wehub-resource-sync
109 lines
4.1 KiB
C++
109 lines
4.1 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/tile_grad_kernel.h"
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#include "paddle/phi/backends/xpu/enforce_xpu.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/full_kernel.h"
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namespace phi {
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template <typename T, typename Context>
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void TileGradKernel(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& repeat_times,
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DenseTensor* x_grad) {
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// x_grad->numel() may be not 0.
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if (out_grad.numel() == 0) {
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Full<T, Context>(dev_ctx, x_grad->dims(), 0, x_grad);
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return;
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}
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auto x_dims = x.dims();
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auto vec_x_dims = vectorize<int64_t>(x_dims);
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auto repeat_times_data = repeat_times.GetData();
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if (repeat_times_data.size() < vec_x_dims.size()) {
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size_t diff = vec_x_dims.size() - repeat_times_data.size();
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repeat_times_data.insert(repeat_times_data.begin(), diff, 1);
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} else {
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size_t diff = repeat_times_data.size() - vec_x_dims.size();
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vec_x_dims.insert(vec_x_dims.begin(), diff, 1);
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}
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// 1. reshape_dims_vec is the broadcast parameter.
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// 2. reduce_dims_vec is the dimension parameter to compute gradients. For
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// each dimension expanded, the gradients should be summed to original
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// size.
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std::vector<int64_t> reshape_dims_vec;
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std::vector<int64_t> reduce_dims_vec;
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for (size_t i = 0; i < repeat_times_data.size(); ++i) {
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reduce_dims_vec.push_back(reshape_dims_vec.size());
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reshape_dims_vec.push_back(repeat_times_data[i]);
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reshape_dims_vec.push_back(vec_x_dims[i]);
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}
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dev_ctx.template Alloc<T>(x_grad);
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int dims = reduce_dims_vec.size();
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bool just_copy = true;
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for (size_t i = 0; i < repeat_times_data.size(); i++) {
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if (repeat_times_data[i] != 1) {
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just_copy = false;
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break;
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}
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}
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// no need reduce, just copy
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if (just_copy) {
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Copy(dev_ctx, out_grad, dev_ctx.GetPlace(), false, x_grad);
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// TensorCopy may change the dims of dx
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x_grad->Resize(x_dims);
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} else {
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PADDLE_ENFORCE_GE(dims,
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1,
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errors::InvalidArgument(
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"The rank of the input 'Out@GRAD' for tile_grad op "
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"must be greater than or equal to 1, but "
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"the value received is %d.",
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dims));
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PADDLE_ENFORCE_LE(dims,
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MAX_RANK_SUPPORTED,
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errors::InvalidArgument(
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"The rank of the input 'Out@GRAD' for tile_grad op "
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"must be less than or equal "
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"to %d, but the value received is %d.",
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MAX_RANK_SUPPORTED,
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dims));
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using XPUType = typename XPUTypeTrait<T>::Type;
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// int reduce_sum(Context* xpu_ctx, const T* x, T* y, const
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// std::vector<int64_t>& xshape, const std::vector<int64_t>& rdims)
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const auto* out_data = reinterpret_cast<const XPUType*>(out_grad.data<T>());
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auto* x_grad_data = reinterpret_cast<XPUType*>(x_grad->data<T>());
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if (x_grad->numel() > 0) {
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int r = xpu::reduce_sum<XPUType>(dev_ctx.x_context(),
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out_data,
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x_grad_data,
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reshape_dims_vec,
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reduce_dims_vec);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "reduce_sum");
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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(
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tile_grad, XPU, ALL_LAYOUT, phi::TileGradKernel, float, phi::bfloat16) {}
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