blob: 4ea0db56aae250900031d8e82b86ccbc05c81bfd [file] [log] [blame]
# mypy: allow-untyped-defs
from __future__ import annotations
import contextlib
import dataclasses
import warnings
import weakref
from dataclasses import dataclass
from typing import (
Any,
Callable,
ClassVar,
ContextManager,
Dict,
List,
Optional,
Tuple,
Type,
TYPE_CHECKING,
Union,
)
from typing_extensions import TypeAlias
import torch
from torch._C._autograd import CreationMeta
from torch._C._functorch import (
_add_batch_dim,
_unwrap_functional_tensor,
_wrap_functional_tensor,
get_unwrapped,
is_batchedtensor,
is_functorch_wrapped_tensor,
is_gradtrackingtensor,
is_legacy_batchedtensor,
maybe_get_bdim,
maybe_get_level,
peek_interpreter_stack,
)
from torch._logging import trace_structured
from torch.utils._mode_utils import no_dispatch
from torch.utils._python_dispatch import is_traceable_wrapper_subclass
from torch.utils.weak import WeakIdKeyDictionary
if TYPE_CHECKING:
from torch._C._functorch import CInterpreter
from torch._guards import Source
# Import here to avoid cycle
from torch._subclasses.fake_tensor import FakeTensorMode
# Import the following modules during type checking to enable code intelligence features,
# Do not import unconditionally, as they import sympy and importing sympy is very slow
from torch.fx.experimental.symbolic_shapes import ShapeEnv, SymbolicContext
DimList = List
def safe_is_leaf(t):
try:
return t.is_leaf
except RuntimeError:
# inference mode can trigger this
return False
def safe_grad(t):
with warnings.catch_warnings():
warnings.filterwarnings("ignore", "The .grad attribute of a Tensor")
return t.grad
def assert_eq(a, b):
assert a == b, f"{a} != {b}"
def assert_metadata_eq(
assert_eq,
m1: Union[MetaTensorDesc, torch.Tensor],
m2: torch.Tensor,
*,
skip_symbolic=False,
skip_leaf=False,
):
if isinstance(m1, torch.Tensor):
m1 = MetaTensorDescriber().describe_tensor(m1)
def go(m1, m2):
assert_eq(m1.dtype, m2.dtype)
if not skip_symbolic:
assert_eq(m1.shape, m2.shape)
assert_eq(m1.requires_grad, m2.requires_grad)
if not skip_leaf:
assert_eq(m1.is_leaf, m2.is_leaf)
# MetaTensorDesc doesn't store grad_fn; inferred from leaf
# assert_eq(m1.grad_fn is None, m2.grad_fn is None)
assert_eq(m1.is_sparse, m2.is_sparse)
assert_eq(m1.is_inference, m2.is_inference())
assert_eq(m1.is_conj, m2.is_conj())
assert_eq(m1.is_neg, m2.is_neg())
assert_eq(m1.grad is not None, safe_grad(m2) is not None)
if m1.grad is not None:
go(m1.grad, safe_grad(m2))
if m1.is_sparse:
assert_eq(m1.dense_dim, m2.dense_dim())
assert_eq(m1.sparse_dim, m2.sparse_dim())
assert_eq(m1.is_coalesced, m2.is_coalesced())
else:
if not skip_symbolic:
assert_eq(m1.stride, m2.stride())
assert_eq(m1.storage_offset, m2.storage_offset())
assert_eq(m1.is_view, m2._is_view())
if m1.is_view:
go(m1.base, m2._base)
# TODO: test if is resizable (no direct query for this atm)
# TODO: audit AutogradMeta to see if it matches
# TODO: test forward AD
return go(m1, m2)
def is_sparse_coo(t):
return isinstance(t, torch.Tensor) and t.layout is torch.sparse_coo
def is_sparse_compressed_layout(layout):
return layout in {
torch.sparse_csr,
torch.sparse_csc,
torch.sparse_bsr,
torch.sparse_bsc,
}
def is_sparse_compressed(t):
return isinstance(t, torch.Tensor) and is_sparse_compressed_layout(t.layout)
def is_sparse_any(t):
return is_sparse_coo(t) or is_sparse_compressed(t)
# Don't use id() directly, because those can get reallocated over time.
MetaStorageId: TypeAlias = int
MetaTensorId: TypeAlias = int
DESCRIBER_NEXT_ID = 0
class MetaTensorDescriber:
"""
Given a Tensor/Storage, generate a MetaTensorDesc/MetaStorageDesc
for it, which is enough information to reconstruct a meta tensor/fake tensor
corresponding to a Tensor as faithfully as possible.
This is a stateful conversion object because we keep track of the IDs
of the tensors/storages passed to us, so we can consistently give
the same ID when we see the same tensor/storage.
"""
def __init__(self, *, copy_data=False):
global DESCRIBER_NEXT_ID
self.id = DESCRIBER_NEXT_ID
DESCRIBER_NEXT_ID += 1
self.next_tensor_id: MetaTensorId = 0
self.next_storage_id: MetaStorageId = 0
# Tensor -> int
self.lookup_tensor = WeakIdKeyDictionary()
# Storage -> int
self.lookup_storage = WeakIdKeyDictionary()
self.copy_data = copy_data
self.traced_tensors = set()
self.traced_storages = set()
def get_tensor_id(self, t: torch.Tensor):
if t not in self.lookup_tensor:
self.lookup_tensor[t] = self.next_tensor_id
self.next_tensor_id += 1
return self.lookup_tensor[t]
def get_storage_id(self, s: torch.UntypedStorage):
if s not in self.lookup_storage:
self.lookup_storage[s] = self.next_storage_id
self.next_storage_id += 1
return self.lookup_storage[s]
def describe_storage(self, s: torch.UntypedStorage, *, trace: bool = False):
r = MetaStorageDesc(
id=self.get_storage_id(s),
size=s.size(),
# NB: We don't do the copy yet; copy happens when we start
# creating the new storages
data=s if self.copy_data else None,
)
if trace and r.id not in self.traced_storages:
trace_structured(
"describe_storage",
metadata_fn=lambda: r.as_json(self.id),
)
self.traced_storages.add(r.id)
return r
def describe_tensor(
self, t: torch.Tensor, *, recurse: bool = True, trace: bool = False
):
is_leaf = safe_is_leaf(t)
is_view = t._is_view()
is_sparse = t.is_sparse
layout = t.layout
is_nested = t.is_nested
is_traceable_wrapper_subclass_v = is_traceable_wrapper_subclass(t)
is_functorch_wrapped = is_functorch_wrapped_tensor(t)
is_mkldnn = t.is_mkldnn
is_batchedtensor_v = is_batchedtensor(t)
is_legacy_batchedtensor_v = is_legacy_batchedtensor(t)
is_gradtrackingtensor_v = is_gradtrackingtensor(t)
is_functorch_batched_or_grad = is_batchedtensor_v or is_gradtrackingtensor_v
is_functional = torch._is_functional_tensor(t)
storage = None
# NB: For compatibility, I default this to zero, as sometimes people
# still have stuffed zero into storage offset even though the tensor
# doesn't meaningfully have an offset
storage_offset = 0
if not (
is_sparse
or is_sparse_compressed_layout(layout)
or (is_nested and not is_traceable_wrapper_subclass_v)
or is_mkldnn
# TODO: TBH, functorch wrapped tensors probably should have
# storage associated with them
or is_functorch_wrapped
or is_legacy_batchedtensor_v
):
# NB: We actually don't use storage to do views, but might as well
# put it in for accuracy
storage = self.describe_storage(t.untyped_storage(), trace=trace)
storage_offset = t.storage_offset()
stride = None
if not (
is_sparse
or is_sparse_compressed_layout(layout)
or (is_nested and not is_traceable_wrapper_subclass_v)
):
# stride/storage_offset are called from is_functorch_wrapped,
# view_from_base, empty_create_subclass,
# sym_sizes_strides_storage_offset (empty_create)
stride = t.stride()
# NB: this technically should refer to functorch unwrapped tensor, but
# I am (perhaps abusively) using it to store both the functorch and
# non-functorch functional tensor
unwrapped = None
autograd_meta_from = None
current_level = None
if is_batchedtensor_v or is_gradtrackingtensor_v:
unwrapped = self.describe_tensor(get_unwrapped(t), trace=trace)
# xla and lazy tensors present as functional tensors, but we want them
# to be handled specially
elif is_functional and t.device.type not in ("xla", "lazy"):
if t._is_view():
raise RuntimeError(
"Cannot safely fakify a view because this process drops the view information right now."
)
if not is_functorch_wrapped:
torch._sync(t)
unwrapped = self.describe_tensor(
torch._from_functional_tensor(t), trace=trace
)
autograd_meta_from = t
else:
reapply_views = torch._C._functionalization_reapply_views_tls()
# NB: has side effects!
unwrapped = self.describe_tensor(
_unwrap_functional_tensor(t, reapply_views), trace=trace
)
# TODO: It's pretty suspicious that functional tensors don't have
# valid level and thus we just grab whatever the current level
# is
current_level = torch._C._functorch.current_level()
maybe_functorch_stack = None
if is_functorch_wrapped:
with torch._functorch.pyfunctorch.temporarily_clear_interpreter_stack() as maybe_functorch_stack:
pass
attrs = None
ctx = None
type_v = None
if is_traceable_wrapper_subclass_v:
assert hasattr(t, "__tensor_flatten__")
raw_attrs, ctx = t.__tensor_flatten__()
attrs = {
attr: self.describe_tensor(getattr(t, attr), trace=trace)
for attr in raw_attrs
}
type_v = type(t)
# TODO: Is it important to enable torch.inference_mode before querying
# these values?
r = MetaTensorDesc(
id=self.get_tensor_id(t),
storage=storage,
is_inference=t.is_inference(),
is_leaf=is_leaf,
requires_grad=t.requires_grad,
# NB: ndim should be OK too but there is a disaster at
# python test/dynamo/test_subclasses.py -k test_user_overidden_property_unsupported
# Actually, this means that we have a little bit of a problem
# here, which is that there is some sensitivity to how exactly an
# access is done if you have a __torch_function__ subclass. Maybe
# should disable torch function before doing accesses?
ndim=t.dim(),
dtype=t.dtype,
is_sparse=is_sparse,
is_mkldnn=is_mkldnn,
is_functorch_wrapped=is_functorch_wrapped,
is_batchedtensor=is_batchedtensor_v,
is_legacy_batchedtensor=is_legacy_batchedtensor_v,
is_gradtrackingtensor=is_gradtrackingtensor_v,
is_view=is_view,
is_conj=t.is_conj(),
is_neg=t.is_neg(),
is_parameter=isinstance(t, torch.nn.Parameter),
is_traceable_wrapper_subclass=is_traceable_wrapper_subclass_v,
is_nested=is_nested,
is_functional=is_functional,
layout=layout,
device=t.device,
size=t.size(),
stride=stride,
storage_offset=storage_offset,
dynamo_dynamic_indices=list(getattr(t, "_dynamo_dynamic_indices", set())),
sparse_dim=t.sparse_dim()
if t.is_sparse or is_sparse_compressed(t)
else None,
dense_dim=t.dense_dim() if t.is_sparse or is_sparse_compressed(t) else None,
is_coalesced=t.is_coalesced() if t.is_sparse else None,
# TODO: I actually think recursing here is correct, but we have at
# least an infinite cycle from base -> values -> base
# https://github.com/pytorch/pytorch/issues/122089
crow_indices=self.describe_tensor(
t.crow_indices(), recurse=False, trace=trace
)
if recurse and t.layout in {torch.sparse_csr, torch.sparse_bsr}
else None,
col_indices=self.describe_tensor(
t.col_indices(), recurse=False, trace=trace
)
if recurse and t.layout in {torch.sparse_csr, torch.sparse_bsr}
else None,
ccol_indices=self.describe_tensor(
t.ccol_indices(), recurse=False, trace=trace
)
if recurse and t.layout in {torch.sparse_csc, torch.sparse_bsc}
else None,
row_indices=self.describe_tensor(
t.row_indices(), recurse=False, trace=trace
)
if recurse and t.layout in {torch.sparse_csc, torch.sparse_bsc}
else None,
values=self.describe_tensor(t.values(), recurse=False, trace=trace)
if recurse and is_sparse_compressed(t)
else None,
grad=self.describe_tensor(safe_grad(t), trace=trace)
if safe_grad(t) is not None
else None,
creation_meta=torch._C._autograd._get_creation_meta(t)
if t._is_view()
else None,
unwrapped=unwrapped,
level=maybe_get_level(t)
if is_batchedtensor_v or is_gradtrackingtensor_v
else None,
bdim=maybe_get_bdim(t) if is_batchedtensor_v else None,
base=self.describe_tensor(t._base, trace=trace)
if recurse and t._is_view() and t._base is not None
else None,
fake_mode=torch._subclasses.fake_tensor.maybe_get_fake_mode(t),
view_func=t._view_func_unsafe,
attrs=attrs,
ctx=ctx,
type=type_v,
# NB: even if functorch is enabled, don't actually save the
# interpreter stack here unless we are actually functorch wrapped;
# it's irrelevant for non-functorch stuff
functorch_stack=maybe_functorch_stack,
autograd_meta_from=autograd_meta_from,
current_level=current_level,
data=t if self.copy_data else None,
)
if trace and r.id not in self.traced_tensors:
trace_structured(
"describe_tensor",
metadata_fn=lambda: r.as_json(self.id),
)
self.traced_tensors.add(r.id)
return r
@dataclass(frozen=True)
class MetaStorageDesc:
id: MetaStorageId
size: int
# NB: this is only populated with copy_data True, it is not directly
# serializable in JSON, you want to do something special here anyway
data: Optional[torch.UntypedStorage]
def as_json(self, describer_id):
return {
"id": self.id,
"describer_id": describer_id,
"size": self.size if isinstance(self.size, int) else repr(self.size),
}
@dataclass(frozen=True)
class MetaTensorDesc:
id: MetaTensorId
ndim: int
dtype: torch.dtype
device: torch.device
# NB: Sometimes, size, stride and storage_offset contain SymInt, in which
# case this is NOT serializable. That only happens when you're
# re-fakeifying a fake tensor with an existing ShapeEnv... maybe we
# can get rid of this use case entirely. Notably, even if we are
# fakeifying a real tensor into a fake tensor with symbolic shapes, the
# size here is NOT dynamic
# NB: These also contain SymInt because wrap_meta_outputs_with_default_device_logic
# goes through this codepath. But it really should not LOL.
# NB: size could potentially be None as you can override it and make it
# throw an error, but we don't currently have any subclasses that do this
# except C++ nested tensor but we're going to have nested int to make this
# defined on NJT
size: Tuple[int, ...]
dynamo_dynamic_indices: List[int]
layout: torch.layout = torch.strided
is_inference: bool = False
is_leaf: bool = False
requires_grad: bool = False
is_sparse: bool = False
is_mkldnn: bool = False
is_functorch_wrapped: bool = False
is_batchedtensor: bool = False
is_legacy_batchedtensor: bool = False
is_gradtrackingtensor: bool = False
is_view: bool = False
is_nested: bool = False
is_traceable_wrapper_subclass: bool = False
is_functional: bool = False
is_conj: bool = False
is_neg: bool = False
is_parameter: bool = False
stride: Optional[Tuple[int, ...]] = None
storage_offset: int = 0
# NB: We have a choice whether or not to store the id or a direct pointer
# to the data structure. For ease of use, we store the data structure,
# but this means that when we serialize, we have to swizzle these pointers
# back into ids (so we have accurate aliasing relationships)
storage: Optional[MetaStorageDesc] = None
sparse_dim: Optional[int] = None # is_sparse, is_sparse_compressed
dense_dim: Optional[int] = None # is_sparse, is_sparse_compressed
is_coalesced: Optional[bool] = None # is_sparse
crow_indices: Optional[MetaTensorDesc] = None # is_sparse_compressed
col_indices: Optional[MetaTensorDesc] = None # is_sparse_compressed
ccol_indices: Optional[MetaTensorDesc] = None # is_sparse_compressed
row_indices: Optional[MetaTensorDesc] = None # is_sparse_compressed
values: Optional[MetaTensorDesc] = None # is_sparse_compressed
unwrapped: Optional[MetaTensorDesc] = None # is_functorch_wrapped
bdim: Optional[int] = None # is_functorch_wrapped
base: Optional[MetaTensorDesc] = None # is_view
attrs: Optional[Dict[str, MetaTensorDesc]] = None # is_traceable_wrapper_subclass
creation_meta: Optional[CreationMeta] = None
grad: Optional[MetaTensorDesc] = None
# Everything below is NOT serializable, need some more work
_UNSERIALIZABLE: ClassVar[List[str]] = [
"ctx",
"type",
"fake_mode",
"view_func",
"level",
"current_level",
"functorch_stack",
"autograd_meta_from",
"data",
]
ctx: Optional[object] = None # is_traceable_wrapper_subclass
type: Optional[Type] = None # is_traceable_wrapper_subclass
fake_mode: Optional[FakeTensorMode] = None
view_func: Optional[
Callable[
[
torch.Tensor,
Callable[[int], int],
Callable[[torch.Tensor], torch.Tensor],
],
torch.Tensor,
]
] = None
# level looks serializable, but actually it is meaningless without
# the functorch_stack below
level: Optional[int] = None # is_functorch_wrapped
current_level: Optional[int] = None
functorch_stack: Optional[List[CInterpreter]] = None
autograd_meta_from: Optional[torch.Tensor] = None
# This is only populated on copy_data, and typically is not used at all,
# except for some of our meta-ification paths that don't properly use
# storage (pro-tip: you should use storage)
data: Optional[torch.Tensor] = None
# Faithfully serializing functorch tensors will not be too difficult.
# We only need to consider grad/vmap interpreters, and their internal
# state is only bools (mostly what the grad enabled/disabled state
# should be in the lower layer). Beyond that, tensors just need to
# precisely indicate which particular interpreter they correspond
# to (we then replace level with a pointer to the interpreter stack.)
# However, this use of functorch is very "non-lexical" so it's not
# entirely clear how to make it all lexical again, so we haven't done
# it for now.
# NB: This will reference numeric IDs, and it is assumed that you've
# already serialized everything this recursively references
def as_json(self, describer_id):
def json(k, v):
# Some best-effort debugging serialization for unserializable
# fields (feel free to add other special cases as appropriate)
if k in ["data", "autograd_meta_from"]:
return None # never repr these
if k in set(MetaTensorDesc._UNSERIALIZABLE):
return repr(v)
if isinstance(v, (torch.device, torch.dtype, torch.layout)):
return repr(v)
if isinstance(v, torch.SymInt):
return repr(v)
if isinstance(v, (tuple, list)):
return [json(k, v1) for v1 in v]
if isinstance(v, (MetaStorageDesc, MetaTensorDesc)):
return v.id
if isinstance(v, CreationMeta):
return str(v)
if k == "attrs" and isinstance(v, dict):
return {k1: v1.id for k1, v1 in v.items()}
return v
r = {
field.name: json(field.name, getattr(self, field.name))
for field in dataclasses.fields(self)
if not (
getattr(self, field.name) is field.default
or (
field.name == "dynamo_dynamic_indices"
and not getattr(self, field.name)
)
)
}
r.update({"describer_id": describer_id})
return r
@property
def shape(self):
return self.size
# A more faithful reproduction would do a copy on the entire
# storage, but this needs to be done carefully because the
# underlying storage could have larger extent than is implied
# by size/stride. The real fix is to properly call
# meta_storage recursively here.
#
# These "safe" functions are intended to be used under no_dispatch() mode.
# The no_dispatch() here is intended to prevent ambient fake tensor mode from
# fakeifying the operation. But if we are given an honest to goodness
# FakeTensor as src, we MUST NOT run the copy/clone operation. A better way
# to do this would be to not use no_dispatch and instead just disable fake
# tensor mode only (allowing for subclass dispatch to occur)
def _safe_copy(dst, src):
if type(src) is not torch.Tensor:
return
dst.copy_(src)
def _safe_clone(src):
if type(src) is not torch.Tensor:
return None
return src.clone()
# This is a class for converting multiple tensors into meta tensors which
# share the same view/storage structure. The operation model is you allocate
# one of these, and then call it repeatedly on all the tensors you want to
# convert. It's important to use the same object for tensors you want to
# share storage because this is how we correlate shared storages to the same
# meta storages. This class will hold weak references to cached tenosrs
# and tensor storages.
class MetaConverter:
def __init__(self, *, copy_data: bool = False):
# Maps MetaStorageId to UntypedStorage
self.storage_memo: weakref.WeakValueDictionary = weakref.WeakValueDictionary()
# Maps MetaTensorId to torch.Tensor (typically a meta tensor or
# FakeTensor)
self.tensor_memo: weakref.WeakValueDictionary = weakref.WeakValueDictionary()
self.hit = 0
self.miss = 0
self.del_hook = None
self.arg_cnt = 0
# Ensures real_storage/real_tensor are populated on the resulting
# metaified storage/tensor. The naming of this attribute is load
# bearing: FakeTensor relies on real tensor being set to exactly this
# value
self.copy_data = copy_data
self.describer = MetaTensorDescriber(copy_data=copy_data)
def successful(self):
return self.hit > 0 and self.miss == 0
def get_tensor_memo(self, t: MetaTensorDesc):
return self.tensor_memo.get(t.id, None)
def set_tensor_memo(self, t: MetaTensorDesc, v):
self.tensor_memo[t.id] = v
def get_storage_memo(self, s: MetaStorageDesc):
return self.storage_memo.get(s.id, None)
def set_storage_memo(self, s: MetaStorageDesc, v):
self.storage_memo[s.id] = v
def meta_storage(self, s: MetaStorageDesc, callback):
# If we are fakeifying a tensor that has a secretly-zero-sized storage,
# Need to make sure to resize the meta storage too.
if self.get_storage_memo(s) is None:
r_s = callback(
lambda: torch.empty(s.size, dtype=torch.uint8, device="meta"),
).untyped_storage()
if self.copy_data:
# NB: no_dispatch is needed because internally storage copy is
# implemented as Tensor operations
with torch.no_grad(), no_dispatch():
assert s.data is not None
r_s.real_storage = s.data.clone()
self.set_storage_memo(s, r_s)
return r_s
else:
return self.get_storage_memo(s)
# This function assumes that it's possible to do the conversion
# NB: name here is used in a conventional way by Dynamo; it corresponds
# precisely to the Source.name() of the tensor we're fakeifying and
# corresponds to a valid Python expression. When we construct sub-names
# as part of this process, we will maintain this invariant! (Even though
# other users of this may not need it this property to be upheld.)
def meta_tensor(
self,
t: MetaTensorDesc,
shape_env: Optional[ShapeEnv] = None,
callback=lambda t: t(),
source: Optional[Source] = None,
symbolic_context: Optional[SymbolicContext] = None,
):
if source is None:
from torch._dynamo.source import ConstantSource
# TODO: make a dedicated UnknownSource for this?
source = ConstantSource(
f"__meta_utils_unknown_tensor{len(self.tensor_memo)}"
)
# This indicates you set no_dispatch() before calling into this
# function. This is an error: we may be creating fake tensors and
# will perform operations on them which need fake tensor mode to
# be active. You will segfault if you are in a no_dispatch() block.
assert not torch._C._dispatch_tls_local_exclude_set().has(
torch._C.DispatchKey.Python
)
arg_cnt = self.arg_cnt
self.arg_cnt += 1
# When we make as_strided calls, we end up generating a guard
# that the new as_strided tensor is in bounds for the old storage
# for the base (since as_strided calls can "bust" out of their
# bounding box.) This guard is unnecessary: if a user is able
# to provide us a tensor with the view base setup this way, we
# don't need to produce a guard, because the fact that they
# were able to produce the view base means its in bounds.
#
# Now, ordinarily, this guard would be harmless. However, the
# generated guard refers to variables bound on the base variable.
# At the moment, Dynamo doesn't actually guard on x._base, because
# according to Voz this results in a lot of spurious invalidations,
# and also if the user doesn't directly make use of _base, its
# pointless anyway (because programs should be parametric over
# whether or not the input tensor is a view or not--unless you're
# mutating the input, but that's a whole 'nother ballgame). So
# for expediency, we suppress these guards so we don't have to
# deal with this (yet, anyway.)
#
# NB: An old version of this code suppressed guards for ALL operations
# happening during meta conversion, not just as_strided calls.
# This is too aggressive: we do duck sizing and 0/1 simplification
# as we allocate variables, and we do need to register guards for
# these cases.
maybe_suppress: Callable[[], Any] = contextlib.nullcontext
if shape_env is not None:
maybe_suppress = shape_env.suppress_guards
def sym_sizes_strides_storage_offset(
t: MetaTensorDesc, src, symbolic_context=symbolic_context
) -> Tuple[Tuple[int, ...], Tuple[int, ...], int]:
assert t.stride is not None
if shape_env is not None:
fake_mode = t.fake_mode
if fake_mode is not None and fake_mode.shape_env is shape_env:
# Don't reallocate the sizes; the shape envs are the same,
# so reuse the old sizes/strides/etc
return (t.size, t.stride, t.storage_offset)
else:
# TODO: deduplicate this
t_size = tuple(
shape_env._maybe_specialize_sym_int_with_hint(sz)
for sz in t.size
)
t_stride = tuple(
shape_env._maybe_specialize_sym_int_with_hint(sd)
for sd in t.stride
)
t_storage_offset = shape_env._maybe_specialize_sym_int_with_hint(
t.storage_offset
)
return shape_env._create_symbolic_sizes_strides_storage_offset(
t_size,
t_stride,
t_storage_offset,
[d in t.dynamo_dynamic_indices for d in range(t.ndim)],
src,
symbolic_context=symbolic_context,
)
else:
return (t.size, t.stride, t.storage_offset)
def empty_create(
inner_t: MetaTensorDesc, inner_src, symbolic_context=symbolic_context
):
(
inner_sizes,
inner_strides,
inner_storage_offset,
) = sym_sizes_strides_storage_offset(inner_t, inner_src, symbolic_context)
return torch.empty_strided(
inner_sizes,
inner_strides,
dtype=inner_t.dtype,
device="meta",
)
# Creates a subclass instance with empty inner tensors according to the specified
# symbolic context.
def empty_create_subclass(
t: MetaTensorDesc,
outer_size,
outer_stride,
symbolic_context=symbolic_context,
callback=callback,
source=source,
):
from torch._dynamo.source import AttrSource
from torch.fx.experimental.symbolic_shapes import SubclassSymbolicContext
assert t.attrs is not None
assert t.type is not None
# NB: t.ctx could be None if the subclass in question has no
# meaningful context
assert symbolic_context is None or isinstance(
symbolic_context, SubclassSymbolicContext
)
# Note: transform_subclass will use __tensor_unflatten__ to generate
# a fresh subclass wrapper with outer sizes / strides according to the
# outer symbolic context (passed in to this function). Inner size / stride
# / storage offset symbols are allocated according to the appropriate inner
# symbolic contexts, after which the checks in transform_subclass() will
# relate them to the outer metadata as possible.
#
# Morally, the code here is same as transform_subclass, but we've
# written it from scratch to read EmptyCreateSubclass
outer_size = outer_size if outer_size is not None else t.size
outer_stride = outer_stride if outer_stride is not None else t.stride
def transform(attr, inner_t):
r = callback(
lambda: empty_create(
inner_t,
AttrSource(source, attr),
symbolic_context=(
None
if symbolic_context is None
else symbolic_context.inner_contexts[attr]
),
)
)
if self.copy_data:
with torch.no_grad(), no_dispatch():
r.real_tensor = torch.empty_strided(
inner_t.size,
inner_t.stride,
dtype=inner_t.dtype,
device=inner_t.device,
)
assert inner_t.data is not None
_safe_copy(r.real_tensor, inner_t.data)
return r
transformed_tensors_dict = {
attr: transform(attr, inner_t) for attr, inner_t in t.attrs.items()
}
sub = t.type.__tensor_unflatten__(
transformed_tensors_dict, t.ctx, outer_size, outer_stride
)
# NB: Purposefully guard here to simplify the inner / outer symbols.
# Using sym_eq() for symbolic comparison can result in an expression that's too
# difficult to guard on, so we use == here.
assert sub.shape == outer_size, (
f"Expected return value from {t.type}__tensor_unflatten__() to have "
f"shape equal to {outer_size}, but got: {sub.shape}"
)
assert sub.stride() == outer_stride, (
f"Expected return value from {t.type}__tensor_unflatten__() to have "
f"stride equal to {outer_stride}, but got: {sub.stride()}"
)
return sub
# Returns an all-dynamic symbolic context used for metafying the given tensor with
# fully dynamic dims. This is useful when fake-ifying intermediate tensors in
# closed-over ViewFunc state, as we don't have symbolic contexts for them, but we
# don't want to over-specialize during view replay.
def all_dynamic_symbolic_context(
t: MetaTensorDesc, source, shape_env, callback
):
from torch._dynamo.source import AttrSource
from torch.fx.experimental.symbolic_shapes import (
DimDynamic,
StatelessSymbolicContext,
SubclassSymbolicContext,
)
view_base_context: Optional[SymbolicContext] = None
if t.is_view:
assert t.base is not None
view_base_context = all_dynamic_symbolic_context(
t.base, AttrSource(source, "_base"), shape_env, callback
)
t_symbolic_context: SymbolicContext
t_dynamic_sizes = [DimDynamic.DYNAMIC] * t.ndim
if t.is_traceable_wrapper_subclass:
assert t.attrs is not None
inner_contexts: Dict[str, SymbolicContext] = {}
for attr, inner in t.attrs.items():
assert isinstance(attr, str)
inner_contexts[attr] = all_dynamic_symbolic_context(
inner, AttrSource(source, attr), shape_env, callback
)
t_symbolic_context = SubclassSymbolicContext(
dynamic_sizes=t_dynamic_sizes,
constraint_sizes=[None] * t.ndim,
inner_contexts=inner_contexts,
tensor_source=source,
view_base_context=view_base_context,
)
else:
t_symbolic_context = StatelessSymbolicContext(
dynamic_sizes=t_dynamic_sizes,
constraint_sizes=[None] * t.ndim,
view_base_context=view_base_context,
)
return t_symbolic_context
# Returns a fake-ified version of an input view tensor t, given an already fake-ified
# base. At a high level, we want two things:
# 1. fake_t should have the same view relationship to the given fake base as the
# input t has to its _base.
# 2. fake_t should have symbolic sizes / strides / storage offset according to the
# appropriate symbolic context (i.e. from the automatic dynamic algorithm).
#
# We currently take different strategies across view types:
# * For dense -> dense views, accomplish both (1) and (2) simultaneously via an
# as_strided() call on the fake-ified base, passing symbolic metadata.
# * For views involving subclasses, perform view replay using view funcs to
# achieve (1). It's necessary for (2) to swap out any closed-over state in
# the view funcs with symbolicized SymInts and fake-ified tensors. Doing this
# avoids specialization (and thus over-eager simplification of symbols) that
# could occur during view replay on the fake-ified base.
#
# Examples:
# * t.unsqueeze(-1) with dense t is a dense -> dense view. It can be modeled
# with an as_strided() call on the fake base passing symbolic metadata.
# * sub.select(dim=0, index=3) is a subclass -> subclass view. The index arg
# is made symbolic to avoid invalid specialization and view replay is then
# done to reconstruct the view.
# * _nested_from_jagged(values, offsets) is a dense -> subclass view
# that returns a subclass instance from a dense values tensor. The offsets
# tensor is closed over in the view func, as it can be considered view metadata.
# First, the offsets tensor is fake-ified according to the inner symbolic
# context and with the correct relationship to the outer size / stride metadata.
# Then view replay is done, swapping in the fake offsets so the view replay output
# is fully fake with no invalid specialization.
def view_from_base(
base: torch.Tensor, t: MetaTensorDesc, source=source, shape_env=shape_env
):
# fake-ify t's metadata according to the outer symbolic context
(sizes, strides, storage_offset) = sym_sizes_strides_storage_offset(
t, source
)
if (
not t.is_traceable_wrapper_subclass
and not is_traceable_wrapper_subclass(base)
):
# Dense -> Dense view case uses as_strided() to construct view relationship.
# TODO: Change this logic to use view replay for consistency?
# It's likely there is no view func available.
with maybe_suppress():
return base.as_strided(sizes, strides, storage_offset)
from torch._dynamo.source import EphemeralSource
from torch.fx.experimental.symbolic_shapes import (
StatelessSymbolicContext,
sym_eq,
)
def symint_visitor_fn(s):
nonlocal symbolic_context
from torch.fx.experimental.symbolic_shapes import DimDynamic
all_static_sizes = (
symbolic_context is not None
and isinstance(symbolic_context, StatelessSymbolicContext)
and all(
x is DimDynamic.STATIC for x in symbolic_context.dynamic_sizes
)
)
# Can't just rely on shape env being None - dynamo always initializes it
if all_static_sizes or shape_env is None:
return s
# NB: The symbol here is expected to be simplified out because we a priori
# allocate inner and outer symbols according to the appropriate symbolic
# contexts and prefer those over this symbol during symbol simplification
# (via usage of EphemeralSource below). This -shouldn't- happen, but if
# this symbol somehow leaks out beyond the view tensor's shape metadata, our
# assumption of it being simplified out will fail and it may be guarded on,
# which will hard error.
sym_source = EphemeralSource("symint_visitor_fn")
symbol = shape_env.create_symbol(s, sym_source)
return shape_env.create_symintnode(symbol, hint=s, source=sym_source)
real_to_fake_mapping = {}
if t.is_traceable_wrapper_subclass:
assert t.attrs is not None
# NB: t.ctx could be None if the subclass in question has no
# meaningful context
assert t.type is not None
# Fake-ify t naively here; this is only done so we can get fake-ified inner
# tensors with the correct relationships to the outer sizes / strides for use
# in view replay. It's done beforehand here because it's not easy to do when
# visiting tensors one-by-one during view replay.
#
# Example:
# Consider a Dense -> NJT view. NJT has (values, offsets) components and we
# want a view of values with the offsets closed over. As the offsets component
# is needed to describe the output view, it's important that it's fakeified
# correctly.
fake_t = empty_create_subclass(
t, outer_size=sizes, outer_stride=strides
)
attrs, _ = fake_t.__tensor_flatten__()
for attr in attrs:
real_to_fake_mapping[t.attrs[attr].id] = getattr(fake_t, attr)
def tensor_visitor_fn(
visited_t: torch.Tensor,
# These arguments are never passed, we just use them to close
# over these relevant values
shape_env=shape_env,
callback=callback,
):
# It's possible to close over an undefined tensor (e.g. NJT's lengths).
if visited_t is None:
return None
# NB: visited_t being a Tensor here is very naughty! Should
# have already been described
# Fake inner tensors of view subclasses will come from the mapping built above.
visited_id = self.describer.get_tensor_id(visited_t)
fake_visited_t = real_to_fake_mapping.get(visited_id, None)
if fake_visited_t is not None:
return fake_visited_t
visited_desc = self.describer.describe_tensor(visited_t)
# For other closed-over tensor state, fake-ify it as all dynamic with an
# ephemeral source. This avoids invalid specialization during view replay.
# If we find that in practice the usage of ephemeral sources isn't enough
# to guarantee that we don't have guards on these symbols, we may need to
# explicitly suppress guards (as is done for _base in the dense -> dense
# view case).
temp_source = EphemeralSource("tensor_visitor_fn")
return self.meta_tensor(
visited_desc,
shape_env,
callback,
source=temp_source,
symbolic_context=all_dynamic_symbolic_context(
visited_desc, temp_source, shape_env, callback
),
)
# Replay the view, swapping out any non-symbolic SymInts or real tensors
# for symbolic SymInts or fake tensors.
assert t.view_func is not None
# NB: we do NOT suppress guards here, we need to remove ephemeral
# sources
fake_t = t.view_func(base, symint_visitor_fn, tensor_visitor_fn)
# Ensure the output has symbolic shapes according to the outer symbolic context.
# These checks should simplify out any symbols created for closed-over view func
# SymInts.
torch._check(sym_eq(fake_t.size(), sizes))
torch._check(sym_eq(fake_t.stride(), strides))
torch._check(sym_eq(fake_t.storage_offset(), storage_offset))
return fake_t
if self.get_tensor_memo(t) is None:
GRAD_TENSOR_SENTINEL_VALUE = -2
with torch.inference_mode(t.is_inference):
if t.is_sparse:
is_leaf = t.is_leaf
# The lambda function below is similar to
# `t.to(device='meta')` except the latter
# preserves nnz value
r = callback(
lambda: torch.ops.aten._sparse_coo_tensor_with_dims(
t.sparse_dim,
t.dense_dim,
t.size,
dtype=t.dtype,
layout=torch.sparse_coo,
device="meta",
)
)
if self.copy_data:
# Pray that sparse clone doesn't lose information
assert t.data is not None
with torch.no_grad(), no_dispatch():
r.real_tensor = _safe_clone(t.data)
assert safe_is_leaf(r), "the callback you passed in doesn't detach"
# Note [is_coalesced is dispatched]
# Strangely enough, is_coalesced() is a dispatched operator,
# which means that it will get caught by fake tensor mode.
# Ordinarily this would error, but there's some logic in
# fake tensor ensure this doesn't happen.
r._coalesced_(t.is_coalesced)
if t.requires_grad:
r.requires_grad = True
if t.requires_grad and not is_leaf:
# This should probably use DelayedError,
# but clone is fine for now for sparse tensors.
# (DelayedError does not work for sparse because it causes
# the Fake sparse tensor to "lose" its fakeness)
r = r.clone()
with torch.enable_grad():
r._coalesced_(t.is_coalesced)
elif is_sparse_compressed_layout(t.layout):
is_leaf = t.is_leaf
if t.layout in {torch.sparse_bsr, torch.sparse_bsc}:
assert t.sparse_dim is not None
assert t.dense_dim is not None
assert t.values is not None
batch_dim = t.ndim - t.sparse_dim - t.dense_dim
blocksize = t.values.shape[batch_dim + 1 : batch_dim + 3]
else:
blocksize = ()
if t.layout in {torch.sparse_csr, torch.sparse_bsr}:
assert t.crow_indices is not None
index_dtype = t.crow_indices.dtype
else:
assert t.ccol_indices is not None
index_dtype = t.ccol_indices.dtype
r = callback(
lambda: torch.ops.aten._sparse_compressed_tensor_with_dims(
0,
t.dense_dim,
t.shape,
blocksize,
index_dtype,
layout=t.layout,
dtype=t.dtype,
device="meta",
)
)
if self.copy_data:
# Pray sparse clone doesn't lose information
assert t.data is not None
with torch.no_grad(), no_dispatch():
r.real_tensor = _safe_clone(t.data)
assert safe_is_leaf(r), "the callback you passed in doesn't detach"
if t.requires_grad:
r.requires_grad = True
if t.requires_grad and not is_leaf:
r = torch._C._functions.DelayedError(
"Internal error: Tried to backward() through example input",
1,
)(r)
elif t.is_nested and not t.is_traceable_wrapper_subclass:
# TODO: Handle this better in Dynamo?
# There are checks there now, but this can still be triggered by a dense
# tensor graph input that is a view of a strided NT.
from torch._dynamo.exc import unimplemented
unimplemented(
"strided nested tensors are not supported by meta conversion"
)
elif t.is_mkldnn:
is_leaf = t.is_leaf
sizes, strides, _storage_offset = sym_sizes_strides_storage_offset(
t, source
)
# TODO: This doesn't seem right, where's the MKLDNN'ness
# lol
r = callback(
lambda: torch.empty_strided(
sizes, strides, dtype=t.dtype, device="meta"
)
)
if self.copy_data:
with torch.no_grad(), no_dispatch():
assert t.size is not None
assert t.stride is not None
r.real_tensor = torch.empty_strided(
t.size, t.stride, dtype=t.dtype, device=t.device
)
assert t.data is not None
_safe_copy(r.real_tensor, t.data)
assert safe_is_leaf(r), "the callback you passed in doesn't detach"
if t.requires_grad:
r.requires_grad = True
if t.requires_grad and not is_leaf:
r = torch._C._functions.DelayedError(
"Internal error: Tried to backward() through example input",
1,
)(r)
elif t.is_functorch_wrapped:
if t.is_view:
from torch._dynamo.exc import unimplemented
unimplemented(
"view functorch tensors are not supported by meta conversion"
)
# Wraps a functorch tensor class (BatchedTensor, GradTrackingTensor)
# in a FakeTensor
def _to_fake_tensor(t: MetaTensorDesc):
# TODO: why aren't the recursive calls going to
# meta_tensor
if t.is_batchedtensor:
assert t.unwrapped is not None
assert t.level is not None
assert t.bdim is not None
ft = _to_fake_tensor(t.unwrapped)
lvl = t.level
bdim = t.bdim
# You cannot create functorch tensors without
# having the ambient funtorch interpreter stack
# available, as the level refers to things in the
# stack
with torch._functorch.pyfunctorch.temporarily_restore_interpreter_stack(
t.functorch_stack
):
r = _add_batch_dim(ft, bdim, lvl)
elif t.is_gradtrackingtensor:
assert t.unwrapped is not None
assert t.level is not None
disable_functorch = torch._C._DisableFuncTorch
with disable_functorch():
ft = _to_fake_tensor(t.unwrapped)
lvl = t.level
if lvl == GRAD_TENSOR_SENTINEL_VALUE:
r = ft
else:
with torch._functorch.pyfunctorch.temporarily_restore_interpreter_stack(
t.functorch_stack
):
r = torch._C._functorch._wrap_for_grad(ft, lvl)
is_leaf = t.is_leaf
if t.requires_grad and safe_is_leaf(r):
r.requires_grad = True
elif t.requires_grad and not is_leaf:
r = torch._C._functions.DelayedError( # type: ignore[assignment]
"Internal error: Tried to backward() through example input",
1,
)(
r # type: ignore[arg-type]
)
elif t.is_functional:
assert t.unwrapped is not None
assert t.current_level is not None
ft = self.meta_tensor(
t.unwrapped,
shape_env=shape_env,
callback=callback,
# NB: reuse these exactly, we treat the
# functional tensor as "invisible".
# TODO: Actually this all probably doesn't
# work, take a closer look.
source=source,
symbolic_context=symbolic_context,
)
r = _wrap_functional_tensor(ft, t.current_level)
# TODO: is_leaf/requires_grad?
else:
assert t.stride is not None
sizes = t.size
strides = t.stride
r = callback(
lambda: torch.empty_strided(
sizes,
strides,
dtype=t.dtype,
device="meta",
)
)
if self.copy_data:
with torch.no_grad(), no_dispatch():
r.real_tensor = torch.empty_strided( # type: ignore[attr-defined]
t.size,
t.stride,
dtype=t.dtype,
device=t.device,
)
assert t.data is not None
_safe_copy(r.real_tensor, t.data) # type: ignore[attr-defined]
return r
r = _to_fake_tensor(t)
elif t.is_functional and t.device.type not in ["xla", "lazy"]:
assert t.unwrapped is not None
assert not t.is_functorch_wrapped # handled above
unwrapped = self.meta_tensor(
t.unwrapped,
shape_env=shape_env,
callback=callback,
source=source,
symbolic_context=symbolic_context,
)
r = torch._to_functional_tensor(unwrapped)
torch._mirror_autograd_meta_to(t.autograd_meta_from, r) # type: ignore[attr-defined]
elif t.is_view:
# Construct views in two steps: recursively meta-fy their
# base, and then create view(s) off that. NB: doing it
# directly from storage is WRONG because this won't cause
# version counters to get shared.
assert t.base is not None
base_symbolic_context = None
if shape_env and symbolic_context is not None:
from torch.fx.experimental.symbolic_shapes import (
StatelessSymbolicContext,
)
assert isinstance(symbolic_context, StatelessSymbolicContext)
# NB: This should generally be set when the input is a view,
# but the exception right now is for fake-ifying grads, which is
# a work in progress.
if symbolic_context.view_base_context is not None:
base_symbolic_context = symbolic_context.view_base_context
base = self.meta_tensor(
t.base,
shape_env,
callback,
source=torch._dynamo.source.AttrSource(source, "_base"),
symbolic_context=base_symbolic_context,
)
def is_c_of_r(complex_dtype, real_dtype):
return (
utils.is_complex_dtype(complex_dtype)
and utils.corresponding_real_dtype(complex_dtype)
== real_dtype
)
# In some situations, MetaConverter may be called in a
# context where autograd is disabled. For the _is_view
# assert to pass, we have to setup the autograd view
# metadata anyway. Do this by reenabling the
# ADInplaceOrView key. This is kind of a hack.
old_exclude = torch._C._dispatch_tls_is_dispatch_key_excluded(
torch._C.DispatchKey.ADInplaceOrView
)
torch._C._dispatch_tls_set_dispatch_key_excluded(
torch._C.DispatchKey.ADInplaceOrView, False
)
try:
if base.dtype == t.dtype:
pass
elif is_c_of_r(base.dtype, t.dtype):
base = torch.view_as_real(base)
elif is_c_of_r(t.dtype, base.dtype):
base = torch.view_as_complex(base)
else:
# This is not guaranteed to succeed. If it fails, it
# means there is another dtype-converting view function
# that hasn't been handled here
base = base.view(t.dtype)
# This is very tricky. Naively, you might expect this
# to hold:
#
# if t.requires_grad and not safe_is_leaf(t)
# assert t._base.requires_grad
#
# But it's not true! As you can see in the following
# program:
#
# x = torch.zeros(4)
# y = x.view(1, 4)
# y.requires_grad = True
# z = y.view(1, 1, 4)
# assert z._base is x
#
# So we may have to do *two* views out of the base to
# recreate this situation.
if t.is_leaf:
# Leaf views that track view metadata are created by
# creating a view inside a no_grad block
with torch.no_grad():
r = view_from_base(base, t)
# As it's a leaf, we can directly assign requires_grad
r.requires_grad = t.requires_grad
else:
if t.base.requires_grad == t.requires_grad:
# Easy case, just run the view op
with torch.enable_grad():
r = view_from_base(base, t)
# NB: We don't actaully faithfully replicate
# autograd connectivity, but that doesn't matter
# today. See following for more info:
# https://gist.github.com/soulitzer/e03f015b314c3f5fcf80888c69390913
else:
# Obscure case. Create a leaf view and give it the
# correct requires_grad, then do the final view.
# NB: Can't have a non-leaf without requiring grad!
assert t.requires_grad
with torch.no_grad():
mid = base.view(base.shape)
mid.requires_grad = t.requires_grad
with torch.enable_grad():
r = view_from_base(mid, t)
# The CreationMeta influences whether or not inplace
# mutation is an error or not. So we need to make
# sure we properly propagate this as well.
assert t.creation_meta is not None
torch._C._autograd._set_creation_meta(r, t.creation_meta)
finally:
torch._C._dispatch_tls_set_dispatch_key_excluded(
torch._C.DispatchKey.ADInplaceOrView, old_exclude
)
else:
is_leaf = t.is_leaf
# Graph-Break for wrapped tensors
if (
not (t.is_batchedtensor or t.is_gradtrackingtensor)
and t.is_functorch_wrapped
) or t.is_legacy_batchedtensor:
return NotImplemented
(
sizes,
strides,
storage_offset,
) = sym_sizes_strides_storage_offset(t, source, symbolic_context)
# If we have a subclass that desugars into dense tensors,
# perform our callback on each inner tensor.
if t.is_traceable_wrapper_subclass:
r = empty_create_subclass(
t, outer_size=sizes, outer_stride=strides
)
else:
r = callback(
lambda: torch.empty_strided(
sizes,
strides,
dtype=t.dtype,
device="meta",
)
)
if self.copy_data:
with torch.no_grad(), no_dispatch():
assert t.size is not None
assert t.stride is not None
r.real_tensor = torch.empty_strided(
t.size, t.stride, dtype=t.dtype, device=t.device
)
_safe_copy(r.real_tensor, t.data)
assert safe_is_leaf(r), "the callback you passed in doesn't detach"
if t.requires_grad:
r.requires_grad = t.requires_grad
if not is_leaf:
# Fake up some autograd history.
# Note: we *used* to call .clone() here to mock up some autograd history.
# This is bad for subclasses.
# Consider the case where you have a wrapper subclass that is contiguous,
# but its inner tensor is noncontiguous().
# .clone() (or other ops) will have the side effect of changing
# the metadata of the inner tensor.
# So instead, we now have a dedicated fn to set autograd history,
# without inadvertently changing other metadata.
r = torch._C._functions.DelayedError(
"Internal error: Tried to backward() through example input",
1,
)(r)
s = t.storage
assert s is not None
if s.id not in self.storage_memo and (
r.is_nested
or (
r.stride() == strides
and r.storage_offset() == storage_offset
)
):
# You're normal and happy, install the fresh storage into the memo
self.set_storage_memo(s, r.untyped_storage())
if self.copy_data:
r.untyped_storage().real_storage = (
r.real_tensor.untyped_storage()
)
else:
# You're in crazy town; somehow you gave us a tensor
# that wasn't a view, but had nonzero storage offset,
# nontrivial strides (such that clone() couldn't
# preserve them), or already aliases with another
# tensor's storage. The most typical way to end
# up here is with set_. So use set_ to bludgeon this
# in.
r_s = self.meta_storage(s, callback=callback)
# NB: In principle, this should always work, but there
# is some subtle difference in the autograd metadata
# that means we will backprop the set_ call, even if
# r is declared as an input to grad.
# See https://github.com/pytorch/pytorch/issues/87956
# for the reproducer.
# NB: The in_kernel_invocation_manager here is necessary
# for fake tensor. If we run the set_ call with fake
# tensor on, r will improperly report that it is NOT a
# meta tensor but a cpu tensor, and then the set_ call
# will fail due to device mismatch. no_dispatch() is
# not enough, because the fake tensor will still claim
# to be a CPU tensor and you'll end up in the CPU
# kernel. Arguably this is a hack; a cleaner way to
# solve this is to have a FakeStorage concept which
# would report it's CPU device--no problem now! But
# this is difficult to do because we don't have storage
# subclasses. Relevant test is
# DynamicShapesFunctionTests::test_add_dynamic_shapes in
# test/dynamo/test_dynamic_shapes.py
maybe_fake_mgr: ContextManager[None] = contextlib.nullcontext()
from torch._subclasses.fake_tensor import (
in_kernel_invocation_manager,
maybe_get_fake_mode,
)
mb_fake_mode = maybe_get_fake_mode(r)
if mb_fake_mode is not None:
maybe_fake_mgr = in_kernel_invocation_manager(mb_fake_mode)
with torch.no_grad(), maybe_suppress():
with maybe_fake_mgr:
r.set_(r_s, storage_offset, sizes, strides)
if self.copy_data:
with torch.no_grad(), no_dispatch():
r.real_tensor.set_(
r_s.real_storage,
t.storage_offset,
t.size,
t.stride,
)
if t.grad is not None:
from torch._dynamo.source import AttrSource
# TODO: Use a valid grad-specific symbolic context instead of recycling
# the one from t. This isn't correct if e.g. t._is_view() != t.grad._is_view().
r.grad = self.meta_tensor(
t.grad,
shape_env,
callback,
source=AttrSource(source, "grad"),
symbolic_context=symbolic_context,
)
torch._C._set_conj(r, t.is_conj)
torch._C._set_neg(r, t.is_neg)
# This can be skipped if necessary for performance reasons
skip_leaf = (
t.is_gradtrackingtensor and t.level == GRAD_TENSOR_SENTINEL_VALUE
)
assert_metadata_eq(assert_eq, t, r, skip_symbolic=True, skip_leaf=skip_leaf)
# Thanks to storage resizing, it's possible to end up with a tensor
# that advertises a real size, but has a storage that actually has zero bytes.
# Need to reflect this in the generated FakeTensor.
if t.storage is not None and t.storage.size == 0:
r.untyped_storage().resize_(0)
if t.is_parameter:
r._is_param = True
self.set_tensor_memo(t, r)
return self.get_tensor_memo(t)
def __call__(
self,
t,
shape_env=None,
*,
callback=lambda t: t(),
source=None,
symbolic_context=None,
# Controls whether or not we should dump the tensor metadata to structured logs
# when source is not None. Because we refakify after Dynamo is done,
# we don't want to dump info again from AOTAutograd, it is redundant.
trace=True,
):
# TODO: zero tensors? We appear to have eliminated them by
# excluding complex for now
# Filter out cases we don't support
# TODO: This can probably be simplified quite a bit
if isinstance(t, torch.Tensor) or is_traceable_wrapper_subclass(t):
if (
# Lazy tensors are not supported. Note that XLA is
# implemented on top of lazy tensor, not excluded here; we
# have some special handling for it; this is for XLA Dynamo
# integration
t.device.type == "lazy"
or
# Quantization is not supported
t.is_quantized
or
# Views out of sparse tensors not currently supported (plain
# sparse is supported htough)
(t._is_view() and t._base is not None and t._base.is_sparse)
):
self.miss += 1
return NotImplemented
else:
self.hit += 1
elif torch.overrides.is_tensor_like(t):
self.miss += 1
return NotImplemented
else:
# non-Tensor types don't count as hit or miss
return t
if source is None:
trace = False
# Describe the tensor. NB: do NOT disable ambient modes, we may need
# to query them when figuring out what to put in here
t_desc = self.describer.describe_tensor(t, trace=trace)
if trace:
trace_structured(
"describe_source",
metadata_fn=lambda: {
"describer_id": self.describer.id,
"id": t_desc.id,
"source": source.name(),
},
)
# Do the meta-fication. Here, we disable all the ambient modes, to
# better simulate what would be like to re-fakeify from a fresh
# process
with contextlib.ExitStack() as exit_stack:
exit_stack.enter_context(torch._dispatch.python.suspend_functionalization())
st = peek_interpreter_stack()
if st is not None:
exit_stack.enter_context(
torch._functorch.pyfunctorch.temporarily_clear_interpreter_stack()
)
r = self.meta_tensor(
t_desc,
shape_env=shape_env,
callback=callback,
source=source,
symbolic_context=symbolic_context,
)
if type(t) is torch.nn.Parameter:
# NB: Cannot directly use Parameter constructor
# because that would force a detach, not desirable
r._is_param = True
# TODO: return the description for later
return r
import torch._prims_common as utils