#!/usr/bin/env python3
# -*- coding: UTF-8 -*-
# ----------------------------------------------------------------------------
# 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.
# ----------------------------------------------------------------------------
__golden__ = {
    "kernel": {
        "matmul_all_reduce": "matmul_all_reduce_golden"
    }
}

import numpy as np
import torch

def matmul_all_reduce_golden(
    x1,
    x2,
    bias=None,
    x3=None,
    antiquant_scale=None,
    antiquant_offset=None,
    dequant_scale=None,
    pertoken_scale=None,
    comm_quant_scale_1=None,
    comm_quant_scale_2=None,
    group=None,
    reduce_op: str = "sum",
    is_trans_a: bool = False,
    is_trans_b: bool = False,
    comm_turn: int = 0,
    antiquant_group_size: int = 0,
    group_size: int = 0,
    y_dtype: int = 0,
    comm_quant_mode: int = 0,
    **kwargs
):
    if is_trans_b:
        x2 = x2.t()
    output = torch.matmul(x1, x2)
    if bias is not None:
        output = torch.add(output, bias)
    return output