Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

fix 2.4.1ckpt #3629

Merged
merged 1 commit into from
Sep 23, 2024
Merged

fix 2.4.1ckpt #3629

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
6 changes: 1 addition & 5 deletions composer/trainer/_patch_pytorch.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -945,8 +945,7 @@ def unshard_with_sync(self):

if version.parse(torch.__version__) >= version.parse('2.4.0') and version.parse(
torch.__version__,
) < version.parse('2.4.1'):
# 2.4.0 only patch
) < version.parse('2.4.2'):
# PyTorch issue: https://github.com/pytorch/pytorch/issues/133923
from torch.distributed.checkpoint.metadata import STATE_DICT_TYPE
from typing import Mapping, Collection
Expand Down Expand Up @@ -1003,9 +1002,6 @@ def _traverse_obj(path: OBJ_PATH, value: STATE_DICT_ITEM) -> None:
for key, value in state_dict.items():
_traverse_obj((str(key),), value)

if version.parse(torch.__version__) >= version.parse('2.4.0') and version.parse(
torch.__version__,
) < version.parse('2.4.2'):
# Save original FlatParamHandle.unshard to revert back to when dropping automicrobatching hooks
from torch.distributed.fsdp._flat_param import FlatParamHandle
original_unshard = FlatParamHandle.unshard
Expand Down
69 changes: 30 additions & 39 deletions composer/utils/checkpoint.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -623,50 +623,41 @@ def dist_cp_load(
load_planner: Optional[LoadPlanner] = None,
):
if version.parse(torch.__version__) >= version.parse('2.4.0'):
if version.parse(torch.__version__) < version.parse('2.4.1'):
# PyTorch 2.4.0
from torch.distributed.checkpoint.utils import CheckpointException
try:
dist_cp.load(
state_dict=state_dict,
storage_reader=storage_reader,
planner=load_planner,
)
except CheckpointException as e:
checkpoint_metadata = storage_reader.read_metadata().state_dict_metadata
if 'state.metadata' in checkpoint_metadata and 'state.metadata.composer_env_info.composer_version' not in checkpoint_metadata:
# Torch 2.4 changed the way how state dict is flattened. It broke backward compatibility.
# Torch issue: https://github.com/pytorch/pytorch/issues/133923.
# We override the traverse_state_dict so that the load planner could
# use the old way of flattening the state dict
log.debug('Trying to load checkpointing saved before torch 2.4')

import torch.distributed.checkpoint._nested_dict as nested_dict
import torch.distributed.checkpoint._sharded_tensor_utils as sharded_tensor_util
from torch.distributed.checkpoint._traverse import traverse_state_dict as traverse_2_4_0

from composer.trainer._patch_pytorch import traverse_state_dict as backward_compatible_traverse

nested_dict.traverse_state_dict = backward_compatible_traverse
sharded_tensor_util.traverse_state_dict = backward_compatible_traverse

dist_cp.load(
state_dict=state_dict,
storage_reader=storage_reader,
planner=load_planner,
)
# Revert the override
nested_dict.traverse_state_dict = traverse_2_4_0
sharded_tensor_util.traverse_state_dict = traverse_2_4_0
else:
raise e
else:
# PyTorch 2.4.1
from torch.distributed.checkpoint.utils import CheckpointException
try:
dist_cp.load(
state_dict=state_dict,
storage_reader=storage_reader,
planner=load_planner,
)
except CheckpointException as e:
checkpoint_metadata = storage_reader.read_metadata().state_dict_metadata
if 'state.metadata' in checkpoint_metadata and 'state.metadata.composer_env_info.composer_version' not in checkpoint_metadata:
# Torch 2.4 changed the way how state dict is flattened. It broke backward compatibility.
# Torch issue: https://github.com/pytorch/pytorch/issues/133923.
# We override the traverse_state_dict so that the load planner could
# use the old way of flattening the state dict
log.debug('Trying to load checkpointing saved before torch 2.4')

import torch.distributed.checkpoint._nested_dict as nested_dict
import torch.distributed.checkpoint._sharded_tensor_utils as sharded_tensor_util
from torch.distributed.checkpoint._traverse import traverse_state_dict as traverse_2_4_0

from composer.trainer._patch_pytorch import traverse_state_dict as backward_compatible_traverse

nested_dict.traverse_state_dict = backward_compatible_traverse
sharded_tensor_util.traverse_state_dict = backward_compatible_traverse

dist_cp.load(
state_dict=state_dict,
storage_reader=storage_reader,
planner=load_planner,
)
# Revert the override
nested_dict.traverse_state_dict = traverse_2_4_0
sharded_tensor_util.traverse_state_dict = traverse_2_4_0
else:
raise e
else:
dist_cp.load_state_dict(
state_dict=state_dict,
Expand Down
Loading