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Refine concat_op #8669
Refine concat_op #8669
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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"); | ||
you may not use this file except in compliance with the License. | ||
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 | ||
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. */ | ||
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#include "paddle/fluid/operators/math/concat.h" | ||
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namespace paddle { | ||
namespace operators { | ||
namespace math { | ||
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/* | ||
* All tensors' dimension should be the same and the values of | ||
* each dimension are the same, except the axis dimension. | ||
*/ | ||
template <typename T> | ||
class ConcatFunctor<platform::CPUDeviceContext, T> { | ||
public: | ||
void operator()(const platform::CPUDeviceContext& context, | ||
const std::vector<framework::Tensor>& input, const int axis, | ||
framework::Tensor* output) { | ||
// TODO(zcd): Add input data validity checking | ||
int num = input.size(); | ||
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int rows = 1; | ||
auto dim_0 = input[0].dims(); | ||
for (int i = 0; i < axis; ++i) { | ||
rows *= dim_0[i]; | ||
} | ||
int out_rows = rows, out_cols = 0; | ||
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std::vector<int64_t> input_cols(input.size()); | ||
for (int i = 0; i < num; ++i) { | ||
int t_cols = input[i].numel() / rows; | ||
out_cols += t_cols; | ||
input_cols[i] = t_cols; | ||
} | ||
auto& cpu_place = boost::get<platform::CPUPlace>(context.GetPlace()); | ||
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// computation | ||
for (int k = 0; k < out_rows; ++k) { | ||
T* dst_ptr = output->data<T>() + k * out_cols; | ||
int col_idx = 0; | ||
for (int j = 0; j < num; ++j) { | ||
int col_len = input_cols[j]; | ||
const T* src_prt = input[j].data<T>() + k * col_len; | ||
memory::Copy(cpu_place, dst_ptr + col_idx, cpu_place, src_prt, | ||
sizeof(T) * col_len); | ||
col_idx += col_len; | ||
} | ||
} | ||
} | ||
}; | ||
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/* | ||
* All tensors' dimension should be the same and the values of | ||
* each dimension are the same, except the axis dimension. | ||
*/ | ||
template <typename T> | ||
class ConcatGradFunctor<platform::CPUDeviceContext, T> { | ||
public: | ||
void operator()(const platform::CPUDeviceContext& context, | ||
const framework::Tensor& input, const int axis, | ||
std::vector<framework::Tensor>& outputs) { | ||
// TODO(zcd): Add input data validity checking | ||
int num = outputs.size(); | ||
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int input_rows = 1; | ||
auto dim_0 = outputs[0].dims(); | ||
for (int i = 0; i < axis; ++i) { | ||
input_rows *= dim_0[i]; | ||
} | ||
int input_cols = 0; | ||
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std::vector<int64_t> output_cols(outputs.size()); | ||
for (int i = 0; i < num; ++i) { | ||
int t_cols = outputs[i].numel() / input_rows; | ||
input_cols += t_cols; | ||
output_cols[i] = t_cols; | ||
} | ||
auto& cpu_place = boost::get<platform::CPUPlace>(context.GetPlace()); | ||
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// computation | ||
for (int k = 0; k < input_rows; ++k) { | ||
const T* src_ptr = input.data<T>() + k * input_cols; | ||
int col_idx = 0; | ||
for (int j = 0; j < num; ++j) { | ||
int col_len = output_cols[j]; | ||
T* dst_ptr = outputs[j].data<T>() + k * col_len; | ||
memory::Copy(cpu_place, dst_ptr, cpu_place, src_ptr + col_idx, | ||
sizeof(T) * col_len); | ||
col_idx += col_len; | ||
} | ||
} | ||
} | ||
}; | ||
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template class ConcatFunctor<platform::CPUDeviceContext, int>; | ||
template class ConcatFunctor<platform::CPUDeviceContext, int64_t>; | ||
template class ConcatFunctor<platform::CPUDeviceContext, float>; | ||
template class ConcatFunctor<platform::CPUDeviceContext, double>; | ||
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template class ConcatGradFunctor<platform::CPUDeviceContext, int>; | ||
template class ConcatGradFunctor<platform::CPUDeviceContext, int64_t>; | ||
template class ConcatGradFunctor<platform::CPUDeviceContext, float>; | ||
template class ConcatGradFunctor<platform::CPUDeviceContext, double>; | ||
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There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Why need a functor? We can just write the code into op.cc/cu There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Currently, |
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} // namespace math | ||
} // namespace operators | ||
} // namespace paddle |
There was a problem hiding this comment.
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Same with above, it is redundant with the
stridememcpywithaxis
.There was a problem hiding this comment.
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This functor doesn't process the GPU data, so it is not redundant with
stridememcpywithaxis
.For GPU data, In some case, the functor is slower than
stridememcpywithaxis
.