mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
2ee6b97464
Moving DTensor to be in the public namespace, to formally add the documentation page that includes all the public APIs. This includes: * many path renames and path import fixes * a dedicated doc page without too much content yet (adding in the next PRs) * To preserve the BC for users still using the `torch.distributed._tensor`, I added a shim script to redirect old path calls to the new module The BC preserving is evidented by the fact that all DTensor tests are still working without changing the public imports. So it's safe to land the changes Pull Request resolved: https://github.com/pytorch/pytorch/pull/133113 Approved by: https://github.com/XilunWu ghstack dependencies: #133305, #133306
387 lines
13 KiB
Python
387 lines
13 KiB
Python
# mypy: allow-untyped-defs
|
|
import io
|
|
from typing import Any, Callable, cast, Dict, List
|
|
|
|
import torch
|
|
import torch.distributed as dist
|
|
from torch._utils import _get_device_module
|
|
from torch.distributed._shard.metadata import ShardMetadata
|
|
from torch.distributed._shard.sharded_tensor import ShardedTensor
|
|
from torch.distributed.tensor import DTensor
|
|
from torch.distributed.tensor._utils import compute_local_shape_and_global_offset
|
|
|
|
from .metadata import (
|
|
BytesStorageMetadata,
|
|
ChunkStorageMetadata,
|
|
MetadataIndex,
|
|
STATE_DICT_TYPE,
|
|
STORAGE_TYPES,
|
|
TensorProperties,
|
|
TensorStorageMetadata,
|
|
)
|
|
from .planner import (
|
|
LoadItemType,
|
|
ReadItem,
|
|
SavePlan,
|
|
TensorWriteData,
|
|
WriteItem,
|
|
WriteItemType,
|
|
)
|
|
from .resharding import (
|
|
_check_shard_metadata_pair_overlap,
|
|
_shards_get_overlap_region_wrt_saved_tensor,
|
|
)
|
|
|
|
|
|
__all__: List[str] = ["create_read_items_for_chunk_list"]
|
|
|
|
|
|
def _create_chunk_from_tensor(tensor: torch.Tensor) -> ChunkStorageMetadata:
|
|
return ChunkStorageMetadata(
|
|
offsets=torch.Size([0] * len(tensor.size())), sizes=tensor.size()
|
|
)
|
|
|
|
|
|
def _chunk_for_shard(shard_md: ShardMetadata) -> ChunkStorageMetadata:
|
|
return ChunkStorageMetadata(
|
|
offsets=torch.Size(shard_md.shard_offsets),
|
|
sizes=torch.Size(shard_md.shard_sizes),
|
|
)
|
|
|
|
|
|
def _sharded_tensor_metadata(
|
|
sharded_tensor: ShardedTensor, shard_md: ShardMetadata
|
|
) -> TensorWriteData:
|
|
shard_properties = sharded_tensor.metadata().tensor_properties
|
|
|
|
properties = TensorProperties(
|
|
dtype=shard_properties.dtype,
|
|
layout=shard_properties.layout,
|
|
requires_grad=shard_properties.requires_grad,
|
|
memory_format=shard_properties.memory_format,
|
|
pin_memory=shard_properties.pin_memory,
|
|
)
|
|
|
|
return TensorWriteData(
|
|
chunk=_chunk_for_shard(shard_md),
|
|
properties=properties,
|
|
size=sharded_tensor.metadata().size,
|
|
)
|
|
|
|
|
|
def _create_write_items_for_dtensor(fqn: str, tensor: DTensor) -> WriteItem:
|
|
sizes, offsets = compute_local_shape_and_global_offset(
|
|
tensor.shape, tensor.device_mesh, tensor.placements
|
|
)
|
|
sizes, offsets = torch.Size(sizes), torch.Size(offsets)
|
|
|
|
return WriteItem(
|
|
index=MetadataIndex(fqn, offsets),
|
|
type=WriteItemType.SHARD,
|
|
tensor_data=TensorWriteData(
|
|
chunk=ChunkStorageMetadata(
|
|
offsets=offsets,
|
|
sizes=sizes,
|
|
),
|
|
properties=TensorProperties.create_from_tensor(tensor.to_local()),
|
|
size=tensor.size(),
|
|
),
|
|
)
|
|
|
|
|
|
def _create_write_item_for_shard(
|
|
fqn: str, sharded_tensor: ShardedTensor, shard_md: ShardMetadata
|
|
) -> WriteItem:
|
|
offsets = torch.Size(shard_md.shard_offsets)
|
|
return WriteItem(
|
|
index=MetadataIndex(fqn, offsets),
|
|
type=WriteItemType.SHARD,
|
|
tensor_data=_sharded_tensor_metadata(sharded_tensor, shard_md),
|
|
)
|
|
|
|
|
|
def _create_write_item_for_tensor(fqn: str, tensor: torch.Tensor) -> WriteItem:
|
|
offsets = torch.Size([0] * len(tensor.size()))
|
|
return WriteItem(
|
|
index=MetadataIndex(fqn, offsets),
|
|
type=WriteItemType.TENSOR,
|
|
tensor_data=TensorWriteData(
|
|
chunk=ChunkStorageMetadata(offsets=offsets, sizes=tensor.size()),
|
|
properties=TensorProperties.create_from_tensor(tensor),
|
|
size=tensor.size(),
|
|
),
|
|
)
|
|
|
|
|
|
def _create_write_item_for_bytesio(fqn: str, bytes: Any):
|
|
return WriteItem(
|
|
index=MetadataIndex(fqn),
|
|
type=WriteItemType.BYTE_IO,
|
|
)
|
|
|
|
|
|
def _create_read_item_for_byteio(
|
|
dest_index, dest_offset, storage_index, storage_offset, length
|
|
):
|
|
return ReadItem(
|
|
type=LoadItemType.BYTE_IO,
|
|
dest_index=dest_index,
|
|
dest_offsets=torch.Size((dest_offset,)),
|
|
storage_index=storage_index,
|
|
storage_offsets=torch.Size((storage_offset,)),
|
|
lengths=torch.Size((length,)),
|
|
)
|
|
|
|
|
|
def _create_read_item_for_tensor(
|
|
dest_index, dest_offsets, storage_index, storage_offsets, lengths
|
|
):
|
|
return ReadItem(
|
|
type=LoadItemType.TENSOR,
|
|
dest_index=dest_index,
|
|
dest_offsets=torch.Size(dest_offsets),
|
|
storage_index=storage_index,
|
|
storage_offsets=torch.Size(storage_offsets),
|
|
lengths=torch.Size(lengths),
|
|
)
|
|
|
|
|
|
def create_read_items_for_chunk_list(
|
|
fqn: str,
|
|
checkpoint_md: TensorStorageMetadata,
|
|
local_chunks: List[ChunkStorageMetadata],
|
|
) -> List[ReadItem]:
|
|
"""
|
|
Create a list of ``ReadItem`` based on the checkpoint and local chunks.
|
|
|
|
This applies the resharding algorithm and computes the reads needed
|
|
to satisfy ``local_chunks`` with a checkpoint described by ``checkpoint_md``.
|
|
|
|
Args:
|
|
fqn (str) : The state_dict FQN to pass to ``ReadItem``.
|
|
checkpoint_md (TensorStorageMetadata): metadata for a given tensor
|
|
from a checkpoint.
|
|
local_chunks (List[ChunkStorageMetadata]): Local chunks that needs to be
|
|
loaded.
|
|
|
|
Returns:
|
|
A list of ``ReadItem`` that will satisfy all input chunks.
|
|
"""
|
|
read_items = []
|
|
# this is a naive quadratic algo that can be optimized later
|
|
for idx, shard in enumerate(local_chunks):
|
|
for storage_idx, storage_md in enumerate(checkpoint_md.chunks):
|
|
if not _check_shard_metadata_pair_overlap(shard, storage_md):
|
|
continue
|
|
|
|
storage_offsets = []
|
|
dest_offsets = []
|
|
lengths = []
|
|
for (
|
|
dim,
|
|
offset_for_saved_tensor,
|
|
offset_for_current_tensor,
|
|
length,
|
|
) in _shards_get_overlap_region_wrt_saved_tensor(
|
|
saved_shard=storage_md, current_shard=shard
|
|
):
|
|
storage_offsets.append(offset_for_saved_tensor)
|
|
dest_offsets.append(offset_for_current_tensor)
|
|
lengths.append(length)
|
|
|
|
read_items.append(
|
|
_create_read_item_for_tensor(
|
|
dest_index=MetadataIndex(fqn, shard.offsets, idx),
|
|
dest_offsets=dest_offsets,
|
|
storage_index=MetadataIndex(fqn, storage_md.offsets, storage_idx),
|
|
storage_offsets=storage_offsets,
|
|
lengths=lengths,
|
|
)
|
|
)
|
|
return read_items
|
|
|
|
|
|
def _create_default_metadata_only_plan(state_dict: STATE_DICT_TYPE) -> SavePlan:
|
|
requests = []
|
|
for fqn, obj in state_dict.items():
|
|
if isinstance(obj, DTensor):
|
|
requests.append(_create_write_items_for_dtensor(fqn, obj))
|
|
elif isinstance(obj, ShardedTensor):
|
|
for shard_md in obj.metadata().shards_metadata:
|
|
requests.append(_create_write_item_for_shard(fqn, obj, shard_md))
|
|
elif isinstance(obj, torch.Tensor):
|
|
requests.append(_create_write_item_for_tensor(fqn, obj))
|
|
else:
|
|
requests.append(_create_write_item_for_bytesio(fqn, obj))
|
|
return SavePlan(requests)
|
|
|
|
|
|
def _create_write_items(fqn: str, object: Any) -> List[WriteItem]:
|
|
if hasattr(object, "__create_write_items__"):
|
|
# DTensor implements _Checkpointable
|
|
return object.__create_write_items__(fqn, object)
|
|
elif isinstance(object, ShardedTensor):
|
|
return [
|
|
_create_write_item_for_shard(fqn, object, shard.metadata)
|
|
for shard in object.local_shards()
|
|
]
|
|
elif isinstance(object, torch.Tensor):
|
|
return [_create_write_item_for_tensor(fqn, object)]
|
|
else:
|
|
return [_create_write_item_for_bytesio(fqn, object)]
|
|
|
|
|
|
def _create_chunk_from_dtensor(tensor: DTensor) -> ChunkStorageMetadata:
|
|
sizes, offsets = compute_local_shape_and_global_offset(
|
|
tensor.shape, tensor.device_mesh, tensor.placements
|
|
)
|
|
sizes, offsets = torch.Size(sizes), torch.Size(offsets)
|
|
return ChunkStorageMetadata(
|
|
offsets=offsets,
|
|
sizes=sizes,
|
|
)
|
|
|
|
|
|
def _create_chunk_list(tensor: torch.Tensor) -> List[ChunkStorageMetadata]:
|
|
if hasattr(tensor, "__create_chunk_list__"):
|
|
# DTensor implements _Checkpointable
|
|
local_chunks = tensor.__create_chunk_list__() # type: ignore[attr-defined]
|
|
elif isinstance(tensor, ShardedTensor):
|
|
local_chunks = [
|
|
_chunk_for_shard(shard.metadata) for shard in tensor.local_shards()
|
|
]
|
|
elif isinstance(tensor, torch.Tensor):
|
|
local_chunks = [_create_chunk_from_tensor(tensor)]
|
|
else:
|
|
raise ValueError(
|
|
"Unsupported Type, expecting one of [Tensor, DTensor, ShardedTensor] "
|
|
f",but got {type(tensor)}"
|
|
)
|
|
|
|
return local_chunks
|
|
|
|
|
|
def _create_read_items(fqn: str, md: STORAGE_TYPES, obj: Any) -> List[ReadItem]:
|
|
if not isinstance(md, BytesStorageMetadata):
|
|
try:
|
|
local_chunks = _create_chunk_list(obj)
|
|
except ValueError as ex:
|
|
raise ValueError(
|
|
f"Invalid checkpoint metadata for {fqn}, "
|
|
+ f"expected BytesStorageMetadata but found {type(md)}",
|
|
) from ex
|
|
|
|
return create_read_items_for_chunk_list(fqn, md, local_chunks)
|
|
else:
|
|
return [
|
|
_create_read_item_for_byteio(
|
|
dest_index=MetadataIndex(fqn),
|
|
dest_offset=0,
|
|
storage_index=MetadataIndex(fqn),
|
|
storage_offset=0,
|
|
length=0,
|
|
)
|
|
]
|
|
|
|
|
|
def _init_state_dict(state_dict: Dict[str, Any]) -> Any:
|
|
"""
|
|
Initializes meta tensor if the meta tensor is DTensor or torch.Tensor.
|
|
"""
|
|
|
|
def dtensor_func(value: DTensor):
|
|
device = getattr(value, "device", None)
|
|
if device == torch.device("meta"):
|
|
device_type = dist.distributed_c10d._get_pg_default_device().type
|
|
device = cast(
|
|
torch.device, _get_device_module(device_type).current_device()
|
|
)
|
|
new_local_tensor = torch.empty_like(value.to_local(), device=device)
|
|
# We need to pass shape and stride explicitly, since DTensor might be
|
|
# sharded unevenly.
|
|
dtensor = DTensor.from_local(
|
|
new_local_tensor,
|
|
device_mesh=value.device_mesh,
|
|
placements=value.placements,
|
|
shape=value.size(),
|
|
stride=value.stride(),
|
|
)
|
|
return dtensor
|
|
else:
|
|
return value
|
|
|
|
def sharded_tensor_func(value: Any):
|
|
device = getattr(value, "device", None)
|
|
if device == torch.device("meta"):
|
|
raise RuntimeError(
|
|
f"Found unsupported type {type(value)} for meta device loading."
|
|
)
|
|
else:
|
|
return value
|
|
|
|
def tensor_func(value: torch.Tensor):
|
|
device = getattr(value, "device", None)
|
|
if device == torch.device("meta"):
|
|
device_type = dist.distributed_c10d._get_pg_default_device().type
|
|
device = cast(
|
|
torch.device, _get_device_module(device_type).current_device()
|
|
)
|
|
tensor = torch.empty_like(value, device=device)
|
|
return tensor
|
|
else:
|
|
return value
|
|
|
|
_iterate_state_dict(
|
|
state_dict,
|
|
dtensor_func,
|
|
sharded_tensor_func,
|
|
tensor_func,
|
|
)
|
|
|
|
|
|
def _iterate_state_dict(
|
|
iter_object: Any,
|
|
dtensor_func: Callable,
|
|
sharded_tensor_func: Callable,
|
|
tensor_func: Callable,
|
|
):
|
|
"""
|
|
Iterate through the state dict, applying the given functions to each tensor type
|
|
and update the state dict in place.
|
|
|
|
Args:
|
|
iter_object (Any): the target state_dict.
|
|
sharded_tensor_func (Callable): the function to apply to ShardedTensor
|
|
dtensor_func (Callable): the function to apply to DTensor
|
|
tensor_func (Callable): the function to apply to Tensor
|
|
|
|
# TODO: let state_dict_util._iterate_state_dict() to support in place option
|
|
so we don't need to have two versions of _iterate_state_dict.
|
|
"""
|
|
|
|
if isinstance(iter_object, DTensor):
|
|
return dtensor_func(iter_object)
|
|
elif isinstance(iter_object, ShardedTensor):
|
|
return sharded_tensor_func(iter_object)
|
|
elif isinstance(iter_object, torch.Tensor):
|
|
return tensor_func(iter_object)
|
|
elif (
|
|
isinstance(iter_object, (int, float, str, bytes, io.BytesIO))
|
|
or iter_object is None
|
|
):
|
|
return iter_object
|
|
elif isinstance(iter_object, dict):
|
|
for key, value in iter_object.items():
|
|
iter_object[key] = _iterate_state_dict(
|
|
value, dtensor_func, sharded_tensor_func, tensor_func
|
|
)
|
|
return iter_object
|
|
elif isinstance(iter_object, (list, tuple)):
|
|
ret = [
|
|
_iterate_state_dict(v, dtensor_func, sharded_tensor_func, tensor_func)
|
|
for v in iter_object
|
|
]
|
|
if isinstance(iter_object, tuple):
|
|
ret = tuple(ret) # type: ignore[assignment]
|
|
return ret
|