* Copyright (c) 2026 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_div_operation.cpp
* \brief
*/
#include <nlohmann/json.hpp>
#include "test_operation.h"
using namespace tile_fwk::test_operation;
namespace {
struct DivOpFuncArgs : public OpFuncArgs {
DivOpFuncArgs(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 DivOpMetaData {
explicit DivOpMetaData(const OpFunc& opFunc, const nlohmann::json& test_data)
: opFunc_(opFunc), test_data_(test_data)
{}
OpFunc opFunc_;
nlohmann::json test_data_;
};
void UpdateInputBrcViewShape(
std::vector<int64_t>& inputBrcViewShape, const std::vector<SymbolicScalar>& inputsShape,
const std::vector<SymbolicScalar>& outputsShape)
{
for (size_t i = 0; i < inputsShape.size(); i++) {
if (inputsShape[i] == 1 && outputsShape[i] != 1) {
inputBrcViewShape[i] = 1;
}
}
}
void UpdateInputBrcVaildShape(
std::vector<SymbolicScalar>& inputValidShape, const std::vector<SymbolicScalar>& inputsShape,
const std::vector<SymbolicScalar>& outputsShape)
{
for (size_t i = 0; i < inputsShape.size(); i++) {
if (inputsShape[i] == 1 && outputsShape[i] != 1) {
inputValidShape[i] = 1;
}
}
}
void UpdateOffset(
std::vector<SymbolicScalar>& offset, const std::vector<SymbolicScalar>& inputsShape,
const std::vector<SymbolicScalar>& outputsShape)
{
for (size_t i = 0; i < inputsShape.size(); i++) {
if (inputsShape[i] == 1 && outputsShape[i] != 1) {
offset[i] = 0;
}
}
}
static void DivOperationExeFunc2Dims(
const std::vector<Tensor>& inputs, std::vector<Tensor>& outputs, const OpFuncArgs* opArgs)
{
FUNCTION("main", {inputs[0], inputs[1]}, {outputs[0]})
{
std::vector<SymbolicScalar> firstInputsShape = {inputs[0].GetShape()[0], inputs[0].GetShape()[1]};
std::vector<SymbolicScalar> secondInputsShape = {inputs[1].GetShape()[0], inputs[1].GetShape()[1]};
std::vector<SymbolicScalar> outputsShape = {outputs[0].GetShape()[0], outputs[0].GetShape()[1]};
auto args = static_cast<const DivOpFuncArgs*>(opArgs);
std::vector<int64_t> viewShape = {args->viewShape_[0], args->viewShape_[1]};
std::vector<int64_t> firstInputViewShape = viewShape;
std::vector<int64_t> secondInputViewShape = viewShape;
UpdateInputBrcViewShape(firstInputViewShape, firstInputsShape, outputsShape);
UpdateInputBrcViewShape(secondInputViewShape, secondInputsShape, outputsShape);
const int bloop = CeilDiv(outputsShape[0], viewShape[0]);
const int sloop = CeilDiv(outputsShape[1], viewShape[1]);
LOOP("LOOP_L0_bIdx", FunctionType::DYNAMIC_LOOP, bIdx, LoopRange(0, bloop, 1))
{
LOOP("LOOP_L1_sIdx", FunctionType::DYNAMIC_LOOP, sIdx, LoopRange(0, sloop, 1))
{
std::vector<SymbolicScalar> firstInputValidShape = {
std::min(firstInputsShape[0] - bIdx * firstInputViewShape[0], firstInputViewShape[0]),
std::min(firstInputsShape[1] - sIdx * firstInputViewShape[1], firstInputViewShape[1])};
std::vector<SymbolicScalar> secondInputValidShape = {
std::min(secondInputsShape[0] - bIdx * secondInputViewShape[0], secondInputViewShape[0]),
std::min(secondInputsShape[1] - sIdx * secondInputViewShape[1], secondInputViewShape[1])};
std::vector<SymbolicScalar> firstOffset = {
bIdx * firstInputViewShape[0], sIdx * firstInputViewShape[1]};
std::vector<SymbolicScalar> secondOffset = {
bIdx * secondInputViewShape[0], sIdx * secondInputViewShape[1]};
UpdateInputBrcVaildShape(firstInputValidShape, firstInputsShape, outputsShape);
UpdateInputBrcVaildShape(secondInputValidShape, secondInputsShape, outputsShape);
UpdateOffset(firstOffset, firstInputsShape, outputsShape);
UpdateOffset(secondOffset, secondInputsShape, outputsShape);
Tensor tileTensor0 = View(inputs[0], firstInputViewShape, firstInputValidShape, firstOffset);
Tensor tileTensor1 = View(inputs[1], secondInputViewShape, secondInputValidShape, secondOffset);
TileShape::Current().SetVecTile(args->tileShape_);
auto res = Div(tileTensor0, tileTensor1);
Assemble(res, {bIdx * viewShape[0], sIdx * viewShape[1]}, outputs[0]);
}
}
}
}
static void DivOperationExeFunc3Dims(
const std::vector<Tensor>& inputs, std::vector<Tensor>& outputs, const OpFuncArgs* opArgs)
{
FUNCTION("main", {inputs[0], inputs[1]}, {outputs[0]})
{
std::vector<SymbolicScalar> firstInputsShape = {
inputs[0].GetShape()[0], inputs[0].GetShape()[1], inputs[0].GetShape()[2]};
std::vector<SymbolicScalar> secondInputsShape = {
inputs[1].GetShape()[0], inputs[1].GetShape()[1], inputs[1].GetShape()[2]};
std::vector<SymbolicScalar> outputsShape = {
outputs[0].GetShape()[0], outputs[0].GetShape()[1], outputs[0].GetShape()[2]};
auto args = static_cast<const DivOpFuncArgs*>(opArgs);
std::vector<int64_t> viewShape = {args->viewShape_[0], args->viewShape_[1], args->viewShape_[2]};
std::vector<int64_t> firstInputViewShape = viewShape;
std::vector<int64_t> secondInputViewShape = viewShape;
UpdateInputBrcViewShape(firstInputViewShape, firstInputsShape, outputsShape);
UpdateInputBrcViewShape(secondInputViewShape, secondInputsShape, outputsShape);
const int bloop = CeilDiv(outputsShape[0], viewShape[0]);
const int sloop = CeilDiv(outputsShape[1], viewShape[1]);
const int nloop = CeilDiv(outputsShape[2], viewShape[2]);
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))
{
std::vector<SymbolicScalar> firstInputValidShape = {
std::min(firstInputsShape[0] - bIdx * firstInputViewShape[0], firstInputViewShape[0]),
std::min(firstInputsShape[1] - sIdx * firstInputViewShape[1], firstInputViewShape[1]),
std::min(firstInputsShape[2] - nIdx * firstInputViewShape[2], firstInputViewShape[2])};
std::vector<SymbolicScalar> secondInputValidShape = {
std::min(secondInputsShape[0] - bIdx * secondInputViewShape[0], secondInputViewShape[0]),
std::min(secondInputsShape[1] - sIdx * secondInputViewShape[1], secondInputViewShape[1]),
std::min(secondInputsShape[2] - nIdx * secondInputViewShape[2], secondInputViewShape[2])};
std::vector<SymbolicScalar> firstOffset = {
bIdx * firstInputViewShape[0], sIdx * firstInputViewShape[1], nIdx * firstInputViewShape[2]};
std::vector<SymbolicScalar> secondOffset = {
bIdx * secondInputViewShape[0], sIdx * secondInputViewShape[1], nIdx * secondInputViewShape[2]};
UpdateInputBrcVaildShape(firstInputValidShape, firstInputsShape, outputsShape);
UpdateInputBrcVaildShape(secondInputValidShape, secondInputsShape, outputsShape);
UpdateOffset(firstOffset, firstInputsShape, outputsShape);
UpdateOffset(secondOffset, secondInputsShape, outputsShape);
Tensor tileTensor0 = View(inputs[0], firstInputViewShape, firstInputValidShape, firstOffset);
Tensor tileTensor1 = View(inputs[1], secondInputViewShape, secondInputValidShape, secondOffset);
TileShape::Current().SetVecTile(args->tileShape_);
auto res = Div(tileTensor0, tileTensor1);
Assemble(res, {bIdx * viewShape[0], sIdx * viewShape[1], nIdx * viewShape[2]}, outputs[0]);
}
}
}
}
}
static void DivOperationExeFunc4Dims(
const std::vector<Tensor>& inputs, std::vector<Tensor>& outputs, const OpFuncArgs* opArgs)
{
FUNCTION("main", {inputs[0], inputs[1]}, {outputs[0]})
{
std::vector<SymbolicScalar> firstInputsShape = {
inputs[0].GetShape()[0], inputs[0].GetShape()[1], inputs[0].GetShape()[2], inputs[0].GetShape()[3]};
std::vector<SymbolicScalar> secondInputsShape = {
inputs[1].GetShape()[0], inputs[1].GetShape()[1], inputs[1].GetShape()[2], inputs[1].GetShape()[3]};
std::vector<SymbolicScalar> outputsShape = {
outputs[0].GetShape()[0], outputs[0].GetShape()[1], outputs[0].GetShape()[2], outputs[0].GetShape()[3]};
auto args = static_cast<const DivOpFuncArgs*>(opArgs);
std::vector<int64_t> viewShape = {
args->viewShape_[0], args->viewShape_[1], args->viewShape_[2], args->viewShape_[3]};
std::vector<int64_t> firstInputViewShape = viewShape;
std::vector<int64_t> secondInputViewShape = viewShape;
UpdateInputBrcViewShape(firstInputViewShape, firstInputsShape, outputsShape);
UpdateInputBrcViewShape(secondInputViewShape, secondInputsShape, outputsShape);
const int bloop = CeilDiv(outputsShape[0], viewShape[0]);
const int sloop = CeilDiv(outputsShape[1], viewShape[1]);
const int nloop = CeilDiv(outputsShape[2], viewShape[2]);
const int mloop = CeilDiv(outputsShape[3], viewShape[3]);
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, nIdx, LoopRange(0, nloop, 1))
{
LOOP("LOOP_L3_nIdx", FunctionType::DYNAMIC_LOOP, mIdx, LoopRange(0, mloop, 1))
{
std::vector<SymbolicScalar> firstInputValidShape = {
std::min(firstInputsShape[0] - bIdx * firstInputViewShape[0], firstInputViewShape[0]),
std::min(firstInputsShape[1] - sIdx * firstInputViewShape[1], firstInputViewShape[1]),
std::min(firstInputsShape[2] - nIdx * firstInputViewShape[2], firstInputViewShape[2]),
std::min(firstInputsShape[3] - mIdx * firstInputViewShape[3], firstInputViewShape[3])};
std::vector<SymbolicScalar> secondInputValidShape = {
std::min(secondInputsShape[0] - bIdx * secondInputViewShape[0], secondInputViewShape[0]),
std::min(secondInputsShape[1] - sIdx * secondInputViewShape[1], secondInputViewShape[1]),
std::min(secondInputsShape[2] - nIdx * secondInputViewShape[2], secondInputViewShape[2]),
std::min(secondInputsShape[3] - mIdx * secondInputViewShape[3], secondInputViewShape[3])};
std::vector<SymbolicScalar> firstOffset = {
bIdx * firstInputViewShape[0], sIdx * firstInputViewShape[1], nIdx * firstInputViewShape[2],
mIdx * firstInputViewShape[3]};
std::vector<SymbolicScalar> secondOffset = {
bIdx * secondInputViewShape[0], sIdx * secondInputViewShape[1],
nIdx * secondInputViewShape[2], mIdx * secondInputViewShape[3]};
UpdateInputBrcVaildShape(firstInputValidShape, firstInputsShape, outputsShape);
UpdateInputBrcVaildShape(secondInputValidShape, secondInputsShape, outputsShape);
UpdateOffset(firstOffset, firstInputsShape, outputsShape);
UpdateOffset(secondOffset, secondInputsShape, outputsShape);
Tensor tileTensor0 = View(inputs[0], firstInputViewShape, firstInputValidShape, firstOffset);
Tensor tileTensor1 = View(inputs[1], secondInputViewShape, secondInputValidShape, secondOffset);
TileShape::Current().SetVecTile(args->tileShape_);
auto res = Div(tileTensor0, tileTensor1);
Assemble(
res, {bIdx * viewShape[0], sIdx * viewShape[1], nIdx * viewShape[2], mIdx * viewShape[3]},
outputs[0]);
}
}
}
}
}
}
static void DivOperationExeFunc5Dims(
const std::vector<Tensor>& inputs, std::vector<Tensor>& outputs, const OpFuncArgs* opArgs)
{
FUNCTION("main", {inputs[0], inputs[1]}, {outputs[0]})
{
std::vector<SymbolicScalar> firstInputsShape = {
inputs[0].GetShape()[0], inputs[0].GetShape()[1], inputs[0].GetShape()[2], inputs[0].GetShape()[3],
inputs[0].GetShape()[4]};
std::vector<SymbolicScalar> secondInputsShape = {
inputs[1].GetShape()[0], inputs[1].GetShape()[1], inputs[1].GetShape()[2], inputs[1].GetShape()[3],
inputs[1].GetShape()[4]};
std::vector<SymbolicScalar> outputsShape = {
outputs[0].GetShape()[0], outputs[0].GetShape()[1], outputs[0].GetShape()[2], outputs[0].GetShape()[3],
outputs[0].GetShape()[4]};
auto args = static_cast<const DivOpFuncArgs*>(opArgs);
std::vector<int64_t> viewShape = {
args->viewShape_[0], args->viewShape_[1], args->viewShape_[2], args->viewShape_[3], args->viewShape_[4]};
std::vector<int64_t> firstInputViewShape = viewShape;
std::vector<int64_t> secondInputViewShape = viewShape;
UpdateInputBrcViewShape(firstInputViewShape, firstInputsShape, outputsShape);
UpdateInputBrcViewShape(secondInputViewShape, secondInputsShape, outputsShape);
const int bloop = CeilDiv(outputsShape[0], viewShape[0]);
const int sloop = CeilDiv(outputsShape[1], viewShape[1]);
const int nloop = CeilDiv(outputsShape[2], viewShape[2]);
const int mloop = CeilDiv(outputsShape[3], viewShape[3]);
const int qloop = CeilDiv(outputsShape[4], viewShape[4]);
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, nIdx, LoopRange(0, nloop, 1))
{
LOOP("LOOP_L3_nIdx", FunctionType::DYNAMIC_LOOP, mIdx, LoopRange(0, mloop, 1))
{
LOOP("LOOP_L4_nIdx", FunctionType::DYNAMIC_LOOP, qIdx, LoopRange(0, qloop, 1))
{
std::vector<SymbolicScalar> firstInputValidShape = {
std::min(firstInputsShape[0] - bIdx * firstInputViewShape[0], firstInputViewShape[0]),
std::min(firstInputsShape[1] - sIdx * firstInputViewShape[1], firstInputViewShape[1]),
std::min(firstInputsShape[2] - nIdx * firstInputViewShape[2], firstInputViewShape[2]),
std::min(firstInputsShape[3] - mIdx * firstInputViewShape[3], firstInputViewShape[3]),
std::min(firstInputsShape[4] - qIdx * firstInputViewShape[4], firstInputViewShape[4])};
std::vector<SymbolicScalar> secondInputValidShape = {
std::min(
secondInputsShape[0] - bIdx * secondInputViewShape[0], secondInputViewShape[0]),
std::min(
secondInputsShape[1] - sIdx * secondInputViewShape[1], secondInputViewShape[1]),
std::min(
secondInputsShape[2] - nIdx * secondInputViewShape[2], secondInputViewShape[2]),
std::min(
secondInputsShape[3] - mIdx * secondInputViewShape[3], secondInputViewShape[3]),
std::min(
secondInputsShape[4] - qIdx * secondInputViewShape[4], secondInputViewShape[4])};
std::vector<SymbolicScalar> firstOffset = {
bIdx * firstInputViewShape[0], sIdx * firstInputViewShape[1],
nIdx * firstInputViewShape[2], mIdx * firstInputViewShape[3],
qIdx * firstInputViewShape[4]};
std::vector<SymbolicScalar> secondOffset = {
bIdx * secondInputViewShape[0], sIdx * secondInputViewShape[1],
nIdx * secondInputViewShape[2], mIdx * secondInputViewShape[3],
qIdx * secondInputViewShape[4]};
UpdateInputBrcVaildShape(firstInputValidShape, firstInputsShape, outputsShape);
UpdateInputBrcVaildShape(secondInputValidShape, secondInputsShape, outputsShape);
UpdateOffset(firstOffset, firstInputsShape, outputsShape);
UpdateOffset(secondOffset, secondInputsShape, outputsShape);
Tensor tileTensor0 =
View(inputs[0], firstInputViewShape, firstInputValidShape, firstOffset);
Tensor tileTensor1 =
View(inputs[1], secondInputViewShape, secondInputValidShape, secondOffset);
TileShape::Current().SetVecTile(args->tileShape_);
auto res = Div(tileTensor0, tileTensor1);
Assemble(
res,
{bIdx * viewShape[0], sIdx * viewShape[1], nIdx * viewShape[2], mIdx * viewShape[3],
qIdx * viewShape[4]},
outputs[0]);
}
}
}
}
}
}
}
class DivOperationTest : public npu::tile_fwk::stest::TestSuite_STest_Ops_Aihac_param<DivOpMetaData> {};
INSTANTIATE_TEST_SUITE_P(
TestDiv, DivOperationTest,
::testing::ValuesIn(GetOpMetaData<DivOpMetaData>(
{DivOperationExeFunc2Dims, DivOperationExeFunc3Dims, DivOperationExeFunc4Dims, DivOperationExeFunc5Dims},
"Div")));
TEST_P(DivOperationTest, TestDiv)
{
auto test_data = GetParam().test_data_;
auto args = DivOpFuncArgs(GetViewShape(test_data), GetTileShape(test_data));
auto testCase = CreateTestCaseDesc<DivOpMetaData>(GetParam(), &args);
TestExecutor::runTest(testCase);
}
}
