torch/distributed/checkpoint/default_planner.py - platform/external/pytorch - Git at Google

 # Copyright (c) Meta Platforms, Inc. and affiliates

 import dataclasses
 import io
 import logging
 import operator
 from collections import ChainMap
 from functools import reduce
 from typing import List, Tuple, Dict, Any, Union, cast

 import torch

 from torch.distributed._shard._utils import narrow_tensor_by_index
 from torch.distributed._tensor import DTensor


 from torch.distributed.checkpoint.planner import (
     SavePlanner,
     LoadPlanner,
     SavePlan,
     LoadPlan,
     ReadItem,
     WriteItem,
     WriteItemType,
 )

 from torch.distributed.checkpoint.metadata import (
     BytesStorageMetadata,
     ChunkStorageMetadata,
     TensorStorageMetadata,
     MetadataIndex,
     Metadata,
     STATE_DICT_TYPE,
     STORAGE_TYPES,
 )

 from torch.distributed.checkpoint.planner_helpers import (
     _create_read_items,
     _create_write_items,
     _create_default_metadata_only_plan,
 )

 from torch.distributed.checkpoint._nested_dict import (
     FLATTEN_MAPPING,
     flatten_state_dict,
 )
 from torch.distributed.checkpoint._sharded_tensor_utils import (
     _flatten_sharded_tensors,
 )
 from torch.distributed.checkpoint._dedup_tensors import dedup_tensors
 from torch.distributed.checkpoint.utils import find_state_dict_object
 from torch.distributed.checkpoint._traverse import set_element

 logger: logging.Logger = logging.getLogger(__name__)


 __all__ = [
     "DefaultSavePlanner",
     "DefaultLoadPlanner",
     "create_default_local_load_plan",
     "create_default_global_load_plan",
     "create_default_local_save_plan",
     "create_default_global_save_plan",
 ]


 # TODO: Update docstrings for default_planner.py
 class DefaultSavePlanner(SavePlanner):
     mappings: FLATTEN_MAPPING

     def __init__(
         self,
         flatten_state_dict: bool = True,
         flatten_sharded_tensors: bool = True,
         dedup_replicated_tensors: bool = True,
     ) -> None:
         self.flatten_state_dict = flatten_state_dict
         self.flatten_sharded_tensors = flatten_sharded_tensors
         self.dedup_replicated_tensors = dedup_replicated_tensors
         self.mappings = {}

     def set_up_planner(
         self, state_dict: STATE_DICT_TYPE, is_coordinator: bool
     ) -> None:
         if self.flatten_state_dict:
             state_dict, self.mappings = flatten_state_dict(state_dict)
         if self.flatten_sharded_tensors:
             state_dict = _flatten_sharded_tensors(state_dict)
         self.state_dict = state_dict
         self.is_coordinator = is_coordinator

     def create_local_plan(self) -> SavePlan:
         plan = create_default_local_save_plan(
             self.state_dict, self.is_coordinator
         )
         if self.flatten_state_dict:
             plan = dataclasses.replace(plan, planner_data=self.mappings)
         self.plan = plan

         return self.plan

     def create_global_plan(
         self, all_plans: List[SavePlan]
     ) -> Tuple[List[SavePlan], Metadata]:
         if self.dedup_replicated_tensors:
             all_plans = dedup_tensors(all_plans)

         global_plan, metadata = create_default_global_save_plan(all_plans)

         if self.flatten_state_dict:
             # | does not work for Python 3.8 or older version.
             # merged_mappings = reduce(
             #     lambda x, y: x | y, (p.planner_data for p in global_plan)
             # )
             planner_data_dict = [p.planner_data for p in global_plan]
             merged_mappings = dict(ChainMap(*planner_data_dict))
             metadata = dataclasses.replace(
                 metadata, planner_data=merged_mappings
             )

         if not _validate_global_plan(global_plan, metadata):
             raise ValueError("Failed to validate global plan")

         self.global_plan = global_plan
         self.metadata = metadata

         return self.global_plan, self.metadata

     def finish_plan(self, new_plan: SavePlan) -> SavePlan:
         self.plan = new_plan
         return new_plan

     def resolve_data(
         self, write_item: WriteItem
     ) -> Union[torch.Tensor, io.BytesIO]:
         object = self.lookup_object(write_item.index)
         return self.transform_object(write_item, object)

     def lookup_object(self, index: MetadataIndex) -> Any:
         """
         This is an extension from the planner interface to make it easy to extend the default planner
         """
         return find_state_dict_object(self.state_dict, index)

     def transform_object(self, write_item: WriteItem, object: Any):
         """
         This is an extension from the planner interface to make it easy to extend the default planner
         """
         if write_item.type == WriteItemType.BYTE_IO:
             bytes = io.BytesIO()
             torch.save(object, bytes)
             object = bytes
         return object


 class DefaultLoadPlanner(LoadPlanner):
     """
     DefaultLoadPlanner that adds multiple features on top of LoadPlanner.

     In particular it adds the following:

     flatten_state_dict: Handle state_dict with nested dicts
     flatten_sharded_tensors: For FSDP in 2D parallel mode
     """

     original_state_dict: STATE_DICT_TYPE
     mappings: FLATTEN_MAPPING

     def __init__(
         self,
         flatten_state_dict: bool = True,
         flatten_sharded_tensors: bool = True,
     ) -> None:
         self.flatten_state_dict = flatten_state_dict
         self.flatten_sharded_tensors = flatten_sharded_tensors
         self.original_state_dict = {}
         self.mappings = {}

     def set_up_planner(
         self,
         state_dict: STATE_DICT_TYPE,
         metadata: Metadata,
         is_coordinator: bool,
     ) -> None:
         self.original_state_dict = state_dict

         if self.flatten_sharded_tensors:
             state_dict = _flatten_sharded_tensors(state_dict)

         if self.flatten_state_dict:
             state_dict, self.mappings = flatten_state_dict(state_dict)

         self.state_dict = state_dict
         self.metadata = metadata
         self.is_coordinator = is_coordinator

     def create_local_plan(self) -> LoadPlan:
         return create_default_local_load_plan(self.state_dict, self.metadata)

     def create_global_plan(self, global_plan: List[LoadPlan]) -> List[LoadPlan]:
         return create_default_global_load_plan(global_plan)

     def finish_plan(self, new_plan: LoadPlan) -> LoadPlan:
         return new_plan

     def load_bytes(self, read_item: ReadItem, value: io.BytesIO) -> None:
         if self.flatten_state_dict:
             set_element(
                 self.original_state_dict,
                 self.mappings[read_item.dest_index.fqn],
                 torch.load(value),
             )
         else:
             self.state_dict[read_item.dest_index.fqn] = torch.load(value)

     def resolve_tensor(self, read_item: ReadItem):
         tensor = self.lookup_tensor(read_item.dest_index)
         return self.transform_tensor(read_item, tensor)

     def commit_tensor(self, read_item: ReadItem, tensor: torch.Tensor) -> None:
         pass

     def lookup_tensor(self, index: MetadataIndex) -> torch.Tensor:
         """
         This is an extension from the planner interface to make it easy to extend the default planner
         """
         return find_state_dict_object(self.state_dict, index)

     def transform_tensor(self, read_item: ReadItem, tensor: torch.Tensor):
         """
         This is an extension from the planner interface to make it easy to extend the default planner
         """
         return narrow_tensor_by_index(
             tensor, read_item.dest_offsets, read_item.lengths
         )


 def create_default_local_load_plan(
     state_dict: Dict[str, Any],
     metadata: Metadata,
 ) -> LoadPlan:
     requests = []
     """
     Create the ``LoadPlan`` used by DefaultLoadPlanner.

     It produces one read item per value in ``state_dict`` using the metadata in ``metadata``.

     The default behavior is to match key exactly between state_dict and metadata.
     It handles resharding by issuing multiple read requests against storage in order to match
     load requirements.
     """

     for fqn, obj in state_dict.items():
         md = metadata.state_dict_metadata[fqn]
         # Since DTensor supports submesh, adding extra check to ensure _create_read_items()
         # gets called only when the current rank is part of the mesh for the corresponding DTensor.
         if isinstance(obj, DTensor):
             if obj.device_mesh.get_coordinate() is not None:
                 requests += _create_read_items(fqn, md, obj)
         else:
             requests += _create_read_items(fqn, md, obj)

     return LoadPlan(requests)


 def create_default_global_load_plan(
     all_plans: List[LoadPlan],
 ) -> List[LoadPlan]:
     """
     Create global load plan used by DefaultLoadPlanner.

     The default load behavior involved no global coordination and this function
     currently doesn't change the local plans.
     """
     return all_plans


 def create_default_local_save_plan(
     state_dict: Dict[str, Any], is_coordinator: bool
 ) -> SavePlan:
     """
     Create the ``SavePlan`` used by DefaultSavePlanner.

     On non-coordinator ranks, this function ignores tensors and non-tensor objects,
     only producing writes for ShardedTensor objects.

     On the coordinator rank, produce writes for all values.
     """
     requests = []
     for fqn, obj in state_dict.items():
         # Since DTensor supports submesh, adding extra check to ensure _create_write_items()
         # gets called only when the current rank is part of the mesh for the corresponding DTensor.
         if isinstance(obj, DTensor):
             if obj.device_mesh.get_coordinate() is not None:
                 requests += _create_write_items(fqn, obj)
         elif isinstance(obj, (torch.Tensor)) or is_coordinator:
             requests += _create_write_items(fqn, obj)

     return SavePlan(requests)


 def create_default_global_save_plan(
     all_plans: List[SavePlan],
     rewrite_index_hints: bool = True,
 ) -> Tuple[List[SavePlan], Metadata]:
     """
     Create the global plan and metadata used by DefaultSavePlanner.

     Metadata is produced by concatenating the metadata of all ``WriteItem`` from the supplied plans.

     The only global planning change is to update index hints in all ``MetadataIndex`` objects if
     ``rewrite_index_hints`` is True.
     """
     md: Dict[str, STORAGE_TYPES] = {}
     new_plans = []
     for plan in all_plans:
         new_items = []
         for item in plan.items:
             if not item.type == WriteItemType.SHARD:
                 assert item.index.fqn not in md

             if item.type == WriteItemType.BYTE_IO:
                 md[item.index.fqn] = BytesStorageMetadata()
                 new_items.append(item)
             else:
                 assert item.tensor_data is not None
                 tensor_md = cast(
                     TensorStorageMetadata,
                     md.setdefault(
                         item.index.fqn,
                         TensorStorageMetadata(
                             properties=item.tensor_data.properties,
                             size=item.tensor_data.size,
                             chunks=[],
                         ),
                     ),
                 )
                 new_item = item
                 if rewrite_index_hints:
                     new_index = dataclasses.replace(
                         item.index, index=len(tensor_md.chunks)
                     )
                     new_item = dataclasses.replace(item, index=new_index)
                 new_items.append(new_item)

                 assert (
                     item.tensor_data.chunk is not None
                 ), f"""
                     Cannot create MD for tensor without bounds.
                     FQN: {item.index.fqn}
                 """
                 tensor_md.chunks.append(item.tensor_data.chunk)
         new_plans.append(dataclasses.replace(plan, items=new_items))
     return (new_plans, Metadata(md))


 def _create_default_local_metadata(state_dict: STATE_DICT_TYPE) -> Metadata:
     """
     Return the ``Metadata`` if DefaultSavePlanner was used to checkpoint ``state_dict``.
     """
     plan = _create_default_metadata_only_plan(state_dict)
     _, md = create_default_global_save_plan([plan])
     return md


 def _check_box_overlap(
     box0: ChunkStorageMetadata, box1: ChunkStorageMetadata
 ) -> bool:
     """
     Checks if two boxes overlap. Tuples are (offset, lengths)
     """

     # For each dim of each shard, check if one shard resides on the other
     # end of second shard with respect to that dim. As an example for a 2D
     # shard, we would check if one shard is above or on the left of the
     # other shard.
     ndims = len(box0.offsets)
     for i in range(ndims):
         if box0.offsets[i] >= box1.offsets[i] + box1.sizes[i]:
             return False
         if box1.offsets[i] >= box0.offsets[i] + box0.sizes[i]:
             return False

     return True


 def _check_box_bounds(
     outer_box_size: torch.Size, inner_box: ChunkStorageMetadata
 ) -> bool:
     for i in range(len(outer_box_size)):
         if inner_box.offsets[i] < 0:
             return False
         if inner_box.sizes[i] < 0:
             return False
         if inner_box.offsets[i] + inner_box.sizes[i] > outer_box_size[i]:
             return False

     return True


 def _validate_global_plan(
     global_plan: List[SavePlan], metadata: Metadata
 ) -> bool:
     all_good = True
     for key, value in metadata.state_dict_metadata.items():
         if isinstance(value, BytesStorageMetadata):
             continue
         if len(value.size) == 0:
             continue
         chunks_volume = 0
         for chunk_idx, chunk0 in enumerate(value.chunks):
             # Compute the volume
             if not _check_box_bounds(value.size, chunk0):
                 logger.warning(
                     """
                         key:%s has out of bounds chunk:
                         tensor-size:%s chunk: %s
                     """, key, value.size, chunk0
                 )
                 all_good = False
             chunks_volume += reduce(operator.mul, chunk0.sizes, 1)

             # Check for overlap
             for chunk1 in value.chunks[chunk_idx + 1 :]:
                 if _check_box_overlap(chunk0, chunk1):
                     logger.warning(
                         "key:%s has overlapping chunks: %s %s", key, chunk0, chunk1
                     )
                     all_good = False

         # Check whether combined chunk cover the whole tensor
         tensor_volume = reduce(operator.mul, value.size, 1)
         if chunks_volume != tensor_volume:
             logger.warning(
                 """
                     key:%s invalid fill tensor-volume:
                     %s chunks-volume: %s
                 """, key, tensor_volume, chunks_volume
             )
             all_good = False

     return all_good
	# Copyright (c) Meta Platforms, Inc. and affiliates

	import dataclasses
	import io
	import logging
	import operator
	from collections import ChainMap
	from functools import reduce
	from typing import List, Tuple, Dict, Any, Union, cast

	import torch

	from torch.distributed._shard._utils import narrow_tensor_by_index
	from torch.distributed._tensor import DTensor


	from torch.distributed.checkpoint.planner import (
	SavePlanner,
	LoadPlanner,
	SavePlan,
	LoadPlan,
	ReadItem,
	WriteItem,
	WriteItemType,
	)

	from torch.distributed.checkpoint.metadata import (
	BytesStorageMetadata,
	ChunkStorageMetadata,
	TensorStorageMetadata,
	MetadataIndex,
	Metadata,
	STATE_DICT_TYPE,
	STORAGE_TYPES,
	)

	from torch.distributed.checkpoint.planner_helpers import (
	_create_read_items,
	_create_write_items,
	_create_default_metadata_only_plan,
	)

	from torch.distributed.checkpoint._nested_dict import (
	FLATTEN_MAPPING,
	flatten_state_dict,
	)
	from torch.distributed.checkpoint._sharded_tensor_utils import (
	_flatten_sharded_tensors,
	)
	from torch.distributed.checkpoint._dedup_tensors import dedup_tensors
	from torch.distributed.checkpoint.utils import find_state_dict_object
	from torch.distributed.checkpoint._traverse import set_element

	logger: logging.Logger = logging.getLogger(__name__)


	__all__ = [
	"DefaultSavePlanner",
	"DefaultLoadPlanner",
	"create_default_local_load_plan",
	"create_default_global_load_plan",
	"create_default_local_save_plan",
	"create_default_global_save_plan",
	]


	# TODO: Update docstrings for default_planner.py
	class DefaultSavePlanner(SavePlanner):
	mappings: FLATTEN_MAPPING

	def __init__(
	self,
	flatten_state_dict: bool = True,
	flatten_sharded_tensors: bool = True,
	dedup_replicated_tensors: bool = True,
	) -> None:
	self.flatten_state_dict = flatten_state_dict
	self.flatten_sharded_tensors = flatten_sharded_tensors
	self.dedup_replicated_tensors = dedup_replicated_tensors
	self.mappings = {}

	def set_up_planner(
	self, state_dict: STATE_DICT_TYPE, is_coordinator: bool
	) -> None:
	if self.flatten_state_dict:
	state_dict, self.mappings = flatten_state_dict(state_dict)
	if self.flatten_sharded_tensors:
	state_dict = _flatten_sharded_tensors(state_dict)
	self.state_dict = state_dict
	self.is_coordinator = is_coordinator

	def create_local_plan(self) -> SavePlan:
	plan = create_default_local_save_plan(
	self.state_dict, self.is_coordinator
	)
	if self.flatten_state_dict:
	plan = dataclasses.replace(plan, planner_data=self.mappings)
	self.plan = plan

	return self.plan

	def create_global_plan(
	self, all_plans: List[SavePlan]
	) -> Tuple[List[SavePlan], Metadata]:
	if self.dedup_replicated_tensors:
	all_plans = dedup_tensors(all_plans)

	global_plan, metadata = create_default_global_save_plan(all_plans)

	if self.flatten_state_dict:
	# \| does not work for Python 3.8 or older version.
	# merged_mappings = reduce(
	# lambda x, y: x \| y, (p.planner_data for p in global_plan)
	# )
	planner_data_dict = [p.planner_data for p in global_plan]
	merged_mappings = dict(ChainMap(*planner_data_dict))
	metadata = dataclasses.replace(
	metadata, planner_data=merged_mappings
	)

	if not _validate_global_plan(global_plan, metadata):
	raise ValueError("Failed to validate global plan")

	self.global_plan = global_plan
	self.metadata = metadata

	return self.global_plan, self.metadata

	def finish_plan(self, new_plan: SavePlan) -> SavePlan:
	self.plan = new_plan
	return new_plan

	def resolve_data(
	self, write_item: WriteItem
	) -> Union[torch.Tensor, io.BytesIO]:
	object = self.lookup_object(write_item.index)
	return self.transform_object(write_item, object)

	def lookup_object(self, index: MetadataIndex) -> Any:
	"""
	This is an extension from the planner interface to make it easy to extend the default planner
	"""
	return find_state_dict_object(self.state_dict, index)

	def transform_object(self, write_item: WriteItem, object: Any):
	"""
	This is an extension from the planner interface to make it easy to extend the default planner
	"""
	if write_item.type == WriteItemType.BYTE_IO:
	bytes = io.BytesIO()
	torch.save(object, bytes)
	object = bytes
	return object


	class DefaultLoadPlanner(LoadPlanner):
	"""
	DefaultLoadPlanner that adds multiple features on top of LoadPlanner.

	In particular it adds the following:

	flatten_state_dict: Handle state_dict with nested dicts
	flatten_sharded_tensors: For FSDP in 2D parallel mode
	"""

	original_state_dict: STATE_DICT_TYPE
	mappings: FLATTEN_MAPPING

	def __init__(
	self,
	flatten_state_dict: bool = True,
	flatten_sharded_tensors: bool = True,
	) -> None:
	self.flatten_state_dict = flatten_state_dict
	self.flatten_sharded_tensors = flatten_sharded_tensors
	self.original_state_dict = {}
	self.mappings = {}

	def set_up_planner(
	self,
	state_dict: STATE_DICT_TYPE,
	metadata: Metadata,
	is_coordinator: bool,
	) -> None:
	self.original_state_dict = state_dict

	if self.flatten_sharded_tensors:
	state_dict = _flatten_sharded_tensors(state_dict)

	if self.flatten_state_dict:
	state_dict, self.mappings = flatten_state_dict(state_dict)

	self.state_dict = state_dict
	self.metadata = metadata
	self.is_coordinator = is_coordinator

	def create_local_plan(self) -> LoadPlan:
	return create_default_local_load_plan(self.state_dict, self.metadata)

	def create_global_plan(self, global_plan: List[LoadPlan]) -> List[LoadPlan]:
	return create_default_global_load_plan(global_plan)

	def finish_plan(self, new_plan: LoadPlan) -> LoadPlan:
	return new_plan

	def load_bytes(self, read_item: ReadItem, value: io.BytesIO) -> None:
	if self.flatten_state_dict:
	set_element(
	self.original_state_dict,
	self.mappings[read_item.dest_index.fqn],
	torch.load(value),
	)
	else:
	self.state_dict[read_item.dest_index.fqn] = torch.load(value)

	def resolve_tensor(self, read_item: ReadItem):
	tensor = self.lookup_tensor(read_item.dest_index)
	return self.transform_tensor(read_item, tensor)

	def commit_tensor(self, read_item: ReadItem, tensor: torch.Tensor) -> None:
	pass

	def lookup_tensor(self, index: MetadataIndex) -> torch.Tensor:
	"""
	This is an extension from the planner interface to make it easy to extend the default planner
	"""
	return find_state_dict_object(self.state_dict, index)

	def transform_tensor(self, read_item: ReadItem, tensor: torch.Tensor):
	"""
	This is an extension from the planner interface to make it easy to extend the default planner
	"""
	return narrow_tensor_by_index(
	tensor, read_item.dest_offsets, read_item.lengths
	)


	def create_default_local_load_plan(
	state_dict: Dict[str, Any],
	metadata: Metadata,
	) -> LoadPlan:
	requests = []
	"""
	Create the ``LoadPlan`` used by DefaultLoadPlanner.

	It produces one read item per value in ``state_dict`` using the metadata in ``metadata``.

	The default behavior is to match key exactly between state_dict and metadata.
	It handles resharding by issuing multiple read requests against storage in order to match
	load requirements.
	"""

	for fqn, obj in state_dict.items():
	md = metadata.state_dict_metadata[fqn]
	# Since DTensor supports submesh, adding extra check to ensure _create_read_items()
	# gets called only when the current rank is part of the mesh for the corresponding DTensor.
	if isinstance(obj, DTensor):
	if obj.device_mesh.get_coordinate() is not None:
	requests += _create_read_items(fqn, md, obj)
	else:
	requests += _create_read_items(fqn, md, obj)

	return LoadPlan(requests)


	def create_default_global_load_plan(
	all_plans: List[LoadPlan],
	) -> List[LoadPlan]:
	"""
	Create global load plan used by DefaultLoadPlanner.

	The default load behavior involved no global coordination and this function
	currently doesn't change the local plans.
	"""
	return all_plans


	def create_default_local_save_plan(
	state_dict: Dict[str, Any], is_coordinator: bool
	) -> SavePlan:
	"""
	Create the ``SavePlan`` used by DefaultSavePlanner.

	On non-coordinator ranks, this function ignores tensors and non-tensor objects,
	only producing writes for ShardedTensor objects.

	On the coordinator rank, produce writes for all values.
	"""
	requests = []
	for fqn, obj in state_dict.items():
	# Since DTensor supports submesh, adding extra check to ensure _create_write_items()
	# gets called only when the current rank is part of the mesh for the corresponding DTensor.
	if isinstance(obj, DTensor):
	if obj.device_mesh.get_coordinate() is not None:
	requests += _create_write_items(fqn, obj)
	elif isinstance(obj, (torch.Tensor)) or is_coordinator:
	requests += _create_write_items(fqn, obj)

	return SavePlan(requests)


	def create_default_global_save_plan(
	all_plans: List[SavePlan],
	rewrite_index_hints: bool = True,
	) -> Tuple[List[SavePlan], Metadata]:
	"""
	Create the global plan and metadata used by DefaultSavePlanner.

	Metadata is produced by concatenating the metadata of all ``WriteItem`` from the supplied plans.

	The only global planning change is to update index hints in all ``MetadataIndex`` objects if
	``rewrite_index_hints`` is True.
	"""
	md: Dict[str, STORAGE_TYPES] = {}
	new_plans = []
	for plan in all_plans:
	new_items = []
	for item in plan.items:
	if not item.type == WriteItemType.SHARD:
	assert item.index.fqn not in md

	if item.type == WriteItemType.BYTE_IO:
	md[item.index.fqn] = BytesStorageMetadata()
	new_items.append(item)
	else:
	assert item.tensor_data is not None
	tensor_md = cast(
	TensorStorageMetadata,
	md.setdefault(
	item.index.fqn,
	TensorStorageMetadata(
	properties=item.tensor_data.properties,
	size=item.tensor_data.size,
	chunks=[],
	),
	),
	)
	new_item = item
	if rewrite_index_hints:
	new_index = dataclasses.replace(
	item.index, index=len(tensor_md.chunks)
	)
	new_item = dataclasses.replace(item, index=new_index)
	new_items.append(new_item)

	assert (
	item.tensor_data.chunk is not None
	), f"""
	Cannot create MD for tensor without bounds.
	FQN: {item.index.fqn}
	"""
	tensor_md.chunks.append(item.tensor_data.chunk)
	new_plans.append(dataclasses.replace(plan, items=new_items))
	return (new_plans, Metadata(md))


	def _create_default_local_metadata(state_dict: STATE_DICT_TYPE) -> Metadata:
	"""
	Return the ``Metadata`` if DefaultSavePlanner was used to checkpoint ``state_dict``.
	"""
	plan = _create_default_metadata_only_plan(state_dict)
	_, md = create_default_global_save_plan([plan])
	return md


	def _check_box_overlap(
	box0: ChunkStorageMetadata, box1: ChunkStorageMetadata
	) -> bool:
	"""
	Checks if two boxes overlap. Tuples are (offset, lengths)
	"""

	# For each dim of each shard, check if one shard resides on the other
	# end of second shard with respect to that dim. As an example for a 2D
	# shard, we would check if one shard is above or on the left of the
	# other shard.
	ndims = len(box0.offsets)
	for i in range(ndims):
	if box0.offsets[i] >= box1.offsets[i] + box1.sizes[i]:
	return False
	if box1.offsets[i] >= box0.offsets[i] + box0.sizes[i]:
	return False

	return True


	def _check_box_bounds(
	outer_box_size: torch.Size, inner_box: ChunkStorageMetadata
	) -> bool:
	for i in range(len(outer_box_size)):
	if inner_box.offsets[i] < 0:
	return False
	if inner_box.sizes[i] < 0:
	return False
	if inner_box.offsets[i] + inner_box.sizes[i] > outer_box_size[i]:
	return False

	return True


	def _validate_global_plan(
	global_plan: List[SavePlan], metadata: Metadata
	) -> bool:
	all_good = True
	for key, value in metadata.state_dict_metadata.items():
	if isinstance(value, BytesStorageMetadata):
	continue
	if len(value.size) == 0:
	continue
	chunks_volume = 0
	for chunk_idx, chunk0 in enumerate(value.chunks):
	# Compute the volume
	if not _check_box_bounds(value.size, chunk0):
	logger.warning(
	"""
	key:%s has out of bounds chunk:
	tensor-size:%s chunk: %s
	""", key, value.size, chunk0
	)
	all_good = False
	chunks_volume += reduce(operator.mul, chunk0.sizes, 1)

	# Check for overlap
	for chunk1 in value.chunks[chunk_idx + 1 :]:
	if _check_box_overlap(chunk0, chunk1):
	logger.warning(
	"key:%s has overlapping chunks: %s %s", key, chunk0, chunk1
	)
	all_good = False

	# Check whether combined chunk cover the whole tensor
	tensor_volume = reduce(operator.mul, value.size, 1)
	if chunks_volume != tensor_volume:
	logger.warning(
	"""
	key:%s invalid fill tensor-volume:
	%s chunks-volume: %s
	""", key, tensor_volume, chunks_volume
	)
	all_good = False

	return all_good