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Internal. #3795

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19 changes: 10 additions & 9 deletions src/maxtext/trainers/pre_train/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -58,7 +58,8 @@
)
from maxtext.common.gcloud_stub import cloud_diagnostics as _cloud_diag, is_decoupled
from maxtext.common.gcloud_stub import vertex_tensorboard_modules
from maxtext.common.metric_logger import MetricLogger, record_activation_metrics
from maxtext.common import metric_logger
from maxtext.common.metric_logger import record_activation_metrics
from maxtext.trainers.post_train.dpo.dpo_utils import _merge_dpo_state, _split_dpo_state, dpo_loss_fn
from maxtext.utils import exceptions
from maxtext.utils import gcs_utils
Expand Down Expand Up @@ -570,10 +571,10 @@ def train_loop(config, recorder, state=None):

start_step = get_first_step(model, state) # this is the start_step for training
prof = profiler.Profiler(config, offset_step=start_step)
metric_logger = MetricLogger(config=config, learning_rate_schedule=learning_rate_schedule)
metric_logger_instance = metric_logger.MetricLogger(config=config, learning_rate_schedule=learning_rate_schedule)

# Write train config params, num model params, and XLA flags to tensorboard
metric_logger.write_setup_info_to_tensorboard(state.params)
metric_logger_instance.write_setup_info_to_tensorboard(state.params)

_job_completed_gracefully = False
try:
Expand Down Expand Up @@ -611,7 +612,7 @@ def train_loop(config, recorder, state=None):
assert eval_data_iterator
# Explicitly reset the eval iterator and counters before starting the eval loop
eval_data_iterator.reset()
metric_logger.reset_eval_metrics()
metric_logger_instance.reset_eval_metrics()

eval_step_count = 0
# pylint: disable=not-callable
Expand All @@ -622,11 +623,11 @@ def train_loop(config, recorder, state=None):
break
with jax.set_mesh(mesh), nn_partitioning.axis_rules(config.logical_axis_rules):
eval_metrics = p_eval_step(state, eval_batch, nextrng)
metric_logger.record_eval_metrics(step, metrics=eval_metrics)
metric_logger_instance.record_eval_metrics(step, metrics=eval_metrics)
max_logging.log(f"Completed eval step {eval_step_count}")
eval_step_count += 1
metric_logger.record_eval_metrics(step, eval_step_count=eval_step_count)
if metric_logger.cumulative_eval_metrics["scalar"]["eval/avg_loss"] <= config.target_eval_loss:
metric_logger_instance.record_eval_metrics(step, eval_step_count=eval_step_count)
if metric_logger_instance.cumulative_eval_metrics["scalar"]["eval/avg_loss"] <= config.target_eval_loss:
prof.deactivate()
raise exceptions.StopTraining(f"Target loss {config.target_eval_loss=} is achieved.")

Expand All @@ -635,7 +636,7 @@ def train_loop(config, recorder, state=None):
if step == start_step:
max_utils.print_mem_stats("After params initialized")

metric_logger.buffer_and_write_train_metrics(metrics, step, step_time_delta)
metric_logger_instance.buffer_and_write_train_metrics(metrics, step, step_time_delta)

if config.save_checkpoint_on_completion:
state_to_save = state if not config.use_dpo else _split_dpo_state(state)[0]
Expand All @@ -650,7 +651,7 @@ def train_loop(config, recorder, state=None):
finally:
if _job_completed_gracefully:
record_goodput(recorder, RECORD_JOB_END_TIME)
metric_logger.flush_metrics_and_cleanup()
metric_logger_instance.flush_metrics_and_cleanup()

return state

Expand Down
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