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Set up TensorRT configurations for external use, and add a test.
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| # Description: | ||
| # Wrap NVIDIA TensorRT (http://developer.nvidia.com/tensorrt) with tensorflow. | ||
| # APIs are meant to change over time. | ||
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| package(default_visibility = ["//tensorflow:__subpackages__"]) | ||
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| licenses(["notice"]) # Apache 2.0 | ||
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| exports_files(["LICENSE"]) | ||
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| load("//tensorflow:tensorflow.bzl", "tf_cuda_cc_test") | ||
| load( | ||
| "@local_config_tensorrt//:build_defs.bzl", | ||
| "if_tensorrt", | ||
| ) | ||
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| tf_cuda_cc_test( | ||
| name = "tensorrt_test_cc", | ||
| size = "small", | ||
| srcs = ["tensorrt_test.cc"], | ||
| tags = [ | ||
| "manual", | ||
| "notap", | ||
| ], | ||
| deps = [ | ||
| "//tensorflow/core:lib", | ||
| "//tensorflow/core:test", | ||
| "//tensorflow/core:test_main", | ||
| ] + if_tensorrt([ | ||
| "@local_config_cuda//cuda:cuda_headers", | ||
| "@local_config_tensorrt//:nv_infer", | ||
| ]), | ||
| ) | ||
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| filegroup( | ||
| name = "all_files", | ||
| srcs = glob( | ||
| ["**/*"], | ||
| exclude = [ | ||
| "**/METADATA", | ||
| "**/OWNERS", | ||
| ], | ||
| ), | ||
| visibility = ["//tensorflow:__subpackages__"], | ||
| ) |
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| /* Copyright 2018 The TensorFlow Authors. All 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. | ||
| ==============================================================================*/ | ||
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| #include "tensorflow/core/platform/logging.h" | ||
| #include "tensorflow/core/platform/test.h" | ||
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| #if GOOGLE_CUDA | ||
| #if GOOGLE_TENSORRT | ||
| #include "cuda/include/cuda.h" | ||
| #include "cuda/include/cuda_runtime_api.h" | ||
| #include "tensorrt/include/NvInfer.h" | ||
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| namespace tensorflow { | ||
| namespace { | ||
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| class Logger : public nvinfer1::ILogger { | ||
| public: | ||
| void log(nvinfer1::ILogger::Severity severity, const char* msg) override { | ||
| switch (severity) { | ||
| case Severity::kINFO: | ||
| LOG(INFO) << msg; | ||
| break; | ||
| case Severity::kWARNING: | ||
| LOG(WARNING) << msg; | ||
| break; | ||
| case Severity::kINTERNAL_ERROR: | ||
| case Severity::kERROR: | ||
| LOG(ERROR) << msg; | ||
| break; | ||
| default: | ||
| break; | ||
| } | ||
| } | ||
| }; | ||
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| class ScopedWeights { | ||
| public: | ||
| ScopedWeights(float value) : value_(value) { | ||
| w.type = nvinfer1::DataType::kFLOAT; | ||
| w.values = &value_; | ||
| w.count = 1; | ||
| } | ||
| const nvinfer1::Weights& get() { return w; } | ||
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| private: | ||
| float value_; | ||
| nvinfer1::Weights w; | ||
| }; | ||
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| const char* kInputTensor = "input"; | ||
| const char* kOutputTensor = "output"; | ||
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| // Creates a network to compute y=2x+3. | ||
| nvinfer1::IHostMemory* CreateNetwork() { | ||
| Logger logger; | ||
| nvinfer1::IBuilder* builder = nvinfer1::createInferBuilder(logger); | ||
| ScopedWeights weights(2.0); | ||
| ScopedWeights bias(3.0); | ||
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| nvinfer1::INetworkDefinition* network = builder->createNetwork(); | ||
| // Add the input. | ||
| auto input = network->addInput(kInputTensor, nvinfer1::DataType::kFLOAT, | ||
| nvinfer1::DimsCHW{1, 1, 1}); | ||
| EXPECT_NE(input, nullptr); | ||
| // Add the hidden layer. | ||
| auto layer = network->addFullyConnected(*input, 1, weights.get(), bias.get()); | ||
| EXPECT_NE(layer, nullptr); | ||
| // Mark the output. | ||
| auto output = layer->getOutput(0); | ||
| output->setName(kOutputTensor); | ||
| network->markOutput(*output); | ||
| // Build the engine | ||
| builder->setMaxBatchSize(1); | ||
| builder->setMaxWorkspaceSize(1 << 10); | ||
| auto engine = builder->buildCudaEngine(*network); | ||
| EXPECT_NE(engine, nullptr); | ||
| // Serialize the engine to create a model, then close everything. | ||
| nvinfer1::IHostMemory* model = engine->serialize(); | ||
| network->destroy(); | ||
| engine->destroy(); | ||
| builder->destroy(); | ||
| return model; | ||
| } | ||
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| // Executes the network. | ||
| void Execute(nvinfer1::IExecutionContext& context, const float* input, | ||
| float* output) { | ||
| const nvinfer1::ICudaEngine& engine = context.getEngine(); | ||
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| // We have two bindings: input and output. | ||
| ASSERT_EQ(engine.getNbBindings(), 2); | ||
| const int input_index = engine.getBindingIndex(kInputTensor); | ||
| const int output_index = engine.getBindingIndex(kOutputTensor); | ||
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| // Create GPU buffers and a stream | ||
| void* buffers[2]; | ||
| ASSERT_EQ(0, cudaMalloc(&buffers[input_index], sizeof(float))); | ||
| ASSERT_EQ(0, cudaMalloc(&buffers[output_index], sizeof(float))); | ||
| cudaStream_t stream; | ||
| ASSERT_EQ(0, cudaStreamCreate(&stream)); | ||
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| // Copy the input to the GPU, execute the network, and copy the output back. | ||
| // | ||
| // Note that since the host buffer was not created as pinned memory, these | ||
| // async copies are turned into sync copies. So the following synchronization | ||
| // could be removed. | ||
| ASSERT_EQ(0, cudaMemcpyAsync(buffers[input_index], input, sizeof(float), | ||
| cudaMemcpyHostToDevice, stream)); | ||
| context.enqueue(1, buffers, stream, nullptr); | ||
| ASSERT_EQ(0, cudaMemcpyAsync(output, buffers[output_index], sizeof(float), | ||
| cudaMemcpyDeviceToHost, stream)); | ||
| cudaStreamSynchronize(stream); | ||
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| // Release the stream and the buffers | ||
| cudaStreamDestroy(stream); | ||
| ASSERT_EQ(0, cudaFree(buffers[input_index])); | ||
| ASSERT_EQ(0, cudaFree(buffers[output_index])); | ||
| } | ||
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| TEST(TensorrtTest, BasicFunctions) { | ||
| // Create the network model. | ||
| nvinfer1::IHostMemory* model = CreateNetwork(); | ||
| // Use the model to create an engine and then an execution context. | ||
| Logger logger; | ||
| nvinfer1::IRuntime* runtime = nvinfer1::createInferRuntime(logger); | ||
| nvinfer1::ICudaEngine* engine = | ||
| runtime->deserializeCudaEngine(model->data(), model->size(), nullptr); | ||
| model->destroy(); | ||
| nvinfer1::IExecutionContext* context = engine->createExecutionContext(); | ||
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| // Execute the network. | ||
| float input = 1234; | ||
| float output; | ||
| Execute(*context, &input, &output); | ||
| EXPECT_EQ(output, input * 2 + 3); | ||
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| // Destroy the engine. | ||
| context->destroy(); | ||
| engine->destroy(); | ||
| runtime->destroy(); | ||
| } | ||
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| } // namespace | ||
| } // namespace tensorflow | ||
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| #endif // GOOGLE_TENSORRT | ||
| #endif // GOOGLE_CUDA |
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