Merge pull request #5310 from qingqing01/fix_max_pool
Refine sequence max-pooling and add unit testing of gradient check.mobile_baidu
Tao Luo
7 years ago
committed by
GitHub
commit
0977cbfb41
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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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
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/operators/math/sequence_pooling.h"
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#include "paddle/operators/math/math_function.h"
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namespace paddle {
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namespace operators {
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namespace math {
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template <typename T>
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class MaxSeqPoolFunctor<platform::CPUPlace, T> {
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public:
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void operator()(const platform::DeviceContext& context,
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const framework::LoDTensor& input, framework::Tensor* output,
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framework::Tensor* index) {
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auto in_dims = input.dims();
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auto out_dims = output->dims();
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auto idx_dims = index->dims();
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PADDLE_ENFORCE_GT(in_dims.size(), 1);
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PADDLE_ENFORCE_GT(out_dims.size(), 1);
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for (int64_t i = 1; i < in_dims.size(); ++i) {
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PADDLE_ENFORCE_EQ(in_dims[i], out_dims[i]);
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}
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PADDLE_ENFORCE_EQ(idx_dims, out_dims);
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auto starts = input.lod()[0];
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const T* in_data = input.data<T>();
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T* out_data = output->data<T>();
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int* max_index = index->data<int>();
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int64_t num_seq = out_dims[0];
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int64_t dim = output->numel() / num_seq;
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for (int64_t i = 0; i < num_seq; ++i) {
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for (int64_t k = 0; k < dim; ++k) {
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out_data[i * dim + k] = in_data[starts[i] * dim + k];
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max_index[i * dim + k] = starts[i];
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}
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for (size_t j = starts[i] + 1; j < starts[i + 1]; ++j) {
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for (int64_t k = 0; k < dim; ++k) {
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if (in_data[j * dim + k] > out_data[i * dim + k]) {
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out_data[i * dim + k] = in_data[j * dim + k];
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max_index[i * dim + k] = j;
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}
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}
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}
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}
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}
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};
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template <typename T>
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class MaxSeqPoolGradFunctor<platform::CPUPlace, T> {
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public:
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void operator()(const platform::DeviceContext& context,
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const framework::Tensor& out_grad,
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const framework::Tensor& index,
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framework::LoDTensor* in_grad) {
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auto og_dims = out_grad.dims();
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auto ig_dims = in_grad->dims();
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auto idx_dims = index.dims();
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PADDLE_ENFORCE_GT(og_dims.size(), 1);
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PADDLE_ENFORCE_GT(ig_dims.size(), 1);
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for (int64_t i = 1; i < og_dims.size(); ++i) {
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PADDLE_ENFORCE_EQ(og_dims[i], ig_dims[i]);
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}
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PADDLE_ENFORCE_EQ(idx_dims, og_dims);
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const T* og_data = out_grad.data<T>();
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const int* max_index = index.data<int>();
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T* ig_data = in_grad->data<T>();
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SetConstant<platform::CPUPlace, T> set_zero;
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set_zero(context, in_grad, static_cast<T>(0.0));
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int64_t num_seq = og_dims[0];
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int64_t dim = out_grad.numel() / num_seq;
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for (int64_t i = 0; i < num_seq; ++i) {
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for (int64_t j = 0; j < dim; ++j) {
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int step_id = max_index[i * dim + j];
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ig_data[step_id * dim + j] = og_data[i * dim + j];
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}
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}
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}
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};
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template class MaxSeqPoolFunctor<platform::CPUPlace, float>;
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template class MaxSeqPoolFunctor<platform::CPUPlace, double>;
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template class MaxSeqPoolGradFunctor<platform::CPUPlace, float>;
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template class MaxSeqPoolGradFunctor<platform::CPUPlace, double>;
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} // namespace math
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} // namespace operators
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} // namespace paddle
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@ -0,0 +1,136 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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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
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/operators/math/math_function.h"
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#include "paddle/operators/math/sequence_pooling.h"
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namespace paddle {
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namespace operators {
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namespace math {
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#define FLT_MAX __FLT_MAX__
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template <typename T>
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__global__ void KeMaxSequencePool(const T* input, const size_t* starts,
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T* output, int* index, int64_t num_seq,
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int64_t dim) {
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int dim_idx = threadIdx.x;
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int seq_id = blockIdx.x;
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if (seq_id >= num_seq) return;
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size_t start = starts[seq_id];
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size_t end = starts[seq_id + 1];
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for (int64_t i = dim_idx; i < dim; i += blockDim.x) {
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T max_val = static_cast<T>(-FLT_MAX);
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int max_id = -1;
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for (size_t step_id = start; step_id < end; step_id++) {
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if (max_val < input[step_id * dim + i]) {
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max_val = input[step_id * dim + i];
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max_id = step_id;
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}
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}
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output[seq_id * dim + i] = max_val;
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index[seq_id * dim + i] = max_id;
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}
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}
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template <typename T>
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class MaxSeqPoolFunctor<platform::GPUPlace, T> {
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public:
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void operator()(const platform::DeviceContext& context,
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const framework::LoDTensor& input, framework::Tensor* output,
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framework::Tensor* index) {
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auto in_dims = input.dims();
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auto out_dims = output->dims();
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auto idx_dims = index->dims();
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PADDLE_ENFORCE_GT(in_dims.size(), static_cast<int64_t>(1));
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PADDLE_ENFORCE_GT(out_dims.size(), 1);
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for (int64_t i = 1; i < in_dims.size(); ++i) {
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PADDLE_ENFORCE_EQ(in_dims[i], out_dims[i]);
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}
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PADDLE_ENFORCE_EQ(idx_dims, out_dims);
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auto starts = input.lod()[0];
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const T* in_data = input.data<T>();
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T* out_data = output->data<T>();
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int* max_index = index->data<int>();
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int64_t num_seq = out_dims[0];
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int64_t dim = output->numel() / num_seq;
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dim3 threads(256, 1);
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dim3 grid(num_seq, 1);
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auto stream =
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reinterpret_cast<const platform::CUDADeviceContext&>(context).stream();
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KeMaxSequencePool<T><<<grid, threads, 0, stream>>>(
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in_data, starts.data(), out_data, max_index, num_seq, dim);
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}
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};
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template <typename T>
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__global__ void KeMaxSequencePoolGrad(const T* out_grad, const int* max_index,
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T* in_grad, int64_t num_seq,
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int64_t dim) {
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int idx = threadIdx.x + blockIdx.x * blockDim.x;
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int col_idx = idx % dim;
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if (idx < num_seq * dim) {
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int step_id = max_index[idx];
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in_grad[step_id * dim + col_idx] = out_grad[idx];
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}
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}
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template <typename T>
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class MaxSeqPoolGradFunctor<platform::GPUPlace, T> {
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public:
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void operator()(const platform::DeviceContext& context,
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const framework::Tensor& out_grad,
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const framework::Tensor& index,
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framework::LoDTensor* in_grad) {
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auto og_dims = out_grad.dims();
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auto idx_dims = index.dims();
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auto ig_dims = in_grad->dims();
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PADDLE_ENFORCE_GT(og_dims.size(), static_cast<int64_t>(1));
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PADDLE_ENFORCE_GT(ig_dims.size(), static_cast<int64_t>(1));
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for (int64_t i = 1; i < og_dims.size(); ++i) {
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PADDLE_ENFORCE_EQ(og_dims[i], ig_dims[i]);
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}
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PADDLE_ENFORCE_EQ(idx_dims, og_dims);
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const T* og_data = out_grad.data<T>();
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const int* max_index = index.data<int>();
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T* ig_data = in_grad->data<T>();
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SetConstant<platform::GPUPlace, T> set_zero;
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set_zero(context, in_grad, static_cast<T>(0.0));
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int64_t num_seq = og_dims[0];
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int64_t dim = out_grad.numel() / num_seq;
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unsigned int blocks = (num_seq * dim + 128 - 1) / 128;
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dim3 threads(128, 1);
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dim3 grid(blocks, 1);
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auto stream =
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reinterpret_cast<const platform::CUDADeviceContext&>(context).stream();
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KeMaxSequencePoolGrad<T><<<grid, threads, 0, stream>>>(
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og_data, max_index, ig_data, num_seq, dim);
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}
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};
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template class MaxSeqPoolFunctor<platform::GPUPlace, float>;
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template class MaxSeqPoolFunctor<platform::GPUPlace, double>;
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template class MaxSeqPoolGradFunctor<platform::GPUPlace, float>;
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template class MaxSeqPoolGradFunctor<platform::GPUPlace, double>;
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} // namespace math
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} // namespace operators
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} // namespace paddle
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@ -0,0 +1,45 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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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
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#pragma once
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#include "paddle/framework/lod_tensor.h"
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#include "paddle/framework/tensor.h"
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#include "paddle/platform/device_context.h"
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namespace paddle {
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namespace operators {
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namespace math {
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#define FLT_MAX __FLT_MAX__
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template <typename Place, typename T>
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class MaxSeqPoolFunctor {
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public:
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void operator()(const platform::DeviceContext& context,
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const framework::LoDTensor& input, framework::Tensor* output,
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framework::Tensor* index);
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};
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template <typename Place, class T>
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class MaxSeqPoolGradFunctor {
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public:
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void operator()(const platform::DeviceContext& context,
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const framework::Tensor& out_grad,
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const framework::Tensor& index,
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framework::LoDTensor* in_grad);
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};
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} // namespace math
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} // namespace operators
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} // namespace paddle
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