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Merge branch 'fix-6678' of github.com:pkuyym/Paddle into fix-7691

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add_depthwiseConv_op_gpu
yangyaming 7 years ago
parent 9fea1d4385 b07ca1de1f
commit c198523885
4 changed files with 330 additions and 0 deletions
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130
paddle/operators/sequence_reshape_op.cc
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/* Copyright (c) 2018 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_reshape_op.h"
#include "paddle/framework/ddim.h"
namespace paddle {
namespace operators {
class SequenceReshapeOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of SequenceReshapeOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of SequenceReshapeOp should not be null.");
auto x_dims = ctx->GetInputDim("X");
auto x_numel = product(x_dims);
PADDLE_ENFORCE_EQ(x_dims.size(), 2U, "Rank of Input(X) should be 2.");
int new_dim = ctx->Attrs().Get<int>("new_dim");
ctx->SetOutputDim("Out",
{x_numel / new_dim, static_cast<int64_t>(new_dim)});
}
};
class SequenceReshapeOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SequenceReshapeOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(LoDTensor, default LoDTensor<float>) A 2-D LoDTensor with shape "
"being [N, M].");
AddOutput("Out",
"(LoDTensor, default LoDTensor<float>) A 2-D LoDTensor with "
"shape [T, new_dim] where T is calculated based on X.lod, M and "
"new_dim.");
AddAttr<int>("new_dim", "Sequence dimension of the output LoDTensor.");
AddComment(R"DOC(
Sequence Reshape Operator.
This operator will rearrange the input sequences. The new dimension is set by
attribute and length of each sequence may change longer or shorter which is
decided by original length, original dimension and new dimension. The following
example will help to illustrate the function of this operator:
x is a LoDTensor:
x.lod = [[0, 2, 6]]
x.data = [[1, 2], [3, 4],
[5, 6], [7, 8], [9, 10], [11, 12]]
x.dims = [6, 2]
set new_dim = 4
then out is a LoDTensor:
out.lod = [[0, 1, 3]]
out.data = [[1, 2, 3, 4],
[5, 6, 7, 8], [9, 10, 11, 12]]
out.dims = [3, 4]
Currently, only 1-level LoDTensor is supported and please make sure (original
length * original dimension) can be divided by new_dim with no remainder for
each sequence.
)DOC");
}
};
class SequenceReshapeGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(
ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) of SequenceReshapeGradOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of SequenceReshapeGradOp should not be null.");
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
ctx->ShareLoD("X", /*->*/ framework::GradVarName("X"));
}
};
class SequenceReshapeGradOpMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
auto* op_desc_ptr = new framework::OpDesc();
op_desc_ptr->SetType("sequence_reshape_grad");
op_desc_ptr->SetInput("X", Input("X"));
op_desc_ptr->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op_desc_ptr->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op_desc_ptr->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDesc>(op_desc_ptr);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(sequence_reshape, ops::SequenceReshapeOp,
ops::SequenceReshapeOpMaker, ops::SequenceReshapeGradOpMaker);
REGISTER_OPERATOR(sequence_reshape_grad, ops::SequenceReshapeGradOp);
REGISTER_OP_CPU_KERNEL(
sequence_reshape,
ops::SequenceReshapeKernel<paddle::platform::CPUDeviceContext, float>,
ops::SequenceReshapeKernel<paddle::platform::CPUDeviceContext, double>,
ops::SequenceReshapeKernel<paddle::platform::CPUDeviceContext, int>,
ops::SequenceReshapeKernel<paddle::platform::CPUDeviceContext, int64_t>);
REGISTER_OP_CPU_KERNEL(
sequence_reshape_grad,
ops::SequenceReshapeGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::SequenceReshapeGradKernel<paddle::platform::CPUDeviceContext, double>,
ops::SequenceReshapeGradKernel<paddle::platform::CPUDeviceContext, int64_t>,
ops::SequenceReshapeGradKernel<paddle::platform::CPUDeviceContext, int>);

30
paddle/operators/sequence_reshape_op.cu
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/* Copyright (c) 2018 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_reshape_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
sequence_reshape,
ops::SequenceReshapeKernel<paddle::platform::CUDADeviceContext, float>,
ops::SequenceReshapeKernel<paddle::platform::CUDADeviceContext, double>,
ops::SequenceReshapeKernel<paddle::platform::CUDADeviceContext, int>,
ops::SequenceReshapeKernel<paddle::platform::CUDADeviceContext, int64_t>);
REGISTER_OP_CUDA_KERNEL(
sequence_reshape_grad,
ops::SequenceReshapeGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::SequenceReshapeGradKernel<paddle::platform::CUDADeviceContext, double>,
ops::SequenceReshapeGradKernel<paddle::platform::CUDADeviceContext,
int64_t>,
ops::SequenceReshapeGradKernel<paddle::platform::CUDADeviceContext, int>);

86
paddle/operators/sequence_reshape_op.h
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/* Copyright (c) 2018 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/op_registry.h"
#include "paddle/operators/math/math_function.h"
namespace paddle {
namespace operators {
using LoDTensor = framework::LoDTensor;
template <typename DeviceContext, typename T>
class SequenceReshapeKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* in = context.Input<LoDTensor>("X");
auto* out = context.Output<LoDTensor>("Out");
int out_width = context.Attr<int>("new_dim");
auto in_dims = in->dims();
int64_t in_width = in_dims[1];
auto& in_lod = in->lod();
PADDLE_ENFORCE_EQ(in_lod.size(), 1UL,
"Only support one level sequence now.");
PADDLE_ENFORCE_EQ(
in_dims[0], in_lod[0].back(),
"Inconsistent size between X.shape[0] and X.lod()[0].back().");
auto in_lod_l0 = in_lod[0];
int seq_num = in_lod_l0.size() - 1;
if (in_width == out_width) {
out->set_lod(in->lod());
} else {
auto& out_lod = *out->mutable_lod();
out_lod.resize(1);
out_lod[0].resize(seq_num + 1);
out_lod[0][0] = 0;
for (int i = 0; i < seq_num; ++i) {
size_t seq_len = in_lod_l0[i + 1] - in_lod_l0[i];
size_t offset = 0;
offset = (seq_len * in_width) / out_width;
PADDLE_ENFORCE_EQ(offset * out_width, seq_len * in_width,
"Please make sure (sequence_length * dimension) can "
"be divided by new_dim with no remainder for each "
"sequence. The %dth sequence is invalid.",
i + 1);
out_lod[0][i + 1] = out_lod[0][i] + offset;
}
}
framework::Copy(*in, context.GetPlace(), out);
out->Resize({static_cast<int64_t>(out->lod()[0].back()), out_width});
}
};
template <typename DeviceContext, typename T>
class SequenceReshapeGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* x_tensor_ptr = context.Input<LoDTensor>("X");
auto* outg_tensor_ptr =
context.Input<LoDTensor>(framework::GradVarName("Out"));
auto* xg_tensor_ptr =
context.Output<LoDTensor>(framework::GradVarName("X"));
xg_tensor_ptr->mutable_data<T>(context.GetPlace());
framework::Copy(*outg_tensor_ptr, context.GetPlace(), xg_tensor_ptr);
xg_tensor_ptr->Resize(x_tensor_ptr->dims());
}
};
} // namespace operators
} // namespace paddle

84
python/paddle/v2/fluid/tests/test_sequence_reshape.py
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# Copyright (c) 2018 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.
import unittest
import numpy as np
import math
from op_test import OpTest
class TestSequenceReshape(OpTest):
def setUp(self):
self.op_type = 'sequence_reshape'
dimension = 12
x_lod = [[0, 4, 5, 8, 11]]
x = np.random.uniform(0.1, 1, [11, 24]).astype('float32')
self.inputs = {'X': (x, x_lod)}
self.attrs = {'new_dim': dimension}
out, out_lod = self.compute_output(x, x_lod, dimension)
self.outputs = {'Out': (out, out_lod)}
def compute_output(self, x, x_lod, dimension):
x_width = x.shape[1]
out_lod = [[0]]
for i in xrange(len(x_lod[0]) - 1):
seq_len = x_lod[0][i + 1] - x_lod[0][i]
offset = (seq_len * x_width) / dimension
assert int(offset) * dimension == seq_len * x_width
out_lod[0].append(out_lod[0][-1] + int(offset))
out = np.zeros(shape=(out_lod[0][-1], dimension)).astype('float32')
out.ravel()[:] = x.ravel()[:]
return out, out_lod
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["X"], "Out")
class TestSequenceReshape_reduce(TestSequenceReshape):
def setUp(self):
self.op_type = 'sequence_reshape'
dimension = 24
x_lod = [[0, 4, 6, 8, 12]]
x = np.random.uniform(0.1, 1, [12, 12]).astype('float32')
self.inputs = {'X': (x, x_lod)}
self.attrs = {'new_dim': dimension}
out, out_lod = self.compute_output(x, x_lod, dimension)
self.outputs = {'Out': (out, out_lod)}
class TestSequenceReshape_same(TestSequenceReshape):
def setUp(self):
self.op_type = 'sequence_reshape'
dimension = 12
x_lod = [[0, 4, 6, 8, 12]]
x = np.random.uniform(0.1, 1, [12, 12]).astype('float32')
self.inputs = {'X': (x, x_lod)}
self.attrs = {'new_dim': dimension}
out, out_lod = self.compute_output(x, x_lod, dimension)
self.outputs = {'Out': (out, out_lod)}
if __name__ == '__main__':
unittest.main()
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