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97 lines
3.6 KiB
97 lines
3.6 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/softmax_op.h"
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namespace paddle {
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namespace operators {
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class SoftmaxOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"),
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"Input(X) of SoftmaxOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of SoftmaxOp should not be null.");
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auto x_dims = ctx->GetInputDim("X");
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PADDLE_ENFORCE(x_dims.size() == 2UL,
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"The input of softmax op must be a matrix.");
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ctx->SetOutputDim("Out", x_dims);
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ctx->ShareLoD("X", /*->*/ "Out");
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}
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};
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class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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SoftmaxOpMaker(OpProto* proto, OpAttrChecker* op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("X",
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"The input tensor of softmax. "
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"2-D with shape [batch_size, input_feature_dimensions].");
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AddOutput("Out", "The normalized values with the same shape as X.");
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AddComment(R"DOC(
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Softmax Operator.
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The input of the softmax operator is a 2-D tensor with shape N x K (N is the
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batch_size, K is the dimension of input feature). The output tensor has the
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same shape as the input tensor.
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For each row of the input tensor, the softmax operator squashes the
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K-dimensional vector of arbitrary real values to a K-dimensional vector of real
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values in the range [0, 1] that add up to 1.
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It computes the exponential of the given dimension and the sum of exponential
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values of all the other dimensions in the K-dimensional vector input.
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Then the ratio of the exponential of the given dimension and the sum of
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exponential values of all the other dimensions is the output of the softmax
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operator.
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For each row $i$ and each column $j$ in Input(X), we have:
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$$Out[i, j] = \frac{\exp(X[i, j])}{\sum_j(exp(X[i, j])}$$
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)DOC");
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}
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};
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class SoftmaxOpGrad : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("Out"), "Input(Out) should be not null.");
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PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
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"Input(Out@GRAD) should be not null.");
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PADDLE_ENFORCE_EQ(ctx->GetInputDim("Out"),
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ctx->GetInputDim(framework::GradVarName("Out")),
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"Input(Out) and its gradients should have a same shape.");
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ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
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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_OP(softmax, ops::SoftmaxOp, ops::SoftmaxOpMaker, softmax_grad,
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ops::SoftmaxOpGrad);
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REGISTER_OP_CPU_KERNEL(
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softmax, ops::SoftmaxKernel<paddle::platform::CPUDeviceContext, float>);
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REGISTER_OP_CPU_KERNEL(
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softmax_grad,
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ops::SoftmaxGradKernel<paddle::platform::CPUDeviceContext, float>);
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