// 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 #include #include #include #include // NOLINT #include #ifdef PADDLE_WITH_CUDA #include "paddle/fluid/platform/cuda_device_guard.h" #endif #include "gflags/gflags.h" #include "glog/logging.h" #include "paddle/fluid/framework/garbage_collector.h" namespace paddle { namespace framework { DEFINE_double( eager_delete_tensor_gb, -1.0, "Memory size threshold (GB) when the garbage collector clear tensors." "Disabled when this value is less than 0"); DEFINE_bool(fast_eager_deletion_mode, true, "Fast eager deletion mode. If enabled, memory would release " "immediately without waiting GPU kernel ends."); DEFINE_double(memory_fraction_of_eager_deletion, 1.0, "Fraction of eager deletion. If less than 1.0, all variables in " "the program would be sorted according to its memory size, and " "only the FLAGS_memory_fraction_of_eager_deletion of the largest " "variables would be deleted."); GarbageCollector::GarbageCollector(const platform::Place &place, size_t max_memory_size) : max_memory_size_((std::max)(max_memory_size, static_cast(1))) { garbages_.reset(new GarbageQueue()); dev_ctx_ = platform::DeviceContextPool::Instance().Get(place); } CPUGarbageCollector::CPUGarbageCollector(const platform::CPUPlace &place, size_t max_memory_size) : GarbageCollector(place, max_memory_size) {} void CPUGarbageCollector::ClearCallback(const std::function &callback) { callback(); } #ifdef PADDLE_WITH_CUDA UnsafeFastGPUGarbageCollector::UnsafeFastGPUGarbageCollector( const platform::CUDAPlace &place, size_t max_memory_size) : GarbageCollector(place, max_memory_size) {} void UnsafeFastGPUGarbageCollector::ClearCallback( const std::function &callback) { callback(); } DefaultStreamGarbageCollector::DefaultStreamGarbageCollector( const platform::CUDAPlace &place, size_t max_memory_size) : GarbageCollector(place, max_memory_size) {} void DefaultStreamGarbageCollector::Wait() const { static_cast(this->dev_ctx_) ->WaitStreamCallback(); } void DefaultStreamGarbageCollector::ClearCallback( const std::function &callback) { static_cast(this->dev_ctx_) ->AddStreamCallback(callback); } StreamGarbageCollector::StreamGarbageCollector(const platform::CUDAPlace &place, size_t max_memory_size) : GarbageCollector(place, max_memory_size) { platform::CUDADeviceGuard guard(place.device); PADDLE_ENFORCE(cudaStreamCreate(&stream_)); callback_manager_.reset(new platform::StreamCallbackManager(stream_)); } StreamGarbageCollector::~StreamGarbageCollector() { auto place = boost::get(this->dev_ctx_->GetPlace()); platform::CUDADeviceGuard guard(place.device); PADDLE_ENFORCE(cudaStreamSynchronize(stream_)); PADDLE_ENFORCE(cudaStreamDestroy(stream_)); } cudaStream_t StreamGarbageCollector::stream() const { return stream_; } void StreamGarbageCollector::Wait() const { callback_manager_->Wait(); } void StreamGarbageCollector::ClearCallback( const std::function &callback) { callback_manager_->AddCallback(callback); } #endif int64_t GetEagerDeletionThreshold() { return FLAGS_eager_delete_tensor_gb < 0 ? -1 : static_cast(FLAGS_eager_delete_tensor_gb * (static_cast(1) << 30)); } bool IsFastEagerDeletionModeEnabled() { return FLAGS_fast_eager_deletion_mode; } void SetEagerDeletionMode(double threshold, double fraction, bool fast_mode) { FLAGS_eager_delete_tensor_gb = threshold; FLAGS_memory_fraction_of_eager_deletion = fraction; FLAGS_fast_eager_deletion_mode = fast_mode; } double GetEagerDeletionMemoryFraction() { return FLAGS_memory_fraction_of_eager_deletion; } } // namespace framework } // namespace paddle