outputs), expected_node_order
+
+
+# Graph structure:
+# x2  x1
+# |   |
+# Test0
+# |
+# int0  x0
+# |    /
+# Test1
+# |
+# int1  x3
+# |    /
+# Test2 -> out (graph_output)
+def toposort_multi_tier_input_graph():
+    inputs = [Variable(name="x0"), Variable(name="x1"), Variable(name="x2"), Variable(name="x3")]
+    int0, int1 = [Variable(name="intermediate0"), Variable(name="intermediate1")]
+    outputs = [Variable(name="out")]
+    x0, x1, x2, x3 = inputs
+    nodes = [
+        Node(op="Add", name="Test2", inputs=[int1, x3], outputs=outputs),
+        Node(op="Add", name="Test0", inputs=[x2, x1], outputs=[int0]),
+        Node(op="Add", name="Test1", inputs=[int0, x0], outputs=[int1]),
+    ]
+    expected_node_order = [nodes[1], nodes[2], nodes[0]]
+    return Graph(nodes=nodes, inputs=inputs, outputs=outputs), expected_node_order
+
+
+TOPOSORT_TEST_CASES = [
+    toposort_linear_graph(),
+    toposort_multi_tier_output_graph(),
+    toposort_multi_tier_input_graph(),
+]
+
+class TestGraph(object):
+    def test_generate_name(self):
+        graph = Graph()
+        names = set()
+        num_names = 100
+        # This function should not return the same name more than once
+        for idx in range(num_names):
+            names.add(graph._generate_name("name"))
+        assert len(names) == 100
+
+
+    def test_register(self):
+        @Graph.register()
+        def add(self, a, b):
+            return self.layer(op="Add", inputs=[a, b], outputs=["add_out"])
+
+        graph = Graph()
+        [output] = graph.add("a", "b")
+        assert "add_out" in output.name
+        assert len(graph.nodes) == 1
+        assert graph.nodes[-1].op == "Add"
+
+
+    def test_register_opset(self):
+        @Graph.register(opsets=[11])
+        def add(self, a, b):
+            return self.layer(op="Add", inputs=[a, b], outputs=["add_out"])
+
+        @Graph.register(opsets=[10])
+        def add(self, a, b):
+            return self.layer(op="Add-10", inputs=[a, b], outputs=["add_out"])
+
+        graph = Graph()
+        [output] = graph.add("a", "b")
+        assert "add_out" in output.name
+        assert len(graph.nodes) == 1
+        assert graph.nodes[-1].op == "Add"
+
+        graph_opset10 = Graph(opset=10)
+        [output] = graph_opset10.add("a", "b")
+        assert "add_out" in output.name
+        assert len(graph_opset10.nodes) == 1
+        assert graph_opset10.nodes[-1].op == "Add-10"
+
+
+    def test_layer_with_attrs(self):
+        graph = Graph()
+        outputs = graph.layer(op="Add", name="node", attrs={"fake_attr": 0})
+        assert len(graph.nodes) == 1
+        assert graph.nodes[-1].op == "Add"
+        assert graph.nodes[-1].name == "node"
+        assert graph.nodes[-1].attrs["fake_attr"] == 0
+
+
+    def test_layer_with_tensors(self):
+        x0 = Variable("x0")
+        x1 = Variable("x1")
+        y0 = Variable("y0")
+        y1 = Variable("y1")
+        graph = Graph()
+
+        outputs = graph.layer(op="Fake", inputs=[x0, x1], outputs=[y0, y1])
+        assert outputs == [y0, y1]
+        assert len(graph.nodes) == 1
+        assert graph.nodes[-1].inputs == [x0, x1]
+        assert graph.nodes[-1].outputs == outputs
+
+
+    def test_layer_with_strings(self):
+        x0 = "x0"
+        x1 = "x1"
+        y0 = "y0"
+        y1 = "y1"
+        graph = Graph()
+
+        outputs = graph.layer(op="Fake", inputs=[x0, x1], outputs=[y0, y1])
+        assert len(graph.nodes) == 1
+        assert [prefix in tensor.name for prefix, tensor in zip([x0, x1], graph.nodes[-1].inputs)]
+        assert [prefix in tensor.name for prefix, tensor in zip([y0, y1], graph.nodes[-1].outputs)]
+        assert graph.nodes[-1].outputs == outputs
+
+
+    def test_layer_with_arrays(self):
+        x0 = np.array([1])
+        x1 = np.array([1])
+        y0 = "y0"
+        y1 = "y1"
+        graph = Graph()
+
+        outputs = graph.layer(op="Fake", inputs=[x0, x1], outputs=[y0, y1])
+        assert [prefix in tensor.name for prefix, tensor in zip([y0, y1], graph.nodes[-1].outputs)]
+        assert len(graph.nodes) == 1
+        assert graph.nodes[-1].inputs[0].values == x0
+        assert graph.nodes[-1].inputs[1].values == x1
+        assert graph.nodes[-1].outputs == outputs
+
+
+    def test_tensors(self):
+        graph, nodes, tensors = tensors_linear_graph()
+        graph_tensors = graph.tensors()
+        for name, tensor in tensors.items():
+            assert name in graph_tensors
+            assert tensor is graph_tensors[name]
+
+        for name, tensor in graph_tensors.items():
+            assert name in tensors
+            assert tensor is tensors[name]
+
+
+    def test_tensors_check_duplicates(self):
+        inputs = [Variable(name="x")]
+        outputs = [Variable(name="x")] # Distinct tensors with the same name
+        nodes = [
+            Node(op="Add", name="Test", inputs=inputs, outputs=outputs),
+        ]
+        graph = Graph(nodes=nodes, inputs=inputs, outputs=outputs)
+
+        with pytest.raises(OnnxGraphSurgeonException):
+            graph.tensors(check_duplicates=True)
+
+
+    @pytest.mark.parametrize("graph", GRAPH_TEST_CASES)
+    def test_get_used_node_ids(self, graph):
+        graph_used_nodes = copy.copy(graph.nodes)
+        graph_used_tensors = copy.copy(list(graph.tensors().values()))
+
+        unused_tensor = Variable(name="Unused")
+        unused_node = Node(op="Unused", inputs=[graph.inputs[0]], outputs=[unused_tensor])
+        graph.nodes.append(unused_node)
+
+        with graph.node_ids():
+            used_node_ids, used_tensors = graph._get_used_node_ids()
+            assert len(used_node_ids) == len(graph.nodes) - 1
+            assert all([node.id in used_node_ids for node in graph_used_nodes])
+            assert unused_node.id not in used_node_ids
+            assert unused_tensor not in used_tensors
+            assert all([used_tensor in used_tensors for used_tensor in graph_used_tensors])
+
+
+    @pytest.mark.parametrize("toposort_test_case", TOPOSORT_TEST_CASES)
+    def test_topologically_sort(self, toposort_test_case):
+        graph, expected_node_order = toposort_test_case
+        assert graph.nodes != expected_node_order
+        graph.toposort()
+        assert graph.nodes == expected_node_order
+
+
+    def test_cleanup_multi_tier(self):
+        graph, _ = toposort_multi_tier_output_graph()
+        tensor = graph.outputs.pop()
+        unused_node = tensor.inputs[0]
+        graph.cleanup() # Should remove just the Test2 node as out1 is still an output.
+        assert unused_node not in graph.nodes
+        assert len(graph.nodes) == 2
+        assert len(graph.outputs) == 2
+
+        tensor_map = graph.tensors()
+        assert tensor.name not in tensor_map
+
+
+    def test_cleanup_intermediate_tensors(self):
+        graph, _  = toposort_linear_graph()
+        graph.toposort()
+        graph_output = graph.outputs[0]
+
+        dummy = Variable("dummy")
+        # Add unused tensor to a node in the middle of the graph.
+        # Since it does not contribute to graph outputs, it should be removed.
+        graph.nodes[1].outputs.append(dummy)
+
+        graph.cleanup()
+        assert dummy not in graph.nodes[1].outputs
+        assert graph.outputs[0] == graph_output # Graoh outputs will never be removed
+
+
+    def test_cleanup_independent_path(self):
+        graph, _ = toposort_linear_graph()
+        # Build out a path totally unrelated to rest of the graph
+        indep0 = Variable(name="indep0")
+        indep1 = Variable(name="indep1")
+        node = Node(op="IndepTest", inputs=[indep0], outputs=[indep1])
+        graph.inputs.append(indep0) # Unused inputs should be removed as well
+        graph.nodes.append(node)
+        graph.cleanup()
+        assert indep0 not in graph.inputs
+        assert node not in graph.nodes
+
+        tensor_map = graph.tensors()
+        assert indep0.name not in tensor_map
+        assert indep1.name not in tensor_map
+
+
+    def test_deep_copy(self):
+        def make_graph():
+            graph, _ = toposort_multi_tier_output_graph()
+            graph.outputs.pop()
+            return graph
+
+        graph = make_graph()
+        new_graph = copy.deepcopy(graph)
+        assert graph == new_graph
+
+        # Running cleanup on the first graph should not affect the copy
+        graph.cleanup()
+        assert graph != new_graph
+        assert new_graph == make_graph()
+
+
+    def test_fold_constants(self):
+        # Graph:
+        # c = (a + b)
+        # output = input + c
+        # Should fold to:
+        # output = input + c
+        inp = Variable("input", shape=(1, 3), dtype=np.float32)
+        a = Constant("a", values=np.ones(shape=(1, 3), dtype=np.float32))
+        b = Constant("b", values=np.ones(shape=(1, 3), dtype=np.float32))
+        c = Variable("c", shape=(1, 3), dtype=np.float32)
+        out = Variable("output", shape=(1, 3), dtype=np.float32)
+
+        nodes = [
+            Node("Add", inputs=[a, b], outputs=[c]),
+            Node("Add", inputs=[inp, c], outputs=[out]),
+        ]
+        graph = Graph(nodes=nodes, inputs=[inp], outputs=[out])
+
+        graph.fold_constants().cleanup()
+
+        # Extra node should be removed
+        assert len(graph.nodes) == 1
+        assert graph.nodes[0].inputs[0] == inp
+        assert graph.nodes[0].inputs[1] == c
+        # Value should be computed correctly
+        assert np.all(graph.nodes[0].inputs[1].values == np.ones(shape=(1, 3), dtype=np.float32) * 2)
+
+
+    def test_fold_constants_one_hop(self):
+        # Graph:
+        # c = (a + b)
+        # e = (c + d)
+        # output = input + e
+        # Should fold to:
+        # output = input + e
+        inp = Variable("input", shape=(1, 3), dtype=np.float32)
+        a = Constant("a", values=np.ones(shape=(1, 3), dtype=np.float32))
+        b = Constant("b", values=np.ones(shape=(1, 3), dtype=np.float32))
+        c = Variable("c", shape=(1, 3), dtype=np.float32)
+        d = Constant("d", values=np.ones(shape=(1, 3), dtype=np.float32))
+        e = Variable("e", shape=(1, 3), dtype=np.float32)
+        out = Variable("output", shape=(1, 3), dtype=np.float32)
+
+        nodes = [
+            Node("Add", inputs=[a, b], outputs=[c]),
+            Node("Add", inputs=[c, d], outputs=[e]),
+            Node("Add", inputs=[inp, e], outputs=[out]),
+        ]
+
+        graph = Graph(nodes=nodes, inputs=[inp], outputs=[out])
+
+        graph.fold_constants().cleanup()
+
+        # Extra nodes should be removed
+        assert len(graph.nodes) == 1
+        assert graph.nodes[0].inputs[0] == inp
+        assert graph.nodes[0].inputs[1] == e
+        # Value should be computed correctly
+        assert np.all(graph.nodes[0].inputs[1].values == np.ones(shape=(1, 3), dtype=np.float32) * 3)
+
+
+    def test_fold_constants_no_foldable_constants(self):
+        inp0 = Variable("input0", shape=(1, 3), dtype=np.float32)
+        inp1 = Variable("input1", shape=(1, 3), dtype=np.float32)
+        out = Variable("output", shape=(1, 3), dtype=np.float32)
+
+        nodes = [
+            Node("Add", inputs=[inp0, inp1], outputs=[out])
+        ]
+
+        graph = Graph(nodes=nodes, inputs=[inp0, inp1], outputs=[out])
+
+        graph.fold_constants().cleanup()
+
+        assert len(graph.nodes) == 1
+        assert graph.nodes[0].inputs == [inp0, inp1]