Correctly support resuming with dataset without length

src/transformers/trainer.py

@@ -2205,7 +2205,7 @@ def _inner_training_loop(
             max_steps = args.max_steps
             # Setting a very large number of epochs so we go as many times as necessary over the iterator.
             num_train_epochs = sys.maxsize
-            num_update_steps_per_epoch = max_steps
+            num_update_steps_per_epoch = None
             num_examples = total_train_batch_size * args.max_steps
             num_train_samples = args.max_steps * total_train_batch_size
             if args.include_tokens_per_second:
@@ -2355,15 +2355,28 @@ def _inner_training_loop(
             self.state = TrainerState.load_from_json(os.path.join(resume_from_checkpoint, TRAINER_STATE_NAME))
             self.compare_trainer_and_checkpoint_args(self.args, self.state)
             self._load_callback_state()
-            epochs_trained = int(self.state.global_step // num_update_steps_per_epoch)
-            if not args.ignore_data_skip:
-                steps_trained_in_current_epoch = self.state.global_step % (num_update_steps_per_epoch)
-                steps_trained_in_current_epoch *= args.gradient_accumulation_steps
+            if num_update_steps_per_epoch is not None:
+                epochs_trained = int(self.state.global_step // num_update_steps_per_epoch)
+                if not args.ignore_data_skip:
+                    steps_trained_in_current_epoch = self.state.global_step % (num_update_steps_per_epoch)
+                    steps_trained_in_current_epoch *= args.gradient_accumulation_steps
+                else:
+                    steps_trained_in_current_epoch = 0
             else:
-                steps_trained_in_current_epoch = 0
+                # If the dataloader does not have a length, we cannot restore the number of trained epochs.
+                # In the following loop, we repeatedly iterate over the dataloader to skip the first
+                # `steps_trained_in_current_epoch` steps and increment `epochs_trained` accordingly.
+                epochs_trained = 0
+                steps_trained_in_current_epoch = self.state.global_step * args.gradient_accumulation_steps
+                if args.ignore_data_skip:
+                    raise ValueError(
+                        "The dataloader does not have a length, so it is impossible to restore the number of trained"
+                        " epochs. Please disable the `ignore_data_skip` option."
+                    )
 
             logger.info("  Continuing training from checkpoint, will skip to saved global_step")
-            logger.info(f"  Continuing training from epoch {epochs_trained}")
+            if num_update_steps_per_epoch is not None:
+                logger.info(f"  Continuing training from epoch {epochs_trained}")
             logger.info(f"  Continuing training from global step {self.state.global_step}")
             if not args.ignore_data_skip:
                 logger.info(
@@ -2410,6 +2423,26 @@ def _inner_training_loop(
             if hasattr(epoch_dataloader, "set_epoch"):
                 epoch_dataloader.set_epoch(epoch)
 
+            steps_skipped = 0
+            rng_to_sync = False
+            if steps_trained_in_current_epoch > 0 and num_update_steps_per_epoch is None:
+                # Since the dataloader does not have a length, we just loop until the required number of steps.
+                # Every time we reach the end of the dataloader, we increment epoch and reset the iterator.
+                epoch_iterator = iter(epoch_iterator)
+                epoch_over = False
+                while steps_trained_in_current_epoch > 0:
+                    try:
+                        next(epoch_iterator)
+                        steps_trained_in_current_epoch -= 1
+                        steps_skipped += 1
+                    except StopIteration:
+                        epoch_over = True
+                        break
+                if epoch_over:
+                    continue
+                assert steps_trained_in_current_epoch == 0
+                rng_to_sync = True
+
             # Reset the past mems state at the beginning of each epoch if necessary.
             if args.past_index >= 0:
                 self._past = None
@@ -2424,8 +2457,6 @@ def _inner_training_loop(
             if epoch == epochs_trained and resume_from_checkpoint is not None and steps_trained_in_current_epoch == 0:
                 self._load_rng_state(resume_from_checkpoint)
 
-            rng_to_sync = False
-            steps_skipped = 0
             if steps_trained_in_current_epoch > 0:
                 epoch_dataloader = skip_first_batches(epoch_dataloader, steps_trained_in_current_epoch)
                 steps_skipped = steps_trained_in_current_epoch
@@ -2575,13 +2606,6 @@ def _inner_training_loop(
                     if is_torch_xla_available():
                         xm.mark_step()
                     break
-            if step < 0:
-                logger.warning(
-                    "There seems not to be a single sample in your epoch_iterator, stopping training at step"
-                    f" {self.state.global_step}! This is expected if you're using an IterableDataset and set"
-                    f" num_steps ({max_steps}) higher than the number of available samples."
-                )
-                self.control.should_training_stop = True
 
             self.control = self.callback_handler.on_epoch_end(args, self.state, self.control)
             self._maybe_log_save_evaluate(tr_loss, grad_norm, model, trial, epoch, ignore_keys_for_eval)

tests/trainer/test_trainer.py

@@ -2958,6 +2958,44 @@ def test_resume_training_with_frozen_params(self):
             self.assertEqual(b, b1)
             self.check_trainer_state_are_the_same(state, state1)
 
+    @parameterized.expand([(9, 1), (10, 1), (11, 1), (20, 1), (21, 1), (9, 2)])
+    def test_resume_training_with_iterable_dataset(self, dataset_length, gradient_accumulation_steps):
+        with tempfile.TemporaryDirectory() as tmpdir:
+
+            def get_trainer():
+                config = RegressionModelConfig()
+                train_dataset = SampleIterableDataset(length=dataset_length)
+                model = RegressionRandomPreTrainedModel(config)
+                args = RegressionTrainingArguments(
+                    output_dir=tmpdir,
+                    learning_rate=0.1,
+                    max_steps=20,
+                    save_steps=10,
+                    per_device_train_batch_size=1,
+                    gradient_accumulation_steps=gradient_accumulation_steps,
+                )
+                return Trainer(model=model, args=args, train_dataset=train_dataset)
+
+            # Train from scratch.
+            trainer = get_trainer()
+            trainer.train()
+            self.assertEqual(trainer.state.global_step, 20)
+            (a, b) = trainer.model.a.item(), trainer.model.b.item()
+            state = dataclasses.asdict(trainer.state)
+
+            # Train from a checkpoint.
+            checkpoint = os.path.join(tmpdir, "checkpoint-10")
+            trainer = get_trainer()
+            trainer.train(resume_from_checkpoint=checkpoint)
+            self.assertEqual(trainer.state.global_step, 20)
+            (a1, b1) = trainer.model.a.item(), trainer.model.b.item()
+            state1 = dataclasses.asdict(trainer.state)
+
+            # Check that the resumed model is the same as the original one.
+            self.assertEqual(a, a1)
+            self.assertEqual(b, b1)
+            self.check_trainer_state_are_the_same(state, state1)
+
     def test_load_best_model_at_end(self):
         total = int(self.n_epochs * 64 / self.batch_size)
         with tempfile.TemporaryDirectory() as tmpdir: