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import torch
import torch.fx
import warnings
import functools
import builtins
from typing import Any, Callable, Dict, Optional, Union
def embedding_override(self, input):
return torch.empty(*input.shape, self.weight.shape[-1], device='meta')
def nn_layernorm_override(self, input):
return input
def torch_relu_override(x):
return x
def torch_nn_relu_override(self, x):
return x
def functional_relu_override(x, inplace=False):
assert not inplace, 'dont support inplace functional.relu for metatensor analysis'
return x
def torch_where_override(condition, x, y):
# torch.where returns the broadcasted tensor of condition, x, and y,
# so hack it by using addition
return condition.to(device='meta') + x.to(device='meta') + y.to(device='meta')
def torch_abs_override(input, *, out=None):
assert out is None, 'Dont support in-place abs for MetaTensor analysis'
return input
manual_meta_overrides : Dict[Callable, Callable] = {
torch.nn.Embedding: embedding_override,
torch.nn.LayerNorm: nn_layernorm_override,
torch.relu: torch_relu_override,
torch.nn.functional.relu: functional_relu_override,
torch.nn.ReLU: torch_nn_relu_override,
torch.where: torch_where_override,
torch.abs: torch_abs_override,
}
def gen_constructor_wrapper(target):
@functools.wraps(target)
def wrapper(*args, **kwargs):
proxy = None
def check_has_proxy(v):
if isinstance(v, torch.fx.Proxy):
nonlocal proxy
proxy = v
torch.fx.node.map_aggregate(args, check_has_proxy)
torch.fx.node.map_aggregate(kwargs, check_has_proxy)
if proxy is not None:
return proxy.tracer.create_proxy('call_function', target, args, kwargs)
else:
return target(*args, **kwargs)
return wrapper, target
class MetaProxy(torch.fx.Proxy):
def install_tensor_meta(self, tensor_meta):
self._tensor_meta = tensor_meta
def size(self, dim=None):
if hasattr(self, '_tensor_meta') and self._tensor_meta is not None:
return self._tensor_meta.size(*[dim] if dim else [])
return self.tracer.create_proxy('call_method', 'size', (self, dim) if dim else (self,), {})
def dim(self):
if hasattr(self, '_tensor_meta') and self._tensor_meta is not None:
return self._tensor_meta.dim()
return self.tracer.create_proxy('call_method', 'dim', (self,), {})
@property
def shape(self):
if hasattr(self, '_tensor_meta') and self._tensor_meta is not None:
return self._tensor_meta.shape
return self.tracer.create_proxy('call_function', builtins.getattr, (self, 'shape'), {})
@property
def dtype(self):
if hasattr(self, '_tensor_meta') and self._tensor_meta is not None:
return self._tensor_meta.dtype
return self.tracer.create_proxy('call_function', builtins.getattr, (self, 'dtype'), {})
@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')
def __getattr__(self, k):
if k == '_tensor_meta':
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 MetaAttribute(self, k)
class MetaAttribute(MetaProxy):
def __init__(self, root, attr: str):
self.root = root
self.attr = attr
self.tracer = root.tracer
self._node = None
@property
def node(self):
# the node for attributes is added lazily, since most will just be method calls
# which do not rely on the getitem call
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(MetaAttribute):
pass
def proxys_to_metas(v):
if isinstance(v, MetaDeviceAttribute):
return 'meta'
if isinstance(v, torch.fx.Proxy):
assert isinstance(v, MetaProxy), f'Expected MetaProxy but got {type(v)}'
assert hasattr(v, '_tensor_meta'), 'MetaProxy does not have an associated meta'
return v._tensor_meta
return v
class MetaTracer(torch.fx.Tracer):
allow_insert_stateless_mods : bool = True
_TORCH_METHODS_TO_PATCH = ['arange', 'zeros', 'ones', 'full_like', 'eye']
def create_proxy(self, kind, target, args, kwargs, name=None, type_expr=None, proxy_factory_fn=None):
rv = super().create_proxy(kind, target, args, kwargs, name, type_expr, proxy_factory_fn)
if kind == 'placeholder' and target in self.meta_args:
rv.install_tensor_meta(self.meta_args[target])
return rv
if target in self.orig_fns:
# NOTE: tensor constructors in PyTorch define the `device` argument as
# *kwargs-only*. That is why this works. If you add methods to
# _TORCH_METHODS_TO_PATCH that do not define `device` as kwarg-only,
# this will break and you will likely see issues where we cannot infer
# the size of the output.
if 'device' in kwargs:
kwargs['device'] = 'meta'
try:
args_metas = torch.fx.node.map_aggregate(args, proxys_to_metas)
kwargs_metas = torch.fx.node.map_aggregate(kwargs, proxys_to_metas)
if kind == 'call_function':
meta_target = manual_meta_overrides.get(target, target)
meta_out = meta_target(*args_metas, **kwargs_metas)
elif kind == 'call_method':
meta_out = getattr(args_metas[0], target)(*args_metas[1:], **kwargs_metas)
elif kind == 'call_module':
assert hasattr(self, 'orig_forward')
self._disable_module_getattr = True
try:
mod = self.root.get_submodule(target)
mod_type = type(mod)
if mod_type in manual_meta_overrides:
meta_out = manual_meta_overrides[mod_type](mod, *args_metas, **kwargs_metas)
else:
meta_out = self.orig_forward(*args_metas, **kwargs_metas)
finally:
self._disable_module_getattr = False
elif kind == 'get_attr':
self._disable_module_getattr = True
try:
attr_itr = self.root
atoms = target.split('.')
for atom in atoms:
attr_itr = getattr(attr_itr, atom)
assert isinstance(attr_itr, torch.Tensor)
meta_out = attr_itr.to(device='meta')
finally:
self._disable_module_getattr = False
else:
return rv
# TODO
assert isinstance(rv, torch.fx.Proxy), 'Dont support composite output yet'
rv.install_tensor_meta(meta_out)
except Exception as e:
warnings.warn(f'Could not compute metadata for {kind} target {target}: {e}')
return rv
def getattr(self, attr, attr_val, parameter_proxy_cache):
if getattr(self, '_disable_module_getattr', False):
return attr_val
else:
return super().getattr(attr, attr_val, parameter_proxy_cache)
def call_module(self, m, forward, args, kwargs):
self.orig_forward = forward
return super().call_module(m, forward, args, kwargs)
def _insert_module_as_submodule(self, mod: torch.nn.Module) -> str:
"""
Helper method which tries to insert a module that was not declared as submodule.
"""
idx = 0
mod_name = mod.__class__.__name__.lower()
path = f"{mod_name}_{idx}"
while hasattr(self.root, path):
path = f"{mod_name}_{idx}"
idx += 1
self.root.add_module(path, mod)
return path
def path_of_module(self, mod: torch.nn.Module) -> str:
try:
return super().path_of_module(mod)
except NameError as e:
if self.allow_insert_stateless_mods and len(list(mod.parameters())) == 0 and len(list(mod.buffers())) == 0:
path = self._insert_module_as_submodule(mod)
self.prev_module = path
return path
raise
def proxy(self, node):
return MetaProxy(node, self)
def trace(self, root, meta_args : Dict[str, torch.Tensor], concrete_args=None):
assert isinstance(meta_args, dict)
self.meta_args = meta_args
self.patched_torch_methods = {
target: gen_constructor_wrapper(getattr(torch, target)) for target in self._TORCH_METHODS_TO_PATCH
}
self.orig_fns = set()
for name, (wrapper, orig) in self.patched_torch_methods.items():
setattr(torch, name, wrapper)
self.orig_fns.add(orig)
try:
graph = super().trace(root, concrete_args)
graph._tracer_extras = {'meta_args': meta_args}
return graph
finally:
for name, (_, orig) in self.patched_torch_methods.items():
setattr(torch, name, orig)
def symbolic_trace(root : Union[torch.nn.Module, Callable[..., Any]],
meta_args : Optional[Dict[str, torch.Tensor]] = None,
concrete_args: Optional[Dict[str, Any]] = None) -> torch.fx.GraphModule:
tracer = MetaTracer()
graph = tracer.trace(root, meta_args, concrete_args)
name = root.__class__.__name__ if isinstance(root, torch.nn.Module) else root.__name__
gm = torch.fx.GraphModule(tracer.root, graph, name)
return gm