feature/mul converter (#10841)
Yan Chunwei
7 years ago
committed by
GitHub
parent
8f7b020ba8
commit
f5fc9c3bc1
@ -1,7 +1,10 @@
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nv_test(test_op_converter SRCS test_op_converter.cc mul_op.cc conv2d_op.cc DEPS ${FLUID_CORE_MODULES})
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# Add TRT tests
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nv_test(test_op_converter SRCS test_op_converter.cc mul_op.cc conv2d_op.cc DEPS ${FLUID_CORE_MODULES} tensorrt_engine)
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# This test is not stable
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# See https://paddleci.ngrok.io/viewLog.html?tab=buildLog&buildTypeId=Paddle_PrCi2&buildId=36834&_focus=8828
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#nv_test(test_trt_activation_op SRCS test_activation_op.cc activation_op.cc io_converter.cc
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# DEPS ${FLUID_CORE_MODULES} activation_op tensorrt_engine
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# SERIAL)
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nv_test(test_io_converter SRCS test_io_converter.cc io_converter.cc DEPS dynload_cuda dynamic_loader lod_tensor)
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nv_test(test_trt_mul_op SRCS test_mul_op.cc mul_op.cc
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DEPS ${FLUID_CORE_MODULES} tensorrt_engine mul_op SERIAL)
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@ -0,0 +1,47 @@
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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 <gtest/gtest.h>
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/inference/tensorrt/convert/ut_helper.h"
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namespace paddle {
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namespace inference {
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namespace tensorrt {
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TEST(MulOpConverter, main) {
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TRTConvertValidation validator(10, 1000);
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validator.DeclInputVar("mul-X", nvinfer1::Dims2(10, 6));
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validator.DeclInputVar("mul-Y", nvinfer1::Dims2(6, 10));
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validator.DeclOutputVar("mul-Out", nvinfer1::Dims2(10, 10));
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// Prepare Op description
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framework::OpDesc desc;
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desc.SetType("mul");
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desc.SetInput("X", {"mul-X"});
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desc.SetInput("Y", {"mul-Y"});
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desc.SetOutput("Out", {"mul-Out"});
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LOG(INFO) << "set OP";
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validator.SetOp(*desc.Proto());
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LOG(INFO) << "execute";
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validator.Execute(10);
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}
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} // namespace tensorrt
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} // namespace inference
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} // namespace paddle
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USE_OP(mul);
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@ -0,0 +1,156 @@
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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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/*
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* This file implements a UT framework to make the validation of transforming
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* Fluid Op to TRT Layer.
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*/
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#pragma once
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#include "paddle/fluid/framework/lod_tensor.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/inference/analysis/helper.h"
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#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
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#include "paddle/fluid/inference/tensorrt/engine.h"
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namespace paddle {
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namespace inference {
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namespace tensorrt {
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/*
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* Get a random float value between [low, high]
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*/
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float random(float low, float high) {
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static std::random_device rd;
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static std::mt19937 mt(rd());
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std::uniform_real_distribution<double> dist(1.0, 10.0);
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return dist(mt);
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}
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void RandomizeTensor(framework::LoDTensor* tensor, const platform::Place& place,
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const platform::DeviceContext& ctx) {
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auto dims = tensor->dims();
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size_t num_elements = analysis::AccuDims(dims, dims.size());
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PADDLE_ENFORCE_GT(num_elements, 0);
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auto* data = tensor->mutable_data<float>(place);
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for (size_t i = 0; i < num_elements; i++) {
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*(data + i) = random(0., 1.);
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}
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}
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/*
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* Help to validate the correctness between Fluid Op and the corresponding TRT
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* layer.
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*/
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class TRTConvertValidation {
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public:
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TRTConvertValidation() = delete;
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TRTConvertValidation(int batch_size, int workspace_size = 1 << 10) {
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// create engine.
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engine_.reset(new TensorRTEngine(10, 1 << 10, &stream_));
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engine_->InitNetwork();
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PADDLE_ENFORCE_EQ(cudaStreamCreate(&stream_), 0);
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}
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// Declare a Variable as input with random initialization.
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void DeclInputVar(const std::string& name, const nvinfer1::Dims& dims) {
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DeclVar(name, dims);
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// Declare TRT inputs.
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engine_->DeclareInput(name, nvinfer1::DataType::kFLOAT, dims);
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}
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void DeclOutputVar(const std::string& name, const nvinfer1::Dims& dims) {
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DeclVar(name, dims);
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}
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void DeclVar(const std::string& name, const nvinfer1::Dims& dims) {
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platform::CPUPlace place;
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platform::CPUDeviceContext ctx(place);
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// Init Fluid tensor.
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std::vector<int> dim_vec(dims.nbDims);
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for (int i = 0; i < dims.nbDims; i++) {
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dim_vec[i] = dims.d[i];
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}
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auto* x = scope_.Var(name);
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auto* x_tensor = x->GetMutable<framework::LoDTensor>();
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x_tensor->Resize(framework::make_ddim(dim_vec));
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RandomizeTensor(x_tensor, place, ctx);
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}
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void SetOp(const framework::proto::OpDesc& desc) {
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op_ = framework::OpRegistry::CreateOp(desc);
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OpConverter op_converter;
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op_converter.ConvertOp(desc, engine_.get());
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engine_->FreezeNetwork();
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// Declare outputs.
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op_desc_.reset(new framework::OpDesc(desc, nullptr, nullptr));
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// Set Inputs.
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for (const auto& input : op_desc_->InputArgumentNames()) {
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auto* var = scope_.FindVar(input);
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PADDLE_ENFORCE(var);
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auto tensor = var->GetMutable<framework::LoDTensor>();
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engine_->SetInputFromCPU(
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input, static_cast<void*>(tensor->data<float>()),
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sizeof(float) *
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analysis::AccuDims(tensor->dims(), tensor->dims().size()));
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}
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}
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void Execute(int batch_size) {
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// Execute Fluid Op
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// Execute TRT
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platform::CPUPlace place;
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platform::CPUDeviceContext ctx(place);
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engine_->Execute(batch_size);
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op_->Run(scope_, place);
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ASSERT_FALSE(op_desc_->OutputArgumentNames().empty());
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for (const auto& output : op_desc_->OutputArgumentNames()) {
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std::vector<float> fluid_out;
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std::vector<float> trt_out(200);
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engine_->GetOutputInCPU(output, &trt_out[0], 200 * sizeof(float));
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auto* var = scope_.FindVar(output);
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auto tensor = var->GetMutable<framework::LoDTensor>();
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framework::TensorToVector(*tensor, ctx, &fluid_out);
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// Compare two output
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ASSERT_FALSE(fluid_out.empty());
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for (size_t i = 0; i < fluid_out.size(); i++) {
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EXPECT_LT(std::abs(fluid_out[i] - trt_out[i]), 0.001);
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}
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}
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}
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framework::Scope& scope() { return scope_; }
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private:
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std::unique_ptr<TensorRTEngine> engine_;
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cudaStream_t stream_;
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framework::Scope scope_;
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std::unique_ptr<framework::OperatorBase> op_;
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std::unique_ptr<framework::OpDesc> op_desc_;
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};
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} // namespace tensorrt
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} // namespace inference
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} // namespace paddle
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