Merge branch 'add_cudnn_lstm' of https://github.com/PaddlePaddle/Paddle into add_cudnn_lstm
chengduozh
6 years ago
commit
1013d6d05d
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/* Copyright (c) 2018 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_cudnn_op.h"
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#include <string>
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#ifdef PADDLE_WITH_CUDA
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#include "paddle/fluid/platform/cudnn_helper.h"
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#endif
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namespace paddle {
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namespace operators {
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class CudnnLSTMOp : 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("Input"),
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"Input(Input) of LSTM should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("W"),
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"Input(Weight) of LSTM should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("InitH"),
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"Input(init_h) of LSTM should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("InitC"),
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"Input(init_c) of LSTM should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Cache"),
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"Input(Cache) of LSTM should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of LSTM should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("last_h"),
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"Output(last_h) of LSTM should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("last_c"),
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"Output(last_c) of LSTM should not be null.");
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auto in_dims = ctx->GetInputDim("Input");
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PADDLE_ENFORCE_EQ(in_dims.size(), 3, "Input(X)'s rank must be 3.");
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ctx->SetOutputDim("Out", ctx->GetInputDim("Input"));
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ctx->SetOutputDim("last_h", ctx->GetInputDim("InitH"));
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ctx->SetOutputDim("last_c", ctx->GetInputDim("InitC"));
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}
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};
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class CudnnLSTMOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput(
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"Input",
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"(Tensor) RNN input tensor, which support variable-time length input "
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"sequence."
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"The shape of the Tensor MUST be ( seq_len * batch_size * input_size)"
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"seq_len is the total time step in this mini-batch (CAN be change in "
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"different batch)"
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"batch_size is the instance number of this batch"
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"input_size is the hidden size of the input."
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"input_hidden_size and the hidden_size in the next may not be same");
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AddInput("InitH",
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"(Tensor) the initial hidden state of the LSTM"
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"input. This is a tensor with shape (num_layers x batch_size x "
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"hidden_size)"
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"and When is_bidirec is True, the shape will be (num_layers*2 x "
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"batch_size x hidden_size)");
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AddInput("InitC",
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"(Tensor) the initial cell state of the LSTm "
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"input. This is a tensor with shape (num_layers x batch_size x "
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"hidden_size)"
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"and When is_bidirec is True, the shape will be (num_layers*2 x "
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"batch_size x hidden_size)");
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AddInput("W",
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"(Tensor) the learnable hidden-hidden weights."
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" The shape is (N), where N is total weight size of the LSTM. "
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" cudnn concatenate all the weight to one Tensor");
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AddInput("Cache",
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"The cache of dropout op, a RAW type variable including random "
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"number generator states and some descriptors, which is used in "
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"cudnn kernel.")
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.AsDispensable();
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AddOutput("Out",
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"(Tensor) the hidden state of LSTM operator. "
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"The shape is ( seq_len x batch_size x hidden_size) if "
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"is_bidirec is False"
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"and When is_bidirec is True, the shape will be ( seq_len x "
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"batch_size x hidden_size * 2) ");
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AddOutput("last_h",
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"(Tensor) the hidden state of the last step. "
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"The shape is ( num_layers x batch_size x hidden_size) if "
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"is_bidirec is False"
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"and When is_bidirec is True, the shape will be (num_layers*2 x "
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"batch_size x hidden_size)");
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AddOutput("last_c",
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"(Tensor) the cell state of the last step"
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"The shape is ( num_layers x batch_size x hidden_size) if "
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"is_bidirec is False"
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"and When is_bidirect is True, the shape will be (num_layers*2 x "
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"batch_size x hidden_size*2)");
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AddAttr<int>("max_len",
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"max length of the LSTM op"
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"the first dim of the Input can NOT be greater than max_len")
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.SetDefault(20);
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AddAttr<float>(
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"dropout_prob",
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"dropout prob of the dropout op"
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"the dropout ONLY work between lstm layers, not between time steps"
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"There is no dropout work on the Out tensor")
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.SetDefault(0.0);
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AddAttr<bool>("is_bidirec",
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"is_bidirec"
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"if it is bidirection rnn"
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"The will affect the shape of the Out, last_h, and last_c")
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.SetDefault(false);
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AddAttr<int>("input_size", "input size ot the Input Tensor").SetDefault(10);
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AddAttr<int>("hidden_size", "hidden size of the LSTM").SetDefault(100);
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AddAttr<int>("num_layers", "the total layer number of the LSTM")
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.SetDefault(1);
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AddAttr<bool>("is_test", "True if in test phase.").SetDefault(false);
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AddAttr<int>("seed", "seed to used if fix_seed is True").SetDefault(-1);
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AddComment(R"DOC(
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CUDNN LSTM implementation
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A four-gate Long Short-Term Memory network with no peephole connections.
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In the forward pass the output ht and cell output ct for a given iteration can be computed from the recurrent input ht-1,
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the cell input ct-1 and the previous layer input xt given matrices W, R and biases bW, bR from the following equations:
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$$ i_t = sigmoid(W_{ix}x_{t} + W_{ih}h_{t-1} + bx_i + bh_i) $$
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$$ f_t = sigmoid(W_{fx}x_{t} + W_{fh}h_{t-1} + bx_f + bh_f) $$
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$$ o_t = sigmoid(W_{ox}x_{t} + W_{oh}h_{t-1} + bx_o + bh_o) $$
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$$ \\tilde{c_t} = tanh(W_{cx}x_t + W_{ch}h_{t-1} + bx_c + bh_c) $$
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$$ c_t = f_t \\odot c_{t-1} + i_t \\odot \\tilde{c_t} $$
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$$ h_t = o_t \\odot tanh(c_t) $$
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- W terms denote weight matrices (e.g. $W_{ix}$ is the matrix
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of weights from the input gate to the input)
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- The b terms denote bias vectors ($bx_i$ and $bh_i$ are the input gate bias vector).
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- sigmoid is the logistic sigmoid function.
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- $i, f, o$ and $c$ are the input gate, forget gate, output gate,
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and cell activation vectors, respectively, all of which have the same size as
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the cell output activation vector $h$.
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- The $\odot$ is the element-wise product of the vectors.
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- `tanh` is the activation functions.
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- $\tilde{c_t}$ is also called candidate hidden state,
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which is computed based on the current input and the previous hidden state.
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Where sigmoid is the sigmoid operator: sigmoid(x) = 1 / (1 + e^-x), * represents a point-wise multiplication,
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X represensts a matrix multiplication
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)DOC");
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}
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};
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class CudnnLSTMGradOp : 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("Input"),
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"Input(Input) of LSTM should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("W"), "Input(W) of LSTM should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("last_h"),
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"Input(last_h) of LSTM should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("last_c"),
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"Input(last_c) of LSTM should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Cache"),
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"Input(last_c) of LSTM should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("InitH"),
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"Input(init_h) of LSTM should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("InitC"),
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"Input(init_c) of LSTM should not be null.");
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auto SetOutGradDim = [&ctx](const std::string& name) {
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auto g_name = framework::GradVarName(name);
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if (ctx->HasOutput(g_name)) {
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ctx->SetOutputDim(g_name, ctx->GetInputDim(name));
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}
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};
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SetOutGradDim("Input");
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SetOutGradDim("W");
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SetOutGradDim("InitH");
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SetOutGradDim("InitC");
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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(cudnn_lstm, ops::CudnnLSTMOp, ops::CudnnLSTMOpMaker,
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paddle::framework::DefaultGradOpDescMaker<true>);
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REGISTER_OPERATOR(cudnn_lstm_grad, ops::CudnnLSTMGradOp);
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REGISTER_OP_CPU_KERNEL(
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cudnn_lstm,
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ops::CudnnLSTMKernel<paddle::platform::CPUDeviceContext, float>);
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REGISTER_OP_CPU_KERNEL(
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cudnn_lstm_grad,
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ops::CudnnLSTMGradKernel<paddle::platform::CPUDeviceContext, float>);
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File diff suppressed because it is too large
Load Diff
@ -0,0 +1,45 @@
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/* Copyright (c) 2018 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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#pragma once
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#include <string>
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/operators/math/blas.h"
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#include "paddle/fluid/operators/math/detail/activation_functions.h"
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#include "paddle/fluid/operators/math/lstm_compute.h"
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#include "paddle/fluid/operators/math/sequence2batch.h"
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namespace paddle {
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namespace operators {
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using LoDTensor = framework::LoDTensor;
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using Tensor = framework::Tensor;
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template <typename DeviceContext, typename T>
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class CudnnLSTMKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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PADDLE_THROW(
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"CPU is not support for this kernel now. Will be add in the future");
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}
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};
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template <typename DeviceContext, typename T>
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class CudnnLSTMGradKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {}
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};
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} // namespace operators
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} // namespace paddle
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