// Copyright (c) 2021 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 "paddle/fluid/eager/utils.h" #include "paddle/fluid/eager/api/utils/global_utils.h" #include "paddle/fluid/eager/api/utils/hook_utils.h" #include "paddle/fluid/eager/tensor_wrapper.h" #include "paddle/pten/api/all.h" #include "paddle/pten/common/layout.h" #include "paddle/pten/core/tensor_meta.h" #include "paddle/fluid/framework/data_layout.h" #include "paddle/fluid/framework/pten_utils.h" #include "paddle/fluid/framework/variable.h" PADDLE_DEFINE_EXPORTED_bool(retain_grad_for_all_tensor, true, "retain grad for all tensor"); namespace egr { /** * Implementation of Eager Utils. **/ AutogradMeta* EagerUtils::autograd_meta(egr::EagerTensor* target) { auto* p_autograd_meta = target->get_autograd_meta(); if (!p_autograd_meta) { auto p_autograd_meta_ptr = std::make_shared(); p_autograd_meta = p_autograd_meta_ptr.get(); target->set_autograd_meta(p_autograd_meta_ptr); } return static_cast(p_autograd_meta); } AutogradMeta* EagerUtils::unsafe_autograd_meta(const egr::EagerTensor& target) { auto* p_autograd_meta = target.get_autograd_meta(); PADDLE_ENFORCE(p_autograd_meta, paddle::platform::errors::Fatal( "Null autograd_meta gotten from unsafe_autograd_meta()")); return static_cast(p_autograd_meta); } std::vector EagerUtils::unsafe_autograd_meta( const std::vector& targets) { std::vector metas; metas.reserve(targets.size()); for (const egr::EagerTensor& t : targets) { metas.emplace_back(unsafe_autograd_meta(t)); } return metas; } AutogradMeta* EagerUtils::nullable_autograd_meta( const egr::EagerTensor& target) { auto* p_autograd_meta = target.get_autograd_meta(); if (!p_autograd_meta) return nullptr; return static_cast(p_autograd_meta); } std::vector EagerUtils::nullable_autograd_meta( const std::vector& targets) { std::vector metas; metas.reserve(targets.size()); for (const egr::EagerTensor& t : targets) { metas.emplace_back(nullable_autograd_meta(t)); } return metas; } std::vector EagerUtils::autograd_meta( std::vector* targets) { std::vector ret; ret.reserve(targets->size()); // for autograd_meta we can tolerent it has nullptr. for (auto& t : (*targets)) { auto* p_autograd_meta = autograd_meta(&t); ret.push_back(static_cast(p_autograd_meta)); } return ret; } std::pair EagerUtils::OutRankInfo( const egr::EagerTensor& target) { return unsafe_autograd_meta(target)->OutRankInfo(); } std::shared_ptr EagerUtils::grad_node( const egr::EagerTensor& target) { auto* meta = nullable_autograd_meta(target); if (meta) { return meta->GetMutableGradNode(); } else { return nullptr; } } void EagerUtils::SetHistory(std::vector* autograd_metas, const std::shared_ptr& grad_node) { for (const auto& autograd_meta : *autograd_metas) { autograd_meta->SetGradNode(grad_node); } } void EagerUtils::SetHistory(AutogradMeta* autograd_meta, const std::shared_ptr& grad_node) { autograd_meta->SetGradNode(grad_node); } void EagerUtils::SetOutRankWithSlot(std::vector* targets, size_t slot_id) { // Set OutRankInfo from 0 to size of targets for (size_t i = 0; i < targets->size(); i++) { (*targets)[i]->SetSingleOutRankWithSlot(slot_id, i); } } void EagerUtils::SetOutRankWithSlot(AutogradMeta* target, size_t slot_id) { target->SetSingleOutRankWithSlot(slot_id, 0); } /* ---- Tensor -> Var ---- */ std::vector> EagerUtils::SyncToVars( const egr::EagerTensor& tensor) { // TODO(jiabin): No const cast here. We should call SyncToVar in Python_C // wrapper const_cast(&tensor)->SyncToVar( paddle::framework::proto::VarType_Type_LOD_TENSOR); return {std::make_shared(tensor)}; } std::vector> EagerUtils::SyncToVars( const std::vector& tensors) { // TODO(jiabin): No const cast here. We should call SyncToVar in Python_C // wrapper std::vector> res; size_t num = tensors.size(); res.reserve(num); for (size_t i = 0; i < num; i++) { const_cast(&(tensors[i])) ->SyncToVar(paddle::framework::proto::VarType_Type_LOD_TENSOR); res.emplace_back(new EagerTensor(tensors[i])); } return res; } static std::shared_ptr TrySyncToVar( egr::EagerTensor* tensor) { if (tensor->initialized() || tensor->Var().IsInitialized()) { tensor->SyncToVar(paddle::framework::proto::VarType_Type_LOD_TENSOR); } return std::shared_ptr(tensor, [&](egr::EagerTensor* ptr) {}); } std::vector> EagerUtils::TrySyncToVars( egr::EagerTensor* tensor) { return {TrySyncToVar(tensor)}; } std::vector> EagerUtils::TrySyncToVars( std::vector* tensors) { std::vector> res; size_t num = tensors->size(); res.reserve(num); for (size_t i = 0; i < num; i++) { res.emplace_back(TrySyncToVar(&(*tensors)[i])); } return res; } std::vector> EagerUtils::TrySyncToVars( const std::vector& tensors) { std::vector> res; size_t num = tensors.size(); res.reserve(num); for (size_t i = 0; i < num; i++) { res.emplace_back(TrySyncToVar(tensors[i])); } return res; } /* ---- VarBase -> Tensor ---- */ std::vector> EagerUtils::SyncToTensors( const egr::EagerTensor& tensor) { // TODO(jiabin): No const cast here. We should call SyncToTensor in Python_C // wrapper const_cast(&tensor)->SyncToTensor(); return {std::make_shared(tensor)}; } std::vector> EagerUtils::SyncToTensors( const std::vector& tensors) { // TODO(jiabin): No const cast here. We should call SyncToTensor in Python_C // wrapper std::vector> res; size_t num = tensors.size(); res.reserve(num); for (size_t i = 0; i < num; i++) { const_cast(&(tensors[i]))->SyncToTensor(); res.emplace_back(new EagerTensor(tensors[i])); } return res; } std::vector> EagerUtils::ConstructDuplicableOutput( const size_t num) { std::vector> res; res.reserve(num); for (size_t i = 0; i < num; i++) { res.emplace_back( new EagerTensor(egr::Controller::Instance().GenerateUniqueName())); } return res; } std::vector EagerUtils::GetOutputs( const std::vector>& outs) { std::vector res; res.reserve(outs.size()); for (const auto& out : outs) { PADDLE_ENFORCE_NOT_NULL( out.get(), paddle::platform::errors::Fatal( "Eager Tensor %s is null and cannot be copied. " "We are tring to Get Output tensor from its " "shared_ptr, this error may indicate some outputs " "are nullptr", out->name())); res.emplace_back((*(out.get()))); } return res; } egr::EagerTensor EagerUtils::GetOutput( const std::shared_ptr& out) { PADDLE_ENFORCE_NOT_NULL( out.get(), paddle::platform::errors::Fatal( "Eager Tensor %s is null and cannot be copied. We " "are tring to Get Output tensor from its shared_ptr, " "this error may indicate output is nullptr", out->name())); return EagerTensor((*(out.get()))); } EagerTensor EagerUtils::RecoverTensorWrapper( TensorWrapper* tw, const std::shared_ptr& grad_node) { return tw->recover(grad_node); } std::vector EagerUtils::RecoverTensorWrapper( std::vector* tw, const std::shared_ptr& grad_node) { std::vector ret; for (auto& t : *tw) { ret.emplace_back(t.recover(grad_node)); } return ret; } void EagerUtils::CheckAndRetainGrad(const egr::EagerTensor& tensor) { VLOG(6) << "Check RetainGradForTensor: " << tensor.name(); if (FLAGS_retain_grad_for_all_tensor) { VLOG(6) << "RetainGradForTensor: " << tensor.name(); egr::egr_utils_api::RetainGradForTensor(tensor); } } void EagerUtils::CheckAndRetainGrad( const std::vector& tensors) { if (FLAGS_retain_grad_for_all_tensor) { for (auto& tensor : tensors) { VLOG(6) << "RetainGradForTensor: " << tensor.name(); egr::egr_utils_api::RetainGradForTensor(tensor); } } } paddle::experimental::Tensor EagerUtils::SyncToPtenTensors( const egr::EagerTensor& tensor) { const_cast(&tensor)->SyncToTensor(); return *tensor.Tensor().get(); } std::vector EagerUtils::SyncToPtenTensors( const std::vector& tensors) { std::vector res; size_t num = tensors.size(); res.reserve(num); for (size_t i = 0; i < num; i++) { const_cast(&(tensors[i]))->SyncToTensor(); res.push_back(*tensors[i].Tensor().get()); } return res; } egr::EagerTensor EagerUtils::CreateEagerTensorFromTensor( const paddle::experimental::Tensor& tensor) { egr::EagerTensor ret; ret.set_tensor(std::make_shared(tensor)); return ret; } std::vector EagerUtils::CreateEagerTensorFromTensor( const std::vector& tensors) { std::vector res; size_t num = tensors.size(); res.reserve(num); for (size_t i = 0; i < num; i++) { egr::EagerTensor tmp; tmp.set_tensor(std::make_shared(tensors[i])); res.emplace_back(std::move(tmp)); } return res; } } // namespace egr