The cuDNN Frontend API is a C++ header-only library that demonstrates how to use the cuDNN C backend API. The cuDNN C backend API is documented in the cuDNN developer guide.
In order to include the entire library, include the cudnn_frontend header file cudnn_frontend.h into your compilation unit.
Each cudnnBackendDescriptorType_t documented in the enum is organized into its header file.
- cudnn_frontend_Tensor.h -> CUDNN_BACKEND_TENSOR_DESCRIPTOR
- cudnn_frontend_ConvDesc.h -> CUDNN_BACKEND_CONVOLUTION_DESCRIPTOR
- cudnn_frontend_PointWiseDesc.h -> CUDNN_BACKEND_POINTWISE_DESCRIPTOR
- cudnn_frontend_MatMulDesc.h -> CUDNN_BACKEND_MATMUL_DESCRIPTOR
- cudnn_frontend_ReductionDesc.h -> CUDNN_BACKEND_REDUCTION_DESCRIPTOR
- cudnn_frontend_Operation.h -> CUDNN_BACKEND_OPERATION_*_DESCRIPTOR
- cudnn_frontend_OperationGraph.h -> CUDNN_BACKEND_OPERATIONGRAPH_DESCRIPTOR
- cudnn_frontend_Heuristics.h -> CUDNN_BACKEND_ENGINEHEUR_DESCRIPTOR
- cudnn_frontend_Engine.h -> CUDNN_BACKEND_ENGINE_DESCRIPTOR
- cudnn_frontend_EngineConfig.h -> CUDNN_BACKEND_ENGINECFG_DESCRIPTOR
- cudnn_frontend_ExecutionPlan.h -> CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR
- cudnn_frontend_ExecutionPlan.h -> CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR
- cudnn_frontend_VariantPack.h -> CUDNN_BACKEND_VARIANT_PACK_DESCRIPTOR
- cudnn_frontend_find_plan.h -> Implements the
cudnnFindPlanfunction - cudnn_frontend_get_plan.h -> Implements the
cudnnGetPlanfunction - cudnn_frontend_Filters.h -> List of helpful utility functions to filter out execution plans
- cudnn_frontend_ExecutionPlanCache.h -> Describes and implements the execution plan caching.
- cudnn_frontend_Logging.h -> Implements a basic logging framework for cudnn_frontend
- cudnn_frontend_utils.h
- cudnn_frontend_EngineFallbackList.h -> Provides a fallback engine id if the heuristics do not provide an executable engine.
Multiple samples of convolution, dgrad, wgrad and convBiasAct are added in samples/test_list.cpp and samples/conv_sample.cpp.
Samples of runtime fusion are added in samples/test_list.cpp and samples/fusion_sample.cpp.
Sample tests are written using the Catch2 C++ test framework.
- Provide CUDA according to: https://cmake.org/cmake/help/latest/module/FindCUDAToolkit.html
- CUDNN_PATH has the cudnn installation.
- Headers are in CUDNN_PATH/include
- Libraries are in CUDNN_PATH/lib or CUDNN_PATH/lib64 or CUDNN_PATH/lib/x64
mkdir build; cd build
cmake -DCUDNN_PATH=/path/to/cudnn -DCUDAToolkit_ROOT=/path/to/cuda ../
cmake --build . -j16
bin/samples
Prior to cuDNN V8, cuDNN provided cudnnFindConvolution* and cudnnGetConvolution* functions, which provided a way to sample all the algorithms for a given problem and study the run times. This can be further used to cache the best algorithms for a given problem. In cuDNN V8, this has been replaced with cudnnFindPlan and cudnnGetPlan.
In order to use cudnnFindPlan, a user needs to provide:
- Source for a pruned list of
engineConfigs for the given problem statement - Filter function to Filter out the execution plan based on the prerequisite conditions
The cudnnFindPlan in turn
- Creates a set of execution plans that are supported
- Execute each filtered plan and ranks them in order of the execution plan runtime
The most common engineConfig generation is the built-in heuristics of cuDNN V8. Generally, this is appended with the fallback list. An example of usage can be seen in run_from_cudnn_find(...) function in conv_sample.cpp.
Errata filter gives the cuDNN team an opportunity to block certain faulty kernels from being executed. cuDNN team can eitherprovide a json file which blocks certain engine configs from being executed. The users can augment to this list if they find certain characteristics to be undesirable (Eg. Bad memory access, Execution plan failure). Users can either declare the json file statically or load from a file during runtime using the environment variable "CUDNN_ERRATA_JSON_FILE".
version : 1 - Mandatory. Tells the format version of the json.
rules : [] - Mandatory. Array of rule object which identifies the engine config
rule_id : "" - Optional. Used to uniquely identify a rule. Has no purpose other than being easy to debug.
operation : "" - Mandatory. Stringified version of the operation graph.
engine : "" - Mandatory. Stringified version of the engine ID.
knob : "" - Optional. Stringified version of the knob. If specified only the engineConfig for the engine matching the knobs will be blocked. Else, all possible combination of knobs for the engine will be blocked.
cudnn_version_start : 0 - Optional. Denotes the cudnn version after which the engine started having issues.
cudnn_version_end : -1 - Optional. Denotes the cudnn_version when the issue was fixed. "-1" denotes its an ongoing issue.
arch : "" - Optional. Architectures where this kernel might be faulty.
PS: The errata filter note is still in beta version. We may add/modify certain features as necessary.
cuDNN through heuristics provides a way to query a list of good engine configs. Based on this query we build the cudnn_frontend_find_plan function which runs all the engineConfig(s) on the given user system and returns a sorted list of plans. This process of running multiple plans through several iterations is time consuming. The ExecutionPlanCache allows the user to build a cache with operation graph as the key to query an execution plan. It is the responsibilty of the user to maintain different caches for different types of operation_graphs (For eg. different cache for convolutionForward compared to Dgrad or Wgrad). The is_fastest_plan_stable builds on top of this by making sure the same plan is chosen by the cudnnFind multiple times.
- void add_plan_to_cache(const cudnn_frontend::OperationGraph &op_graph, const cudnn_frontend::ExecutionPlan &plan) : Creates a mapping between the operation graph and executionPlan
- bool get_plan_from_cache(const cudnn_frontend::OperationGraph &op_graph, const cudnn_frontend::ExecutionPlan *&plan) : Sets the executionPlan in the plan pointer and returns true if found.
- cudnnFindPlanAndCache(cudnnHandle_t handle, cudnn_frontend::OperationGraph &opGraph, cudnn_frontend::VariantPack const &variantPack, cudnn_frontend::ExecutionPlanCache &cache, Predicate pred) -> cudnn_frontend::ExecutionPlan
The above API chains the output of cudnn_frontend_find_plan and caches the result for future usage.
PS: ExecutionPlanCaching today supports only single operation operation_graphs.
cuDNN v8.4 and above provides exeuction plan serialization and deserialization to save the execution plan as a string in JSON format. The execution plan can be then restored from that string at a later point, and this also saves compilation time compared to rebuilding the plan from scratch. Currently, this is an experimental feature that only supports the runtime fusion engine. No forward/backward or cross-device compatibility guarantee is offered at this time.
- std::string cudnn_frontend::ExecutionPlan_v8::getJsonRepresentation() : Serialize the execution plan into a string in JSON format.
- cudnn_frontend::ExecutionPlan_v8&& cudnn_frontend::ExecutionPlanBuilder_v8::loadFromJson(const std::string &json_plan) : Deserialize from a string containing the JSON representation of the execution plan.
cuDNN Frontend API logging records execution flow through cuDNN frontend API. This functionality is disabled by default, and can be enabled through methods described in this section.
| Environment variables | CUDNN_FRONTEND_LOG_INFO=0 | CUDNN_FRONTEND_LOG_INFO=1 |
|---|---|---|
| CUDNN_FRONTEND_LOG_FILE not set | No Logging | No Logging |
| CUDNN_FRONTEND_LOG_FILE set to stdout or stderr | No Logging | Logging to cout or cerr |
| CUDNN_FRONTEND_LOG_FILE set to filename.txt | No Logging | Logging to the filename |
Calling cudnn_frontend::isLoggingEnabled() = true|false has same effect of setting the environment variable.
Calling cudnn_frontend::getStream() = stream_name can be used to assign the output stream directly.
Documentation can be found at https://nvidia.github.io/cudnn-frontend/
Support, resources, and information about cuDNN can be found online at https://developer.nvidia.com/cudnn.
For questions or to provide feedback, please contact [email protected].