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@ -29,9 +29,9 @@ class ConditionalOp : public framework::OperatorBase {
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protected:
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std::vector<const framework::LoDTensor *> InputTensors(
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const framework::Scope &scope) const {
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const framework::Scope &scope, const std::string &in_name) const {
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std::vector<const framework::LoDTensor *> retv;
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auto xs = Inputs("X");
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auto xs = Inputs(in_name);
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retv.resize(xs.size(), nullptr);
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std::transform(
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xs.begin(), xs.end(), retv.begin(),
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@ -81,12 +81,18 @@ class ConditionalBlockOp : public ConditionalOp {
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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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auto xs = InputTensors(scope);
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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, "Cond");
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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, "Input");
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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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@ -110,11 +116,11 @@ class ConditionalBlockOp : public ConditionalOp {
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class ConditionalBlockOpProtoMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X",
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"The conditional variable of this operator. If X is empty, the "
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AddInput("Cond",
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"The conditional variable of this operator. If Cond is empty, the "
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"whole sub-block will not be executed.")
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.AsDuplicable();
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AddInput("Params", "The input variables of the sub-block.").AsDuplicable();
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AddInput("Input", "The input variables of the sub-block.").AsDuplicable();
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AddOutput("Out", "The output variables of the sub-block.").AsDuplicable();
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AddOutput("Scope",
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"(std::vector<Scope*>) The step scope of conditional block. To "
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@ -123,13 +129,18 @@ class ConditionalBlockOpProtoMaker : public framework::OpProtoAndCheckerMaker {
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AddAttr<framework::BlockDesc *>(
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"sub_block", "The step block of conditional block operator");
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AddAttr<bool>("is_scalar_condition",
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"the input X is used as scalar "
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"condition")
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"The conditional variable (Cond) is used as scalar "
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"condition.")
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.SetDefault(false);
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AddComment(R"DOC(Conditional block operator
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Run the sub-block if X is not empty. Params is the other inputs and Out is the
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outputs of the sub-block.
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If `is_scalar_condition` is True, the conditional variable (Cond) is a scalar,
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run the operators in sub-block if Cond is True.
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If `is_scalar_condition` is False, the conditional variable (Cond) is a vector or
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tensor, run the operators in sub-block if all of input variables are not empty.
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)DOC");
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}
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};
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@ -145,12 +156,12 @@ class ConditionalBlockGradOp : public ConditionalOp {
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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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auto xs = this->InputTensors(scope);
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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, "Cond");
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need_run = ScalarCondition(xs);
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} else {
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auto xs = this->InputTensors(scope, "Input");
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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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@ -166,11 +177,11 @@ class ConditionalBlockGradOp : public ConditionalOp {
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auto *block = Attr<framework::BlockDesc *>("sub_block");
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exec.Run(*block->Program(), &cur_scope, block->ID(), false);
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AssignLocalGradientToGlobal(dev_place, cur_scope, Inputs("Params"),
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Outputs(framework::GradVarName("Params")));
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AssignLocalGradientToGlobal(dev_place, cur_scope, Inputs("Input"),
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Outputs(framework::GradVarName("Input")));
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AssignLocalGradientToGlobal(dev_place, cur_scope, Inputs("X"),
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Outputs(framework::GradVarName("X")));
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AssignLocalGradientToGlobal(dev_place, cur_scope, Inputs("Cond"),
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Outputs(framework::GradVarName("Cond")));
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}
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}
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@ -199,15 +210,15 @@ class ConditionalBlockGradOp : public ConditionalOp {
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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("X"));
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if (context->HasInputs("Params")) {
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PADDLE_ENFORCE(context->HasOutputs(framework::GradVarName("Params")));
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context->SetOutputsDim(framework::GradVarName("Params"),
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context->GetInputsDim("Params"));
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PADDLE_ENFORCE(context->HasInputs("Cond"));
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if (context->HasInputs("Input")) {
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PADDLE_ENFORCE(context->HasOutputs(framework::GradVarName("Input")));
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context->SetOutputsDim(framework::GradVarName("Input"),
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context->GetInputsDim("Input"));
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}
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if (context->HasOutputs(framework::GradVarName("X"))) {
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context->SetOutputsDim(framework::GradVarName("X"),
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context->GetInputsDim("X"));
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if (context->HasOutputs(framework::GradVarName("Cond"))) {
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context->SetOutputsDim(framework::GradVarName("Cond"),
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context->GetInputsDim("Cond"));
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}
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}
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};
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@ -220,14 +231,15 @@ class ConditionalBlockGradMaker : public framework::SingleGradOpDescMaker {
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std::unique_ptr<framework::OpDesc> Apply() const override {
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auto grad_op = new framework::OpDesc();
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grad_op->SetType("conditional_block_grad");
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grad_op->SetInput("X", Input("X"));
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grad_op->SetInput("Params", Input("Params"));
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grad_op->SetInput("Cond", Input("Cond"));
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grad_op->SetInput("Input", Input("Input"));
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grad_op->SetInput("Out", Output("Out"));
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grad_op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
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grad_op->SetInput("Scope", Output("Scope"));
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grad_op->SetOutput(framework::GradVarName("X"), InputGrad("X", false));
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grad_op->SetOutput(framework::GradVarName("Params"),
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InputGrad("Params", false));
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grad_op->SetOutput(framework::GradVarName("Cond"),
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InputGrad("Cond", false));
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grad_op->SetOutput(framework::GradVarName("Input"),
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InputGrad("Input", false));
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grad_op->SetBlockAttr("sub_block", this->grad_block_[0]);
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grad_op->SetAttr("is_scalar_condition", GetAttr("is_scalar_condition"));
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return std::unique_ptr<framework::OpDesc>(grad_op);
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tensor-tang
7 years ago