wehub-resource-sync
107 lines
4.3 KiB
Plaintext
107 lines
4.3 KiB
Plaintext
/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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/funcs/eigen/eigen_function.h"
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namespace phi {
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namespace funcs {
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template <typename T, int Rank>
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struct EigenBroadcast<Eigen::GpuDevice, T, Rank> {
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using Array = Eigen::DSizes<int64_t, Rank>;
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using InType =
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Eigen::TensorMap<Eigen::Tensor<const T, Rank, Eigen::RowMajor, int64_t>>;
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using InType32BitIndex =
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Eigen::TensorMap<Eigen::Tensor<const T, Rank, Eigen::RowMajor, int64_t>,
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Eigen::Aligned>;
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using OutType =
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Eigen::TensorMap<Eigen::Tensor<T, Rank, Eigen::RowMajor, int64_t>>;
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using OutType32BitIndex =
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Eigen::TensorMap<Eigen::Tensor<T, Rank, Eigen::RowMajor, int64_t>,
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Eigen::Aligned>;
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static void Eval(const Eigen::GpuDevice& dev,
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OutType out,
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InType in,
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const Array& bcast) {
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out.device(dev) = in.broadcast(bcast);
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}
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static void Eval(const Eigen::GpuDevice& dev,
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OutType32BitIndex out,
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InType32BitIndex in,
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const Array& bcast) {
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out.device(dev) = in.broadcast(bcast);
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}
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};
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template <typename T, int Rank>
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struct EigenBroadcastGrad<Eigen::GpuDevice, T, Rank> {
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using Array = Eigen::DSizes<int64_t, Rank>;
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using Array2 = Eigen::DSizes<int64_t, Rank * 2>;
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using InType =
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Eigen::TensorMap<Eigen::Tensor<const T, 1, Eigen::RowMajor, int64_t>>;
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using OutType =
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Eigen::TensorMap<Eigen::Tensor<T, 1, Eigen::RowMajor, int64_t>>;
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static void Eval(const Eigen::GpuDevice& dev,
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OutType out,
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InType in,
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const Array& reduce_dims,
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const Array2& reshape_dims) {
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out.device(dev) =
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in.reshape(reshape_dims).sum(reduce_dims).reshape(out.dimensions());
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}
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};
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#define INSTANTIATION(FUNCTOR, T) \
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template struct FUNCTOR<Eigen::GpuDevice, T, 1>; \
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template struct FUNCTOR<Eigen::GpuDevice, T, 2>; \
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template struct FUNCTOR<Eigen::GpuDevice, T, 3>; \
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template struct FUNCTOR<Eigen::GpuDevice, T, 4>; \
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template struct FUNCTOR<Eigen::GpuDevice, T, 5>; \
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template struct FUNCTOR<Eigen::GpuDevice, T, 6>; \
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template struct FUNCTOR<Eigen::GpuDevice, T, 7>; \
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template struct FUNCTOR<Eigen::GpuDevice, T, 8>; \
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template struct FUNCTOR<Eigen::GpuDevice, T, 9>
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INSTANTIATION(EigenBroadcast, bool);
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INSTANTIATION(EigenBroadcast, dtype::float16);
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INSTANTIATION(EigenBroadcast, dtype::bfloat16);
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INSTANTIATION(EigenBroadcast, float);
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INSTANTIATION(EigenBroadcast, double);
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INSTANTIATION(EigenBroadcast, dtype::complex<float>);
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INSTANTIATION(EigenBroadcast, dtype::complex<double>);
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INSTANTIATION(EigenBroadcast, int);
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INSTANTIATION(EigenBroadcast, int64_t);
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INSTANTIATION(EigenBroadcastGrad, bool);
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INSTANTIATION(EigenBroadcastGrad, float);
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INSTANTIATION(EigenBroadcastGrad, dtype::bfloat16);
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INSTANTIATION(EigenBroadcastGrad, dtype::float16);
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INSTANTIATION(EigenBroadcastGrad, double);
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INSTANTIATION(EigenBroadcastGrad, dtype::complex<float>);
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INSTANTIATION(EigenBroadcastGrad, dtype::complex<double>);
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INSTANTIATION(EigenBroadcastGrad, int);
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INSTANTIATION(EigenBroadcastGrad, int64_t);
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INSTANTIATION(EigenBroadcastGrad, int8_t);
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INSTANTIATION(EigenBroadcastGrad, uint8_t);
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INSTANTIATION(EigenBroadcastGrad, int16_t);
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INSTANTIATION(EigenBroadcastGrad, phi::float8_e4m3fn);
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INSTANTIATION(EigenBroadcastGrad, phi::float8_e5m2);
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template struct EigenBroadcastGrad<Eigen::GpuDevice, float, 0>;
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template struct EigenBroadcastGrad<Eigen::GpuDevice, dtype::float16, 0>;
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template struct EigenBroadcastGrad<Eigen::GpuDevice, double, 0>;
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template struct EigenBroadcastGrad<Eigen::GpuDevice, int, 0>;
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template struct EigenBroadcastGrad<Eigen::GpuDevice, int64_t, 0>;
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#undef INSTANTIATION
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} // namespace funcs
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} // namespace phi
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