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Initialize the sequence softmax operator.

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tonyyang-svail-feed-op-desgin
Liu Yiqun 8 years ago
parent e53dc8a2e4
commit f14a7966b0
5 changed files with 205 additions and 1 deletions
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2
paddle/operators/sequence_avg_pool_op.cc
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@ -36,7 +36,7 @@ class SequenceAvgPoolOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_GE(
dims[0],
/*batch size = */ static_cast<int64_t>(lod[0].size() - 1),
"The first dimension of Input(X) must be large than batch size.");
"The first dimension of Input(X) must be larger than batch size.");
dims[0] = lod[0].size() - 1;
ctx.Output<framework::LoDTensor>("Out")->Resize({dims});
}

82
paddle/operators/sequence_softmax_op.cc
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@ -0,0 +1,82 @@
/* 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/sequence_softmax_op.h"
namespace paddle {
namespace operators {
class SequenceSoftmaxOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(
ctx.InputVar("X"), "Input(X) of SequenceSoftmaxOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(
ctx.OutputVar("Out"),
"Output(Out) of SequenceSoftmaxOp should not be null.");
auto *x = ctx.Input<framework::LoDTensor>("X");
auto dims = x->dims();
auto lod = x->lod();
PADDLE_ENFORCE_EQ(lod.size(), 1UL, "Only support one level sequence now.");
PADDLE_ENFORCE_GE(
dims[0],
/* batch_size */ static_cast<int64_t>(lod[0].size() - 1),
"The first dimension of Input(X) should be larger than batch size.");
PADDLE_ENFORCE_EQ(x->numel(), static_cast<int64_t>(lod[0].size() - 1),
"The width of each timestep in Input(X) of "
"SequenceSoftmaxOp should be 1.");
dims[0] = lod[0].size() - 1;
ctx.Output<framework::LoDTensor>("Out")->Resize({dims});
}
};
class SequenceSoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SequenceSoftmaxOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "(LoDTensor)");
AddOutput("Out", "(LoDTensor)");
AddComment(R"DOC(
Softmax of Sequence.
)DOC");
}
};
class SequenceSoftmaxGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(sequence_softmax, ops::SequenceSoftmaxOp,
ops::SequenceSoftmaxOpMaker, sequence_softmax_grad,
ops::SequenceSoftmaxGradOp);
REGISTER_OP_CPU_KERNEL(
sequence_softmax,
ops::SequenceSoftmaxKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
sequence_softmax_grad,
ops::SequenceSoftmaxGradKernel<paddle::platform::GPUPlace, float>);

25
paddle/operators/sequence_softmax_op.cu
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@ -0,0 +1,25 @@
/* 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. */
#define EIGEN_USE_GPU
#include "paddle/operators/sequence_softmax_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
sequence_softmax,
ops::SequenceSoftmaxKernel<paddle::platform::GPUPlace, float>)
REGISTER_OP_GPU_KERNEL(
sequence_softmax_grad,
ops::SequenceSoftmaxGradKernel<paddle::platform::GPUPlace, float>);

62
paddle/operators/sequence_softmax_op.h
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@ -0,0 +1,62 @@
/* 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 "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/softmax_function.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename Place, typename T>
class SequenceSoftmaxKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* x = ctx.Input<LoDTensor>("X");
auto* out = ctx.Output<LoDTensor>("Out");
auto lod = x->lod();
const size_t level = lod.size();
out->mutable_data<T>(ctx.GetPlace());
for (int i = 0; i < static_cast<int>(lod[level].size()) - 1; ++i) {
int start_pos = static_cast<int>(lod[level][i]);
int end_pos = static_cast<int>(lod[level][i + 1]);
Tensor x_i = x->Slice<T>(start_pos, end_pos);
Tensor out_i = out->Slice<T>(start_pos, end_pos);
math::SoftmaxFunctor<Place, T>()(&x_i, &out_i, ctx);
}
}
};
template <typename Place, typename T>
class SequenceSoftmaxGradKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& ctx) const override {}
};
} // namespace operators
} // namespace paddle

35
python/paddle/v2/framework/tests/test_sequence_softmax_op.py
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@ -0,0 +1,35 @@
import unittest
import numpy as np
from op_test import OpTest
def stable_softmax(x):
"""Compute the softmax of vector x in a numerically stable way."""
shiftx = x - np.max(x)
exps = np.exp(shiftx)
return exps / np.sum(exps)
class TestSequenceSoftmaxOp(OpTest):
def setUp(self):
self.op_type = "sequence_softmax"
x = np.random.uniform(0.1, 1, (11, 1)).astype("float32")
lod = [[0, 4, 5, 8, 11]]
out = np.zeros((11, 1)).astype("float32")
for i in range(4):
sub_x = x[lod[0][i]:lod[0][i + 1], :]
sub_x = sub_x.reshape(1, lod[0][i + 1] - lod[0][i])
sub_out = stable_softmax(sub_x)
out[lod[0][i]:lod[0][i + 1], :] = sub_out.reshape(
lod[0][i + 1] - lod[0][i], 1)
self.inputs = {"X": (x, lod)}
self.outputs = {"Out": out}
def test_check_output(self):
self.check_output()
if __name__ == "__main__":
unittest.main()
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