Source code for torch._utils
import copyreg
import functools
import logging
import sys
import traceback
import warnings
from collections import defaultdict
from typing import Any, Callable, DefaultDict, Generic, List, Optional
from typing_extensions import ParamSpec
import torch
def _type(self, dtype=None, non_blocking=False, **kwargs):
"""Returns the type if `dtype` is not provided, else casts this object to
the specified type.
If this is already of the correct type, no copy is performed and the
original object is returned.
Args:
dtype (type or string): The desired type
non_blocking (bool): If ``True``, and the source is in pinned memory
and destination is on the GPU or vice versa, the copy is performed
asynchronously with respect to the host. Otherwise, the argument
has no effect.
**kwargs: For compatibility, may contain the key ``async`` in place of
the ``non_blocking`` argument. The ``async`` arg is deprecated.
"""
non_blocking = _get_async_or_non_blocking("type", non_blocking, kwargs)
if dtype is None:
return self.__module__ + "." + self.__class__.__name__
if isinstance(dtype, str):
dtype = _import_dotted_name(dtype)
if dtype == type(self):
return self
if self.is_sparse:
if not dtype.is_sparse:
raise RuntimeError("Cannot cast sparse tensor to dense tensor")
new_module_name = dtype.__module__.replace(".sparse", "")
new_values_type_name = new_module_name + "." + dtype.__name__
new_values = torch.Tensor._values(self).type(new_values_type_name, non_blocking)
new_indices_type_name = new_module_name + ".LongTensor"
new_indices = torch.Tensor._indices(self).type(
new_indices_type_name, non_blocking
)
return dtype(new_indices, new_values, self.size())
if dtype.is_sparse:
raise RuntimeError("Cannot cast dense tensor to sparse tensor")
return dtype(self.size()).copy_(self, non_blocking)
def _hpu(self, device=None, non_blocking=False, **kwargs):
"""Returns a copy of this object in HPU memory.
If this object is already in HPU memory and on the correct device, then
no copy is performed and the original object is returned.
Args:
device (int): The destination HPU id. Defaults to the current device.
non_blocking (bool): If ``True`` and the source is in pinned memory,
the copy will be asynchronous with respect to the host. Otherwise,
the argument has no effect.
**kwargs: For compatibility, may contain the key ``async`` in place of
the ``non_blocking`` argument.
"""
non_blocking = _get_async_or_non_blocking("hpu", non_blocking, kwargs)
hpu = getattr(torch, "hpu", None)
assert hpu is not None, "HPU device module is not loaded"
if self.is_hpu:
if device is None:
device = hpu.current_device()
if self.get_device() == device:
return self
else:
if device is None:
device = -1
with hpu.device(device):
assert not self.is_sparse, "sparse storage is not supported for HPU tensors"
untyped_storage = torch.UntypedStorage(self.size(), device=torch.device("hpu"))
untyped_storage.copy_(self, non_blocking)
return untyped_storage
def _cuda(self, device=None, non_blocking=False, **kwargs):
"""Returns a copy of this object in CUDA memory.
If this object is already in CUDA memory and on the correct device, then
no copy is performed and the original object is returned.
Args:
device (int): The destination GPU id. Defaults to the current device.
non_blocking (bool): If ``True`` and the source is in pinned memory,
the copy will be asynchronous with respect to the host. Otherwise,
the argument has no effect.
**kwargs: For compatibility, may contain the key ``async`` in place of
the ``non_blocking`` argument.
"""
non_blocking = _get_async_or_non_blocking("cuda", non_blocking, kwargs)
if self.is_cuda:
if device is None:
device = torch.cuda.current_device()
if self.get_device() == device:
return self
else:
if device is None:
device = -1
with torch.cuda.device(device):
if self.is_sparse:
new_type = getattr(torch.cuda.sparse, self.__class__.__name__)
indices = torch.Tensor._indices(self).cuda(device, non_blocking)
values = torch.Tensor._values(self).cuda(device, non_blocking)
return new_type(indices, values, self.size())
else:
untyped_storage = torch.UntypedStorage(
self.size(), device=torch.device("cuda")
)
untyped_storage.copy_(self, non_blocking)
return untyped_storage
def _get_async_or_non_blocking(function_name, non_blocking, kwargs):
"""Return the non-blocking flag given the function name and kwargs.
Args:
function_name (str): the name of the function being used.
non_blocking (bool): the default value.
**kwargs (dict): the kwargs passed to the function.
"""
if not kwargs:
return non_blocking
if len(kwargs) != 1 or "async" not in kwargs:
message = "{}() got an unexpected keyword argument '{}'"
argument = list(kwargs.keys()).pop()
raise TypeError(message.format(function_name, argument))
warnings.warn("'async' is deprecated; use 'non_blocking'")
return kwargs["async"]
# Note [Don't serialize hooks]
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Since time immemorial, we have serialized the backward hooks associated with
# variables. This kind of half-worked--Python can pickle global functions
# (but not closures!)--but there were problems.
#
# - It's fragile. If you serialize a backward hook into a saved
# model, and then you rename the function associated with the hook,
# now your saved model is broken and you can't load it anymore.
#
# - It's not actually used. The standard recommendation is to
# serialize the *state_dict* of a model, not the model itself
# (since this is more stable to code changes affecting the model
# serialization), and the state dict saves "data" only, thus
# stripping the backward hooks. In some cases, hooks are
# essential to the well-functioning of a model (e.g., DDP),
# but DDP already manages readding the hooks!
#
# - We didn't serialize them in many cases. Prior to #10220, we
# were dropping backward hooks in ForkingPickler. We "fixed" this
# to be convenient with other serialization sites, but lack of
# serializing backward hooks wasn't actually the root cause of
# the bug.
#
# With these cases in mind, we have decided that a better strategy
# is to just NOT serialize hooks at all.
#
# Since this is a BC-breaking change, we should warn when we previously
# serialized a hook, but no longer do so. This will be done by adding a special
# sentinel property to hooks will be used to suppress this warning. If a hook
# has the property _torch_serialize_ignore, we will not emit a warning if we
# attempt to serialize a Tensor with this hook attached to it.
#
# By the way, when _backward_hooks is skipped, we must give an EMPTY
# OrderedDict(), if you pass a None you'll run afoul #12219.
# TODO: Once we decide to break serialization FC, `storage` no longer needs to
# be a TypedStorage
def _rebuild_tensor(storage, storage_offset, size, stride):
# first construct a tensor with the correct dtype/device
t = torch.empty((0,), dtype=storage.dtype, device=storage._untyped_storage.device)
return t.set_(storage._untyped_storage, storage_offset, size, stride)
def get_tensor_metadata(tensor):
# Tensor's Metadata for serializing.
# Currently, this only returns a dict[string, bool] specifing whether
# `conj` or `neg` bit is set.
assert isinstance(tensor, torch.Tensor)
return torch._C._get_tensor_metadata(tensor) # type: ignore[attr-defined]
def set_tensor_metadata(tensor, metadata):
# See `get_tensor_metadata` above
assert isinstance(metadata, dict)
assert isinstance(tensor, torch.Tensor)
torch._C._set_tensor_metadata(tensor, metadata) # type: ignore[attr-defined]
def _rebuild_tensor_v2(
storage, storage_offset, size, stride, requires_grad, backward_hooks, metadata=None
):
tensor = _rebuild_tensor(storage, storage_offset, size, stride)
tensor.requires_grad = requires_grad
if metadata:
set_tensor_metadata(tensor, metadata)
# NB: This line exists only for backwards compatibility; the
# general expectation is that backward_hooks is an empty
# OrderedDict. See Note [Don't serialize hooks]
tensor._backward_hooks = backward_hooks
return tensor
def _rebuild_tensor_v3(
storage,
storage_offset,
size,
stride,
requires_grad,
backward_hooks,
dtype,
metadata=None,
):
t = torch.empty(
(0,),
dtype=dtype,
device=storage._untyped_storage.device,
requires_grad=requires_grad,
)
t.set_(storage._untyped_storage, storage_offset, size, stride)
if metadata:
set_tensor_metadata(t, metadata)
t._backward_hooks = backward_hooks
return t
_sparse_tensors_to_validate: List["torch.Tensor"] = []
# In _legacy_load() in serialization.py we unpickle storages after the sparse
# tensors have been already unpickled. Those storages contain data necessary for
# validating sparse tensors: indices and values. That's why sparse tensors are
# first unpickled without any validation, and then this function is called just
# before _legacy_load() returns, so that all the sparse tensors can be validated
# in bulk.
#
# The same procedure must be followed by _load() in serialization.py because due
# to Pickler semantics, we have to use the same (non-validating) function for
# unpickling sparse tensors, regardless of the caller.
def _validate_loaded_sparse_tensors():
try:
for t in _sparse_tensors_to_validate:
if t.layout is torch.sparse_coo:
torch._validate_sparse_coo_tensor_args(
t._indices(), t._values(), t.size(), t.is_coalesced()
)
elif t.layout in {
torch.sparse_csr,
torch.sparse_csc,
torch.sparse_bsr,
torch.sparse_bsc,
}:
# TODO: Validation currently involves an expensive traversal
# on CPU, which may include a device transfer.
if t.layout in {torch.sparse_csr, torch.sparse_bsr}:
compressed_indices, plain_indices = (
t.crow_indices(),
t.col_indices(),
)
else:
compressed_indices, plain_indices = (
t.ccol_indices(),
t.row_indices(),
)
torch._validate_sparse_compressed_tensor_args(
compressed_indices, plain_indices, t.values(), t.size(), t.layout
)
else:
raise NotImplementedError(
f"_validate_loaded_sparse_tensors for layout `{t.layout}`"
)
finally:
_sparse_tensors_to_validate.clear()
def _rebuild_sparse_tensor(layout, data):
"""
Rebuilds a sparse tensor from its sparse storage representation.
Args:
layout (str): The sparse storage layout of the tensor.
data (tuple): The tensor's sparse storage representation.
"""
if layout == torch.sparse_coo:
if len(data) == 3:
# For BC:
indices, values, size = data
is_coalesced = None
else:
indices, values, size, is_coalesced = data
result = torch.sparse_coo_tensor(
indices, values, size, check_invariants=False, is_coalesced=is_coalesced
)
_sparse_tensors_to_validate.append(result)
return result
elif layout in {
torch.sparse_csr,
torch.sparse_csc,
torch.sparse_bsr,
torch.sparse_bsc,
}:
compressed_indices, plain_indices, values, size = data
result = torch.sparse_compressed_tensor(
compressed_indices,
plain_indices,
values,
size,
layout=layout,
check_invariants=False,
)
_sparse_tensors_to_validate.append(result)
return result
raise NotImplementedError(f"rebuilding sparse tensor for layout {layout}")
def _rebuild_nested_tensor(buffer, sizes, strides, storage_offsets):
return torch._nested_view_from_buffer(buffer, sizes, strides, storage_offsets)
def _rebuild_device_tensor_from_numpy(data, dtype, device, requires_grad):
tensor = torch.from_numpy(data).to(dtype=dtype, device=device)
tensor.requires_grad = requires_grad
return tensor
# Should not be used, only here to be able to load Tensors serialized with older versions of pytorch
_rebuild_xla_tensor = _rebuild_device_tensor_from_numpy
def _rebuild_meta_tensor_no_storage(dtype, size, stride, requires_grad):
return torch.empty_strided(
size, stride, dtype=dtype, device="meta", requires_grad=requires_grad
)
def _rebuild_wrapper_subclass(
cls, dtype, size, stride, storage_offset, layout, device, requires_grad
):
return torch.Tensor._make_wrapper_subclass( # type: ignore[attr-defined]
cls,
size,
strides=stride,
storage_offset=storage_offset,
layout=layout,
device=device,
requires_grad=requires_grad,
)
# TODO: Once we decide to break serialization FC, `storage` no longer needs to
# be a TypedStorage
def _rebuild_qtensor(
storage,
storage_offset,
size,
stride,
quantizer_params,
requires_grad,
backward_hooks,
):
qscheme = quantizer_params[0]
if qscheme == torch.per_tensor_affine:
_, scale, zero_point = quantizer_params
tensor = torch._empty_affine_quantized(
size,
scale=scale,
zero_point=zero_point,
dtype=storage.dtype,
device=storage.device,
)
elif qscheme in (torch.per_channel_affine, torch.per_channel_affine_float_qparams):
_, scales, zero_points, axis = quantizer_params
if type(scales) is list and type(zero_points) is list:
if qscheme == torch.per_channel_affine:
scales = torch.tensor(scales, dtype=torch.double, device=storage.device)
zero_points = torch.tensor(
zero_points, dtype=torch.long, device=storage.device
)
else:
scales = torch.tensor(scales, dtype=torch.float, device=storage.device)
zero_points = torch.tensor(
zero_points, dtype=torch.float, device=storage.device
)
tensor = torch._empty_per_channel_affine_quantized(
size,
scales=scales,
zero_points=zero_points,
axis=axis,
dtype=storage.dtype,
device=storage.device,
)
else:
raise RuntimeError(f"Can't deserialize quantized tensor with qscheme {qscheme}")
tensor.set_(storage, storage_offset, size, stride)
tensor.requires_grad = requires_grad
# NB: This line exists only for backwards compatibility; the
# general expectation is that backward_hooks is an empty
# OrderedDict. See Note [Don't serialize hooks]
tensor._backward_hooks = backward_hooks
return tensor
def _rebuild_parameter(data, requires_grad, backward_hooks):
param = torch.nn.Parameter(data, requires_grad)
# NB: This line exists only for backwards compatibility; the
# general expectation is that backward_hooks is an empty
# OrderedDict. See Note [Don't serialize hooks]
param._backward_hooks = backward_hooks
return param
def _rebuild_parameter_with_state(data, requires_grad, backward_hooks, state):
param = torch.nn.Parameter(data, requires_grad)
# NB: This line exists only for backwards compatibility; the
# general expectation is that backward_hooks is an empty
# OrderedDict. See Note [Don't serialize hooks]
param._backward_hooks = backward_hooks
# Restore state on Parameter like python attr.
param = _set_obj_state(param, state)
return param
def _get_obj_state(obj):
# Get the state of the python subclass
# This loosely mimicks the function on the object class but since Tensor do not inherit
# from it, we cannot call that function directly
# https://github.com/python/cpython/blob/c83919bd635f4433f1c6ae8504996a9fe3c215e5/Objects/typeobject.c#L4891
# Note that starting with Python 3.11, this `__getstate__` is always defined and thus
# the else branch will never be taken.
getstate_fn = getattr(obj, "__getstate__", None)
if getstate_fn:
state = getstate_fn()
else:
slots_to_save = copyreg._slotnames(obj.__class__) # type: ignore[attr-defined]
if slots_to_save:
state = (
obj.__dict__,
{
name: getattr(obj, name)
for name in slots_to_save
if hasattr(obj, name)
},
)
else:
state = obj.__dict__
return state
def _set_obj_state(obj, state):
if isinstance(state, tuple):
if not len(state) == 2:
raise RuntimeError(f"Invalid serialized state: {state}")
dict_state = state[0]
slots_state = state[1]
else:
dict_state = state
slots_state = None
# Starting with Python 3.11, the __dict__ attribute is lazily created
# and is serialized as None when not needed.
if dict_state:
for k, v in dict_state.items():
setattr(obj, k, v)
if slots_state:
for k, v in slots_state.items():
setattr(obj, k, v)
return obj
def _import_dotted_name(name):
components = name.split(".")
obj = __import__(components[0])
for component in components[1:]:
obj = getattr(obj, component)
return obj
def _flatten_dense_tensors(tensors):
"""Flatten dense tensors into a contiguous 1D buffer. Assume tensors are of
same dense type.
Since inputs are dense, the resulting tensor will be a concatenated 1D
buffer. Element-wise operation on this buffer will be equivalent to
operating individually.
Args:
tensors (Iterable[Tensor]): dense tensors to flatten.
Returns:
A contiguous 1D buffer containing input tensors.
"""
return torch._C._nn.flatten_dense_tensors(tensors)
def _flatten_sparse_tensors(tensors):
"""Flatten sparse tensors into two contiguous 1D buffers, one of indices and
one of values. Assume tensors are of same sparse type.
Args:
tensors (Iterable[Tensor]): sparse tensors to flatten.
Returns:
A tuple of two contiguous 1D buffers, one containing input tensors'
indices and the other containing the values.
"""
flat_indices = torch._C._nn.flatten_dense_tensors(
[torch.Tensor._indices(t) for t in tensors]
)
flat_values = torch._C._nn.flatten_dense_tensors(
[torch.Tensor._values(t) for t in tensors]
)
return flat_indices, flat_values
def _unflatten_dense_tensors(flat, tensors):
"""View a flat buffer using the sizes of tensors. Assume that tensors are of
same dense type, and that flat is given by _flatten_dense_tensors.
Args:
flat (Tensor): flattened dense tensors to unflatten.
tensors (Iterable[Tensor]): dense tensors whose sizes will be used to
unflatten flat.
Returns:
Unflattened dense tensors with sizes same as tensors and values from
flat.
"""
return torch._C._nn.unflatten_dense_tensors(flat, tensors)
def _unflatten_sparse_tensors(flat, tensors):
"""View flat buffer (containing indices and values) using the sizes of
tensors. Assume that tensors are of same sparse type, and that flat is given
by _flatten_sparse_tensors.
Args:
flat (tuple(Tensor, Tensor)): flattened indices and values of sparse
tensors to unflatten.
tensors (Iterable[Tensor]): sparse tensors whose sizes will be used to
unflatten flat.
Returns:
Unflattened sparse tensors with sizes same as tensors and values from
flat.
"""
flat_indices, flat_values = flat
indices = torch._C._nn.unflatten_dense_tensors(
flat_indices, [torch.Tensor._indices(t) for t in tensors]
)
values = torch._C._nn.unflatten_dense_tensors(
flat_values, [torch.Tensor._values(t) for t in tensors]
)
outputs = []
for t, i, v in zip(tensors, indices, values):
outputs.append(t.new(i, v, t.size()))
return tuple(outputs)
def _reorder_tensors_as(tensors, ordered_tensors):
"""Assume that tensors are of same order as ordered_tensors within their
types, e.g., from _take_tensors. Reorder them to be of same order as
ordered_tensors.
Args:
tensors (Iterable[Tensor]): tensors to be reordered. They should be of
the same order as ordered_tensors within their own types.
ordered_tensors (Iterable[Tensor]): tensors whose order will be the
reference.
Returns:
Ordered tuple of tensors with contents from tensors and order of
ordered_tensors.
"""
type_dict = defaultdict(list)
for tensor in tensors:
type_dict[tensor.type()].append(tensor)
type_dict_ = {t: iter(coll) for t, coll in type_dict.items()}
return tuple(next(type_dict_[tensor.type()]) for tensor in ordered_tensors)
def _take_tensors(tensors, size_limit):
"""Group tensors into chunks. This generator yields a chunk at each time,
each containing tensors of same type up to certain byte limit in total size.
Args:
tensors (Sequence): A sequence of tensors to be separated into chunks.
size_limit (int): The limit of each chunk in bytes.
Yields:
Blocks of tensors of same type and within size_limit. The yielded
tensors are only ordered as the original sequence within its types.
"""
buf_dict: DefaultDict[str, List] = defaultdict(lambda: [[], 0])
for tensor in tensors:
t = tensor.type()
if tensor.is_sparse:
indices = torch.Tensor._indices(tensor)
values = torch.Tensor._values(tensor)
size = (
indices.numel() * indices.element_size()
+ values.numel() * values.element_size()
)
else:
size = tensor.numel() * tensor.element_size()
buf_and_size = buf_dict[t]
if buf_and_size[1] + size > size_limit and buf_and_size[1] > 0:
yield buf_and_size[0]
buf_and_size = buf_dict[t] = [[], 0]
buf_and_size[0].append(tensor)
buf_and_size[1] += size
for buf, _ in buf_dict.values():
if len(buf) > 0:
yield buf
# annotation decorator to get annotations in a way that is compatible
# with both Python 2 and 3
def annotate(ret, **kwargs):
def dec(fun):
fun.__annotations__ = dict(kwargs)
fun.__annotations__["return"] = ret
return fun
return dec
def render_call(fn, args, kwargs):
str_fn = torch.overrides.resolve_name(fn)
if str_fn is None:
str_fn = str(fn)
str_args: List[str] = []
with torch._tensor_str.printoptions(threshold=0, edgeitems=0):
str_args.extend(repr(a) for a in args)
str_args.extend(f"{k}={repr(v)}" for k, v in kwargs.items())
r = f"{str_fn}({', '.join(str_args)})"
return r
# NOTE [ Python Traceback Reference Cycle Problem ]
#
# When using sys.exc_info(), it is important to **not** store the exc_info[2],
# which is the traceback, because otherwise you will run into the traceback
# reference cycle problem, i.e., the traceback holding reference to the frame,
# and the frame (which holds reference to all the object in its temporary scope)
# holding reference the traceback.
class KeyErrorMessage(str):
r"""str subclass that returns itself in repr"""
def __repr__(self):
return self
class ExceptionWrapper:
r"""Wraps an exception plus traceback to communicate across threads"""
def __init__(self, exc_info=None, where="in background"):
# It is important that we don't store exc_info, see
# NOTE [ Python Traceback Reference Cycle Problem ]
if exc_info is None:
exc_info = sys.exc_info()
self.exc_type = exc_info[0]
self.exc_msg = "".join(traceback.format_exception(*exc_info))
self.where = where
def reraise(self):
r"""Reraises the wrapped exception in the current thread"""
# Format a message such as: "Caught ValueError in DataLoader worker
# process 2. Original Traceback:", followed by the traceback.
msg = f"Caught {self.exc_type.__name__} {self.where}.\nOriginal {self.exc_msg}"
if self.exc_type == KeyError:
# KeyError calls repr() on its argument (usually a dict key). This
# makes stack traces unreadable. It will not be changed in Python
# (https://bugs.python.org/issue2651), so we work around it.
msg = KeyErrorMessage(msg)
elif getattr(self.exc_type, "message", None):
# Some exceptions have first argument as non-str but explicitly
# have message field
raise self.exc_type(message=msg)
try:
exception = self.exc_type(msg)
except TypeError:
# If the exception takes multiple arguments, don't try to
# instantiate since we don't know how to
raise RuntimeError(msg) from None
raise exception
def _get_available_device_type():
if torch.cuda.is_available():
return "cuda"
if hasattr(torch, "xpu") and torch.xpu.is_available(): # type: ignore[attr-defined]
return "xpu"
custom_backend_name = torch._C._get_privateuse1_backend_name()
custom_device_mod = getattr(torch, custom_backend_name, None)
if custom_device_mod and custom_device_mod.is_available():
return custom_backend_name
# add more available device types here
return None
def _get_device_attr(get_member):
device_type = _get_available_device_type()
if device_type and device_type.lower() == "cuda":
return get_member(torch.cuda)
if device_type and device_type.lower() == "xpu":
return get_member(torch.xpu) # type: ignore[attr-defined]
if device_type == torch._C._get_privateuse1_backend_name():
return get_member(getattr(torch, device_type))
# add more available device types here
return None
def _get_current_device_index():
# current device index
return _get_device_attr(lambda m: m.current_device())
def _get_all_device_indices():
# all device index
return _get_device_attr(lambda m: list(range(m.device_count())))
def _get_devices_properties(device_ids):
# all device properties
return [_get_device_attr(lambda m: m.get_device_properties(i)) for i in device_ids]
def get_current_device_index() -> int:
r"""Checks if there are CUDA devices available and
returns the device index of the current default CUDA device.
Returns -1 in case there are no CUDA devices available.
Arguments: ``None``
"""
if torch.cuda.device_count() > 0:
return torch.cuda.current_device()
return -1
def _get_device_index(
device: Any, optional: bool = False, allow_cpu: bool = False
) -> int:
r"""Gets the device index from :attr:`device`, which can be a torch.device
object, a Python integer, or ``None``.
If :attr:`device` is a torch.device object, returns the device index if it
has index. Note that for a device without a specified index,
i.e., ``torch.device('xxx')``, this will return the current default
device of that type if :attr:`optional` is ``True``. If :attr:`allow_cpu` is ``True``,
CPU devices will be accepted and ``-1`` will be returned in this case.
If :attr:`device` is a Python integer, it is returned as is.
If :attr:`device` is ``None``, this will return the current default
device of the supported runtime platform if :attr:`optional` is ``True``.
i.e., the current default CUDA device will be returned if CUDA runtime is supported.
"""
if isinstance(device, str):
device = torch.device(device)
device_idx: Optional[int] = None
if isinstance(device, torch.device):
if not allow_cpu and device.type == "cpu":
raise ValueError(f"Expected a non cpu device, but got: {device}")
device_idx = -1 if device.type == "cpu" else device.index
if isinstance(device, int):
device_idx = device
if device_idx is None:
if optional:
# The eager API _get_current_device_index uses `lambda` functions which are
# not supported in JIT and hence not scriptable. The JIT equivalent API to get
# the current device index is `get_current_device_index()` which can
# be scripted. We use is_scripting to check the mode we are in and call the
# appropriate API.
if torch.jit.is_scripting():
device_idx = get_current_device_index()
else:
device_idx = _get_current_device_index()
else:
raise ValueError(
f"Expected a torch.device with a specified index or an integer, but got:{device}"
)
return device_idx
def _handle_complex(tensor):
"""
Returns a real view of a tensor if complex dtype else just the tensor
need to check if a UninitializedParameter because otherwise checking is_complex is an error for a LazyModule
"""
return (
torch.view_as_real(tensor)
if not isinstance(tensor, torch.nn.UninitializedParameter)
and tensor.is_complex()
else tensor
)
def _element_size(dtype):
"""
Returns the element size for a dtype, in bytes
"""
if not isinstance(dtype, torch.dtype):
raise RuntimeError(f"expected torch.dtype, but got {type(dtype)}")
if dtype.is_complex:
return torch.finfo(dtype).bits >> 2
elif dtype.is_floating_point:
return torch.finfo(dtype).bits >> 3
elif dtype == torch.bool:
# NOTE: torch.bool is not supported in torch.iinfo()
return 1
else:
return torch.iinfo(dtype).bits >> 3
class _ClassPropertyDescriptor:
def __init__(self, fget, fset=None):
self.fget = fget
def __get__(self, instance, owner=None):
if owner is None:
owner = type(instance)
return self.fget.__get__(instance, owner)()
def classproperty(func):
if not isinstance(func, (classmethod, staticmethod)):
func = classmethod(func)
return _ClassPropertyDescriptor(func)
def is_compiling() -> bool:
"""
Indicates whether we are tracing/compiling with torch.compile() or torch.export().
TODO(khabinov): we should deprecate this function and use torch.compiler.is_compiling().
"""
return torch.compiler.is_compiling()
def _functionalize_sync(t):
# This code lives in python instead of C++ since conditioning on a certain python subclass
# is much more of a pain in C++.
from torch._subclasses.functional_tensor import FunctionalTensor
if isinstance(t, FunctionalTensor):
# If a FunctionalTensorMode is active while syncing, we don't want it to intercept any ops that get called
# when we sync our inner tensor.
# Why?
# (1) If there are input mutations in the graph, then they will be re-applied during
# AOTAutograd when we call _sync() from inside of our functionalization kernels.
# (2) _sync() causes us to regenerate our updated the tensor from the updated base,
# which dispatches to a bunch of view ops
# (3) The input to these view ops is our inner FunctionalTensorWrapper
# (since the sync was called from C++), not the python FunctionalTensor
# (4) if a python FunctionalTensorMode is active, it will complain when it intercepts
# the view op, since it will see an input that is a C++ FunctionalTensorWrapper
# (aka a normal torch.Tensor) instead of a python `FunctionalTensor).
maybe_functional_mode = torch._C._unset_dispatch_mode(
torch._C._TorchDispatchModeKey.FUNCTIONAL
)
try:
torch._functionalize_sync(t.elem) # type: ignore[attr-defined]
finally:
if maybe_functional_mode is not None:
torch._C._set_dispatch_mode(maybe_functional_mode)
else:
torch._functionalize_sync(t) # type: ignore[attr-defined]
@functools.lru_cache(2)
def _get_device_module(device_type: str):
device_module = getattr(torch, device_type, None)
if device_module is None:
raise RuntimeError(
f"Device '{device_type}' does not have a corresponding module registered as 'torch.{device_type}'."
)
return device_module
def _dummy_type(name: str) -> type:
def get_err_fn(is_init: bool):
def err_fn(obj, *args, **kwargs):
if is_init:
class_name = obj.__class__.__name__
else:
class_name = obj.__name__
raise RuntimeError(f"Tried to instantiate dummy base class {class_name}")
return err_fn
return type(
name, (object,), {"__init__": get_err_fn(True), "__new__": get_err_fn(False)}
)
class _LazySeedTracker:
# Since seeding is memory-less, only track the latest seed.
# Note: `manual_seed_all` followed by `manual_seed` overwrites
# the seed on current device. We track the order of **latest**
# calls between these two API.
def __init__(self):
self.manual_seed_all_cb = None
self.manual_seed_cb = None
self.call_order = []
def queue_seed_all(self, cb, traceback):
self.manual_seed_all_cb = (cb, traceback)
# update seed_all to be latest
self.call_order = [self.manual_seed_cb, self.manual_seed_all_cb]
def queue_seed(self, cb, traceback):
self.manual_seed_cb = (cb, traceback)
# update seed to be latest
self.call_order = [self.manual_seed_all_cb, self.manual_seed_cb]
def get_calls(self) -> List:
return self.call_order
logger = logging.getLogger(__name__)
P = ParamSpec("P")
class CallbackRegistry(Generic[P]):
def __init__(self, name: str):
self.name = name
self.callback_list: List[Callable[P, None]] = []
def add_callback(self, cb: Callable[P, None]) -> None:
self.callback_list.append(cb)
def fire_callbacks(self, *args: P.args, **kwargs: P.kwargs) -> None:
for cb in self.callback_list:
try:
cb(*args, **kwargs)
except Exception as e:
logger.exception(
"Exception in callback for %s registered with gpu trace", self.name
)
