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from typing import Any, Dict, Optional
import torch.distributed as dist
from .storage import (
StorageReader,
)
from .planner import LoadPlanner
from .default_planner import DefaultLoadPlanner
from .utils import _DistWrapper
def load_state_dict(
state_dict: Dict[str, Any],
storage_reader: StorageReader,
process_group: Optional[dist.ProcessGroup] = None,
coordinator_rank: int = 0,
no_dist: bool = False,
planner: LoadPlanner = None
) -> None:
"""
Load a distributed state_dict in SPMD style.
Each rank will try to read the least amount of data necessary
to fullfill the requested `state_dict`.
When loading ShardedTensor instances, each rank only
reads data for their local shards.
All tensors in ``state_dict`` must be allocated on their
destination device prior to calling this function.
All non-tensor data is loaded using `torch.load()` and modified in place
on state_dict.
Users must call `load_state_dict` on the root module to ensure load
pos-processing and non-tensor data properly propagates.
This function can be used for local inference and load a checkpoint
produced by ``save_state_dict`` without having a process group initialized
by passing ``no_dist=True`` and by using Tensors instead of ShardedTensors.
Args:
state_dict (Dict[str, Any]) : The state_dict to load. Note that this
state dict will updated in places.
storage_reader (StorageReader): StorageReader used to load data from.
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 load in SPMD style. Default to False
Returns:
None.
Examples
>>> # xdoctest: +SKIP
>>> my_model = MyModule()
>>> optimizer = Adagrad(my_model.parameters())
>>> model_state_dict = my_model.state_dict()
>>> fs_storage_loader = torch.distributed._shard.checkpoint.FileSystemLoader("/checkpoint/1")
>>> torch.distributed._shard.checkpoint.load_state_dict(
>>> state_dict=model_state_dict,
>>> storage_reader=fs_storage_loader,
>>> )
>>> # module.load_state_dict() function might have customized steps
>>> # to flush the state_dict, must call it to
>>> # ensure correct behavior.
>>> my_model.load_state_dict(model_state_dict)
.. note:: load_state_dict uses collectives to coordinate reads 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()``
"""
distW = _DistWrapper(process_group, not no_dist, coordinator_rank)
if planner is None:
planner = DefaultLoadPlanner()
def local_step():
assert planner is not None
metadata = storage_reader.read_metadata()
planner.init(state_dict, metadata, distW.is_coordinator)
storage_reader.init(metadata, distW.is_coordinator)
local_plan = planner.create_local_plan()
local_plan = storage_reader.prepare_local_plan(local_plan)
return local_plan
def global_step(all_local_plans):
assert planner is not None
all_local_plans = planner.create_global_plan(all_local_plans)
all_local_plans = storage_reader.prepare_global_plan(all_local_plans)
return all_local_plans
central_plan = distW.reduce_scatter("plan", local_step, global_step)
def read_data():
assert planner is not None
final_local_plan = planner.finish_plan(central_plan)
all_reads = storage_reader.read_data(final_local_plan, planner)
all_reads.wait()
return None
_ = distW.all_gather("read", read_data)
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