wehub-resource-sync
135 lines
4.5 KiB
C++
135 lines
4.5 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/transpose_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/complex_kernel.h"
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namespace phi {
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template <typename T, typename Context>
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void TransposeGradKernel(const Context& dev_ctx,
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const DenseTensor& out_grad,
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const std::vector<int>& axis,
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DenseTensor* x_grad) {
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using XPUType = typename XPUTypeTrait<T>::Type;
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dev_ctx.template Alloc<T>(x_grad);
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if (x_grad->numel() == 0) {
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return;
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}
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size_t axis_size = axis.size();
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if (axis_size == 0) {
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Copy<Context>(dev_ctx, out_grad, dev_ctx.GetPlace(), false, x_grad);
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return;
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}
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std::vector<int64_t> formatted_axis(axis.begin(), axis.end());
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for (size_t i = 0; i < axis_size; i++) {
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if (axis[i] < 0) {
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formatted_axis[i] = axis[i] + axis_size;
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}
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}
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std::vector<int64_t> reversed_axis(axis.begin(), axis.end());
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for (size_t i = 0; i < axis_size; i++) {
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reversed_axis[formatted_axis[i]] = i;
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}
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std::vector<int64_t> out_grad_dim_vec = vectorize<int64_t>(out_grad.dims());
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int r = xpu::transpose<XPUType>(
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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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out_grad_dim_vec,
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reversed_axis);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "transpose_grad");
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}
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#ifdef PADDLE_WITH_XPU_FFT
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template <>
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void TransposeGradKernel<phi::complex64, XPUContext>(
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const XPUContext& dev_ctx,
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const DenseTensor& out_grad,
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const std::vector<int>& axis,
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DenseTensor* x_grad) {
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using T = phi::complex64;
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dev_ctx.template Alloc<T>(x_grad);
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if (x_grad->numel() == 0) {
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return;
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}
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size_t axis_size = axis.size();
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if (axis_size == 0) {
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Copy<XPUContext>(dev_ctx, out_grad, dev_ctx.GetPlace(), false, x_grad);
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return;
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}
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std::vector<int64_t> formatted_axis(axis.begin(), axis.end());
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for (size_t i = 0; i < axis_size; i++) {
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if (axis[i] < 0) {
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formatted_axis[i] = axis[i] + axis_size;
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}
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}
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std::vector<int64_t> reversed_axis(axis.begin(), axis.end());
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for (size_t i = 0; i < axis_size; i++) {
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reversed_axis[formatted_axis[i]] = i;
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}
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// The current complex number implementation uses separate real/imaginary
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// parts,resulting in redundant operations and performance
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// penalties.Optimization should address this in future iterations.
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DenseTensor real_out, imag_out;
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real_out.Resize(x_grad->dims());
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imag_out.Resize(x_grad->dims());
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dev_ctx.template Alloc<float>(&real_out);
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dev_ctx.template Alloc<float>(&imag_out);
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const DenseTensor real = Real<T, XPUContext>(dev_ctx, out_grad);
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const DenseTensor imag = Imag<T, XPUContext>(dev_ctx, out_grad);
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std::vector<int64_t> out_grad_dim_vec = vectorize<int64_t>(out_grad.dims());
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int r = xpu::transpose<float>(dev_ctx.x_context(),
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real.data<float>(),
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real_out.data<float>(),
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out_grad_dim_vec,
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reversed_axis);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "transpose_grad");
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r = xpu::transpose<float>(dev_ctx.x_context(),
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imag.data<float>(),
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imag_out.data<float>(),
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out_grad_dim_vec,
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reversed_axis);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "transpose_grad");
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phi::ComplexKernel<float>(dev_ctx, real_out, imag_out, x_grad);
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}
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#endif
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} // namespace phi
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PD_REGISTER_KERNEL(transpose_grad,
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XPU,
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ALL_LAYOUT,
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phi::TransposeGradKernel,
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float,
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phi::float16,
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phi::bfloat16,
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#ifdef PADDLE_WITH_XPU_FFT
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phi::complex64,
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#endif
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int64_t,
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int,
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bool) {
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}
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