wehub-resource-sync
99 lines
3.0 KiB
C++
99 lines
3.0 KiB
C++
// Copyright (c) 2022 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/reduce_mean_grad_kernel.h"
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#include "paddle/phi/backends/xpu/enforce_xpu.h"
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#include "paddle/phi/backends/xpu/xpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/xpu/reduce.h"
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namespace phi {
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template <typename T, typename Context>
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void ReduceMeanGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& out_grad,
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const IntArray& dims,
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bool keep_dim,
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bool reduce_all,
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DenseTensor* x_grad) {
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if (x_grad && x_grad->numel() == 0) {
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dev_ctx.template Alloc<T>(x_grad);
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return;
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}
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using XPUType = typename XPUTypeTrait<T>::Type;
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reduce_all = recompute_reduce_all(x, dims, reduce_all);
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dev_ctx.template Alloc<T>(x_grad);
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const XPUType* dy_data = reinterpret_cast<const XPUType*>(out_grad.data<T>());
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XPUType* x_data = reinterpret_cast<XPUType*>(x_grad->data<T>());
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auto reduce_dims = dims.GetData();
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std::vector<int64_t> xdims = vectorize<int64_t>(x.dims());
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std::vector<int64_t> ydims = vectorize<int64_t>(out_grad.dims());
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int64_t reduce_numel = 1;
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if (reduce_all) {
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reduce_dims.clear();
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for (size_t d = 0; d < xdims.size(); ++d) {
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reduce_dims.push_back(d);
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}
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}
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for (auto& d : reduce_dims) {
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if (d < 0) {
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d = d + xdims.size();
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}
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reduce_numel *= xdims[d];
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}
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if (keep_dim != true) {
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sort(reduce_dims.begin(), reduce_dims.end());
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for (auto& d : reduce_dims) {
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ydims.insert(ydims.begin() + d, 1);
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}
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}
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float val = 1.0f / static_cast<float>(reduce_numel);
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int r = xpu::constant(
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dev_ctx.x_context(), x_data, x.numel(), static_cast<XPUType>(val));
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant");
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// use [1] to replace [], because xpu not support []
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if (xdims.size() == 0) {
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xdims = std::vector<int64_t>({1});
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}
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if (ydims.size() == 0) {
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ydims = std::vector<int64_t>({1});
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}
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r = xpu::broadcast_mul(
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dev_ctx.x_context(), x_data, dy_data, x_data, xdims, ydims);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "broadcast_mul");
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}
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} // namespace phi
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PD_REGISTER_KERNEL(mean_grad,
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XPU,
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ALL_LAYOUT,
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phi::ReduceMeanGradKernel,
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float,
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phi::float16,
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phi::bfloat16) {}
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