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334 lines
11 KiB
334 lines
11 KiB
/* 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 <algorithm>
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#include <map>
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#include <set>
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#include <sstream>
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#include <string>
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#include <utility>
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#include <vector>
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#include "paddle/fluid/framework/feed_fetch_method.h"
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#include "paddle/fluid/inference/api/api_impl.h"
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#include "paddle/fluid/inference/api/timer.h"
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#include "paddle/fluid/platform/profiler.h"
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DEFINE_bool(profile, false, "Turn on profiler for fluid");
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namespace paddle {
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namespace {
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using paddle::inference::Timer;
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template <class T>
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std::string num2str(T a) {
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std::stringstream istr;
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istr << a;
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return istr.str();
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}
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} // namespace
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void NativePaddlePredictor::PrepareFeedFetch() {
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for (auto *op : inference_program_->Block(0).AllOps()) {
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if (op->Type() == "feed") {
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int idx = boost::get<int>(op->GetAttr("col"));
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if (feeds_.size() <= static_cast<size_t>(idx)) {
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feeds_.resize(idx + 1);
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}
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feeds_[idx] = op;
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feed_names_[op->Output("Out")[0]] = idx;
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} else if (op->Type() == "fetch") {
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int idx = boost::get<int>(op->GetAttr("col"));
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if (fetchs_.size() <= static_cast<size_t>(idx)) {
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fetchs_.resize(idx + 1);
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}
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fetchs_[idx] = op;
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}
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}
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}
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bool NativePaddlePredictor::Init(
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std::shared_ptr<framework::Scope> parent_scope) {
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VLOG(3) << "Predictor::init()";
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#if !defined(_WIN32)
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if (FLAGS_profile) {
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LOG(WARNING) << "Profiler is actived, might affect the performance";
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LOG(INFO) << "You can turn off by set gflags '-profile false'";
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auto tracking_device = config_.use_gpu ? platform::ProfilerState::kAll
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: platform::ProfilerState::kCPU;
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platform::EnableProfiler(tracking_device);
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}
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#endif
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if (config_.use_gpu) {
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place_ = paddle::platform::CUDAPlace(config_.device);
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} else {
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place_ = paddle::platform::CPUPlace();
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}
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if (parent_scope) {
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scope_ = parent_scope;
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sub_scope_ = &(parent_scope->NewScope());
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PADDLE_ENFORCE_NOT_NULL(sub_scope_, "create sub scope fail");
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} else {
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paddle::framework::InitDevices(false);
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scope_.reset(new paddle::framework::Scope());
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}
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executor_.reset(new paddle::framework::Executor(place_));
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// Initialize the inference program
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if (!config_.model_dir.empty()) {
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// Parameters are saved in separate files sited in
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// the specified `dirname`.
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inference_program_ = paddle::inference::Load(executor_.get(), scope_.get(),
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config_.model_dir);
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} else if (!config_.prog_file.empty() && !config_.param_file.empty()) {
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// All parameters are saved in a single file.
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// The file names should be consistent with that used
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// in Python API `fluid.io.save_inference_model`.
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inference_program_ = paddle::inference::Load(
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executor_.get(), scope_.get(), config_.prog_file, config_.param_file);
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} else {
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LOG(ERROR) << "fail to load inference model.";
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return false;
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}
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ctx_ = executor_->Prepare(*inference_program_, 0);
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if (config_._use_mkldnn) {
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executor_->EnableMKLDNN(*inference_program_);
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}
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executor_->CreateVariables(*inference_program_,
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sub_scope_ ? sub_scope_ : scope_.get(), 0);
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// Get the feed_target_names and fetch_target_names
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PrepareFeedFetch();
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return true;
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}
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NativePaddlePredictor::~NativePaddlePredictor() {
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#if !defined(_WIN32)
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if (FLAGS_profile) {
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platform::DisableProfiler(platform::EventSortingKey::kTotal,
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"./profile.log");
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}
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#endif
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if (sub_scope_) {
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scope_->DeleteScope(sub_scope_);
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}
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}
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bool NativePaddlePredictor::Run(const std::vector<PaddleTensor> &inputs,
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std::vector<PaddleTensor> *output_data,
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int batch_size) {
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VLOG(3) << "Predictor::predict";
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Timer timer;
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timer.tic();
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// set feed variable
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std::vector<framework::LoDTensor> feeds;
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framework::Scope *scope = sub_scope_ != nullptr ? sub_scope_ : scope_.get();
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if (!SetFeed(inputs, scope)) {
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LOG(ERROR) << "fail to set feed";
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return false;
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}
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// Run the inference program
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// if share variables, we need not create variables
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VLOG(4) << "Run prepared context";
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executor_->RunPreparedContext(ctx_.get(), scope,
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false, /* don't create local scope each time*/
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false /* don't create variable eatch time */);
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VLOG(4) << "Finish prepared context";
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// get fetch variable
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if (!GetFetch(output_data, scope)) {
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LOG(ERROR) << "fail to get fetches";
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return false;
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}
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VLOG(3) << "predict cost: " << timer.toc() << "ms";
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return true;
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}
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std::unique_ptr<PaddlePredictor> NativePaddlePredictor::Clone() {
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VLOG(3) << "Predictor::clone";
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std::unique_ptr<PaddlePredictor> cls(new NativePaddlePredictor(config_));
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if (!dynamic_cast<NativePaddlePredictor *>(cls.get())->Init(scope_)) {
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LOG(ERROR) << "fail to call Init";
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return nullptr;
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}
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#ifdef __clang__
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// fix clang compile error
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return cls;
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#else
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// fix manylinux compile error.
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return std::move(cls);
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#endif
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}
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bool NativePaddlePredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
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framework::Scope *scope) {
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VLOG(3) << "Predictor::set_feed";
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if (inputs.size() != feeds_.size()) {
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LOG(ERROR) << "wrong feed input size, need " << feeds_.size() << " but get "
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<< inputs.size();
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return false;
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}
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for (size_t i = 0; i < inputs.size(); ++i) {
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framework::LoDTensor input;
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framework::DDim ddim = framework::make_ddim(inputs[i].shape);
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void *input_ptr;
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if (inputs[i].dtype == PaddleDType::INT64) {
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input_ptr = input.mutable_data<int64_t>(ddim, platform::CPUPlace());
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} else if (inputs[i].dtype == PaddleDType::FLOAT32) {
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input_ptr = input.mutable_data<float>(ddim, platform::CPUPlace());
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} else {
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LOG(ERROR) << "unsupported feed type " << inputs[i].dtype;
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return false;
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}
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// TODO(panyx0718): Init LoDTensor from existing memcpy to save a copy.
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std::memcpy(static_cast<void *>(input_ptr), inputs[i].data.data(),
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inputs[i].data.length());
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// TODO(Superjomn) Low performance, need optimization for heavy LoD copy.
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framework::LoD lod;
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for (auto &level : inputs[i].lod) {
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lod.emplace_back(level);
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}
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input.set_lod(lod);
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int idx = -1;
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if (config_.specify_input_name) {
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idx = feed_names_[inputs[i].name];
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} else {
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idx = boost::get<int>(feeds_[i]->GetAttr("col"));
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}
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framework::SetFeedVariable(scope, input, "feed", idx);
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}
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return true;
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}
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template <typename T>
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void NativePaddlePredictor::GetFetchOne(const framework::LoDTensor &fetch,
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PaddleTensor *output) {
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std::vector<int> shape;
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auto dims_i = fetch.dims();
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auto lod = fetch.lod();
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const T *output_ptr = fetch.data<T>();
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auto num = fetch.numel();
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std::vector<T> data;
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if (0 == lod.size()) {
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std::copy(output_ptr, output_ptr + num, std::back_inserter(data));
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for (int j = 0; j < dims_i.size(); ++j) {
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shape.push_back(dims_i[j]);
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}
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} else {
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// for batch detection
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// image[0] -> output[0] shape {145, 6}
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// image[1] -> output[1] shape {176, 6}
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// then,
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// the batch output shape {321, 6}
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// the lod {{0, 145, 321}}
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// so we should append output[0] to {176, 6}
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size_t max_dim = 0;
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for (size_t j = 1; j < lod[0].size(); j++) {
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max_dim = std::max(max_dim, lod[0][j] - lod[0][j - 1]);
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}
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size_t common_dim = lod[0].back() == 0 ? 0 : num / lod[0].back();
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if (max_dim > 0) {
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data.resize((lod[0].size() - 1) * max_dim * common_dim, 0);
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}
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for (size_t j = 1; j < lod[0].size(); j++) {
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size_t start = lod[0][j - 1] * common_dim;
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size_t end = lod[0][j] * common_dim;
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if (end > start) {
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std::copy(output_ptr + start, output_ptr + end,
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data.begin() + (j - 1) * max_dim * common_dim);
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}
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}
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shape.push_back(lod[0].size() - 1);
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shape.push_back(max_dim);
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for (int j = 1; j < dims_i.size(); ++j) {
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shape.push_back(dims_i[j]);
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}
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}
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output->shape = shape;
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auto &buffer = output->data;
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if (buffer.empty() || buffer.length() < sizeof(T) * data.size()) {
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buffer.Resize(sizeof(T) * data.size());
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}
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std::memcpy(buffer.data(), data.data(), sizeof(T) * data.size());
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// copy LoD
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for (const auto &level : fetch.lod()) {
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output->lod.emplace_back(level);
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}
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}
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bool NativePaddlePredictor::GetFetch(std::vector<PaddleTensor> *outputs,
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framework::Scope *scope) {
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VLOG(3) << "Predictor::get_fetch";
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outputs->resize(fetchs_.size());
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for (size_t i = 0; i < fetchs_.size(); ++i) {
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int idx = boost::get<int>(fetchs_[i]->GetAttr("col"));
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PADDLE_ENFORCE((size_t)idx == i);
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framework::LoDTensor &fetch =
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framework::GetFetchVariable(*scope, "fetch", idx);
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auto type = fetch.type();
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auto output = &(outputs->at(i));
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if (type == typeid(float)) {
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GetFetchOne<float>(fetch, output);
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output->dtype = PaddleDType::FLOAT32;
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} else if (type == typeid(int64_t)) {
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GetFetchOne<int64_t>(fetch, output);
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output->dtype = PaddleDType::INT64;
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} else {
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LOG(ERROR) << "unknown type, only support float32 and int64 now.";
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}
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}
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return true;
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}
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template <>
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std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
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NativeConfig, PaddleEngineKind::kNative>(const NativeConfig &config) {
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VLOG(3) << "create NativePaddlePredictor";
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if (config.use_gpu) {
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// 1. GPU memeroy
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PADDLE_ENFORCE_GT(
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config.fraction_of_gpu_memory, 0.f,
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"fraction_of_gpu_memory in the config should be set to range (0., 1.]");
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PADDLE_ENFORCE_GE(config.device, 0, "Invalid device id %d", config.device);
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std::vector<std::string> flags;
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if (config.fraction_of_gpu_memory >= 0.0f ||
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config.fraction_of_gpu_memory <= 0.95f) {
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flags.push_back("dummpy");
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std::string flag = "--fraction_of_gpu_memory_to_use=" +
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num2str<float>(config.fraction_of_gpu_memory);
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flags.push_back(flag);
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VLOG(3) << "set flag: " << flag;
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framework::InitGflags(flags);
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}
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}
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std::unique_ptr<PaddlePredictor> predictor(new NativePaddlePredictor(config));
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if (!dynamic_cast<NativePaddlePredictor *>(predictor.get())->Init(nullptr)) {
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return nullptr;
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}
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#ifdef __clang__
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// fix clang compile error
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return predictor;
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#else
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return std::move(predictor);
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#endif
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}
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} // namespace paddle
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