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b8b3c26d3d
Subsumes half of https://github.com/pytorch/pytorch/pull/113605 We support fakeifying an already fake tensor, which will give you a new fake tensor mirroring the same structure as the original fake tensor, which is what is needed by https://github.com/pytorch/pytorch/issues/113643 . However, when this refakeification happens, we will naively reallocate all new sizes for all of the fake tensor. This is the right thing to do if you are re-fakeifying on a fresh ShapeEnv (because you're reparametrizing the sizes or something), but if you have two fake tensor modes which are sharing a shape environment, you would actually rather just reuse the original sizes/strides/offset from the original fake tensor. This ends up being pretty simple. I recommend viewing with whitespace diff turned off. There's some fuzz around jagged tensor handling; that code is probably not quite right, but I fixed it for this particular case in the most straightforward way. Signed-off-by: Edward Z. Yang <ezyang@meta.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/113651 Approved by: https://github.com/albanD, https://github.com/eellison, https://github.com/bdhirsh
750 lines
35 KiB
Python
750 lines
35 KiB
Python
import contextlib
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import warnings
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import weakref
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from typing import ContextManager, List, Optional, Tuple, TYPE_CHECKING
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import torch
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from torch._C._functorch import (
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_unwrap_functional_tensor,
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_wrap_functional_tensor,
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current_level,
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peek_interpreter_stack,
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TransformType,
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)
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from torch._guards import Source
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from torch.multiprocessing.reductions import StorageWeakRef
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from torch.utils._python_dispatch import (
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is_traceable_wrapper_subclass,
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transform_subclass,
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)
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from torch.utils.weak import WeakIdRef
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if TYPE_CHECKING:
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# Import the following modules during type checking to enable code intelligence features,
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# Do not import unconditionally, as they import sympy and importing sympy is very slow
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from torch.fx.experimental.symbolic_shapes import DimConstraint, DimDynamic
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DimList = List
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def safe_is_leaf(t):
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try:
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return t.is_leaf
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except RuntimeError:
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# inference mode can trigger this
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return False
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def safe_grad(t):
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with warnings.catch_warnings():
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warnings.filterwarnings("ignore", "The .grad attribute of a Tensor")
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return t.grad
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def assert_eq(a, b):
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assert a == b, f"{a} != {b}"
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def assert_metadata_eq(assert_eq, m1, m2, *, skip_symbolic=False):
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def go(m1, m2):
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assert_eq(m1.dtype, m2.dtype)
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if not skip_symbolic:
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assert_eq(m1.shape, m2.shape)
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assert_eq(m1.requires_grad, m2.requires_grad)
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assert_eq(m1.is_leaf, m2.is_leaf)
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assert_eq(m1.grad_fn is None, m2.grad_fn is None)
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assert_eq(m1.is_sparse, m2.is_sparse)
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assert_eq(m1.is_inference(), m2.is_inference())
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assert_eq(m1.is_conj(), m2.is_conj())
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assert_eq(m1.is_neg(), m2.is_neg())
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assert_eq(safe_grad(m1) is not None, safe_grad(m2) is not None)
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if safe_grad(m1) is not None:
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go(safe_grad(m1), safe_grad(m2))
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if m1.is_sparse:
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assert_eq(m1.dense_dim(), m2.dense_dim())
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assert_eq(m1.sparse_dim(), m2.sparse_dim())
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assert_eq(m1.is_coalesced(), m2.is_coalesced())
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else:
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if not skip_symbolic:
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assert_eq(m1.stride(), m2.stride())
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assert_eq(m1.storage_offset(), m2.storage_offset())
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assert_eq(m1._is_view(), m2._is_view())
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if m1._is_view():
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go(m1._base, m2._base)
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# TODO: test if is resizable (no direct query for this atm)
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# TODO: audit AutogradMeta to see if it matches
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# TODO: test forward AD
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return go(m1, m2)
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# This is a class for converting multiple tensors into meta tensors which
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# share the same view/storage structure. The operation model is you allocate
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# one of these, and then call it repeatedly on all the tensors you want to
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# convert. It's important to use the same object for tensors you want to
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# share storage because this is how we correlate shared storages to the same
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# meta storages. This class will hold weak references to cached tenosrs
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# and tensor storages.
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class MetaConverter:
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def __init__(self):
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self.storage_memo = {}
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self.tensor_memo: weakref.WeakValueDictionary = weakref.WeakValueDictionary()
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self.maybe_storages_to_delete = []
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self.check_expired_frequency = 128
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self.check_expired_count = 0
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self.hit = 0
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self.miss = 0
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self.del_hook = None
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self.arg_cnt = 0
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def successful(self):
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return self.hit > 0 and self.miss == 0
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def check_for_expired_weak_storages(self):
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new_li = []
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stor_to_delete = []
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for obj in self.maybe_storages_to_delete:
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if not obj.expired():
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new_li.append(obj)
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else:
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stor_to_delete.append(obj)
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for obj in stor_to_delete:
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self.storage_memo.pop(obj, None)
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self.maybe_storages_to_delete = new_li
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# if for some reason we have aquired many storages which have not expired
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# even though a tensor with their storage has expired (aliasing or otherwise)
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# check for expired storages less often so as to bound the amount of work we
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# do checking for expired storages
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self.check_expired_frequency = max(
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self.check_expired_frequency, len(self.maybe_storages_to_delete)
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)
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def get_tensor_memo(self, t):
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return self.tensor_memo.get(WeakIdRef(t), None)
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def set_tensor_memo(self, t, v):
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# hold a weak ref to self, otherwise it will be kept alive
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# by the del_ten closure
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self_weak_ref = weakref.ref(self)
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if t.is_sparse or t.is_mkldnn:
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weak_st = None
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else:
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weak_st = StorageWeakRef(t._typed_storage())
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tensor_ref_key = WeakIdRef(t)
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def del_ten():
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# tensor outlives the converter
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self_ref = self_weak_ref()
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if self_ref is None:
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return
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# on shutdown, tensor_ref_key may not be in memo
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self_ref.tensor_memo.pop(tensor_ref_key, None)
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if weak_st and weak_st.expired():
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self_ref.storage_memo.pop(weak_st, None)
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elif weak_st is not None:
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# [expired-storages]
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# NB: even though the tensor has died,
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# the deallocation of its storage can take longer,
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# even when the storage has no other uses/views.
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# In this case, the StorageWeakRef object will be kept alive
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# longer than it needs to be, however the storage itself
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# will be deallocated. We retain the possibly dead storages
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# and periodically check if any of them are expired and
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# can be freed.
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self_ref.maybe_storages_to_delete.append(weak_st)
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weakref.finalize(t, del_ten)
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self.tensor_memo[tensor_ref_key] = v
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# NB: doesn't actually return a storage, because meta storage is
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# not supported
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def meta_storage(self, s, callback):
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# NB: TypedStorage is freshly allocated and cannot be used as hash
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# key index.
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# Use a Weak Ref to s in order to not leak memory
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swr = StorageWeakRef(s)
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if swr not in self.storage_memo:
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self.storage_memo[swr] = callback(
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lambda: torch.empty(s.size(), dtype=torch.uint8, device="meta")
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).untyped_storage()
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return self.storage_memo[swr]
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# This function assumes that it's possible to do the conversion
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# NB: name here is used in a conventional way by Dynamo; it corresponds
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# precisely to the Source.name() of the tensor we're fakeifying and
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# corresponds to a valid Python expression. When we construct sub-names
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# as part of this process, we will maintain this invariant! (Even though
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# other users of this may not need it this property to be upheld.)
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def meta_tensor(
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self,
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t,
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shape_env=None,
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callback=lambda t: t(),
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source: Optional[Source] = None,
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dynamic_dims: "Optional[DimList[DimDynamic]]" = None,
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constraint_dims: "Optional[DimList[DimConstraint]]" = None,
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):
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from torch._subclasses.fake_tensor import FakeTensor
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if source is None:
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from torch._dynamo.source import ConstantSource
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# TODO: make a dedicated UnknownSource for this?
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source = ConstantSource(
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f"__meta_utils_unknown_tensor{len(self.tensor_memo)}"
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)
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# This indicates you set no_dispatch() before calling into this
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# function. This is an error: we may be creating fake tensors and
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# will perform operations on them which need fake tensor mode to
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# be active. You will segfault if you are in a no_dispatch() block.
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assert not torch._C._dispatch_tls_local_exclude_set().has(
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torch._C.DispatchKey.Python
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)
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arg_cnt = self.arg_cnt
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self.arg_cnt += 1
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# When we make as_strided calls, we end up generating a guard
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# that the new as_strided tensor is in bounds for the old storage
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# for the base (since as_strided calls can "bust" out of their
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# bounding box.) This guard is unnecessary: if a user is able
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# to provide us a tensor with the view base setup this way, we
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# don't need to produce a guard, because the fact that they
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# were able to produce the view base means its in bounds.
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#
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# Now, ordinarily, this guard would be harmless. However, the
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# generated guard refers to variables bound on the base variable.
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# At the moment, Dynamo doesn't actually guard on x._base, because
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# according to Voz this results in a lot of spurious invalidations,
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# and also if the user doesn't directly make use of _base, its
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# pointless anyway (because programs should be parametric over
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# whether or not the input tensor is a view or not--unless you're
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# mutating the input, but that's a whole 'nother ballgame). So
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# for expediency, we suppress these guards so we don't have to
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# deal with this (yet, anyway.)
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#
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# NB: An old version of this code suppressed guards for ALL operations
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# happening during meta conversion, not just as_strided calls.
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# This is too aggressive: we do duck sizing and 0/1 simplification
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# as we allocate variables, and we do need to register guards for
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# these cases.
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maybe_suppress = contextlib.nullcontext
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if shape_env is not None:
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maybe_suppress = shape_env.suppress_guards
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def sym_sizes_strides_storage_offset(
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t, src
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) -> Tuple[Tuple[int, ...], Tuple[int, ...], int]:
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if shape_env is not None:
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if isinstance(t, FakeTensor) and t.fake_mode.shape_env is shape_env:
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# Don't reallocate the sizes; the shape envs are the same,
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# so reuse the old sizes/strides/etc
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return (t.size(), t.stride(), t.storage_offset())
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else:
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return shape_env.create_symbolic_sizes_strides_storage_offset(
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t,
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src,
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# Assume that the set of dims that are dynamic are the same between
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# the wrapper tensor and any inner tensors.
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# We can revisit this if this assumption does not hold
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# for any important subclasses later.
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dynamic_dims=dynamic_dims,
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constraint_dims=constraint_dims,
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)
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else:
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assert dynamic_dims is None
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assert constraint_dims is None
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return (t.size(), t.stride(), t.storage_offset())
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# see expired-storages
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self.check_expired_count += 1
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if self.check_expired_count >= self.check_expired_frequency:
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self.check_for_expired_weak_storages()
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self.check_expired_count = 0
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if self.get_tensor_memo(t) is None:
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with torch.inference_mode(t.is_inference()):
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if t.is_sparse:
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is_leaf = safe_is_leaf(t)
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r = callback(
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lambda: torch.ops.aten._sparse_coo_tensor_with_dims(
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t.sparse_dim(),
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t.dense_dim(),
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t.shape,
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dtype=t.dtype,
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layout=torch.sparse_coo,
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device="meta",
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)
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)
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assert safe_is_leaf(r), "the callback you passed in doesn't detach"
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# Note [is_coalesced is dispatched]
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# Strangely enough, is_coalesced() is a dispatched operator,
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# which means that it will get caught by fake tensor mode.
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# Ordinarily this would error, but there's some logic in
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# fake tensor ensure this doesn't happen.
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r._coalesced_(t.is_coalesced())
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if t.requires_grad:
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r.requires_grad = True
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if t.requires_grad and not is_leaf:
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with torch.enable_grad():
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r = r.clone()
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r._coalesced_(t.is_coalesced())
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elif t.is_mkldnn:
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is_leaf = safe_is_leaf(t)
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sizes, strides, _storage_offset = sym_sizes_strides_storage_offset(
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t, source
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)
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r = callback(
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lambda: torch.empty_strided(
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sizes, strides, dtype=t.dtype, device="meta"
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)
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)
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assert safe_is_leaf(r), "the callback you passed in doesn't detach"
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if t.requires_grad:
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r.requires_grad = True
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if t.requires_grad and not is_leaf:
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with torch.enable_grad():
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r = r.clone()
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elif t._is_view():
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# Construct views in two steps: recursively meta-fy their
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# base, and then create view(s) off that. NB: doing it
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# directly from storage is WRONG because this won't cause
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# version counters to get shared.
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assert t._is_view()
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from torch._dynamo.source import AttrSource
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from torch.fx.experimental.symbolic_shapes import DimDynamic
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if shape_env and not t.is_nested and not t._base.is_nested:
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base_dynamic_dims = [DimDynamic.STATIC] * t._base.dim()
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else:
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base_dynamic_dims = None
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base = self.meta_tensor(
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t._base,
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shape_env,
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callback,
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source=AttrSource(source, "_base"),
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dynamic_dims=base_dynamic_dims,
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)
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def is_c_of_r(complex_dtype, real_dtype):
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return (
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utils.is_complex_dtype(complex_dtype)
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and utils.corresponding_real_dtype(complex_dtype)
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== real_dtype
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)
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# In some situations, MetaConverter may be called in a
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# context where autograd is disabled. For the _is_view
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# assert to pass, we have to setup the autograd view
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# metadata anyway. Do this by reenabling the
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# ADInplaceOrView key. This is kind of a hack.
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old_exclude = torch._C._dispatch_tls_is_dispatch_key_excluded(
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torch._C.DispatchKey.ADInplaceOrView
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)
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torch._C._dispatch_tls_set_dispatch_key_excluded(
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torch._C.DispatchKey.ADInplaceOrView, False
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)
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try:
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if base.dtype == t.dtype:
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pass
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elif is_c_of_r(base.dtype, t.dtype):
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base = torch.view_as_real(base)
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elif is_c_of_r(t.dtype, base.dtype):
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base = torch.view_as_complex(base)
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else:
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# This is not guaranteed to succeed. If it fails, it
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# means there is another dtype-converting view function
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# that hasn't been handled here
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base = base.view(t.dtype)
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# This is very tricky. Naively, you might expect this
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# to hold:
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#
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# if t.requires_grad and not safe_is_leaf(t)
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# assert t._base.requires_grad
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#
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# But it's not true! As you can see in the following
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# program:
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#
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# x = torch.zeros(4)
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# y = x.view(1, 4)
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# y.requires_grad = True
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# z = y.view(1, 1, 4)
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# assert z._base is x
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#
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# So we may have to do *two* views out of the base to
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# recreate this situation.
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def _view_from_base(base, t):
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if t.is_nested:
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# Nested tensors do not support as_strided, and
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# hence,always have _view_func available.
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#
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# The unsafe version of _view_func omits
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# checking whether the base passed in has the same
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# metadata as the original base the view_func
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# was originally executed with. (1) It is OK here,
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# because we're calling it on the meta-ified base,
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# so the metadata is guaranteed to be the same.
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# (2) It is necessary because we don't actually
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# want to guard on the base's metadata here.
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return t._view_func_unsafe(base)
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else:
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(
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sizes,
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strides,
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storage_offset,
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) = sym_sizes_strides_storage_offset(t, source)
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return base.as_strided(sizes, strides, storage_offset)
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if safe_is_leaf(t):
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# Leaf views that track view metadata are created by
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# creating a view inside a no_grad block
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with torch.no_grad(), maybe_suppress():
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r = _view_from_base(base, t)
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# As it's a leaf, we can directly assign requires_grad
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r.requires_grad = t.requires_grad
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else:
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if t._base.requires_grad == t.requires_grad:
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# Easy case, just run the view op
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with torch.enable_grad(), maybe_suppress():
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r = _view_from_base(base, t)
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# NB: We don't actaully faithfully replicate
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# autograd connectivity, but that doesn't matter
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# today. See following for more info:
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# https://gist.github.com/soulitzer/e03f015b314c3f5fcf80888c69390913
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else:
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# Obscure case. Create a leaf view and give it the
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# correct requires_grad, then do the final view.
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# NB: Can't have a non-leaf without requiring grad!
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assert t.requires_grad
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with torch.no_grad():
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mid = base.view(base.shape)
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mid.requires_grad = t.requires_grad
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with torch.enable_grad(), maybe_suppress():
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r = _view_from_base(mid, t)
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# The CreationMeta influences whether or not inplace
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# mutation is an error or not. So we need to make
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# sure we properly propagate this as well.
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torch._C._autograd._set_creation_meta(
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r, torch._C._autograd._get_creation_meta(t)
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)
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finally:
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torch._C._dispatch_tls_set_dispatch_key_excluded(
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torch._C.DispatchKey.ADInplaceOrView, old_exclude
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)
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else:
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is_leaf = safe_is_leaf(t)
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if not t.is_nested:
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# Nested tensor subclasses have special logic for
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# creating symbolic size/strides/storage_offset
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(
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sizes,
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strides,
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storage_offset,
|
|
) = sym_sizes_strides_storage_offset(t, source)
|
|
|
|
def empty_create(inner_t, inner_src):
|
|
(
|
|
inner_sizes,
|
|
inner_strides,
|
|
inner_storage_offset,
|
|
) = sym_sizes_strides_storage_offset(inner_t, inner_src)
|
|
return torch.empty_strided(
|
|
inner_sizes,
|
|
inner_strides,
|
|
dtype=inner_t.dtype,
|
|
device="meta",
|
|
)
|
|
|
|
# If we have a subclass that desugars into dense tensors,
|
|
# perform our callback on each inner tensor.
|
|
if is_traceable_wrapper_subclass(t):
|
|
# Note: transform_subclass will use __tensor_unflatten__ to generate
|
|
# a fresh subclass wrapper, which is why sizes/strides are not passed in
|
|
# to the creation function here.
|
|
# We assume that if the inner tensors of the subclass are given symbolic sizes,
|
|
# their sizes will be used to construct the (symbolic) sizes of the wrapper tensor.
|
|
from torch._dynamo.source import AttrSource
|
|
|
|
if t.is_nested:
|
|
# Avoid circular import
|
|
from torch._dynamo.source import (
|
|
TensorProperty,
|
|
TensorPropertySource,
|
|
)
|
|
|
|
# For nested tensors, manually do transform_subclass
|
|
# so we can insert some special processing on ctx
|
|
attrs, ctx = t.__tensor_flatten__()
|
|
transformed_tensors_dict = {}
|
|
orig_shape_env = None
|
|
for attr in attrs:
|
|
inner_t = getattr(t, attr)
|
|
if orig_shape_env is None:
|
|
orig_shape_env = (
|
|
inner_t.fake_mode.shape_env
|
|
if isinstance(inner_t, FakeTensor)
|
|
else None
|
|
)
|
|
transformed_tensors_dict[attr] = callback(
|
|
lambda: empty_create(
|
|
inner_t, AttrSource(source, attr)
|
|
)
|
|
)
|
|
# We expect JaggedTensor to have a 'ragged_size' in
|
|
# its context
|
|
assert isinstance(ctx, dict)
|
|
assert "ragged_size" in ctx
|
|
assert isinstance(t._size[1], torch.SymInt)
|
|
if orig_shape_env is shape_env:
|
|
# It's already fake and the shape envs line up, reuse the old size
|
|
# Do not assert singleton_int; it may already
|
|
# be a variable
|
|
ctx["ragged_size"] = t._size[1]
|
|
else:
|
|
assert t._size[1].node.singleton_int() is not None
|
|
# Replace the eager ragged size with our freshly
|
|
# allocated jagged size that has a source
|
|
ctx["ragged_size"] = shape_env.create_symintnode(
|
|
shape_env.create_symbol(
|
|
t._size[1],
|
|
TensorPropertySource(
|
|
source, TensorProperty.SIZE, 1
|
|
),
|
|
),
|
|
hint=t._size[1],
|
|
)
|
|
r = type(t).__tensor_unflatten__(
|
|
transformed_tensors_dict, ctx
|
|
)
|
|
else:
|
|
r = transform_subclass(
|
|
t,
|
|
lambda attr, inner_t: callback(
|
|
lambda: empty_create(
|
|
inner_t,
|
|
AttrSource(source, attr),
|
|
)
|
|
),
|
|
)
|
|
else:
|
|
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 = t.requires_grad
|
|
if not is_leaf:
|
|
# Fake up some autograd history.
|
|
with torch.enable_grad():
|
|
# preserve_format is the default, but we want to
|
|
# emphasize how important it is to preserve
|
|
# format here
|
|
r = r.clone(memory_format=torch.preserve_format)
|
|
|
|
# Graph-Break for wrapped tensors
|
|
if torch._C._functorch.is_functorch_wrapped_tensor(t):
|
|
return NotImplemented
|
|
|
|
s = t.untyped_storage()
|
|
swr = StorageWeakRef(s)
|
|
if swr 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.storage_memo[swr] = r.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 maybe_fake_mgr, torch.no_grad():
|
|
r.set_(r_s, storage_offset, sizes, strides)
|
|
|
|
if safe_grad(t) is not None:
|
|
from torch._dynamo.source import AttrSource
|
|
|
|
r.grad = self.meta_tensor(
|
|
safe_grad(t),
|
|
shape_env,
|
|
callback,
|
|
source=AttrSource(source, "grad"),
|
|
dynamic_dims=dynamic_dims,
|
|
constraint_dims=constraint_dims,
|
|
)
|
|
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
|
|
assert_metadata_eq(assert_eq, t, r, skip_symbolic=True)
|
|
self.set_tensor_memo(t, r)
|
|
|
|
return self.get_tensor_memo(t)
|
|
|
|
def __call__(
|
|
self,
|
|
t,
|
|
shape_env=None,
|
|
*,
|
|
callback=lambda t: t(),
|
|
ignore_subclass=False,
|
|
source=None,
|
|
dynamic_dims=None,
|
|
constraint_dims=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 (ignore_subclass and isinstance(t, torch.Tensor))
|
|
or is_traceable_wrapper_subclass(t)
|
|
or isinstance(t, FakeTensor)
|
|
):
|
|
if t.device.type != "xla" and any(
|
|
[
|
|
t.is_sparse_csr,
|
|
t.layout in [torch.sparse_csc, torch.sparse_bsr, torch.sparse_bsc],
|
|
t.is_quantized,
|
|
t._is_view() and t._base is not None and t._base.is_sparse,
|
|
torch._is_functional_tensor(t),
|
|
t.device.type in ("lazy"),
|
|
# 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.
|
|
|
|
if torch._is_functional_tensor(t) and t.device.type != "lazy":
|
|
if t._is_view():
|
|
raise RuntimeError(
|
|
"Cannot safely fakify a view because this process drops the view information right now."
|
|
)
|
|
|
|
st = peek_interpreter_stack()
|
|
assert (
|
|
st is None or st.key() == TransformType.Functionalize
|
|
), "Expect st to be either None or have Functionalize transform key."
|
|
if st is None:
|
|
# the case of AOTAutograd
|
|
torch._sync(t)
|
|
unwrap_t = torch._from_functional_tensor(t)
|
|
with torch._dispatch.python.suspend_functionalization():
|
|
fake_t = self.meta_tensor(
|
|
unwrap_t,
|
|
shape_env=shape_env,
|
|
callback=callback,
|
|
source=source,
|
|
dynamic_dims=dynamic_dims,
|
|
constraint_dims=constraint_dims,
|
|
)
|
|
out = torch._to_functional_tensor(fake_t)
|
|
torch._mirror_autograd_meta_to(fake_t, out)
|
|
return out
|
|
else:
|
|
# torch.func.functionalize
|
|
reapply_views = torch._C._functionalization_reapply_views_tls()
|
|
unwrap_t = _unwrap_functional_tensor(t, reapply_views)
|
|
pop_st_ctx = (
|
|
torch._functorch.pyfunctorch.temporarily_pop_interpreter_stack()
|
|
)
|
|
with pop_st_ctx:
|
|
fake_t = self.meta_tensor(
|
|
unwrap_t,
|
|
shape_env=shape_env,
|
|
callback=callback,
|
|
source=source,
|
|
dynamic_dims=dynamic_dims,
|
|
constraint_dims=constraint_dims,
|
|
)
|
|
return _wrap_functional_tensor(fake_t, current_level())
|
|
self.miss += 1
|
|
return NotImplemented
|
|
else:
|
|
self.hit += 1
|
|
# When ignoring subclasses, we treat the input tensor "as if" it
|
|
# were a normal tensor and create a non-subclassed fake tensor
|
|
# that, modulo type and attributes, resembles the original tensor.
|
|
# This can be helpful if you're planning to simulate the subclassness
|
|
# by hand, e.g., as is done in Dynamo
|
|
ctx = contextlib.nullcontext()
|
|
if ignore_subclass:
|
|
ctx = torch._C.DisableTorchFunctionSubclass()
|
|
with ctx:
|
|
r = self.meta_tensor(
|
|
t,
|
|
shape_env=shape_env,
|
|
callback=callback,
|
|
source=source,
|
|
dynamic_dims=dynamic_dims,
|
|
constraint_dims=constraint_dims,
|
|
)
|
|
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
|
|
return r
|
|
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
|
|
|
|
|
|
import torch._prims_common as utils
|