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232 lines
8.7 KiB
232 lines
8.7 KiB
/* Copyright (c) 2016 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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#pragma once
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#include <memory>
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#include <string>
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#include <unordered_set>
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#include <vector>
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#include "paddle/fluid/framework/executor.h"
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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// StepScopes manages the scopes inside Recurrent Op.
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//
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// if is_train = False, then
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// there are two scopes for the RNN and just support forward
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// else
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// the len(scopes) == seq_len
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//
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// if is_backward = True, then
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// reversely access scopes, delete useless ex-scope
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// else
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// access scopes from beginning to end
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class StepScopes {
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public:
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StepScopes(const platform::DeviceContext &dev_ctx,
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const framework::Scope &parent,
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std::vector<framework::Scope *> *scopes, bool is_train,
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size_t seq_len, bool is_backward = false);
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// Get the current scope
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framework::Scope &CurScope();
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// Get the ex-scope, which is the scope in previous time step
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framework::Scope &ExScope();
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// Move to next time step when forwarding
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void ForwardNext();
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// Delete ex-scope after using it, then move to next time step when
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// backwarding
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void BackwardNext(const platform::DeviceContext &dev_ctx,
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framework::Scope *parent_scope);
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private:
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framework::Scope &GetScope(size_t scope_id) const;
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size_t counter_;
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std::vector<framework::Scope *> *scopes_;
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bool is_train_;
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bool is_backward_;
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};
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// Base class for RecurrentOp/RecurrentGradOp
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// Some common protected functions for RecurrentOp/RecurrentGradOp
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class RecurrentBase : public framework::OperatorBase {
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public:
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static const char kInputs[];
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static const char kInitialStates[];
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static const char kParameters[];
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static const char kOutputs[];
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static const char kStepScopes[];
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static const char kHasStates[];
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static const char kExStates[];
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static const char kStates[];
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static const char kStepBlock[];
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static const char kReverse[];
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static const char kIsTrain[];
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static const char kSkipEagerDeletionVars[];
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static const char kInputGrads[];
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static const char kOutputGrads[];
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static const char kParamGrads[];
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static const char kInitStateGrads[];
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RecurrentBase(const std::string &type,
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const framework::VariableNameMap &inputs,
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const framework::VariableNameMap &outputs,
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const framework::AttributeMap &attrs);
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protected:
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// Get SequenceLength from Scope
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// The sequence length is got from input tensor. The input tensor's
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// dimension should be [SEQ_LEN, ..., ...]. The first of the tensor's shape
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// is SEQ_LEN. The second of the tensor's shape could be the batch size or
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// nested sequence length.
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int64_t GetSequenceLength(const framework::Scope &scope) const;
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// for src_tensor, dst_tensor in zip(map(src_scope.FindVar, src_vars),
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// map(dst_scope.Var, dst_vars)):
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// dst_tensor.ShareDataWith(src_tensor)
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static void LinkTensor(const framework::Scope &src_scope,
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const std::vector<std::string> &src_vars,
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framework::Scope *dst_scope,
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const std::vector<std::string> &dst_vars);
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// for src_tensor, dst_tensor in zip(map(src_scope.FindVar, src_vars),
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// map(dst_scope.Var, dst_vars)):
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// callback(src_tensor, &dst_tensor)
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template <typename Callback>
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static void LinkTensorWithCallback(const framework::Scope &src_scope,
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const std::vector<std::string> &src_vars,
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framework::Scope *dst_scope,
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const std::vector<std::string> &dst_vars,
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Callback callback,
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bool is_backward = false) {
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PADDLE_ENFORCE_EQ(src_vars.size(), dst_vars.size());
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for (size_t i = 0; i < dst_vars.size(); ++i) {
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VLOG(10) << "Link " << src_vars[i] << " to " << dst_vars[i];
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AccessTensor(src_scope, src_vars[i], dst_scope, dst_vars[i], callback,
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is_backward);
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}
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}
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// for src_tensor, dst_tensor in zip(map(src_scope.FindVar, src_vars),
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// map(dst_scope.FindVar, dst_vars)):
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// callback(src_tensor, &dst_tensor)
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template <typename Callback>
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static void LinkTensorWithCallback(const framework::Scope &src_scope,
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const std::vector<std::string> &src_vars,
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const framework::Scope &dst_scope,
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const std::vector<std::string> &dst_vars,
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Callback callback,
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bool is_backward = false) {
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PADDLE_ENFORCE_EQ(src_vars.size(), dst_vars.size());
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for (size_t i = 0; i < dst_vars.size(); ++i) {
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VLOG(10) << "Link " << src_vars[i] << " to " << dst_vars[i];
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AccessTensor(src_scope, src_vars[i], dst_scope, dst_vars[i], callback,
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is_backward);
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}
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}
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// (seq_len, shape) -> return [seq_len] + list(shape)
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static framework::DDim PrependDims(size_t seq_len,
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const framework::DDim &src);
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private:
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template <typename Callback>
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static void AccessTensor(const framework::Scope &src_scope,
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const std::string &src_var_name,
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framework::Scope *dst_scope,
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const std::string &dst_var_name, Callback callback,
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bool is_backward = false) {
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auto *src_var = src_scope.FindVar(src_var_name);
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if (is_backward && src_var == nullptr) {
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return;
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}
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PADDLE_ENFORCE_NOT_NULL(src_var, "%s is not found.", src_var_name);
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auto &src_tensor = src_var->Get<framework::LoDTensor>();
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auto *dst_var = dst_scope->Var(dst_var_name);
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auto *dst_tensor = dst_var->GetMutable<framework::LoDTensor>();
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callback(src_tensor, dst_tensor);
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}
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template <typename Callback>
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static void AccessTensor(const framework::Scope &src_scope,
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const std::string &src_var_name,
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const framework::Scope &dst_scope,
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const std::string &dst_var_name, Callback callback,
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bool is_backward = false) {
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auto *dst_var = dst_scope.FindVar(dst_var_name);
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if (is_backward && dst_var == nullptr) {
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return;
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}
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auto *src_var = src_scope.FindVar(src_var_name);
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PADDLE_ENFORCE_NOT_NULL(src_var, "%s is not found.", src_var_name);
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auto &src_tensor = src_var->Get<framework::LoDTensor>();
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PADDLE_ENFORCE_NOT_NULL(dst_var, "%s is not found.", dst_var_name);
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auto *dst_tensor = dst_var->GetMutable<framework::LoDTensor>();
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callback(src_tensor, dst_tensor);
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}
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};
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class RecurrentOp : public RecurrentBase {
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public:
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RecurrentOp(const std::string &type, const framework::VariableNameMap &inputs,
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const framework::VariableNameMap &outputs,
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const framework::AttributeMap &attrs);
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private:
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void RunImpl(const framework::Scope &scope,
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const platform::Place &place) const override;
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private:
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StepScopes CreateStepScopes(const platform::DeviceContext &dev_ctx,
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const framework::Scope &scope,
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size_t seq_len) const;
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};
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class RecurrentGradOp : public RecurrentBase {
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public:
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RecurrentGradOp(const std::string &type,
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const framework::VariableNameMap &inputs,
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const framework::VariableNameMap &outputs,
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const framework::AttributeMap &attrs);
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private:
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void RunImpl(const framework::Scope &scope,
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const platform::Place &place) const override;
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StepScopes CreateStepScopes(const platform::DeviceContext &dev_ctx,
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const framework::Scope &scope,
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size_t seq_len) const;
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std::unordered_set<std::string> List2Set(
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const std::vector<std::string> &list) const;
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std::unordered_set<std::string> LocalVarNames(
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const framework::Scope &scope) const;
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static std::vector<std::string> GradVarLists(
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const std::vector<std::string> &var_names);
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};
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} // namespace operators
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} // namespace paddle
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