通信缓冲区

    self.input_buffers = [None] * self.num_stages
    self.output_buffers = [None] * self.num_stages
    self.comm_events = [None] * self.num_stages
    
    # 异步操作队列
    self.forward_queue = asyncio.Queue()
    self.backward_queue = asyncio.Queue()
    self.result_queue = asyncio.Queue()
    
    # 启动异步任务
    self.loop = asyncio.get_event_loop()
    self.tasks = []
    
async def forward_async(self, input_ids: torch.Tensor) -> torch.Tensor:
    """异步前向传播"""
    batch_size = input_ids.shape[0]
    num_microbatches = (batch_size + self.microbatch_size - 1) // self.microbatch_size
    
    # 分割微批次
    microbatches = []
    for i in range(num_microbatches):
        start_idx = i * self.microbatch_size
        end_idx = min((i + 1) * self.microbatch_size, batch_size)
        microbatch = input_ids[start_idx:end_idx]
        microbatches.append(microbatch)
    
    # 启动异步任务
    results = []
    for microbatch in microbatches:
        # 将微批次加入队列
        await self.forward_queue.put(microbatch)
    
    # 等待所有结果
    for _ in range(num_microbatches):
        result = await self.result_queue.get()
        results.append(result)
    
    # 合并结果
    final_output = torch.cat(results, dim=0)
    return final_output

async def process_pipeline(self):
    """处理流水线操作"""
    while True:
        # 获取下一个微批次
        microbatch = await self.forward_queue.get()
        
        # 初始化输入缓冲区
        if self.input_buffers[0] is None:
            self.input_buffers[0] = microbatch
        else:
            self.input_buffers[0] = torch.cat([self.input_buffers[0], microbatch], dim=0)
        
        # 处理流水线
        outputs = []
        for i in range(self.num_stages):
            if self.input_buffers[i] is not None:
                # 处理当前阶段的输入
                stage = self.stages[i]
                output = stage(self.input_buffers[i])
                
                if i < self.num_stages - 1:
                    # 通信到下一个阶段
                    if self.overlap_communication:
                        # 异步通信
                        asyncio.create_task(self._async_send_to_stage(i + 1, output))
                    else:
                        # 同步通信
                        self.input_buffers[i + 1] = output.to(self.stages[i + 1].device)
                
                outputs.append(output)
        
        # 将最终输出加入结果队列
        if len(outputs) > 0:
            await self.result_queue.put(outputs[-1])
        
        # 清理缓冲区
        for i in range(self.num_stages):
            if self.input_buffers[i] is not None:
                # 检查是否完成了微批次处理
                if self.input_buffers[i].shape[0] >= self.microbatch_size:
                    # 只保留部分数据用于下一个微批次
                    remaining = self.input_buffers[i].shape[0] - self.microbatch_size
                    if remaining > 0:
                        self.input_buffers[i] = self.input_buffers[i][-remaining:]
                    else:
                        self.input_buffers[i] = None

async def _async_send_to_stage(self, stage_idx: int, tensor: torch.Tensor):
    """异步发送到指定阶段"""
    # 这里应该使用实际的异步通信机制
    # 简化处理，直接设置缓冲区
    self.input_buffers[stage_idx] = tensor.to(self.stages[stage_idx].device)

def start_processing(self):
    """启动处理任务"""
    # 启动流水线处理任务
    task = self.loop.create_task(self.process_pipeline())
    self.tasks.append(task)

def stop_processing(self):
    """停止处理任务"""
    for task in self.tasks:
        task.cancel()
​

class OptimizedCommunication:
"""优化的通信管理"""

def __init__(self, num_stages: int, devices: List[torch.device]):
    self.num_stages = num_stages
    self.devices = devices
    self.communication_overhead = 0.0
    self.comm_count = 0
    
    # 通信缓存
    self.cache_enabled = True
    self.communication_cache = {}
    self.cache_hits = 0
    self.cache_misses = 0

def async_send(self, source_device: torch.device, 
              target_device: torch.device, 
              tensor: torch.Tensor, 
              priority: str = 'normal') -> torch.Tensor:
    """异步发送张量"""
    start_time = time.time()
    
    # 检查通信缓存
    cache_key = (tensor.data_ptr(), str(target_device))
    if self.cache_enabled and cache_key in self.communication_cache:
        self.cache_hits += 1
        cached_tensor = self.communication_cache[cache_key]
        return cached_tensor
    
    # 模拟异步通信
    if source_device == target_device:
        # 本地传输，无通信开销
        result = tensor.to(target_device)
    else:
        # 跨设备通信
        # 简化处理，实际应该使用NCCL或其他通信库
        time.sleep(0.001)  # 模拟通信延迟
        result = tensor.to(target_device)
        
        # 更新通信统计
        self.communication_overhead += time.time() - start_time
        self.comm_count += 1
    
    # 缓存结果
    if self.cache_enabled:
        self.communication_cache[cache_key] = result
        self.cache_misses += 1
    
    return result

def batch_communication(self, tensors: List[Tuple[torch.device, torch.device, torch.Tensor]]) -> List[torch.Tensor]:
    """批量通信优化"""
    # 按目标设备分组
    grouped_comm = {}
    for source_device, target_device, tensor in tensors:
        if target_device not in grouped_comm:
            grouped_comm[target_device] = []
        grouped_comm[target_device].append((source_device, tensor))
    
    # 批量处理
    results = []
    for target_device, comm_list in grouped_comm.items():
        # 这里应该实现真正的批量通信
        # 简化处理，逐个处理
        for source_device, tensor in comm_list:
            result = self.async_send(source_device, target_device, tensor)
            results.append(result)
    
    return results

def get_statistics(self) -> Dict:
    """获取通信统计信息"""
    avg_comm_time = self.communication_overhead / max(self.comm_count, 1)
    cache_hit_rate = self.cache_hits / (self.cache_hits + self.cache_misses) if (self.cache_hits + self.cache_misses) > 0 else 0
    
    return {
        'communication_overhead': self.communication_overhead,
        'comm_count': self.comm_count,
        'avg_communication_time': avg_comm_time,
        'cache_enabled': self.cache_enabled,
        'cache_hits': self.cache_hits,
        'cache_misses': self.cache_misses,
        'cache_hit_rate': cache_hit_rate,
        'num_stages': self.num_stages
    }
​

演示异步流水线通信

async def demonstrate_async_pipeline():
"""演示异步流水线通信"""
print("\n异步流水线通信演示")
print("-" * 50)

# 创建设备
devices = [torch.device(f'cuda:{i}') for i in range(4)]

# 创建测试层
layers = []
d_model = 256
for i in range(12):
    if i % 4 == 0:
        layers.append(nn.MultiheadAttention(d_model, 4))
    else:
        layers.append(nn.Linear(d_model, d_model))

# 创建流水线模型
pipeline_model = PipelineParallelModel(layers, 4, devices)

# 创建异步流水线
async_pipeline = AsyncPipeline(pipeline_model, microbatch_size=4, overlap_communication=True)

# 启动处理
async_pipeline.start_processing()

# 创建输入
batch_size = 16
seq_len = 32
input_ids = torch.randint(0, 10000, (batch_size, seq_len))

# 异步前向传播
start_time = time.time()
output = await async_pipeline.forward_async(input_ids)
async_time = time.time() - start_time

print(f"输入形状: {input_ids.shape}")
print(f"输出形状: {output.shape}")
print(f"异步流水线时间: {async_time:.4f}s")

# 停止处理
async_pipeline.stop_processing()

# 通信统计
comm_stats = async_pipeline.comm_manager.get_statistics()
print(f"\n通信统计:")
print(f"通信开销: {comm_stats['communication_overhead']:.4f}s")
print(f"平均通信时间: {comm_stats['avg_communication_time']:.4f}s")
print(f"缓存命中率: {comm_stats['cache_hit_rate']:.2%}")
​

运行异步演示

asyncio.run(demonstrate_async_pipeline())

## 2. 微批次调度策略

### 2.1 智能微批次管理

**动态微批次调整算法**

```python
class MicrobatchScheduler:
    """微批次调度器"""
    
    def __init__(self, 
                 base_batch_size: int,
                 max_batch_size: int,
                 min_batch_size: int = 1,
                 adaptive_scaling: bool = True):
        self.base_batch_size = base_batch_size
        self.max_batch_size = max_batch_size
        self.min_batch_size = min_batch_size
        self.adaptive_scaling = adaptive_scaling
        
        # 性能监控
        self.performance_history = []
        self.batch_size_history = []
        self.latency_history = []
        self.throughput_history = []
        
        # 自适应参数
        self.current_batch_size = base_batch_size
        self.performance_threshold = 0.95  # 性能阈值
        self.scaling_factor = 1.5
        self.shrink_factor = 0.7
    
    def schedule_microbatches(self, 
                            total_batch_size: int, 
                            gpu_memory_info: Dict) -> List[int]:
        """调度微批次大小"""
        if not self.adaptive_scaling:
            # 固定微批次大小
            return self._fixed_microbatch_schedule(total_batch_size)
        else:
            # 自适应微批次大小
            return self._adaptive_microbatch_schedule(total_batch_size, gpu_memory_info)
    
    def _fixed_microbatch_schedule(self, total_batch_size: int) -> List[int]:
        """固定微批次调度"""
        num_microbatches = (total_batch_size + self.current_batch_size - 1) // self.current_batch_size
        microbatch_sizes = []
        
        remaining = total_batch_size
        for i in range(num_microbatches):
            size = min(self.current_batch_size, remaining)
            microbatch_sizes.append(size)
            remaining -= size
        
        return microbatch_sizes
    
    def _adaptive_microbatch_schedule(self, 
                                    total_batch_size: int, 
                                    gpu_memory_info: Dict) -> List[int]:
        """自适应微批次调度"""
        # 获取GPU内存信息
        available_memory = gpu_memory_info.get('available_memory', 0)
        used_memory = gpu_memory_info.get('used_memory', 0)
        
        # 计算最大可用的微批次大小
        memory_per_token = 0.001  # GB per token (简化估计)
        max_tokens = available_memory / memory_per_token
        max_microbatch_size = min(int(max_tokens), self.max_batch_size)
        
        # 根据历史性能调整微批次大小
        if len(self.performance_history) > 10:
            recent_performance = sum(self.performance_history[-10:]) / 10
            if recent_performance > self.performance_threshold:
                # 性能良好，可以增加微批次大小
                self.current_batch_size = min(
                    int(self.current_batch_size * self.scaling_factor), 
                    max_microbatch_size
                )
            else:
                # 性能不佳，减少微批次大小
                self.current_batch_size = max(
                    int(self.current_batch_size * self.shrink_factor), 
                    self.min_batch_size
                )
        
        # 确保微批次大小在合理范围内
        self.current_batch_size = max(
            self.min_batch_size, 
            min(self.current_batch_size, max_microbatch_size)
        )
        
        # 生成微批次调度
        return self._fixed_microbatch_schedule(total_batch_size)
    
    def update_performance_metrics(self, 
                                 batch_size: int, 
                                 latency: float, 
                                 throughput: float):
        """更新性能指标"""
        self.batch_size_history.append(batch_size)
        self.latency_history.append(latency)
        self.throughput_history.append(throughput)
        
        # 计算性能分数（吞吐量与延迟的比值）
        performance_score = throughput / latency if latency > 0 else 0
        self.performance_history.append(performance_score)
        
        # 保持历史记录长度
        max_history = 100
        if len(self.performance_history) > max_history:
            self.performance_history = self.performance_history[-max_history:]
        if len(self.batch_size_history) > max_history:
            self.batch_size_history = self.batch_size_history[-max_history:]
        if len(self.latency_history) > max_history:
            self.latency_history = self.latency_history[-max_history:]
        if len(self.throughput_history) > max_history:
            self.throughput_history = self.throughput_history[-max_history:]
    
    def get_optimal_batch_size(self, target_latency: float = None) -> int:
        """获取最优批次大小"""
        if target_latency is None:
            # 使用历史数据预测最优批次大小
            if len(self.performance_history) > 0:
                best_performance = max(self.performance_history)
                best_batch_size = self.batch_size_history[
                    self.performance_history.index(best_performance)
                ]
                return best_batch_size
            else:
                return self.base_batch_size
        else:
            # 根据延迟目标调整批次大小
            if len(self.latency_history) > 0:
                # 找到满足延迟条件的最大批次大小
                for i in range(len(self.latency_history) - 1, -1, -1):
                    if self.latency_history[i] <= target_latency:
                        return self.batch_size_history[i]
            
            return self.min_batch_size
    
    def get_statistics(self) -> Dict:
        """获取调度器统计信息"""
        return {
            'current_batch_size': self.current_batch_size,
            'base_batch_size': self.base_batch_size,
            'max_batch_size': self.max_batch_size,
            'min_batch_size': self.min_batch_size,
            'adaptive_scaling': self.adaptive_scaling,
            'performance_history': self.performance_history[-10:],  # 最近10个
            'batch_size_history': self.batch_size_history[-10:],
            'latency_history': self.latency_history[-10:],
            'throughput_history': self.throughput_history[-10:],
            'average_performance': sum(self.performance_history) / len(self.performance_history) if self.performance_history else 0,
            'average_latency': sum(self.latency_history) / len(self.latency_history) if self.latency_history else 0,
            'average_throughput': sum(self.throughput_history) / len(self.throughput_history) if self.throughput_history else 0
        }

class DynamicMicrobatchManager:
    """动态微批次管理器"""
    
    def __init__(self, 
                 model: nn.Module,
                 devices: List[torch.device],
                 base_batch_size: int = 8,
                 max_batch_size: int = 32):
        self.model = model
        self.devices = devices
        self.schedulers = [MicrobatchScheduler(base_batch_size, max_batch_size) for _ in devices]
        
        # GPU监控
        self.gpu_monitors = {}
        for device in devices:
            self.gpu_monitors[device] = self._create_gpu_monitor(device)
    
    def _create_gpu_monitor(self, device: torch.device) -> Dict:
        """创建GPU监控器"""
        return {
            'device': device,
            'memory_info': None,
            'utilization': 0.0,
            'temperature': 0.0,
            'power_usage': 0.0
        }
    
    def update_gpu_status(self):
        """更新GPU状态"""
        for device, monitor in self.gpu_monitors.items():
            try:
                # 获取GPU内存信息
                memory_info = torch.cuda.memory_allocated(device)
                memory_total = torch.cuda.get_device_properties(device).total_memory
                memory_free = memory_total - memory_info
                
                monitor['memory_info'] = {
                    'used_memory': memory_info,
                    'free_memory': memory_free,
                    'total_memory': memory_total,
                    'utilization': memory_info / memory_total
                }
                
                # 模拟获取其他信息（实际应该使用nvidia-ml-py）
                monitor['utilization'] = min(1.0, memory_info / (memory_total * 0.9))
​