| 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 |
| |
| 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): |
| # 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: |
| 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(t.size(), t.stride(), t.storage_offset()) |
| 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, t.storage_offset(), t.size(), t.stride()) |
| |
| 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): |
| # 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) |
| 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 |
