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Fix deepspeed for PPOv2Trainer #2080

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merged 1 commit into from
Sep 19, 2024
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Fix deepspeed for PPOv2Trainer #2080

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18 changes: 13 additions & 5 deletions trl/trainer/ppov2_trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -39,7 +39,7 @@
)
from transformers.integrations import get_reporting_integration_callbacks
from transformers.trainer import DEFAULT_CALLBACKS, DEFAULT_PROGRESS_CALLBACK
from transformers.trainer_callback import CallbackHandler, PrinterCallback
from transformers.trainer_callback import CallbackHandler, ExportableState, PrinterCallback

from ..core import masked_mean, masked_whiten
from ..models.utils import unwrap_model_for_generation
Expand Down Expand Up @@ -174,17 +174,20 @@ def __init__(
#########
### trainer specifics
#########
self.state = OnlineTrainerState(
is_local_process_zero=self.is_local_process_zero(),
is_world_process_zero=self.is_world_process_zero(),
)
default_callbacks = DEFAULT_CALLBACKS + get_reporting_integration_callbacks(self.args.report_to)
self.callbacks = default_callbacks if callbacks is None else default_callbacks + callbacks
self.callback_handler = CallbackHandler(
self.callbacks, self.model, self.tokenizer, self.optimizer, self.lr_scheduler
)
self.add_callback(PrinterCallback if self.args.disable_tqdm else DEFAULT_PROGRESS_CALLBACK)
self.control = TrainerControl()
self.state = OnlineTrainerState(
is_local_process_zero=self.is_local_process_zero(),
is_world_process_zero=self.is_world_process_zero(),
stateful_callbacks=[
cb for cb in self.callback_handler.callbacks + [self.control] if isinstance(cb, ExportableState)
],
)
self.current_flos = 0
self.hp_search_backend = None
self.is_deepspeed_enabled = getattr(self.accelerator.state, "deepspeed_plugin", None) is not None
Expand Down Expand Up @@ -311,6 +314,11 @@ def repeat_generator():
self.state.save_steps = args.save_steps
self.control = self.callback_handler.on_train_begin(args, self.state, self.control)

# backward compatibility
if self.is_deepspeed_enabled:
self.deepspeed = self.model
self.model_wrapped = self.model

for update in range(1, args.num_total_batches + 1):
self.state.episode += 1 * args.batch_size
data = next(iter_dataloader)
Expand Down
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