[bugfix] Fix dataloading for iterable datasets and limit_train_batches (

#7306)

* bugfix-dataloading

* rm-logs

* Update CHANGELOG.md

* Update test_dataloaders.py

* Update test_dataloaders.py

* Update training_loop.py

* Update test_dataloaders.py

* Update CHANGELOG.md

* Update CHANGELOG.md

* Update test_dataloaders.py

* Update training_loop.py

* Update training_loop.py

* comments

* address comments

* more tests

* Update progress.py

* Update test_dataloaders.py

* Update test_dataloaders.py

* Update training_loop.py

* Update training_loop.py

* test ckpt fix?

* update again

CHANGELOG.md

@@ -297,6 +297,12 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 ### Fixed
 
 
+- Fixed NaN errors in progress bars when training with iterable datasets with no length defined ([#7306](https://github.com/PyTorchLightning/pytorch-lightning/pull/7306))
+
+
+- Fixed validation being skipped for iterable datasets with no length defined ([#7306](https://github.com/PyTorchLightning/pytorch-lightning/pull/7306))
+
+
 - Fixed attaching train and validation dataloaders when `reload_dataloaders_every_epoch=True` and `num_sanity_val_steps=0` ([#7207](https://github.com/PyTorchLightning/pytorch-lightning/pull/7207))
 
 

pytorch_lightning/callbacks/model_checkpoint.py

@@ -231,9 +231,7 @@ def on_train_batch_end(
         self.save_checkpoint(trainer)
 
     def on_validation_end(self, trainer, pl_module) -> None:
-        """
-        checkpoints can be saved at the end of the val loop
-        """
+        """ Save a checkpoint at the end of the validation stage. """
         skip = (
             self._should_skip_saving_checkpoint(trainer) or self._every_n_val_epochs < 1
             or (trainer.current_epoch + 1) % self._every_n_val_epochs != 0

pytorch_lightning/callbacks/progress.py

@@ -20,6 +20,7 @@
 """
 import importlib
 import io
+import math
 import os
 import sys
 
@@ -397,7 +398,7 @@ def on_train_epoch_start(self, trainer, pl_module):
         super().on_train_epoch_start(trainer, pl_module)
         total_train_batches = self.total_train_batches
         total_val_batches = self.total_val_batches
-        if total_train_batches != float('inf'):
+        if total_train_batches != float('inf') and total_val_batches != float('inf'):
             # val can be checked multiple times per epoch
             val_checks_per_epoch = total_train_batches // trainer.val_check_batch
             total_val_batches = total_val_batches * val_checks_per_epoch
@@ -407,7 +408,9 @@ def on_train_epoch_start(self, trainer, pl_module):
 
     def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
         super().on_train_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
-        if self._should_update(self.train_batch_idx, self.total_train_batches + self.total_val_batches):
+        total_batches = self.total_train_batches + self.total_val_batches
+        total_batches = convert_inf(total_batches)
+        if self._should_update(self.train_batch_idx, total_batches):
             self._update_bar(self.main_progress_bar)
             self.main_progress_bar.set_postfix(trainer.progress_bar_dict)
 
@@ -422,7 +425,7 @@ def on_validation_start(self, trainer, pl_module):
 
     def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
         super().on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
-        if self._should_update(self.val_batch_idx, self.total_val_batches):
+        if self._should_update(self.val_batch_idx, convert_inf(self.total_val_batches)):
             self._update_bar(self.val_progress_bar)
             self._update_bar(self.main_progress_bar)
 
@@ -479,7 +482,7 @@ def print(
             s = sep.join(map(str, args))
             active_progress_bar.write(s, end=end, file=file, nolock=nolock)
 
-    def _should_update(self, current, total):
+    def _should_update(self, current, total) -> bool:
         return self.is_enabled and (current % self.refresh_rate == 0 or current == total)
 
     def _update_bar(self, bar: Optional[tqdm]) -> None:
@@ -496,8 +499,8 @@ def _update_bar(self, bar: Optional[tqdm]) -> None:
 
 
 def convert_inf(x: Optional[Union[int, float]]) -> Optional[Union[int, float]]:
-    """ The tqdm doesn't support inf values. We have to convert it to None. """
-    if x == float('inf'):
+    """ The tqdm doesn't support inf/nan values. We have to convert it to None. """
+    if x is None or math.isinf(x) or math.isnan(x):
         return None
     return x
 

pytorch_lightning/trainer/training_loop.py

@@ -484,9 +484,9 @@ def run_training_epoch(self):
             self.trainer.logger_connector.log_train_step_metrics(batch_output)
 
             # -----------------------------------------
-            # VALIDATE IF NEEDED + CHECKPOINT CALLBACK
+            # VALIDATE IF NEEDED
             # -----------------------------------------
-            should_check_val = self.should_check_val_fx(batch_idx, is_last_batch)
+            should_check_val = self._should_check_val_fx(batch_idx, is_last_batch)
             if should_check_val:
                 self.trainer.validating = True
                 self.trainer.run_evaluation()
@@ -535,7 +535,7 @@ def run_training_epoch(self):
         # log epoch metrics
         self.trainer.logger_connector.log_train_epoch_end_metrics(epoch_output)
 
-        should_check_val = self.should_check_val_fx(batch_idx, is_last_batch, on_epoch=True)
+        should_check_val = self._should_check_val_fx(batch_idx, is_last_batch, on_epoch=True)
         should_skip_eval = self.trainer.evaluation_loop.should_skip_evaluation(self.trainer.num_val_batches)
         should_train_only = self.trainer.disable_validation or should_skip_eval
 
@@ -825,19 +825,34 @@ def should_accumulate(self):
         is_final_batch = self._num_training_batches_reached()
         return not (accumulation_done or is_final_batch)
 
-    def should_check_val_fx(self, batch_idx, is_last_batch, on_epoch=False):
-        # decide if we should run validation
-        is_val_check_batch = (batch_idx + 1) % self.trainer.val_check_batch == 0
-        is_val_check_epoch = (self.trainer.current_epoch + 1) % self.trainer.check_val_every_n_epoch == 0
-        can_check_val = self.trainer.enable_validation and is_val_check_epoch
-        is_last_batch_for_infinite_dataset = is_last_batch and self.trainer.val_check_batch == float("inf")
-        epoch_end_val_check = (batch_idx + 1) % self.trainer.num_training_batches == 0
+    def _should_check_val_fx(self, batch_idx: int, is_last_batch: bool, on_epoch: bool = False) -> bool:
+        """ Decide if we should run validation. """
+
+        if not self.trainer.enable_validation:
+            return False
+
+        # check if this epoch is eligible to run validation
+        if (self.trainer.current_epoch + 1) % self.trainer.check_val_every_n_epoch != 0:
+            return False
 
-        should_check_val = ((is_val_check_batch and epoch_end_val_check) or self.trainer.should_stop
-                            or is_last_batch_for_infinite_dataset
-                            ) if on_epoch else (is_val_check_batch and not epoch_end_val_check)
+        # val_check_batch is inf for iterable datasets with no length defined
+        # TODO: let training/eval loop handle logic around limit_*_batches and val_check_batch
+        is_val_check_batch = False
+        if isinstance(self.trainer.limit_train_batches, int) and self.trainer.val_check_batch == float('inf'):
+            is_val_check_batch = (batch_idx + 1) % self.trainer.limit_train_batches == 0
+        elif self.trainer.val_check_batch != float('inf'):
+            is_val_check_batch = (batch_idx + 1) % self.trainer.val_check_batch == 0
 
-        return should_check_val and can_check_val
+        # Note: num_training_batches is also inf for iterable datasets with no length defined
+        epoch_end_val_check = (batch_idx + 1) % self.trainer.num_training_batches == 0
+        is_last_batch_for_infinite_dataset = is_last_batch and self.trainer.val_check_batch == float("inf")
+
+        if on_epoch:
+            return (
+                is_val_check_batch and epoch_end_val_check
+            ) or self.trainer.should_stop or is_last_batch_for_infinite_dataset
+        else:
+            return is_val_check_batch and not epoch_end_val_check
 
     def build_train_args(self, batch, batch_idx, opt_idx, hiddens):
         # enable not needing to add opt_idx to training_step

tests/helpers/boring_model.py

@@ -14,7 +14,7 @@
 from typing import Optional
 
 import torch
-from torch.utils.data import DataLoader, Dataset, Subset
+from torch.utils.data import DataLoader, Dataset, IterableDataset, Subset
 
 from pytorch_lightning import LightningDataModule, LightningModule
 
@@ -60,6 +60,31 @@ def __len__(self):
         return self.len
 
 
+class RandomIterableDataset(IterableDataset):
+
+    def __init__(self, size: int, count: int):
+        self.count = count
+        self.size = size
+
+    def __iter__(self):
+        for _ in range(self.count):
+            yield torch.randn(self.size)
+
+
+class RandomIterableDatasetWithLen(IterableDataset):
+
+    def __init__(self, size: int, count: int):
+        self.count = count
+        self.size = size
+
+    def __iter__(self):
+        for _ in range(len(self)):
+            yield torch.randn(self.size)
+
+    def __len__(self):
+        return self.count
+
+
 class BoringModel(LightningModule):
 
     def __init__(self):