// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. #include "paddle/phi/kernels/transpose_grad_kernel.h" #include "paddle/phi/backends/xpu/enforce_xpu.h" #include "paddle/phi/core/kernel_registry.h" namespace phi { template void TransposeGradKernel(const Context& dev_ctx, const DenseTensor& out_grad, const std::vector& axis, DenseTensor* x_grad) { using XPUType = typename XPUTypeTrait::Type; dev_ctx.template Alloc(x_grad); if (x_grad->numel() == 0) { return; } if (axis.size() == 0) { phi::Copy(dev_ctx, out_grad, dev_ctx.GetPlace(), false, x_grad); return; } std::vector reversed_axis(axis); for (size_t i = 0; i < axis.size(); i++) { reversed_axis[axis[i]] = i; } int ndims = axis.size(); std::vector out_shape_host(ndims, 0); for (int i = 0; i < ndims; ++i) { out_shape_host[i] = out_grad.dims()[i]; } int r = xpu::transpose( dev_ctx.x_context(), reinterpret_cast(out_grad.data()), reinterpret_cast(x_grad->data()), out_shape_host, reversed_axis); PADDLE_ENFORCE_XDNN_SUCCESS(r, "transpose_grad"); } } // namespace phi PD_REGISTER_KERNEL(transpose_grad, XPU, ALL_LAYOUT, phi::TransposeGradKernel, float, phi::dtype::float16) {}