Experimental MetaTensorTracer
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76003
Approved by: https://github.com/jansel
diff --git a/test/test_fx_experimental.py b/test/test_fx_experimental.py
index 33e6abf..2ab9ae3 100644
--- a/test/test_fx_experimental.py
+++ b/test/test_fx_experimental.py
@@ -26,6 +26,7 @@
)
from torch.fx.experimental.rewriter import RewritingTracer
from torch.fx.experimental.schema_type_annotation import AnnotateTypesWithSchema
+from torch.fx.experimental.meta_tracer import MetaTracer
from torch.fx.graph_module import GraphModule
from torch.fx.node import Node
from torch.fx.operator_schemas import (
@@ -667,6 +668,28 @@
# Confirm that the output is correct
self.assertEqual(traced(3, 3), m(3, 3))
+ def test_meta_tracer(self):
+ mt = MetaTracer()
+
+ class MetaTracerTestModule(torch.nn.Module):
+ def __init__(self):
+ super().__init__()
+ self.emb = torch.nn.Embedding(num_embeddings=42, embedding_dim=16)
+ self.layernorm = torch.nn.LayerNorm(16)
+
+ def forward(self, x):
+ emb = self.emb(x)
+ lol = self.layernorm(emb)
+ return torch.relu(lol) if lol.shape[0] < 30 else torch.sigmoid(lol)
+
+ mttm = MetaTracerTestModule()
+ for BS in [15, 35]:
+ x = torch.zeros(BS, dtype=torch.long).random_(42)
+ graph = mt.trace(mttm, meta_args={'x' : x.to(device='meta')})
+ gm = torch.fx.GraphModule(mttm, graph)
+ torch.testing.assert_close(gm(x), mttm(x))
+
+
def test_call_to_assert_with_msg(self):
class M(torch.nn.Module):
def forward(self, a, b):
diff --git a/torch/fx/experimental/meta_tracer.py b/torch/fx/experimental/meta_tracer.py
new file mode 100644
index 0000000..0180392
--- /dev/null
+++ b/torch/fx/experimental/meta_tracer.py
@@ -0,0 +1,256 @@
+import torch
+import torch.fx
+import warnings
+import functools
+import builtins
+
+from typing import Callable, Dict
+
+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 _module_getattr(self, attr, attr_val, parameter_proxy_cache):
+ if getattr(self, '_disable_module_getattr', False):
+ return attr_val
+ else:
+ return super()._module_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:
+ return super().trace(root, concrete_args)
+ finally:
+ for name, (_, orig) in self.patched_torch_methods.items():
+ setattr(torch, name, orig)
