开源盘古 Ultra-MoE-718B 模型量化：W8A8动态量化技术

开源盘古 Ultra-MoE-718B 模型量化：W8A8动态量化技术【免费下载链接】openPangu-Ultra-MoE-718B-model昇腾原生的开源盘古 Ultra-MoE-718B 语言模型项目地址: http...

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611人浏览 · 2025-08-28 14:15:46

尚绮令Imogen · 2025-08-28 14:15:46 发布

开源盘古 Ultra-MoE-718B 模型量化：W8A8动态量化技术

【免费下载链接】openPangu-Ultra-MoE-718B-model 昇腾原生的开源盘古 Ultra-MoE-718B 语言模型项目地址: https://ai.gitcode.com/ascend-tribe/openpangu-ultra-moe-718b-model

引言

在大规模语言模型部署中，内存占用和推理速度是两大关键挑战。openPangu-Ultra-MoE-718B作为7180亿参数的混合专家模型，通过W8A8动态量化技术实现了显著的性能优化。本文将深入解析该模型的量化实现原理、技术架构和实际应用效果。

W8A8动态量化技术概述

W8A8（Weight 8-bit, Activation 8-bit）动态量化是一种先进的模型压缩技术，它将权重和激活值都量化为8位整数，同时保持模型精度。相比传统的FP16/BF16精度，W8A8量化可减少约50%的内存占用和提升推理速度。

量化基本原理

mermaid

盘古Ultra-MoE-718B量化架构

核心量化类结构

mermaid

量化参数管理

量化过程中需要管理多种参数类型：

参数类型	数据类型	作用	存储格式
weight_scale	FP32/BF16	权重缩放因子	per-channel
weight_offset	FP16	权重偏移量	per-channel
input_scale	FP32	输入缩放因子	per-tensor
input_offset	INT8	输入偏移量	per-tensor

动态量化核心实现

def apply_mlp(hidden_states: torch.Tensor,
              w1: torch.Tensor,
              w1_scale: torch.Tensor,
              w2: torch.Tensor,
              w2_scale: torch.Tensor,
              group_list: torch.Tensor,
              dynamic_scale: torch.Tensor = None,
              group_list_type: int = 1) -> torch.Tensor:
    """
    MLP动态量化前向传播
    gate_up_proj → swiglu → down_proj
    """
    if dynamic_scale is None:
        unquantized_hidden_states = hidden_states
        hidden_states, pertoken_scale = torch_npu.npu_dynamic_quant(hidden_states)
        dispose_tensor(unquantized_hidden_states)
    else:
        pertoken_scale = dynamic_scale

    # gate_up_proj量化计算
    hidden_states = torch_npu.npu_grouped_matmul(
        x=[hidden_states],
        weight=[w1],
        scale=[w1_scale],
        per_token_scale=[pertoken_scale],
        split_item=2,
        group_list_type=group_list_type,
        group_type=0,
        group_list=group_list,
        output_dtype=w2_scale.dtype)[0]

    # swiglu激活函数
    hidden_states = torch_npu.npu_swiglu(hidden_states)
    hidden_states, swiglu_out_scale = torch_npu.npu_dynamic_quant(hidden_states)

    # down_proj量化计算
    hidden_states = torch_npu.npu_grouped_matmul(
        x=[hidden_states],
        weight=[w2],
        scale=[w2_scale],
        per_token_scale=[swiglu_out_scale],
        split_item=2,
        group_list_type=group_list_type,
        group_type=0,
        group_list=group_list,
        output_dtype=w2_scale.dtype)[0]

    return hidden_states

MoE专家路由的量化优化

专家选择与量化集成

mermaid

多专家并行量化处理

def fused_experts_with_mc2(
    hidden_states: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    w1_scale: torch.Tensor,
    w2_scale: torch.Tensor,
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
    top_k: int,
    expert_map: torch.Tensor = None,
    moe_all_to_all_group_name: str = "",
    log2phy: torch.Tensor = None,
    global_redundant_expert_num: int = 0,
    shared_experts: Optional[Any] = None,
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
    """
    MC2架构下的多专家量化融合
    """
    if log2phy is not None:
        topk_ids = log2phy[topk_ids]
    
    # MC2分布式量化分发
    kwargs_mc2 = {
        "x": hidden_states,
        "expert_ids": topk_ids,
        "expert_shard_type": 0,
        "shared_expert_rank_num": 0,
        "moe_expert_num": len(expert_map) + global_redundant_expert_num,
        "global_bs": 0,
        "expert_scales": topk_weights.to(torch.float32),
    }
    
    # 量化模式配置
    stage1_kwargs = {
        "scales": None,
        "quant_mode": 2,  # 动态量化模式
        "group_ep": moe_all_to_all_group_name,
        # ... 其他配置参数
    }
    kwargs_mc2.update(stage1_kwargs)
    
    output = torch_npu.npu_moe_distribute_dispatch(**kwargs_mc2)
    expand_x, dynamic_scale, expand_idx, expert_token_nums, ep_recv_counts, _, expand_scales = output[0:7]
    
    # 应用量化MLP
    down_out_list = apply_mlp(expand_x, w1, w1_scale, w2, w2_scale, 
                             expert_token_nums, dynamic_scale=dynamic_scale)
    
    # MC2量化结果组合
    kwargs_mc2 = {
        "expand_x": down_out_list,
        "expert_ids": topk_ids,
        "expand_idx": expand_idx,
        "expert_scales": topk_weights.to(torch.float32),
        # ... 其他参数
    }
    
    hidden_states = torch_npu.npu_moe_distribute_combine(**kwargs_mc2)
    return hidden_states

性能优化技术

内存优化策略

优化技术	效果	实现方式
权重共享量化	减少50%存储	同一专家共享量化参数
动态scale计算	实时精度调整	per-token量化缩放
内存复用	减少分配开销	dispose_tensor机制
格式转换优化	提升计算效率	ACL_FORMAT_FRACTAL_NZ

计算加速技术

class AscendW8A8DynamicLinearMethod:
    def apply(self, layer, x, bias=None, tp_rank=0):
        config = getattr(layer, "_ascend_quant_config", {})
        if not isinstance(x, tuple):
            output_dtype = config.get("output_dtype", x.dtype)
            quantized_x, dynamic_scale = torch_npu.npu_dynamic_quant(x)
        else:
            output_dtype = config["output_dtype"]
            quantized_x, dynamic_scale = x
        
        pertoken_scale = dynamic_scale if config.get("pertoken_scale", True) else None

        # 使用NPU专用量化矩阵乘法
        output = torch_npu.npu_quant_matmul(
            quantized_x,
            layer.weight,
            layer.weight_scale,
            pertoken_scale=pertoken_scale,
            bias=bias,
            output_dtype=output_dtype,
        )
        
        return (output, dynamic_scale) if config.get("return_scale", False) else output

实际部署指南

量化配置示例

# 量化参数配置
quant_config = {
    "output_dtype": torch.bfloat16,
    "pertoken_scale": True,
    "return_scale": False,
    "quant_mode": "dynamic",  # 动态量化模式
}

# 应用量化到线性层
linear_layer._ascend_quant_config = quant_config
quantized_output = ascend_quant_method.apply(linear_layer, input_tensor)