mirror of https://github.com/hpcaitech/ColossalAI
125 lines
3.6 KiB
Python
125 lines
3.6 KiB
Python
import operator
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import torch
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from torch.fx.proxy import Proxy, Attribute
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from typing import List, Union, Any
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__all__ = ['ColoProxy']
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class ColoProxy(Proxy):
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"""
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ColoProxy is a proxy class which uses meta tensor to handle data-dependent control flow. The original torch.fx proxy
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cannot be used to infer the condition statement, with this proxy, torch.fx can still run even with if statements.
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Usage:
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proxy = tracer.create_proxy(...)
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proxy.meta_data = torch.empty(4, 2, device='meta')
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print(len(proxy)) # expect output 4
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"""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self._meta_data = None
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@property
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def meta_data(self):
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return self._meta_data
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@meta_data.setter
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def meta_data(self, data: Any):
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self._meta_data = data
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@property
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def has_meta_data(self):
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return self._meta_data is not None
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def _assert_meta_data_is_tensor(self):
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assert torch.is_tensor(
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self._meta_data) and self._meta_data.is_meta, f'Meta data is not a meta tensor for {self.node.name}'
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def _assert_has_meta_data(self):
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assert self._meta_data is not None, f'Meta data is not set for {self.node.name}'
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@property
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def device(self):
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# Hack so we can track when devices are used. During meta-tensor propagation,
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# replace these values with a constant 'meta'
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return MetaDeviceAttribute(self, "device")
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@property
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def dtype(self):
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self._assert_meta_data_is_tensor()
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return self.meta_data.dtype
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@property
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def shape(self):
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self._assert_meta_data_is_tensor()
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return self.meta_data.shape
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@property
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def ndim(self):
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return self.dim()
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def dim(self):
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self._assert_meta_data_is_tensor()
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return self.meta_data.dim()
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def size(self, dim: int = None):
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self._assert_meta_data_is_tensor()
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if dim is not None:
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return self.meta_data.size(dim=dim)
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else:
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# size(dim=None) will trigger runtime error for meta tensor
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return self.meta_data.size()
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def __len__(self):
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self._assert_has_meta_data()
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return len(self.meta_data)
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def __bool__(self):
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self._assert_has_meta_data()
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return self.meta_data
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def __getattr__(self, k):
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if k == "meta_data":
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return self.__getattribute__(k)
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# note: not added to the graph yet, if this is a method call
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# we peephole optimize to the method invocation
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return Attribute(self, k)
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def __setitem__(self, indices, values):
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return self.tracer.create_proxy("call_function", operator.setitem, (self, indices, values), {})
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def __contains__(self, key):
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if self.node.op == "placeholder":
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# this is used to handle like
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# if x in kwargs
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# we don't handle this case for now
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return False
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return super().__contains__(key)
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class ColoAttribute(ColoProxy):
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def __init__(self, root, attr: str):
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# this class is copied from torch.fx.Attribute
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# but inherits ColoProxy
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self.root = root
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self.attr = attr
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self.tracer = root.tracer
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self._node = None
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@property
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def node(self):
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if self._node is None:
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self._node = self.tracer.create_proxy("call_function", getattr, (self.root, self.attr), {}).node
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return self._node
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def __call__(self, *args, **kwargs):
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return self.tracer.create_proxy("call_method", self.attr, (self.root,) + args, kwargs)
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class MetaDeviceAttribute(ColoAttribute):
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pass
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