wehub-resource-sync
328 lines
12 KiB
C++
328 lines
12 KiB
C++
/* 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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#pragma once
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#ifndef _USE_MATH_DEFINES
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#define _USE_MATH_DEFINES
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#endif
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#ifndef NOMINMAX
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#define NOMINMAX
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#endif
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#include "paddle/phi/kernels/funcs/eigen/extensions.h"
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namespace phi {
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namespace funcs {
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template <typename EigenDevice, typename T, int Rank>
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struct EigenBroadcast {
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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 EigenDevice& dev,
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OutType out,
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InType in,
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const Array& bcast);
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static void Eval(const EigenDevice& dev,
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OutType32BitIndex out,
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InType32BitIndex in,
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const Array& bcast);
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};
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template <typename EigenDevice, typename T, int Rank>
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struct EigenBroadcastGrad {
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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 EigenDevice& 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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};
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template <typename EigenDevice, typename T, int Rank>
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struct EigenConstant {
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using Type =
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Eigen::TensorMap<Eigen::Tensor<T, Rank, Eigen::RowMajor, int64_t>>;
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static void Eval(const EigenDevice& dev, Type out, const T value);
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};
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template <typename EigenDevice, typename T>
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struct EigenSign {
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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 EigenDevice& dev, OutType out, const InType& in);
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};
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template <typename EigenDevice, typename T, int Rank>
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struct EigenReverse {
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using Array = Eigen::DSizes<bool, 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 OutType =
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Eigen::TensorMap<Eigen::Tensor<T, Rank, Eigen::RowMajor, int64_t>>;
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static void Eval(const EigenDevice& dev,
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OutType out,
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const InType& in,
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const Array& reverse);
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};
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template <typename EigenDevice, typename T>
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struct EigenAdd {
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using InType = Eigen::TensorMap<
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Eigen::
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TensorFixedSize<const T, Eigen::Sizes<>, Eigen::RowMajor, int64_t>>;
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using OutType = Eigen::TensorMap<
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Eigen::TensorFixedSize<T, Eigen::Sizes<>, Eigen::RowMajor, int64_t>>;
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static void Eval(const EigenDevice& dev,
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OutType out,
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const InType& in,
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const T value);
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};
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template <typename EigenDevice, typename T>
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struct EigenSub {
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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 EigenDevice& dev,
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OutType out,
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const InType& left,
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const InType& right);
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};
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template <typename EigenDevice, typename T>
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struct EigenDiv {
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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 EigenDevice& dev,
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OutType out,
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const InType& in,
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const T value);
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};
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template <typename EigenDevice, typename T, int Rank>
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struct EigenSlice {
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using Array = Eigen::DSizes<int64_t, Rank>;
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using Array32Bit = 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 EigenDevice& dev,
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OutType out,
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const InType& in,
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const Array& offsets,
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const Array& extents);
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static void Eval(const EigenDevice& dev,
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OutType32BitIndex out,
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const InType32BitIndex& in,
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const Array32Bit& offsets,
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const Array32Bit& extents);
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};
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template <typename EigenDevice, typename T, int Rank>
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struct EigenPad {
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using Array = std::array<std::pair<int64_t, int64_t>, Rank>;
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using Array32Bit = std::array<std::pair<int, int>, 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 EigenDevice& dev,
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OutType out,
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const InType& in,
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const Array& padding,
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const T value);
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static void Eval32(const EigenDevice& dev,
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OutType32BitIndex out,
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const InType32BitIndex& in,
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const Array32Bit& padding,
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const T value);
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};
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template <typename EigenDevice, typename T>
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struct EigenScale {
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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 EigenDevice& dev,
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OutType out,
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const InType& in,
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const T scale,
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const T bias,
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const bool bias_after_scale);
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};
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template <typename EigenDevice, typename T>
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struct EigenErf {
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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 EigenDevice& dev, OutType out, const InType& in);
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};
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template <typename EigenDevice, typename T>
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struct EigenErfGrad {
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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 EigenDevice& dev,
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OutType din,
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const InType& in,
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const InType& dout);
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};
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template <typename EigenDevice, typename T>
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struct EigenRankLoss {
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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 EigenDevice& dev,
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OutType out,
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const InType& label,
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const InType& left,
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const InType& right);
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};
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template <typename EigenDevice, typename T>
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struct EigenRankLossGrad {
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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 EvalLeft(const EigenDevice& dev,
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OutType dleft,
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const InType& dout,
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const InType& label,
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const InType& left,
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const InType& right);
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static void EvalRight(const EigenDevice& dev,
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OutType dright,
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const InType& dout,
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const InType& label,
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const InType& left,
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const InType& right);
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};
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template <typename EigenDevice, typename T>
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struct EigenLogLoss {
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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 EigenDevice& dev,
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OutType out,
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const InType& pred,
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const InType& label,
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const T& epsilon);
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};
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template <typename EigenDevice, typename T>
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struct EigenLogLossGrad {
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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 EigenDevice& dev,
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OutType dpred,
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const InType& dloss,
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const InType& pred,
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const InType& label,
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const T& epsilon);
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};
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template <typename EigenDevice, typename T>
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struct EigenHingeLoss {
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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 EigenDevice& dev,
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OutType loss,
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const InType& pred,
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const InType& label);
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};
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template <typename EigenDevice, typename T>
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struct EigenHingeLossGrad {
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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 EigenDevice& dev,
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OutType dpred,
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const InType& dloss,
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const InType& pred,
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const InType& label);
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};
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template <typename EigenDevice, typename T>
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struct EigenL1Norm {
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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 = Eigen::TensorMap<
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Eigen::TensorFixedSize<T, Eigen::Sizes<>, Eigen::RowMajor, int64_t>>;
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static void Eval(const EigenDevice& dev, OutType out, const InType& in);
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};
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template <typename EigenDevice, typename T>
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struct EigenL1NormGrad {
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using Array = Eigen::DSizes<int64_t, 1>;
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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 EigenDevice& dev,
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OutType din,
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const InType& dout,
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const InType& in,
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const Array& bcast);
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};
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} // namespace funcs
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} // namespace phi
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