/* 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 "paddle/fluid/operators/softmax_op.h" #include #ifdef PADDLE_WITH_CUDA #include "paddle/fluid/platform/cudnn_helper.h" #endif #ifdef PADDLE_WITH_MKLDNN #include "paddle/fluid/platform/mkldnn_helper.h" #endif namespace paddle { namespace operators { class SoftmaxOp : public framework::OperatorWithKernel { public: using framework::OperatorWithKernel::OperatorWithKernel; void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of SoftmaxOp should not be null."); PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) of SoftmaxOp should not be null."); ctx->SetOutputDim("Out", ctx->GetInputDim("X")); ctx->ShareLoD("X", /*->*/ "Out"); } protected: framework::OpKernelType GetExpectedKernelType( const framework::ExecutionContext& ctx) const override { // choose cudnn kernel if the runtime supported. framework::LibraryType library_{framework::LibraryType::kPlain}; std::string data_format = ctx.Attr("data_format"); framework::DataLayout layout_ = framework::StringToDataLayout(data_format); #ifdef PADDLE_WITH_CUDA if (platform::CanCUDNNBeUsed(ctx)) { library_ = framework::LibraryType::kCUDNN; } #endif #ifdef PADDLE_WITH_MKLDNN if (library_ == framework::LibraryType::kPlain && platform::CanMKLDNNBeUsed(ctx)) { library_ = framework::LibraryType::kMKLDNN; layout_ = framework::DataLayout::kMKLDNN; } #endif auto input_data_type = framework::ToDataType(ctx.Input("X")->type()); if (input_data_type == framework::proto::VarType::FP16) { PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()), "float16 can only be used on GPU place"); } return framework::OpKernelType(input_data_type, ctx.GetPlace(), layout_, library_); } }; class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker { public: void Make() override { AddInput("X", "The input tensor of softmax, " "whose last dimension is the input_feature_dimensions."); AddOutput("Out", "The normalized values with the same shape as X.") .Reuse("X"); AddAttr( "use_cudnn", "(bool, default false) Only used in cudnn kernel, need install cudnn") .SetDefault(false); AddAttr( "data_format", "(string, default NCHW) Only used in " "An optional string from: \"NHWC\", \"NCHW\". " "Defaults to \"NHWC\". Specify the data format of the output data, " "the input will be transformed automatically. ") .SetDefault("AnyLayout"); AddAttr("use_mkldnn", "(bool, default false) Only used in mkldnn kernel") .SetDefault(false); AddAttr("is_test", "Disable epsilon adding to softmax results. Used by MKLDNN.") .SetDefault(false); AddComment(R"DOC( Softmax Operator. The input of the softmax operator is a tensor of any rank. The output tensor has the same shape as the input. The input tensor will first be logically flattened to a 2-D matrix. The matrix's second dimension(row length) is as same as the last dimension of the input tensor, and the first dimension(column length) is the product of all other dimensions of the input tensor. For each row of the matrix, the softmax operator squashes the K-dimensional(K is the width of the matrix, which is also the size of the input tensor's last dimension) vector of arbitrary real values to a K-dimensional vector of real values in the range [0, 1] that add up to 1. It computes the exponential of the given dimension and the sum of exponential values of all the other dimensions in the K-dimensional vector input. Then the ratio of the exponential of the given dimension and the sum of exponential values of all the other dimensions is the output of the softmax operator. For each row $i$ and each column $j$ in the matrix, we have: $$Out[i, j] = \frac{\exp(X[i, j])}{\sum_j(exp(X[i, j])}$$ )DOC"); } }; class SoftmaxOpGrad : public framework::OperatorWithKernel { public: using framework::OperatorWithKernel::OperatorWithKernel; void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("Out"), "Input(Out) should be not null."); PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), "Input(Out@GRAD) should be not null."); PADDLE_ENFORCE_EQ(ctx->GetInputDim("Out"), ctx->GetInputDim(framework::GradVarName("Out")), "Input(Out) and its gradients should have a same shape."); ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim(framework::GradVarName("Out"))); } protected: framework::OpKernelType GetExpectedKernelType( const framework::ExecutionContext& ctx) const override { // choose cudnn kernel if the runtime supported. framework::LibraryType library_{framework::LibraryType::kPlain}; std::string data_format = ctx.Attr("data_format"); framework::DataLayout layout_ = framework::StringToDataLayout(data_format); #ifdef PADDLE_WITH_CUDA if (platform::CanCUDNNBeUsed(ctx)) { library_ = framework::LibraryType::kCUDNN; } #endif #ifdef PADDLE_WITH_MKLDNN if (library_ == framework::LibraryType::kPlain && platform::CanMKLDNNBeUsed(ctx)) { library_ = framework::LibraryType::kMKLDNN; layout_ = framework::DataLayout::kMKLDNN; } #endif auto input_data_type = framework::ToDataType( ctx.Input(framework::GradVarName("Out"))->type()); if (input_data_type == framework::proto::VarType::FP16) { PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()), "float16 can only be used on GPU place"); } return framework::OpKernelType(input_data_type, ctx.GetPlace(), layout_, library_); } }; class SoftmaxOpGradMaker : public framework::SingleGradOpDescMaker { public: using framework::SingleGradOpDescMaker::SingleGradOpDescMaker; protected: std::unique_ptr Apply() const override { auto* op = new framework::OpDesc(); op->SetType("softmax_grad"); op->SetInput("Out", Output("Out")); op->SetInput(framework::GradVarName("Out"), OutputGrad("Out")); op->SetAttrMap(Attrs()); op->SetOutput(framework::GradVarName("X"), InputGrad("X")); return std::unique_ptr(op); } }; } // namespace operators } // namespace paddle namespace ops = paddle::operators; REGISTER_OPERATOR(softmax, ops::SoftmaxOp, ops::SoftmaxOpMaker, ops::SoftmaxOpGradMaker); REGISTER_OPERATOR(softmax_grad, ops::SoftmaxOpGrad); REGISTER_OP_CPU_KERNEL( softmax, ops::SoftmaxKernel, ops::SoftmaxKernel); REGISTER_OP_CPU_KERNEL( softmax_grad, ops::SoftmaxGradKernel, ops::SoftmaxGradKernel);