| import torch |
| import torch.utils.hooks |
| from torch._namedtensor_internals import check_serializing_named_tensor |
| import os |
| import threading |
| import multiprocessing |
| from multiprocessing.util import register_after_fork |
| from multiprocessing.reduction import ForkingPickler |
| 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(object): |
| r"""A weak reference to a Storage. |
| |
| The cdata member is a Python number containing the integer representation of |
| the Storage pointer.""" |
| |
| 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] |
| |
| def expired(self): |
| return torch.Storage._expired(self.cdata) # type: ignore[attr-defined] |
| |
| def __del__(self): |
| self._free_weak_ref(self.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) |
| 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) |
| |
| t = torch._utils._rebuild_tensor( |
| torch.storage.TypedStorage(wrap_storage=storage._untyped(), dtype=dtype), |
| 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): |
| storage = tensor.storage() |
| |
| 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. |
| # |
| if storage.is_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 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 cls._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(fd, size) |
| shared_cache[fd_id(fd)] = StorageWeakRef(storage) |
| return storage |
| finally: |
| os.close(fd) |
| |
| |
| def rebuild_storage_filename(cls, manager, handle, size): |
| storage = storage_from_cache(cls, handle) |
| if storage is not None: |
| return storage._shared_decref() |
| storage = cls._new_shared_filename(manager, handle, size) |
| 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) |
| |
| # Use for torch.storage.TypedStorage |
| def reduce_typed_storage(storage): |
| return (rebuild_typed_storage, (storage._storage, storage.dtype)) |
| |
| def rebuild_typed_storage_child(storage, storage_type): |
| return storage_type(wrap_storage=storage) |
| |
| # Use for child classes of torch.storage.TypedStorage, like torch.FloatStorage |
| def reduce_typed_storage_child(storage): |
| return (rebuild_typed_storage_child, (storage._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_() |
| cache_key = metadata[1] |
| rebuild = rebuild_storage_filename |
| 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_() |
| 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) |
