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import weakref
import torch
from torch.multiprocessing.reductions import StorageWeakRef
from torch.utils._mode_utils import no_dispatch
def safe_is_leaf(t):
try:
return t.is_leaf
except RuntimeError:
# inference mode can trigger this
return False
# torch.Tensors cannot be used as a key in a dictionary
# because they define a custom __eq__ function which when used
# to resolve hash collisions will throw when comparing tensors:
# "RuntimeError: bool value of Tensor with more than one value is ambiguous."
# To avoid that, we use an object which will hold a Tensor and use
# its id for both hashing and equality.
# In order to use this as a weak key reference, we cannot
# simply use weakref.WeakKeyDictionary because the newly constructed
# WeakTensorRefKey only use would be a dictionary so it would have no strong
# references.
# To get around this issue, we can use it as a normal key, and then set
# `weakref.finalize` to delete the key when its contained tensor dies.
class WeakTensorRefKey(object):
def __init__(self, ten):
self.ten = weakref.ref(ten)
# store id since as soon as ten is deallocated
# the old id will no longer be recoverable, and
# we need to be able to remove the WeakTensorRefKey
# from the dictionary by hashing it to the same
# value it had when ten was alive
self.id = id(self.ten())
def __hash__(self):
return self.id
def __eq__(self, other):
if id(self) == id(other):
return True
return self.id == other.id
# 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 = {}
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(WeakTensorRefKey(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:
weak_st = None
else:
weak_st = StorageWeakRef(t.storage())
tensor_ref_key = WeakTensorRefKey(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):
# 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] = torch.empty(s.size(), dtype=s.dtype, device="meta")
return self.storage_memo[swr]
# This function assumes that it's possible to do the conversion
def meta_tensor(self, t, shape_env=None):
arg_cnt = self.arg_cnt
self.arg_cnt += 1
# Don't make parameters have symbolic shapes; they are assumed to stay
# constant size across training runs
make_symbolic = shape_env is not None and not isinstance(t, torch.nn.Parameter)
def sym(name, x):
if make_symbolic:
return shape_env.create_symint(f"t{arg_cnt}.{name}()", x)
else:
return x
def sym_list(name, xs):
if make_symbolic:
return [
shape_env.create_symint(f"t{arg_cnt}.{name}({i})", x)
for i, x in enumerate(xs)
]
else:
return xs
def sym_size(t):
return sym_list("size", t.size())
def sym_stride(t):
return sym_list("stride", t.stride())
# NB: Although sym_stride variables initially have no correlation
# with size, we will immediately introduce guards based on contiguity.
# Thus, if the input tensor is contiguous, the stride variables
# will typically immediately get reexpressed in terms of the size
# variables.
def sym_storage_offset(t):
return sym("storage_offset", 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 = 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",
)
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_view():
# Construct views in two steps: recursively meta-fy their
# base, and then create the view off that. NB: doing it
# directly from storage is WRONG because this won't cause
# version counters to get shared.
assert t._is_view()
base = self.meta_tensor(t._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
)
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)
with torch.enable_grad():
r = base.as_strided(
sym_size(t), sym_stride(t), sym_storage_offset(t)
)
else:
is_leaf = safe_is_leaf(t)
# Fake up some autograd history.
if t.requires_grad:
r = torch.empty(
(0,), dtype=t.dtype, device="meta", requires_grad=True
)
if not is_leaf:
with torch.enable_grad():
# The backward function here will be wrong, but
# that's OK; our goal is just to get the metadata
# looking as close as possible; we're not going to
# actually try to backward() on these produced
# metas. TODO: would be safer to install some
# sort of unsupported grad_fn here
r = r.clone()
else:
r = torch.empty((0,), dtype=t.dtype, device="meta")
# As long as meta storage is not supported, need to prevent
# redispatching on set_(Storage, ...) which will choke with
# meta storage
s = self.meta_storage(t.storage())
with no_dispatch():
with torch.no_grad():
r.set_(s, sym_storage_offset(t), sym_size(t), sym_stride(t))
torch._C._set_conj(r, t.is_conj())
torch._C._set_neg(r, t.is_neg())
self.set_tensor_memo(t, r)
return self.get_tensor_memo(t)
def __call__(self, t, shape_env=None):
# TODO: zero tensors? We appear to have eliminated them by
# excluding complex for now
from torch._subclasses.fake_tensor import FakeTensor
if (
type(t) is torch.Tensor
or type(t) is torch.nn.Parameter
or isinstance(t, FakeTensor)
):
if any(
[
t.is_sparse_csr,
t.layout in [torch.sparse_csc, torch.sparse_bsr, torch.sparse_bsc],
t.is_mkldnn,
t.is_quantized,
t.is_nested,
t._is_view() and t._base is not None and t._base.is_sparse,
torch._is_functional_tensor(t),
# these are supported in meta conversion but the fallbacks
# don't work
t.is_neg(),
t.is_conj(),
t.device.type in ("lazy", "meta"),
# We need a way to test if a tensor is batched but there
# is no official APi to do it
# torch._C._is_batched(t),
]
):
# TODO: sparse should support meta
# NB technically to('meta') does work but our logging
# instrumentation will see the meta conversions and the
# tests all break so we just exclude this. In any case
# the to conversion isn't really right anyhow.
self.miss += 1
return t
else:
self.hit += 1
r = self.meta_tensor(t, shape_env=shape_env)
if type(t) is torch.nn.Parameter:
r = torch.nn.Parameter(r, requires_grad=r.requires_grad)
return r
elif torch.overrides.is_tensor_like(t):
# Blindly converting tensor subclasses to meta can cause
# unpredictable problems; e.g., FX tests will trace meta
# tensors into their trace / some subclasses don't correctly
# support meta. Trying to YOLO this is more trouble than it's
# worth.
self.miss += 1
return t
else:
# non-Tensor types don't count as hit or miss
return t
import torch._prims_common as utils
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