You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
257 lines
9.9 KiB
257 lines
9.9 KiB
/* Copyright (c) 2016 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 <algorithm>
|
|
#include "paddle/fluid/framework/executor.h"
|
|
#include "paddle/fluid/framework/op_registry.h"
|
|
#include "paddle/fluid/framework/var_type.h"
|
|
|
|
namespace paddle {
|
|
namespace operators {
|
|
|
|
class ConditionalOp : public framework::OperatorBase {
|
|
public:
|
|
ConditionalOp(const std::string &type,
|
|
const framework::VariableNameMap &inputs,
|
|
const framework::VariableNameMap &outputs,
|
|
const framework::AttributeMap &attrs)
|
|
: OperatorBase(type, inputs, outputs, attrs) {}
|
|
|
|
protected:
|
|
std::vector<const framework::LoDTensor *> InputTensors(
|
|
const framework::Scope &scope, const std::string &in_name) const {
|
|
std::vector<const framework::LoDTensor *> retv;
|
|
auto xs = Inputs(in_name);
|
|
retv.resize(xs.size(), nullptr);
|
|
std::transform(
|
|
xs.begin(), xs.end(), retv.begin(),
|
|
[&scope](const std::string &var_name) -> const framework::LoDTensor * {
|
|
auto *var = scope.FindVar(var_name);
|
|
PADDLE_ENFORCE(var != nullptr, "Cannot find variable %s", var_name);
|
|
return &var->Get<framework::LoDTensor>();
|
|
});
|
|
return retv;
|
|
}
|
|
|
|
bool ScalarCondition(
|
|
const std::vector<const framework::LoDTensor *> &ips) const {
|
|
if (!(ips.size() == 1UL && ips[0]->IsInitialized())) {
|
|
PADDLE_THROW("should have one initialized input as condition");
|
|
}
|
|
|
|
PADDLE_ENFORCE(ips[0]->type() == framework::proto::VarType::BOOL &&
|
|
ips[0]->numel() == 1,
|
|
"condition input's data type should be bool, "
|
|
"numel should be 1, actual numel is %d",
|
|
ips[0]->numel());
|
|
bool res = false;
|
|
if (platform::is_gpu_place(ips[0]->place())) {
|
|
#ifdef PADDLE_WITH_CUDA
|
|
framework::LoDTensor cpu_tensor;
|
|
framework::TensorCopy(*ips[0], platform::CPUPlace(), &cpu_tensor);
|
|
platform::DeviceContextPool::Instance().Get(ips[0]->place())->Wait();
|
|
res = cpu_tensor.data<bool>()[0];
|
|
#endif
|
|
} else {
|
|
res = ips[0]->data<bool>()[0];
|
|
}
|
|
return res;
|
|
}
|
|
};
|
|
|
|
class ConditionalBlockOp : public ConditionalOp {
|
|
public:
|
|
ConditionalBlockOp(const std::string &type,
|
|
const framework::VariableNameMap &inputs,
|
|
const framework::VariableNameMap &outputs,
|
|
const framework::AttributeMap &attrs)
|
|
: ConditionalOp(type, inputs, outputs, attrs) {}
|
|
|
|
private:
|
|
void RunImpl(const framework::Scope &scope,
|
|
const platform::Place &dev_place) const override {
|
|
bool need_run;
|
|
if (Attr<bool>("is_scalar_condition")) {
|
|
// When is_scalar_condition is True, the conditional variable is a scalar,
|
|
// whether need to execute the operators in sub-block depends on the
|
|
// conditional variable (Cond).
|
|
auto xs = InputTensors(scope, "Cond");
|
|
need_run = ScalarCondition(xs);
|
|
} else {
|
|
// When is_scalar_condition is False, the conditional variable maybe a
|
|
// vector or tensor, whether need to execute the operators in sub-block
|
|
// depends on the input variables (Input).
|
|
auto xs = InputTensors(scope, "Input");
|
|
need_run = std::all_of(
|
|
xs.begin(), xs.end(),
|
|
[](const framework::LoDTensor *t) { return t->numel() != 0; });
|
|
}
|
|
|
|
if (need_run) {
|
|
auto *scope_var = scope.FindVar(Output("Scope"));
|
|
PADDLE_ENFORCE(scope_var != nullptr, "Must set scope");
|
|
auto *scopes = scope_var->GetMutable<std::vector<framework::Scope *>>();
|
|
scopes->resize(1);
|
|
scopes->front() = &scope.NewScope();
|
|
auto &cur_scope = *scopes->front();
|
|
|
|
framework::Executor exec(dev_place);
|
|
auto *block = Attr<framework::BlockDesc *>("sub_block");
|
|
exec.Run(*block->Program(), &cur_scope, block->ID(), false);
|
|
}
|
|
}
|
|
};
|
|
|
|
class ConditionalBlockOpProtoMaker : public framework::OpProtoAndCheckerMaker {
|
|
public:
|
|
void Make() override {
|
|
AddInput("Cond",
|
|
"The conditional variable of this operator. If Cond is empty, the "
|
|
"whole sub-block will not be executed.")
|
|
.AsDuplicable();
|
|
AddInput("Input", "The input variables of the sub-block.").AsDuplicable();
|
|
AddOutput("Out", "The output variables of the sub-block.").AsDuplicable();
|
|
AddOutput("Scope",
|
|
"(std::vector<Scope*>) The step scope of conditional block. To "
|
|
"unify the conditional block, rnn and while op, the type of "
|
|
"scope is std::vector<Scope*>");
|
|
AddAttr<framework::BlockDesc *>(
|
|
"sub_block", "The step block of conditional block operator");
|
|
AddAttr<bool>("is_scalar_condition",
|
|
"The conditional variable (Cond) is used as scalar "
|
|
"condition.")
|
|
.SetDefault(false);
|
|
AddComment(R"DOC(Conditional block operator
|
|
|
|
If `is_scalar_condition` is True, the conditional variable (Cond) is a scalar,
|
|
run the operators in sub-block if Cond is True.
|
|
|
|
If `is_scalar_condition` is False, the conditional variable (Cond) is a vector or
|
|
tensor, run the operators in sub-block if all of input variables are not empty.
|
|
|
|
|
|
)DOC");
|
|
}
|
|
};
|
|
|
|
class ConditionalBlockGradOp : public ConditionalOp {
|
|
public:
|
|
ConditionalBlockGradOp(const std::string &type,
|
|
const framework::VariableNameMap &inputs,
|
|
const framework::VariableNameMap &outputs,
|
|
const framework::AttributeMap &attrs)
|
|
: ConditionalOp(type, inputs, outputs, attrs) {}
|
|
|
|
private:
|
|
void RunImpl(const framework::Scope &scope,
|
|
const platform::Place &dev_place) const override {
|
|
bool need_run;
|
|
if (Attr<bool>("is_scalar_condition")) {
|
|
auto xs = this->InputTensors(scope, "Cond");
|
|
need_run = ScalarCondition(xs);
|
|
} else {
|
|
auto xs = this->InputTensors(scope, "Input");
|
|
need_run = std::all_of(
|
|
xs.begin(), xs.end(),
|
|
[](const framework::LoDTensor *t) { return t->numel() != 0; });
|
|
}
|
|
|
|
if (need_run) {
|
|
auto *scope_var = scope.FindVar(Input("Scope"));
|
|
PADDLE_ENFORCE(scope_var != nullptr, "Must set scope");
|
|
auto &scopes = scope_var->Get<std::vector<framework::Scope *>>();
|
|
framework::Scope &cur_scope = *scopes[0];
|
|
|
|
framework::Executor exec(dev_place);
|
|
auto *block = Attr<framework::BlockDesc *>("sub_block");
|
|
exec.Run(*block->Program(), &cur_scope, block->ID(), false);
|
|
|
|
AssignLocalGradientToGlobal(dev_place, cur_scope, Inputs("Input"),
|
|
Outputs(framework::GradVarName("Input")));
|
|
|
|
AssignLocalGradientToGlobal(dev_place, cur_scope, Inputs("Cond"),
|
|
Outputs(framework::GradVarName("Cond")));
|
|
}
|
|
}
|
|
|
|
private:
|
|
void AssignLocalGradientToGlobal(
|
|
const platform::Place &place, const framework::Scope &cur_scope,
|
|
const std::vector<std::string> &p_names,
|
|
const std::vector<std::string> &pg_names) const {
|
|
for (size_t i = 0; i < p_names.size(); ++i) {
|
|
auto out_grad_name = pg_names[i];
|
|
auto in_grad_name = framework::GradVarName(p_names[i]);
|
|
auto *in_var = cur_scope.FindVar(in_grad_name);
|
|
if (in_var == nullptr) {
|
|
continue;
|
|
}
|
|
auto new_in_grad_name = cur_scope.Rename(in_grad_name);
|
|
auto assign = framework::OpRegistry::CreateOp(
|
|
"assign", {{"X", {new_in_grad_name}}}, {{"Out", {out_grad_name}}},
|
|
framework::AttributeMap{});
|
|
assign->Run(cur_scope, place);
|
|
cur_scope.Rename(new_in_grad_name, in_grad_name);
|
|
}
|
|
}
|
|
};
|
|
|
|
class ConditionalBlockGradInferShape : public framework::InferShapeBase {
|
|
public:
|
|
void operator()(framework::InferShapeContext *context) const override {
|
|
PADDLE_ENFORCE(context->HasInputs("Cond"));
|
|
if (context->HasInputs("Input")) {
|
|
PADDLE_ENFORCE(context->HasOutputs(framework::GradVarName("Input")));
|
|
context->SetOutputsDim(framework::GradVarName("Input"),
|
|
context->GetInputsDim("Input"));
|
|
}
|
|
if (context->HasOutputs(framework::GradVarName("Cond"))) {
|
|
context->SetOutputsDim(framework::GradVarName("Cond"),
|
|
context->GetInputsDim("Cond"));
|
|
}
|
|
}
|
|
};
|
|
|
|
class ConditionalBlockGradMaker : public framework::SingleGradOpDescMaker {
|
|
public:
|
|
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
|
|
|
|
protected:
|
|
std::unique_ptr<framework::OpDesc> Apply() const override {
|
|
auto grad_op = new framework::OpDesc();
|
|
grad_op->SetType("conditional_block_grad");
|
|
grad_op->SetInput("Cond", Input("Cond"));
|
|
grad_op->SetInput("Input", Input("Input"));
|
|
grad_op->SetInput("Out", Output("Out"));
|
|
grad_op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
|
|
grad_op->SetInput("Scope", Output("Scope"));
|
|
grad_op->SetOutput(framework::GradVarName("Cond"),
|
|
InputGrad("Cond", false));
|
|
grad_op->SetOutput(framework::GradVarName("Input"),
|
|
InputGrad("Input", false));
|
|
grad_op->SetBlockAttr("sub_block", this->grad_block_[0]);
|
|
grad_op->SetAttr("is_scalar_condition", GetAttr("is_scalar_condition"));
|
|
return std::unique_ptr<framework::OpDesc>(grad_op);
|
|
}
|
|
};
|
|
|
|
} // namespace operators
|
|
} // namespace paddle
|
|
|
|
namespace ops = paddle::operators;
|
|
REGISTER_OPERATOR(conditional_block, ops::ConditionalBlockOp,
|
|
ops::ConditionalBlockOpProtoMaker,
|
|
ops::ConditionalBlockGradMaker);
|
|
REGISTER_OPERATOR(conditional_block_grad, ops::ConditionalBlockGradOp,
|
|
ops::ConditionalBlockGradInferShape);
|