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293 lines
12 KiB
293 lines
12 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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#include "paddle/fluid/operators/controlflow/conditional_block_op.h"
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#include "paddle/fluid/operators/assign_op.h"
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#include "paddle/fluid/operators/math/math_function.h"
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namespace paddle {
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namespace operators {
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const char ConditionalOp::kInputs[] = "Input";
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const char ConditionalOp::kOutputs[] = "Out";
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const char ConditionalOp::kCondition[] = "Cond";
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const char ConditionalOp::kScope[] = "Scope";
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const char ConditionalOp::kSkipEagerDeletionVars[] = "skip_eager_deletion_vars";
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class ConditionalBlockOp : public ConditionalOp {
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public:
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ConditionalBlockOp(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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: ConditionalOp(type, inputs, outputs, attrs) {}
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private:
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void RunImpl(const framework::Scope &scope,
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const platform::Place &dev_place) const override {
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bool need_run;
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if (Attr<bool>("is_scalar_condition")) {
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// When is_scalar_condition is True, the conditional variable is a scalar,
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// whether need to execute the operators in sub-block depends on the
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// conditional variable (Cond).
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auto xs = InputTensors(scope, ConditionalOp::kCondition);
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need_run = ScalarCondition(xs);
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} else {
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// When is_scalar_condition is False, the conditional variable maybe a
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// vector or tensor, whether need to execute the operators in sub-block
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// depends on the input variables (Input).
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auto xs = InputTensors(scope, ConditionalOp::kInputs);
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need_run = std::all_of(
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xs.begin(), xs.end(),
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[](const framework::LoDTensor *t) { return t->numel() != 0; });
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}
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if (need_run) {
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auto *scope_var = scope.FindVar(Output(ConditionalOp::kScope));
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PADDLE_ENFORCE(scope_var != nullptr, "Must set scope");
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auto *scopes = scope_var->GetMutable<std::vector<framework::Scope *>>();
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scopes->resize(1);
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scopes->front() = &scope.NewScope();
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auto &cur_scope = *scopes->front();
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framework::Executor exec(dev_place);
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auto *block = Attr<framework::BlockDesc *>("sub_block");
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VLOG(3) << "Conditional block.idx = " << block->ID()
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<< ", scope = " << &cur_scope;
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auto &skip_vars =
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Attr<std::vector<std::string>>(ConditionalOp::kSkipEagerDeletionVars);
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exec.Run(*block->Program(), &cur_scope, block->ID(), false, true,
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skip_vars, /* force_disable_gc */ false,
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/* keep_kid_scopes */ true);
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}
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}
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};
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class ConditionalBlockInferShape : public framework::InferShapeBase {
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public:
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void operator()(framework::InferShapeContext *context) const override {
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PADDLE_ENFORCE_EQ(context->HasInputs(ConditionalOp::kCondition), true,
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platform::errors::InvalidArgument(
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"conditional_block_op must have condition input"));
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}
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};
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class ConditionalBlockGradOp : public ConditionalOp {
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public:
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ConditionalBlockGradOp(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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: ConditionalOp(type, inputs, outputs, attrs) {}
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private:
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void RunImpl(const framework::Scope &scope,
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const platform::Place &dev_place) const override {
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bool need_run;
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if (Attr<bool>("is_scalar_condition")) {
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auto xs = this->InputTensors(scope, ConditionalOp::kCondition);
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need_run = ScalarCondition(xs);
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} else {
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auto xs = this->InputTensors(scope, ConditionalOp::kInputs);
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need_run = std::all_of(
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xs.begin(), xs.end(),
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[](const framework::LoDTensor *t) { return t->numel() != 0; });
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}
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const auto &inputs = Inputs(ConditionalOp::kInputs);
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const auto &outside_grads =
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Outputs(framework::GradVarName(ConditionalOp::kInputs));
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if (need_run) {
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std::vector<std::string> inside_grads;
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inside_grads.reserve(inputs.size());
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for (auto &in : inputs) {
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inside_grads.emplace_back(framework::GradVarName(in));
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}
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auto *scope_var = scope.FindVar(Input(ConditionalOp::kScope));
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PADDLE_ENFORCE_NE(scope_var, nullptr,
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platform::errors::InvalidArgument(
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"Scope must be set in conditional block op"));
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auto &scopes = scope_var->Get<std::vector<framework::Scope *>>();
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PADDLE_ENFORCE_GT(scopes.size(), 0,
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platform::errors::InvalidArgument(
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"Scope must be set in conditional block op"));
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framework::Scope &cur_scope = *scopes[0];
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framework::Executor exec(dev_place);
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auto *block = Attr<framework::BlockDesc *>("sub_block");
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VLOG(3) << "Conditional Grad block.idx = " << block->ID()
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<< ", scope = " << &cur_scope;
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exec.Run(*block->Program(), &cur_scope, block->ID(), false, true,
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inside_grads, /* force_disable_gc */ false,
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/* keep_kid_scopes */ false);
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AssignLocalGradientToParentScope(dev_place, cur_scope, scope,
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inside_grads, outside_grads);
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return;
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}
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AssignZeroToParentScope(dev_place, scope, inputs, outside_grads);
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}
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private:
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void AssignLocalGradientToParentScope(
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const platform::Place &place, const framework::Scope &cur_scope,
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const framework::Scope &parent_scope,
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const std::vector<std::string> &inside_grads,
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const std::vector<std::string> &outside_grads) const {
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for (size_t i = 0; i < outside_grads.size(); ++i) {
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const std::string &outside_grad_name = outside_grads[i];
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const std::string &inside_grad_name = inside_grads[i];
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VLOG(4) << "inside_grad_name = " << inside_grad_name
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<< ", outside_grad_name = " << outside_grad_name;
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framework::Variable *inside_var =
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cur_scope.FindLocalVar(inside_grad_name);
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if (inside_var == nullptr) {
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continue;
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}
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framework::Variable *outside_var =
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parent_scope.FindVar(outside_grad_name);
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if (outside_var == nullptr) {
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continue;
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}
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platform::DeviceContext *dev_ctx =
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platform::DeviceContextPool::Instance().Get(place);
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framework::VisitVarType(*inside_var,
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AssignFunctor(outside_var, *dev_ctx));
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}
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}
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void AssignZeroToParentScope(
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const platform::Place &place, const framework::Scope &scope,
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const std::vector<std::string> &inputs,
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const std::vector<std::string> &outside_grads) const {
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for (size_t i = 0; i < outside_grads.size(); ++i) {
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const std::string &outside_grad_name = outside_grads[i];
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const std::string &input_name = inputs[i];
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VLOG(4) << "input_name = " << input_name
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<< ", outside_grad_name = " << outside_grad_name;
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framework::Variable *input_var = scope.FindVar(input_name);
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if (input_var == nullptr) {
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continue;
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}
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framework::Variable *outside_var = scope.FindVar(outside_grad_name);
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if (outside_var == nullptr) {
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continue;
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}
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if (input_var->IsType<framework::LoDTensor>()) {
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PADDLE_ENFORCE_EQ(outside_var->IsType<framework::LoDTensor>(), true,
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platform::errors::InvalidArgument(
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"Type of outside_var %s is NOT LoDTensor, which "
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"doesn't match input_var %s",
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outside_grad_name, input_name));
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AssignZeroToOutsideTensor(
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place, scope, input_var->Get<framework::LoDTensor>(),
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outside_var->GetMutable<framework::LoDTensor>());
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} else if (input_var->IsType<framework::LoDTensorArray>()) {
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PADDLE_ENFORCE_EQ(outside_var->IsType<framework::LoDTensorArray>(),
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true,
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platform::errors::InvalidArgument(
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"Type of outside_var %s is NOT LoDTensorArray, "
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"which doesn't match input_var %s",
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outside_grad_name, input_name));
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const auto &input_tensors = input_var->Get<framework::LoDTensorArray>();
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auto *outside_tensors =
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outside_var->GetMutable<framework::LoDTensorArray>();
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PADDLE_ENFORCE_EQ(input_tensors.size(), outside_tensors->size(),
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platform::errors::InvalidArgument(
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"LoDTensorArray outside_var %s doen't have same "
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"size as input_var %s",
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outside_grad_name, input_name));
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for (size_t j = 0; j < input_tensors.size(); ++j) {
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AssignZeroToOutsideTensor(place, scope, input_tensors[j],
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&((*outside_tensors)[j]));
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}
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} else {
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// TODO(huihuangzheng): add support for SelectedRows
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PADDLE_THROW(platform::errors::InvalidArgument(
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"Conditional block grad op doesn't support non-LoDTensor output "
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"now"));
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}
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}
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}
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void AssignZeroToOutsideTensor(const platform::Place &place,
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const framework::Scope &cur_scope,
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const framework::LoDTensor &input_tensor,
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framework::LoDTensor *outside_tensor) const {
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if (!input_tensor.IsInitialized() || input_tensor.numel() == 0) {
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return;
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}
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VLOG(4) << "Assigning zero to " << outside_tensor;
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outside_tensor->Resize(input_tensor.dims());
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outside_tensor->mutable_data(place, input_tensor.type());
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const platform::DeviceContext *dev_ctx =
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platform::DeviceContextPool::Instance().Get(place);
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math::set_constant(*dev_ctx, outside_tensor, 0.0f);
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outside_tensor->set_lod(input_tensor.lod());
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}
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};
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class ConditionalBlockGradInferShape : public framework::InferShapeBase {
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public:
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void operator()(framework::InferShapeContext *context) const override {
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PADDLE_ENFORCE(context->HasInputs(ConditionalOp::kCondition));
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if (context->HasInputs(ConditionalOp::kInputs) &&
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context->HasOutputs(framework::GradVarName(ConditionalOp::kInputs))) {
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context->SetOutputsDim(framework::GradVarName(ConditionalOp::kInputs),
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context->GetInputsDim(ConditionalOp::kInputs));
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}
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}
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};
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template <typename T>
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class ConditionalBlockGradMaker : public framework::SingleGradOpMaker<T> {
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public:
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using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
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protected:
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void Apply(GradOpPtr<T> grad_op) const override {
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grad_op->SetType("conditional_block_grad");
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grad_op->SetInput(ConditionalOp::kCondition,
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this->Input(ConditionalOp::kCondition));
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grad_op->SetInput(ConditionalOp::kInputs,
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this->Input(ConditionalOp::kInputs));
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grad_op->SetInput(ConditionalOp::kOutputs,
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this->Output(ConditionalOp::kOutputs));
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grad_op->SetInput(framework::GradVarName(ConditionalOp::kOutputs),
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this->OutputGrad(ConditionalOp::kOutputs));
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grad_op->SetInput(ConditionalOp::kScope,
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this->Output(ConditionalOp::kScope));
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grad_op->SetOutput(framework::GradVarName(ConditionalOp::kInputs),
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this->InputGrad(ConditionalOp::kInputs, false));
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grad_op->SetBlockAttr("sub_block", this->grad_block_[0]);
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grad_op->SetAttr("is_scalar_condition",
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this->GetAttr("is_scalar_condition"));
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OPERATOR(conditional_block, ops::ConditionalBlockOp,
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ops::ConditionalBlockInferShape,
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ops::ConditionalBlockOpProtoMaker,
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ops::ConditionalBlockGradMaker<paddle::framework::OpDesc>);
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REGISTER_OPERATOR(conditional_block_grad, ops::ConditionalBlockGradOp,
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ops::ConditionalBlockGradInferShape);
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