wehub-resource-sync
248 lines
7.9 KiB
C++
248 lines
7.9 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/legacy/elementwise_kernel.h"
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/cpu/elementwise.h"
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#include "paddle/phi/kernels/impl/elementwise_kernel_impl.h"
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namespace phi {
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template <typename T, typename Context>
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void MaximumKernel(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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int axis = -1;
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MaximumRawKernel<T>(dev_ctx, x, y, axis, out);
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}
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template <typename T, typename Context>
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void MinimumKernel(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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int axis = -1;
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MinimumRawKernel<T>(dev_ctx, x, y, axis, out);
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}
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template <typename T, typename Context>
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void RemainderKernel(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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int axis = -1;
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RemainderRawKernel<T>(dev_ctx, x, y, axis, out);
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}
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template <typename T, typename Context>
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void FloorDivideKernel(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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int axis = -1;
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FloorDivideRawKernel<T>(dev_ctx, x, y, axis, out);
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}
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template <typename T, typename Context>
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void TruncDivideKernel(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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int axis = -1;
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dev_ctx.template Alloc<T>(out);
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auto x_dims = x.dims();
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auto y_dims = y.dims();
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if (x_dims.size() >= y_dims.size()) { // NOLINT
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funcs::ElementwiseCompute<funcs::TruncDivideFunctor<T>, T>(
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dev_ctx, x, y, funcs::TruncDivideFunctor<T>(), out, axis);
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} else {
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funcs::ElementwiseCompute<funcs::InverseTruncDivideFunctor<T>, T>(
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dev_ctx, x, y, funcs::InverseTruncDivideFunctor<T>(), out, axis);
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}
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}
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template <typename T, typename Context>
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void ElementwisePowKernel(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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int axis = -1;
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ElementwisePowRawKernel<T>(dev_ctx, x, y, axis, out);
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}
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template <typename T, typename Context>
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void HeavisideKernel(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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// allocate memory for out
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dev_ctx.template Alloc<T>(out);
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auto x_dims = x.dims();
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auto y_dims = y.dims();
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if (x_dims.size() >= y_dims.size()) {
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funcs::ElementwiseCompute<funcs::ElementwiseHeavisideFunctor<T>, T>(
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dev_ctx, x, y, funcs::ElementwiseHeavisideFunctor<T>(), out);
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} else {
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funcs::ElementwiseCompute<funcs::ElementwiseInverseHeavisideFunctor<T>, T>(
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dev_ctx, x, y, funcs::ElementwiseInverseHeavisideFunctor<T>(), out);
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}
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}
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template <typename T, typename Context>
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void CopySignKernel(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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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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dev_ctx.template Alloc<T>(out);
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auto x_dims = x.dims();
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auto y_dims = y.dims();
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if (x_dims.size() >= y_dims.size()) {
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funcs::ElementwiseCompute<funcs::CopySignFunctor<T>, T>(
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dev_ctx, x, y, funcs::CopySignFunctor<T>(), out);
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} else {
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funcs::ElementwiseCompute<funcs::InverseCopySignFunctor<T>, T>(
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dev_ctx, x, y, funcs::InverseCopySignFunctor<T>(), out);
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}
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}
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template <typename T, typename Context>
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void NextafterKernel(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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if (x.numel() == 0 || y.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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dev_ctx.template Alloc<T>(out);
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auto x_dims = x.dims();
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auto y_dims = y.dims();
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if (x_dims.size() >= y_dims.size()) {
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funcs::ElementwiseCompute<funcs::NextafterFunctor<T>, T>(
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dev_ctx, x, y, funcs ::NextafterFunctor<T>(), out);
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} else {
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funcs::ElementwiseCompute<funcs::InverseNextafterFunctor<T>, T>(
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dev_ctx, x, y, funcs::InverseNextafterFunctor<T>(), out);
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}
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}
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} // namespace phi
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// NOTE(chenweihang): using bfloat16 will cause redefine with xpu bfloat16
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// using bfloat16 = ::phi::bfloat16;
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PD_REGISTER_KERNEL(
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fmax, CPU, ALL_LAYOUT, phi::FMaxKernel, float, double, int, int64_t) {}
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PD_REGISTER_KERNEL(
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fmin, CPU, ALL_LAYOUT, phi::FMinKernel, float, double, int, int64_t) {}
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PD_REGISTER_KERNEL(maximum,
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CPU,
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ALL_LAYOUT,
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phi::MaximumKernel,
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float,
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double,
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int,
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int64_t,
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phi::bfloat16) {}
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PD_REGISTER_KERNEL(minimum,
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CPU,
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ALL_LAYOUT,
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phi::MinimumKernel,
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float,
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double,
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int,
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int64_t,
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phi::bfloat16) {}
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PD_REGISTER_KERNEL(remainder,
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CPU,
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ALL_LAYOUT,
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phi::RemainderKernel,
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float,
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double,
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int,
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phi::complex64,
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phi::complex128,
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int64_t) {}
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PD_REGISTER_KERNEL(floor_divide,
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CPU,
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ALL_LAYOUT,
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phi::FloorDivideKernel,
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uint8_t,
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int8_t,
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int16_t,
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int32_t,
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int64_t,
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float,
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double,
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phi::float16,
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phi::bfloat16) {}
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PD_REGISTER_KERNEL(trunc_divide,
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CPU,
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ALL_LAYOUT,
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phi::TruncDivideKernel,
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uint8_t,
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int8_t,
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int16_t,
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int32_t,
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int64_t,
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float,
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double,
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phi::dtype::float16,
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phi::dtype::bfloat16) {}
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PD_REGISTER_KERNEL(elementwise_pow,
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CPU,
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ALL_LAYOUT,
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phi::ElementwisePowKernel,
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float,
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double,
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int,
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int64_t,
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phi::bfloat16,
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phi::complex64,
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phi::complex128) {}
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PD_REGISTER_KERNEL(heaviside,
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CPU,
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ALL_LAYOUT,
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phi::HeavisideKernel,
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float,
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double,
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int,
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int64_t) {}
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PD_REGISTER_KERNEL(copysign,
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CPU,
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ALL_LAYOUT,
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phi::CopySignKernel,
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bool,
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uint8_t,
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int8_t,
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int16_t,
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int,
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int64_t,
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float,
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double,
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phi::float16,
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phi::bfloat16) {}
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PD_REGISTER_KERNEL(
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nextafter, CPU, ALL_LAYOUT, phi::NextafterKernel, float, double) {}
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