wehub-resource-sync
109 lines
3.9 KiB
C++
109 lines
3.9 KiB
C++
// Copyright (c) 2025 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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#ifdef PADDLE_WITH_XPU_FFT
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#include <string>
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#include <vector>
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#include "paddle/phi/common/type_traits.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/fft_kernel.h"
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#include "paddle/common/ddim.h"
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#include "paddle/phi/kernels/empty_kernel.h"
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#include "paddle/phi/kernels/full_kernel.h"
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#include "paddle/phi/kernels/funcs/fft.h"
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#include "paddle/phi/kernels/funcs/fft_fill_conj_xpu.h"
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namespace phi {
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template <typename T, typename Context>
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void FFTC2CKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const std::vector<int64_t>& axes,
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const std::string& normalization,
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bool forward,
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DenseTensor* out) {
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dev_ctx.template Alloc<T>(out);
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if (x.numel() == 0) {
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Full<T, Context>(dev_ctx, out->dims(), 0, out);
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return;
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}
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const auto norm_type = funcs::get_norm_from_string(normalization, forward);
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funcs::FFTC2CFunctor<Context, T, T> fft_c2c_func;
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fft_c2c_func(dev_ctx, x, out, axes, norm_type, forward);
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}
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template <typename T, typename Context>
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void FFTC2RKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const std::vector<int64_t>& axes,
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const std::string& normalization,
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bool forward,
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int64_t last_dim_size UNUSED,
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DenseTensor* out) {
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using R = typename T::value_type; // get real type
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dev_ctx.template Alloc<R>(out);
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if (x.numel() == 0) {
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Full<R, Context>(dev_ctx, out->dims(), 0, out);
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return;
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}
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const auto norm_type = funcs::get_norm_from_string(normalization, forward);
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funcs::FFTC2RFunctor<Context, T, R> fft_c2r_func;
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fft_c2r_func(dev_ctx, x, out, axes, norm_type, forward);
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}
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template <typename T, typename Context>
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void FFTR2CKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const std::vector<int64_t>& axes,
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const std::string& normalization,
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bool forward,
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bool onesided,
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DenseTensor* out) {
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using C = phi::dtype::complex<T>;
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dev_ctx.template Alloc<C>(out);
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if (x.numel() == 0) {
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Full<C, Context>(dev_ctx, out->dims(), 0, out);
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return;
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}
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auto norm_type = funcs::get_norm_from_string(normalization, forward);
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funcs::FFTR2CFunctor<Context, T, C> fft_r2c_func;
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if (onesided) {
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fft_r2c_func(dev_ctx, x, out, axes, norm_type, forward);
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} else {
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DDim onesided_out_shape = x.dims();
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const int64_t last_fft_axis = axes.back();
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const int64_t onesided_last_axis_size =
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out->dims().at(last_fft_axis) / 2 + 1;
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onesided_out_shape[last_fft_axis] = onesided_last_axis_size;
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DenseTensor onesided_out =
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Empty<C, Context>(dev_ctx, vectorize(onesided_out_shape));
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fft_r2c_func(dev_ctx, x, &onesided_out, axes, norm_type, forward);
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funcs::FFTFillConj<Context, C>(dev_ctx, &onesided_out, out, axes);
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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fft_c2c, XPU, ALL_LAYOUT, phi::FFTC2CKernel, phi::complex64) {}
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PD_REGISTER_KERNEL(
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fft_c2r, XPU, ALL_LAYOUT, phi::FFTC2RKernel, phi::complex64) {
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kernel->OutputAt(0).SetDataType(phi::dtype::ToReal(kernel_key.dtype()));
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}
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PD_REGISTER_KERNEL(fft_r2c, XPU, ALL_LAYOUT, phi::FFTR2CKernel, float) {
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kernel->OutputAt(0).SetDataType(phi::dtype::ToComplex(kernel_key.dtype()));
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}
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#endif
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