wehub-resource-sync
58 lines
2.0 KiB
C++
58 lines
2.0 KiB
C++
// Copyright (c) 2023 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/pixel_shuffle_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 PixelShuffleGradKernel(const Context& dev_ctx,
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const DenseTensor& out_grad,
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int upscale_factor,
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const std::string& data_format,
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DenseTensor* x_grad) {
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using XPUType = typename XPUTypeTrait<T>::Type;
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const T* x_ptr = out_grad.data<T>();
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T* y_ptr = dev_ctx.template Alloc<T>(x_grad);
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if (x_grad && x_grad->numel() == 0) {
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return;
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}
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bool is_nchw = data_format == "NCHW";
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int64_t n = out_grad.dims()[0];
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int64_t xc = out_grad.dims()[is_nchw ? 1 : 3];
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int64_t xh = out_grad.dims()[is_nchw ? 2 : 1];
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int64_t xw = out_grad.dims()[is_nchw ? 3 : 2];
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int r = pixel_unshuffle(dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(x_ptr),
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reinterpret_cast<XPUType*>(y_ptr),
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n,
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xc,
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xh,
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xw,
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upscale_factor,
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is_nchw);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "pixel_unshuffle");
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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pixel_shuffle_grad, XPU, ALL_LAYOUT, phi::PixelShuffleGradKernel, float) {}
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