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from typing import Any, cast, 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 torch.utils._pytree import tree_map_only
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 isinstance(object, DTensor):
return [_create_write_items_for_dtensor(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 isinstance(tensor, DTensor):
local_chunks = [_create_chunk_from_dtensor(tensor)]
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: STATE_DICT_TYPE) -> None:
state_dict_assigned_storage = tree_map_only(
torch.Tensor, lambda v: _init_meta_tensor(v), state_dict
)
# The inplace version of tree_map_only, tree_map_only_ doesn't seem to work.
# So we need to temporariy update the each element in the state dict with meta tensor.
for k in state_dict.keys():
state_dict[k] = state_dict_assigned_storage[k]
def _init_meta_tensor(value: Any) -> Any:
"""
Initializes tensor, moves it to device for torch.Tensor/DTensor on meta device.
"""
device = getattr(value, "device", None)
# DCP does the initialization if it's meta tensor/DTensor.
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())
if isinstance(value, DTensor):
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
elif isinstance(value, torch.Tensor):
tensor = torch.empty_like(value, device=device)
return tensor
else:
raise RuntimeError(
f"Found unsupported type {type(value)} for meta device loading."
)
else:
return value
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