mirror of https://github.com/hpcaitech/ColossalAI
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60 lines
1.8 KiB
60 lines
1.8 KiB
from typing import Optional, Tuple
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import torch
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def colo_model_optimizer_usage(optim) -> Tuple[int, int]:
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"""Trace the optimizer memory usage
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Args:
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optim (ShardedOptimV2): an instance of ShardedOptimver
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Returns:
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Tuple[int, int]: cuda/cpu memory usage in Byte
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"""
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if optim is None:
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return 0, 0
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assert hasattr(optim, 'get_memory_usage'), f"{type(optim)} has no attr get_memory_usage()"
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return optim.get_memory_usage()
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def colo_model_mem_usage(model: torch.nn.Module) -> Tuple[int, int]:
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"""
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Trace the model memory usage.
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Args:
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model (torch.nn.Module): a torch model
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Returns:
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Tuple[int, int]: cuda memory usage in Byte, cpu memory usage in Byte
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"""
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if model is None:
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return 0, 0
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def _get_tensor_mem_use(t: Optional[torch.Tensor]):
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if t is None:
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return 0, 0
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assert isinstance(t, torch.Tensor)
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_cpu_mem_usage, _cuda_mem_usage = 0, 0
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if t.device.type == 'cpu':
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_cpu_mem_usage += t.numel() * t.element_size()
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elif t.device.type == 'cuda':
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_cuda_mem_usage += t.numel() * t.element_size()
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return _cuda_mem_usage, _cpu_mem_usage
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cuda_mem_usage = 0
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cpu_mem_usage = 0
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for param in model.parameters():
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if hasattr(param, 'colo_attr'):
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t_cuda, t_cpu = param.colo_attr.get_memory_usage()
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cuda_mem_usage += t_cuda
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cpu_mem_usage += t_cpu
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else:
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t_cuda, t_cpu = _get_tensor_mem_use(param.data)
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cuda_mem_usage += t_cuda
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cpu_mem_usage += t_cpu
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t_cuda, t_cpu = _get_tensor_mem_use(param.grad)
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cuda_mem_usage += t_cuda
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cpu_mem_usage += t_cpu
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return cuda_mem_usage, cpu_mem_usage
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