wehub-resource-sync
95 lines
3.0 KiB
C++
95 lines
3.0 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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#pragma once
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#include <string>
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#include <utility>
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#include <vector>
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/core/kernel_registry.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 PartialSumKernel(const Context& dev_ctx,
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const std::vector<const DenseTensor*>& x,
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int start_index,
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int length,
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DenseTensor* out) {
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PADDLE_ENFORCE_EQ(
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x.size() > 0,
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true,
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common::errors::InvalidArgument("The input should not be null."));
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auto* out_t = dev_ctx.template Alloc<T>(out);
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auto batch_size = x[0]->dims()[0];
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if (length == -1) {
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length = x[0]->dims()[1] - start_index;
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}
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memset(out_t, 0, sizeof(T) * batch_size * length);
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for (size_t i = 0; i < x.size(); ++i) {
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auto* in_t = x[i]->data<T>();
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auto total_len = x[i]->dims()[1];
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for (auto bs_id = 0; bs_id < batch_size; ++bs_id) {
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for (auto k = 0; k < length; ++k) {
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out_t[bs_id * length + k] += in_t[bs_id * total_len + start_index + k];
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}
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}
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}
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}
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template <typename T, typename Context>
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void PartialSumGradientOpKernel(const Context& dev_ctx,
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const std::vector<const DenseTensor*>& x,
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const DenseTensor& out_grad,
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int start_index,
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int length,
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std::vector<DenseTensor*> x_grad) {
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PADDLE_ENFORCE_EQ(
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x.size() > 0,
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true,
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common::errors::InvalidArgument("The input should not be null."));
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auto batch_size = x[0]->dims()[0];
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if (length == -1) {
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length = x[0]->dims()[1] - start_index;
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}
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// initialize
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auto& place = *dev_ctx.eigen_device();
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for (size_t i = 0; i < x_grad.size(); ++i) {
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dev_ctx.template Alloc<T>(x_grad[i]);
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auto dxt = EigenVector<T>::Flatten(*x_grad[i]);
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dxt.device(place) = dxt.constant(static_cast<T>(0));
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}
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auto* out_grad_t = out_grad.data<T>();
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for (size_t i = 0; i < x_grad.size(); ++i) {
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auto* out_t = x_grad[i]->data<T>();
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auto total_len = x[i]->dims()[1];
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for (auto bs_id = 0; bs_id < batch_size; ++bs_id) {
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for (int len = 0; len < length; ++len) {
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out_t[start_index + bs_id * total_len + len] =
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out_grad_t[bs_id * length + len] * static_cast<T>(1);
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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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