mirror of https://github.com/hpcaitech/ColossalAI
95 lines
3.4 KiB
Python
95 lines
3.4 KiB
Python
from typing import Optional
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import torch
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from colossalai.tensor.colo_tensor import ColoTensor
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from colossalai.tensor.param_op_hook import ColoParamOpHookManager
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from .colo_tensor import _convert_output
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WHITE_LIST_FUNCS = {torch.Tensor.__getitem__}
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NO_HOOK_FUNCS = {torch.Tensor.is_floating_point}
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def is_no_hook_op(func) -> bool:
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return (func.__name__.startswith("__") and func not in WHITE_LIST_FUNCS) or func in NO_HOOK_FUNCS
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def filter_colo_parameters(*args, **kwargs):
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param_list = []
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def get_colo_parameters(element) -> None:
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if isinstance(element, list) or isinstance(element, tuple):
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for e in element:
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get_colo_parameters(e)
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elif isinstance(element, dict):
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raise RuntimeError("Found Dict: ColoParameter can't deal with complicated arguments.")
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elif isinstance(element, ColoParameter):
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param_list.append(element)
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return
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for a in args:
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get_colo_parameters(a)
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for v in kwargs.values():
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get_colo_parameters(v)
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return param_list
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def replace_args(args, kwargs, new_args):
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args = new_args[: len(args)]
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for k, v in zip(kwargs.keys(), new_args[len(args) :]):
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kwargs[k] = v
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return tuple(args), kwargs
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class ColoParameter(ColoTensor, torch.nn.Parameter):
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r"""A kind of ColoTensor to be considered as a module parameter."""
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def __new__(cls, data: Optional[torch.Tensor] = None, requires_grad: bool = True) -> "ColoParameter":
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if data is None:
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data = torch.empty(0)
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return torch.Tensor._make_subclass(cls, data, requires_grad)
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@classmethod
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def __torch_function__(cls, func, types, args=..., kwargs=None):
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if kwargs is None:
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kwargs = {}
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if ColoParamOpHookManager.has_hook() and not is_no_hook_op(func):
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params = filter_colo_parameters(*args, **kwargs)
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if len(params) > 0:
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with torch._C.DisableTorchFunction():
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new_args = ColoParamOpHookManager.pre_op(params, *args, *kwargs.values())
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args, kwargs = replace_args(args, kwargs, new_args)
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ret = super().__torch_function__(func, types, args, kwargs)
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with torch._C.DisableTorchFunction():
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ret = ColoParamOpHookManager.post_op(params, ret)
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return _convert_output(ret, func)
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return super().__torch_function__(func, types, args, kwargs)
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def __deepcopy__(self, memo):
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if id(self) in memo:
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return memo[id(self)]
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else:
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with torch._C.DisableTorchFunction():
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data = self.data.clone()
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tensor = ColoParameter(data, self.requires_grad)
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memo[id(self)] = tensor
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return tensor
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def __reduce_ex__(self, proto):
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# Adapted from torch._utils._rebuild_parameter
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# def _rebuild_colo_parameter(data, requires_grad, backward_hooks):
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# colo_param = ColoParameter(data, requires_grad)
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# colo_param._backward_hooks = backward_hooks
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# return colo_param
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# return (
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# _rebuild_colo_parameter,
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# (self.data, self.requires_grad, OrderedDict())
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# )
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# TODO(jzy) we don't support object reflection now.
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# distspec cannot be pickled or rebuilt because it's tightly connected to runtime attribute `process_group`.
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raise NotImplementedError
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