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Standardize SFT model interface with TunixMaxTextAdapter #3584

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Standardize SFT model interface with TunixMaxTextAdapter #3584

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9 changes: 9 additions & 0 deletions src/maxtext/trainers/post_train/sft/train_sft.py
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Original file line number Diff line number Diff line change
Expand Up @@ -49,6 +49,7 @@

from tunix.sft import metrics_logger, peft_trainer, profiler

from maxtext.integration.tunix.tunix_adapter import TunixMaxTextAdapter
from maxtext.configs import pyconfig
from maxtext.trainers.pre_train.train import loss_fn
from maxtext.common.goodput import (
Expand Down Expand Up @@ -135,6 +136,10 @@ def loss_func(model, inputs, inputs_position, inputs_segmentation, targets, targ
"targets_position": targets_position,
"targets_segmentation": targets_segmentation,
}
# If the model is wrapped in TunixMaxTextAdapter, we should unwrap it
# because MaxText's loss_fn expects the raw Transformer interface.
if isinstance(model, TunixMaxTextAdapter):
model = model.base
return loss_fn(model, mt_config, data, dropout_rng=None, params=None, is_train=True)

trainer = trainer.with_loss_fn(loss_func, has_aux=True)
Expand All @@ -147,6 +152,10 @@ def setup_trainer_state(mt_config, goodput_recorder=None):

with maybe_record_goodput(goodput_recorder, GoodputEvent.TPU_INIT):
model, mesh = model_creation_utils.create_nnx_model(mt_config)

# Wrap model with Tunix adapter for consistent interface
model = TunixMaxTextAdapter(model)

learning_rate_schedule = maxtext_utils.create_learning_rate_schedule(mt_config)
# pass in model for muon
optimizer = optimizers.get_optimizer(mt_config, learning_rate_schedule, model)
Expand Down
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