mirror of https://github.com/hpcaitech/ColossalAI
43 lines
1.6 KiB
Python
43 lines
1.6 KiB
Python
import torch
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import torch.distributed as dist
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from colossalai.zero.sharded_param import ShardedTensor
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from typing import Optional
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class ShardedParamV2(object):
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def __init__(self,
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param: torch.nn.Parameter,
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process_group: Optional[dist.ProcessGroup] = None,
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rm_torch_payload=False) -> None:
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self._sharded_data_tensor: ShardedTensor = ShardedTensor(param.data, process_group)
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self.fp16_grad: Optional[torch.Tensor] = None
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self.fp32_grad: Optional[torch.Tensor] = None
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# This attribute must be initialized in ShardedModel
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self.offload_fp32_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 rm_torch_payload:
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self.remove_torch_payload()
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# Backward count for handle local grad accumulation
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# This value will increment by 1 in every pre-bwd hook
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# And will be reset to 0 in every final-bwd hook
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self.bwd_count = 0
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def remove_torch_payload(self):
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self.param.data = torch.empty([], dtype=self.param.dtype, device=self.param.device)
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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 param_is_sharded(self):
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return self._sharded_data_tensor.is_sharded
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