昇腾 910B NPU 平台 ops-transformer 算子全场景性能测试与 PyTorch 注意力对比

环境准备与配置

下载社区版 CANN 工具包

包括 CANN toolkit、CANN legacy、CANN ops-math 关键组件。

# 下载 legacy 包
wget https://ascend-cann.obs.cn-north-4.myhuaweicloud.com/CANN/community/8.5.0.alpha001/Ascend-cann-toolkit_8.5.0.alpha001_linux-aarch64.run
wget https://ascend-cann.obs.cn-north-4.myhuaweicloud.com/CANN/community/8.5.0.alpha001/cann-910b-ops-legacy_8.5.0.alpha001_linux-aarch64.run
wget https://ascend-cann.obs.cn-north-4.myhuaweicloud.com/CANN/community/cann-910b-ops-math_8.3.RC1_linux-aarch64.run
chmod +x Ascend-cann-toolkit_8.5.0.alpha001_linux-aarch64.run cann-910b-ops-legacy_8.5.0.alpha001_linux-aarch64.run cann-910b-ops-math_8.3.RC1_linux-aarch64.run

安装与部署社区版 CANN

# 1. 安装 CANN Toolkit
./Ascend-cann-toolkit_8.5.0.alpha001_linux-aarch64.run --full --force --install-path=$HOME/.local/Ascend

# 2. 安装 legacy 包
./cann-910b-ops-legacy_8.5.0.alpha001_linux-aarch64.run --full --install-path=$HOME/.local/Ascend

# 3. 安装 ops-math 包
./cann-910b-ops-math_8.3.RC1_linux-aarch64.run --full --install-path=$HOME/.local/Ascend

环境变量配置

# 切换到 bash 终端
bash
# 加载配置
. ~/.bashrc
# 定义实际安装路径
TOOLKIT_ROOT="$HOME/.local/Ascend/8.5.0.alpha001"
MATH_ROOT="$HOME/.local/Ascend/8.3.RC1"
export PATH="$TOOLKIT_ROOT/bin:$PATH"
export LD_LIBRARY_PATH="$TOOLKIT_ROOT/lib64:$TOOLKIT_ROOT/opp_legacy/lib64:$MATH_ROOT/ops_math/lib64:$LD_LIBRARY_PATH"
export PYTHONPATH="$TOOLKIT_ROOT/python/site-packages:$PYTHONPATH"
export ASCEND_HOME="$TOOLKIT_ROOT"

ops-transformer 项目安装与依赖构建

# 下载项目源码
git clone <repository_url>
# 安装根目录 requirements.txt 依赖
pip3 install -r requirements.txt

ops-transformer 性能测试

测试脚本准备

通过配置 7 类覆盖不同批次、序列长度、注意力头数的测试场景，调用昇腾内置优化的注意力算子，经预热、计时、显存统计等步骤，输出各场景的平均时延、吞吐量及峰值显存。

import torch
import time
import sys

# ==================== 基础配置 ====================
DEVICE = 0
WARMUP_TIMES = 20
TEST_TIMES = 100
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),
    (2, 2048, 8, 64),
    (32, 128, 4, 64),
]

# ==================== 核心测试函数 ====================
def ascend_flash_attention(query, key, value, mask):
    return torch.nn.functional.scaled_dot_product_attention(
        query, key, value, attn_mask=mask, dropout_p=0.0, is_causal=False
    )

def benchmark(config):
    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):
        ascend_flash_attention(query, key, value, mask)
        torch.npu.synchronize()

    torch.npu.reset_peak_memory_stats()
    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

    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

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("=" * 90)

运行测试脚本

python3 ops_perf_complete.py

测试结果与分析

昇腾内置 ops-transformer 优化效果显著，表现出低时延、高吞吐量、显存控制优秀的核心优势。

• 时延水平：所有场景平均时延均≤0.5ms，最小仅 0.07ms。
• 吞吐量：峰值达 47.2 万样本/秒，常规场景吞吐量均≥1.6 万样本/秒。
• 显存控制：峰值显存最高仅 167MB，无 OOM 风险。
• 稳定性：7 个场景全部测试通过。

原生 PyTorch 注意力 vs ops-transformer：注意力性能对比测试

测试脚本准备

通过实现原生 PyTorch 注意力与昇腾内置 ops-transformer 优化注意力双版本，在 5 个统一配置的测试场景中，输出平均时延、吞吐量、峰值显存及时延优化倍数。

import torch
import time
import sys
import math

DEVICE = 0
WARMUP_TIMES = 20
TEST_TIMES = 50
TORCH_VERSION = torch.__version__
NPU_AVAILABLE = torch.npu.is_available()

TEST_CONFIGS = [
    (4, 256, 4, 64),
    (8, 512, 8, 64),
    (4, 1024, 8, 64),
    (16, 256, 8, 64),
    (8, 512, 16, 64),
]

def ascend_ops_transformer(query, key, value, mask):
    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):
    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)

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

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})"
        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 = "-"
        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 = "-"

        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}")
        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}")
    print("=" * 120)

运行测试脚本

python3 ops_perf_complete.py

测试结果与分析

ops-transformer 通过昇腾硬件深度优化，较原生 PyTorch 注意力实现 2.4-4.7 倍时延降低、同等倍数吞吐量提升及最高 54% 显存节省。

• 时延优化：整体降低 2.4-4.7 倍，长序列和多注意力头场景优化达 4.5-4.7 倍。
• 吞吐量：整体提升 2.4-4.7 倍，峰值达 15.2 万样本/秒。
• 显存控制：显存占用较原生版减少 24%-54%，长序列和多注意力头场景显存近乎减半。

总结

ops-transformer 核心优势在于低部署成本 + 高性能 + 强适配。免额外编译安装，依托昇腾 PyTorch 内置优化可快速落地；较原生 PyTorch 注意力时延降低 2.4-4.7 倍、吞吐量同步提升，显存节省 24%-54%，高复杂度场景优化更突出；适配从小规模到极限场景的多样需求，无需大幅修改代码即可支撑 LLM 任务，显著降低开发与硬件资源成本。