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Add LSTM Operators.

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revert-4814-Add_sequence_project_op
dangqingqing 8 years ago
parent 9efd5422f9
commit 8728b3cce2
7 changed files with 388 additions and 3 deletions
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185
paddle/operators/lstm_op.cc
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/operators/lstm_unit_op.h"
namespace paddle {
namespace operators {
class LSTMOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Input"),
"Input(Input) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Hidden"),
"Output(Hidden) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("H"),
"Output(Cell) of LSTM should not be null.");
auto x_dims = ctx->GetInputDim("Input");
PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank must be 2.");
if (ctx->HasInput("H0")) {
PADDLE_ENFORCE(ctx->HasInput("C0"),
"Input(Cell) and Input(Hidden) of LSTM should not "
"be null at the same time.");
auto h_dims = ctx->GetInputDim("H0");
auto c_dims = ctx->GetInputDim("C0");
PADDLE_ENFORCE(h_dims == c_dims,
"The dimension of Input(H0) and Input(C0) "
"should be the same.");
}
ctx->SetOutputDim("Hidden", x_dims);
ctx->SetOutputDim("Cell", x_dims);
ctx->ShareLoD("Input", "Hidden");
ctx->ShareLoD("Input", "Cell");
}
};
class LSTMOpMaker : public framework::OpProtoAndCheckerMaker {
public:
LSTMOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Input",
"(LoDTensor) the first input is a LodTensor, which support "
"variable-time length input sequence. The underlying tensor in "
"this LoDTenosr is a matrix with shape (T X D), where, T is the "
"total time steps in this mini-batch, D is the hidden size.");
AddInput("H0",
"(Tensor, optional) the initial hidden state is an optional "
"input. This is a tensor with shape (N x D), where N is the "
"batch size, D is the hidden size.");
AddInput("C0",
"(Tensor, optional) the initial cell state is an optional "
"input. This is a tensor with shape (N x D), where N is the "
"batch size. `H0` and `C0` can be NULL but only at the same time");
AddInput("Weight",
"(Tensor) the learnable hidden-hidden weights."
" - The shape is (D x 4*D), where D is the hidden size. "
" - Weight = {W_ih, W_fh, W_ch, W_oh}");
AddInput("Bias",
"(Tensor) the learnable weights, which contains two parts: "
"input-hidden bias weight and peephole connections weight if "
"seting `use_peepholes` True. "
"1. `use_peepholes = False` "
" - The shape is (1 x 4*D). "
" - Bias = {b_i, b_f, b_c, b_o}."
"2. `use_peepholes = True` "
" - The shape is (1 x 7*D). "
" - Bias = {b_i, b_f, b_c, b_o, W_ic, W_fc, W_oc}.");
AddOutput("Hidden",
"(LoDTensor) the hidden state lod tensor of LSTM operator. "
"The shape and lod is the same with the `Input`.");
AddOutput("Cell",
"(LoDTensor) the cell state lod tensor of LSTM operator. "
"The shape and lod is the same with the `Input`.");
AddAttr<bool>("use_peepholes",
"(bool, defalut: True) "
"whether to enable diagonal/peephole connections.")
.SetDefault(true);
AddAttr<std::string>(
"gate_activation",
"(string, defalut: sigmoid)"
"The activation for input gate, forget gate and output "
"gate, `sigmoid` by defalut.")
.SetDefault("sigmoid");
AddAttr<std::string>("cell_activation",
"(string, defalut: tanh)"
"The activation for cell output, `tanh` by defalut.")
.SetDefault("tanh");
AddAttr<std::string>("candidate_activation",
"(string, defalut: tanh)"
"The activation for candidate hidden state, "
"`tanh` by defalut.")
.SetDefault("tanh");
AddComment(R"DOC(Long-Short Term Memory (LSTM) Operator
The defalut implementation is diagonal/peephole connection [1], the formula is
as follows
i_t = \sigma(W_{ix}x_{t} + W_{ih}h_{t-1} + W_{ic}c_{t-1} + b_i)
f_t = \sigma(W_{fx}x_{t} + W_{fh}h_{t-1} + W_{fc}c_{t-1} + b_f)
\tilde{c_t} = act_g(W_{cx}x_t + W_{ch}h_{t-1} + b_c)
o_t = \sigma(W_{ox}x_{t} + W_{oh}h_{t-1} + W_{oc}c_t + b_o)
c_t = f_t ⊙ c_{t-1} + i_t ⊙ \tilde{c_t}
h_t = o_t ⊙ act_h(c_t)
where the W terms denote weight matrices (e.g. \f$W_{xi}\f$ is the matrix
of weights from the input gate to the input), \f$W_{ic}, W_{fc}, W_{oc}\f$
are diagonal weight matrices for peephole connections. In our implenmention,
We use vectors to reprenset these diagonal weight matrices. The b terms
denote bias vectors (\f$b_i\f$ is the input gate bias vector), \f$\sigma\f$
is the non-line actications, such as logistic sigmoid function, and
\f$i, f, o\f$ and \f$c\f$ are respectively the input gate, forget gate,
output gate and cell activation vectors, all of which are the same size as
the cell output activation vector \f$h\f$.
The ⊙ is the element-wise product of the vectors, \f$act_g\f$ and \f$act_h\f$
are the cell input and cell output activation functions, `tanh` is usually
used for them. \f$\tilde{c_t}\f$ is also called candidate hidden state,
which is computed based on the current input and the previous hidden state.
Set `use_peepholes` False to disable peephole connection [2]. The formula
is omitted here.
@note These \f$W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}\f$
operations on the input x_{t} were NOT included in this operator. The
users can choose to use fully-connect operator before LSTM operator.
[1] Hasim Sak, Andrew Senior, and Francoise Beaufays. Long short-term memory
recurrent neural network architectures for large scale acoustic modeling.
INTERSPEECH, 2014.
[2] S. Hochreiter and J. Schmidhuber. Long Short-Term Memory.
Neural Computation, 9(8):1735-1780, 1997.
)DOC");
}
};
class LSTMGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Hidden")),
"Input(Hidden@GRAD) should not be null");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Cell")),
"Input(Cell@GRAD) should not be null");
ctx->SetOutputDim(framework::GradVarName("Weight"),
ctx->GetInputDim("Weight"));
ctx->SetOutputDim(framework::GradVarName("Bias"), ctx->GetInputDim("Bias"));
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(lstm, ops::LSTMOp, ops::LSTMOpMaker, lstm_grad, ops::LSTMGradOp);
REGISTER_OP_CPU_KERNEL(lstm, ops::LSTMKernel<paddle::platform::CPUPlace, float>,
ops::LSTMKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(lstm_grad,
ops::LSTMGradKernel<paddle::platform::CPUPlace, float>,
ops::LSTMGradKernel<paddle::platform::CPUPlace, double>);

38
paddle/operators/lstm_op.h
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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. */
#pragma once
#include "glog/logging.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
using framework::LoDTensor;
using framework::Tensor;
template <typename Place, typename T>
class LSTMKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {}
};
template <typename Place, typename T>
class LSTMGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {}
};
} // namespace operators
} // namespace paddle

1
paddle/operators/lstm_unit_op.h
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namespace paddle {
namespace operators {
using framework::LoDTensor;
using framework::Tensor;
template <typename T>

2
paddle/operators/math/cross_entropy.cu
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template <typename T>
__global__ void CrossEntropyKernel(T* Y, const T* X, const int* label,
const int N, const int D) {
// TOOD(qingqing) define CUDA_1D_KERNEL_LOOP macro in a common file.
// CUDA_1D_KERNEL_LOOP(i, N) {
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N;
i += blockDim.x * gridDim.x) {
PADDLE_ASSERT(label[i] >= 0 && label[i] < D);

26
paddle/operators/math/sequence2batch.cc
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/operators/math/sequence2batch.h"
namespace paddle {
namespace operators {
namespace math {
template class LoDTensor2BatchFunctor<platform::CPUPlace, float>;
template class Batch2LoDTensor2Functor<platform::CPUPlace, float>;
} // namespace math
} // namespace operators
} // namespace paddle

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paddle/operators/math/sequence2batch.cu
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/operators/math/sequence2batch.h"
namespace paddle {
namespace operators {
namespace math {
template class LoDTensor2BatchFunctor<platform::GPUPlace, float>;
template class Batch2LoDTensor2Functor<platform::GPUPlace, float>;
} // namespace math
} // namespace operators
} // namespace paddle

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paddle/operators/math/sequence2batch.h
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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. */
namespace paddle {
namespace operators {
namespace math {
template <typename Place, typename T>
class LoDTensor2BatchFunctor {
public:
void operator()(const platform::DeviceContext& context,
const framework::LoDTensor& lod_tensor,
framework::LoDTensor& batch, const bool is_reverse) const {
auto lods = lod_tensor->lod();
PADDLE_ENFORCE_EQ(lod.size(), 1UL, "Only support one level sequence now.");
auto lod = lods[0];
// Calculate the length of each sequence and
// sort sequence index by the length.
// example: sequences = {s0, s1, s2}
// s0: 0 0 0 0, s1: 1 1 1 1 1, s2: 2 2 2
// seq_info[3] = {(4, 5, 1), (0, 4, 0), (9, 3, 2)}
//
struct SeqInfo {
SeqInfo(int start, int length, int seq_idx)
: start(start), length(length), seqIdx(seq_idx) {}
int start;
int length;
int seq_idx;
};
std::vector<SeqInfo> seq_info;
for (size_t seq_id = 0; seq_id < lod.size(); ++seq_id) {
int length = lod[seq_id + 1] - lod[seq_id];
seq_info.emplace_back(lod[seq_id], length, seq_id);
}
std::sort(seq_info.begin(), seq_info.end(),
[](SeqInfo a, SeqInfo b) { return a.length > b.length; });
// calculate the start position of each batch
// (numBatch equal the maxLength of sequences)
// example: sequences = {s0, s1, s2}
// s0: 0 0 0 0, s1: 1 1 1 1 1, s2: 2 2 2
// num_batch = 5,
// batchIndex = {b0, b1, b2, b3, b4}
// b0: 1 0 2, b1: 1 0 2, b2: 1 0 2, b3: 1 0, b4: 1
// batch_start_positions[6] = {0, 3, 6, 9, 11, 12}
// seq2batch_idx[12] = {4, 0, 9,
// 5, 1, 10,
// 6, 2, 11,
// 7, 3,
// 8}
// The batch number represents batch size after rearranging the
// input LodTensor. It is also the maximum length of input sequence.
auto batch_lods = batch->lod();
if (!batch_lods) {
batch_lods->resize(2);
}
// batch_lods[0] is the start positions for batch LoDTensor
int num_batch = (size_t)seq_info[0].length;
batch_lods[0]->resize(num_batch + 1);
// batch_lods[1] is the raw index in the input LoDTensor
auto dims = lod_tensor->dims();
batch_lods[1]->resize(dims[0]);
auto* batch_starts = batch_lods[0].data();
auto* seq2batch_idx = batch_lods[1].data();
batch_starts[0] = 0;
for (size_t n = 0; n < num_batch; n++) {
int batch_id = batch_starts[n];
for (size_t i = 0; i < seq_info.size(); ++i) {
size_t seq_len = seq_info[i].length;
int start = seq_info[i].start;
if (n < seq_len) {
if (!is_reverse) {
seq2batch_idx[batch_id] = start + n;
} else {
seq2batch_idx[batch_id] = start + seq_len - 1 - n;
}
batch_id++;
} else {
break;
}
}
batch_starts[n + 1] = batch_id;
}
}
}
template <typename Place, typename T>
class Batch2LoDTensor2Functor {
public:
void operator()(const platform::DeviceContext& context,
const framework::LoDTensor& batch,
framework::LoDTensor& lod_tensor,
const bool is_reverse) const;
} // namespace math
} // namespace operators
} // namespace paddle
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