Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into feature/simplify_delay_logic
Yu Yang
7 years ago
commit
c0f4e0332c
@ -0,0 +1,53 @@
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if(NOT DEFINED SPHINX_THEME)
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set(SPHINX_THEME default)
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endif()
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if(NOT DEFINED SPHINX_THEME_DIR)
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set(SPHINX_THEME_DIR)
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endif()
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# configured documentation tools and intermediate build results
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set(BINARY_BUILD_DIR_EN "${CMAKE_CURRENT_BINARY_DIR}/en/_build")
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# Sphinx cache with pickled ReST documents
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set(SPHINX_CACHE_DIR_EN "${CMAKE_CURRENT_BINARY_DIR}/en/_doctrees")
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# HTML output director
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set(SPHINX_HTML_DIR_EN "${CMAKE_CURRENT_BINARY_DIR}/en/html")
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configure_file(
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"${CMAKE_CURRENT_SOURCE_DIR}/../templates/conf.py.en.in"
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"${BINARY_BUILD_DIR_EN}/conf.py"
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@ONLY)
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sphinx_add_target(paddle_mobile_docs
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html
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${BINARY_BUILD_DIR_EN}
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${SPHINX_CACHE_DIR_EN}
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${CMAKE_CURRENT_SOURCE_DIR}
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${SPHINX_HTML_DIR_EN})
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add_dependencies(paddle_mobile_docs gen_proto_py paddle_python)
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# configured documentation tools and intermediate build results
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set(BINARY_BUILD_DIR_CN "${CMAKE_CURRENT_BINARY_DIR}/cn/_build")
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# Sphinx cache with pickled ReST documents
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set(SPHINX_CACHE_DIR_CN "${CMAKE_CURRENT_BINARY_DIR}/cn/_doctrees")
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# HTML output director
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set(SPHINX_HTML_DIR_CN "${CMAKE_CURRENT_BINARY_DIR}/cn/html")
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configure_file(
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"${CMAKE_CURRENT_SOURCE_DIR}/../templates/conf.py.cn.in"
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"${BINARY_BUILD_DIR_CN}/conf.py"
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@ONLY)
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sphinx_add_target(paddle_mobile_docs_cn
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html
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${BINARY_BUILD_DIR_CN}
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${SPHINX_CACHE_DIR_CN}
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${CMAKE_CURRENT_SOURCE_DIR}
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${SPHINX_HTML_DIR_CN})
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add_dependencies(paddle_mobile_docs_cn gen_proto_py paddle_python)
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@ -0,0 +1,9 @@
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移动端
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=====
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.. toctree::
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:maxdepth: 1
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cross_compiling_for_android_cn.md
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cross_compiling_for_ios_cn.md
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cross_compiling_for_raspberry_cn.md
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@ -0,0 +1,9 @@
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Mobile
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======
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.. toctree::
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:maxdepth: 1
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cross_compiling_for_android_en.md
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cross_compiling_for_ios_en.md
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cross_compiling_for_raspberry_en.md
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@ -0,0 +1 @@
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nv_test(test_tensorrt SRCS test_tensorrt.cc DEPS dynload_cuda device_context dynamic_loader)
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@ -0,0 +1,155 @@
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/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include <glog/logging.h>
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#include <gtest/gtest.h>
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#include "NvInfer.h"
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#include "cuda.h"
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#include "cuda_runtime_api.h"
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#include "paddle/fluid/platform/dynload/tensorrt.h"
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namespace dy = paddle::platform::dynload;
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class Logger : public nvinfer1::ILogger {
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public:
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void log(nvinfer1::ILogger::Severity severity, const char* msg) override {
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switch (severity) {
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case Severity::kINFO:
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LOG(INFO) << msg;
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break;
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case Severity::kWARNING:
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LOG(WARNING) << msg;
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break;
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case Severity::kINTERNAL_ERROR:
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case Severity::kERROR:
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LOG(ERROR) << msg;
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break;
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default:
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break;
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}
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}
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};
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class ScopedWeights {
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public:
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ScopedWeights(float value) : value_(value) {
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w.type = nvinfer1::DataType::kFLOAT;
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w.values = &value_;
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w.count = 1;
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}
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const nvinfer1::Weights& get() { return w; }
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private:
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float value_;
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nvinfer1::Weights w;
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};
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// The following two API are implemented in TensorRT's header file, cannot load
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// from the dynamic library. So create our own implementation and directly
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// trigger the method from the dynamic library.
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nvinfer1::IBuilder* createInferBuilder(nvinfer1::ILogger& logger) {
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return static_cast<nvinfer1::IBuilder*>(
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dy::createInferBuilder_INTERNAL(&logger, NV_TENSORRT_VERSION));
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}
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nvinfer1::IRuntime* createInferRuntime(nvinfer1::ILogger& logger) {
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return static_cast<nvinfer1::IRuntime*>(
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dy::createInferRuntime_INTERNAL(&logger, NV_TENSORRT_VERSION));
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}
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const char* kInputTensor = "input";
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const char* kOutputTensor = "output";
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// Creates a network to compute y = 2x + 3
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nvinfer1::IHostMemory* CreateNetwork() {
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Logger logger;
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// Create the engine.
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nvinfer1::IBuilder* builder = createInferBuilder(logger);
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ScopedWeights weights(2.);
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ScopedWeights bias(3.);
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nvinfer1::INetworkDefinition* network = builder->createNetwork();
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// Add the input
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auto input = network->addInput(kInputTensor, nvinfer1::DataType::kFLOAT,
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nvinfer1::DimsCHW{1, 1, 1});
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EXPECT_NE(input, nullptr);
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// Add the hidden layer.
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auto layer = network->addFullyConnected(*input, 1, weights.get(), bias.get());
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EXPECT_NE(layer, nullptr);
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// Mark the output.
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auto output = layer->getOutput(0);
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output->setName(kOutputTensor);
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network->markOutput(*output);
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// Build the engine.
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builder->setMaxBatchSize(1);
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builder->setMaxWorkspaceSize(1 << 10);
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auto engine = builder->buildCudaEngine(*network);
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EXPECT_NE(engine, nullptr);
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// Serialize the engine to create a model, then close.
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nvinfer1::IHostMemory* model = engine->serialize();
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network->destroy();
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engine->destroy();
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builder->destroy();
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return model;
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}
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void Execute(nvinfer1::IExecutionContext& context, const float* input,
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float* output) {
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const nvinfer1::ICudaEngine& engine = context.getEngine();
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// Two binds, input and output
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ASSERT_EQ(engine.getNbBindings(), 2);
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const int input_index = engine.getBindingIndex(kInputTensor);
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const int output_index = engine.getBindingIndex(kOutputTensor);
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// Create GPU buffers and a stream
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void* buffers[2];
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ASSERT_EQ(0, cudaMalloc(&buffers[input_index], sizeof(float)));
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ASSERT_EQ(0, cudaMalloc(&buffers[output_index], sizeof(float)));
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cudaStream_t stream;
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ASSERT_EQ(0, cudaStreamCreate(&stream));
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// Copy the input to the GPU, execute the network, and copy the output back.
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ASSERT_EQ(0, cudaMemcpyAsync(buffers[input_index], input, sizeof(float),
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cudaMemcpyHostToDevice, stream));
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context.enqueue(1, buffers, stream, nullptr);
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ASSERT_EQ(0, cudaMemcpyAsync(output, buffers[output_index], sizeof(float),
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cudaMemcpyDeviceToHost, stream));
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cudaStreamSynchronize(stream);
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// Release the stream and the buffers
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cudaStreamDestroy(stream);
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ASSERT_EQ(0, cudaFree(buffers[input_index]));
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ASSERT_EQ(0, cudaFree(buffers[output_index]));
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}
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TEST(TensorrtTest, BasicFunction) {
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// Create the network serialized model.
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nvinfer1::IHostMemory* model = CreateNetwork();
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// Use the model to create an engine and an execution context.
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Logger logger;
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nvinfer1::IRuntime* runtime = createInferRuntime(logger);
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nvinfer1::ICudaEngine* engine =
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runtime->deserializeCudaEngine(model->data(), model->size(), nullptr);
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model->destroy();
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nvinfer1::IExecutionContext* context = engine->createExecutionContext();
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// Execute the network.
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float input = 1234;
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float output;
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Execute(*context, &input, &output);
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EXPECT_EQ(output, input * 2 + 3);
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// Destroy the engine.
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context->destroy();
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engine->destroy();
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runtime->destroy();
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}
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@ -0,0 +1,30 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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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
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limitations under the License. */
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#include "paddle/fluid/platform/dynload/tensorrt.h"
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namespace paddle {
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namespace platform {
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namespace dynload {
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std::once_flag tensorrt_dso_flag;
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void *tensorrt_dso_handle;
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#define DEFINE_WRAP(__name) DynLoad__##__name __name
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TENSORRT_RAND_ROUTINE_EACH(DEFINE_WRAP);
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} // namespace dynload
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} // namespace platform
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} // namespace paddle
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@ -0,0 +1,69 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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||||
You may obtain a copy of the License at
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||||
|
||||
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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#pragma once
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#include <NvInfer.h>
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#include <dlfcn.h>
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#include <mutex> // NOLINT
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#include "paddle/fluid/platform/dynload/dynamic_loader.h"
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#include "paddle/fluid/platform/enforce.h"
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namespace paddle {
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namespace platform {
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namespace dynload {
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extern std::once_flag tensorrt_dso_flag;
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extern void* tensorrt_dso_handle;
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#ifdef PADDLE_USE_DSO
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#define DECLARE_DYNAMIC_LOAD_TENSORRT_WRAP(__name) \
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struct DynLoad__##__name { \
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template <typename... Args> \
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auto operator()(Args... args) -> decltype(__name(args...)) { \
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using tensorrt_func = decltype(__name(args...)) (*)(Args...); \
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std::call_once(tensorrt_dso_flag, []() { \
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tensorrt_dso_handle = \
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paddle::platform::dynload::GetTensorRtDsoHandle(); \
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PADDLE_ENFORCE(tensorrt_dso_handle, "load tensorrt so failed"); \
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}); \
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void* p_##__name = dlsym(tensorrt_dso_handle, #__name); \
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PADDLE_ENFORCE(p_##__name, "load %s failed", #__name); \
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return reinterpret_cast<tensorrt_func>(p_##__name)(args...); \
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} \
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}; \
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extern DynLoad__##__name __name
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#else
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#define DECLARE_DYNAMIC_LOAD_TENSORRT_WRAP(__name) \
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struct DynLoad__##__name { \
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template <typename... Args> \
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tensorrtResult_t operator()(Args... args) { \
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return __name(args...); \
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} \
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}; \
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extern DynLoad__##__name __name
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#endif
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#define TENSORRT_RAND_ROUTINE_EACH(__macro) \
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__macro(createInferBuilder_INTERNAL); \
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__macro(createInferRuntime_INTERNAL);
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TENSORRT_RAND_ROUTINE_EACH(DECLARE_DYNAMIC_LOAD_TENSORRT_WRAP)
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} // namespace dynload
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} // namespace platform
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} // namespace paddle
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