// Copyright (c) 2018 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/operators/isfinite_op.h" #include namespace paddle { namespace framework { class InferShapeContext; class OpDesc; template class EmptyGradOpMaker; } // namespace framework namespace imperative { class OpBase; } // namespace imperative namespace platform { class CPUDeviceContext; } // namespace platform } // namespace paddle namespace paddle { namespace operators { class OverflowOp : public framework::OperatorWithKernel { public: OverflowOp(const std::string &type, const framework::VariableNameMap &inputs, const framework::VariableNameMap &outputs, const framework::AttributeMap &attrs) : OperatorWithKernel(type, inputs, outputs, attrs) {} void InferShape(framework::InferShapeContext *ctx) const override { OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "isfinite"); OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "isfinite"); ctx->SetOutputDim("Out", {1}); } protected: framework::OpKernelType GetExpectedKernelType( const framework::ExecutionContext &ctx) const override { int dtype = -1; auto *x_var = ctx.InputVar("X"); if (x_var->IsType()) { dtype = framework::TransToProtoVarType( x_var->Get().type()); } else if (x_var->IsType()) { dtype = framework::TransToProtoVarType( x_var->Get().value().type()); } else { PADDLE_ENFORCE_EQ( true, false, platform::errors::InvalidArgument( "The input type mismatch, the type of Input(X) must be Tensor or " "SelectedRows, please check your input.")); } return framework::OpKernelType(framework::proto::VarType::Type(dtype), ctx.GetPlace()); } }; class OverflowOpMaker : public framework::OpProtoAndCheckerMaker { public: void Make() override { AddInput("X", "(Tensor) The input tensors of overflow operator."); AddOutput("Out", "(Tensor) 1-dim tensor, contains a bool scalar. The output " "tensor of overflow operator."); AddComment(string::Sprintf(R"DOC( Overflow %s operator. $$Out = any(X)$$ If any X contains Inf or Nan, the Out will generate a indicator. Out = Inf if any X contains Inf, Out = Nan if any X contains Nan, Out = 0 if no Inf/Nan detected. If X contains both Inf/Nan, it will return the first indicator it meeted. %s )DOC", GetName(), GetComments())); } protected: virtual std::string GetName() const = 0; virtual std::string GetComments() const = 0; }; } // namespace operators } // namespace paddle namespace ops = paddle::operators; #define REGISTER_OP_MAKER(op_type, comment) \ namespace paddle { \ namespace operators { \ class _##op_type##OverflowOpMaker \ : public ::paddle::operators::OverflowOpMaker { \ protected: \ std::string GetName() const { return #op_type; } \ std::string GetComments() const { return comment; } \ }; \ } \ } \ REGISTER_OPERATOR( \ op_type, ops::OverflowOp, ops::_##op_type##OverflowOpMaker, \ paddle::framework::EmptyGradOpMaker, \ paddle::framework::EmptyGradOpMaker) #define REGISTER_OVERFLOW_CPU_KERNEL(op_type, functor) \ REGISTER_OP_CPU_KERNEL( \ op_type, \ ops::OverflowKernel, \ ops::OverflowKernel, \ ops::OverflowKernel, \ ops::OverflowKernel); REGISTER_OP_MAKER(isinf, "isinf(X)"); REGISTER_OP_MAKER(isnan, "isnan(X)"); REGISTER_OP_MAKER(isfinite, "isfinite(X)"); REGISTER_OP_CPU_KERNEL(isinf, ops::OverflowKernel, ops::OverflowKernel, ops::OverflowKernel, ops::OverflowKernel); REGISTER_OP_CPU_KERNEL(isnan, ops::OverflowKernel, ops::OverflowKernel, ops::OverflowKernel, ops::OverflowKernel); REGISTER_OP_CPU_KERNEL(isfinite, ops::OverflowKernel, ops::OverflowKernel, ops::OverflowKernel, ops::OverflowKernel);