analysis_predictor.cc

// 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.

#include <memory>
#include "paddle/fluid/framework/ir/pass.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/inference/analysis/analyzer.h"
#include "paddle/fluid/inference/api/api_impl.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/inference/utils/singleton.h"

namespace paddle {

using inference::analysis::Argument;
using inference::Singleton;
using inference::analysis::Analyzer;
using framework::proto::ProgramDesc;

/* This predictor is based on the original native predictor with IR and Analysis
 * support. It will optimize IR and Parameters in the runtime.
 * TODO(Superjomn) Replace the Navive predictor?
 */
class AnalysisPredictor : public NativePaddlePredictor {
 public:
  explicit AnalysisPredictor(const NativeConfig& config)
      : NativePaddlePredictor(config), config_(config) {}

  bool Init(const std::shared_ptr<framework::Scope>& parent_scope) {
    VLOG(3) << "Predictor::init()";
    if (config_.use_gpu) {
      place_ = paddle::platform::CUDAPlace(config_.device);
    } else {
      place_ = paddle::platform::CPUPlace();
    }
    PADDLE_ENFORCE(!parent_scope);
    if (parent_scope) {
      scope_ = parent_scope;
      sub_scope_ = &(parent_scope->NewScope());
    } else {
      paddle::framework::InitDevices(false);
      scope_.reset(new paddle::framework::Scope());
    }

    executor_.reset(new paddle::framework::Executor(place_));

    // Initialize the inference program
    if (!config_.model_dir.empty()) {
      // Parameters are saved in separate files sited in
      // the specified `dirname`.
      inference_program_ = paddle::inference::Load(
          executor_.get(), scope_.get(), config_.model_dir);
    } else if (!config_.prog_file.empty() && !config_.param_file.empty()) {
      // All parameters are saved in a single file.
      // The file names should be consistent with that used
      // in Python API `fluid.io.save_inference_model`.
      inference_program_ = paddle::inference::Load(
          executor_.get(), scope_.get(), config_.prog_file, config_.param_file);
    } else {
      LOG(ERROR) << "fail to load inference model.";
      return false;
    }

    OptimizeInferenceProgram();
    ctx_ = executor_->Prepare(*inference_program_, 0);

    VLOG(5) << "to create variables";
    PADDLE_ENFORCE(scope_.get());
    executor_->CreateVariables(*inference_program_,
                               sub_scope_ ? sub_scope_ : scope_.get(), 0);

    // Get the feed_target_names and fetch_target_names
    feed_target_names_ = inference_program_->GetFeedTargetNames();
    fetch_target_names_ = inference_program_->GetFetchTargetNames();
    return true;
  }

  bool Run(const std::vector<PaddleTensor>& inputs,
           std::vector<PaddleTensor>* output_data,
           int batch_size = -1) override {
    return NativePaddlePredictor::Run(inputs, output_data, batch_size);
  }

  void OptimizeInferenceProgram() {
    LOG(INFO) << "optimize begin";
    FLAGS_IA_enable_ir = true;
    FLAGS_IA_enable_tensorrt_subgraph_engine = false;
    FLAGS_IA_output_storage_path = "";  // Don't output the model.
    // Analyze inference_program
    Argument argument;
    if (!config_.model_dir.empty()) {
      argument.fluid_model_dir.reset(new std::string(config_.model_dir));
    } else {
      PADDLE_ENFORCE(
          !config_.param_file.empty(),
          "Either model_dir or (param_file, prog_file) should be set.");
      PADDLE_ENFORCE(!config_.prog_file.empty());
      argument.fluid_model_program_path.reset(
          new std::string(config_.prog_file));
      argument.fluid_model_param_path.reset(
          new std::string(config_.param_file));
    }
    argument.origin_program_desc.reset(
        new ProgramDesc(*inference_program_->Proto()));
    Singleton<Analyzer>::Global().Run(&argument);
    CHECK(argument.transformed_program_desc);
    VLOG(5) << "to prepare executor";
    // LOG(INFO) << "transformed_parogram_desc " <<
    // argument.transformed_program_desc->DebugString();
    inference_program_.reset(
        new framework::ProgramDesc(*argument.transformed_program_desc));
    PADDLE_ENFORCE(argument.Has("param_scope"));
    // Update scope.
    scope_.reset(argument.Release<framework::Scope>("param_scope"));
    LOG(INFO) << "optimize end ==";
  }

 private:
  NativeConfig config_;
};

template <>
std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
    NativeConfig, PaddleEngineKind::kAnalysis>(const NativeConfig& config) {
  VLOG(3) << "create NativePredictor";
  if (config.use_gpu) {
    // 1. GPU memeroy
    PADDLE_ENFORCE_GT(
        config.fraction_of_gpu_memory, 0.f,
        "fraction_of_gpu_memory in the config should be set to range (0., 1.]");
    PADDLE_ENFORCE_GE(config.device, 0, "Invalid device id %d", config.device);
    std::vector<std::string> flags;
    if (config.fraction_of_gpu_memory >= 0.0f ||
        config.fraction_of_gpu_memory <= 0.95f) {
      flags.push_back("dummpy");
      std::string flag = "--fraction_of_gpu_memory_to_use=" +
                         std::to_string(config.fraction_of_gpu_memory);
      flags.push_back(flag);
      VLOG(3) << "set flag: " << flag;
      framework::InitGflags(flags);
    }
  }

  std::unique_ptr<PaddlePredictor> predictor(new AnalysisPredictor(config));
  if (!dynamic_cast<AnalysisPredictor*>(predictor.get())->Init(nullptr)) {
    return nullptr;
  }
  return predictor;
}

}  // namespace paddle

USE_PASS(fc_fuse_pass);
USE_PASS(graph_viz_pass);
USE_PASS(infer_clean_graph_pass);