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import os
from transformers import TrainerCallback, is_tensorboard_available
from transformers.integrations import rewrite_logs
class LogFlosCallback(TrainerCallback):
"""
A :class:`~transformers.TrainerCallback` that adds current flos to every log.
"""
def on_log(self, args, state, control, logs=None, **kwargs):
logs["total_flos"] = state.total_flos
class TensorBoardFloIndexedCallback(TrainerCallback):
"""
A :class:`~transformers.TrainerCallback` that sends the logs to `TensorBoard
<https://www.tensorflow.org/tensorboard>`__.
Args:
tb_writer (:obj:`SummaryWriter`, `optional`):
The writer to use. Will instantiate one if not set.
"""
def __init__(self, tb_writer=None):
has_tensorboard = is_tensorboard_available()
assert (
has_tensorboard
), "TensorBoardCallback requires tensorboard to be installed. Either update your PyTorch version or install tensorboardX."
if has_tensorboard:
try:
from torch.utils.tensorboard import SummaryWriter # noqa: F401
self._SummaryWriter = SummaryWriter
except ImportError:
try:
from tensorboardX import SummaryWriter
self._SummaryWriter = SummaryWriter
except ImportError:
self._SummaryWriter = None
else:
self._SummaryWriter = None
self.tb_writer = tb_writer
def _init_summary_writer(self, args, log_dir=None):
log_dir = log_dir or args.logging_dir
if self._SummaryWriter is not None:
self.tb_writer = self._SummaryWriter(log_dir=log_dir)
def on_train_begin(self, args, state, control, **kwargs):
if not state.is_world_process_zero:
return
log_dir = None
if state.is_hyper_param_search:
trial_name = state.trial_name
if trial_name is not None:
log_dir = os.path.join(args.logging_dir, trial_name)
self._init_summary_writer(args, log_dir)
if self.tb_writer is not None:
self.tb_writer.add_text("args", args.to_json_string())
if "model" in kwargs:
model = kwargs["model"]
if hasattr(model, "config") and model.config is not None:
model_config_json = model.config.to_json_string()
self.tb_writer.add_text("model_config", model_config_json)
# Version of TensorBoard coming from tensorboardX does not have this method.
if hasattr(self.tb_writer, "add_hparams"):
self.tb_writer.add_hparams(args.to_sanitized_dict(), metric_dict={})
def on_log(self, args, state, control, logs=None, **kwargs):
if not state.is_world_process_zero:
return
if self.tb_writer is None:
self._init_summary_writer(args)
if self.tb_writer is not None:
logs = rewrite_logs(logs)
self.tb_writer.add_scalar("Conversion/x steps - y flos", state.total_flos, state.global_step)
self.tb_writer.add_scalar("Conversion/x flos - y steps", state.global_step, state.total_flos)
for k, v in logs.items():
if isinstance(v, (int, float)):
self.tb_writer.add_scalar(f"Flos/{k}", v, state.total_flos)
self.tb_writer.add_scalar(f"Steps/{k}", v, state.global_step)
self.tb_writer.flush()
def on_train_end(self, args, state, control, **kwargs):
if self.tb_writer:
self.tb_writer.close()
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