* 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.
*/
#include "grouped_matmul.h"
#include "opdev/op_log.h"
#include "opdev/op_dfx.h"
#include "opdev/shape_utils.h"
#include "opdev/make_op_executor.h"
using namespace op;
namespace l0op {
OP_TYPE_REGISTER(GroupedMatmul);
const aclTensorList *GroupedMatmul(const aclTensorList *x,
const aclTensorList *weight,
const aclTensorList *biasOptional,
const aclTensorList *scaleOptional,
const aclTensorList *offsetOptional,
const aclTensorList *antiquantScaleOptional,
const aclTensorList *antiquantOffsetOptional,
const aclTensor *groupListOptional,
const aclTensor *perTokenScaleOptional,
int64_t splitItem,
op::DataType yDtype,
bool transposeWeight,
bool transposeX,
int64_t groupType,
int64_t groupListType,
int64_t actType,
const aclIntArray *tuningConfig,
size_t outLength,
aclOpExecutor *executor) {
L0_DFX(GroupedMatmul, x, weight, biasOptional, scaleOptional, offsetOptional, antiquantScaleOptional,
antiquantOffsetOptional, groupListOptional, perTokenScaleOptional, splitItem, yDtype,
transposeWeight, transposeX, groupType, groupListType, actType, tuningConfig, outLength);
std::vector<const aclTensor*> tensorsVec;
const aclTensor *x0 = x->Size() > 0 ? (*x)[0] : nullptr;
if (x0 == nullptr) {
OP_LOGE(ACLNN_ERR_PARAM_INVALID, "(*x)[0] is nullptr.");
return nullptr;
}
for (size_t i(0); i < outLength; ++i) {
tensorsVec.emplace_back(executor->AllocTensor(yDtype, x0->GetStorageFormat(), x0->GetOriginalFormat()));
}
int64_t outputDtype = yDtype == DataType::DT_INT32 ? 2 : -1;
auto out = executor->AllocTensorList(tensorsVec.data(), outLength);
auto ret = INFER_SHAPE(GroupedMatmul,
OP_INPUT(x, weight, biasOptional, scaleOptional, offsetOptional, antiquantScaleOptional,
antiquantOffsetOptional, groupListOptional, perTokenScaleOptional),
OP_OUTPUT(out),
OP_ATTR(splitItem, outputDtype, transposeWeight, transposeX, groupType, groupListType, actType, tuningConfig));
if (ret != ACLNN_SUCCESS) {
OP_LOGE(ACLNN_ERR_PARAM_INVALID, "InferShape failed.");
return nullptr;
}
ret = ADD_TO_LAUNCHER_LIST_AICORE(GroupedMatmul,
OP_INPUT(x, weight, biasOptional, scaleOptional, offsetOptional,
antiquantScaleOptional, antiquantOffsetOptional, groupListOptional,
perTokenScaleOptional),
OP_OUTPUT(out),
OP_ATTR(splitItem, outputDtype, transposeWeight, transposeX, groupType, groupListType,
actType, tuningConfig));
if (ret != ACLNN_SUCCESS) {
OP_LOGE(ACLNN_ERR_PARAM_INVALID, "ADD_TO_LAUNCHER_LIST_AICORE failed.");
return nullptr;
}
return out;
}
}