* Copyright (c) 2025 Huawei Technologies Co., Ltd.
* This program is free software, you can redistribute it and/or modify it under the terms and conditions of
* CANN Open Software License Agreement Version 2.0 (the "License").
* Please refer to the License for details. You may not use this file except in compliance with the License.
* THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED,
* INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
* See LICENSE in the root of the software repository for the full text of the License.
*/
* \file test_prelu_operation.cpp
* \brief
*/
#include "test_operation.h"
using namespace tile_fwk::test_operation;
namespace {
struct PReLUOpFuncArgs : public OpFuncArgs {
PReLUOpFuncArgs(const std::vector<int64_t>& viewShape, const std::vector<int64_t>& tileShape)
: viewShape_(viewShape), tileShape_(tileShape)
{}
std::vector<int64_t> viewShape_;
std::vector<int64_t> tileShape_;
};
struct PReLUOpMetaData {
explicit PReLUOpMetaData(const OpFunc& opFunc, const nlohmann::json& test_data)
: opFunc_(opFunc), test_data_(test_data)
{}
OpFunc opFunc_;
nlohmann::json test_data_;
};
static void PReLUOperationExeFunc1Dim(
const std::vector<Tensor>& inputs, std::vector<Tensor>& outputs, const OpFuncArgs* opArgs)
{
FUNCTION("main", {inputs[0], inputs[1]}, {outputs[0]})
{
auto args = static_cast<const PReLUOpFuncArgs*>(opArgs);
SymbolicScalar firstDim = inputs[0].GetShape()[0];
const int firstViewShape = args->viewShape_[0];
const int bloop = CeilDiv(firstDim, firstViewShape);
LOOP("LOOP_L0_bIdx", FunctionType::DYNAMIC_LOOP, bIdx, LoopRange(0, bloop, 1))
{
auto tileTensor0 = View(
inputs[0], {firstViewShape},
{std::min(firstDim - bIdx * firstViewShape, firstViewShape)},
{bIdx * firstViewShape});
auto tileTensor1 = View(inputs[1], {1}, {1}, {0});
TileShape::Current().SetVecTile(args->tileShape_);
auto res = PReLU(tileTensor0, tileTensor1);
Assemble(res, {bIdx * firstViewShape}, outputs[0]);
}
}
}
static void PReLUOperationExeFunc2Dims(
const std::vector<Tensor>& inputs, std::vector<Tensor>& outputs, const OpFuncArgs* opArgs)
{
FUNCTION("main", {inputs[0], inputs[1]}, {outputs[0]})
{
auto args = static_cast<const PReLUOpFuncArgs*>(opArgs);
SymbolicScalar firstDim = inputs[0].GetShape()[0];
SymbolicScalar secondDim = inputs[0].GetShape()[1];
const int firstViewShape = args->viewShape_[0];
const int secondViewShape = args->viewShape_[1];
const int bloop = CeilDiv(firstDim, firstViewShape);
const int sloop = CeilDiv(secondDim, secondViewShape);
LOOP("LOOP_L0_bIdx", FunctionType::DYNAMIC_LOOP, bIdx, LoopRange(0, bloop, 1))
{
LOOP("LOOP_L1_sIdx", FunctionType::DYNAMIC_LOOP, sIdx, LoopRange(0, sloop, 1))
{
auto tileTensor0 = View(
inputs[0], {firstViewShape, secondViewShape},
{std::min(firstDim - bIdx * firstViewShape, firstViewShape),
std::min(secondDim - sIdx * secondViewShape, secondViewShape)},
{bIdx * firstViewShape, sIdx * secondViewShape});
auto weightViewShape = std::min(secondDim - sIdx * secondViewShape, secondViewShape);
auto tileTensor1 = View(inputs[1], {secondViewShape}, {weightViewShape}, {sIdx * secondViewShape});
TileShape::Current().SetVecTile(args->tileShape_);
auto res = PReLU(tileTensor0, tileTensor1);
Assemble(res, {bIdx * firstViewShape, sIdx * secondViewShape}, outputs[0]);
}
}
}
}
static void PReLUOperationExeFunc3Dims(
const std::vector<Tensor>& inputs, std::vector<Tensor>& outputs, const OpFuncArgs* opArgs)
{
FUNCTION("main", {inputs[0], inputs[1]}, {outputs[0]})
{
auto args = static_cast<const PReLUOpFuncArgs*>(opArgs);
SymbolicScalar firstDim = inputs[0].GetShape()[0];
SymbolicScalar secondDim = inputs[0].GetShape()[1];
SymbolicScalar thirdDim = inputs[0].GetShape()[2];
const int firstViewShape = args->viewShape_[0];
const int secondViewShape = args->viewShape_[1];
const int thirdViewShape = args->viewShape_[2];
const int bloop = CeilDiv(firstDim, firstViewShape);
const int sloop = CeilDiv(secondDim, secondViewShape);
const int nloop = CeilDiv(thirdDim, thirdViewShape);
LOOP("LOOP_L0_bIdx", FunctionType::DYNAMIC_LOOP, bIdx, LoopRange(0, bloop, 1))
{
LOOP("LOOP_L1_sIdx", FunctionType::DYNAMIC_LOOP, sIdx, LoopRange(0, sloop, 1))
{
LOOP("LOOP_L2_nIdx", FunctionType::DYNAMIC_LOOP, nIdx, LoopRange(0, nloop, 1))
{
auto tileTensor0 = View(
inputs[0], {firstViewShape, secondViewShape, thirdViewShape},
{std::min(firstDim - bIdx * firstViewShape, firstViewShape),
std::min(secondDim - sIdx * secondViewShape, secondViewShape),
std::min(thirdDim - nIdx * thirdViewShape, thirdViewShape)},
{bIdx * firstViewShape, sIdx * secondViewShape, nIdx * thirdViewShape});
auto weightViewShape = std::min(secondDim - sIdx * secondViewShape, secondViewShape);
auto tileTensor1 = View(inputs[1], {secondViewShape}, {weightViewShape}, {sIdx * secondViewShape});
TileShape::Current().SetVecTile(args->tileShape_);
auto res = PReLU(tileTensor0, tileTensor1);
Assemble(res, {bIdx * firstViewShape, sIdx * secondViewShape, nIdx * thirdViewShape}, outputs[0]);
}
}
}
}
}
static void PReLUOperationExeFunc4Dims(
const std::vector<Tensor>& inputs, std::vector<Tensor>& outputs, const OpFuncArgs* opArgs)
{
FUNCTION("main", {inputs[0], inputs[1]}, {outputs[0]})
{
auto args = static_cast<const PReLUOpFuncArgs*>(opArgs);
SymbolicScalar firstDim = inputs[0].GetShape()[0];
SymbolicScalar secondDim = inputs[0].GetShape()[1];
SymbolicScalar thirdDim = inputs[0].GetShape()[2];
SymbolicScalar fourthDim = inputs[0].GetShape()[3];
const int firstViewShape = args->viewShape_[0];
const int secondViewShape = args->viewShape_[1];
const int thirdViewShape = args->viewShape_[2];
const int fourthViewShape = args->viewShape_[3];
const int bloop = CeilDiv(firstDim, firstViewShape);
const int sloop = CeilDiv(secondDim, secondViewShape);
const int mloop = CeilDiv(thirdDim, thirdViewShape);
const int nloop = CeilDiv(fourthDim, fourthViewShape);
LOOP("LOOP_L0_bIdx", FunctionType::DYNAMIC_LOOP, bIdx, LoopRange(0, bloop, 1))
{
LOOP("LOOP_L1_sIdx", FunctionType::DYNAMIC_LOOP, sIdx, LoopRange(0, sloop, 1))
{
LOOP("LOOP_L2_mIdx", FunctionType::DYNAMIC_LOOP, mIdx, LoopRange(0, mloop, 1))
{
LOOP("LOOP_L3_nIdx", FunctionType::DYNAMIC_LOOP, nIdx, LoopRange(0, nloop, 1))
{
auto tileTensor0 = View(
inputs[0], {firstViewShape, secondViewShape, thirdViewShape, fourthViewShape},
{std::min(firstDim - bIdx * firstViewShape, firstViewShape),
std::min(secondDim - sIdx * secondViewShape, secondViewShape),
std::min(thirdDim - mIdx * thirdViewShape, thirdViewShape),
std::min(fourthDim - nIdx * fourthViewShape, fourthViewShape)},
{bIdx * firstViewShape, sIdx * secondViewShape, mIdx * thirdViewShape,
nIdx * fourthViewShape});
auto weightViewShape = std::min(secondDim - sIdx * secondViewShape, secondViewShape);
auto tileTensor1 =
View(inputs[1], {secondViewShape}, {weightViewShape}, {sIdx * secondViewShape});
TileShape::Current().SetVecTile(args->tileShape_);
auto res = PReLU(tileTensor0, tileTensor1);
Assemble(
res,
{bIdx * firstViewShape, sIdx * secondViewShape, mIdx * thirdViewShape,
nIdx * fourthViewShape},
outputs[0]);
}
}
}
}
}
}
class PReLUOperationTest : public npu::tile_fwk::stest::TestSuite_STest_Ops_Aihac_param<PReLUOpMetaData> {};
INSTANTIATE_TEST_SUITE_P(
TestPReLU, PReLUOperationTest,
::testing::ValuesIn(GetOpMetaData<PReLUOpMetaData, 1>(
{PReLUOperationExeFunc1Dim, PReLUOperationExeFunc2Dims,
PReLUOperationExeFunc3Dims, PReLUOperationExeFunc4Dims}, "PReLU")));
TEST_P(PReLUOperationTest, TestPReLU)
{
auto test_data = GetParam().test_data_;
auto args = PReLUOpFuncArgs(GetViewShape(test_data), GetTileShape(test_data));
auto testCase = CreateTestCaseDesc<PReLUOpMetaData>(GetParam(), &args);
TestExecutor::runTest(testCase);
}
}
