昇腾 910B NPU 平台 ops-transformer 算子性能测试与 PyTorch 对比

前言

基于昇腾 910B NPU 配合 CANN 8.2+ 环境，我们聚焦于 ops-transformer 算子的完整部署与性能验证。通过清晰的环境配置、依赖安装、多场景性能测试以及与原生 PyTorch 注意力的对比实验，直观呈现其在低时延、高吞吐量及显存优化上的核心优势，为 LLM 训练/推理等 NLP 任务提供高效、可落地的算子应用参考。

前提条件

开发环境准备

确保计算资源已配置为 NPU 类型，以利用昇腾芯片的专用算力执行 AI 算子。硬件建议配置如下：

NPU 硬件：NPU basic · 1 * NPU 910B
CPU：64GB
容器镜像：ubuntu22.04-py3.11-cann8.2.rc1-sglang-main-notebook（或兼容版本）

环境依赖安装

在 Ubuntu 22.04 + Python 3.11 + CANN 8.2 环境中，Python、GCC、CMake 通常已预装完成。若需手动确认或补全，请检查以下版本要求：

Python >= 3.7.0
GCC >= 7.3.0
CMake >= 3.16.0
pigz（可选，建议 >= 2.4，提升打包速度）
dos2unix
Gawk
googletest（仅执行单元测试时依赖，建议 release-1.11.0）

项目根目录提供了 install_deps.sh 脚本用于一键自动化安装依赖。若遇到系统不支持的情况，可按以下步骤手动独立配置：

安装 Gawk
安装 dos2unix
安装 zlib（pigz 依赖）
安装 pigz
安装 googletest
配置环境变量

环境依赖项验证

运行以下脚本可快速验证关键工具链是否就绪：

import sys
import subprocess
import os

def get_command_version(cmd, version_pattern):
    """执行命令并提取版本号"""
    try:
        result = subprocess.run(cmd, capture_output=True, text=True, check=True)
        for line in result.stdout.splitlines():
            if version_pattern in line:
                return line.strip()
        return "未知版本"
    except (subprocess.CalledProcessError, FileNotFoundError):
         

 ():
    
    python_version = sys.version.split()[]
    ()

    
    gcc_version = get_command_version([, ], )
    gcc_ver = gcc_version.split()[-]    gcc_version  gcc_version
    ()

    
    cmake_version = get_command_version([, ], )
    cmake_ver = cmake_version.split()[]  (cmake_version.split()) >=   cmake_version
    ()

    
    pigz_version = get_command_version([, ], )
    pigz_ver = pigz_version.split()[]    pigz_version  pigz_version
    ()

    
    dos2unix_version = get_command_version([, ], )
    dos2unix_ver = dos2unix_version.split()[]    dos2unix_version  dos2unix_version
    ()

    
    gawk_version = get_command_version([, ], )
    gawk_ver = gawk_version.split()[]  (gawk_version.split()) >=   gawk_version
    ()

    
    gtest_inc = os.path.expanduser()
    gtest_lib = os.path.expanduser()
     os.path.exists(gtest_inc)  os.path.exists(gtest_lib):
        ()

 __name__ == :
    main()

import torch import time import sys # ==================== 基础配置 ==================== DEVICE = 0 WARMUP_TIMES = 20 # 预热次数：避免 NPU 冷启动误差 TEST_TIMES = 100 # 测试次数：取平均值，结果更稳定 TORCH_VERSION = torch.__version__ NPU_AVAILABLE = torch.npu.is_available() # ==================== 多场景测试配置 ==================== # 配置格式：(batch_size, seq_len, num_heads, head_dim) TEST_CONFIGS = [ (4, 256, 4, 64), # 小规模（基础验证场景） (8, 512, 8, 64), # 中规模（常见 LLM 基础配置） (4, 1024, 8, 64), # 长序列（考验长文本处理扩展性） (16, 256, 8, 64), # 大批次（考验高并发吞吐量） (8, 512, 16, 64), # 多注意力头（考验并行计算能力） (2, 2048, 8, 64), # 超长序列（极限场景验证） (32, 128, 4, 64), # 超大批次（极限并发场景） ] # ==================== 核心测试函数 ==================== def ascend_flash_attention(query, key, value, mask): """昇腾 PyTorch 内置的 ops-transformer 优化注意力算子""" return torch.nn.functional.scaled_dot_product_attention( query, key, value, attn_mask=mask, dropout_p=0.0, is_causal=False ) def benchmark(config): """单场景性能测试函数""" batch, seq_len, heads, head_dim = config # 构造符合规范的 NPU 输入张量 query = torch.randn(batch, heads, seq_len, head_dim, dtype=torch.float32).npu() key = torch.randn(batch, heads, seq_len, head_dim, dtype=torch.float32).npu() value = torch.randn(batch, heads, seq_len, head_dim, dtype=torch.float32).npu() mask = torch.ones(batch, 1, seq_len, seq_len, dtype=torch.bool).npu() # 预热阶段 for _ in range(WARMUP_TIMES): ascend_flash_attention(query, key, value, mask) torch.npu.synchronize() # 重置显存统计 torch.npu.reset_peak_memory_stats() # 计时测试阶段 start_time = time.time() for _ in range(TEST_TIMES): ascend_flash_attention(query, key, value, mask) torch.npu.synchronize() total_time = time.time() - start_time # 计算核心性能指标 avg_latency = (total_time / TEST_TIMES) * 1000 throughput = (TEST_TIMES * batch) / total_time peak_memory = torch.npu.max_memory_allocated() / 1024 / 1024 return avg_latency, throughput, peak_memory # ==================== 主程序 ==================== if __name__ == "__main__": print("=" * 90) print("📋 ops-transformer 完整性能测试报告") print("=" * 90) print(f"【环境信息】") print(f" PyTorch 版本：{TORCH_VERSION}") print(f" NPU 设备可用：{'✅' if NPU_AVAILABLE else '❌'}") print(f" 测试设备：NPU-{DEVICE}") print(f" 预热次数：{WARMUP_TIMES} | 测试次数：{TEST_TIMES}") print(f" 数据精度：float32") print("=" * 90) if not NPU_AVAILABLE: print("❌ 错误：NPU 环境未配置就绪") sys.exit(1) print(f"\n{'测试场景':<30} {'平均时延(ms)':<15} {'吞吐量 (样本/秒)':<20} {'峰值显存 (MB)':<15}") print("-" * 90) for idx, config in enumerate(TEST_CONFIGS, 1): batch, seq_len, heads, head_dim = config scene_name = f"场景{idx} (B{batch}, S{seq_len}, H{heads}, D{head_dim})" try: latency, throughput, memory = benchmark(config) print(f"{scene_name:<30} {latency:<15.2f} {throughput:<20.0f} {memory:<15.0f}") except Exception as e: print(f"{scene_name:<30} {'❌ 测试失败':<15} {'-':<20} {'-':<15}") print(f"{'':<30} 错误信息：{str(e)[:60]}...") print("=" * 90)

场景	配置（batch, seq_len, heads）	核心测试目标
小规模	(4, 256, 4)	基础性能（快速验证）
中规模	(8, 512, 8)	常见 LLM 场景（平衡性能）
长序列	(4, 1024, 8)	扩展性（考验长文本处理）
大批次	(16, 256, 8)	吞吐量（高并发场景）
多注意力头	(8, 512, 16)	并行能力（复杂模型场景）

import torch import time import sys import math # ==================== 基础配置 ==================== DEVICE = 0 WARMUP_TIMES = 20 TEST_TIMES = 50 TORCH_VERSION = torch.__version__ NPU_AVAILABLE = torch.npu.is_available() # ==================== 多场景测试配置 ==================== TEST_CONFIGS = [ (4, 256, 4, 64), (8, 512, 8, 64), (4, 1024, 8, 64), (16, 256, 8, 64), (8, 512, 16, 64), ] # ==================== 双版本注意力实现 ==================== def ascend_ops_transformer(query, key, value, mask): """昇腾内置 ops-transformer 优化注意力""" return torch.nn.functional.scaled_dot_product_attention( query, key, value, attn_mask=mask, dropout_p=0.0, is_causal=False ) def torch_vanilla_attention(query, key, value, mask): """原生 PyTorch 注意力（基准版本）""" d_k = query.size(-1) scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k) if mask is not None: scores = scores.masked_fill(mask == 0, -1e9) attn_weights = torch.softmax(scores, dim=-1) return torch.matmul(attn_weights, value) # ==================== 通用基准测试函数 ==================== def benchmark(attention_func, config, version_name): batch, seq_len, heads, head_dim = config query = torch.randn(batch, heads, seq_len, head_dim, dtype=torch.float32).npu() key = torch.randn(batch, heads, seq_len, head_dim, dtype=torch.float32).npu() value = torch.randn(batch, heads, seq_len, head_dim, dtype=torch.float32).npu() mask = torch.ones(batch, 1, seq_len, seq_len, dtype=torch.bool).npu() for _ in range(WARMUP_TIMES): attention_func(query, key, value, mask) torch.npu.synchronize() torch.npu.reset_peak_memory_stats() start_time = time.time() for _ in range(TEST_TIMES): attention_func(query, key, value, mask) torch.npu.synchronize() total_time = time.time() - start_time avg_latency = (total_time / TEST_TIMES) * 1000 throughput = (TEST_TIMES * batch) / total_time peak_memory = torch.npu.max_memory_allocated() / 1024 / 1024 return avg_latency, throughput, peak_memory # ==================== 主程序 ==================== if __name__ == "__main__": print("=" * 120) print("📋 ops-transformer vs 原生 PyTorch 注意力性能对比测试") print("=" * 120) print(f"【环境信息】") print(f" PyTorch 版本：{TORCH_VERSION}") print(f" NPU 设备可用：{'✅' if NPU_AVAILABLE else '❌'}") print(f" 测试设备：NPU-{DEVICE}") print(f" 预热次数：{WARMUP_TIMES} | 测试次数：{TEST_TIMES}") print(f" 数据精度：float32") print("=" * 120) if not NPU_AVAILABLE: print("❌ 错误：NPU 环境未配置就绪") sys.exit(1) print(f"\n{'测试场景':<30} {'版本':<20} {'平均时延 (ms)':<15} {'吞吐量 (样本/秒)':<20} {'峰值显存 (MB)':<15} {'优化倍数 (时延)':<10}") print("-" * 120) for idx, config in enumerate(TEST_CONFIGS, 1): batch, seq_len, heads, head_dim = config scene_name = f"场景{idx} (B{batch}, S{seq_len}, H{heads})" # 1. 测试原生 PyTorch 注意力 try: vanilla_latency, vanilla_throughput, vanilla_memory = benchmark(torch_vanilla_attention, config, "原生 PyTorch 注意力") except Exception as e: vanilla_latency = vanilla_throughput = vanilla_memory = "-" vanilla_err = str(e)[:40] # 2. 测试 ops-transformer try: ops_latency, ops_throughput, ops_memory = benchmark(ascend_ops_transformer, config, "ops-transformer 优化") opt_multiple = f"{vanilla_latency / ops_latency:.1f}x" if vanilla_latency != "-" else "-" except Exception as e: ops_latency = ops_throughput = ops_memory = opt_multiple = "-" ops_err = str(e)[:40] # 输出原生版结果 if vanilla_latency != "-": print(f"{scene_name:<30} {'原生 PyTorch 注意力':<20} {vanilla_latency:<15.2f} {vanilla_throughput:<20.0f} {vanilla_memory:<15.0f} {'-':<10}") else: print(f"{scene_name:<30} {'原生 PyTorch 注意力':<20} {'❌ 测试失败':<15} {'-':<20} {'-':<15} {'-':<10}") # 输出优化版结果 if ops_latency != "-": print(f"{scene_name:<30} {'ops-transformer 优化':<20} {ops_latency:<15.2f} {ops_throughput:<20.0f} {ops_memory:<15.0f} {opt_multiple:<10}") else: print(f"{scene_name:<30} {'ops-transformer 优化':<20} {'❌ 测试失败':<15} {'-':<20} {'-':<15} {'-':<10}") print("=" * 120)

昇腾 910B NPU 平台 ops-transformer 算子性能测试与 PyTorch 对比