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https://github.com/zebrajr/pytorch.git
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e9e7363854
I'm testing out turning on re-inplacing + functionalization by default with the AOTAutograd + eager backend on torchbench + huggingface models. This PR contains a few bug fixes from turning re-inplacing on: (1) Handle more gracefully when FakeTensorMode is already turned on when you call reinplace (2) More robust detection for when an inplace variant of an op exists (the dumb bug was that `pow.Scalar` doesn't have an inplace variant, even though there are several overloads of `pow_`. None of them are eligible though (3) Avoid re-inplacing when it would require resizing the input buffer. This isn't allowed, because inplace ops aren't allowed to resize their inputs. For the last one, I gave the two main examples in more detail in the comments. Important cases are: ``` # This should not be re-inplaced at all; the op broadcasts, so this would require resizing the self tensor torch.add(tensor[1, 4], tensor[4, 4]) # This should not be re-inplaced, because the inplace and out-of-place variants of the op return different dtypes torch.ge(a, b) # However, this means that today when functionalization functionalists a `torch.ge_(a, b)` call, reinplacing won't properly de-functionalize it. I mentioned that optimization is worth adding later in the comments ``` (4) There's some logic around keeping `storage_to_nodes` up to date when we see a view op: if we re-inplace `out = a.add(...)`, and later in the program we encounter a "later_node",`out.view(..)`, and need to replace it with `a.view(...)`, then we need to update some metadata structures. I had to fix that logic: specifically, if "later_node" isn't a dispatcher op, (e.g. if it's an FX output node), I wasn't properly handling the case where the node's fake_meta info was not a tensor. Pull Request resolved: https://github.com/pytorch/pytorch/pull/83626 Approved by: https://github.com/ezyang
284 lines
12 KiB
Python
284 lines
12 KiB
Python
import weakref
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import torch
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from torch.multiprocessing.reductions import StorageWeakRef
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from torch.utils._mode_utils import no_dispatch
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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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# torch.Tensors cannot be used as a key in a dictionary
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# because they define a custom __eq__ function which when used
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# to resolve hash collisions will throw when comparing tensors:
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# "RuntimeError: bool value of Tensor with more than one value is ambiguous."
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# To avoid that, we use an object which will hold a Tensor and use
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# its id for both hashing and equality.
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# In order to use this as a weak key reference, we cannot
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# simply use weakref.WeakKeyDictionary because the newly constructed
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# WeakTensorRefKey only use would be a dictionary so it would have no strong
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# references.
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# To get around this issue, we can use it as a normal key, and then set
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# `weakref.finalize` to delete the key when its contained tensor dies.
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class WeakTensorRefKey(object):
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def __init__(self, ten):
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self.ten = weakref.ref(ten)
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# store id since as soon as ten is deallocated
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# the old id will no longer be recoverable, and
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# we need to be able to remove the WeakTensorRefKey
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# from the dictionary by hashing it to the same
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# value it had when ten was alive
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self.id = id(self.ten())
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def __hash__(self):
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return self.id
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def __eq__(self, other):
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if id(self) == id(other):
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return True
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return self.id == other.id
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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 = {}
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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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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(WeakTensorRefKey(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:
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weak_st = None
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else:
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weak_st = StorageWeakRef(t.storage())
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tensor_ref_key = WeakTensorRefKey(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):
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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] = torch.empty(s.size(), dtype=s.dtype, device="meta")
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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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def meta_tensor(self, t):
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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 = 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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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_view():
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# Construct views in two steps: recursively meta-fy their
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# base, and then create the view 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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base = self.meta_tensor(t._base)
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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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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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with torch.enable_grad():
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r = base.as_strided(t.size(), t.stride(), t.storage_offset())
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else:
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is_leaf = safe_is_leaf(t)
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# Fake up some autograd history.
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if t.requires_grad:
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r = torch.empty(
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(0,), dtype=t.dtype, device="meta", requires_grad=True
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)
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if not is_leaf:
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with torch.enable_grad():
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# The backward function here will be wrong, but
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# that's OK; our goal is just to get the metadata
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# looking as close as possible; we're not going to
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# actually try to backward() on these produced
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# metas. TODO: would be safer to install some
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# sort of unsupported grad_fn here
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r = r.clone()
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else:
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r = torch.empty((0,), dtype=t.dtype, device="meta")
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# As long as meta storage is not supported, need to prevent
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# redispatching on set_(Storage, ...) which will choke with
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# meta storage
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s = self.meta_storage(t.storage())
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with no_dispatch():
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with torch.no_grad():
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r.set_(s, t.storage_offset(), t.size(), t.stride())
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torch._C._set_conj(r, t.is_conj())
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torch._C._set_neg(r, t.is_neg())
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self.set_tensor_memo(t, r)
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return self.get_tensor_memo(t)
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def __call__(self, t):
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# TODO: zero tensors? We appear to have eliminated them by
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# excluding complex for now
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from torch._subclasses.fake_tensor import FakeTensor
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if (
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type(t) is torch.Tensor
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or type(t) is torch.nn.Parameter
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or isinstance(t, FakeTensor)
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):
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if any(
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[
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t.is_sparse_csr,
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t.layout in [torch.sparse_csc, torch.sparse_bsr, torch.sparse_bsc],
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t.is_mkldnn,
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t.is_quantized,
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t.is_nested,
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t._is_view() and t._base is not None and t._base.is_sparse,
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torch._is_functional_tensor(t),
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# these are supported in meta conversion but the fallbacks
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# don't work
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t.is_neg(),
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t.is_conj(),
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t.device.type in ("lazy", "meta"),
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# We need a way to test if a tensor is batched but there
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# is no official APi to do it
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# torch._C._is_batched(t),
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]
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):
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# TODO: sparse should support meta
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# NB technically to('meta') does work but our logging
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# instrumentation will see the meta conversions and the
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# tests all break so we just exclude this. In any case
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# the to conversion isn't really right anyhow.
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self.miss += 1
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return t
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else:
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self.hit += 1
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r = self.meta_tensor(t)
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if type(t) is torch.nn.Parameter:
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r = torch.nn.Parameter(r, requires_grad=r.requires_grad)
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return r
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elif torch.overrides.is_tensor_like(t):
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# Blindly converting tensor subclasses to meta can cause
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# unpredictable problems; e.g., FX tests will trace meta
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# tensors into their trace / some subclasses don't correctly
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# support meta. Trying to YOLO this is more trouble than it's
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# worth.
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self.miss += 1
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return t
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else:
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# non-Tensor types don't count as hit or miss
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return t
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import torch._prims_common as utils
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