CANN性能分析与调优工具链实践：从profiling到性能优化的完整流程

哈__2026-02-10 10:51

本文基于CANN开源社区的多个仓库进行应用案例讲解

CANN组织地址：https://atomgit.com/cann

atc仓库地址：https://atomgit.com/cann/atc

runtime仓库地址：https://atomgit.com/cann/runtime

前言

性能优化是深度学习模型部署的关键环节。CANN提供了完整的性能分析工具链，包括profiling工具、性能数据采集、可视化分析等功能。

本文将展示如何使用CANN的性能分析工具定位性能瓶颈，并通过各种优化手段提升模型推理和训练性能。

Profiling基础

1. 启用性能分析

python 复制代码 
import torch
import torch_npu
from torch_npu.profiler import Profile

class PerformanceProfiler:
    def __init__(self, output_path='./profiling_data'):
        self.output_path = output_path
  
    def profile_model(self, model, input_data, warmup_steps=10, profile_steps=20):
        """对模型进行性能分析"""
        model.eval()
      
        # 预热
        print(f"预热 {warmup_steps} 步...")
        with torch.no_grad():
            for _ in range(warmup_steps):
                _ = model(input_data)
      
        # 开始profiling
        print(f"开始profiling {profile_steps} 步...")
        with Profile(
            activities=[ProfilerActivity.CPU, ProfilerActivity.NPU],
            record_shapes=True,
            profile_memory=True,
            with_stack=True
        ) as prof:
            with torch.no_grad():
                for _ in range(profile_steps):
                    _ = model(input_data)
      
        # 保存结果
        prof.export_chrome_trace(f"{self.output_path}/trace.json")
      
        return prof
  
    def print_summary(self, prof):
        """打印性能摘要"""
        print("\n=== 性能摘要 ===")
        print(prof.key_averages().table(
            sort_by="npu_time_total",
            row_limit=20
        ))
  
    def analyze_memory(self, prof):
        """分析内存使用"""
        print("\n=== 内存分析 ===")
        print(prof.key_averages().table(
            sort_by="npu_memory_usage",
            row_limit=10
        ))

# 使用示例
import torchvision.models as models

# 创建模型
model = models.resnet50().npu()
input_data = torch.randn(32, 3, 224, 224).npu()

# 性能分析
profiler = PerformanceProfiler()
prof = profiler.profile_model(model, input_data)

# 查看结果
profiler.print_summary(prof)
profiler.analyze_memory(prof)

2. 算子级别分析

python 复制代码 
class OperatorProfiler:
    def __init__(self):
        self.op_stats = {}
  
    def profile_operators(self, model, input_data, iterations=100):
        """分析每个算子的性能"""
        import time
      
        model.eval()
      
        # 注册hook
        hooks = []
      
        def make_hook(name):
            def hook(module, input, output):
                if name not in self.op_stats:
                    self.op_stats[name] = {
                        'count': 0,
                        'total_time': 0,
                        'input_shape': [],
                        'output_shape': []
                    }
              
                # 记录形状
                if isinstance(input, tuple):
                    self.op_stats[name]['input_shape'] = [
                        list(i.shape) if hasattr(i, 'shape') else None 
                        for i in input
                    ]
              
                if hasattr(output, 'shape'):
                    self.op_stats[name]['output_shape'] = list(output.shape)
              
                self.op_stats[name]['count'] += 1
          
            return hook
      
        # 为每个模块注册hook
        for name, module in model.named_modules():
            if len(list(module.children())) == 0:  # 叶子节点
                hook = module.register_forward_hook(make_hook(name))
                hooks.append(hook)
      
        # 运行推理
        with torch.no_grad():
            for _ in range(iterations):
                _ = model(input_data)
      
        # 移除hooks
        for hook in hooks:
            hook.remove()
      
        return self.op_stats
  
    def print_operator_stats(self):
        """打印算子统计"""
        print("\n=== 算子统计 ===")
        print(f"{'算子名称':<50} {'调用次数':<10} {'输入形状':<30} {'输出形状':<30}")
        print("-" * 120)
      
        for name, stats in sorted(self.op_stats.items(), 
                                  key=lambda x: x[1]['count'], 
                                  reverse=True):
            input_shape = str(stats['input_shape'])[:28]
            output_shape = str(stats['output_shape'])[:28]
          
            print(f"{name:<50} {stats['count']:<10} {input_shape:<30} {output_shape:<30}")

# 使用示例
op_profiler = OperatorProfiler()
op_stats = op_profiler.profile_operators(model, input_data)
op_profiler.print_operator_stats()

性能指标监控

1. 实时性能监控

python 复制代码 
import time
import psutil
import torch
import torch_npu

class PerformanceMonitor:
    def __init__(self):
        self.metrics = {
            'latency': [],
            'throughput': [],
            'npu_utilization': [],
            'memory_usage': []
        }
  
    def measure_latency(self, model, input_data, iterations=100):
        """测量延迟"""
        model.eval()
        latencies = []
      
        # 预热
        with torch.no_grad():
            for _ in range(10):
                _ = model(input_data)
      
        # 测量
        with torch.no_grad():
            for _ in range(iterations):
                torch.npu.synchronize()
                start = time.time()
              
                _ = model(input_data)
              
                torch.npu.synchronize()
                end = time.time()
              
                latencies.append((end - start) * 1000)  # 转换为毫秒
      
        self.metrics['latency'] = latencies
      
        return {
            'mean': sum(latencies) / len(latencies),
            'min': min(latencies),
            'max': max(latencies),
            'p50': sorted(latencies)[len(latencies) // 2],
            'p95': sorted(latencies)[int(len(latencies) * 0.95)],
            'p99': sorted(latencies)[int(len(latencies) * 0.99)]
        }
  
    def measure_throughput(self, model, batch_size, input_shape, duration=10):
        """测量吞吐量"""
        model.eval()
      
        input_data = torch.randn(batch_size, *input_shape).npu()
      
        total_samples = 0
        start_time = time.time()
      
        with torch.no_grad():
            while time.time() - start_time < duration:
                _ = model(input_data)
                total_samples += batch_size
      
        elapsed = time.time() - start_time
        throughput = total_samples / elapsed
      
        self.metrics['throughput'].append(throughput)
      
        return throughput
  
    def get_npu_utilization(self):
        """获取NPU利用率"""
        # 这里使用torch_npu的API获取NPU信息
        npu_id = torch.npu.current_device()
      
        # 获取NPU利用率（示例）
        utilization = torch.npu.utilization(npu_id)
      
        self.metrics['npu_utilization'].append(utilization)
      
        return utilization
  
    def get_memory_usage(self):
        """获取内存使用情况"""
        npu_id = torch.npu.current_device()
      
        allocated = torch.npu.memory_allocated(npu_id) / 1024**3  # GB
        reserved = torch.npu.memory_reserved(npu_id) / 1024**3    # GB
      
        memory_info = {
            'allocated': allocated,
            'reserved': reserved,
            'free': reserved - allocated
        }
      
        self.metrics['memory_usage'].append(memory_info)
      
        return memory_info
  
    def print_report(self):
        """打印性能报告"""
        print("\n=== 性能报告 ===")
      
        if self.metrics['latency']:
            latencies = self.metrics['latency']
            print(f"\n延迟统计 (ms):")
            print(f"  平均: {sum(latencies) / len(latencies):.2f}")
            print(f"  最小: {min(latencies):.2f}")
            print(f"  最大: {max(latencies):.2f}")
            print(f"  P50: {sorted(latencies)[len(latencies) // 2]:.2f}")
            print(f"  P95: {sorted(latencies)[int(len(latencies) * 0.95)]:.2f}")
            print(f"  P99: {sorted(latencies)[int(len(latencies) * 0.99)]:.2f}")
      
        if self.metrics['throughput']:
            print(f"\n吞吐量: {self.metrics['throughput'][-1]:.2f} samples/sec")
      
        if self.metrics['memory_usage']:
            mem = self.metrics['memory_usage'][-1]
            print(f"\n内存使用:")
            print(f"  已分配: {mem['allocated']:.2f} GB")
            print(f"  已保留: {mem['reserved']:.2f} GB")
            print(f"  空闲: {mem['free']:.2f} GB")

# 使用示例
monitor = PerformanceMonitor()

# 测量延迟
latency_stats = monitor.measure_latency(model, input_data)
print(f"延迟统计: {latency_stats}")

# 测量吞吐量
throughput = monitor.measure_throughput(model, batch_size=32, input_shape=(3, 224, 224))
print(f"吞吐量: {throughput:.2f} samples/sec")

# 获取资源使用情况
memory_info = monitor.get_memory_usage()
print(f"内存使用: {memory_info}")

# 打印完整报告
monitor.print_report()

2. 批量大小优化

python 复制代码 
class BatchSizeOptimizer:
    def __init__(self, model, input_shape):
        self.model = model
        self.input_shape = input_shape
        self.monitor = PerformanceMonitor()
  
    def find_optimal_batch_size(self, min_batch=1, max_batch=256, step=2):
        """寻找最优批量大小"""
        results = []
      
        batch_size = min_batch
        while batch_size <= max_batch:
            try:
                print(f"\n测试批量大小: {batch_size}")
              
                # 创建输入
                input_data = torch.randn(batch_size, *self.input_shape).npu()
              
                # 测量性能
                throughput = self.monitor.measure_throughput(
                    self.model,
                    batch_size,
                    self.input_shape,
                    duration=5
                )
              
                memory_info = self.monitor.get_memory_usage()
              
                results.append({
                    'batch_size': batch_size,
                    'throughput': throughput,
                    'memory_allocated': memory_info['allocated']
                })
              
                print(f"  吞吐量: {throughput:.2f} samples/sec")
                print(f"  内存: {memory_info['allocated']:.2f} GB")
              
                batch_size *= step
              
            except RuntimeError as e:
                if "out of memory" in str(e):
                    print(f"  批量大小 {batch_size} 超出内存限制")
                    break
                else:
                    raise e
      
        # 找到最优批量大小
        if results:
            optimal = max(results, key=lambda x: x['throughput'])
          
            print(f"\n=== 最优批量大小 ===")
            print(f"批量大小: {optimal['batch_size']}")
            print(f"吞吐量: {optimal['throughput']:.2f} samples/sec")
            print(f"内存使用: {optimal['memory_allocated']:.2f} GB")
          
            return optimal
      
        return None

# 使用示例
optimizer = BatchSizeOptimizer(model, input_shape=(3, 224, 224))
optimal_config = optimizer.find_optimal_batch_size(min_batch=1, max_batch=256)

模型优化技术

1. 算子融合

python 复制代码 
import torch
import torch_npu

class OperatorFusion:
    @staticmethod
    def fuse_conv_bn(conv, bn):
        """融合卷积和BatchNorm"""
        # 获取参数
        conv_weight = conv.weight
        conv_bias = conv.bias if conv.bias is not None else torch.zeros(conv.out_channels)
      
        bn_weight = bn.weight
        bn_bias = bn.bias
        bn_mean = bn.running_mean
        bn_var = bn.running_var
        bn_eps = bn.eps
      
        # 计算融合后的权重和偏置
        bn_std = torch.sqrt(bn_var + bn_eps)
        fused_weight = conv_weight * (bn_weight / bn_std).reshape(-1, 1, 1, 1)
        fused_bias = (conv_bias - bn_mean) * bn_weight / bn_std + bn_bias
      
        # 创建新的卷积层
        fused_conv = torch.nn.Conv2d(
            conv.in_channels,
            conv.out_channels,
            conv.kernel_size,
            conv.stride,
            conv.padding,
            conv.dilation,
            conv.groups,
            bias=True
        )
      
        fused_conv.weight.data = fused_weight
        fused_conv.bias.data = fused_bias
      
        return fused_conv
  
    @staticmethod
    def fuse_model(model):
        """融合模型中的算子"""
        # 遍历模型
        modules_to_fuse = []
      
        for name, module in model.named_modules():
            if isinstance(module, torch.nn.Sequential):
                # 查找Conv+BN模式
                for i in range(len(module) - 1):
                    if isinstance(module[i], torch.nn.Conv2d) and \
                       isinstance(module[i+1], torch.nn.BatchNorm2d):
                        modules_to_fuse.append((name, i))
      
        # 执行融合
        for module_name, idx in modules_to_fuse:
            parent = dict(model.named_modules())[module_name]
            conv = parent[idx]
            bn = parent[idx + 1]
          
            fused = OperatorFusion.fuse_conv_bn(conv, bn)
          
            # 替换
            parent[idx] = fused
            parent[idx + 1] = torch.nn.Identity()
      
        return model

# 使用示例
# 融合前
model_original = models.resnet50().npu()
input_data = torch.randn(32, 3, 224, 224).npu()

monitor = PerformanceMonitor()
latency_before = monitor.measure_latency(model_original, input_data)

print(f"融合前延迟: {latency_before['mean']:.2f} ms")

# 融合后
model_fused = OperatorFusion.fuse_model(model_original)
latency_after = monitor.measure_latency(model_fused, input_data)

print(f"融合后延迟: {latency_after['mean']:.2f} ms")
print(f"加速比: {latency_before['mean'] / latency_after['mean']:.2f}x")

2. 混合精度训练

python 复制代码 
class MixedPrecisionTrainer:
    def __init__(self, model, optimizer, loss_scale=128.0):
        self.model = model
        self.optimizer = optimizer
        self.loss_scale = loss_scale
      
        # 转换为FP16
        self.model = self.model.half()
  
    def train_step(self, inputs, targets):
        """训练一步"""
        # 前向传播（FP16）
        outputs = self.model(inputs.half())
        loss = torch.nn.functional.cross_entropy(outputs, targets)
      
        # 缩放损失
        scaled_loss = loss * self.loss_scale
      
        # 反向传播
        self.optimizer.zero_grad()
        scaled_loss.backward()
      
        # 缩放梯度
        for param in self.model.parameters():
            if param.grad is not None:
                param.grad.data = param.grad.data / self.loss_scale
      
        # 更新参数
        self.optimizer.step()
      
        return loss.item()
  
    def train_epoch(self, dataloader):
        """训练一个epoch"""
        self.model.train()
        total_loss = 0
      
        for batch_idx, (inputs, targets) in enumerate(dataloader):
            inputs = inputs.npu()
            targets = targets.npu()
          
            loss = self.train_step(inputs, targets)
            total_loss += loss
          
            if (batch_idx + 1) % 100 == 0:
                avg_loss = total_loss / (batch_idx + 1)
                print(f"Batch {batch_idx + 1}, Loss: {avg_loss:.4f}")
      
        return total_loss / len(dataloader)

# 使用示例
model = models.resnet50().npu()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

trainer = MixedPrecisionTrainer(model, optimizer)

# 假设有dataloader
# for epoch in range(10):
#     loss = trainer.train_epoch(dataloader)
#     print(f"Epoch {epoch + 1}, Loss: {loss:.4f}")

3. 动态shape优化

python 复制代码 
class DynamicShapeOptimizer:
    def __init__(self, model):
        self.model = model
        self.shape_cache = {}
  
    def optimize_for_shape(self, input_shape):
        """为特定shape优化模型"""
        shape_key = str(input_shape)
      
        if shape_key in self.shape_cache:
            return self.shape_cache[shape_key]
      
        # 创建示例输入
        dummy_input = torch.randn(*input_shape).npu()
      
        # 追踪模型
        traced_model = torch.jit.trace(self.model, dummy_input)
      
        # 优化
        traced_model = torch.jit.optimize_for_inference(traced_model)
      
        # 缓存
        self.shape_cache[shape_key] = traced_model
      
        return traced_model
  
    def infer(self, input_data):
        """推理"""
        input_shape = tuple(input_data.shape)
        optimized_model = self.optimize_for_shape(input_shape)
      
        with torch.no_grad():
            output = optimized_model(input_data)
      
        return output

# 使用示例
model = models.resnet50().npu()
optimizer = DynamicShapeOptimizer(model)

# 不同shape的输入
shapes = [
    (1, 3, 224, 224),
    (8, 3, 224, 224),
    (32, 3, 224, 224)
]

for shape in shapes:
    input_data = torch.randn(*shape).npu()
  
    # 第一次会优化并缓存
    start = time.time()
    output = optimizer.infer(input_data)
    elapsed = time.time() - start
  
    print(f"Shape {shape}: {elapsed*1000:.2f} ms")

内存优化

1. 梯度累积

python 复制代码 
class GradientAccumulation:
    def __init__(self, model, optimizer, accumulation_steps=4):
        self.model = model
        self.optimizer = optimizer
        self.accumulation_steps = accumulation_steps
  
    def train_step(self, inputs, targets, step):
        """训练一步（带梯度累积）"""
        # 前向传播
        outputs = self.model(inputs)
        loss = torch.nn.functional.cross_entropy(outputs, targets)
      
        # 缩放损失
        loss = loss / self.accumulation_steps
      
        # 反向传播
        loss.backward()
      
        # 每accumulation_steps步更新一次
        if (step + 1) % self.accumulation_steps == 0:
            self.optimizer.step()
            self.optimizer.zero_grad()
      
        return loss.item() * self.accumulation_steps
  
    def train_epoch(self, dataloader):
        """训练一个epoch"""
        self.model.train()
        total_loss = 0
      
        for batch_idx, (inputs, targets) in enumerate(dataloader):
            inputs = inputs.npu()
            targets = targets.npu()
          
            loss = self.train_step(inputs, targets, batch_idx)
            total_loss += loss
      
        return total_loss / len(dataloader)

# 使用示例
model = models.resnet50().npu()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

# 使用梯度累积，相当于batch_size扩大4倍
trainer = GradientAccumulation(model, optimizer, accumulation_steps=4)

2. 检查点技术

python 复制代码 
from torch.utils.checkpoint import checkpoint

class CheckpointModel(torch.nn.Module):
    def __init__(self, original_model):
        super().__init__()
        self.model = original_model
  
    def forward(self, x):
        # 使用检查点减少内存
        # 只保存部分中间结果，需要时重新计算
      
        # 假设模型有多个stage
        if hasattr(self.model, 'layer1'):
            x = checkpoint(self.model.layer1, x)
            x = checkpoint(self.model.layer2, x)
            x = checkpoint(self.model.layer3, x)
            x = checkpoint(self.model.layer4, x)
          
            x = self.model.avgpool(x)
            x = torch.flatten(x, 1)
            x = self.model.fc(x)
        else:
            x = self.model(x)
      
        return x

# 使用示例
original_model = models.resnet50().npu()
checkpoint_model = CheckpointModel(original_model).npu()

# 对比内存使用
input_data = torch.randn(32, 3, 224, 224).npu()

# 原始模型
torch.npu.reset_peak_memory_stats()
_ = original_model(input_data)
original_memory = torch.npu.max_memory_allocated() / 1024**3

# 检查点模型
torch.npu.reset_peak_memory_stats()
_ = checkpoint_model(input_data)
checkpoint_memory = torch.npu.max_memory_allocated() / 1024**3

print(f"原始模型内存: {original_memory:.2f} GB")
print(f"检查点模型内存: {checkpoint_memory:.2f} GB")
print(f"节省: {(1 - checkpoint_memory/original_memory)*100:.1f}%")

分布式性能优化

1. 通信优化

python 复制代码 
import torch.distributed as dist

class CommunicationOptimizer:
    def __init__(self, model):
        self.model = model
  
    def overlap_communication_computation(self, inputs, targets):
        """重叠通信和计算"""
        # 前向传播
        outputs = self.model(inputs)
        loss = torch.nn.functional.cross_entropy(outputs, targets)
      
        # 反向传播
        loss.backward()
      
        # 异步梯度同步
        handles = []
        for param in self.model.parameters():
            if param.grad is not None:
                handle = dist.all_reduce(param.grad, async_op=True)
                handles.append(handle)
      
        # 等待通信完成
        for handle in handles:
            handle.wait()
      
        return loss.item()
  
    def gradient_compression(self, threshold=0.01):
        """梯度压缩"""
        for param in self.model.parameters():
            if param.grad is not None:
                # 只传输大于阈值的梯度
                mask = torch.abs(param.grad) > threshold
                compressed_grad = param.grad * mask
              
                # 同步压缩后的梯度
                dist.all_reduce(compressed_grad)
              
                param.grad = compressed_grad

# 使用示例（需要在分布式环境中）
# dist.init_process_group(backend='hccl')
# model = models.resnet50().npu()
# optimizer = CommunicationOptimizer(model)

2. 流水线并行

python 复制代码 
class PipelineParallel:
    def __init__(self, model, num_stages=4):
        self.num_stages = num_stages
        self.stages = self.split_model(model)
  
    def split_model(self, model):
        """将模型分割为多个stage"""
        # 简化示例：假设模型有4个layer
        stages = []
      
        if hasattr(model, 'layer1'):
            stages.append(torch.nn.Sequential(
                model.conv1,
                model.bn1,
                model.relu,
                model.maxpool,
                model.layer1
            ))
            stages.append(model.layer2)
            stages.append(model.layer3)
            stages.append(torch.nn.Sequential(
                model.layer4,
                model.avgpool,
                torch.nn.Flatten(),
                model.fc
            ))
      
        return stages
  
    def forward_pipeline(self, inputs, micro_batch_size):
        """流水线前向传播"""
        # 将输入分成多个micro-batch
        micro_batches = torch.split(inputs, micro_batch_size)
      
        # 流水线执行
        outputs = []
        intermediate = [None] * len(micro_batches)
      
        for stage_idx, stage in enumerate(self.stages):
            stage_outputs = []
          
            for batch_idx, micro_batch in enumerate(micro_batches):
                if stage_idx == 0:
                    x = micro_batch
                else:
                    x = intermediate[batch_idx]
              
                x = stage(x)
              
                if stage_idx < len(self.stages) - 1:
                    intermediate[batch_idx] = x
                else:
                    stage_outputs.append(x)
          
            if stage_idx == len(self.stages) - 1:
                outputs = stage_outputs
      
        return torch.cat(outputs, dim=0)

# 使用示例
model = models.resnet50().npu()
pipeline = PipelineParallel(model, num_stages=4)

input_data = torch.randn(32, 3, 224, 224).npu()
output = pipeline.forward_pipeline(input_data, micro_batch_size=8)

推理优化

1. 模型量化

python 复制代码 
import torch.quantization as quantization

class ModelQuantizer:
    def __init__(self, model):
        self.model = model
  
    def quantize_dynamic(self):
        """动态量化（仅权重）"""
        quantized_model = quantization.quantize_dynamic(
            self.model,
            {torch.nn.Linear, torch.nn.Conv2d},
            dtype=torch.qint8
        )
      
        return quantized_model
  
    def quantize_static(self, calibration_data):
        """静态量化（权重+激活）"""
        # 准备模型
        self.model.eval()
        self.model.qconfig = quantization.get_default_qconfig('fbgemm')
      
        # 准备量化
        prepared_model = quantization.prepare(self.model)
      
        # 校准
        with torch.no_grad():
            for data in calibration_data:
                prepared_model(data)
      
        # 转换
        quantized_model = quantization.convert(prepared_model)
      
        return quantized_model
  
    def compare_performance(self, original_model, quantized_model, test_data):
        """对比性能"""
        monitor = PerformanceMonitor()
      
        # 原始模型
        latency_original = monitor.measure_latency(original_model, test_data)
        memory_original = monitor.get_memory_usage()
      
        # 量化模型
        latency_quantized = monitor.measure_latency(quantized_model, test_data)
        memory_quantized = monitor.get_memory_usage()
      
        print("\n=== 量化对比 ===")
        print(f"原始模型延迟: {latency_original['mean']:.2f} ms")
        print(f"量化模型延迟: {latency_quantized['mean']:.2f} ms")
        print(f"加速比: {latency_original['mean'] / latency_quantized['mean']:.2f}x")
        print(f"\n原始模型内存: {memory_original['allocated']:.2f} GB")
        print(f"量化模型内存: {memory_quantized['allocated']:.2f} GB")
        print(f"内存节省: {(1 - memory_quantized['allocated']/memory_original['allocated'])*100:.1f}%")

# 使用示例
model = models.resnet50().npu()
quantizer = ModelQuantizer(model)

# 动态量化
quantized_model = quantizer.quantize_dynamic()

# 对比性能
test_data = torch.randn(32, 3, 224, 224).npu()
quantizer.compare_performance(model, quantized_model, test_data)

2. 模型剪枝

python 复制代码 
import torch.nn.utils.prune as prune

class ModelPruner:
    def __init__(self, model):
        self.model = model
  
    def prune_unstructured(self, amount=0.3):
        """非结构化剪枝"""
        parameters_to_prune = []
      
        for name, module in self.model.named_modules():
            if isinstance(module, (torch.nn.Conv2d, torch.nn.Linear)):
                parameters_to_prune.append((module, 'weight'))
      
        # 全局剪枝
        prune.global_unstructured(
            parameters_to_prune,
            pruning_method=prune.L1Unstructured,
            amount=amount
        )
      
        # 移除剪枝重参数化
        for module, param_name in parameters_to_prune:
            prune.remove(module, param_name)
      
        return self.model
  
    def prune_structured(self, amount=0.3):
        """结构化剪枝（通道剪枝）"""
        for name, module in self.model.named_modules():
            if isinstance(module, torch.nn.Conv2d):
                prune.ln_structured(
                    module,
                    name='weight',
                    amount=amount,
                    n=2,
                    dim=0  # 剪枝输出通道
                )
                prune.remove(module, 'weight')
      
        return self.model
  
    def iterative_pruning(self, dataloader, epochs=10, prune_rate=0.2):
        """迭代剪枝"""
        for epoch in range(epochs):
            # 训练
            self.train_epoch(dataloader)
          
            # 剪枝
            current_amount = prune_rate * (epoch + 1) / epochs
            self.prune_unstructured(amount=current_amount)
          
            print(f"Epoch {epoch + 1}, 剪枝率: {current_amount*100:.1f}%")
      
        return self.model
  
    def train_epoch(self, dataloader):
        """训练一个epoch"""
        self.model.train()
        # 训练逻辑
        pass
  
    def calculate_sparsity(self):
        """计算稀疏度"""
        total_params = 0
        zero_params = 0
      
        for param in self.model.parameters():
            total_params += param.numel()
            zero_params += (param == 0).sum().item()
      
        sparsity = zero_params / total_params
      
        print(f"\n模型稀疏度: {sparsity*100:.2f}%")
        print(f"总参数: {total_params:,}")
        print(f"零参数: {zero_params:,}")
      
        return sparsity

# 使用示例
model = models.resnet50().npu()
pruner = ModelPruner(model)

# 剪枝前
print("剪枝前:")
pruner.calculate_sparsity()

# 执行剪枝
pruned_model = pruner.prune_unstructured(amount=0.3)

# 剪枝后
print("\n剪枝后:")
pruner.calculate_sparsity()

3. 知识蒸馏

python 复制代码 
class KnowledgeDistillation:
    def __init__(self, teacher_model, student_model, temperature=3.0, alpha=0.7):
        self.teacher = teacher_model
        self.student = student_model
        self.temperature = temperature
        self.alpha = alpha
      
        self.teacher.eval()
  
    def distillation_loss(self, student_logits, teacher_logits, labels):
        """蒸馏损失"""
        # 软标签损失
        soft_loss = torch.nn.functional.kl_div(
            torch.nn.functional.log_softmax(student_logits / self.temperature, dim=1),
            torch.nn.functional.softmax(teacher_logits / self.temperature, dim=1),
            reduction='batchmean'
        ) * (self.temperature ** 2)
      
        # 硬标签损失
        hard_loss = torch.nn.functional.cross_entropy(student_logits, labels)
      
        # 组合损失
        loss = self.alpha * soft_loss + (1 - self.alpha) * hard_loss
      
        return loss
  
    def train_step(self, inputs, labels, optimizer):
        """训练一步"""
        # 教师模型推理
        with torch.no_grad():
            teacher_logits = self.teacher(inputs)
      
        # 学生模型推理
        student_logits = self.student(inputs)
      
        # 计算损失
        loss = self.distillation_loss(student_logits, teacher_logits, labels)
      
        # 反向传播
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
      
        return loss.item()
  
    def train_epoch(self, dataloader, optimizer):
        """训练一个epoch"""
        self.student.train()
        total_loss = 0
      
        for batch_idx, (inputs, labels) in enumerate(dataloader):
            inputs = inputs.npu()
            labels = labels.npu()
          
            loss = self.train_step(inputs, labels, optimizer)
            total_loss += loss
          
            if (batch_idx + 1) % 100 == 0:
                avg_loss = total_loss / (batch_idx + 1)
                print(f"Batch {batch_idx + 1}, Loss: {avg_loss:.4f}")
      
        return total_loss / len(dataloader)

# 使用示例
teacher = models.resnet50(pretrained=True).npu()
student = models.resnet18().npu()

distiller = KnowledgeDistillation(teacher, student)
optimizer = torch.optim.SGD(student.parameters(), lr=0.01)

# 训练
# for epoch in range(10):
#     loss = distiller.train_epoch(dataloader, optimizer)
#     print(f"Epoch {epoch + 1}, Loss: {loss:.4f}")

端到端优化案例

1. ResNet推理优化

python 复制代码 
class OptimizedResNetInference:
    def __init__(self, model_path):
        # 加载模型
        self.model = models.resnet50()
        self.model.load_state_dict(torch.load(model_path))
        self.model.eval()
      
        # 优化1: 算子融合
        self.model = OperatorFusion.fuse_model(self.model)
      
        # 优化2: 转换为FP16
        self.model = self.model.half()
      
        # 优化3: 移到NPU
        self.model = self.model.npu()
      
        # 优化4: JIT编译
        dummy_input = torch.randn(1, 3, 224, 224).half().npu()
        self.model = torch.jit.trace(self.model, dummy_input)
        self.model = torch.jit.optimize_for_inference(self.model)
  
    def preprocess(self, image):
        """预处理"""
        from torchvision import transforms
      
        transform = transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            transforms.Normalize(
                mean=[0.485, 0.456, 0.406],
                std=[0.229, 0.224, 0.225]
            )
        ])
      
        return transform(image)
  
    def infer(self, image):
        """推理"""
        # 预处理
        input_tensor = self.preprocess(image).unsqueeze(0).half().npu()
      
        # 推理
        with torch.no_grad():
            output = self.model(input_tensor)
      
        # 后处理
        probabilities = torch.nn.functional.softmax(output[0], dim=0)
      
        return probabilities
  
    def batch_infer(self, images, batch_size=32):
        """批量推理"""
        results = []
      
        for i in range(0, len(images), batch_size):
            batch_images = images[i:i+batch_size]
          
            # 预处理
            batch_tensors = torch.stack([
                self.preprocess(img) for img in batch_images
            ]).half().npu()
          
            # 推理
            with torch.no_grad():
                outputs = self.model(batch_tensors)
          
            # 后处理
            probabilities = torch.nn.functional.softmax(outputs, dim=1)
            results.extend(probabilities.cpu().numpy())
      
        return results

# 使用示例
from PIL import Image

# 创建优化后的推理器
inferencer = OptimizedResNetInference('resnet50.pth')

# 单张图像推理
image = Image.open('test.jpg')
probs = inferencer.infer(image)
print(f"Top-1 概率: {probs.max().item():.4f}")

# 批量推理
images = [Image.open(f'test_{i}.jpg') for i in range(100)]
results = inferencer.batch_infer(images, batch_size=32)
print(f"处理了 {len(results)} 张图像")

2. 性能基准测试

python 复制代码 
class PerformanceBenchmark:
    def __init__(self):
        self.results = {}
  
    def benchmark_model(self, model, input_shape, batch_sizes=[1, 8, 16, 32, 64]):
        """对模型进行基准测试"""
        model.eval()
        monitor = PerformanceMonitor()
      
        print(f"\n=== 基准测试: {model.__class__.__name__} ===")
        print(f"输入形状: {input_shape}")
      
        for batch_size in batch_sizes:
            try:
                input_data = torch.randn(batch_size, *input_shape).npu()
              
                # 测量延迟
                latency_stats = monitor.measure_latency(model, input_data)
              
                # 测量吞吐量
                throughput = monitor.measure_throughput(
                    model,
                    batch_size,
                    input_shape,
                    duration=5
                )
              
                # 测量内存
                memory_info = monitor.get_memory_usage()
              
                self.results[batch_size] = {
                    'latency_mean': latency_stats['mean'],
                    'latency_p99': latency_stats['p99'],
                    'throughput': throughput,
                    'memory': memory_info['allocated']
                }
              
                print(f"\nBatch Size: {batch_size}")
                print(f"  延迟 (mean): {latency_stats['mean']:.2f} ms")
                print(f"  延迟 (p99): {latency_stats['p99']:.2f} ms")
                print(f"  吞吐量: {throughput:.2f} samples/sec")
                print(f"  内存: {memory_info['allocated']:.2f} GB")
              
            except RuntimeError as e:
                if "out of memory" in str(e):
                    print(f"\nBatch Size {batch_size}: 内存不足")
                    break
                else:
                    raise e
  
    def compare_models(self, models_dict, input_shape, batch_size=32):
        """对比多个模型"""
        print(f"\n=== 模型对比 (Batch Size: {batch_size}) ===")
        print(f"{'模型':<20} {'延迟(ms)':<15} {'吞吐量(samples/s)':<20} {'内存(GB)':<15}")
        print("-" * 70)
      
        for name, model in models_dict.items():
            model.eval()
            monitor = PerformanceMonitor()
          
            input_data = torch.randn(batch_size, *input_shape).npu()
          
            latency = monitor.measure_latency(model, input_data)
            throughput = monitor.measure_throughput(model, batch_size, input_shape, duration=5)
            memory = monitor.get_memory_usage()
          
            print(f"{name:<20} {latency['mean']:<15.2f} {throughput:<20.2f} {memory['allocated']:<15.2f}")

# 使用示例
benchmark = PerformanceBenchmark()

# 单模型基准测试
model = models.resnet50().npu()
benchmark.benchmark_model(model, input_shape=(3, 224, 224))

# 多模型对比
models_dict = {
    'ResNet18': models.resnet18().npu(),
    'ResNet50': models.resnet50().npu(),
    'ResNet101': models.resnet101().npu()
}
benchmark.compare_models(models_dict, input_shape=(3, 224, 224), batch_size=32)

总结

CANN性能分析与优化要点：

  • Profiling工具：性能数据采集和分析
  • 性能监控：延迟、吞吐量、资源使用
  • 模型优化：算子融合、混合精度、动态shape
  • 内存优化：梯度累积、检查点技术
  • 分布式优化：通信优化、流水线并行
  • 推理优化：量化、剪枝、知识蒸馏
  • 端到端优化：综合应用各种优化技术

通过CANN的atc和runtime工具，可以系统地分析和优化模型性能，实现高效的AI应用部署。

相关链接

atc仓库地址：https://atomgit.com/cann/atc

runtime仓库地址：https://atomgit.com/cann/runtime

CANN组织地址：https://atomgit.com/cann

相关推荐
九.九17 小时前
ops-transformer：AI 处理器上的高性能 Transformer 算子库
人工智能·深度学习·transformer
春日见17 小时前
拉取与合并：如何让个人分支既包含你昨天的修改，也包含 develop 最新更新
大数据·人工智能·深度学习·elasticsearch·搜索引擎
恋猫de小郭17 小时前
AI 在提高你工作效率的同时，也一直在增加你的疲惫和焦虑
前端·人工智能·ai编程
deephub17 小时前
Agent Lightning：微软开源的框架无关 Agent 训练方案，LangChain/AutoGen 都能用
人工智能·microsoft·langchain·大语言模型·agent·强化学习
大模型RAG和Agent技术实践17 小时前
从零构建本地AI合同审查系统：架构设计与流式交互实战（完整源代码）
人工智能·交互·智能合同审核
老邋遢17 小时前
第三章-AI知识扫盲看这一篇就够了
人工智能
互联网江湖17 小时前
Seedance2.0炸场：长短视频们“修坝”十年，不如AI放水一天？
人工智能
PythonPioneer18 小时前
在AI技术迅猛发展的今天，传统职业该如何“踏浪前行”？
人工智能
冬奇Lab18 小时前
一天一个开源项目（第20篇）：NanoBot - 轻量级AI Agent框架，极简高效的智能体构建工具
人工智能·开源·agent
阿里巴巴淘系技术团队官网博客19 小时前
设计模式Trustworthy Generation：提升RAG信赖度
人工智能·设计模式
热门推荐
01GitHub 镜像站点02Claude Code + GLM4.7 避坑指南：解决 Unable to connect to Anthropic services03UV安装并设置国内源04243 行 microGPT：把“训练 + 推理”拆到骨头里05Zsh/Bash Conda设置延迟启动，启动速度优化06MIUI显示/隐藏5G开关的方法，信号弱时开启手机Wifi通话方法07openclaw配置教程（linux+局域网ollama）08AI 规范驱动开发“三剑客”深度对比：Spec-Kit、Kiro 与 OpenSpec 实战指南09openclaw 配置飞书 报错 应用未建立长连接10在Trae中使用Pencil MCP