wehub-resource-sync
147 lines
5.9 KiB
C++
147 lines
5.9 KiB
C++
/* Copyright (c) 2021 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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#include "paddle/phi/common/int_array.h"
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#include "paddle/phi/common/scalar.h"
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#include "paddle/phi/core/enforce.h"
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#include "paddle/phi/core/meta_tensor.h"
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namespace phi {
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// Common InferMeta Functions for 0-nary operators(no input tensor), The format
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// like:
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//
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// 1. void [FunctionDesc|OpName]InferMeta(..., MetaTensor* out)
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//
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// NOTE: The name "InferShape" may be not appropriate. "InferMeta" may be good.
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// Because functions in this file not only can infer shape, but also need
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// infer lod or other useful data.
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//
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// The InferMeta Functions in this file are arranged in alphabetic order.
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PADDLE_API void ArangeInferMeta(const Scalar& start,
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const Scalar& end,
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const Scalar& step,
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DataType dtype,
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MetaTensor* out);
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PADDLE_API void RangeInferMeta(const Scalar& start,
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const Scalar& end,
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const Scalar& step,
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DataType dtype,
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MetaTensor* out);
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PADDLE_API void AssignValueInferMeta(const std::vector<int>& shape,
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DataType dtype,
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MetaTensor* out);
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PADDLE_API void CommInitAllInferMeta(const std::vector<int>& devices,
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int ring_id);
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PADDLE_API void CreateVecShapeInferMeta(const std::vector<int64_t>& shape,
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DataType dtype,
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MetaTensor* out);
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PADDLE_API void CreateArrayInferMeta(DataType dtype, MetaTensor* out);
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PADDLE_API void CreateInferMeta(const IntArray& shape,
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DataType dtype,
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MetaTensor* out,
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MetaConfig config = MetaConfig());
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PADDLE_API void CreateInferMetaBase(const std::vector<int64_t>& shape,
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DataType dtype,
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DataLayout layout,
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MetaTensor* out);
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PADDLE_API void DataInferMeta(const std::string& name,
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const phi::IntArray& shape,
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DataType data_type,
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MetaTensor* out);
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PADDLE_API void EyeInferMeta(const Scalar& num_rows,
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const Scalar& num_columns,
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DataType dtype,
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MetaTensor* out,
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MetaConfig config = MetaConfig());
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PADDLE_API void GaussianInferMeta(const IntArray& shape,
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double mean,
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double std,
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int seed,
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DataType dtype,
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MetaTensor* out);
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PADDLE_API void LoadInferMeta(MetaTensor* out,
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MetaConfig config = MetaConfig());
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PADDLE_API void RandpermInferMeta(int n, DataType dtype, MetaTensor* out);
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PADDLE_API void RandintInferMeta(
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int low, int high, const IntArray& shape, DataType dtype, MetaTensor* out);
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PADDLE_API void RandomInferMeta(const MetaTensor& x, MetaTensor* out);
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PADDLE_API void PartialRecvInferMeta(int peer,
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DataType dtype,
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const std::vector<int>& out_shape,
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int num,
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int id,
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MetaTensor* out);
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PADDLE_API void PRecvInferMeta(const int peer,
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DataType dtype,
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const std::vector<int>& out_shape,
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const bool dynamic_shape,
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MetaTensor* out);
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PADDLE_API void PRecvArrayInferMeta(int peer,
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DataType dtype,
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const std::vector<int>& out_shape,
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MetaTensor* out);
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PADDLE_API void RecvV2InferMeta(const int ring_id,
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const bool dynamic_shape,
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const int peer,
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const std::vector<int>& out_shape,
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DataType dtype,
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MetaTensor* out);
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PADDLE_API void SeedInferMeta(int seed, MetaTensor* out);
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PADDLE_API void TruncatedGaussianRandomInferMeta(const std::vector<int>& shape,
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float mean,
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float std,
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int seed,
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float a,
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float b,
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DataType dtype,
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MetaTensor* out);
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PADDLE_API void UniformRandomInferMeta(const IntArray& shape,
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DataType dtype,
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MetaTensor* out);
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PADDLE_API void TrilIndicesInferMeta(
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int rows, int cols, int offset, DataType dtype, MetaTensor* out);
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PADDLE_API void TriuIndicesInferMeta(
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int row, int col, int offset, DataType dtype, MetaTensor* out);
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PADDLE_API void ReadFileInferMeta(const std::string& filename, MetaTensor* out);
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} // namespace phi
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