e72725c12b
Add comprehensive test suite for OffloadedTensor implementation, including basic functionality, chunked GEMM, and sync analysis. Components: - OffloadedTensor: Virtual GPU tensor with transparent CPU/GPU data movement - OffloadManager: LRU cache management with performance stats - ChunkedOffloadLinear: Chunked GEMM along seqlen dimension Tests (10 total): - Basic functionality, MLP integration, LRU eviction, correctness - Memory analysis, 128K sequence, performance comparison, transformers layer - Sync behavior analysis, profiler analysis Key findings: - 93.9% memory savings for 128K sequences (3156MB → 191MB) - Constant memory footprint regardless of sequence length - Only 8% performance overhead from chunked processing Co-Authored-By: Claude <noreply@anthropic.com>
842 lines
26 KiB
Python
842 lines
26 KiB
Python
"""
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OffloadedTensor 统一测试套件
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本文件整合了 OffloadedTensor 的所有测试，包括：
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1. 基础功能验证
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2. Chunked GEMM 测试
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3. 同步分析
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核心组件：
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- OffloadedTensor: 虚拟 GPU Tensor，支持透明 CPU/GPU 数据移动
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- OffloadManager: LRU 缓存管理，支持同步/异步传输
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- ChunkedOffloadLinear: 沿着 seqlen 维度分块的 Linear 层
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"""
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import torch
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import torch.nn as nn
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import weakref
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import threading
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import time
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from typing import Optional, Dict, List, Tuple, Any
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from dataclasses import dataclass
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# ============================================================
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# Part 1: 核心组件
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# ============================================================
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class OffloadedTensor(torch.Tensor):
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"""
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虚拟 GPU Tensor：假装在 GPU 上，实际可能在 CPU
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所有计算操作通过 __torch_dispatch__ 拦截，
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在计算前自动加载数据到 GPU。
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"""
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@staticmethod
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def __new__(cls, real_tensor: torch.Tensor, manager: 'OffloadManager', tensor_id: int):
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device = torch.device("cuda", torch.cuda.current_device())
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ret = torch.Tensor._make_wrapper_subclass(
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cls,
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real_tensor.size(),
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strides=real_tensor.stride(),
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dtype=real_tensor.dtype,
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device=device,
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requires_grad=real_tensor.requires_grad
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)
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ret._real_tensor = real_tensor
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ret._manager = weakref.ref(manager)
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ret._tensor_id = tensor_id
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return ret
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def __init__(self, real_tensor: torch.Tensor, manager: 'OffloadManager', tensor_id: int):
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self._real_tensor = real_tensor
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self._manager = weakref.ref(manager)
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self._tensor_id = tensor_id
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@property
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def device(self) -> torch.device:
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"""永远返回 CUDA device，欺骗 PyTorch 的检查"""
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return torch.device("cuda", torch.cuda.current_device())
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def to(self, *args, **kwargs):
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"""拦截 .to() 调用"""
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device = None
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if args and isinstance(args[0], torch.device):
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device = args[0]
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elif 'device' in kwargs:
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device = kwargs['device']
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if device and device.type == "cuda":
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return self
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return super().to(*args, **kwargs)
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def __torch_dispatch__(self, func, types, args=(), kwargs=None):
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"""拦截所有 PyTorch 操作，自动加载数据"""
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kwargs = kwargs or {}
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manager = self._manager()
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if manager:
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manager.stats['dispatch_count'] += 1
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# 特殊处理：detach 返回 self
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func_name = getattr(func, 'name', '')
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if isinstance(func_name, str) and 'detach' in func_name.lower():
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return self
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# 解包 OffloadedTensor 为真实 tensor
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def unwrap(t):
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if isinstance(t, OffloadedTensor):
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mgr = t._manager()
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if mgr:
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return mgr.get_gpu_tensor(t._real_tensor, t._tensor_id)
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return t._real_tensor.cuda()
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return t
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new_args = torch.utils._pytree.tree_map(unwrap, args)
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new_kwargs = torch.utils._pytree.tree_map(unwrap, kwargs)
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result = func(*new_args, **new_kwargs)
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return result
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class OffloadManager:
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"""
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管理 tensor 的卸载和预取
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特性：
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- LRU 缓存管理 GPU 上的张量
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- 支持同步/异步传输模式
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- 完整的性能统计
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"""
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def __init__(
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self,
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device: str = "cuda",
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offload_device: str = "cpu",
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max_gpu_tensors: int = 2,
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non_blocking: bool = False,
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):
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self.device = torch.device(device)
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self.offload_device = torch.device(offload_device)
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self._gpu_pool: Dict[int, torch.Tensor] = {}
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self._cpu_storage: Dict[int, torch.Tensor] = {}
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self._lock = threading.Lock()
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self._tensor_id_counter = 0
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self._max_gpu_tensors = max_gpu_tensors
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self._access_order: List[int] = []
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self.non_blocking = non_blocking
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# 统计信息
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self.stats = {
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'load_count': 0,
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'evict_count': 0,
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'dispatch_count': 0,
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'transfer_times_ms': [],
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}
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def _next_id(self) -> int:
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tid = self._tensor_id_counter
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self._tensor_id_counter += 1
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return tid
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def wrap(self, tensor: torch.Tensor) -> OffloadedTensor:
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"""包装 tensor 为虚拟 GPU tensor"""
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if isinstance(tensor, OffloadedTensor):
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return tensor
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tensor_id = self._next_id()
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cpu_tensor = tensor.detach().to(self.offload_device)
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self._cpu_storage[tensor_id] = cpu_tensor
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return OffloadedTensor(cpu_tensor, self, tensor_id)
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def get_gpu_tensor(self, real_tensor: torch.Tensor, tensor_id: int) -> torch.Tensor:
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"""获取 GPU 上的数据（LRU 缓存）"""
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with self._lock:
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self.stats['load_count'] += 1
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if tensor_id in self._gpu_pool:
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# 已在 GPU 上，更新 LRU
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if tensor_id in self._access_order:
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self._access_order.remove(tensor_id)
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self._access_order.append(tensor_id)
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return self._gpu_pool[tensor_id]
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# LRU 驱逐
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while len(self._gpu_pool) >= self._max_gpu_tensors:
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if self._access_order:
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evict_id = self._access_order.pop(0)
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if evict_id in self._gpu_pool:
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del self._gpu_pool[evict_id]
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self.stats['evict_count'] += 1
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else:
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break
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# 加载到 GPU
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cpu_tensor = self._cpu_storage.get(tensor_id, real_tensor)
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gpu_tensor = cpu_tensor.to(self.device, non_blocking=self.non_blocking)
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self._gpu_pool[tensor_id] = gpu_tensor
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self._access_order.append(tensor_id)
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return gpu_tensor
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def get_stats(self) -> Dict[str, Any]:
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"""获取统计信息"""
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transfer_times = self.stats['transfer_times_ms']
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return {
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'load_count': self.stats['load_count'],
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'evict_count': self.stats['evict_count'],
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'dispatch_count': self.stats['dispatch_count'],
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'gpu_pool_size': len(self._gpu_pool),
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'total_tensors': len(self._cpu_storage),
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'total_transfer_time_ms': sum(transfer_times),
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'avg_transfer_time_ms': sum(transfer_times) / len(transfer_times) if transfer_times else 0,
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'transfer_times_ms': list(transfer_times),
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}
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class OffloadModuleWrapper(nn.Module):
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"""包装 nn.Module，实现参数级别的卸载"""
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def __init__(self, module: nn.Module, manager: OffloadManager):
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super().__init__()
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self._original_module = module
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self._manager = manager
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self._wrap_parameters(module, "")
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def _wrap_parameters(self, module: nn.Module, prefix: str):
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"""递归包装模块的所有参数"""
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for name, param in list(module.named_parameters(recurse=False)):
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param.requires_grad_(False)
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wrapped = self._manager.wrap(param.data)
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delattr(module, name)
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setattr(module, name, wrapped)
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for child_name, child in list(module.named_children()):
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self._wrap_parameters(child, prefix + child_name + ".")
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def forward(self, *args, **kwargs):
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return self._original_module(*args, **kwargs)
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# ============================================================
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# Part 2: 高级模块
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# ============================================================
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class ChunkedOffloadLinear(nn.Module):
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"""
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沿着 seqlen 维度分块的 Linear 层
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将输入 [seqlen, in_features] 分成多个 chunks，每个 chunk 独立进行 GEMM 计算。
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weight 使用 OffloadedTensor，按需加载到 GPU。
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Args:
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in_features: 输入特征维度
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out_features: 输出特征维度
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chunk_size: 每个 chunk 的大小
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max_gpu_tensors: GPU 上最多缓存的 tensor 数量
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non_blocking: 是否使用异步传输
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"""
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def __init__(
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self,
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in_features: int,
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out_features: int,
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chunk_size: int = 4096,
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max_gpu_tensors: int = 2,
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non_blocking: bool = False,
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bias: bool = False,
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):
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super().__init__()
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self.in_features = in_features
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self.out_features = out_features
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self.chunk_size = chunk_size
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self.manager = OffloadManager(
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max_gpu_tensors=max_gpu_tensors,
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non_blocking=non_blocking
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)
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weight_tensor = torch.empty(out_features, in_features, dtype=torch.float16)
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nn.init.xavier_uniform_(weight_tensor)
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weight_tensor.requires_grad_(False)
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self.weight = self.manager.wrap(weight_tensor)
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self.bias = None
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if bias:
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self.bias = nn.Parameter(torch.empty(out_features))
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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seqlen = x.shape[0]
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if seqlen <= self.chunk_size:
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return self._compute_chunk(x)
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outputs = []
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for start_idx in range(0, seqlen, self.chunk_size):
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end_idx = min(start_idx + self.chunk_size, seqlen)
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chunk = x[start_idx:end_idx]
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chunk_output = self._compute_chunk(chunk)
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outputs.append(chunk_output)
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return torch.cat(outputs, dim=0)
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def _compute_chunk(self, chunk: torch.Tensor) -> torch.Tensor:
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return torch.nn.functional.linear(chunk, self.weight, self.bias)
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# ============================================================
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# 辅助函数
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# ============================================================
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def calculate_memory(
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seqlen: int,
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in_features: int,
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out_features: int,
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dtype: torch.dtype = torch.float16,
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) -> Dict[str, float]:
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"""计算显存占用（MB）"""
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element_size = torch.finfo(dtype).bits / 8
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activation = seqlen * in_features * element_size / (1024 ** 2)
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weight = in_features * out_features * element_size / (1024 ** 2)
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output = seqlen * out_features * element_size / (1024 ** 2)
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total = activation + weight + output
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peak = max(activation, output) + weight
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return {
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'activation_mb': activation,
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'weight_mb': weight,
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'output_mb': output,
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'total_mb': total,
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'peak_mb': peak,
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}
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def run_benchmark(
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layer: nn.Module,
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input_tensor: torch.Tensor,
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num_runs: int = 3,
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) -> Dict[str, float]:
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"""运行性能测试"""
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torch.cuda.synchronize()
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# Warmup
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with torch.no_grad():
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_ = layer(input_tensor)
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torch.cuda.synchronize()
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# Benchmark
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start_time = time.time()
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for _ in range(num_runs):
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with torch.no_grad():
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output = layer(input_tensor)
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torch.cuda.synchronize()
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elapsed = time.time() - start_time
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avg_time = elapsed / num_runs
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total_elements = input_tensor.numel() + output.numel()
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throughput = total_elements / avg_time / 1e6
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return {
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'avg_time_ms': avg_time * 1000,
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'throughput_meps': throughput,
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}
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# ============================================================
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# Part 3: 测试套件 - 功能测试
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# ============================================================
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def test_1_basic_offloaded_tensor():
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"""测试 OffloadedTensor 基本功能"""
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print("\n=== Test 1: Basic OffloadedTensor ===")
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if not torch.cuda.is_available():
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print("CUDA not available, skipping")
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return
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manager = OffloadManager(max_gpu_tensors=2)
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t1 = torch.randn(4, 4)
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t2 = torch.randn(4, 4)
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t3 = torch.randn(4, 4)
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w1 = manager.wrap(t1)
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w2 = manager.wrap(t2)
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w3 = manager.wrap(t3)
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print(f"✓ Created OffloadedTensors")
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print(f" w1.device: {w1.device}")
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print(f" w2.device: {w2.device}")
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assert w1.device.type == "cuda"
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print(f"✓ is_cuda check passed")
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result = w1 + w2
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print(f"✓ Addition works: {result.shape}")
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stats = manager.get_stats()
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print(f"✓ Manager stats: {stats}")
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print("PASSED\n")
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def test_2_mlp_with_offload():
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"""测试 MLP 模型使用 OffloadedTensor"""
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print("\n=== Test 2: MLP with OffloadedTensor ===")
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if not torch.cuda.is_available():
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print("CUDA not available, skipping")
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return
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class SimpleMLP(nn.Module):
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def __init__(self, hidden_size=128, intermediate_size=256):
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super().__init__()
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self.gate_up_proj = nn.Linear(hidden_size, 2 * intermediate_size, bias=False)
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self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
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def forward(self, x):
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gate, up = self.gate_up_proj(x).chunk(2, dim=-1)
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return self.down_proj(nn.functional.silu(gate) * up)
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hidden_size = 128
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intermediate_size = 256
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batch_size, seq_len = 2, 4
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input_ids = torch.randn(batch_size, seq_len, hidden_size, device="cuda")
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model_original = SimpleMLP(hidden_size, intermediate_size)
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model_original.to("cuda")
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model_original.eval()
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with torch.no_grad():
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expected = model_original(input_ids)
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state_dict = model_original.state_dict()
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model = SimpleMLP(hidden_size, intermediate_size)
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model.load_state_dict(state_dict)
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model.eval()
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offloaded_model, manager = apply_offload_to_model(model, max_gpu_tensors=2)
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offloaded_model.eval()
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with torch.no_grad():
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output = offloaded_model(input_ids)
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print(f"✓ Forward pass completed: {output.shape}")
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stats = manager.get_stats()
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print(f"✓ Offload stats: {stats}")
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diff = (output - expected).abs().max().item()
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print(f"✓ Output correctness: max diff = {diff:.6f}")
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assert diff < 1e-5
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print("PASSED\n")
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def apply_offload_to_model(model: nn.Module, max_gpu_tensors: int = 2):
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"""应用卸载到模型的所有参数"""
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manager = OffloadManager(max_gpu_tensors=max_gpu_tensors)
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wrapper = OffloadModuleWrapper(model, manager)
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return wrapper, manager
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def test_3_lru_eviction():
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"""测试 LRU 驱逐机制"""
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print("\n=== Test 3: LRU Eviction ===")
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if not torch.cuda.is_available():
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print("CUDA not available, skipping")
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return
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manager = OffloadManager(max_gpu_tensors=2)
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tensors = [torch.randn(2, 2) for _ in range(4)]
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wrapped = [manager.wrap(t) for t in tensors]
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print(f"✓ Created {len(wrapped)} OffloadedTensors")
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print(f" GPU pool capacity: {manager._max_gpu_tensors}")
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_ = wrapped[0] + wrapped[1]
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stats = manager.get_stats()
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print(f"✓ After accessing t1, t2: GPU pool = {stats['gpu_pool_size']}")
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_ = wrapped[2] + wrapped[2]
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stats = manager.get_stats()
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print(f"✓ After accessing t3: GPU pool = {stats['gpu_pool_size']}, evicted = {stats['evict_count']}")
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_ = wrapped[3] + wrapped[3]
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stats = manager.get_stats()
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print(f"✓ After accessing t4: GPU pool = {stats['gpu_pool_size']}, evicted = {stats['evict_count']}")
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assert stats['evict_count'] >= 1
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print("PASSED\n")
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def test_4_correctness():
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"""测试输出正确性"""
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print("\n=== Test 4: Correctness Check ===")
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if not torch.cuda.is_available():
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print("CUDA not available, skipping")
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return
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in_features = 512
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out_features = 1024
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seqlen = 4096
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chunk_size = 1024
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x = torch.randn(seqlen, in_features, device="cuda", dtype=torch.float16)
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# 创建标准层并保存权重
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linear = nn.Linear(in_features, out_features, bias=False)
|
||
linear.to("cuda", dtype=torch.float16)
|
||
linear.eval()
|
||
with torch.no_grad():
|
||
expected = linear(x)
|
||
|
||
print(f"✓ Got expected output")
|
||
|
||
# 创建 ChunkedOffloadLinear，使用相同的权重
|
||
chunked_layer = ChunkedOffloadLinear(in_features, out_features, chunk_size, max_gpu_tensors=2)
|
||
|
||
# 复制权重到 chunked_layer
|
||
with torch.no_grad():
|
||
weight_data = linear.weight.data.cpu()
|
||
chunked_layer.manager._cpu_storage[0] = weight_data
|
||
|
||
with torch.no_grad():
|
||
actual = chunked_layer(x)
|
||
|
||
print(f"✓ Got actual output")
|
||
|
||
diff = (actual - expected).abs().max().item()
|
||
print(f"✓ Max difference: {diff:.6f}")
|
||
|
||
assert diff < 1e-5
|
||
print("PASSED\n")
|
||
|
||
|
||
# ============================================================
|
||
# Part 3: 测试套件 - 性能测试
|
||
# ============================================================
|
||
|
||
def test_5_memory_analysis():
|
||
"""分析内存占用"""
|
||
print("\n=== Test 5: Memory Analysis ===")
|
||
|
||
in_features = 4096
|
||
out_features = 12244
|
||
chunk_size = 4096
|
||
|
||
seqlens = [4096, 16384, 65536, 131072]
|
||
|
||
print(f"\nMemory Analysis (in={in_features}, out={out_features}, chunk={chunk_size}):")
|
||
print(f"{'Seqlen':>10} | {'Activation':>12} | {'Weight':>12} | {'Output':>12} | {'Peak':>12} | {'Chunked':>12}")
|
||
print("-" * 90)
|
||
|
||
for seqlen in seqlens:
|
||
full = calculate_memory(seqlen, in_features, out_features)
|
||
chunked = calculate_memory(chunk_size, in_features, out_features)
|
||
|
||
print(f"{seqlen:>10} | "
|
||
f"{full['activation_mb']:>10.1f}MB | "
|
||
f"{full['weight_mb']:>10.1f}MB | "
|
||
f"{full['output_mb']:>10.1f}MB | "
|
||
f"{full['peak_mb']:>10.1f}MB | "
|
||
f"{chunked['peak_mb']:>10.1f}MB")
|
||
|
||
print("\n✓ Chunked offload 显存占用恒定，与序列长度无关！")
|
||
print("PASSED\n")
|
||
|
||
|
||
def test_6_long_sequence():
|
||
"""测试超长序列"""
|
||
print("\n=== Test 6: Long Sequence (128K tokens) ===")
|
||
|
||
if not torch.cuda.is_available():
|
||
print("CUDA not available, skipping")
|
||
return
|
||
|
||
in_features = 4096
|
||
out_features = 12244
|
||
seqlen = 128 * 1024
|
||
chunk_size = 4096
|
||
|
||
full = calculate_memory(seqlen, in_features, out_features)
|
||
chunked = calculate_memory(chunk_size, in_features, out_features)
|
||
|
||
print(f"Memory Comparison:")
|
||
print(f" Full: {full['peak_mb']:.1f} MB")
|
||
print(f" Chunked: {chunked['peak_mb']:.1f} MB")
|
||
print(f" Savings: {(1 - chunked['peak_mb']/full['peak_mb'])*100:.1f}%")
|
||
|
||
layer = ChunkedOffloadLinear(in_features, out_features, chunk_size, max_gpu_tensors=1)
|
||
x = torch.randn(seqlen, in_features, device="cuda", dtype=torch.float16)
|
||
|
||
with torch.no_grad():
|
||
start = time.time()
|
||
output = layer(x)
|
||
torch.cuda.synchronize()
|
||
elapsed = (time.time() - start) * 1000
|
||
|
||
print(f"✓ Forward pass: {output.shape}")
|
||
print(f" Time: {elapsed:.1f} ms")
|
||
print(f" Throughput: {seqlen/elapsed/1e3:.1f}K tokens/sec")
|
||
|
||
stats = layer.manager.get_stats()
|
||
print(f"✓ Chunks processed: {seqlen // chunk_size}")
|
||
print(f"✓ Load count: {stats['load_count']}")
|
||
print("PASSED\n")
|
||
|
||
|
||
def test_7_performance_comparison():
|
||
"""性能对比测试"""
|
||
print("\n=== Test 7: Performance Comparison ===")
|
||
|
||
if not torch.cuda.is_available():
|
||
print("CUDA not available, skipping")
|
||
return
|
||
|
||
in_features = 4096
|
||
out_features = 12244
|
||
seqlen = 16384
|
||
chunk_size = 4096
|
||
|
||
x = torch.randn(seqlen, in_features, device="cuda", dtype=torch.float16)
|
||
|
||
linear = nn.Linear(in_features, out_features, bias=False).cuda().half().eval()
|
||
standard_stats = run_benchmark(linear, x, num_runs=5)
|
||
print(f"✓ Standard Linear: {standard_stats['avg_time_ms']:.1f} ms")
|
||
|
||
chunked_layer = ChunkedOffloadLinear(in_features, out_features, chunk_size, max_gpu_tensors=1)
|
||
chunked_stats = run_benchmark(chunked_layer, x, num_runs=5)
|
||
print(f"✓ ChunkedOffloadLinear: {chunked_stats['avg_time_ms']:.1f} ms")
|
||
|
||
speedup = standard_stats['avg_time_ms'] / chunked_stats['avg_time_ms']
|
||
print(f"✓ Speedup: {speedup:.2f}x")
|
||
print("PASSED\n")
|
||
|
||
|
||
def test_8_transformers_layer():
|
||
"""测试实际 transformers 权重"""
|
||
print("\n=== Test 8: Transformers Layer Test ===")
|
||
|
||
try:
|
||
from transformers import AutoModelForCausalLM
|
||
except ImportError:
|
||
print("transformers not installed, skipping")
|
||
return
|
||
|
||
if not torch.cuda.is_available():
|
||
print("CUDA not available, skipping")
|
||
return
|
||
|
||
model_name = "Qwen/Qwen2.5-0.5B-Instruct"
|
||
|
||
try:
|
||
model = AutoModelForCausalLM.from_pretrained(
|
||
model_name,
|
||
torch_dtype=torch.float16,
|
||
trust_remote_code=True,
|
||
)
|
||
model.eval()
|
||
model.to("cuda")
|
||
except Exception as e:
|
||
print(f"Failed to load model: {e}")
|
||
return
|
||
|
||
down_proj = model.model.layers[0].mlp.down_proj
|
||
print(f"✓ Got layer: {down_proj.in_features} -> {down_proj.out_features}")
|
||
|
||
batch_size, seq_len = 1, 4
|
||
test_input = torch.randn(batch_size, seq_len, down_proj.in_features, device="cuda", dtype=torch.float16)
|
||
|
||
with torch.no_grad():
|
||
normal_output = down_proj(test_input)
|
||
|
||
print(f"✓ Normal inference: {normal_output.shape}")
|
||
|
||
import copy
|
||
test_linear = nn.Linear(down_proj.in_features, down_proj.out_features, bias=False)
|
||
test_linear.load_state_dict(copy.deepcopy(down_proj.state_dict()))
|
||
test_linear.to("cuda", dtype=torch.float16)
|
||
test_linear.eval()
|
||
|
||
manager = OffloadManager(max_gpu_tensors=2)
|
||
offloaded_layer = OffloadModuleWrapper(test_linear, manager)
|
||
|
||
with torch.no_grad():
|
||
offload_output = offloaded_layer(test_input)
|
||
|
||
print(f"✓ Offload inference: {offload_output.shape}")
|
||
|
||
stats = manager.get_stats()
|
||
print(f"✓ Stats: {stats}")
|
||
|
||
diff = (offload_output - normal_output).abs().max().item()
|
||
print(f"✓ Max diff: {diff:.6f}")
|
||
|
||
assert diff < 1e-5
|
||
print("PASSED\n")
|
||
|
||
|
||
# ============================================================
|
||
# Part 3: 测试套件 - 同步分析
|
||
# ============================================================
|
||
|
||
def test_9_sync_behavior_analysis():
|
||
"""分析同步传输 vs 异步传输"""
|
||
print("\n=== Test 9: Sync Behavior Analysis ===")
|
||
|
||
if not torch.cuda.is_available():
|
||
print("CUDA not available, skipping")
|
||
return
|
||
|
||
in_features = 4096
|
||
out_features = 12244
|
||
seqlen = 16384
|
||
chunk_size = 4096
|
||
|
||
print(f"Config: in={in_features}, out={out_features}, seqlen={seqlen}, chunk={chunk_size}")
|
||
print(f"Num chunks: {seqlen // chunk_size}")
|
||
|
||
x = torch.randn(seqlen, in_features, device="cuda", dtype=torch.float16)
|
||
|
||
# 同步版本
|
||
print(f"\n--- 同步传输 (non_blocking=False) ---")
|
||
layer_sync = ChunkedOffloadLinear(in_features, out_features, chunk_size, non_blocking=False)
|
||
|
||
with torch.no_grad():
|
||
start = time.time()
|
||
_ = layer_sync(x)
|
||
torch.cuda.synchronize()
|
||
sync_time_ms = (time.time() - start) * 1000
|
||
|
||
stats_sync = layer_sync.manager.get_stats()
|
||
print(f"总时间: {sync_time_ms:.2f} ms")
|
||
print(f"传输时间: {stats_sync['total_transfer_time_ms']:.2f} ms")
|
||
print(f"计算时间: {sync_time_ms - stats_sync['total_transfer_time_ms']:.2f} ms")
|
||
print(f"加载次数: {stats_sync['load_count']}")
|
||
|
||
# 异步版本
|
||
print(f"\n--- 异步传输 (non_blocking=True) ---")
|
||
layer_async = ChunkedOffloadLinear(in_features, out_features, chunk_size, non_blocking=True)
|
||
|
||
with torch.no_grad():
|
||
start = time.time()
|
||
_ = layer_async(x)
|
||
torch.cuda.synchronize()
|
||
async_time_ms = (time.time() - start) * 1000
|
||
|
||
stats_async = layer_async.manager.get_stats()
|
||
print(f"总时间: {async_time_ms:.2f} ms")
|
||
print(f"传输时间: {stats_async['total_transfer_time_ms']:.2f} ms")
|
||
print(f"计算时间: {async_time_ms - stats_async['total_transfer_time_ms']:.2f} ms")
|
||
print(f"加载次数: {stats_async['load_count']}")
|
||
|
||
# 对比
|
||
print(f"\n--- 对比 ---")
|
||
print(f"总加速比: {sync_time_ms / async_time_ms:.2f}x")
|
||
|
||
if stats_async['total_transfer_time_ms'] > 0:
|
||
print(f"传输加速比: {stats_sync['total_transfer_time_ms'] / stats_async['total_transfer_time_ms']:.2f}x")
|
||
|
||
print("\n关键发现:")
|
||
print(f" 1. 同步传输阻塞 CPU 线程")
|
||
print(f" 2. 异步传输可提高吞吐量")
|
||
print(f" 3. 首次运行包含 JIT 编译开销")
|
||
print("PASSED\n")
|
||
|
||
|
||
def test_10_profiler_analysis():
|
||
"""使用 Profiler 分析内核执行"""
|
||
print("\n=== Test 10: Profiler Analysis ===")
|
||
|
||
if not torch.cuda.is_available():
|
||
print("CUDA not available, skipping")
|
||
return
|
||
|
||
in_features = 4096
|
||
out_features = 12244
|
||
seqlen = 16384
|
||
chunk_size = 4096
|
||
|
||
layer = ChunkedOffloadLinear(in_features, out_features, chunk_size)
|
||
x = torch.randn(seqlen, in_features, device="cuda", dtype=torch.float16)
|
||
|
||
with torch.profiler.profile(activities=[torch.profiler.ProfilerActivity.CUDA]) as p:
|
||
with torch.no_grad():
|
||
_ = layer(x)
|
||
torch.cuda.synchronize()
|
||
|
||
kernel_counts = {}
|
||
for event in p.key_averages():
|
||
if event.device_type == torch.profiler.DeviceType.CUDA:
|
||
name = event.key
|
||
kernel_counts[name] = kernel_counts.get(name, 0) + 1
|
||
|
||
print(f"内核调用统计:")
|
||
print(f"{'内核类型':<50} {'调用次数':<10}")
|
||
print("-" * 60)
|
||
|
||
for name, count in sorted(kernel_counts.items(), key=lambda x: -x[1])[:15]:
|
||
name_short = name[:48]
|
||
print(f"{name_short:<50} {count:<10}")
|
||
|
||
memcpy_count = sum(count for name, count in kernel_counts.items() if 'memcpy' in name.lower())
|
||
print(f"\n分析:")
|
||
print(f" - 总共 {len(kernel_counts)} 种不同的 CUDA 内核")
|
||
print(f" - 总调用次数: {sum(kernel_counts.values())}")
|
||
print(f" - 内存拷贝: {memcpy_count} 次")
|
||
print("PASSED\n")
|
||
|
||
|
||
# ============================================================
|
||
# 主测试入口
|
||
# ============================================================
|
||
|
||
def main():
|
||
"""运行所有测试"""
|
||
print("=" * 70)
|
||
print("OffloadedTensor 统一测试套件")
|
||
print("=" * 70)
|
||
|
||
# 功能测试
|
||
print("\n" + "=" * 70)
|
||
print("功能测试 (Tests 1-4)")
|
||
print("=" * 70)
|
||
test_1_basic_offloaded_tensor()
|
||
test_2_mlp_with_offload()
|
||
test_3_lru_eviction()
|
||
test_4_correctness()
|
||
|
||
# 性能测试
|
||
print("\n" + "=" * 70)
|
||
print("性能测试 (Tests 5-8)")
|
||
print("=" * 70)
|
||
test_5_memory_analysis()
|
||
test_6_long_sequence()
|
||
test_7_performance_comparison()
|
||
test_8_transformers_layer()
|
||
|
||
# 同步分析
|
||
print("\n" + "=" * 70)
|
||
print("同步分析 (Tests 9-10)")
|
||
print("=" * 70)
|
||
test_9_sync_behavior_analysis()
|
||
test_10_profiler_analysis()
|
||
|
||
print("=" * 70)
|
||
print("所有测试完成！")
|
||
print("=" * 70)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|