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ColossalAI/colossalai/fx/proxy.py

75 lines
2.1 KiB

import operator
import torch
from torch.fx.proxy import Proxy, Attribute
__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_tensor = torch.empty(4, 2, device='meta')
print(len(proxy)) # expect output 4
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._meta_tensor = None
@property
def meta_tensor(self):
return self._meta_tensor
@meta_tensor.setter
def meta_tensor(self, tensor: torch.Tensor):
assert tensor is None or tensor.is_meta, 'Expected to receive a meta tensor, but got a non-meta tensor'
self._meta_tensor = tensor
@property
def has_meta_tensor(self):
return self.meta_tensor is not None
def _assert_has_meta(self):
assert self.has_meta_tensor, f'Meta tensor is not set for {self.node.name}'
@property
def dtype(self):
self._assert_has_meta()
return self.meta_tensor.dtype
@property
def shape(self):
self._assert_has_meta()
return self.meta_tensor.shape
def dim(self):
self._assert_has_meta()
return self.meta_tensor.dim()
def size(self, dim: int = None):
self._assert_has_meta()
return self.meta_tensor.size(dim=dim)
def __len__(self):
self._assert_has_meta()
return len(self.meta_tensor)
def __bool__(self):
self._assert_has_meta()
return self.meta_tensor
def __getattr__(self, k):
if k == "metadata":
return self.meta_tensor
# 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), {})