mirror of https://github.com/hpcaitech/ColossalAI
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
125 lines
3.6 KiB
125 lines
3.6 KiB
import operator
|
|
import torch
|
|
from torch.fx.proxy import Proxy, Attribute
|
|
from typing import List, Union, Any
|
|
|
|
__all__ = ['ColoProxy']
|
|
|
|
|
|
class ColoProxy(Proxy):
|
|
"""
|
|
ColoProxy is a proxy class which uses meta tensor to handle data-dependent control flow. The original torch.fx proxy
|
|
cannot be used to infer the condition statement, with this proxy, torch.fx can still run even with if statements.
|
|
|
|
Usage:
|
|
proxy = tracer.create_proxy(...)
|
|
proxy.meta_data = torch.empty(4, 2, device='meta')
|
|
print(len(proxy)) # expect output 4
|
|
|
|
"""
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
self._meta_data = None
|
|
|
|
@property
|
|
def meta_data(self):
|
|
return self._meta_data
|
|
|
|
@meta_data.setter
|
|
def meta_data(self, data: Any):
|
|
self._meta_data = data
|
|
|
|
@property
|
|
def has_meta_data(self):
|
|
return self._meta_data is not None
|
|
|
|
def _assert_meta_data_is_tensor(self):
|
|
assert torch.is_tensor(
|
|
self._meta_data) and self._meta_data.is_meta, f'Meta data is not a meta tensor for {self.node.name}'
|
|
|
|
def _assert_has_meta_data(self):
|
|
assert self._meta_data is not None, f'Meta data is not set for {self.node.name}'
|
|
|
|
@property
|
|
def device(self):
|
|
# Hack so we can track when devices are used. During meta-tensor propagation,
|
|
# replace these values with a constant 'meta'
|
|
return MetaDeviceAttribute(self, "device")
|
|
|
|
@property
|
|
def dtype(self):
|
|
self._assert_meta_data_is_tensor()
|
|
return self.meta_data.dtype
|
|
|
|
@property
|
|
def shape(self):
|
|
self._assert_meta_data_is_tensor()
|
|
return self.meta_data.shape
|
|
|
|
@property
|
|
def ndim(self):
|
|
return self.dim()
|
|
|
|
def dim(self):
|
|
self._assert_meta_data_is_tensor()
|
|
return self.meta_data.dim()
|
|
|
|
def size(self, dim: int = None):
|
|
self._assert_meta_data_is_tensor()
|
|
if dim is not None:
|
|
return self.meta_data.size(dim=dim)
|
|
else:
|
|
# size(dim=None) will trigger runtime error for meta tensor
|
|
return self.meta_data.size()
|
|
|
|
def __len__(self):
|
|
self._assert_has_meta_data()
|
|
return len(self.meta_data)
|
|
|
|
def __bool__(self):
|
|
self._assert_has_meta_data()
|
|
return self.meta_data
|
|
|
|
def __getattr__(self, k):
|
|
if k == "meta_data":
|
|
return self.__getattribute__(k)
|
|
# note: not added to the graph yet, if this is a method call
|
|
# we peephole optimize to the method invocation
|
|
return Attribute(self, k)
|
|
|
|
def __setitem__(self, indices, values):
|
|
return self.tracer.create_proxy("call_function", operator.setitem, (self, indices, values), {})
|
|
|
|
def __contains__(self, key):
|
|
if self.node.op == "placeholder":
|
|
# this is used to handle like
|
|
# if x in kwargs
|
|
# we don't handle this case for now
|
|
return False
|
|
return super().__contains__(key)
|
|
|
|
|
|
class ColoAttribute(ColoProxy):
|
|
|
|
def __init__(self, root, attr: str):
|
|
# this class is copied from torch.fx.Attribute
|
|
# but inherits ColoProxy
|
|
self.root = root
|
|
self.attr = attr
|
|
self.tracer = root.tracer
|
|
self._node = None
|
|
|
|
@property
|
|
def node(self):
|
|
if self._node is None:
|
|
self._node = self.tracer.create_proxy("call_function", getattr, (self.root, self.attr), {}).node
|
|
return self._node
|
|
|
|
def __call__(self, *args, **kwargs):
|
|
return self.tracer.create_proxy("call_method", self.attr, (self.root,) + args, kwargs)
|
|
|
|
|
|
class MetaDeviceAttribute(ColoAttribute):
|
|
pass
|