def gather(data, dst=0, group=None):
"""
Run gather on arbitrary picklable data (not necessarily tensors).
Args:
data: any picklable object
dst (int): destination rank
group: a torch process group. By default, will use a group which
contains all ranks on gloo backend.
Returns:
list[data]: on dst, a list of data gathered from each rank. Otherwise,
an empty list.
"""
if get_world_size() == 1:
return [data]
if group is None:
group = _get_global_gloo_group()
world_size = dist.get_world_size(group=group)
if world_size == 1:
return [data]
rank = dist.get_rank(group=group)
if rank == dst:
output = [None for _ in range(world_size)]
dist.gather_object(data, output, dst=dst, group=group)
return output
else:
dist.gather_object(data, None, dst=dst, group=group)
return []
def gather(
self,
dst: int = 0,
out: Optional[torch.Tensor] = None,
) -> None:
"""
Creates a full :class:`Tensor` on rank ``dst`` by gathering all shards of the
sharded tensor.
The API needs to be called on all ranks in SPMD fashion. All ranks should have
the same ``dst``. ``out`` should be a tensor of the same size as the overall
size of the sharded tensor on ``dst`` and ``None`` on all other ranks.
Args:
dst(int): The rank where full tensor is constructed.
Default: 0
out (:class `torch.Tensor`, optional): The output full tensor.
Must to be provided ONLY on ``dst`` rank.
Default: ``None``
"""
rank = dist.get_rank(self._process_group)
full_size = self.metadata().size
_validate_output_tensor_for_gather(rank, dst, full_size, out)
local_shards = self.local_shards()
world_size = dist.get_world_size(self._process_group)
gathered_shards: List[Optional[List[Shard]]] = [None] * world_size if rank == dst else []
# TODO: see how we could use dist.gather() instead of dist.gather_object
# as the latter one involves pickling on CPU, see more context
# https://github.com/pytorch/pytorch/issues/73935
dist.gather_object(
obj=local_shards,
object_gather_list=gathered_shards,
dst=dst,
group=self._process_group,
)
if rank == dst:
if out is None:
raise ValueError("`out` Tensor must be provided on dst rank!")
dims = len(full_size)
for shards in gathered_shards:
if shards is None:
raise RuntimeError(
'Gathered shards cannot be None on dst rank {dst}'
)
for shard in shards:
metadata = shard.metadata
tensor = shard.tensor
out_narrow_view = out
for dim in range(dims):
out_narrow_view = out_narrow_view.narrow(
dim,
metadata.shard_offsets[dim],
metadata.shard_sizes[dim],
)
out_narrow_view.copy_(tensor)
def test_gather_object(self):
output = [None] * dist.get_world_size() if self.rank == 0 else None
dist.gather_object(obj=self.rank, object_gather_list=output)
if self.rank == 0:
for i, v in enumerate(output):
self.assertEqual(i, v, f"rank: {self.rank}")
def gather_object(self, object: T) -> Optional[List[T]]:
"""
Same as c10d::gather_object but works without distributed enabled.
"""
if self.use_dist:
gather_objs = cast(List[T], [None] * dist.get_world_size(
self.group)) if self.is_coordinator else None
dist.gather_object(obj=object,
object_gather_list=gather_objs
if self.is_coordinator else None,
dst=self.coordinator_rank,
group=self.group)
result = gather_objs
else:
result = [object]
return result
def synchronize_between_processes(self):
if dist.is_initialized():
# Bypass NCCL (which forces CUDA-only sync)
if dist.get_backend() == "nccl":
group = dist.new_group(backend="gloo")
else:
group = dist.group.WORLD
my_rank = dist.get_rank()
output = [None for _ in range(dist.get_world_size())]
dist.gather_object(self.predictions,
output if my_rank == 0 else None,
dst=0,
group=group)
return list(itertools.chain.from_iterable(output)), my_rank == 0
else:
return self.predictions, True
def save_state_dict(
state_dict: Dict[str, Any],
storage_writer: StorageWriter,
process_group: Optional[dist.ProcessGroup] = None,
coordinator_rank: int = 0,
no_dist: bool = False
) -> None:
"""
Save a distributed model in SPMD style.
This function is different from ``torch.save()`` as it handles
``ShardedTensor`` by having each rank only save their local shards.
To produce a state_dict with ShardedTensor instances you must call
``_register_state_dict_hook`` on the top module with value
`torch.distributed._shard.sharded_tensor.state_dict_hook` prior to
calling `state_dict()` on the top module.
There is no guarantees of Backwards Compatibility across PyTorch versions
for saved state_dicts.
If using the `process_group` argument, make sure that only its ranks
call `save_state_dict` and that all data in state_dict belong to it.
This function can be used to save a state_dict with an intialized process
group by passing ``no_dist=True``. This can be used to produce a checkpoint
that can consumed by load_state_dict is a SPMD fashion.
Args:
state_dict (Dict[str, Any]) : A state_dict
storage_writer (StorageWriter): Instance of StorageWrite use to perform writes.
process_group (ProcessGroup): ProcessGroup to be used for cross-rank synchronization
coordinator_rank (int): Rank to use to coordinate the checkpoint, rank0 is used by default
no_dist (bool): Don't attempt to save in SPMD style. Default to False
Example:
>>> my_model = MyModule()
>>> # We must call this function prior to state_dict()
>>> my_model._register_state_dict_hook(state_dict_hook)
>>> model_state_dict = my_model.state_dict()
>>> fs_storage_writer = torch.distributed._shard.checkpoint.FileSystemWriter("/checkpoint/1")
>>> torch.distributed._shard.checkpoint.save_state_dict(
>>> state_dict=model_state_dict,
>>> storage_writer=fs_stroage_writer,
>>> )
.. note:: save_state_dict uses collectives to coordinate writes across ranks.
For NCCL-based process groups, internal tensor representations of objects
must be moved to the GPU device before communication takes place. In this
case, the device used is given by ``torch.cuda.current_device()`` and it
is the user's responsibility to ensure that this is set so that each rank
has an individual GPU, via ``torch.cuda.set_device()``
"""
is_coordinator = no_dist or dist.get_rank(process_group) == coordinator_rank
exceptions: List[Optional[BaseException]] = [None]
if is_coordinator:
try:
storage_writer.prepare()
except BaseException as e:
exceptions = [e]
# Writing can only start once prepare has finished
if not no_dist:
dist.broadcast_object_list(exceptions, group=process_group, src=coordinator_rank)
if exceptions[0] is not None:
raise CheckpointException("failed to prepare storage", {coordinator_rank : exceptions[0]})
rank_write_error: Optional[BaseException]
try:
(
metadata,
bytes_write_requests,
tensor_write_requests,
) = _prepare(state_dict, is_coordinator, process_group)
combined_writes: List[Union[TensorWriteRequest, BytesWriteRequest]] = []
combined_writes.extend(tensor_write_requests)
combined_writes.extend(bytes_write_requests)
storage_writer.prepare_storage(combined_writes)
bytes_futures = storage_writer.write_bytes(bytes_write_requests)
tensor_futures = storage_writer.write_tensors(tensor_write_requests)
torch.futures.wait_all([bytes_futures, tensor_futures])
rank_write_error = None
except BaseException as e:
rank_write_error = e
all_errors: List[Optional[BaseException]]
# collect all write errors
if not no_dist:
all_errors = [None] * dist.get_world_size(process_group)
dist.gather_object(
obj=rank_write_error,
object_gather_list=all_errors if is_coordinator else None,
dst=coordinator_rank
)
else:
all_errors = [rank_write_error]
result: List[Optional[CheckpointException]] = [None]
if is_coordinator:
message: Optional[str] = None
# gather produces an array of arrays, flatten it
if any(all_errors):
message = "Failed to write data"
else:
try:
storage_writer.finish(metadata=metadata)
except BaseException as e:
all_errors[coordinator_rank] = e
message = "Failed to finish checkpoint"
if message is not None:
node_failures = {i: err for i, err in enumerate(all_errors) if err is not None}
result[0] = CheckpointException(message, node_failures)
if not no_dist:
dist.broadcast_object_list(
result,
group=process_group,
src=coordinator_rank)
if result[0] is not None:
raise result[0]
import torch.distributed as dist
if dist.get_rank() == 0:
objects = ["f", 1]
else:
objects = [None, None]
# ruleid: pickles-in-torch-distributed
dist.broadcast_object_list(objects, src=0)
# ruleid: pickles-in-torch-distributed
dist.all_gather_object(output, gather_objects[dist.get_rank()])
# ruleid: pickles-in-torch-distributed
dist.gather_object(gather_objects[dist.get_rank()],
output if dist.get_rank() == 0 else None,
dst=0)
# ruleid: pickles-in-torch-distributed
dist.scatter_object_list(output_list, objects, src=0)
