Source code for torch.distributed.checkpoint.default_planner

# 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._shard.sharded_tensor import ShardedTensor

from torch.distributed.checkpoint.planner import (

from torch.distributed.checkpoint.metadata import (

from torch.distributed.checkpoint.planner_helpers import (

from torch.distributed.checkpoint._nested_dict import (
from torch.distributed.checkpoint._sharded_tensor_utils import (
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(__file__)

__all__ = [

# TODO: Update docstrings for
[docs]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)
[docs] 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)
[docs] 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(), bytes) object = bytes return object
[docs]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
[docs] 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)
[docs] 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] 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(): if isinstance(obj, ShardedTensor) or is_coordinator: requests += _create_write_items(fqn, obj) return SavePlan(requests) def create_default_global_save_plan( all_plans: List[SavePlan], ) -> 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. """ 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(, size=item.tensor_data.size, chunks=[], ), ), ) 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): if not _check_box_bounds(value.size, chunk0): logger.warning( f""" key:{key} has out of bounds chunk: tensor-size:{value.size} chunk: {chunk0} """ ) all_good = False chunks_volume += reduce(operator.mul, chunk0.sizes, 1) for chunk1 in value.chunks[chunk_idx + 1 :]: if _check_box_overlap(chunk0, chunk1): logger.warning( f"key:{key} has overlapping chunks: {chunk0} {chunk1}" ) all_good = False tensor_volume = reduce(operator.mul, value.size, 1) if chunks_volume != tensor_volume: logger.warning( f""" key:{key} invalid fill tensor-volume: {tensor_volume} chunks-volume: {chunks_volume} """ ) all_good = False return all_good


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