提交 bea41444 编写于 作者: Y yangyaming

Refine the implementation and add unit test.

上级 f20617be
......@@ -27,9 +27,8 @@ class SequenceReshapeOp : public framework::OperatorWithKernel {
"Output(Out) of SequenceReshapeOp should not be null.");
auto x_dims = ctx->GetInputDim("X");
PADDLE_ENFORCE_EQ(x_dims.size(), 2U, "Rank of Input(X) should be 2.");
int dimension = ctx->Attrs().Get<int>("dimension");
ctx->SetOutputDim("Out", {{x_dims[0], static_cast<int64_t>(dimension)}});
ctx->ShareLoD("X", /*->*/ "Out");
int dimension = ctx->Attrs().Get<int>("new_dim");
ctx->SetOutputDim("Out", {x_dims[0], static_cast<int64_t>(dimension)});
}
};
......@@ -37,11 +36,41 @@ class SequenceReshapeOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SequenceReshapeOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "");
AddOutput("Out", "");
AddAttr<int>("dimension", "");
AddAttr<bool>("is_padding", "Default padding zero.");
AddComment(R"DOC()DOC");
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 = [[0.1, 0.2], [0.3, 0.4],
[0.5, 0.6], [0.7, 0.8], [0.9, 1.0], [1.1, 1.2]]
x.dims = [6, 2]
set new_dim = 4
then out is a LoDTensor:
out.lod = [[0, 1, 3]]
out.data = [[0.1, 0.2, 0.3, 0.4],
[0.5, 0.6, 0.7, 0.8], [0.9, 1.0, 1.1, 1.2]]
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");
}
};
......@@ -63,12 +92,29 @@ class SequenceReshapeGradOp : public framework::OperatorWithKernel {
}
};
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("Out", Output("Out"));
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::SequenceReshapeOpMaker, ops::SequenceReshapeGradOpMaker);
REGISTER_OPERATOR(sequence_reshape_grad, ops::SequenceReshapeGradOp);
REGISTER_OP_CPU_KERNEL(
sequence_reshape,
......
/* 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_reshape_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
sequence_reshape,
ops::SequenceReshapeKernel<paddle::platform::CUDADeviceContext, float>);
REGISTER_OP_CUDA_KERNEL(
sequence_reshape_grad,
ops::SequenceReshapeGradKernel<paddle::platform::CUDADeviceContext, float>);
......@@ -26,53 +26,63 @@ class SequenceReshapeKernel : public framework::OpKernel<T> {
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>("dimension");
bool whether_padding = context.Attr<bool>("whether_padding");
int out_width = context.Attr<int>("new_dim");
const T* p_in_data = in->data<T>();
T* p_out_data = out->mutable_data<T>(context.GetPlace());
// compute shape for output
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_GE(
in_dims[0],
/* batch size = */ static_cast<int64_t>(in_lod[0].size() - 1),
"The 1st dimension of Input(X) must be equal or larger than batch "
"size.");
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;
auto& out_lod = *out->mutable_lod();
out_lod.push_back(std::vector<size_t>({0}));
size_t offset = 0;
out_lod.resize(1);
out_lod[0].clear();
out_lod[0].push_back(0);
for (int i = 0; i < seq_num; ++i) {
size_t seq_len = in_lod_l0[i + 1] - in_lod_l0[i];
if (whether_padding) {
offset += std::ceil((float)(seq_len * in_width) / out_width);
} else {
offset += (seq_len * in_width) / out_width;
}
out_lod[0].push_back(offset);
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);
PADDLE_ENFORCE_GT(offset, 0,
"Illegal operation, length of the %dth sequence become "
"to 0 after reshaped.",
i + 1);
out_lod[0].push_back(out_lod[0].back() + offset);
}
out->Resize({{static_cast<int64_t>(out_lod[0].back()), out_width}});
out->mutable_data<T>(context.GetPlace());
out->Resize({static_cast<int64_t>(out_lod[0].back()), out_width});
T* p_out_data = out->mutable_data<T>(context.GetPlace());
math::set_constant(context.device_context(), out, 0.0f);
for (int i = 0; i < seq_num; ++i) {
size_t in_offset = in_lod_l0[i] * in_width;
size_t out_offset = out_lod[0][i] * out_width;
size_t bytes = sizeof(T) * (in_lod_l0[i + 1] - in_lod_l0[i]) * in_width;
size_t in_count = (in_lod_l0[i + 1] - in_lod_l0[i]) * in_width;
size_t out_count = (out_lod[0][i + 1] - out_lod[0][i]) * out_width;
size_t bytes = sizeof(T) * std::min(in_count, out_count);
if (platform::is_cpu_place(context.GetPlace())) {
std::memcpy(p_out_data + out_offset, p_in_data + in_offset, bytes);
memory::Copy(boost::get<platform::CPUPlace>(context.GetPlace()),
p_out_data + out_offset,
boost::get<platform::CPUPlace>(context.GetPlace()),
p_in_data + in_offset, bytes);
} else {
#ifdef PADDLE_WITH_CUDA
auto& dev_ctx = context.template device_context<DeviceContext>();
auto& dev_ctx =
context.template device_context<platform::CUDADeviceContext>();
memory::Copy(boost::get<platform::CUDAPlace>(context.GetPlace()),
p_out_data + out_offset,
boost::get<platform::CUDAPlace>(context.GetPlace()),
......@@ -103,16 +113,23 @@ class SequenceReshapeGradKernel : public framework::OpKernel<T> {
auto& out_lod = out_tensor_ptr->lod();
int out_width = out_tensor_ptr->dims()[1];
math::set_constant(context.device_context(), x_grad_tensor_ptr, 0.0f);
for (int i = 0; i < seq_num; ++i) {
size_t src_offset = out_lod[0][i] * out_width;
size_t dst_offset = x_lod[0][i] * x_width;
size_t bytes = sizeof(T) * (x_lod[0][i + 1] - x_lod[0][i]) * x_width;
size_t src_count = (out_lod[0][i + 1] - out_lod[0][i]) * out_width;
size_t dst_count = (x_lod[0][i + 1] - x_lod[0][i]) * x_width;
size_t bytes = sizeof(T) * std::min(src_count, dst_count);
if (platform::is_cpu_place(context.GetPlace())) {
std::memcpy(p_x_grad_data + dst_offset, p_out_grad_data + src_offset,
bytes);
memory::Copy(boost::get<platform::CPUPlace>(context.GetPlace()),
p_x_grad_data + dst_offset,
boost::get<platform::CPUPlace>(context.GetPlace()),
p_out_grad_data + src_offset, bytes);
} else {
#ifdef PADDLE_WITH_CUDA
auto& dev_ctx = context.template device_context<DeviceContext>();
auto& dev_ctx =
context.template device_context<platform::CUDADeviceContext>();
memory::Copy(boost::get<platform::CUDAPlace>(context.GetPlace()),
p_x_grad_data + dst_offset,
boost::get<platform::CUDAPlace>(context.GetPlace()),
......
# 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')
for i in xrange(len(x_lod[0]) - 1):
x_offset = x_lod[0][i] * x_width
out_offset = out_lod[0][i] * dimension
out_count = (out_lod[0][i + 1] - out_lod[0][i]) * dimension
x_count = (x_lod[0][i + 1] - x_lod[0][i]) * x_width
count = min(out_count, x_count)
out.ravel()[out_offset:out_offset + count] = x.ravel()[
x_offset:x_offset + count]
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)}
if __name__ == '__main__':
unittest.main()
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