mirror of https://github.com/hpcaitech/ColossalAI
108 lines
3.7 KiB
Python
108 lines
3.7 KiB
Python
import torch
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from colossalai.zero.sharded_param import ShardedTensor
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from typing import Optional, Tuple
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from colossalai.zero.sharded_param.tensor_utils import colo_tensor_mem_usage
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from .tensorful_state import StatefulTensor, TensorState
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from typing import List
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EMPTY_TENSOR_DICT = {}
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def get_empty_tensor(device: torch.device, dtype: torch.dtype):
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key = (device, dtype)
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if key not in EMPTY_TENSOR_DICT:
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EMPTY_TENSOR_DICT[key] = torch.empty(0, dtype=dtype, device=device)
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return EMPTY_TENSOR_DICT[key]
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class ShardedParamV2(object):
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def __init__(self, param: torch.nn.Parameter, set_data_none: bool = False) -> None:
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self._sharded_data_tensor: ShardedTensor = ShardedTensor(param.data)
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self.saved_grad: StatefulTensor = StatefulTensor(None, TensorState.FREE)
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# This attribute must be initialized in ShardedModel
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self.offload_grad: bool = False
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# make sure the shared param is the only owner of payload
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# The param.data maybe used to init the other part of the model.
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# For example: File "resnet.py", line 190, in __init__
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# nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
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# So we can not empty the .data at this time
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self.param = param
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if set_data_none:
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self.set_data_none()
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def get_payload_tensors(self) -> List[StatefulTensor]:
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"""returns stateful tensors kept by this class.
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"""
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return [self._sharded_data_tensor]
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def set_data_none(self):
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self.param.data = get_empty_tensor(self.sharded_data_tensor.device, self.sharded_data_tensor.dtype)
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def set_grad_none(self):
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self.saved_grad.set_null()
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@property
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def sharded_data_tensor(self):
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return self._sharded_data_tensor
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@property
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def data_payload(self):
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return self.sharded_data_tensor.payload
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@property
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def grad_payload(self):
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assert not self.saved_grad.is_null()
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return self.saved_grad.payload
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@property
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def param_is_sharded(self):
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return self.sharded_data_tensor.is_sharded
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def reset_data_payload(self, tensor: torch.Tensor):
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assert type(tensor) is torch.Tensor
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assert tensor.requires_grad is False
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self.sharded_data_tensor.reset_payload(tensor)
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def reset_grad_payload(self, tensor: torch.Tensor):
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assert type(tensor) is torch.Tensor
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assert tensor.requires_grad is False
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self.saved_grad.reset_payload(tensor)
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def get_memory_usage(self) -> Tuple[int, int]:
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"""
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get the memory usage of the param, including data and grad
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Returns:
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Tuple[int, int]: cuda mem usage in Byte, cpu memory usage in Byte
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"""
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cuda_mem_use, cpu_mem_use = 0, 0
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def _update_mem_use(t: Optional[torch.Tensor]):
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if t is None:
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return
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assert isinstance(t, torch.Tensor)
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nonlocal cuda_mem_use
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nonlocal cpu_mem_use
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t_cuda, t_cpu = colo_tensor_mem_usage(t)
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cuda_mem_use += t_cuda
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cpu_mem_use += t_cpu
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address_set = set()
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_update_mem_use(self.data_payload)
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address_set.add(self.data_payload.data_ptr())
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if not self.saved_grad.is_null() and self.saved_grad.data_ptr() not in address_set:
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_update_mem_use(self.grad_payload)
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address_set.add(self.saved_grad.data_ptr())
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if self.param.data is not None and self.param.data.data_ptr() not in address_set:
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_update_mem_use(self.param.data)
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address_set.add(self.param.data.data_ptr())
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if self.param.grad is not None and self.param.grad.data_ptr() not in address_set:
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_update_mem_use(self.param.grad)
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return cuda_mem_use, cpu_mem_use
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