wehub-resource-sync
98 lines
3.4 KiB
C++
98 lines
3.4 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/elementwise_multiply_kernel.h"
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#include <memory>
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#include <string>
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#include "paddle/phi/backends/xpu/xpu_context.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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#include "paddle/phi/kernels/elementwise_add_kernel.h"
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#include "paddle/phi/kernels/elementwise_subtract_kernel.h"
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#include "paddle/phi/kernels/funcs/elementwise_base.h"
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#include "paddle/phi/kernels/xpu/elementwise.h"
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namespace phi {
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template <typename T, typename Context>
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void MultiplyKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& y,
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DenseTensor* out) {
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using XPUType = typename XPUTypeTrait<T>::Type;
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if (out->numel() == 0) {
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dev_ctx.template Alloc<T>(out);
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return;
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}
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auto f = [](xpu::Context* xpu_ctx,
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const XPUType* x,
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const XPUType* y,
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XPUType* z,
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const std::vector<int64_t>& xshape,
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const std::vector<int64_t>& yshape) {
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return xpu::broadcast_mul<XPUType>(xpu_ctx, x, y, z, xshape, yshape);
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};
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XPUElementwise<T, XPUType>(dev_ctx, x, y, -1, out, f);
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}
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#ifdef PADDLE_WITH_XPU_FFT
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template <>
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void MultiplyKernel<phi::complex64, XPUContext>(const XPUContext& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& y,
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DenseTensor* out) {
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using T = phi::complex64;
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if (out->numel() == 0) {
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dev_ctx.template Alloc<T>(out);
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return;
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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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const DenseTensor x_real = Real<T, XPUContext>(dev_ctx, x);
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const DenseTensor x_imag = Imag<T, XPUContext>(dev_ctx, x);
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const DenseTensor y_real = Real<T, XPUContext>(dev_ctx, y);
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const DenseTensor y_imag = Imag<T, XPUContext>(dev_ctx, y);
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DenseTensor real_out = Subtract<float, XPUContext>(
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dev_ctx,
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Multiply<float, XPUContext>(dev_ctx, x_real, y_real),
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Multiply<float, XPUContext>(dev_ctx, x_imag, y_imag));
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DenseTensor imag_out = Add<float, XPUContext>(
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dev_ctx,
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Multiply<float, XPUContext>(dev_ctx, x_real, y_imag),
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Multiply<float, XPUContext>(dev_ctx, x_imag, y_real));
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phi::ComplexKernel<float>(dev_ctx, real_out, imag_out, out);
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}
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#endif
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} // namespace phi
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PD_REGISTER_KERNEL(multiply,
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XPU,
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ALL_LAYOUT,
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phi::MultiplyKernel,
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bool,
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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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float,
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int,
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int64_t) {
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}
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