mirror of https://github.com/hpcaitech/ColossalAI
130 lines
3.9 KiB
Python
130 lines
3.9 KiB
Python
import operator
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from typing import Any, List, Union
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import torch
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from torch.fx.proxy import Attribute, Proxy
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from colossalai.fx.tracer.meta_patch import meta_patched_function
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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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Example::
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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.node._meta_data = None
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@property
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def meta_data(self):
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return self.node._meta_data
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@meta_data.setter
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def meta_data(self, data: Any):
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self.node._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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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 __int__(self):
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self._assert_has_meta_data()
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return int(self.meta_data)
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def __float__(self):
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self._assert_has_meta_data()
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return float(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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return ColoAttribute(self, k)
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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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def extract_meta(*args, **kwargs):
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"""
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This function is copied from _tracer_utils.py to avoid circular import issue.
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"""
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def _convert(val):
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if isinstance(val, ColoProxy):
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return val.meta_data
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elif isinstance(val, (list, tuple)):
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return type(val)([_convert(ele) for ele in val])
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return val
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new_args = [_convert(val) for val in args]
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new_kwargs = {k: _convert(v) for k, v in kwargs.items()}
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return new_args, new_kwargs
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class ColoAttribute(ColoProxy):
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def __init__(self, root, attr: str):
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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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proxy = self.tracer.create_proxy("call_function", getattr, (self.root, self.attr), {})
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if not isinstance(proxy, ColoProxy):
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meta_args, meta_kwargs = extract_meta(*(self.root, self.attr))
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meta_out = getattr(*meta_args, **meta_kwargs)
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proxy = ColoProxy(proxy.node)
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proxy.meta_data = meta_out
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self._node = proxy.node
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return self._node
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def __call__(self, *args, **kwargs):
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proxy = self.tracer.create_proxy("call_method", self.attr, (self.root,) + args, kwargs)
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if not isinstance(proxy, ColoProxy):
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meta_args, meta_kwargs = extract_meta(*((self.root,) + args), **kwargs)
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method = getattr(meta_args[0].__class__, self.attr)
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if meta_patched_function.has(method):
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meta_target = meta_patched_function.get(method)
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elif meta_patched_function.has(method.__name__):
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meta_target = meta_patched_function.get(method.__name__)
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else:
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meta_target = method
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meta_out = meta_target(*meta_args, **meta_kwargs)
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proxy = ColoProxy(proxy.node)
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proxy.meta_data = meta_out
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return proxy
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