wehub-resource-sync
102 lines
3.8 KiB
C++
102 lines
3.8 KiB
C++
// Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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 <Python.h>
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#include <vector>
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#include "paddle/fluid/ir_adaptor/translator/program_translator.h"
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#include "paddle/phi/api/include/tensor.h"
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#include "paddle/phi/common/data_type.h"
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#include "paddle/phi/common/scalar.h"
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#include "paddle/pir/include/core/value.h"
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#include "paddle/utils/optional.h"
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namespace paddle {
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namespace pybind {
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using Value = pir::Value;
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using IntArray = paddle::experimental::IntArray;
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using Scalar = paddle::experimental::Scalar;
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using IntVector = std::vector<int64_t>;
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void ExpandAsPreProcess(Tensor* x,
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paddle::optional<Tensor>* y,
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std::vector<int64_t>* target_shape);
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void ExpandAsPreProcess(Value* x,
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paddle::optional<pir::Value>* y,
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std::vector<int64_t>* target_shape);
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void RollPreProcess(Tensor* x, IntArray* shifts, IntVector* axis);
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void RollPreProcess(Value* x, Value* shifts, IntVector* axis);
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void BinCountPreProcess(Tensor* x,
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paddle::optional<Tensor>* weights,
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Scalar* minlength);
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void BinCountPreProcess(Value* x,
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paddle::optional<Value>* weights,
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Value* minlength);
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void LogsumexpPreProcess(Tensor* x, std::vector<int>* axis, bool* reduce_all);
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void LogsumexpPreProcess(Value* x, std::vector<int>* axis, bool* reduce_all);
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void SumPreProcess(Value* x, Value* axis);
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void IsClosePreProcess(Value* x, Value* y, Value* rtol, Value* atol);
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void AllClosePreProcess(Value* x, Value* y, Value* rtol, Value* atol);
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void GridSamplePreProcess(Tensor* x,
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Tensor* grid,
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std::string* mode,
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std::string* padding_mode,
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bool* align_corners);
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void GridSamplePreProcess(Value* x,
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Value* grid,
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std::string* mode,
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std::string* padding_mode,
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bool* align_corners);
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// Addmm broadcast validation for dygraph
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void AddmmPreProcess(Tensor* input, Tensor* x, Tensor* y);
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// Addmm broadcast validation for static graph
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void AddmmPreProcess(pir::Value* input, pir::Value* x, pir::Value* y);
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// Baddbmm broadcast validation for dygraph
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void BaddbmmPreProcess(Tensor* input, Tensor* x, Tensor* y);
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// Baddbmm broadcast validation for static graph
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void BaddbmmPreProcess(pir::Value* input, pir::Value* x, pir::Value* y);
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// Renorm preprocessing: handle negative axis
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void NegativeAxisPreProcess(Tensor* x, int* axis);
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void NegativeAxisPreProcess(Value* x, int* axis);
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void PixelShufflePreProcess(std::string* data_format);
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// Eigh input validation: check shape >= 2D, last two dims equal, UPLO is 'L' or
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// 'U'
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void EighPreProcess(Tensor* x, std::string* UPLO);
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void EighPreProcess(Value* x, std::string* UPLO);
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// Cholesky input validation: check shape >= 2D, last two dims equal
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void CholeskyPreProcess(Tensor* x, bool* upper);
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void CholeskyPreProcess(Value* x, bool* upper);
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// Inplace API broadcast validation for dygraph
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void InplaceShapePreProcess(Tensor* x, Tensor* y);
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// Inplace API broadcast validation for static graph
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void InplaceShapePreProcess(pir::Value* x, pir::Value* y);
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} // namespace pybind
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} // namespace paddle
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