diff --git a/paddle/fluid/CMakeLists.txt b/paddle/fluid/CMakeLists.txt index 1edc6f960c5e7..f4b68ec65b9d2 100644 --- a/paddle/fluid/CMakeLists.txt +++ b/paddle/fluid/CMakeLists.txt @@ -12,6 +12,5 @@ add_subdirectory(operators) add_subdirectory(string) add_subdirectory(pybind) add_subdirectory(eager) - # NOTE: please add subdirectory inference at last. add_subdirectory(inference) diff --git a/paddle/fluid/eager/CMakeLists.txt b/paddle/fluid/eager/CMakeLists.txt index 88c05163602af..a79b451b54431 100644 --- a/paddle/fluid/eager/CMakeLists.txt +++ b/paddle/fluid/eager/CMakeLists.txt @@ -1 +1,3 @@ add_subdirectory(tests) +cc_library(grad_node_info SRCS grad_node_info.cc DEPS pten pten_api) +cc_library(autograd_meta SRCS autograd_meta.cc DEPS pten pten_api) diff --git a/paddle/fluid/eager/autograd_meta.cc b/paddle/fluid/eager/autograd_meta.cc new file mode 100644 index 0000000000000..999f02c8d1ec6 --- /dev/null +++ b/paddle/fluid/eager/autograd_meta.cc @@ -0,0 +1,17 @@ +// 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/autograd_meta.h" +// We Leave this to make autograd meta can be compiled as a single target. +namespace egr {} // namespace egr diff --git a/paddle/fluid/eager/autograd_meta.h b/paddle/fluid/eager/autograd_meta.h new file mode 100644 index 0000000000000..7f46136416752 --- /dev/null +++ b/paddle/fluid/eager/autograd_meta.h @@ -0,0 +1,155 @@ +// 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. + +#pragma once + +#include "paddle/fluid/eager/grad_node_info.h" + +namespace egr { + +using AbstractAutogradMeta = paddle::experimental::AbstractAutogradMeta; +/** + * + * AutogradMeta is what record the backward info for tensor. When we run + * computation + * graph eagerly, we can not build a static paddle program like static mode do, + * so we + * need a new method to record forward info to trace backward when we finish all + * forward + * computation. This require our AutogradMeta class record following main + * members + * + * 1. grad_op: + * Grad_op indicate the grad operation of the forward op + * + * 2. grad: + * Grad is the gradient of forward Tensor, which should be compute after + * backward computation + * + * NOTE: grad should only be available when current tensor is a leaf tensor, and + * for non-leaf + * tensor grad is only available while user set `retain_grad` option as `true`. + * + * TODO(jiabin) : support hooks + * 3. hooks: + * Hooks are some computation logic which only attached with backward operation, + * it registered + * by user and run before accumulator. + * + * 4.overrided_stop_gradient_ + * This member is used to finish some auto-prune related work, which indicate + * user set stop_gradient + * should overrided the result indicated by framework. All non-parameter + * tensor's stop_gradient + * properties should be true. We will pass stop_gradient when we find one who + * need it. + * + * NOTE: AutogradMeta is inherited from AbstractAutogradMeta which is defined + * in tensor's deps, + * we did this to avoid additional dependency on Autograd. In eager execution, + * we will cast + * AbstractAutogradMeta as AutogradMeta to use it. + * + * **/ + +// No other AutogradMeta class should be derivated from AbstractAutogradMeta. +// It's only used by +class AutogradMeta : public AbstractAutogradMeta { + public: + explicit AutogradMeta(const Edge& edge = Edge()) { + out_slot_id_ = edge.GetEdgeRankInfo().first; + out_rank_ = edge.GetEdgeRankInfo().second; + grad_node_ = edge.GetMutableGradNode(); + } + + ~AutogradMeta() override = default; + + const egr::EagerTensor& Grad() const { return grad_; } + + egr::EagerTensor* MutableGrad() { return &grad_; } + + void SetGradNode(const std::shared_ptr& grad_node) { + PADDLE_ENFORCE_NOT_NULL( + grad_node.get(), + paddle::platform::errors::InvalidArgument( + "Should Not set NULL as GradNode pointer, since " + "our default Edge and autogradMeta has nullptr for " + "grad node. Set Nullptr will lead error.")); + grad_node_ = grad_node; + } + + std::shared_ptr GetMutableGradNode() const { + return grad_node_; + } + + GradNodeBase* GradNode() const { return grad_node_.get(); } + + void SetSingleOutRankWithSlot(size_t slot_id, size_t rank) { + out_slot_id_ = slot_id; + out_rank_ = rank; + } + + std::pair OutRankInfo() + const { + return std::make_pair(out_slot_id_, out_rank_); + } + + bool IsInitialized() { return grad_node_.get(); } + + // TODO(jiabin): This may cause error, since -1 still can indication true; + bool StopGradient() const { return stop_gradient_ != 0; } + + int NumericStopGradient() const { return stop_gradient_; } + + void SetStopGradient(bool stop_gradient) { + stop_gradient_ = static_cast(stop_gradient); + } + + bool Persistable() const { return persistable_; } + + void SetPersistable(bool persistable) { persistable_ = persistable; } + + private: + // TODO(jiabin) :Should we use pointer instead of object? + egr::EagerTensor grad_; + + // GradNodeBase is base class of all grad op which is a + // wrapper for grad op. This class will make grad op easy + // to be traced. + std::shared_ptr grad_node_; + + /** + * Why we need slot id here? + * Because in paddle most of our operators inputs and outputs + * are assemble in form of {"slot name", vector}. + * So its better for us to set a slot id to fit this format. **/ + size_t out_slot_id_; + + // output rank of forward op, this is a vital num, since + // we are now trying to make our forward output is as same + // sequence as backward input. In case of tracing backward + // sequence we need to record output rank in slot here. + size_t out_rank_; + + // TODO(jiabin) :Support hooks here and store it in AutogradMeta + + // Stop gradient flag to indicate should we compute backward + int stop_gradient_{-1}; + + bool persistable_{false}; + + // TODO(jiabin) :Support Quantum here and add cache mechanism as + // VarCache defined in VarBase +}; +} // namespace egr diff --git a/paddle/fluid/eager/grad_node_info.cc b/paddle/fluid/eager/grad_node_info.cc new file mode 100644 index 0000000000000..a1c25f6766a53 --- /dev/null +++ b/paddle/fluid/eager/grad_node_info.cc @@ -0,0 +1,241 @@ +// 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/grad_node_info.h" +#include "paddle/fluid/eager/autograd_meta.h" +#include "paddle/pten/common/data_type.h" +#include "paddle/pten/core/dense_tensor.h" + +#include "paddle/fluid/framework/var_type.h" +#include "paddle/fluid/platform/enforce.h" +#include "paddle/fluid/platform/errors.h" + +#include "glog/logging.h" + +/** + * Implementation of GradNodeBase, Edge and InputBuffer. +**/ +namespace egr { + +GradNodeBase::GradNodeBase(size_t bwd_in_slot_num, size_t bwd_out_slot_num) { + bwd_in_meta_.resize(bwd_in_slot_num); + bwd_out_meta_.resize(bwd_out_slot_num); + // adj_edges has the same num as backward outputs + adj_edges_.resize(bwd_out_slot_num); +} + +void GradNodeBase::AddEdges(const std::vector& metas, + size_t slot_id) { + PADDLE_ENFORCE_LT( + slot_id, adj_edges_.size(), + paddle::platform::errors::InvalidArgument( + "Given slot id is out of range of adj_edges outter size, " + "adj_edges is designed to has the same size of grad " + "inputs's slot num.")); + for (const auto& meta : metas) { + // adj_edges has as same rank as fwd inputs, and record it's output rank + // from + // its pre-ops + adj_edges_[slot_id].emplace_back(meta->GetMutableGradNode(), + meta->OutRankInfo()); + } +} + +void GradNodeBase::AddEdges(const AutogradMeta& meta, size_t slot_id) { + PADDLE_ENFORCE_LT( + slot_id, adj_edges_.size(), + paddle::platform::errors::InvalidArgument( + "Given slot id is out of range of adj_edges outter size, " + "adj_edges is designed to has the same size of grad " + "inputs's slot num.")); + adj_edges_[slot_id].emplace_back(meta.GetMutableGradNode(), + meta.OutRankInfo()); +} + +const std::vector& GradNodeBase::InputMeta() const { + return bwd_in_meta_; +} + +const std::vector& GradNodeBase::OutputMeta() const { + return bwd_out_meta_; +} + +void GradNodeBase::SetGradInMeta(const std::vector& fwd_out, + size_t slot_rank) { + size_t slot_size = fwd_out.size(); + PADDLE_ENFORCE_LE( + slot_rank, (bwd_in_meta_.size() - 1), + paddle::platform::errors::InvalidArgument( + "Slot Rank should less equal than bwd_in_meta_ size, since " + "bwd_in_meta_ is designed to hold as same num as backward " + "inputs.")); + auto& meta = bwd_in_meta_.at(slot_rank); + PADDLE_ENFORCE_EQ(meta.IsInitialized(), false, + paddle::platform::errors::PreconditionNotMet( + "Bwd_in_meta should only be init once, addition " + "initialization for it is forbidden. If you got this " + "error, it indicates bugs in framework.")); + // Init stop gradient vector before use to avoid push back + meta.Init(slot_size); + for (size_t i = 0; i < slot_size; i++) { + if (fwd_out[i]->StopGradient()) { + // Set Stop Gradient only when its true or non-initialized autograd_meta, + // since all default value is false. + meta.SetStopGradient(i, fwd_out[i]->StopGradient()); + } + } +} + +void GradNodeBase::SetGradInMeta(const AutogradMeta& fwd_out, + size_t slot_rank) { + PADDLE_ENFORCE_LE( + slot_rank, (bwd_in_meta_.size() - 1), + paddle::platform::errors::InvalidArgument( + "Slot Rank should less equal than bwd_in_meta_ size, since " + "bwd_in_meta_ is designed to hold as same num as backward " + "inputs.")); + auto& meta = bwd_in_meta_.at(slot_rank); + PADDLE_ENFORCE_EQ(meta.IsInitialized(), false, + paddle::platform::errors::PreconditionNotMet( + "Bwd_in_meta should only be init once, Additional " + "initialization for it is forbidden. If you got this " + "error, it indicates bugs in framework.")); + // Init stop gradient vector before use to avoid push back + VLOG(7) << "Init bwd_in_meta_ with slot rank: " << slot_rank; + meta.Init(1); + meta.SetStopGradient(0, fwd_out.StopGradient()); +} + +void GradNodeBase::SetGradOutMeta(const std::vector& fwd_in, + size_t slot_rank) { + size_t slot_size = fwd_in.size(); + PADDLE_ENFORCE_LE( + slot_rank, (bwd_out_meta_.size() - 1), + paddle::platform::errors::InvalidArgument( + "Slot Rank should less equal than bwd_out_meta_ size, " + "since bwd_out_meta_ is designed to hold as same num as " + "backward outputs.")); + auto& meta = bwd_out_meta_.at(slot_rank); + PADDLE_ENFORCE_EQ(meta.IsInitialized(), false, + paddle::platform::errors::PreconditionNotMet( + "Bwd_out_meta should only be init once. Additional " + "initialization for it is forbidden. If you got this " + "error, it indicates bugs in framework.")); + // Init stop gradient vector before use to avoid push back + meta.Init(slot_size); + for (size_t i = 0; i < slot_size; i++) { + if (fwd_in[i]->StopGradient()) { + // Set Stop Gradient only when its true or non-initialized autograd_meta, + // since all default value is false. + meta.SetStopGradient(i, fwd_in[i]->StopGradient()); + } + } +} + +void GradNodeBase::SetGradOutMeta(const AutogradMeta& fwd_in, + size_t slot_rank) { + PADDLE_ENFORCE_LE( + (slot_rank + 1), bwd_out_meta_.size(), + paddle::platform::errors::InvalidArgument( + "Slot Rank should less equal than bwd_out_meta_ size, " + "since bwd_out_meta_ is designed to hold as same num as " + "backward outputs.")); + auto& meta = bwd_out_meta_.at(slot_rank); + PADDLE_ENFORCE_EQ(meta.IsInitialized(), false, + paddle::platform::errors::PreconditionNotMet( + "Bwd_out_meta should only be init once. Additional " + "initialization for it is forbidden. If you got this " + "error, it indicates bugs in framework.")); + // Init stop gradient vector before use to avoid push back + meta.Init(1); + meta.SetStopGradient(0, fwd_in.StopGradient()); +} + +void GradNodeBase::SetDefaultGradInOutMeta() { + PADDLE_ENFORCE((bwd_out_meta_.size() == 1) && (bwd_in_meta_.size() == 1), + paddle::platform::errors::PreconditionNotMet( + "We can only support 1 input and 1 output in default grad " + "meta setter, other size of inputs and outputs should " + "create with Setter and Getters")); + // Default stop_gradient is false and slot id is 0, slot size is 1; + bwd_out_meta_[0].Init(1); + bwd_in_meta_[0].Init(1); +} + +const std::vector>& GradNodeBase::GetEdges() const { + return adj_edges_; +} + +void GradNodeBase::RegisterGradientHook( + size_t slot_id, size_t rank, + const std::function& hook) { + gradient_hooks_.emplace_back(std::make_tuple(slot_id, rank, hook)); +} + +void GradNodeBase::RegisterReduceHook(const std::function& hook) { + reduce_hooks_.emplace_back(hook); +} + +std::vector> GradNodeBase::ApplyGradientHooks( + const std::vector>& tensors) { + std::vector> outs(tensors.size()); + for (auto& tuple : gradient_hooks_) { + size_t slot_id = std::get<0>(tuple); + size_t rank = std::get<1>(tuple); + std::function& hook = + std::get<2>(tuple); + + PADDLE_ENFORCE(slot_id < tensors.size(), + paddle::platform::errors::Fatal( + "Slot_id from registered hook should be smaller than " + "slot size of grad_tensors")); + + PADDLE_ENFORCE(rank < tensors[slot_id].size(), + paddle::platform::errors::Fatal( + "rank of slot %d from registered hook should be smaller " + "than rank size of grad_tensors", + slot_id)); + + std::vector& slot_out = outs[slot_id]; + slot_out.resize(tensors[slot_id].size()); + egr::EagerTensor& out = slot_out[rank]; + if (!out.defined() || !out.initialized()) { + out = hook(tensors[slot_id][rank]); + } else { + // TODO(jiabin): Why this? + out = hook(out); + } + } + + for (size_t i = 0; i < outs.size(); i++) { + if (outs[i].empty() && (!tensors[i].empty())) { + outs[i].resize(tensors[i].size()); + } + // TODO(Jiabin): Optimize this if we only add hook slot by slot + for (size_t j = 0; j < outs[i].size(); j++) { + if (!outs[i][j].defined() || !outs[i][j].initialized()) { + outs[i][j] = tensors[i][j]; + } + } + } + + return outs; +} + +void GradNodeBase::ApplyReduceHooks() { + for (auto& hook : reduce_hooks_) { + hook(); + } +} +} // namespace egr diff --git a/paddle/fluid/eager/grad_node_info.h b/paddle/fluid/eager/grad_node_info.h new file mode 100644 index 0000000000000..6a4053e837894 --- /dev/null +++ b/paddle/fluid/eager/grad_node_info.h @@ -0,0 +1,231 @@ +// 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. + +#pragma once + +#include "paddle/fluid/eager/eager_tensor.h" +#include "paddle/pten/api/all.h" +#include "paddle/pten/include/core.h" + +namespace egr { +/** + * GradNodeBase is base class of all grad node, which is what should be used by + * eager execution, we define most of backward autograd members here, and for + * each Operator, they should hold their onw forward Inputs as TensorWrapper. + * + * The GradNodeBase will be held in autograd_meta, and it is also a member of + * Edge, which indicates the edge of backward graph. + * + * TODO:(yangzhanlue) GradNodeBase will also in charge of get the correct input + * from GradOpDescMaker to GradNodeBase. + * + * NOTE:GradNodeBase has a method named run, this method should be overrided by + * the + * specific derived class, it will prepare backward inputs and double backward's + * depends. Then, it will call C++ API of backward kernel functions to finish + * backward computation. + * + * NOTE:GradNodeBase holds its own inputs and Outputs + * + * Edge is defined to descripe depend of backward, an Edge is what linked + * between two + * node, it should contain a Node and rank of this Node (this is used to + * indicate which + * input of grad this edge belong). + * */ +class Edge; +class AutogradMeta; + +/** + * GradSlotMeta is used to Record Forward Tensor info to backward, since paddle + * has lots of operators + * whose backward logic is depends on if it has some specific inputs or outputs. + * So, we need a meta info + * to record it's needs. + * **/ +class GradSlotMeta { + public: + GradSlotMeta() = default; + void Init(size_t size) { + size_ = static_cast(size); + stop_gradient_.resize(size, false); + } + + bool IsInitialized() const { return size_ != -1; } + bool IsStopGradient(size_t rank) const { return stop_gradient_[rank]; } + int Size() const { return size_; } + void SetStopGradient(size_t rank, bool stop_gradient = true) { + stop_gradient_.at(rank) = stop_gradient; + } + + private: + int size_{-1}; + std::vector stop_gradient_{false}; +}; + +class GradNodeBase { + public: + GradNodeBase() = default; + GradNodeBase(size_t bwd_in_slot_num, size_t bwd_out_slot_num); + // TODO(jiabin): Should we have other constructor here? + virtual ~GradNodeBase() = default; + + /** + * operator() designed to contian the real backward execution logic, it should + * be + * overrided by derived class defined for each operator. It accepts a vector + * of + * Tensor which contains grads input of current operator + * + * Note: why we need backward inputs and outputs construct as vector of vector + * of egr::EagerTensor? + * Since all of paddle op composite in form of {"Slot name ", vector}, + * so, vector of vector + * is better choice to fit this format. + * **/ + virtual std::vector> operator()( + const std::vector>& grads) = 0; + + /** + * AddEdges is designed to set input tensors' backward Node as current + * node's Edges. + * This method should be call in forward code and for double backward depends + * computation. + * + * This one is called slot by slot + * **/ + void AddEdges(const std::vector& metas, size_t slot_id); + void AddEdges(const AutogradMeta& meta, size_t slot_id); + + /** + * GetEdges is designed to get all edges of current node**/ + const std::vector>& GetEdges() const; + + /** + * Get Input Meta of current Grad node**/ + const std::vector& InputMeta() const; + /** + * Get Output Meta of current Grad node**/ + const std::vector& OutputMeta() const; + /** + * Set bwd ins and outs info with forward vars + * **/ + + void SetGradInMeta(const std::vector& fwd_out, + size_t slot_rank); + void SetGradInMeta(const AutogradMeta& fwd_out, size_t slot_rank); + + void SetGradOutMeta(const std::vector& fwd_in, + size_t slot_rank); + void SetGradOutMeta(const AutogradMeta& fwd_in, size_t slot_rank); + + /** + * Default setters for Grad in/out meta this should be used for same special + * Node which will not create by user + * **/ + void SetDefaultGradInOutMeta(); + /** + * Register GradientHook or ReduceHook + * **/ + void RegisterGradientHook( + size_t slot_id, size_t rank, + const std::function& hook); + void RegisterReduceHook(const std::function& hook); + + /** + * Apply GradientHook or ReduceHook + * **/ + inline bool GradientHooksRegistered() { return gradient_hooks_.size() != 0; } + inline bool ReduceHooksRegistered() { return reduce_hooks_.size() != 0; } + + std::vector> ApplyGradientHooks( + const std::vector>& tensors); + void ApplyReduceHooks(); + + private: + // TODO(jiabin): Use SmallVector instead after merge PR from develop + + // Edges recorded the backward related node info, which indicate all edges + // linked + // by this Grad Node. + // Why we need vector>: Edges is as same rank as bwd output. + std::vector> adj_edges_; + + // bwd_out_meta_ is used to record Grad output info for backward + std::vector bwd_out_meta_; + + // bwd_in_meta_ used to record Grad input info for backward + std::vector bwd_in_meta_; + // Gradient Hooks + // Customer may register a list of hooks which will be called in order during + // backward + // Each entry consists one pair of + std::vector>> + gradient_hooks_; + std::vector> reduce_hooks_; +}; + +class Edge { + public: + // Default constructor for Edges in order to construct it for AutogradMeta + Edge() : in_slot_id_(0), in_rank_(0), grad_node_(nullptr) {} + + // In real use cases we should create Edge from grad node and input rank which + // indicate which edge it is. + // Since we have slot design in operators we will have to locate an edge with + // slot + // and rank. + Edge(const std::shared_ptr& grad_node, size_t in_slot_id, + size_t in_rank) + : in_slot_id_(in_slot_id), in_rank_(in_rank), grad_node_(grad_node) {} + + Edge(const std::shared_ptr& grad_node, + const std::pair& rank_info) + : in_slot_id_(rank_info.first), + in_rank_(rank_info.second), + grad_node_(grad_node) {} + + GradNodeBase* GetGradNode() const { return grad_node_.get(); } + + std::shared_ptr GetMutableGradNode() const { + return grad_node_; + } + + std::pair GetEdgeRankInfo() const { + return std::make_pair(in_slot_id_, in_rank_); + } + + void SetEdgeRankInfo(size_t slot_id, size_t in_rank) { + in_slot_id_ = slot_id; + in_rank_ = in_rank; + } + + void SetEdgeRankInfo( + const std::pair& edge_rank) { + in_slot_id_ = edge_rank.first; + in_rank_ = edge_rank.second; + } + + // Currently we use grad_node_ to identify if a edge is initialized. + bool IsInitialized() const { return grad_node_.get(); } + + private: + size_t in_slot_id_; + size_t in_rank_; + std::shared_ptr grad_node_; +}; + +} // namespace egr diff --git a/paddle/fluid/eager/tests/data_structure_tests/CMakeLists.txt b/paddle/fluid/eager/tests/data_structure_tests/CMakeLists.txt index 907fcd101ba69..21e63b6480c73 100644 --- a/paddle/fluid/eager/tests/data_structure_tests/CMakeLists.txt +++ b/paddle/fluid/eager/tests/data_structure_tests/CMakeLists.txt @@ -1 +1,3 @@ -cc_test(test_egr_ds_eager_tensor SRCS eager_tensor_test.cc DEPS ${eager_deps}) +cc_test(test_egr_ds_eager_tensor SRCS eager_tensor_test.cc DEPS ${eager_deps} ) +cc_test(test_egr_ds_auotgrad_meta SRCS autograd_meta_test.cc DEPS ${eager_deps} grad_node_info) +cc_test(test_egr_ds_grad_node_info SRCS grad_node_info_test.cc DEPS ${eager_deps} grad_node_info) diff --git a/paddle/fluid/eager/tests/data_structure_tests/autograd_meta_test.cc b/paddle/fluid/eager/tests/data_structure_tests/autograd_meta_test.cc new file mode 100644 index 0000000000000..96845569ca0c5 --- /dev/null +++ b/paddle/fluid/eager/tests/data_structure_tests/autograd_meta_test.cc @@ -0,0 +1,82 @@ +// 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 "glog/logging.h" +#include "gtest/gtest.h" + +#include "paddle/fluid/eager/autograd_meta.h" +#include "paddle/fluid/eager/eager_tensor.h" +#include "paddle/fluid/eager/grad_node_info.h" +#include "paddle/fluid/eager/tests/data_structure_tests/grad_node_test.h" +#include "paddle/pten/api/lib/utils/allocator.h" + +TEST(AutogradMeta, Constructor) { + egr::EagerTensor et1; + auto auto_grad = std::make_shared(); + et1.set_autograd_meta(auto_grad); + auto* tmp_auto = static_cast(et1.get_autograd_meta()); + CHECK_EQ(tmp_auto->OutRankInfo().first, size_t(0)); + CHECK_EQ(tmp_auto->OutRankInfo().second, size_t(0)); + CHECK(tmp_auto->IsInitialized() == false); +} + +TEST(AutogradMeta, MemberFunction) { + egr::EagerTensor et1; + auto auto_grad = std::make_shared(); + et1.set_autograd_meta(auto_grad); + auto* tmp_auto = static_cast(et1.get_autograd_meta()); + VLOG(6) << "Test Grad"; + CHECK(tmp_auto->Grad().defined() == false); + auto* grad_t = tmp_auto->MutableGrad(); + pten::DenseTensorMeta meta = pten::DenseTensorMeta( + pten::DataType::FLOAT32, paddle::framework::make_ddim({1, 2})); + std::shared_ptr dt = std::make_shared( + std::make_shared( + paddle::platform::CPUPlace()), + meta); + auto* dt_ptr = dt->mutable_data(); + dt_ptr[0] = 5.0f; + dt_ptr[1] = 10.0f; + grad_t->set_impl(dt); + VLOG(6) << "Test Mutable Grad"; + auto impl_ptr = + std::dynamic_pointer_cast(tmp_auto->Grad().impl()); + CHECK_EQ(impl_ptr->data()[0], 5.0f); + CHECK_EQ(impl_ptr->data()[1], 10.0f); + VLOG(6) << "Test IsInitialized"; + CHECK(tmp_auto->IsInitialized() == false); + VLOG(6) << "Test GradNodeSetter Getter"; + auto grad_node = std::make_shared(); + tmp_auto->SetGradNode(grad_node); + CHECK(tmp_auto->IsInitialized() == true); + auto tmp_grad_node = tmp_auto->GetMutableGradNode(); + std::dynamic_pointer_cast(tmp_grad_node)->val_ = + 5.0; + CHECK_EQ(dynamic_cast(tmp_auto->GradNode())->val_, + 5.0); + VLOG(6) << "Test rank Setter Getter"; + CHECK_EQ(tmp_auto->OutRankInfo().first, size_t(0)); + CHECK_EQ(tmp_auto->OutRankInfo().second, size_t(0)); + tmp_auto->SetSingleOutRankWithSlot(2, 3); + CHECK_EQ(tmp_auto->OutRankInfo().first, size_t(2)); + CHECK_EQ(tmp_auto->OutRankInfo().second, size_t(3)); + VLOG(6) << "Test stop gradient Setter Getter"; + CHECK_EQ(tmp_auto->NumericStopGradient(), -1); + tmp_auto->SetStopGradient(true); + CHECK(tmp_auto->StopGradient() == true); + VLOG(6) << "Test Persistable Setter Getter"; + CHECK(tmp_auto->Persistable() == false); + tmp_auto->SetPersistable(true); + CHECK(tmp_auto->Persistable() == true); +} diff --git a/paddle/fluid/eager/tests/data_structure_tests/eager_tensor_test.cc b/paddle/fluid/eager/tests/data_structure_tests/eager_tensor_test.cc index a528867d44203..a02f0bec456bf 100644 --- a/paddle/fluid/eager/tests/data_structure_tests/eager_tensor_test.cc +++ b/paddle/fluid/eager/tests/data_structure_tests/eager_tensor_test.cc @@ -18,9 +18,6 @@ #include "paddle/fluid/eager/eager_tensor.h" #include "paddle/pten/api/lib/utils/allocator.h" -// TODO(jiabin): remove nolint here!!! -using namespace egr; // NOLINT - namespace eager_test { using AbstractAutogradMeta = paddle::experimental::AbstractAutogradMeta; class AutogradMetaTest : public AbstractAutogradMeta { @@ -30,8 +27,8 @@ class AutogradMetaTest : public AbstractAutogradMeta { }; } TEST(EagerTensor, Constructor) { - EagerTensor et1 = EagerTensor(); - EagerTensor et2 = EagerTensor("et2"); + egr::EagerTensor et1 = egr::EagerTensor(); + egr::EagerTensor et2 = egr::EagerTensor("et2"); CHECK_EQ(et1.defined(), false); CHECK_EQ(et2.name(), "et2"); @@ -45,18 +42,18 @@ TEST(EagerTensor, Constructor) { auto* dt_ptr = dt->mutable_data(); dt_ptr[0] = 5.0f; dt_ptr[1] = 10.0f; - EagerTensor et3 = EagerTensor(dt); + egr::EagerTensor et3 = egr::EagerTensor(dt); auto* et3_ptr = std::dynamic_pointer_cast(et3.impl())->data(); CHECK_EQ(et3_ptr[0], 5.0f); CHECK_EQ(et3_ptr[1], 10.0f); // copy constructor - EagerTensor et4(et3); + egr::EagerTensor et4(et3); auto* et4_ptr = std::dynamic_pointer_cast(et4.impl())->data(); CHECK_EQ(et4_ptr[0], 5.0f); CHECK_EQ(et4_ptr[1], 10.0f); - EagerTensor et5(std::move(et4)); + egr::EagerTensor et5(std::move(et4)); auto* et5_ptr = std::dynamic_pointer_cast(et5.impl())->data(); CHECK_EQ(et5_ptr[0], 5.0f); @@ -64,7 +61,7 @@ TEST(EagerTensor, Constructor) { } TEST(EagerTensor, MemberFunction) { - EagerTensor et3; + egr::EagerTensor et3; pten::DenseTensorMeta meta = pten::DenseTensorMeta( pten::DataType::FLOAT32, paddle::framework::make_ddim({1, 2})); std::shared_ptr dt = std::make_shared( @@ -95,7 +92,7 @@ TEST(EagerTensor, MemberFunction) { std::dynamic_pointer_cast(et3.impl())->data(); CHECK_EQ(dt3_ptr[0], 5.0f); CHECK_EQ(dt3_ptr[1], 10.0f); - EagerTensor et4 = et3; + egr::EagerTensor et4 = et3; VLOG(6) << "copy ="; CHECK(et4.initialized() == true); auto* dt4_ptr = @@ -103,7 +100,7 @@ TEST(EagerTensor, MemberFunction) { CHECK_EQ(dt4_ptr[0], 5.0f); CHECK_EQ(dt4_ptr[1], 10.0f); VLOG(6) << "move ="; - EagerTensor et5 = std::move(et4); + egr::EagerTensor et5 = std::move(et4); auto* dt5_ptr = std::dynamic_pointer_cast(et5.impl())->data(); CHECK_EQ(dt5_ptr[0], 5.0f); diff --git a/paddle/fluid/eager/tests/data_structure_tests/grad_node_info_test.cc b/paddle/fluid/eager/tests/data_structure_tests/grad_node_info_test.cc new file mode 100644 index 0000000000000..abc200f7130ff --- /dev/null +++ b/paddle/fluid/eager/tests/data_structure_tests/grad_node_info_test.cc @@ -0,0 +1,159 @@ +// 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 "glog/logging.h" +#include "gtest/gtest.h" + +#include "paddle/fluid/eager/autograd_meta.h" +#include "paddle/fluid/eager/eager_tensor.h" +#include "paddle/fluid/eager/grad_node_info.h" +#include "paddle/fluid/eager/tests/data_structure_tests/grad_node_test.h" +#include "paddle/pten/api/lib/utils/allocator.h" + +TEST(GradNodeInfo, GradSlotMeta) { + auto grad_slot = egr::GradSlotMeta(); + CHECK(grad_slot.IsInitialized() == false); + VLOG(6) << "Init GradSlotMeta"; + grad_slot.Init(2); + CHECK(grad_slot.IsInitialized() == true); + VLOG(6) << "Set SetStopGradient"; + grad_slot.SetStopGradient(0); + CHECK(grad_slot.IsStopGradient(0) == true); + CHECK_EQ(grad_slot.Size(), 2); +} + +TEST(GradNodeInfo, GradNodeBase) { + VLOG(6) << "Construct Grad Node"; + auto grad_test_node0 = std::make_shared( + /* val */ 5.0, /* in_num */ 2, /* out_num */ 2); + auto grad_test_node1 = std::make_shared(); + std::vector> grads; + pten::DenseTensorMeta meta = pten::DenseTensorMeta( + pten::DataType::FLOAT32, paddle::framework::make_ddim({1, 1})); + std::shared_ptr dt = std::make_shared( + std::make_shared( + paddle::platform::CPUPlace()), + meta); + auto* dt_ptr = dt->mutable_data(); + dt_ptr[0] = 5.0f; + egr::EagerTensor et1(dt); + grads = {{et1}}; + VLOG(6) << "Test Grad Node Call"; + auto res = (*grad_test_node0)(grads); + CHECK_EQ(std::dynamic_pointer_cast(res[0][0].impl()) + ->data()[0], + 6.0f); + VLOG(6) << "Test Add Edges"; + egr::Edge edge0(grad_test_node1, 1, 2); + auto auto_grad0 = std::make_shared(edge0); + egr::Edge edge1(grad_test_node1, 3, 4); + auto auto_grad1 = std::make_shared(edge1); + grad_test_node0->AddEdges((*auto_grad0.get()), 0); + CHECK_EQ(grad_test_node0->GetEdges()[0][0].GetEdgeRankInfo().first, + size_t(1)); + CHECK_EQ(grad_test_node0->GetEdges()[0][0].GetEdgeRankInfo().second, + size_t(2)); + std::vector metas = {auto_grad1.get()}; + grad_test_node0->AddEdges(metas, 1); + CHECK_EQ(grad_test_node0->GetEdges()[1][0].GetEdgeRankInfo().first, + size_t(3)); + CHECK_EQ(grad_test_node0->GetEdges()[1][0].GetEdgeRankInfo().second, + size_t(4)); + + VLOG(6) << "Test Set Meta and Get Meta"; + auto_grad1->SetStopGradient(true); + grad_test_node0->SetGradInMeta(metas, 0); + grad_test_node0->SetGradInMeta(*auto_grad1.get(), 1); + grad_test_node0->SetGradOutMeta(metas, 0); + grad_test_node0->SetGradOutMeta(*auto_grad1.get(), 1); + CHECK_EQ(grad_test_node0->InputMeta()[0].Size(), 1); + CHECK_EQ(grad_test_node0->InputMeta()[1].Size(), 1); + CHECK(grad_test_node0->OutputMeta()[0].IsStopGradient(0)); + CHECK(grad_test_node0->OutputMeta()[1].IsStopGradient(0)); + + VLOG(6) << "Test Default Set Meta and Get Meta"; + auto grad_test_node2 = std::make_shared( + /* val */ 5.0, /* in_num */ 1, /* out_num */ 1); + grad_test_node2->SetDefaultGradInOutMeta(); + CHECK(grad_test_node2->OutputMeta()[0].IsInitialized()); + CHECK(grad_test_node2->OutputMeta()[0].IsStopGradient(0) == false); + CHECK_EQ(grad_test_node2->OutputMeta()[0].Size(), 1); + + VLOG(6) << "Test Gradient Hook"; + auto gradient_hook = [](const egr::EagerTensor& et) -> egr::EagerTensor { + egr::EagerTensor res; + pten::DenseTensorMeta meta = pten::DenseTensorMeta( + pten::DataType::FLOAT32, paddle::framework::make_ddim({1, 1})); + std::shared_ptr dt = std::make_shared( + std::make_shared( + paddle::platform::CPUPlace()), + meta); + auto* dt_ptr = dt->mutable_data(); + dt_ptr[0] = 6.0f; + auto* et_ptr = + std::dynamic_pointer_cast(et.impl())->data(); + dt_ptr[0] += et_ptr[0]; + res.set_impl(dt); + VLOG(6) << "Running Gradient Hook"; + return res; + }; + grad_test_node0->RegisterGradientHook(0, 0, gradient_hook); + // 5 + 6 + auto grad_hook_res = grad_test_node0->ApplyGradientHooks(grads); + CHECK_EQ( + std::dynamic_pointer_cast(grad_hook_res[0][0].impl()) + ->data()[0], + 11.0); + + VLOG(6) << "Test Reduce Hook"; + auto reduce_hook = [&](void) -> void { + auto* et_ptr = std::dynamic_pointer_cast(et1.impl()) + ->mutable_data(); + et_ptr[0] = 100.0; + VLOG(6) << "Running Reduce Hook"; + }; + grad_test_node0->RegisterReduceHook(reduce_hook); + grad_test_node0->ApplyReduceHooks(); + CHECK_EQ(std::dynamic_pointer_cast(et1.impl()) + ->data()[0], + 100.0); +} + +TEST(GradNodeInfo, Edge) { + auto grad_test_node0 = std::make_shared(5, 2, 2); + VLOG(6) << "Test Construct Edge"; + egr::Edge edge0 = egr::Edge(); + CHECK(edge0.IsInitialized() == false); + egr::Edge edge1 = egr::Edge(grad_test_node0, size_t(0), size_t(0)); + CHECK(edge1.IsInitialized() == true); + egr::Edge edge2 = + egr::Edge(grad_test_node0, std::make_pair(size_t(1), size_t(0))); + VLOG(6) << "Test Set Edge's Grad Node"; + auto* grad_node = edge1.GetGradNode(); + CHECK_EQ(grad_node->InputMeta().size(), size_t(2)); + auto mt_grad_node = edge1.GetMutableGradNode(); + auto auto_grad1 = std::make_shared(); + std::vector metas = {auto_grad1.get()}; + // Uninitialized AutogradMeta indicates + mt_grad_node->SetGradInMeta(metas, 0); + CHECK(grad_node->InputMeta()[0].IsStopGradient(0) == true); + VLOG(6) << "Test Get/Set Edge Rank Info"; + CHECK_EQ(edge2.GetEdgeRankInfo().first, size_t(1)); + CHECK_EQ(edge2.GetEdgeRankInfo().second, size_t(0)); + edge2.SetEdgeRankInfo(2, 3); + CHECK_EQ(edge2.GetEdgeRankInfo().first, size_t(2)); + CHECK_EQ(edge2.GetEdgeRankInfo().second, size_t(3)); + edge2.SetEdgeRankInfo(std::make_pair(size_t(4), size_t(5))); + CHECK_EQ(edge2.GetEdgeRankInfo().first, size_t(4)); + CHECK_EQ(edge2.GetEdgeRankInfo().second, size_t(5)); +} diff --git a/paddle/fluid/eager/tests/data_structure_tests/grad_node_test.h b/paddle/fluid/eager/tests/data_structure_tests/grad_node_test.h new file mode 100644 index 0000000000000..ddea70da791a0 --- /dev/null +++ b/paddle/fluid/eager/tests/data_structure_tests/grad_node_test.h @@ -0,0 +1,48 @@ +// 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. +#pragma once +#include "glog/logging.h" +#include "gtest/gtest.h" + +#include "paddle/fluid/eager/autograd_meta.h" +#include "paddle/fluid/eager/eager_tensor.h" +#include "paddle/fluid/eager/grad_node_info.h" +#include "paddle/pten/api/lib/utils/allocator.h" + +namespace eager_test { +class GradTestNode : public egr::GradNodeBase { + public: + ~GradTestNode() override = default; + GradTestNode(float val, int in_num, int out_num) + : GradNodeBase(in_num, out_num), val_(val) {} + GradTestNode() : GradNodeBase() { val_ = 1.0; } + std::vector> operator()( + const std::vector>& grads) override { + val_ = std::dynamic_pointer_cast(grads[0][0].impl()) + ->data()[0]; + pten::DenseTensorMeta meta = pten::DenseTensorMeta( + pten::DataType::FLOAT32, paddle::framework::make_ddim({1, 1})); + std::shared_ptr dt = std::make_shared( + std::make_shared( + paddle::platform::CPUPlace()), + meta); + auto* dt_ptr = dt->mutable_data(); + dt_ptr[0] = 6.0f; + egr::EagerTensor et1(dt); + std::vector> res = {{et1}}; + return res; + } + float val_; +}; +} // namespace eager_test diff --git a/paddle/pten/api/include/tensor.h b/paddle/pten/api/include/tensor.h index 476dddb4e2cbd..454d206c1c237 100644 --- a/paddle/pten/api/include/tensor.h +++ b/paddle/pten/api/include/tensor.h @@ -473,7 +473,7 @@ class PD_DLL_DECL Tensor final { * Tensor name: used for adapt original execution mechanism and debug analysis * in the development of new dygraph. It may be removed in the future. */ - std::string name_; + std::string name_{""}; }; } // namespace experimental