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image-handler/image-handler10.0/venv/lib/python3.12/site-packages/torch/multiprocessing/reductions.py

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import multiprocessing
import os
import threading
from multiprocessing.reduction import ForkingPickler
from multiprocessing.util import register_after_fork
from typing import Union
import torch
import torch.utils.hooks
from torch._namedtensor_internals import check_serializing_named_tensor
try:
# Early load resource_sharer to prevent a partially initialized instance
# from being inherited in a forked child process. The reduce_storage method
# requires this module indirectly through DupFd(). The built-in mp.Queue
# class pickles arguments in a background thread which may overlap with the
# fork.
import multiprocessing.resource_sharer
except ImportError:
pass
class StorageWeakRef:
r"""A weak reference to a Storage.
The cdata member is a Python number containing the integer representation of
the Storage pointer.
"""
__slots__ = ["cdata", "_free_weak_ref"]
def __init__(self, storage):
self.cdata = storage._weak_ref()
# Save a direct reference to _free_weak_ref because the `torch` module
# might be cleared during Python shutdown before this module is cleared.
self._free_weak_ref = torch.Storage._free_weak_ref # type: ignore[attr-defined]
@classmethod
def from_weakref(cls, cdata):
instance = cls.__new__(cls)
instance.cdata = cdata
instance._free_weak_ref = torch.Storage._free_weak_ref # type: ignore[attr-defined]
return instance
def expired(self):
return torch.Storage._expired(self.cdata) # type: ignore[attr-defined]
def __del__(self):
self._free_weak_ref(self.cdata)
def __hash__(self):
return self.cdata
def __eq__(self, other):
if id(self) == id(other):
return True
return self.cdata == other.cdata
class SharedCache(dict):
"""Dictionary from multiprocessing handles to StorageWeakRef."""
def __init__(self):
# free_dead_references() is called if the len exceeds the current
# limit. The limit scales with the number of remaining live objects.
self.limit = 128
# `fork` inherits lock state, so in case we fork when the lock is held,
# we register a function to reset the lock to a new object to avoid
# possible deadlocks, following python multiprocessing library design.
self._after_fork()
register_after_fork(self, SharedCache._after_fork)
def _after_fork(self):
self.lock = threading.Lock()
def get(self, key):
with self.lock:
return dict.get(self, key)
def __setitem__(self, key, storage_ref):
with self.lock:
dict.__setitem__(self, key, storage_ref)
if len(self) > self.limit:
self.free_dead_references()
def free_dead_references(self):
live = 0
for key, storage_ref in list(self.items()):
if storage_ref.expired():
del self[key]
else:
live += 1
self.limit = max(128, live * 2)
# mapping from handles to StorageWeakRef objects
shared_cache = SharedCache()
def rebuild_event(device, handle):
return torch.cuda.Event.from_ipc_handle(device, handle)
def reduce_event(event):
handle = event.ipc_handle()
return (rebuild_event, (event.device, handle))
def rebuild_tensor(cls, storage, metadata):
storage_offset, size, stride, requires_grad = metadata
t = torch._utils._rebuild_tensor(storage, storage_offset, size, stride)
if cls == torch.nn.parameter.Parameter:
# we have to pass requires_grad into constructor, rather than set it as an
# attribute later, because it's an important check for Integer Tensors to
# have requires_grad=False (or else they raise an error)
t = torch.nn.parameter.Parameter(t, requires_grad=requires_grad)
else:
t.requires_grad = requires_grad
return t
def rebuild_cuda_tensor(
tensor_cls,
tensor_size,
tensor_stride,
tensor_offset,
storage_cls,
dtype,
storage_device,
storage_handle,
storage_size_bytes,
storage_offset_bytes,
requires_grad,
ref_counter_handle,
ref_counter_offset,
event_handle,
event_sync_required,
):
# If storage_handle is None, storage points to nullptr.
if storage_handle is None or storage_size_bytes == 0:
storage = storage_cls(0, dtype=dtype, device=storage_device, _internal=True)
else:
storage = storage_from_cache(
storage_cls, (storage_handle, storage_offset_bytes)
)
if storage is None:
torch.cuda._lazy_init()
storage = storage_cls._new_shared_cuda(
storage_device,
storage_handle,
storage_size_bytes,
storage_offset_bytes,
ref_counter_handle,
ref_counter_offset,
event_handle,
event_sync_required,
)
shared_cache[(storage_handle, storage_offset_bytes)] = StorageWeakRef(
storage
)
else:
# We already ref counting this Storage, but producer needs new ref-counters to be released.
storage_cls._release_ipc_counter(
ref_counter_handle, ref_counter_offset, device=storage_device
)
_storage = (
storage
if isinstance(storage, torch.UntypedStorage)
else storage._untyped_storage
)
t = torch._utils._rebuild_tensor(
torch.storage.TypedStorage(wrap_storage=_storage, dtype=dtype, _internal=True),
tensor_offset,
tensor_size,
tensor_stride,
)
if tensor_cls == torch.nn.parameter.Parameter:
# It is crucial for integer tensors to receive
# the requires_grad=False as an argument in the constructor
t = torch.nn.parameter.Parameter(t, requires_grad=requires_grad)
else:
t.requires_grad = requires_grad
return t
def reduce_tensor(tensor):
if tensor.requires_grad and not tensor.is_leaf:
raise RuntimeError(
"Cowardly refusing to serialize non-leaf tensor which requires_grad, "
"since autograd does not support crossing process boundaries. "
"If you just want to transfer the data, call detach() on the tensor "
"before serializing (e.g., putting it on the queue)."
)
check_serializing_named_tensor(tensor)
torch.utils.hooks.warn_if_has_hooks(tensor)
# Note [CUDA IPC and the caching allocator]
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# When you send a CUDA tensor over IPC, you might expect that you will
# get out the same storage from the other end. However, the CUDA caching
# allocator makes it difficult to preserve this invariant. Consider
# the following situation: a tensor of size 0x100 points to offset 0x20 of
# a storage at 0xA100 of size 0x100. (For simplicity, all of these
# sizes are given in bytes). HOWEVER, with the caching allocator, this storage
# might be part of a larger cudaMalloc allocation 0xA000 of size 0x4000.
#
# When we want to send this CUDA tensor over IPC, we must send the
# *entire* cudaMalloc allocation, i.e., the 0xA000 region, not just
# the storage 0xA100 (because that is what CUDA supports). So, on the
# other end, there simply isn't any way to say, "Wait, you gave me
# a bigger region (0xA000) than the one I wanted (0xA100)".
#
# OK, so if you sent the cudaMalloc allocation, can you just wrap that up as
# one storage itself? No, because this cudaMalloc allocation might contain
# storages of mixed types: float, bytes, double... If you make the entire
# allocation a single storage of a type A, we'll hit an error when constructing
# a tensor of type B on the storage.
#
# cudaIpcMemHandle is an identifier to access the sender cudaMalloc allocation on the
# receiver side. However, cudaIpcMemHandles from each device in a given process may
# only be opened by one context per device per other process.
# If we open and close a memory handle multiples times in a process, CUDA is allowed
# to give it a different address; similarly, once we close the memory, we're not
# allowed to access it(and the storage/tensor built on top of it), even if it is
# still live in the original process. As we cannot make a cudaMalloc allocation
# to a single storage in one go, this requires us to cache the device pointer for
# each cudaIpcMemHandle on C++ side to reconstruct types of storages, while keep
# the old ones alives.
# See [https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__DEVICE.html]
#
# This is fine, because all we need to do is to save our position in the allocation,
# and reconstruct storage and tensor from it.
# 0xA000 -> -------CUDA Allocation------
# | |
# | |
# | |
# | |
# 0xA100 -> --------storage1 begin------
# | |
# 0xA120 -> --------tensor1 begin ------
# | |
# | |
# | |
# | |
# | |
# 0xA160 -> --------tensor1 end---------
# | |
# | |
# | |
# 0xA200 -> --------storage1 end--------
# | |
# 0xE000 -> --------CUDA allocation-----
#
# To send tensor1, the following info are required from sender to receiver for
# storage recontruction.
# 1. cudaIpcMemHandle of 0xA000(which can be mapped to a basePtr in receiver process).
# basePtr may not be exactly 0xA000 since it's a different process.
# 2. offset(0xA100) of storage1 in the CUDA allocation.
# 3. size of storage1(0x100).
#
# On receiver side:
# 1. Get the devPtr of the MemHandle to access the memory, reconstruct a storage
# of the same type using (basePtr, offset, size).
# 2. we can reconstruct the tensor on top of the reconstructed storage
# Tensor(size=0x040, offset=0x020, storage=Storage(data=basePtr+0xA100, size=0x0100))
#
# This strategy has a few implications:
#
# 1. When we serialize a CUDA tensor for IPC, we cannot do it all in one
# go (non-compositionally), and this requires to have a global map
# memHandle -> devPtr for each process.
#
# 2. We MUST NOT let the new IPC tensor be resizable. Originally, a resize
# of the storage beyond 0x100 would merely have caused us to do a
# reallocation. You don't really want to do this, but if you did,
# all that would happen is that you would lose IPC sharing. But if
# you do this in the new world, we will happily let you write out of
# bounds of your "allocation", clobbering unrelated data in the cached
# allocator block. BAD!
#
# By the way, in old versions of PyTorch, we supported this situation
# natively using a "storage view", which permitted multiple storages to be
# views on each other. But this was the *only* use of storage views, so we
# eliminated it so that we could just use tensor views to implement the same
# thing.
#
# TODO: Handle distinguishing between subclass and non-subclass versions of NT better
# https://github.com/pytorch/pytorch/issues/110543
from torch.nested._internal.nested_tensor import NestedTensor
if tensor.is_nested and not isinstance(tensor, NestedTensor):
return reduce_nested_tensor(tensor)
if tensor.layout in {
torch.sparse_coo,
torch.sparse_csr,
torch.sparse_bsr,
torch.sparse_csc,
torch.sparse_bsc,
}:
return reduce_sparse_tensor(tensor)
storage = tensor._typed_storage()
if storage._untyped_storage.device.type == "cuda":
(
device,
handle,
storage_size_bytes,
storage_offset_bytes,
ref_counter_handle,
ref_counter_offset,
event_handle,
event_sync_required,
) = storage._share_cuda_()
tensor_offset = tensor.storage_offset()
shared_cache[handle] = StorageWeakRef(storage)
# _backward_hooks purposely omitted here, see
# Note [Don't serialize hooks]
return (
rebuild_cuda_tensor,
(
type(tensor),
tensor.size(),
tensor.stride(),
tensor_offset, # tensor offset in its storage
type(storage),
tensor.dtype,
device,
handle, # identifier which CUDA allocation is the storage in.
storage_size_bytes, # size(in bytes) of the storage
storage_offset_bytes, # offset(in bytes) of the storage in the CUDA allocation
tensor.requires_grad,
ref_counter_handle,
ref_counter_offset,
event_handle,
event_sync_required,
),
)
# _backward_hooks purposely omitted here, see Note [Don't serialize hooks]
metadata = (
tensor.storage_offset(),
tensor.size(),
tensor.stride(),
tensor.requires_grad,
)
return (rebuild_tensor, (type(tensor), storage, metadata))
def rebuild_nested_tensor(
rebuild_buffer_func,
rebuild_buffer_args,
rebuild_sizes_func,
rebuild_sizes_args,
rebuild_strides_func,
rebuild_strides_args,
rebuild_offsets_func,
rebuild_offsets_args,
):
buffer = rebuild_buffer_func(*rebuild_buffer_args)
sizes = rebuild_sizes_func(*rebuild_sizes_args)
strides = rebuild_strides_func(*rebuild_strides_args)
offsets = rebuild_offsets_func(*rebuild_offsets_args)
return torch._nested_view_from_buffer_copy(buffer, sizes, strides, offsets)
def reduce_nested_tensor(nt):
rebuild_buffer_func, rebuild_buffer_args = reduce_tensor(nt.values())
rebuild_sizes_func, rebuild_sizes_args = reduce_tensor(nt._nested_tensor_size())
rebuild_strides_func, rebuild_strides_args = reduce_tensor(
nt._nested_tensor_strides()
)
rebuild_offsets_func, rebuild_offsets_args = reduce_tensor(
nt._nested_tensor_storage_offsets()
)
return (
rebuild_nested_tensor,
(
rebuild_buffer_func,
rebuild_buffer_args,
rebuild_sizes_func,
rebuild_sizes_args,
rebuild_strides_func,
rebuild_strides_args,
rebuild_offsets_func,
rebuild_offsets_args,
),
)
def rebuild_sparse_coo_tensor(
rebuild_indices_func,
rebuild_indices_args,
rebuild_values_func,
rebuild_values_args,
shape,
is_coalesced,
):
indices = rebuild_indices_func(*rebuild_indices_args)
values = rebuild_values_func(*rebuild_values_args)
return torch.sparse_coo_tensor(indices, values, shape, is_coalesced=is_coalesced)
def rebuild_sparse_compressed_tensor(
rebuild_compressed_indices_func,
rebuild_compressed_indices_args,
rebuild_plain_indices_func,
rebuild_plain_indices_args,
rebuild_values_func,
rebuild_values_args,
shape,
layout,
):
compressed_indices = rebuild_compressed_indices_func(
*rebuild_compressed_indices_args
)
plain_indices = rebuild_plain_indices_func(*rebuild_plain_indices_args)
values = rebuild_values_func(*rebuild_values_args)
return torch.sparse_compressed_tensor(
compressed_indices, plain_indices, values, shape, layout=layout
)
def reduce_sparse_tensor(sparse):
if sparse.layout is torch.sparse_coo:
rebuild_indices_func, rebuild_indices_args = reduce_tensor(sparse._indices())
rebuild_values_func, rebuild_values_args = reduce_tensor(sparse._values())
return (
rebuild_sparse_coo_tensor,
(
rebuild_indices_func,
rebuild_indices_args,
rebuild_values_func,
rebuild_values_args,
sparse.shape,
sparse.is_coalesced(),
),
)
else:
if sparse.layout in {torch.sparse_csr, torch.sparse_bsr}:
compressed_indices = sparse.crow_indices()
plain_indices = sparse.col_indices()
elif sparse.layout in {torch.sparse_csc, torch.sparse_bsc}:
compressed_indices = sparse.ccol_indices()
plain_indices = sparse.row_indices()
else:
raise NotImplementedError(sparse.layout)
(
rebuild_compressed_indices_func,
rebuild_compressed_indices_args,
) = reduce_tensor(compressed_indices)
rebuild_plain_indices_func, rebuild_plain_indices_args = reduce_tensor(
plain_indices
)
rebuild_values_func, rebuild_values_args = reduce_tensor(sparse.values())
return (
rebuild_sparse_compressed_tensor,
(
rebuild_compressed_indices_func,
rebuild_compressed_indices_args,
rebuild_plain_indices_func,
rebuild_plain_indices_args,
rebuild_values_func,
rebuild_values_args,
sparse.shape,
sparse.layout,
),
)
def fd_id(fd):
# Returns a tuple which uniquely identifies a file descriptor. In Mac OS,
# this doesn't work with shared memory handles, which is why we don't
# support the "file_descriptor" sharing method on that platform.
stat = os.fstat(fd)
return (stat.st_ino, stat.st_dev)
def storage_from_cache(cls, key):
storage_ref = shared_cache.get(key)
if storage_ref is None:
return None
return torch.UntypedStorage._new_with_weak_ptr(storage_ref.cdata)
def rebuild_storage_fd(cls, df, size):
fd = df.detach()
try:
storage = storage_from_cache(cls, fd_id(fd))
if storage is not None:
return storage
storage = cls._new_shared_fd_cpu(fd, size)
shared_cache[fd_id(fd)] = StorageWeakRef(storage)
return storage
finally:
os.close(fd)
def rebuild_storage_filename(cls, manager, handle, size, dtype=None):
storage: Union[torch.TypedStorage, torch.UntypedStorage] = storage_from_cache(
cls, handle
)
if storage is not None:
return storage._shared_decref()
if dtype is None:
storage = torch.UntypedStorage._new_shared_filename_cpu(manager, handle, size)
else:
byte_size = size * torch._utils._element_size(dtype)
untyped_storage: torch.UntypedStorage = (
torch.UntypedStorage._new_shared_filename_cpu(manager, handle, byte_size)
)
storage = torch.TypedStorage(
wrap_storage=untyped_storage, dtype=dtype, _internal=True
)
shared_cache[handle] = StorageWeakRef(storage)
return storage._shared_decref()
def rebuild_storage_empty(cls):
return cls()
def rebuild_typed_storage(storage, dtype):
return torch.storage.TypedStorage(wrap_storage=storage, dtype=dtype, _internal=True)
# Use for torch.storage.TypedStorage
def reduce_typed_storage(storage):
return (rebuild_typed_storage, (storage._untyped_storage, storage.dtype))
def rebuild_typed_storage_child(storage, storage_type):
return storage_type(wrap_storage=storage, _internal=True)
# Use for child classes of torch.storage.TypedStorage, like torch.FloatStorage
def reduce_typed_storage_child(storage):
return (rebuild_typed_storage_child, (storage._untyped_storage, type(storage)))
def reduce_storage(storage):
from . import get_sharing_strategy
if storage.is_cuda:
raise RuntimeError(
"Cannot pickle CUDA storage; try pickling a CUDA tensor instead"
)
elif get_sharing_strategy() == "file_system":
metadata = storage._share_filename_cpu_()
cache_key = metadata[1]
rebuild = rebuild_storage_filename
if isinstance(storage, torch.TypedStorage):
metadata += (storage.dtype,)
storage._shared_incref()
elif storage.size() == 0:
# This is special cased because Empty tensors
# (with size 0) cannot be mmapped.
return (rebuild_storage_empty, (type(storage),))
else:
fd, size = storage._share_fd_cpu_()
df = multiprocessing.reduction.DupFd(fd)
cache_key = fd_id(fd)
metadata = (df, size)
rebuild = rebuild_storage_fd # type: ignore[assignment]
shared_cache[cache_key] = StorageWeakRef(storage)
return (rebuild, (type(storage),) + metadata)
def init_reductions():
ForkingPickler.register(torch.cuda.Event, reduce_event)
for t in torch._storage_classes:
if t.__name__ == "UntypedStorage":
ForkingPickler.register(t, reduce_storage)
else:
ForkingPickler.register(t, reduce_typed_storage_child)
ForkingPickler.register(torch.storage.TypedStorage, reduce_typed_storage)
for t in torch._tensor_classes:
ForkingPickler.register(t, reduce_tensor)
# TODO: Maybe this should be in tensor_classes? :)
ForkingPickler.register(torch.Tensor, reduce_tensor)
ForkingPickler.register(torch.nn.parameter.Parameter, reduce_tensor)
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