wehub-resource-sync
102 lines
3.3 KiB
C++
102 lines
3.3 KiB
C++
// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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/phi/kernels/row_conv_kernel.h"
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#include <memory>
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#include <string>
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#include <vector>
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#include "paddle/phi/core/enforce.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/core/mixed_vector.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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namespace phi {
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template <typename T, typename Context>
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void RowConvKernel(const Context &dev_ctx,
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const DenseTensor &x_in,
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const DenseTensor &filter_in,
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DenseTensor *out) {
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auto *x = &x_in;
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auto *filter = &filter_in;
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dev_ctx.template Alloc<T>(out);
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bool is_tensor = x->lod().empty();
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int batch_size = 0;
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if (is_tensor) {
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batch_size = static_cast<int>(x->dims()[0]);
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} else {
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batch_size = static_cast<int>(x->lod()[0].size() - 1);
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}
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Vector<size_t> batch_indices(batch_size + 1);
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int input_dim = 0;
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int timesteps = 0;
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if (is_tensor) {
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for (int i = 0; i < batch_size + 1; i++) {
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batch_indices[i] = i;
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}
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input_dim = static_cast<int>(x->dims()[2]);
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timesteps = static_cast<int>(x->dims()[1]);
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} else {
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batch_indices = x->lod()[0];
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input_dim = static_cast<int>(x->dims()[1]);
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}
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size_t num_sequence = batch_indices.size() - 1;
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auto future_context = filter->dims()[0];
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auto weights = EigenMatrix<T>::From(*filter);
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for (size_t i = 0; i < num_sequence; i++) {
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int start = static_cast<int>(batch_indices[i]);
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int end = static_cast<int>(batch_indices[i + 1]);
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int current_timesteps = 0;
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if (is_tensor) {
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current_timesteps = timesteps;
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} else {
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current_timesteps = end - start;
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}
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// int current_timesteps = end - start;
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DenseTensor cur_input_sequence =
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x->Slice(start, end); // Current input sequence
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cur_input_sequence =
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cur_input_sequence.Resize({current_timesteps, input_dim});
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DenseTensor cur_output_sequence =
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out->Slice(start, end); // Current output sequence
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cur_output_sequence =
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cur_output_sequence.Resize({current_timesteps, input_dim});
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auto cip_seq = EigenMatrix<T>::From(cur_input_sequence);
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auto cot_seq = EigenMatrix<T>::From(cur_output_sequence);
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for (int k = 0; k < current_timesteps;
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k++) { // For different time steps in the same sequence
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for (int w = 0; (w < future_context) && ((k + w) < current_timesteps);
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w++) {
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for (int d = 0; d < input_dim; d++) {
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if (w == 0) {
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cot_seq(k, d) = weights(w, d) * cip_seq(k + w, d);
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} else {
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cot_seq(k, d) += weights(w, d) * cip_seq(k + w, d);
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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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} // namespace phi
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PD_REGISTER_KERNEL(row_conv, CPU, ALL_LAYOUT, phi::RowConvKernel, float) {}
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