// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. #include "paddle/pten/kernels/math_kernel.h" #include "paddle/pten/backends/all_context.h" #include "paddle/pten/core/kernel_registry.h" namespace pten { template void MeanKernel(const Context& dev_ctx, const DenseTensor& x, const std::vector& dims, bool keep_dim, DenseTensor* out) { bool reduce_all = false; MeanRawKernel(dev_ctx, x, dims, keep_dim, reduce_all, out); } template void SumKernel(const Context& dev_ctx, const DenseTensor& x, const std::vector& dims, bool keep_dim, DataType out_dtype, DenseTensor* out) { bool reduce_all = false; SumRawKernel(dev_ctx, x, dims, keep_dim, reduce_all, out_dtype, out); } template void AddKernel(const Context& dev_ctx, const DenseTensor& x, const DenseTensor& y, DenseTensor* out) { int axis = -1; AddRawKernel(dev_ctx, x, y, axis, out); } template void SubtractKernel(const Context& dev_ctx, const DenseTensor& x, const DenseTensor& y, DenseTensor* out) { int axis = -1; SubtractRawKernel(dev_ctx, x, y, axis, out); } template void DivideKernel(const Context& dev_ctx, const DenseTensor& x, const DenseTensor& y, DenseTensor* out) { int axis = -1; DivideRawKernel(dev_ctx, x, y, axis, out); } template void MultiplyKernel(const Context& dev_ctx, const DenseTensor& x, const DenseTensor& y, DenseTensor* out) { int axis = -1; MultiplyRawKernel(dev_ctx, x, y, axis, out); } } // namespace pten using complex64 = ::paddle::platform::complex; using complex128 = ::paddle::platform::complex; PT_REGISTER_KERNEL( mean, CPU, ALL_LAYOUT, pten::MeanKernel, float, double, bool) {} PT_REGISTER_KERNEL(sum, CPU, ALL_LAYOUT, pten::SumKernel, bool, float, double, paddle::platform::float16, int, int64_t, complex64, complex128) { kernel->OutputAt(0).SetDataType(paddle::experimental::DataType::UNDEFINED); } PT_REGISTER_KERNEL(add, CPU, ALL_LAYOUT, pten::AddKernel, float, double, int, int64_t, complex64, complex128) {} PT_REGISTER_KERNEL(subtract, CPU, ALL_LAYOUT, pten::SubtractKernel, float, double, int, int64_t, complex64, complex128) {} PT_REGISTER_KERNEL(divide, CPU, ALL_LAYOUT, pten::DivideKernel, float, double, int, int64_t, complex64, complex128) {} PT_REGISTER_KERNEL(multiply, CPU, ALL_LAYOUT, pten::MultiplyKernel, float, double, int, int64_t, bool, complex64, complex128) {} #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) PT_REGISTER_KERNEL(mean, GPU, ALL_LAYOUT, pten::MeanKernel, float, double, bool, paddle::platform::float16) {} PT_REGISTER_KERNEL(sum, GPU, ALL_LAYOUT, pten::SumKernel, bool, float, double, paddle::platform::float16, int, int64_t, complex64, complex128) { kernel->OutputAt(0).SetDataType(paddle::experimental::DataType::UNDEFINED); } PT_REGISTER_KERNEL(add, GPU, ALL_LAYOUT, pten::AddKernel, float, double, int, int64_t, paddle::platform::float16, complex64, complex128) {} PT_REGISTER_KERNEL(subtract, GPU, ALL_LAYOUT, pten::SubtractKernel, float, double, int, int64_t, paddle::platform::float16, complex64, complex128) {} PT_REGISTER_KERNEL(divide, GPU, ALL_LAYOUT, pten::DivideKernel, float, double, int, int64_t, paddle::platform::float16, complex64, complex128) {} PT_REGISTER_KERNEL(multiply, GPU, ALL_LAYOUT, pten::MultiplyKernel, float, double, int, int64_t, bool, paddle::platform::float16, complex64, complex128) {} #endif