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disable only sharding opt in save stage #11200

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Merged
swgu98 merged 1 commit into from
Dec 16, 2025
Merged

disable only sharding opt in save stage #11200

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11 changes: 2 additions & 9 deletions paddlenlp/trainer/trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -815,13 +815,7 @@ def get_metadata_file_name(path):

self._load_scheduler(resume_from_checkpoint)

from .trainer_utils import ShardingOption

should_load_stage1 = self.args.sharding_parallel_degree > 1 and ShardingOption.SHARD_OP in self.args.sharding
logger.debug(f"should_load_stage1 = {should_load_stage1}")
logger.debug(f"sharded_model_from_ema = {self.args.sharded_model_from_ema}")

if should_load_stage1 and self.args.sharded_model_from_ema:
if self.args.sharded_model_from_ema:
ema_states_path = os.path.join(resume_from_checkpoint, EMA_STATE_DIC, f"{dist.get_rank()}_0.distcp")
ema_state_dict = paddle.load(ema_states_path)
ema_master_weights = ema_state_dict.pop("master_weights", None)
Expand All @@ -832,7 +826,6 @@ def get_metadata_file_name(path):
), f"{k} not in ema_master_weights, emas_master_weight keys {ema_master_weights.keys()}"
paddle.assign(ema_master_weights[k], opt_master_weights[k])

ema_state_dict = reshard_util.all_gather_state_dict(ema_state_dict, lambda x: True, self.sharding_group)
self.model.set_state_dict(ema_state_dict)
else:

Expand All @@ -854,7 +847,7 @@ def bf16_filtered_sharded_state_dict(sharded_state_dict):
comm_method=self.args.flex_ckpt_comm_method,
)

if self.args.bf16 and (not self.args.ignore_load_lr_and_optim) and should_load_stage1:
if self.args.bf16 and (not self.args.ignore_load_lr_and_optim):
opt_state_dict = self.optimizer.state_dict()

def recover_params_from_master_weight(opt_state_dict, group):
Expand Down
28 changes: 2 additions & 26 deletions paddlenlp/trainer/utils/zero_cost_checkpoint.py
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Expand Up @@ -1505,37 +1505,13 @@ def __init__(self, args, zcc_manager, timer, unused_arg):
self.sharding_group = self.hcg.get_sharding_parallel_group()

def _manipulate_state_dict_and_config(self, model_to_save, optimizer):
# return model_to_save.sharded_state_dict()

group_getter = GroupGetter(model_to_save)
gids = group_getter.get_group_ids()
from paddlenlp.trainer.utils.sharding_io import (
exclude_parameters_in_state_dict,
filter_sharded_params,
)

# filter_sharded_params = sharded_state_dict_compatibility(filter_sharded_params, return_sharded_state_dict=True)
# exclude_parameters_in_state_dict = sharded_state_dict_compatibility(
# exclude_parameters_in_state_dict, return_sharded_state_dict=True
# )
from paddlenlp.trainer.utils.sharding_io import exclude_parameters_in_state_dict

state_dict = model_to_save.state_dict()
# tmp wa should_save_sharding_stage1_model
if self.args.should_save_sharding_stage1_model or self.args.save_checkpoint_format == "flex_checkpoint":
state_dict = split_model_state(state_dict, group_getter)
for gid in gids:
state_dict[gid] = filter_sharded_params(
state_dict.get(gid, {}),
optimizer,
self.sharding_group,
self.args.save_sharding_stage1_model_include_freeze_params,
)
state_dict = merge_model_state(state_dict)

# tmp wa should_save_sharding_stage1_model
if self.args.bf16 and (
self.args.should_save_sharding_stage1_model or self.args.save_checkpoint_format == "flex_checkpoint"
):
if self.args.bf16:
param_names_in_master_weights = []
optimzier_state_dict = optimizer.state_dict()
optimzier_state_dict = split_opt_state(optimzier_state_dict, group_getter)
Expand Down
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