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