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# mypy: allow-untyped-defs
import dataclasses
import json
import queue
from typing import Any, Optional
import torch
from torch.distributed._shard._utils import narrow_tensor_by_index
from torch.distributed.checkpoint._fsspec_filesystem import FsspecReader, FsspecWriter
from torch.distributed.checkpoint._hf_utils import (
_gen_file_name,
_get_dtype,
_get_safetensors_file_metadata,
_HFStorageInfo,
_metadata_fn,
CUSTOM_METADATA_KEY,
DATA_KEY,
DATA_OFFSETS_KEY,
DEFAULT_EXTRA_METADATA_KEY,
DTYPE_KEY,
SAVED_OFFSETS_KEY,
SHAPE_KEY,
SUFFIX,
)
from torch.distributed.checkpoint.filesystem import SerializationFormat
from torch.distributed.checkpoint.metadata import (
ChunkStorageMetadata,
Metadata,
MetadataIndex,
StorageMeta,
TensorProperties,
TensorStorageMetadata,
)
from torch.distributed.checkpoint.planner import (
LoadPlan,
LoadPlanner,
ReadItem,
SavePlan,
SavePlanner,
WriteItem,
)
from torch.distributed.checkpoint.storage import WriteResult
from torch.futures import Future
__all__ = ["HuggingFaceStorageWriter", "HuggingFaceStorageReader"]
class HuggingFaceStorageWriter(FsspecWriter):
"""
A writer that writes to a huggingface repository in the huggingface format.
Uses Fsspec back-end to communicate with back-end storage.
Fsspec registration of the storage solution is required.
"""
def __init__(
self,
path: str,
fqn_to_index_mapping: Optional[dict[str, int]] = None,
token: Optional[str] = None,
save_sharded: bool = False,
) -> None:
"""
Initialize the huggingface writer pointing to path.
Args:
path: hf directory where the checkpoint will be read from.
Needs to have .safetensors files, but can be from any fsspec supported storage,
including localFS and hf://.
fqn_to_index_mapping: A mapping from tensor FQN to the index of the file that the tensor should be written to.
Indices are from 1 to N, where N is the number of files. If not provided,
the tensors will be written to a single file. If none, then all the tensors on the
same rank will be written to the same file.
token: The token to use to authenticate with huggingface hub.
save_sharded: If True, save the checkpoint as a sharded checkpoint where every rank saves its own shard.
Default is False which assumes full tensors are being saved.
"""
if token is not None:
super().__init__(
path=path,
token=token,
serialization_format=SerializationFormat.SAFETENSORS,
)
else:
super().__init__(
path=path,
serialization_format=SerializationFormat.SAFETENSORS,
)
self._fqn_to_index_mapping: Optional[dict[str, int]] = fqn_to_index_mapping
self._save_sharded = save_sharded
def prepare_global_plan(self, plans: list[SavePlan]) -> list[SavePlan]:
new_plans = []
for i, plan in enumerate(plans, start=1):
storage_data: dict[str, Any] = {}
if self._fqn_to_index_mapping is not None:
storage_data["fqn_to_index_mapping"] = self._fqn_to_index_mapping
if self._save_sharded:
storage_data["shard_index"] = i
new_plans.append(dataclasses.replace(plan, storage_data=storage_data))
return new_plans
def write_data(
self,
plan: SavePlan,
planner: SavePlanner,
) -> Future[list[WriteResult]]:
if len(plan.items) == 0:
fut: Future = Future()
fut.set_result([])
return fut
# storage_plan is a map from key to file index
storage_data: dict[str, Any] = plan.storage_data
storage_plan: Optional[dict[str, int]] = None
shard_index: Optional[int] = None
if "fqn_to_index_mapping" in storage_data:
storage_plan = storage_data["fqn_to_index_mapping"]
if "shard_index" in storage_data:
shard_index = storage_data["shard_index"]
buckets = self._split_by_storage_plan(storage_plan, plan.items)
highest_index = max(storage_plan.values()) if storage_plan is not None else 1
file_queue: queue.Queue = queue.Queue()
for file_index, write_items in buckets.items():
file_name = _gen_file_name(file_index, highest_index, shard_index)
file_queue.put(
(self.fs.concat_path(self.path, file_name), file_name, write_items)
)
return super()._write_data(planner, file_queue)
def finish(self, metadata: Metadata, results: list[list[WriteResult]]) -> None:
if self._save_sharded:
return
metadata_to_write = {}
storage_md = {}
total_size = 0
for wr_list in results:
storage_md.update(
{wr.index.fqn: wr.storage_data.relative_path for wr in wr_list}
)
total_size += sum([wr.storage_data.length for wr in wr_list])
metadata_to_write["metadata"] = {"total_size": total_size}
metadata_to_write["weight_map"] = storage_md
metadata_path = self.fs.concat_path(self.path, f"{_metadata_fn}")
with self.fs.create_stream(metadata_path, "w") as metadata_file:
json.dump(metadata_to_write, metadata_file, indent=2)
def _split_by_storage_plan(
self, storage_plan: Optional[dict[str, int]], items: list[WriteItem]
) -> dict[int, list[WriteItem]]:
# storage_plan is a map from key to index
if storage_plan is None:
return {1: items}
buckets = {}
for item in items:
key = item.index.fqn
idx = storage_plan[key]
if idx not in buckets:
buckets[idx] = [item]
else:
buckets[idx].append(item)
return buckets
@property
def metadata_path(self) -> str:
return _metadata_fn
class HuggingFaceStorageReader(FsspecReader):
"""
A reader that reads from a huggingface repository in the huggingface format.
Uses in Fsspec back-end to communicate with storage.
Fsspec registration of the storage solution is required.
"""
def __init__(self, path: str, token: Optional[str] = None) -> None:
"""
Initialize the huggingface reader pointing to path.
Args:
path: hf directory where the checkpoint will be read from.
Needs to have .safetensors file, but can be from any fsspec supported storage,
including localFS and hf://.
token: The token to use to authenticate with huggingface hub.
"""
if token is not None:
super().__init__(path=path, token=token)
else:
super().__init__(path=path)
def read_data(self, plan: LoadPlan, planner: LoadPlanner) -> Future[None]:
from safetensors import deserialize # type: ignore[import-not-found]
per_file: dict[str, list[ReadItem]] = {}
for read_item in plan.items:
item_md: _HFStorageInfo = self.storage_data[read_item.storage_index]
file_name = item_md.relative_path
per_file.setdefault(file_name, []).append(read_item)
for file_name, reqs in per_file.items():
with self.fs.create_stream(file_name, "rb") as stream:
# TODO: make this more efficient by doing offset reads instead of a
# full deserialization of the file
deserialized = deserialize(stream.read())
deserialized_dict: dict[str, dict[str, Any]] = {
tensor_info[0]: tensor_info[1] for tensor_info in deserialized
}
for req in reqs:
item_md = self.storage_data[req.storage_index]
tensor_bytes = deserialized_dict[req.dest_index.fqn][DATA_KEY]
tensor = torch.frombuffer(
tensor_bytes,
dtype=item_md.dtype,
)
tensor = tensor.reshape(item_md.shape)
tensor = narrow_tensor_by_index(
tensor, req.storage_offsets, req.lengths
)
target_tensor = planner.resolve_tensor(req).detach()
assert target_tensor.size() == tensor.size(), (
f"req {req.storage_index} mismatch sizes {target_tensor.size()} vs {tensor.size()}"
)
target_tensor.copy_(tensor)
planner.commit_tensor(req, target_tensor)
fut: Future = Future()
fut.set_result(None)
return fut
def read_metadata(self) -> Metadata:
state_dict_metadata: dict[str, TensorStorageMetadata] = {}
storage_data: dict[MetadataIndex, _HFStorageInfo] = {}
safetensors_files = []
for file in self.fs.ls(self.path):
if file.endswith(SUFFIX):
safetensors_files.append(file)
for safetensor_file in safetensors_files:
with self.fs.create_stream(safetensor_file, "rb") as f:
safetensors_metadata, _ = _get_safetensors_file_metadata(f)
custom_metadata = safetensors_metadata.get(DEFAULT_EXTRA_METADATA_KEY)
dcp_sharding_info = None
if custom_metadata and custom_metadata.get(CUSTOM_METADATA_KEY):
dcp_sharding_info = json.loads(
custom_metadata.get(CUSTOM_METADATA_KEY)
)
for key, val in safetensors_metadata.items():
if key == DEFAULT_EXTRA_METADATA_KEY:
continue
# construct state_dict_metadata
if dcp_sharding_info is not None:
offset = dcp_sharding_info[key][SAVED_OFFSETS_KEY]
else:
offset = [0] * len(val[SHAPE_KEY])
if key not in state_dict_metadata:
state_dict_metadata[key] = TensorStorageMetadata(
properties=TensorProperties(
dtype=_get_dtype(val[DTYPE_KEY])
),
size=torch.Size(
[
saved + offset
for saved, offset in zip(val[SHAPE_KEY], offset)
]
),
chunks=[
ChunkStorageMetadata(
offsets=torch.Size(offset),
sizes=torch.Size(val[SHAPE_KEY]),
)
],
)
else:
state_dict_metadata[key].chunks.append(
ChunkStorageMetadata(
torch.Size(offset), sizes=torch.Size(val[SHAPE_KEY])
)
)
size = list(state_dict_metadata[key].size)
for i in range(len(size)):
size[i] = max(size[i], val[SHAPE_KEY][i] + offset[i])
state_dict_metadata[key].size = torch.Size(size)
# construct storage data
if dcp_sharding_info is not None:
metadata_index = MetadataIndex(
fqn=key, offset=dcp_sharding_info[key][SAVED_OFFSETS_KEY]
)
else:
metadata_index = MetadataIndex(
fqn=key, offset=[0] * len(val[SHAPE_KEY])
)
storage_data[metadata_index] = _HFStorageInfo(
relative_path=safetensor_file,
offset=val[DATA_OFFSETS_KEY][0],
length=val[DATA_OFFSETS_KEY][1] - val[DATA_OFFSETS_KEY][0],
shape=torch.Size(val[SHAPE_KEY]),
dtype=_get_dtype(val[DTYPE_KEY]),
)
metadata = Metadata(
state_dict_metadata=state_dict_metadata, # type: ignore[arg-type]
storage_data=storage_data,
)
if getattr(metadata, "storage_meta", None) is None:
metadata.storage_meta = StorageMeta()
metadata.storage_meta.load_id = self.load_id # type: ignore[union-attr]
return metadata
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