// Copyright (c) 2020 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. #pragma once #define EIGEN_USE_GPU #include #include "paddle/phi/core/enforce.h" #include "unsupported/Eigen/CXX11/Tensor" namespace phi { namespace funcs { template struct DeviceArray { EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T& operator[](int index) const { return data[index]; } EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T& operator[](int index) { return data[index]; } EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DeviceArray() { for (int i = 0; i < Size; i++) { data[i] = DefaultValue; } } EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DeviceArray(T a0) { data[0] = a0; for (int i = 1; i < Size; i++) { data[i] = DefaultValue; } } EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DeviceArray(T a0, T a1) { data[0] = a0; data[1] = a1; for (int i = 2; i < Size; i++) { data[i] = DefaultValue; } } EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DeviceArray(T a0, T a1, T a2) { data[0] = a0; data[1] = a1; data[2] = a2; for (int i = 3; i < Size; i++) { data[i] = DefaultValue; } } EIGEN_STRONG_INLINE DeviceArray(const std::array& sa) { for (int i = 0; i < Size; i++) { data[i] = sa[i]; } } T data[Size]; }; struct Dim3 : DeviceArray { typedef DeviceArray Base; EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Dim3() : Base() {} EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Dim3(int a0, int a1, int a2) : Base(a0, a1, a2) {} EIGEN_STRONG_INLINE Dim3(const std::array& array) : Base(array) {} }; struct Index3 : DeviceArray { typedef DeviceArray Base; EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index3() : Base() {} EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index3(int a0, int a1, int a2) : Base(a0, a1, a2) {} }; // Flat index with real dimension template EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE IndexType FlatTensorIndex(const Index3& index, const Dim3& dims) { IndexType flat_index = index[0]; for (int i = 1; i < 3; i++) { flat_index = flat_index * dims[i] + index[i]; } return flat_index; } // Convert index to tensor index with dimension. template EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index3 ConvertTensorIndex(IndexType index, const Dim3& dims) { Index3 tensor_index; for (int i = 2; i >= 0; i--) { IndexType new_index = index / dims[i]; tensor_index[i] = static_cast(index - dims[i] * new_index); index = new_index; } return tensor_index; } template IntType CeilOrFloor(IntType x, IntType deviser) { PADDLE_ENFORCE_GT( deviser, 0, phi::errors::InvalidArgument("deviser should be greater than 0, " "but received is:%d", deviser)); PADDLE_ENFORCE_GT( x, 0, phi::errors::InvalidArgument("input should be greater than 0, " "but received is:%d", x)); const IntType round_to_zero = x / deviser; const IntType inte_result = round_to_zero * deviser; if (ceil) { const bool do_adjustment = (round_to_zero >= 0) && (deviser > 0 && x > inte_result); const IntType adjustment = static_cast(do_adjustment); const IntType ceil_val = round_to_zero + adjustment; return ceil_val; } else { const bool do_adjustment = (round_to_zero <= 0) && (deviser > 0 && x < inte_result); const IntType adjustment = static_cast(do_adjustment); const IntType floor_val = round_to_zero - adjustment; return floor_val; } } } // namespace funcs } // namespace phi