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Fix two bugs with --logging_first_step #8193

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merged 5 commits into from
Oct 30, 2020
Merged

Fix two bugs with --logging_first_step #8193

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3247634
make sure that logging_first_step evaluates
abisee Oct 29, 2020
1331a45
fix bug with incorrect loss on logging_first_step
abisee Oct 29, 2020
6c0131a
fix style
abisee Oct 30, 2020
ba17b14
Merge branch 'transformers_official' into logging_first_step
abisee Oct 30, 2020
e245ccd
logging_first_step only logs, not evals
abisee Oct 30, 2020
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6 changes: 5 additions & 1 deletion src/transformers/trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -729,6 +729,7 @@ def train(self, model_path: Optional[str] = None, trial: Union["optuna.Trial", D

tr_loss = torch.tensor(0.0).to(self.args.device)
self._logging_loss_scalar = 0
self._globalstep_last_logged = 0
self._total_flos = self.state.total_flos
model.zero_grad()

Expand Down Expand Up @@ -849,14 +850,17 @@ def _maybe_log_save_evaluate(self, tr_loss, model, trial, epoch):
if self.control.should_log:
logs: Dict[str, float] = {}
tr_loss_scalar = tr_loss.item()
logs["loss"] = (tr_loss_scalar - self._logging_loss_scalar) / self.args.logging_steps
logs["loss"] = (tr_loss_scalar - self._logging_loss_scalar) / (
self.state.global_step - self._globalstep_last_logged
)
# backward compatibility for pytorch schedulers
logs["learning_rate"] = (
self.lr_scheduler.get_last_lr()[0]
if version.parse(torch.__version__) >= version.parse("1.4")
else self.lr_scheduler.get_lr()[0]
)
self._logging_loss_scalar = tr_loss_scalar
self._globalstep_last_logged = self.state.global_step

self.log(logs)

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2 changes: 1 addition & 1 deletion src/transformers/training_args.py
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Expand Up @@ -250,7 +250,7 @@ class TrainingArguments:
warmup_steps: int = field(default=0, metadata={"help": "Linear warmup over warmup_steps."})

logging_dir: Optional[str] = field(default_factory=default_logdir, metadata={"help": "Tensorboard log dir."})
logging_first_step: bool = field(default=False, metadata={"help": "Log and eval the first global_step"})
logging_first_step: bool = field(default=False, metadata={"help": "Log the first global_step"})
logging_steps: int = field(default=500, metadata={"help": "Log every X updates steps."})
save_steps: int = field(default=500, metadata={"help": "Save checkpoint every X updates steps."})
save_total_limit: Optional[int] = field(
Expand Down