api_impl.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 <sys/time.h>
#include <algorithm>
#include <map>
#include <set>
#include <sstream>
#include <string>
#include <utility>
#include <vector>

#include "paddle/fluid/inference/api/api_impl.h"

namespace paddle {
namespace {

// Timer for timer
class Timer {
 public:
  double start;
  double startu;
  void tic() {
    struct timeval tp;
    gettimeofday(&tp, NULL);
    start = tp.tv_sec;
    startu = tp.tv_usec;
  }
  double toc() {
    struct timeval tp;
    gettimeofday(&tp, NULL);
    double used_time_ms =
        (tp.tv_sec - start) * 1000.0 + (tp.tv_usec - startu) / 1000.0;
    return used_time_ms;
  }
};

template <class T>
std::string num2str(T a) {
  std::stringstream istr;
  istr << a;
  return istr.str();
}
}  // namespace

bool NativePaddlePredictor::Init(
    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();
  }
  if (parent_scope) {
    scope_ = parent_scope;
    sub_scope_ = &(parent_scope->NewScope());
    PADDLE_ENFORCE_NOT_NULL(sub_scope_, "create sub scope fail");
  } 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;
  }

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

NativePaddlePredictor::~NativePaddlePredictor() {
  if (sub_scope_) {
    scope_->DeleteScope(sub_scope_);
  }
}

bool NativePaddlePredictor::Run(const std::vector<PaddleTensor> &inputs,
                                std::vector<PaddleTensor> *output_data,
                                int batch_size) {
  VLOG(3) << "Predictor::predict";
  Timer timer;
  timer.tic();
  // set feed variable
  std::map<std::string, const framework::LoDTensor *> feed_targets;
  std::vector<framework::LoDTensor> feeds;
  if (!SetFeed(inputs, &feeds)) {
    LOG(ERROR) << "fail to set feed";
    return false;
  }
  for (size_t i = 0; i < feed_target_names_.size(); ++i) {
    VLOG(4) << "setting " << i << "-th target";
    feed_targets[feed_target_names_[i]] = &feeds[i];
  }
  // get fetch variable
  std::map<std::string, framework::LoDTensor *> fetch_targets;
  std::vector<framework::LoDTensor> fetchs;
  fetchs.resize(fetch_target_names_.size());
  for (size_t i = 0; i < fetch_target_names_.size(); ++i) {
    fetch_targets[fetch_target_names_[i]] = &fetchs[i];
  }
  // Run the inference program
  // if share variables, we need not create variables
  VLOG(4) << "Run prepared context";
  executor_->RunPreparedContext(
      ctx_.get(), sub_scope_ != nullptr ? sub_scope_ : scope_.get(),
      &feed_targets, &fetch_targets,
      false, /* don't create local scope each time*/
      false /* don't create variable eatch time */);
  VLOG(4) << "Finish prepared context";
  if (!GetFetch(fetchs, output_data)) {
    LOG(ERROR) << "fail to get fetches";
    return false;
  }
  VLOG(3) << "predict cost: " << timer.toc() << "ms";
  return true;
}

std::unique_ptr<PaddlePredictor> NativePaddlePredictor::Clone() {
  VLOG(3) << "Predictor::clone";
  std::unique_ptr<PaddlePredictor> cls(new NativePaddlePredictor(config_));

  if (!dynamic_cast<NativePaddlePredictor *>(cls.get())->Init(scope_)) {
    LOG(ERROR) << "fail to call Init";
    return nullptr;
  }
  // fix manylinux compile error.
  return std::move(cls);
}

bool NativePaddlePredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
                                    std::vector<framework::LoDTensor> *feeds) {
  VLOG(3) << "Predictor::set_feed";
  if (inputs.size() != feed_target_names_.size()) {
    LOG(ERROR) << "wrong feed input size.";
    return false;
  }
  for (size_t i = 0; i < feed_target_names_.size(); ++i) {
    framework::LoDTensor input;
    framework::DDim ddim = framework::make_ddim(inputs[i].shape);
    void *input_ptr;
    if (inputs[i].dtype == PaddleDType::INT64) {
      input_ptr = input.mutable_data<int64_t>(ddim, platform::CPUPlace());
    } else if (inputs[i].dtype == PaddleDType::FLOAT32) {
      input_ptr = input.mutable_data<float>(ddim, platform::CPUPlace());
    } else {
      LOG(ERROR) << "unsupported feed type " << inputs[i].dtype;
      return false;
    }

    // TODO(panyx0718): Init LoDTensor from existing memcpy to save a copy.
    std::memcpy(static_cast<void *>(input_ptr), inputs[i].data.data(),
                inputs[i].data.length());
    // TODO(Superjomn) Low performance, need optimization for heavy LoD copy.
    framework::LoD lod;
    for (auto &level : inputs[i].lod) {
      lod.emplace_back(level);
    }
    input.set_lod(lod);

    feeds->push_back(input);
  }
  return true;
}

bool NativePaddlePredictor::GetFetch(
    const std::vector<framework::LoDTensor> &fetchs,
    std::vector<PaddleTensor> *outputs) {
  VLOG(3) << "Predictor::get_fetch";
  outputs->resize(fetchs.size());
  for (size_t i = 0; i < fetchs.size(); ++i) {
    // TODO(panyx0718): Support fetch of other types.
    if (fetchs[i].type() != typeid(float)) {
      LOG(ERROR) << "only support fetching float now.";
      return false;
    }
    std::vector<int> shape;
    auto dims_i = fetchs[i].dims();
    auto lod = fetchs[i].lod();
    const float *output_ptr = fetchs[i].data<float>();
    // const int64_t* output_ptr = fetchs[i].data<int64_t>();
    auto num = fetchs[i].numel();
    std::vector<float> data;
    if (0 == lod.size()) {
      std::copy(output_ptr, output_ptr + num, std::back_inserter(data));
      for (int j = 0; j < dims_i.size(); ++j) {
        shape.push_back(dims_i[j]);
      }
    } else {
      // for batch detection
      // image[0] -> output[0] shape {145, 6}
      // image[1] -> output[1] shape {176, 6}
      // then,
      // the batch output shape {321, 6}
      // the lod {{0, 145, 321}}
      // so we should append output[0] to {176, 6}
      size_t max_dim = 0;
      for (size_t j = 1; j < lod[0].size(); j++) {
        max_dim = std::max(max_dim, lod[0][j] - lod[0][j - 1]);
      }
      size_t common_dim = lod[0].back() == 0 ? 0 : num / lod[0].back();
      if (max_dim > 0) {
        data.resize((lod[0].size() - 1) * max_dim * common_dim, 0);
      }
      for (size_t j = 1; j < lod[0].size(); j++) {
        size_t start = lod[0][j - 1] * common_dim;
        size_t end = lod[0][j] * common_dim;
        if (end > start) {
          std::copy(output_ptr + start, output_ptr + end,
                    data.begin() + (j - 1) * max_dim * common_dim);
        }
      }
      shape.push_back(lod[0].size() - 1);
      shape.push_back(max_dim);
      for (int j = 1; j < dims_i.size(); ++j) {
        shape.push_back(dims_i[j]);
      }
    }

    outputs->at(i).shape = shape;
    auto &buffer = outputs->at(i).data;
    if (buffer.empty() || buffer.length() < sizeof(float) * data.size()) {
      buffer.Resize(sizeof(float) * data.size());
    }
    std::memcpy(buffer.data(), data.data(), buffer.length());
    // copy LoD
    for (const auto &level : fetchs[i].lod()) {
      outputs->at(i).lod.emplace_back(level);
    }
    outputs->at(i).dtype = PaddleDType::FLOAT32;
    // TODO(panyx0718): support other types? fill tensor name? avoid a copy.
  }
  return true;
}

template <>
std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
    NativeConfig, PaddleEngineKind::kNative>(const NativeConfig &config) {
  VLOG(3) << "create NativePaddlePredictor";
  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=" +
                         num2str<float>(config.fraction_of_gpu_memory);
      flags.push_back(flag);
      VLOG(3) << "set flag: " << flag;
      framework::InitGflags(flags);
    }
  }

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

}  // namespace paddle