wehub-resource-sync
78 lines
2.4 KiB
C++
78 lines
2.4 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/roll_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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namespace phi {
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template <typename T, typename Context>
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void RollGradKernel(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& shifts,
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const std::vector<int64_t>& axis,
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DenseTensor* x_grad) {
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using XPUType = typename XPUTypeTrait<T>::Type;
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auto shifts_data = shifts.GetData();
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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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dev_ctx.template Alloc<T>(x_grad);
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DDim input_dim = x.dims();
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std::vector<int64_t> xshape;
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size_t nums = shifts_data.size();
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for (int i = 0; i < input_dim.size(); ++i) {
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xshape.emplace_back(input_dim[i]);
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}
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auto dims = axis;
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// axis = none, reshape to 1-D tensor
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if (dims.size() == 0) {
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dims.push_back(0l);
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input_dim = Dim<1>(x.numel());
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}
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std::vector<int64_t> shifts_in;
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std::vector<int64_t> axis_in;
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for (size_t i = 0; i < nums; ++i) {
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int64_t a = dims[i];
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if (a < 0) {
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a += (input_dim.size());
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}
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axis_in.emplace_back(a);
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int64_t sh = (0 - shifts_data[i]) % input_dim[a];
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if (sh < 0) {
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sh += input_dim[a];
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}
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shifts_in.emplace_back(sh);
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}
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int r = xpu::roll(dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(out_grad.data<T>()),
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reinterpret_cast<XPUType*>(x_grad->data<T>()),
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xshape,
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shifts_in,
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axis_in);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "roll");
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}
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} // namespace phi
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PD_REGISTER_KERNEL(roll_grad, XPU, ALL_LAYOUT, phi::RollGradKernel, float) {}
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