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# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections import defaultdict
from typing import Any
from lightning.pytorch.callbacks.progress import ProgressBar
from lightning.pytorch.utilities.types import STEP_OUTPUT
try:
from megatron.core.num_microbatches_calculator import get_num_microbatches
HAVE_MEGATRON_CORE = True
except (ImportError, ModuleNotFoundError):
HAVE_MEGATRON_CORE = False
from typing_extensions import override
class ProgressPrinter(ProgressBar):
"""
Callback for logging progress in Megatron. Prints status in terms of global batches rather than microbatches.
Recommended over MegatronProgressBar for non-interactive settings
Args:
log_interval (int): determines how frequently (in steps) to print the progress.
skip_accumulate_metrics (list[str]): for all metrics in this list, value logged will
simply reflect the latest value rather than averaging over the log interval.
exclude_metrics (list[str]): any metrics to exclude from logging.
"""
def __init__(
self,
log_interval: int = 1,
skip_accumulate_metrics: list[str] = ["global_step"],
exclude_metrics: list[str] = ["v_num"],
):
self._train_description = "Training"
self._validation_description = "Validation"
self._test_description = "Testing"
self._log_interval = int(log_interval)
# most recent "global_step" will be logged
# rather than averaging over last log_interval steps
self.skip_accumulate_metrics = skip_accumulate_metrics
self.exclude_metrics = exclude_metrics
self.total_metrics_dict = defaultdict(lambda: 0.0)
self._is_disabled = log_interval <= 0
super().__init__()
def format_string(self, prefix, metrics):
log_string = prefix
for metric, val in metrics.items():
if isinstance(val, (float)) and val.is_integer():
val = int(val)
log_string += f' | {metric}: {val}'
else:
log_string += f' | {metric}: {val:.4}'
return log_string
def disable(self):
self._is_disabled = True
def enable(self):
self._is_disabled = False
@property
def is_disabled(self) -> bool:
return self._is_disabled
@property
def average_metrics_dict(self):
average_dict = {}
for key in self.total_metrics_dict:
if key in self.skip_accumulate_metrics or not isinstance(self.total_metrics_dict[key], (int, float)):
average_dict[key] = self.total_metrics_dict[key]
else:
average_dict[key] = self.total_metrics_dict[key] / self.log_interval
return average_dict
@property
def train_description(self):
return self._train_description
@property
def validation_description(self):
return self._validation_description
@property
def test_description(self):
return self._test_description
@property
def log_interval(self):
return self._log_interval
@log_interval.setter
def log_interval(self, val):
self._log_interval = val
@override
def on_sanity_check_start(self, *_: Any) -> None:
self._validation_description = "Sanity checking " + self.validation_description
@override
def on_sanity_check_end(self, *_: Any) -> None:
self._validation_description = "Validation"
@override
def on_train_start(self, trainer, *_):
if trainer.max_steps > 0:
# while resuming from a ckpt use trainer.max_steps as the total for progress bar as trainer.num_training_batches
# is truncated to max_steps - step being resumed at
self.total = trainer.max_steps
else:
self.total = trainer.num_training_batches
## TODO(ashors): handle nan losses
@override
def on_train_batch_end(self, trainer, pl_module, *_, **__):
n = trainer.strategy.current_epoch_step
if self.is_disabled:
return
metrics = self.get_metrics(trainer, pl_module)
for key in metrics:
if key in self.exclude_metrics:
continue
if key in self.skip_accumulate_metrics or not isinstance(metrics[key], (int, float)):
self.total_metrics_dict[key] = metrics[key]
else:
self.total_metrics_dict[key] += metrics[key]
if self.should_log(n):
prefix = self.train_description + f" epoch {trainer.current_epoch}, iteration {n-1}/{self.total-1}"
log_string = self.format_string(prefix, self.average_metrics_dict)
print(log_string)
if getattr(trainer.strategy, "timers", None):
timers = trainer.strategy.timers
megatron_log_string = self.log_megatron_timers(timers)
if megatron_log_string:
print(megatron_log_string, flush=True)
self.total_metrics_dict = defaultdict(lambda: 0.0)
@override
def on_validation_batch_start(
self,
trainer: "pl.Trainer",
pl_module: "pl.LightningModule",
batch: Any,
batch_idx: int,
dataloader_idx: int = 0,
) -> None:
if not self.has_dataloader_changed(dataloader_idx):
return
if float(self.total_val_batches_current_dataloader) == float('inf'):
self.total_validation_steps = float('inf')
else:
self.total_validation_steps = int(self.total_val_batches_current_dataloader / get_num_microbatches())
@override
def on_validation_batch_end(
self,
trainer: "pl.Trainer",
pl_module: "pl.LightningModule",
outputs: STEP_OUTPUT,
batch: Any,
batch_idx: int,
dataloader_idx: int = 0,
) -> None:
if self.is_disabled:
return
n = (batch_idx + 1) / get_num_microbatches()
if self.should_log(n):
print(self.validation_description + f": iteration {int(n)}/{self.total_validation_steps}")
@override
def on_test_batch_start(
self,
trainer: "pl.Trainer",
pl_module: "pl.LightningModule",
batch: Any,
batch_idx: int,
dataloader_idx: int = 0,
) -> None:
if not self.has_dataloader_changed(dataloader_idx):
return
self.total_test_steps = int(self.total_test_batches_current_dataloader / get_num_microbatches())
@override
def on_test_batch_end(
self,
trainer: "pl.Trainer",
pl_module: "pl.LightningModule",
outputs: STEP_OUTPUT,
batch: Any,
batch_idx: int,
dataloader_idx: int = 0,
) -> None:
if self.is_disabled:
return
n = int((batch_idx + 1) / get_num_microbatches())
if self.should_log(n):
print(self.test_description + f": iteration {n}/{self.total_validation_steps}")
def should_log(self, n):
return n % self.log_interval == 0
def log_megatron_timers(self, timers):
output_string = timers.get_all_timers_string(names=None, normalizer=self.log_interval)
if output_string is not None:
return output_string + "\n"
return None
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