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https://github.com/zebrajr/pytorch.git
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5b835682e3
Summary: Remove GPU dependency and register ProfileObserver. Pull Request resolved: https://github.com/pytorch/pytorch/pull/17592 Reviewed By: ezyang Differential Revision: D14265801 Pulled By: mdschatz fbshipit-source-id: f98c0c32653c64a8b087c58ece4f864dfbe1d4b8
83 lines
2.9 KiB
C++
83 lines
2.9 KiB
C++
/**
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* Copyright (c) 2016-present, Facebook, Inc.
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*
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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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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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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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#include "profile_observer.h"
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#include "caffe2/core/logging.h"
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namespace caffe2 {
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void ProfileOperatorObserver::Dump() const {
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static std::mutex loggingMutex;
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std::lock_guard<std::mutex> lock(loggingMutex);
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LOG(INFO) << "--------- Starting operator " << subject_->debug_def().type()
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<< " op#" << getId() << " ---------";
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for (int i = 0; i < subject_->InputSize(); ++i) {
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if (subject_->InputIsTensorType(i, CPU)) {
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const auto& tensor = subject_->Input<Tensor>(i, CPU);
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const auto& name = subject_->debug_def().input(i);
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TensorPrinter printer(name);
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LOG(INFO) << "Input " << i << ": " << printer.MetaStr(tensor);
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} else if (subject_->InputIsTensorType(i, CUDA)) {
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const auto& tensor = subject_->Input<Tensor>(i, CUDA);
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const auto& name = subject_->debug_def().input(i);
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TensorPrinter printer(name);
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LOG(INFO) << "Input " << i << ": " << printer.MetaStr(tensor);
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}
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}
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int a = 0;
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for (const auto& arg : subject_->debug_def().arg()) {
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LOG(INFO) << "Argument " << a << ": " << arg.ShortDebugString();
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++a;
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}
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for (int o = 0; o < subject_->OutputSize(); ++o) {
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if (subject_->OutputIsTensorType(o, CPU)) {
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auto* tensor = subject_->Output<Tensor>(o, CPU);
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const auto& name = subject_->debug_def().output(o);
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TensorPrinter printer(name);
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LOG(INFO) << "Output " << o << ": " << printer.MetaStr(*tensor);
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} else if (subject_->OutputIsTensorType(o, CUDA)) {
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auto* tensor = subject_->Output<Tensor>(o, CUDA);
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const auto& name = subject_->debug_def().output(o);
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TensorPrinter printer(name);
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LOG(INFO) << "Output " << o << ": " << printer.MetaStr(*tensor);
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}
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}
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LOG(INFO) << "--------- Finished operator " << subject_->debug_def().type()
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<< " in " << run_time_ << " ms ---------";
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}
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void ProfileOperatorObserver::Start() {
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start_time_ = timer_.MilliSeconds();
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}
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void ProfileOperatorObserver::Stop() {
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run_time_ = timer_.MilliSeconds() - start_time_;
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Dump();
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}
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std::unique_ptr<ObserverBase<OperatorBase>> ProfileOperatorObserver::rnnCopy(
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OperatorBase* subject,
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int rnn_order) const {
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return std::unique_ptr<ObserverBase<OperatorBase>>(
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new ProfileOperatorObserver(
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subject, netObserver_, net_position_, rnn_order));
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}
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} // namespace caffe2
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