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import cProfile
import inspect
import io
import itertools
import os
import warnings
from contextlib import contextmanager
from functools import wraps
from pstats import Stats
from typing import Any, Callable, cast, Dict, List, Optional, Sequence, TypeVar, Union
import torch
import torch.distributed as dist
from torch.distributed._shard.sharded_tensor import ShardedTensor
from torch.distributed._shard.sharded_tensor.shard import Shard
from torch.distributed._tensor import DTensor
from .api import (
_is_wrapped_exception,
_wrap_exception,
CheckpointException,
WRAPPED_EXCEPTION,
)
from .metadata import MetadataIndex, STATE_DICT_TYPE
__all__ = ["find_tensor_shard", "find_state_dict_object"]
T = TypeVar("T")
R = TypeVar("R")
def _get_failure_dict(
results: List[Union[T, WRAPPED_EXCEPTION]]
) -> Dict[int, WRAPPED_EXCEPTION]:
return cast(
Dict[int, WRAPPED_EXCEPTION],
{i: err for i, err in enumerate(results) if _is_wrapped_exception(err)},
)
def _all_gather_keys(
local_dict: Dict[Any, Any], group: Optional[dist.ProcessGroup] = None
) -> List[Any]:
"""Gathers all keys, and returns them sorted."""
keys = list(local_dict.keys())
gathered_keys: List[List[Any]] = [None] * dist.get_world_size() # type: ignore[list-item]
dist.all_gather_object(gathered_keys, keys, group=group)
return sorted(set(itertools.chain.from_iterable(gathered_keys)))
class _DistWrapper:
"""
This is a wrapper around PG that provides a series of features around object collectives.
It works without distributed initialized, where most collectives turns into nops.
All variants that take functions are exception robust, meaning that if one or more
ranks raise errors, all ranks will observe those.
"""
def __init__(
self,
group: Optional[dist.ProcessGroup],
use_dist: bool,
coordinator_rank: int,
):
self.group = group
self.use_dist = use_dist
self.coordinator_rank = coordinator_rank
if self.use_dist:
self.rank = dist.get_rank(group)
self.is_coordinator = self.rank == coordinator_rank
else:
self.rank = 0
self.is_coordinator = True
def get_rank(self) -> int:
return self.rank
def get_world_size(self) -> int:
if self.use_dist:
return dist.get_world_size(self.group)
return 1
def broadcast_object(self, object: Optional[T]) -> T:
"""Implement functionality similar to c10d::broadcast_object_list but without distributed enabled."""
object_list = [object]
if self.use_dist:
dist.broadcast_object_list(
object_list=object_list,
group=self.group,
src=self.coordinator_rank,
)
return cast(T, object_list[0])
def gather_object(self, object: T) -> Optional[List[T]]:
"""Implement functionality similar to c10d::gather_object but without distributed enabled."""
if self.use_dist:
gather_objs = (
cast(List[T], [None] * dist.get_world_size(self.group))
if self.is_coordinator
else None
)
dist.gather_object(
obj=object,
object_gather_list=gather_objs if self.is_coordinator else None,
dst=self.coordinator_rank,
group=self.group,
)
result = gather_objs
else:
result = [object]
return result
def all_gather_object(self, object: T) -> List[T]:
"""Implement functionality similar to c10d::all_gather_object but without distributed enabled."""
if self.use_dist:
gather_objs = cast(List[T], [None] * dist.get_world_size(self.group))
dist.all_gather_object(
object_list=gather_objs, obj=object, group=self.group
)
else:
gather_objs = [object]
return gather_objs
def scatter_object(self, object_list: Optional[List[T]]) -> T:
"""Implement functionality similar to c10d::scatter_object but without distributed enabled."""
if self.use_dist:
gather_result = cast(List[T], [None])
dist.scatter_object_list(
scatter_object_output_list=gather_result,
scatter_object_input_list=object_list if self.is_coordinator else None,
src=self.coordinator_rank,
group=self.group,
)
local_reply = gather_result[0]
else:
assert object_list is not None
local_reply = object_list[0]
return local_reply
def reduce_scatter(
self,
step: str,
map_fun: Callable[[], T],
reduce_fun: Callable[[List[T]], List[R]],
) -> R:
"""
Compute a value on each rank, then do centralized reduce on a single rank, followed by a scatter.
This method operates in the following way:
Run ``map_fun`` on all ranks
Gather results on rank 0
Call ``reduce_fun`` on all those values
Scatter to each rank part of the result.
"""
local_data: Union[WRAPPED_EXCEPTION, T]
try:
local_data = map_fun()
except BaseException as e:
local_data = _wrap_exception(e)
all_data = self.gather_object(local_data)
all_results: Optional[List[Union[R, CheckpointException]]] = None
if self.is_coordinator:
assert all_data is not None
node_failures = _get_failure_dict(all_data)
if len(node_failures) == 0:
try:
# N.B. why can't mypy cast List[R] to List[Union[R, WRAPPED_EXCEPTION]]?
all_results = cast(
List[Union[R, CheckpointException]],
reduce_fun(cast(List[T], all_data)),
)
except BaseException as e:
node_failures[self.rank] = _wrap_exception(e)
if len(node_failures) > 0:
all_results = [
CheckpointException(step, node_failures)
] * self.get_world_size()
result = self.scatter_object(all_results)
if isinstance(result, CheckpointException):
raise result
return result
def all_reduce(
self,
step: str,
map_fun: Callable[[], T],
reduce_fun: Callable[[List[T]], R],
) -> R:
"""
Compute a value on each rank, then do centralized reduce on a single rank, followed by a broadcast.
This method operates in the following way:
Run ``map_fun`` on all ranks
Gather results on rank 0
Call ``reduce_fun`` on all those values
Broadcast the reduced value to all ranks.
"""
local_data: Union[T, WRAPPED_EXCEPTION]
try:
local_data = map_fun()
except BaseException as e:
local_data = _wrap_exception(e)
all_data = self.gather_object(local_data)
result: Optional[Union[R, CheckpointException]] = None
if self.is_coordinator:
assert all_data is not None
node_failures = _get_failure_dict(all_data)
if len(node_failures) == 0:
try:
result = reduce_fun(cast(List[T], all_data))
except BaseException as e:
node_failures[self.rank] = _wrap_exception(e)
if len(node_failures) > 0:
result = CheckpointException(step, node_failures)
final_result = self.broadcast_object(result)
if isinstance(final_result, CheckpointException):
raise final_result
return cast(R, final_result)
def all_gather(
self,
step: str,
map_fun: Callable[[], T],
) -> List[T]:
"""
Compute a value on each rank, then all_gather them.
This method operates in the following way:
Run ``map_cp`` on all ranks
all_gather the values to all ranks
"""
result: Union[T, WRAPPED_EXCEPTION]
try:
result = map_fun()
except BaseException as e:
result = _wrap_exception(e)
all_results = self.all_gather_object(result)
node_failures = _get_failure_dict(all_results)
if len(node_failures) > 0:
raise CheckpointException(step, node_failures)
return cast(List[T], all_results)
def broadcast(
self,
step: str,
map_fun: Callable[[], T],
) -> T:
"""
Compute a value on rank 0 and broadcast it.
This method operates in the following way:
Run ``map_cp`` on rank 0
broadcast the value
"""
result: Optional[Union[T, CheckpointException]] = None
if self.is_coordinator:
try:
result = map_fun()
except BaseException as e:
result = CheckpointException(step, {self.rank: _wrap_exception(e)})
final_result = self.broadcast_object(result)
if isinstance(final_result, CheckpointException):
raise final_result
return cast(T, final_result)
def _find_shard(tensor: ShardedTensor, index: MetadataIndex) -> Shard:
if index.offset is None:
raise ValueError(
f"Cannot lookup {index.fqn} since its a ShardedTensor and no offset was provided"
)
shards = tensor.local_shards()
# index fast path
if index.index is not None:
if (
len(shards) > index.index
and torch.Size(shards[index.index].metadata.shard_offsets) == index.offset
):
return shards[index.index]
for shard in shards:
if torch.Size(shard.metadata.shard_offsets) == index.offset:
return shard
raise ValueError(f"Could not find shard at '{index.offset}' for FQN: '{index.fqn}'")
def find_tensor_shard(tensor: torch.Tensor, index: MetadataIndex) -> torch.Tensor:
if isinstance(tensor, DTensor):
return tensor.to_local()
if isinstance(tensor, ShardedTensor):
return _find_shard(tensor, index).tensor
if index.offset is not None:
# special case looking up a tensor by origin
if index.offset == torch.Size([0] * len(tensor.size())):
return tensor
raise ValueError(
f"FQN: '{index.fqn}' is not a ShardedTensor, can't find by offset: '{index.offset}'"
)
return tensor
def find_state_dict_object(state_dict: STATE_DICT_TYPE, index: MetadataIndex) -> Any:
if index.fqn not in state_dict:
raise ValueError(f"Could not find FQN: '{index.fqn}'")
obj = state_dict[index.fqn]
if isinstance(obj, torch.Tensor):
return find_tensor_shard(obj, index)
elif index.offset is not None:
raise ValueError(
f"FQN: '{index.fqn}' is not a ShardedTensor, can't find by offset: '{index.offset}'"
)
return obj
def _element_wise_add(a: Sequence[int], b: Sequence[int]) -> List[int]:
return [i_a + i_b for i_a, i_b in zip(a, b)]
def _element_wise_sub(a: Sequence[int], b: Sequence[int]) -> List[int]:
return [i_a - i_b for i_a, i_b in zip(a, b)]
class _ReaderView(io.IOBase):
def __init__(self, base_stream: io.IOBase, offset: int, len: int):
super().__init__()
self.offset = offset
self.len = len
self.base_stream = base_stream
self.seek(0)
def seek(self, __offset: int, __whence: int = os.SEEK_SET) -> int:
if __whence == os.SEEK_SET:
__offset = self.offset + __offset
elif __whence == os.SEEK_END:
__whence = os.SEEK_SET
__offset = (self.offset + self.len) - __offset
return self.base_stream.seek(__offset, __whence)
def tell(self) -> int:
return self.base_stream.tell() - self.offset
def readable(self) -> bool:
return self.base_stream.readable()
def seekable(self) -> bool:
return self.base_stream.seekable()
def readinto(self, b):
return self.base_stream.readinto(b) # type: ignore[attr-defined]
def read(self, size=-1):
return self.base_stream.read(size)
def _create_file_view(file: io.IOBase, offset: int, length: int) -> io.IOBase:
# FIXME (kumpera) torch.load fails if we wrap with io.BufferedReader
return _ReaderView(file, offset, length)
def _normalize_device_info(device_type: str, device_id: int) -> str:
"""Device info normalization."""
if device_type == "cpu":
return "cpu"
return f"{device_type}:{device_id}"
# TODO: integrate with distributed logging flag
ENABLE_PROFILE = False
@contextmanager
def _profile():
# Only log the profiling when it is enable and is on rank0 or dist is not
# avaiable.
if ENABLE_PROFILE and (not dist.is_available() or dist.get_rank() == 0):
profiler = cProfile.Profile()
profiler.enable()
try:
yield
finally:
profiler.disable()
stats = Stats(profiler)
stats.sort_stats("time").print_stats(10)
else:
yield
def _api_bc_check(func):
@wraps(func)
def inner_func(*args, **kwargs) -> Any:
if len(args) == 2:
warnings.warn(
f"The argument order of {func.__name__} has been changed. "
"Please check the document to avoid future breakages."
)
sig = inspect.signature(func)
kwonlyargs = [
p.name for p in sig.parameters.values() if p.kind == p.KEYWORD_ONLY
]
if "storage_writer" in kwonlyargs:
assert "storage_writer" not in kwargs, (args, kwargs)
kwargs["storage_writer"] = args[1]
elif "storage_reader" in kwonlyargs:
assert "storage_reader" not in kwargs, (args, kwargs)
kwargs["storage_reader"] = args[1]
else:
raise RuntimeError(f"Unexpected kwonlyargs = {kwonlyargs}")
return func(args[0], **kwargs)
else:
return func(*args, **kwargs)
return inner_func
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