NaN loss is instantly fatal

mammoth/opts.py

@@ -370,10 +370,6 @@ def _add_train_general_opts(parser):
         default=None,
         help='Criteria to use for early stopping.',
     )
-    group.add(
-        '--max_nan_batches', '-max_nan_batches', type=int, default=0,
-        help='Number of batches that may be skipped due to loss blowout.'
-    )
 
     # GPU
     group = parser.add_argument_group('Computation Environment')

mammoth/trainer.py

@@ -106,7 +106,6 @@ def build_trainer(
         task_queue_manager=task_queue_manager,
         report_stats_from_parameters=opts.report_stats_from_parameters,
         report_training_accuracy=opts.report_training_accuracy,
-        max_nan_batches=opts.max_nan_batches,
     )
     return trainer
 
@@ -154,7 +153,6 @@ def __init__(
         task_queue_manager=None,
         report_stats_from_parameters=False,
         report_training_accuracy=False,
-        max_nan_batches=0,
     ):
         # Basic attributes.
         self.model = model
@@ -177,8 +175,6 @@ def __init__(
         self.earlystopper = earlystopper
         self.dropout = dropout
         self.dropout_steps = dropout_steps
-        self.max_nan_batches = max_nan_batches
-        self.nan_batches = 0
 
         self.task_queue_manager = task_queue_manager
 
@@ -495,37 +491,30 @@ def _gradient_accumulation(
                 )
                 # logger.info(loss)
 
-            try:
-                if loss is not None:
-                    if torch.isnan(loss):
-                        raise NanLossException('Loss blowout')
-                    # loss /= normalization
-                    self.optim.backward(loss)
-
-                if self.report_training_accuracy:
-                    # Slow: requires max over logits, eq, masked_select
-                    batch_stats = Statistics.from_loss_logits_target(
-                        loss.item(),
-                        logits,
-                        target,
-                        padding_idx=self.loss_functions[metadata.tgt_lang].ignore_index,
-                    )
-                else:
-                    batch_stats = Statistics(
-                        loss.item(),
-                        num_tokens,
-                        n_correct=None,
-                    )
+            if loss is not None:
+                if torch.isnan(loss):
+                    raise NanLossException('Loss blowout')
+                # loss /= normalization
+                self.optim.backward(loss)
+
+            if self.report_training_accuracy:
+                # Slow: requires max over logits, eq, masked_select
+                batch_stats = Statistics.from_loss_logits_target(
+                    loss.item(),
+                    logits,
+                    target,
+                    padding_idx=self.loss_functions[metadata.tgt_lang].ignore_index,
+                )
+            else:
+                batch_stats = Statistics(
+                    loss.item(),
+                    num_tokens,
+                    n_correct=None,
+                )
 
-                total_stats.update(batch_stats)
-                report_stats.update(batch_stats)
-                report_stats.update_task_loss(batch_stats.loss, metadata)
-            except NanLossException:
-                traceback.print_exc()
-                logger.info("At step %d, we removed a batch - accum %d", self.optim.training_step, k)
-                self.nan_batches += 1
-                if self.nan_batches >= self.max_nan_batches:
-                    raise NanLossException('Exceeded allowed --max_nan_batches.')
+            total_stats.update(batch_stats)
+            report_stats.update(batch_stats)
+            report_stats.update_task_loss(batch_stats.loss, metadata)
 
         if len(seen_comm_batches) != 1:
             logger.warning('Communication batches out of synch with batch accumulation')