ut_helper.h

/* Copyright (c) 2018 PaddlePaddle 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. */

/*
 * This file implements a UT framework to make the validation of transforming
 * Fluid Op to TRT Layer.
 */

#pragma once

#include <string>
#include <vector>

#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/inference/analysis/helper.h"
#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
#include "paddle/fluid/inference/tensorrt/engine.h"
#include "paddle/fluid/inference/utils/singleton.h"

namespace paddle {
namespace inference {
namespace tensorrt {

/*
 * Get a random float value between [low, high]
 */
float random(float low, float high) {
  static std::random_device rd;
  static std::mt19937 mt(rd());
  std::uniform_real_distribution<double> dist(low, high);
  return dist(mt);
}

void RandomizeTensor(framework::LoDTensor* tensor, const platform::Place& place,
                     const platform::DeviceContext& ctx) {
  auto dims = tensor->dims();
  size_t num_elements = analysis::AccuDims(dims, dims.size());
  PADDLE_ENFORCE_GT(num_elements, 0);
  auto* data = tensor->mutable_data<float>(place);

  for (size_t i = 0; i < num_elements; i++) {
    *(data + i) = random(0., 1.);
  }
}

/*
 * Help to validate the correctness between Fluid Op and the corresponding TRT
 * layer.
 */
class TRTConvertValidation {
 public:
  TRTConvertValidation() = delete;

  TRTConvertValidation(int batch_size,
                       const std::unordered_set<std::string>& parameters,
                       framework::Scope& scope,  // NOLINT
                       int workspace_size = 1 << 10)
      : parameters_(parameters), scope_(scope) {
    // create engine.
    engine_.reset(new TensorRTEngine(batch_size, workspace_size, &stream_));
    engine_->InitNetwork();

    PADDLE_ENFORCE_EQ(cudaStreamCreate(&stream_), 0);
  }

  // Declare a Variable as input with random initialization.
  void DeclInputVar(const std::string& name, const nvinfer1::Dims& dims) {
    DeclVar(name, dims);
    // Declare TRT inputs.
    engine_->DeclareInput(name, nvinfer1::DataType::kFLOAT, dims);
  }

  // Declare a parameter varaible in the scope.
  void DeclParamVar(const std::string& name, const nvinfer1::Dims& dims) {
    DeclVar(name, dims);
  }

  void DeclOutputVar(const std::string& name, const nvinfer1::Dims& dims) {
    DeclVar(name, dims);
  }

  // Declare a variable in a fluid Scope.
  void DeclVar(const std::string& name, const nvinfer1::Dims& dims) {
    platform::CPUPlace place;
    platform::CPUDeviceContext ctx(place);

    // Init Fluid tensor.
    std::vector<int> dim_vec(dims.d, dims.d + dims.nbDims);
    auto* x = scope_.Var(name);
    auto* x_tensor = x->GetMutable<framework::LoDTensor>();
    x_tensor->Resize(framework::make_ddim(dim_vec));
    RandomizeTensor(x_tensor, place, ctx);
  }

  void SetOp(const framework::proto::OpDesc& desc) {
    op_ = framework::OpRegistry::CreateOp(desc);

    Singleton<OpConverter>::Global().ConvertOp(
        desc, parameters_, scope_, engine_.get(), true /*test_mode*/);

    engine_->FreezeNetwork();

    // Declare outputs.
    op_desc_.reset(new framework::OpDesc(desc, nullptr));

    // Set Inputs.
    for (const auto& input : op_desc_->InputArgumentNames()) {
      if (parameters_.count(input)) continue;
      auto* var = scope_.FindVar(input);
      PADDLE_ENFORCE(var);
      auto tensor = var->GetMutable<framework::LoDTensor>();

      engine_->SetInputFromCPU(
          input, static_cast<void*>(tensor->data<void>()),
          sizeof(float) *
              analysis::AccuDims(tensor->dims(), tensor->dims().size()));
    }
  }

  void Execute(int batch_size) {
    // Execute Fluid Op
    platform::CPUPlace place;
    platform::CPUDeviceContext ctx(place);
    op_->Run(scope_, place);
    // Execute TRT.
    engine_->Execute(batch_size);
    cudaStreamSynchronize(*engine_->stream());

    ASSERT_FALSE(op_desc_->OutputArgumentNames().empty());
    const size_t output_space_size = 2000;
    for (const auto& output : op_desc_->OutputArgumentNames()) {
      std::vector<float> fluid_out;
      std::vector<float> trt_out(output_space_size);
      engine_->GetOutputInCPU(output, &trt_out[0], output_space_size);
      cudaStreamSynchronize(*engine_->stream());

      auto* var = scope_.FindVar(output);
      auto tensor = var->GetMutable<framework::LoDTensor>();
      framework::TensorToVector(*tensor, ctx, &fluid_out);
      // Compare two output
      ASSERT_FALSE(fluid_out.empty());
      for (size_t i = 0; i < fluid_out.size(); i++) {
        // Loose the threshold for CI in different machine model.
        EXPECT_LT(std::abs(fluid_out[i] - trt_out[i]), 2e-5);
      }
    }
  }

  framework::Scope& scope() { return scope_; }

 private:
  std::unique_ptr<TensorRTEngine> engine_;
  cudaStream_t stream_;
  std::unique_ptr<framework::OperatorBase> op_;
  std::unique_ptr<framework::OpDesc> op_desc_;
  const std::unordered_set<std::string>& parameters_;
  framework::Scope& scope_;
};

}  // namespace tensorrt
}  // namespace inference
}  // namespace paddle