mirror of https://github.com/hpcaitech/ColossalAI
[fx] An experimental version of ColoTracer.' (#2002)
* [fx] add a symbolic_trace api. * [fx] fix import errors. * [fx] ColoTracer experimental.pull/2099/head^2
Super Daniel
GitHub
parent
4b055351b0
commit
2bf2d1cd3b
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@ -0,0 +1,394 @@
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import enum
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import functools
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import inspect
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from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union
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import torch
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from torch.fx import Graph, Node, Proxy, Tracer
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from torch.utils._pytree import tree_map
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from colossalai.fx import ColoGraphModule, compatibility, is_compatible_with_meta
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if is_compatible_with_meta():
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from colossalai.fx.profiler import MetaTensor
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Target = Union[Callable[..., Any], str]
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Argument = Optional[Union[Tuple[Any, ...], # actually Argument, but mypy can't represent recursive types
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List[Any], # actually Argument
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Dict[str, Any], # actually Argument
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slice, # Slice[Argument, Argument, Argument], but slice is not a templated type in typing
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'Node',]]
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_CScriptMethod = ['add', 'mul', 'sub', 'div']
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_TorchNewMethod = [
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"arange", "zeros", "zeros_like", "ones", "ones_like", "full", "full_like", "empty", "empty_like", "eye", "tensor",
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"finfo"
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]
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_TensorPropertyMethod = ["dtype", "shape", "device", "requires_grad", "grad", "grad_fn", "data"]
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def _truncate_suffix(s: str):
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import re
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return re.sub(r'_\d+$', '', s)
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def is_element_in_list(elements: Union[List[Any], Any], list_: List[Any]):
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if isinstance(elements, (tuple, list, set)):
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for ele in elements:
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if ele not in list_:
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return False, ele
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else:
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if elements not in list_:
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return False, elements
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return True, None
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def default_device():
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return torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
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@compatibility(is_backward_compatible=False)
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class ColoProxy(Proxy):
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def __init__(self, *args, data=None, **kwargs):
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super().__init__(*args, **kwargs)
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self._data = data
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@property
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def data(self):
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return self._data
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@data.setter
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def data(self, args):
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wrap_fn = lambda x: MetaTensor(x) if isinstance(x, torch.Tensor) else x
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self._data = tree_map(wrap_fn, args)
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@classmethod
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def __torch_function__(cls, orig_method, types, args=(), kwargs=None):
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proxy = cls.from_torch_proxy(super().__torch_function__(orig_method, types, args, kwargs))
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unwrap_fn = lambda p: p.data if isinstance(p, ColoProxy) else p
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kwargs = {} if kwargs is None else kwargs
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if proxy.data is None:
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proxy.data = orig_method(*tree_map(unwrap_fn, args), **tree_map(unwrap_fn, kwargs))
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return proxy
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@classmethod
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def from_torch_proxy(cls, proxy: Proxy):
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return cls(proxy.node, proxy.tracer)
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def __repr__(self):
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return f"ColoProxy({self.node.name}, data={self.data})"
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def __len__(self):
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return len(self.data)
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def __int__(self):
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return int(self.data)
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def __index__(self):
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try:
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return int(self.data)
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except:
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return torch.zeros(self.data.shape, dtype=torch.bool).numpy().__index__()
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def __float__(self):
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return float(self.data)
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def __bool__(self):
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return self.data
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def __getattr__(self, k):
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return ColoAttribute(self, k, getattr(self._data, k, None))
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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 __isinstancecheck__(self, type):
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return isinstance(self.data, type)
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@property
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def shape(self):
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return self.data.shape
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@property
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def ndim(self):
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return self.data.ndim
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@property
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def device(self):
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proxy = self.tracer.create_proxy('call_function', getattr, (self, 'device'), {})
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proxy.data = self.data.device
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return proxy
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@property
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def dtype(self):
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proxy = self.tracer.create_proxy('call_function', getattr, (self, 'dtype'), {})
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proxy.data = self.data.dtype
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return proxy
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def to(self, *args, **kwargs):
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return self.tracer.create_proxy('call_method', 'to', (self, *args), {**kwargs})
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def cpu(self, *args, **kwargs):
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return self.tracer.create_proxy('call_method', 'cpu', (self, *args), {**kwargs})
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def cuda(self, *args, **kwargs):
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return self.tracer.create_proxy('call_method', 'cuda', (self, *args), {**kwargs})
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@compatibility(is_backward_compatible=False)
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class ColoAttribute(ColoProxy):
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def __init__(self, root, attr: str, data=None):
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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._data = data
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self._node: Optional[Node] = None
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@property
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def node(self):
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# the node for attributes is added lazily, since most will just be method calls
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# which do not rely on the getitem call
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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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def __repr__(self):
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return f"ColoAttribute({self.node.name}, attr={self.attr})"
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@compatibility(is_backward_compatible=False)
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class ColoTracer(Tracer):
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def __init__(self, trace_act_ckpt: bool = False, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self._disable_module_getattr = False
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self.proxy_buffer_attributes = True
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def proxy(self, node: Node) -> 'ColoProxy':
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return ColoProxy(node, self)
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def create_proxy(self,
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kind: str,
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target: Target,
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args: Tuple[Any, ...],
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kwargs: Dict[str, Any],
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name: Optional[str] = None,
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type_expr: Optional[Any] = None,
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proxy_factory_fn: Callable[[Node], 'Proxy'] = None):
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proxy: ColoProxy = super().create_proxy(kind, target, args, kwargs, name, type_expr, proxy_factory_fn)
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unwrap_fn = lambda p: p.data if isinstance(p, ColoProxy) else p
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if kind == 'placeholder':
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proxy.data = self.meta_args[target] if target in self.meta_args else self.concrete_args.get(
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_truncate_suffix(target), None)
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elif kind == 'get_attr':
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self._disable_module_getattr = True
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try:
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attr_itr = self.root
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atoms = target.split(".")
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for atom in atoms:
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attr_itr = getattr(attr_itr, atom)
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proxy.data = attr_itr
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finally:
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self._disable_module_getattr = False
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elif kind == 'call_function':
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proxy.data = target(*tree_map(unwrap_fn, args), **tree_map(unwrap_fn, kwargs))
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elif kind == 'call_method':
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self._disable_module_getattr = True
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try:
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if target == '__call__':
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proxy.data = unwrap_fn(args[0])(*tree_map(unwrap_fn, args[1:]), **tree_map(unwrap_fn, kwargs))
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else:
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if target not in _TensorPropertyMethod:
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proxy._data = getattr(unwrap_fn(args[0]), target)(*tree_map(unwrap_fn, args[1:]),
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**tree_map(unwrap_fn, kwargs))
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finally:
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self._disable_module_getattr = False
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elif kind == 'call_module':
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mod = self.root.get_submodule(target)
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unwrap_fn = lambda p: p.data if isinstance(p, ColoProxy) else p
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self._disable_module_getattr = True
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try:
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proxy.data = mod.forward(*tree_map(unwrap_fn, args), **tree_map(unwrap_fn, kwargs))
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finally:
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self._disable_module_getattr = True
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return proxy
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def trace(self,
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root: torch.nn.Module,
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concrete_args: Optional[Dict[str, torch.Tensor]] = None,
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meta_args: Optional[Dict[str, torch.Tensor]] = None) -> Graph:
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if meta_args is None:
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meta_args = {}
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if concrete_args is None:
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concrete_args = {}
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# check concrete and meta args have valid names
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sig = inspect.signature(root.forward)
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sig_names = set(sig.parameters.keys())
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meta_arg_names = set(meta_args.keys())
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# update concrete args with default values
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non_meta_arg_names = sig_names - meta_arg_names
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for k, v in sig.parameters.items():
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if k in non_meta_arg_names and \
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k not in concrete_args and \
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v.default is not inspect.Parameter.empty:
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concrete_args[k] = v.default
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# get non concrete arg names
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concrete_arg_names = set(concrete_args.keys())
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non_concrete_arg_names = sig_names - concrete_arg_names
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def _check_arg_name_valid(names):
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success, element = is_element_in_list(names, sig_names)
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if not success:
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raise KeyError(
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f"argument {element} is not found in the signature of {root.__class__.__name__}'s forward function")
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_check_arg_name_valid(meta_arg_names)
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_check_arg_name_valid(concrete_arg_names)
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self.concrete_args = concrete_args
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self.meta_args = meta_args
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with _TorchTensorOverride(self):
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self.graph = super().trace(root, concrete_args=concrete_args)
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self.graph.lint()
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return self.graph
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def _post_check(self, non_concrete_arg_names: Set[str]):
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# This is necessary because concrete args are added as input to the traced module since
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# https://github.com/pytorch/pytorch/pull/55888.
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for node in self.graph.nodes:
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if node.op == "placeholder":
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# Removing default values for inputs as the forward pass will fail with them.
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if node.target in non_concrete_arg_names:
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node.args = ()
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# Without this, torch.jit.script fails because the inputs type is Optional[torch.Tensor].
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# It cannot infer on the attributes and methods the input should have, and fails.
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node.type = torch.Tensor
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# It is a concrete arg so it is not used and should be removed.
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else:
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if hasattr(torch.fx._symbolic_trace, "_assert_is_none"):
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# Newer versions of torch.fx emit an assert statement
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# for concrete arguments; delete those before we delete
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# the concrete arg.
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to_delete = []
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for user in node.users:
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if user.target == torch.fx._symbolic_trace._assert_is_none:
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to_delete.append(user)
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for user in to_delete:
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self.graph.erase_node(user)
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self.graph.erase_node(node)
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# TODO: solves GraphModule creation.
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# Without this, return type annotation "Tuple" is causing code execution failure.
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if node.op == "output":
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node.type = None
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self.graph.lint()
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def _module_getattr(self, attr, attr_val, parameter_proxy_cache):
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if getattr(self, "_disable_module_getattr", False):
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return attr_val
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def maybe_get_proxy_for_attr(attr_val, collection_to_search, parameter_proxy_cache):
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for n, p in collection_to_search:
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if attr_val is p:
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if n not in parameter_proxy_cache:
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kwargs = {}
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if 'proxy_factory_fn' in inspect.signature(self.create_proxy).parameters:
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kwargs['proxy_factory_fn'] = (None if not self.param_shapes_constant else
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lambda node: ColoProxy(self, node, n, attr_val))
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val_proxy = self.create_proxy('get_attr', n, (), {}, **kwargs) # type: ignore[arg-type]
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parameter_proxy_cache[n] = val_proxy
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return parameter_proxy_cache[n]
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return None
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if self.proxy_buffer_attributes and isinstance(attr_val, torch.Tensor):
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maybe_buffer_proxy = maybe_get_proxy_for_attr(attr_val, self.root.named_buffers(), parameter_proxy_cache)
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if maybe_buffer_proxy is not None:
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return maybe_buffer_proxy
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if isinstance(attr_val, torch.nn.Parameter):
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maybe_parameter_proxy = maybe_get_proxy_for_attr(attr_val, self.root.named_parameters(),
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parameter_proxy_cache)
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if maybe_parameter_proxy is not None:
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return maybe_parameter_proxy
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return attr_val
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@compatibility(is_backward_compatible=True)
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def symbolic_trace(
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root: Union[torch.nn.Module, Callable[..., Any]],
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concrete_args: Optional[Dict[str, Any]] = None,
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meta_args: Optional[Dict[str, Any]] = None,
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) -> ColoGraphModule:
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if is_compatible_with_meta():
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if meta_args is not None:
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root.to(default_device())
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wrap_fn = lambda x: MetaTensor(x, fake_device=default_device()) if isinstance(x, torch.Tensor) else x
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graph = ColoTracer().trace(root, concrete_args=concrete_args, meta_args=tree_map(wrap_fn, meta_args))
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root.cpu()
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else:
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graph = Tracer().trace(root, concrete_args=concrete_args)
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else:
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from .tracer import ColoTracer as OrigColoTracer
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graph = OrigColoTracer().trace(root, concrete_args=concrete_args, meta_args=meta_args)
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name = root.__class__.__name__ if isinstance(root, torch.nn.Module) else root.__name__
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return ColoGraphModule(root, graph, name)
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@compatibility(is_backward_compatible=False)
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class _TorchTensorOverride(object):
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def __init__(self, tracer: Tracer):
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self.overrides = {}
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self.tracer = tracer
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def __enter__(self):
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def wrap_tensor_method(target):
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@functools.wraps(target)
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def wrapper(*args, **kwargs):
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is_proxy = any(isinstance(p, ColoProxy) for p in args) | any(
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isinstance(p, ColoProxy) for p in kwargs.values())
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if is_proxy:
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# if the arg is a proxy, then need to record this function called on this proxy
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# e.g. torch.ones(size) where size is an input proxy
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self.tracer._disable_module_getattr = True
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try:
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proxy = self.tracer.create_proxy('call_function', target, args, kwargs)
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finally:
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self.tracer._disable_module_getattr = False
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return proxy
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else:
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return target(*args, **kwargs)
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return wrapper, target
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self.overrides = {
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target: wrap_tensor_method(getattr(torch, target))
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for target in _TorchNewMethod
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if callable(getattr(torch, target))
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}
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for name, (wrapper, orig) in self.overrides.items():
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setattr(torch, name, wrapper)
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def __exit__(self, exc_type, exc_val, exc_tb):
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for name, (wrapper, orig) in self.overrides.items():
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setattr(torch, name, orig)
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