import contextlib
import warnings
import weakref
from typing import ContextManager, Optional
import torch
from torch._guards import Source
from torch.multiprocessing.reductions import StorageWeakRef
from torch.utils.weak import WeakIdRef
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, m2, *, skip_symbolic=False):
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)
assert_eq(m1.is_leaf, m2.is_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(safe_grad(m1) is not None, safe_grad(m2) is not None)
if safe_grad(m1) is not None:
go(safe_grad(m1), 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)
# 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):
self.storage_memo = {}
self.tensor_memo: weakref.WeakValueDictionary = weakref.WeakValueDictionary()
self.maybe_storages_to_delete = []
self.check_expired_frequency = 128
self.check_expired_count = 0
self.hit = 0
self.miss = 0
self.del_hook = None
self.arg_cnt = 0
def successful(self):
return self.hit > 0 and self.miss == 0
def check_for_expired_weak_storages(self):
new_li = []
stor_to_delete = []
for obj in self.maybe_storages_to_delete:
if not obj.expired():
new_li.append(obj)
else:
stor_to_delete.append(obj)
for obj in stor_to_delete:
self.storage_memo.pop(obj, None)
self.maybe_storages_to_delete = new_li
# if for some reason we have aquired many storages which have not expired
# even though a tensor with their storage has expired (aliasing or otherwise)
# check for expired storages less often so as to bound the amount of work we
# do checking for expired storages
self.check_expired_frequency = max(
self.check_expired_frequency, len(self.maybe_storages_to_delete)
)
def get_tensor_memo(self, t):
return self.tensor_memo.get(WeakIdRef(t), None)
def set_tensor_memo(self, t, v):
# hold a weak ref to self, otherwise it will be kept alive
# by the del_ten closure
self_weak_ref = weakref.ref(self)
if t.is_sparse or t.is_mkldnn:
weak_st = None
else:
weak_st = StorageWeakRef(t._typed_storage())
tensor_ref_key = WeakIdRef(t)
def del_ten():
# tensor outlives the converter
self_ref = self_weak_ref()
if self_ref is None:
return
# on shutdown, tensor_ref_key may not be in memo
self_ref.tensor_memo.pop(tensor_ref_key, None)
if weak_st and weak_st.expired():
self_ref.storage_memo.pop(weak_st, None)
elif weak_st is not None:
# [expired-storages]
# NB: even though the tensor has died,
# the deallocation of its storage can take longer,
# even when the storage has no other uses/views.
# In this case, the StorageWeakRef object will be kept alive
# longer than it needs to be, however the storage itself
# will be deallocated. We retain the possibly dead storages
# and periodically check if any of them are expired and
# can be freed.
self_ref.maybe_storages_to_delete.append(weak_st)
weakref.finalize(t, del_ten)
self.tensor_memo[tensor_ref_key] = v
# NB: doesn't actually return a storage, because meta storage is
# not supported
def meta_storage(self, s, callback):
# NB: TypedStorage is freshly allocated and cannot be used as hash
# key index.
# Use a Weak Ref to s in order to not leak memory
swr = StorageWeakRef(s)
if swr not in self.storage_memo:
self.storage_memo[swr] = callback(
lambda: torch.empty(s.size(), dtype=torch.uint8, device="meta")
).untyped_storage()
return self.storage_memo[swr]
# 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, shape_env=None, callback=lambda t: t(), source: Optional[Source] = None
):
if source is None:
from torch._dynamo.source import ConstantSource
# TODO: make a dedicated UnknownSource for this?
source = ConstantSource(f"__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 = contextlib.nullcontext
if shape_env is not None:
maybe_suppress = shape_env.suppress_guards
make_symbolic = shape_env is not None
def sym_sizes_strides_storage_offset(t):
if make_symbolic:
return shape_env.create_symbolic_sizes_strides_storage_offset(t, source)
return (t.size(), t.stride(), t.storage_offset())
# see expired-storages
self.check_expired_count += 1
if self.check_expired_count >= self.check_expired_frequency:
self.check_for_expired_weak_storages()
self.check_expired_count = 0
if self.get_tensor_memo(t) is None:
with torch.inference_mode(t.is_inference()):
if t.is_sparse:
assert shape_env is None, "symbolic on sparse NYI"
is_leaf = safe_is_leaf(t)
r = callback(
lambda: torch.ops.aten._sparse_coo_tensor_with_dims(
t.sparse_dim(),
t.dense_dim(),
t.shape,
dtype=t.dtype,
layout=torch.sparse_coo,
device="meta",
)
)
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:
with torch.enable_grad():
r = r.clone()
r._coalesced_(t.is_coalesced())
elif t.is_mkldnn:
is_leaf = safe_is_leaf(t)
sizes, strides, _storage_offset = sym_sizes_strides_storage_offset(
t
)
r = callback(
lambda: torch.empty_strided(
sizes, strides, dtype=t.dtype, device="meta"
)
)
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:
with torch.enable_grad():
r = r.clone()
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._is_view()
from torch._dynamo.source import AttrSource
base = self.meta_tensor(
t._base, shape_env, callback, source=AttrSource(source, "_base")
)
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.
(
sizes,
strides,
storage_offset,
) = sym_sizes_strides_storage_offset(t)
if safe_is_leaf(t):
# Leaf views that track view metadata are created by
# creating a view inside a no_grad block
with torch.no_grad(), maybe_suppress():
r = base.as_strided(sizes, strides, storage_offset)
# 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(), maybe_suppress():
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