wehub-resource-sync
116 lines
4.3 KiB
C++
116 lines
4.3 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/pad_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 PadGradKernel(const Context& dev_ctx,
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const DenseTensor& d_out,
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const std::vector<int>& paddings,
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const Scalar& pad_value,
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DenseTensor* d_x) {
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using XPUType = typename XPUTypeTrait<T>::Type;
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std::vector<int64_t> pad_left, pad_right;
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std::vector<int64_t> out_shape = vectorize<int64_t>(d_out.dims());
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dev_ctx.template Alloc<T>(d_x);
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if (d_x && d_x->numel() == 0) return;
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for (size_t i = 0; i < paddings.size() / 2; ++i) {
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pad_left.push_back(-paddings[i * 2]);
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pad_right.push_back(-paddings[i * 2 + 1]);
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}
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XPUType value = static_cast<XPUType>(pad_value.to<T>());
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int r = xpu::pad<XPUType>(dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(d_out.data<T>()),
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reinterpret_cast<XPUType*>(d_x->data<T>()),
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out_shape,
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pad_left,
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pad_right,
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value);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "pad");
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}
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#ifdef PADDLE_WITH_XPU_FFT
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template <>
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void PadGradKernel<phi::complex64, XPUContext>(const XPUContext& dev_ctx,
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const DenseTensor& d_out,
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const std::vector<int>& paddings,
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const Scalar& pad_value,
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DenseTensor* d_x) {
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using T = phi::complex64;
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std::vector<int64_t> pad_left, pad_right;
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std::vector<int64_t> out_shape = vectorize<int64_t>(d_out.dims());
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dev_ctx.template Alloc<T>(d_x);
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if (d_x && d_x->numel() == 0) return;
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for (size_t i = 0; i < paddings.size() / 2; ++i) {
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pad_left.push_back(-paddings[i * 2]);
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pad_right.push_back(-paddings[i * 2 + 1]);
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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(d_x->dims());
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imag_out.Resize(d_x->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, d_out);
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const DenseTensor imag = Imag<T, XPUContext>(dev_ctx, d_out);
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T complex_val = pad_value.to<T>();
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float real_part = complex_val.real;
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float imag_part = complex_val.imag;
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int r = xpu::pad<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_shape,
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pad_left,
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pad_right,
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real_part);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "pad");
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r = xpu::pad<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_shape,
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pad_left,
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pad_right,
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imag_part);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "pad");
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phi::ComplexKernel<float>(dev_ctx, real_out, imag_out, d_x);
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}
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#endif
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} // namespace phi
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PD_REGISTER_KERNEL(pad_grad,
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XPU,
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ALL_LAYOUT,
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phi::PadGradKernel,
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float,
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int,
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int16_t,
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int64_t,
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#ifdef PADDLE_WITH_XPU_FFT
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phi::complex64,
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#endif
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phi::bfloat16,
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phi::float16) {
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}
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