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110 lines
4.0 KiB
110 lines
4.0 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/lstm_unit_op.h"
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namespace paddle {
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namespace operators {
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class LstmUnitOp : 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"), "Input(X) of LSTM should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("C_prev"),
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"Input(C_prev) of LSTM should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("C"),
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"Output(C) of LSTM should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("H"),
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"Output(H) of LSTM should not be null.");
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auto x_dims = ctx->GetInputDim("X");
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auto c_prev_dims = ctx->GetInputDim("C_prev");
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PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank must be 2.");
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if (ctx->IsRuntime()) {
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PADDLE_ENFORCE_EQ(x_dims[0], c_prev_dims[0],
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"Batch size of inputs and states must be equal");
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PADDLE_ENFORCE_EQ(x_dims[1], c_prev_dims[1] * 4,
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"Dimension of FC should equal to prev state * 4");
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}
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int b_size = c_prev_dims[0]; // batch size
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int s_dim = c_prev_dims[1]; // state dim
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ctx->SetOutputDim("C", {b_size, s_dim});
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ctx->SetOutputDim("H", {b_size, s_dim});
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}
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};
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class LstmUnitOpMaker : 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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"Lstm unit only applies non-linear activations, please make sure"
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"that linear tranformation has already been applied to `X`. "
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"Linear tranformation can be applied by adding a `fc` layer");
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AddInput(
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"C_prev",
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"The cell state tensor of last time-step in the Lstm Unit operator.");
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AddOutput("C", "The cell tensor of Lstm Unit operator.");
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AddOutput("H", "The hidden state tensor of Lstm Unit operator.");
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AddAttr<float>("forget_bias",
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"(float, default 0.0) "
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"The forget bias of Lstm Unit.")
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.SetDefault(0.0);
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AddComment(R"DOC(
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Lstm Unit Operator
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Equation:
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$$
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i, f, o, j = split(X) \\
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C = C_{prev} * sigm(f + forget\_bias) + sigm(i) * tanh(j) \\
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H = C * sigm(o)
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$$
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)DOC");
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}
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};
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class LstmUnitGradOp : 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(framework::GradVarName("C")),
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"Input(C@GRAD) should not be null");
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PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("H")),
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"Input(H@GRAD) should not be null");
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ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
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ctx->SetOutputDim(framework::GradVarName("C_prev"),
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ctx->GetInputDim("C_prev"));
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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_OPERATOR(lstm_unit, ops::LstmUnitOp, ops::LstmUnitOpMaker,
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paddle::framework::DefaultGradOpDescMaker<true>);
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REGISTER_OPERATOR(lstm_unit_grad, ops::LstmUnitGradOp);
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REGISTER_OP_CPU_KERNEL(lstm_unit,
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ops::LstmUnitKernel<paddle::platform::CPUPlace, float>,
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ops::LstmUnitKernel<paddle::platform::CPUPlace, double>);
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REGISTER_OP_CPU_KERNEL(
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lstm_unit_grad, ops::LstmUnitGradKernel<paddle::platform::CPUPlace, float>,
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ops::LstmUnitGradKernel<paddle::platform::CPUPlace, double>);
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