unary.h

/* Copyright (c) 2021 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

// See Note [ Why still include the fluid headers? ]
#include "paddle/phi/common/int_array.h"
#include "paddle/phi/common/scalar.h"
#include "paddle/phi/core/meta_tensor.h"

namespace phi {

class MetaConfig;

// Common InferMeta Functions for unary operators, The format like:
//
//   void [FunctionDesc|OpName]InferMeta(const MetaTensor& x, ..., MetaTensor*
//   out) {}
//
// NOTE: The name "InferShape" may be not appropriate. "InferMeta" may be good.
// Because functions in this file not only can infer shape, but also need
// infer lod or other useful data.
//
// The InferMeta Functions in this file are arranged in alphabetic order.

void ArgMinMaxInferMeta(const MetaTensor& x,
                        int64_t axis,
                        bool keepdims,
                        bool flatten,
                        int dtype,
                        MetaTensor* out,
                        MetaConfig config = MetaConfig());

void ArgsortInferMeta(const MetaTensor& input,
                      int axis,
                      bool descending,
                      MetaTensor* output,
                      MetaTensor* indices);

void BatchSizeLikeInferMeta(const MetaTensor& x,
                            const std::vector<int>& shape,
                            int x_batch_size_dim,
                            int out_batch_size_dim,
                            MetaTensor* out);

void CastInferMeta(const MetaTensor& x, DataType out_dtype, MetaTensor* out);

void CholeskyInferMeta(const MetaTensor& x, bool upper, MetaTensor* out);

void CopyToInferMeta(const MetaTensor& x,
                     Backend backend,
                     bool blocking,
                     MetaTensor* out);

void CreateLikeInferMeta(const MetaTensor& x, DataType dtype, MetaTensor* out);

void CumsumInferMeta(const MetaTensor& x,
                     int axis,
                     bool flatten,
                     bool exclusive,
                     bool reverse,
                     MetaTensor* out);

void DiagInferMeta(const MetaTensor& x,
                   int offset,
                   float padding_value,
                   MetaTensor* out);

void DiagonalInferMeta(
    const MetaTensor& input, int offset, int axis1, int axis2, MetaTensor* out);

void EighInferMeta(const MetaTensor& x,
                   const std::string& uplo,
                   MetaTensor* out_w,
                   MetaTensor* out_v);

void FlattenInferMeta(const MetaTensor& x,
                      int start_axis,
                      int stop_axis,
                      MetaTensor* out);

void FlattenWithXShapeInferMeta(const MetaTensor& x,
                                int start_axis,
                                int stop_axis,
                                MetaTensor* out,
                                MetaTensor* xshape);

void FlipInferMeta(const MetaTensor& x,
                   const std::vector<int>& axis,
                   MetaTensor* out);

void FullBatchSizeLikeInferMeta(const MetaTensor& x,
                                const std::vector<int>& shape,
                                const Scalar& val,
                                DataType dtype,
                                int x_batch_size_dim,
                                int out_batch_size_dim,
                                MetaTensor* out);

void GumbelSoftmaxInferMeta(const MetaTensor& x,
                            float temperature,
                            bool hard,
                            int axis,
                            MetaTensor* out);
void HistogramInferMeta(
    const MetaTensor& input, int64_t bins, int min, int max, MetaTensor* out);

void IncrementInferMeta(const MetaTensor& x, float value, MetaTensor* out);

void InferMetaFromVecValue(const MetaTensor& x,
                           const std::vector<int64_t>& shape,
                           MetaTensor* out);

void IsEmptyInferMeta(const MetaTensor& x, MetaTensor* out);

void IsfiniteInferMeta(const MetaTensor& input, MetaTensor* out);

void KthvalueInferMeta(const MetaTensor& x,
                       int k,
                       int axis,
                       bool keepdim,
                       MetaTensor* out,
                       MetaTensor* indices,
                       MetaConfig = MetaConfig());

void LogsumexpInferMeta(const MetaTensor& input,
                        const std::vector<int64_t>& axis,
                        bool keepdim,
                        bool reduce_all,
                        MetaTensor* out);

void MatrixPowerInferMeta(const MetaTensor& x, int n, MetaTensor* out);

void MaxOutInferMeta(const MetaTensor& x,
                     int groups,
                     int axis,
                     MetaTensor* out);

void MaxPoolWithIndexInferMeta(const MetaTensor& x,
                               const std::vector<int>& kernel_size,
                               const std::vector<int>& strides,
                               const std::vector<int>& paddings,
                               bool global_pooling,
                               bool adaptive,
                               MetaTensor* out,
                               MetaTensor* mask,
                               MetaConfig config = MetaConfig());

void MeanAllInferMeta(const MetaTensor& x, MetaTensor* out);

void ModeInferMeta(const MetaTensor& x,
                   int axis,
                   bool keepdim,
                   MetaTensor* out,
                   MetaTensor* indices);

void MultinomialInferMeta(const MetaTensor& x,
                          int num_samples,
                          bool replacement,
                          MetaTensor* out);
void NormInferMeta(const MetaTensor& x,
                   int axis,
                   float epsilon,
                   bool is_test,
                   MetaTensor* out,
                   MetaTensor* norm);

void PadInferMeta(const MetaTensor& input,
                  const std::vector<int>& paddings,
                  float pad_value,
                  MetaTensor* out,
                  MetaConfig config = MetaConfig());

void Pad3dInferMeta(const MetaTensor& x,
                    const IntArray& paddings,
                    const std::string& mode,
                    float value,
                    const std::string& data_format,
                    MetaTensor* out,
                    MetaConfig config = MetaConfig());

void PixelShuffleInferMeta(const MetaTensor& x,
                           int upscale_factor,
                           const std::string& data_format,
                           MetaTensor* out);

void PNormInferMeta(const MetaTensor& x,
                    float porder,
                    int axis,
                    float epsilon,
                    bool keepdim,
                    bool asvector,
                    MetaTensor* out);

void PoolInferMeta(const MetaTensor& x,
                   const std::vector<int>& kernel_size,
                   const std::vector<int>& strides,
                   const std::vector<int>& paddings,
                   bool ceil_mode,
                   bool exclusive,
                   const std::string& data_format,
                   const std::string& pooling_type,
                   bool global_pooling,
                   bool adaptive,
                   const std::string& padding_algorithm,
                   MetaTensor* out,
                   MetaConfig config = MetaConfig());

void QrInferMeta(const MetaTensor& x,
                 const std::string& mode,
                 MetaTensor* q,
                 MetaTensor* r);

void RealAndImagInferMeta(const MetaTensor& x, MetaTensor* out);

void ReduceInferMeta(const MetaTensor& x,
                     const std::vector<int64_t>& axis,
                     bool keep_dim,
                     MetaTensor* out);

void ReduceInferMetaBase(const MetaTensor& x,
                         const std::vector<int64_t>& axis,
                         bool keep_dim,
                         bool reduce_all,
                         MetaTensor* out);

void ReshapeInferMeta(const MetaTensor& x,
                      const IntArray& shape,
                      MetaTensor* out,
                      MetaConfig config = MetaConfig());

void ReshapeWithXShapeInferMeta(const MetaTensor& x,
                                const IntArray& shape,
                                MetaTensor* out,
                                MetaTensor* xshape,
                                MetaConfig config = MetaConfig());

void ReverseInferMeta(const MetaTensor& x,
                      const std::vector<int>& axis,
                      MetaTensor* out);

void RollInferMeta(const MetaTensor& x,
                   const IntArray& shifts,
                   const std::vector<int64_t>& axis,
                   MetaTensor* out);

void SetValueInferMeta(const MetaTensor& x, MetaTensor* out);

void ShapeInferMeta(const MetaTensor& input, MetaTensor* out);

void ShardIndexInferMeta(const MetaTensor& in,
                         int index_num,
                         int nshards,
                         int shard_id,
                         int ignore_value,
                         MetaTensor* out,
                         MetaConfig config = MetaConfig());

void SizeInferMeta(const MetaTensor& input, MetaTensor* out);

void SliceRawInferMeta(const MetaTensor& input,
                       const std::vector<int64_t>& axes,
                       const IntArray& starts,
                       const IntArray& ends,
                       const std::vector<int64_t>& infer_flags,
                       const std::vector<int64_t>& decrease_axis,
                       MetaTensor* out,
                       MetaConfig config = MetaConfig());

void SoftmaxInferMeta(const MetaTensor& x, int axis, MetaTensor* out);

void SplitInferMeta(const MetaTensor& x_meta,
                    const IntArray& num_or_sections,
                    const Scalar& axis,
                    std::vector<MetaTensor*> out,
                    MetaConfig config = MetaConfig());

void SqueezeInferMeta(const MetaTensor& x,
                      const std::vector<int>& axes,
                      MetaTensor* xshape,
                      MetaTensor* out);

void StridedSliceRawInferMeta(const MetaTensor& x,
                              const std::vector<int>& axes,
                              const IntArray& starts,
                              const IntArray& ends,
                              const IntArray& strides,
                              const std::vector<int>& infer_flags,
                              const std::vector<int>& decrease_axis,
                              MetaTensor* out,
                              MetaConfig config = MetaConfig());

void StridedSliceInferMeta(const MetaTensor& x,
                           const std::vector<int>& axes,
                           const IntArray& starts,
                           const IntArray& ends,
                           const IntArray& strides,
                           MetaTensor* out,
                           MetaConfig config = MetaConfig());

void SumInferMeta(const MetaTensor& x,
                  const std::vector<int64_t>& axis,
                  DataType dtype,
                  bool keep_dim,
                  MetaTensor* out);

void SumRawInferMeta(const MetaTensor& x,
                     const std::vector<int64_t>& axis,
                     bool keep_dim,
                     bool reduce_all,
                     DataType dtype,
                     MetaTensor* out);

void TemporalShiftInferMeta(const MetaTensor& x,
                            int seg_num,
                            float shift_ratio,
                            const std::string& data_format,
                            MetaTensor* out,
                            MetaConfig config = MetaConfig());

void TileInferMeta(const MetaTensor& x,
                   const IntArray& repeat_times,
                   MetaTensor* out,
                   MetaConfig config = MetaConfig());

void TopKInferMeta(const MetaTensor& x,
                   const Scalar& k_scalar,
                   int axis,
                   bool largest,
                   bool sorted,
                   MetaTensor* out,
                   MetaTensor* indices,
                   MetaConfig config = MetaConfig());

void TraceInferMeta(
    const MetaTensor& x, int offset, int axis1, int axis2, MetaTensor* out);

void TransferLayoutInferMeta(const MetaTensor& x,
                             DataLayout layout,
                             MetaTensor* out);

void TransposeInferMeta(const MetaTensor& x,
                        const std::vector<int>& axis,
                        MetaTensor* out);

void TransposeGradInferMeta(const MetaTensor& x,
                            const std::vector<int>& axis,
                            MetaTensor* out);

void TrilTriuInferMeta(const MetaTensor& x,
                       int diagonal,
                       bool lower,
                       MetaTensor* out);

void UnbindInferMeta(const MetaTensor& x,
                     int axis,
                     std::vector<MetaTensor>* outs);

void UnchangedInferMeta(const MetaTensor& x, MetaTensor* out);

// meta x -> out without change, check if axis in range [-Rank(x), Rank(x)-1]
void UnchangedInferMetaCheckAxis(const MetaTensor& x,
                                 int axis,
                                 MetaTensor* out);

void UnfoldInferMeta(const MetaTensor& x,
                     const std::vector<int>& kernel_sizes,
                     const std::vector<int>& strides,
                     const std::vector<int>& paddings,
                     const std::vector<int>& dilations,
                     MetaTensor* out,
                     MetaConfig config = MetaConfig());

void UniqueInferMeta(const MetaTensor& x,
                     bool return_index,
                     bool return_inverse,
                     bool return_counts,
                     const std::vector<int>& axis,
                     DataType dtype,
                     MetaTensor* out,
                     MetaTensor* indices,
                     MetaTensor* index,
                     MetaTensor* counts);

void UniqueRawInferMeta(const MetaTensor& x,
                        bool return_index,
                        bool return_inverse,
                        bool return_counts,
                        const std::vector<int>& axis,
                        DataType dtype,
                        bool is_sorted,
                        MetaTensor* out,
                        MetaTensor* indices,
                        MetaTensor* index,
                        MetaTensor* counts);

void UnsqueezeInferMeta(const MetaTensor& x,
                        const IntArray& axes,
                        MetaTensor* xshape,
                        MetaTensor* out,
                        MetaConfig config = MetaConfig());

void UnStackInferMeta(const MetaTensor& x,
                      int axis,
                      int num,
                      std::vector<MetaTensor*> outs);

void OneHotRawInferMeta(const MetaTensor& x,
                        int32_t depth,
                        DataType dtype,
                        bool allow_out_of_range,
                        MetaTensor* out);

void OneHotInferMeta(const MetaTensor& x, const Scalar& depth, MetaTensor* out);

void WhereIndexInferMeta(const MetaTensor& condition, MetaTensor* out);

void ChannelShuffleInferMeta(const MetaTensor& x,
                             int groups,
                             const std::string& data_format,
                             MetaTensor* out);

}  // namespace phi