fast_dev_run can be int

CHANGELOG.md

@@ -47,6 +47,12 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added ability for DDP plugin to modify optimizer state saving ([#4675](https://github.com/PyTorchLightning/pytorch-lightning/pull/4675))
 
 
+- Updated `fast_dev_run` to accept integer representing num_batches ([#4629](https://github.com/PyTorchLightning/pytorch-lightning/pull/4629))
+
+
+- Added casting to python types for numpy scalars when logging hparams ([#4647](https://github.com/PyTorchLightning/pytorch-lightning/pull/4647))
+
+
 - Added `prefix` argument in loggers ([#4557](https://github.com/PyTorchLightning/pytorch-lightning/pull/4557))
 
 
@@ -156,6 +162,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Added lambda closure to `manual_optimizer_step` ([#4618](https://github.com/PyTorchLightning/pytorch-lightning/pull/4618))
 
+
 ### Changed
 
 - Change Metrics `persistent` default mode to `False` ([#4685](https://github.com/PyTorchLightning/pytorch-lightning/pull/4685))

docs/source/debugging.rst

@@ -21,17 +21,20 @@ The following are flags that make debugging much easier.
 
 fast_dev_run
 ------------
-This flag runs a "unit test" by running 1 training batch and 1 validation batch.
-The point is to detect any bugs in the training/validation loop without having to wait for
-a full epoch to crash.
+This flag runs a "unit test" by running n if set to ``n`` (int) else 1 if set to ``True`` training and validation batch(es).
+The point is to detect any bugs in the training/validation loop without having to wait for a full epoch to crash.
 
 (See: :paramref:`~pytorch_lightning.trainer.trainer.Trainer.fast_dev_run`
 argument of :class:`~pytorch_lightning.trainer.trainer.Trainer`)
 
 .. testcode::
-
+
+    # runs 1 train, val, test batch and program ends
     trainer = Trainer(fast_dev_run=True)
 
+    # runs 7 train, val, test batches and program ends
+    trainer = Trainer(fast_dev_run=7)
+
 ----------------
 
 Inspect gradient norms

docs/source/trainer.rst

@@ -245,7 +245,7 @@ Example::
     # ddp2 = DistributedDataParallel + dp
     trainer = Trainer(gpus=2, num_nodes=2, accelerator='ddp2')
 
-.. note:: this option does not apply to TPU. TPUs use ```ddp``` by default (over each core)
+.. note:: This option does not apply to TPU. TPUs use ```ddp``` by default (over each core)
 
 You can also modify hardware behavior by subclassing an existing accelerator to adjust for your needs.
 
@@ -632,9 +632,10 @@ fast_dev_run
 
 |
 
-Runs 1 batch of train, test  and val to find any bugs (ie: a sort of unit test).
+Runs n if set to ``n`` (int) else 1 if set to ``True`` batch(es) of train, val and test
+to find any bugs (ie: a sort of unit test).
 
-Under the hood the pseudocode looks like this:
+Under the hood the pseudocode looks like this when running *fast_dev_run* with a single batch:
 
 .. code-block:: python
 
@@ -659,6 +660,16 @@ Under the hood the pseudocode looks like this:
     # runs 1 train, val, test batch and program ends
     trainer = Trainer(fast_dev_run=True)
 
+    # runs 7 train, val, test batches and program ends
+    trainer = Trainer(fast_dev_run=7)
+
+.. note::
+
+    This argument is a bit different from ``limit_train/val/test_batches``. Setting this argument will
+    disable tuner, logger callbacks like ``LearningRateLogger`` and runs for only 1 epoch. This must be
+    used only for debugging purposes. ``limit_train/val/test_batches`` only limits the number of batches and won't
+    disable anything.
+
 gpus
 ^^^^
 
@@ -1200,8 +1211,7 @@ Orders the progress bar. Useful when running multiple trainers on the same node.
     # default used by the Trainer
     trainer = Trainer(process_position=0)
 
-Note:
-    This argument is ignored if a custom callback is passed to :paramref:`~Trainer.callbacks`.
+.. note:: This argument is ignored if a custom callback is passed to :paramref:`~Trainer.callbacks`.
 
 profiler
 ^^^^^^^^

pytorch_lightning/trainer/connectors/debugging_connector.py

@@ -31,16 +31,34 @@ def on_init_start(
             overfit_batches,
             fast_dev_run
     ):
+        if not isinstance(fast_dev_run, (bool, int)):
+            raise MisconfigurationException(
+                f'fast_dev_run={fast_dev_run} is not a valid configuration.'
+                ' It should be either a bool or an int >= 0'
+            )
+
+        if isinstance(fast_dev_run, int) and (fast_dev_run < 0):
+            raise MisconfigurationException(
+                f'fast_dev_run={fast_dev_run} is not a'
+                ' valid configuration. It should be >= 0.'
+            )
 
         self.trainer.fast_dev_run = fast_dev_run
-        if self.trainer.fast_dev_run:
-            limit_train_batches = 1
-            limit_val_batches = 1
-            limit_test_batches = 1
+        fast_dev_run = int(fast_dev_run)
+
+        # set fast_dev_run=True when it is 1, used while logging
+        if fast_dev_run == 1:
+            self.trainer.fast_dev_run = True
+
+        if fast_dev_run:
+            limit_train_batches = fast_dev_run
+            limit_val_batches = fast_dev_run
+            limit_test_batches = fast_dev_run
             self.trainer.num_sanity_val_steps = 0
             self.trainer.max_epochs = 1
             rank_zero_info(
-                'Running in fast_dev_run mode: will run a full train,' ' val and test loop using a single batch'
+                'Running in fast_dev_run mode: will run a full train,'
+                f' val and test loop using {fast_dev_run} batch(es)'
             )
 
         self.trainer.limit_train_batches = _determine_batch_limits(limit_train_batches, 'limit_train_batches')

pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py

@@ -589,7 +589,7 @@ def __gather_result_across_time_and_optimizers(self, epoch_output):
     def log_train_step_metrics(self, batch_output):
         _, batch_log_metrics = self.cached_results.update_logger_connector()
         # when metrics should be logged
-        if self.should_update_logs or self.trainer.fast_dev_run:
+        if self.should_update_logs or self.trainer.fast_dev_run is True:
             # logs user requested information to logger
             grad_norm_dic = batch_output.grad_norm_dic
             if grad_norm_dic is None:

pytorch_lightning/trainer/properties.py

@@ -38,7 +38,7 @@ class TrainerProperties(ABC):
     logger_connector: LoggerConnector
     _state: TrainerState
     global_rank: int
-    fast_dev_run: bool
+    fast_dev_run: Union[int, bool]
     use_dp: bool
     use_ddp: bool
     use_ddp2: bool

pytorch_lightning/trainer/trainer.py

@@ -98,7 +98,7 @@ def __init__(
         overfit_batches: Union[int, float] = 0.0,
         track_grad_norm: Union[int, float, str] = -1,
         check_val_every_n_epoch: int = 1,
-        fast_dev_run: bool = False,
+        fast_dev_run: Union[int, bool] = False,
         accumulate_grad_batches: Union[int, Dict[int, int], List[list]] = 1,
         max_epochs: int = 1000,
         min_epochs: int = 1,
@@ -186,7 +186,8 @@ def __init__(
 
             distributed_backend: deprecated. Please use 'accelerator'
 
-            fast_dev_run: runs 1 batch of train, test and val to find any bugs (ie: a sort of unit test).
+            fast_dev_run: runs n if set to ``n`` (int) else 1 if set to ``True`` batch(es)
+                of train, val and test to find any bugs (ie: a sort of unit test).
 
             flush_logs_every_n_steps: How often to flush logs to disk (defaults to every 100 steps).
 

pytorch_lightning/trainer/training_loop.py

@@ -908,7 +908,7 @@ def build_train_args(self, batch, batch_idx, opt_idx, hiddens):
     def save_loggers_on_train_batch_end(self):
         # when loggers should save to disk
         should_flush_logs = self.trainer.logger_connector.should_flush_logs
-        if should_flush_logs or self.trainer.fast_dev_run:
+        if should_flush_logs or self.trainer.fast_dev_run is True:
             if self.trainer.is_global_zero and self.trainer.logger is not None:
                 self.trainer.logger.save()
 

pytorch_lightning/tuner/batch_size_scaling.py

@@ -70,7 +70,7 @@ def scale_batch_size(trainer,
             or datamodule.
     """
     if trainer.fast_dev_run:
-        rank_zero_warn('Skipping batch size scaler since `fast_dev_run=True`', UserWarning)
+        rank_zero_warn('Skipping batch size scaler since fast_dev_run is enabled.', UserWarning)
         return
 
     if not lightning_hasattr(model, batch_arg_name):

pytorch_lightning/tuner/lr_finder.py

@@ -137,7 +137,7 @@ def lr_find(
 
     """
     if trainer.fast_dev_run:
-        rank_zero_warn('Skipping learning rate finder since `fast_dev_run=True`', UserWarning)
+        rank_zero_warn('Skipping learning rate finder since fast_dev_run is enabled.', UserWarning)
         return
 
     save_path = os.path.join(trainer.default_root_dir, 'lr_find_temp_model.ckpt')

tests/trainer/flags/test_fast_dev_run.py

@@ -4,7 +4,7 @@
 
 
 @pytest.mark.parametrize('tuner_alg', ['batch size scaler', 'learning rate finder'])
-def test_skip_on_fast_dev_run_batch_scaler(tmpdir, tuner_alg):
+def test_skip_on_fast_dev_run_tuner(tmpdir, tuner_alg):
     """ Test that tuner algorithms are skipped if fast dev run is enabled """
 
     hparams = EvalModelTemplate.get_default_hparams()
@@ -16,6 +16,6 @@ def test_skip_on_fast_dev_run_batch_scaler(tmpdir, tuner_alg):
         auto_lr_find=True if tuner_alg == 'learning rate finder' else False,
         fast_dev_run=True
     )
-    expected_message = f'Skipping {tuner_alg} since `fast_dev_run=True`'
+    expected_message = f'Skipping {tuner_alg} since fast_dev_run is enabled.'
     with pytest.warns(UserWarning, match=expected_message):
         trainer.tune(model)

tests/trainer/test_dataloaders.py

@@ -436,9 +436,11 @@ def test_dataloaders_with_limit_num_batches(tmpdir, limit_train_batches, limit_v
 
 
 @mock.patch.dict(os.environ, {"PL_DEV_DEBUG": "1"})
-def test_dataloaders_with_fast_dev_run(tmpdir):
-    """Verify num_batches for train, val & test dataloaders passed with fast_dev_run = True"""
-
+@pytest.mark.parametrize('fast_dev_run', [True, 1, 3, -1, 'temp'])
+def test_dataloaders_with_fast_dev_run(tmpdir, fast_dev_run):
+    """
+    Verify num_batches for train, val & test dataloaders passed with fast_dev_run
+    """
     model = EvalModelTemplate()
     model.val_dataloader = model.val_dataloader__multiple_mixed_length
     model.test_dataloader = model.test_dataloader__multiple_mixed_length
@@ -447,26 +449,47 @@ def test_dataloaders_with_fast_dev_run(tmpdir):
     model.test_step = model.test_step__multiple_dataloaders
     model.test_epoch_end = model.test_epoch_end__multiple_dataloaders
 
-    # train, multiple val and multiple test dataloaders passed with fast_dev_run = True
-    trainer = Trainer(
+    trainer_options = dict(
         default_root_dir=tmpdir,
         max_epochs=2,
-        fast_dev_run=True,
+        fast_dev_run=fast_dev_run,
     )
-    assert trainer.max_epochs == 1
-    assert trainer.num_sanity_val_steps == 0
 
-    trainer.fit(model)
-    assert not trainer.disable_validation
-    assert trainer.num_training_batches == 1
-    assert trainer.num_val_batches == [1] * len(trainer.val_dataloaders)
+    if fast_dev_run == 'temp':
+        with pytest.raises(MisconfigurationException, match='either a bool or an int'):
+            trainer = Trainer(**trainer_options)
+    elif fast_dev_run == -1:
+        with pytest.raises(MisconfigurationException, match='should be >= 0'):
+            trainer = Trainer(**trainer_options)
+    else:
+        trainer = Trainer(**trainer_options)
 
-    trainer.test(ckpt_path=None)
-    assert trainer.num_test_batches == [1] * len(trainer.test_dataloaders)
+        # fast_dev_run is set to True when it is 1
+        if fast_dev_run == 1:
+            fast_dev_run = True
 
-    # verify sanity check batches match as expected
-    num_val_dataloaders = len(model.val_dataloader())
-    assert trainer.dev_debugger.num_seen_sanity_check_batches == trainer.num_sanity_val_steps * num_val_dataloaders
+        assert trainer.fast_dev_run is fast_dev_run
+
+        if fast_dev_run is True:
+            fast_dev_run = 1
+
+        assert trainer.limit_train_batches == fast_dev_run
+        assert trainer.limit_val_batches == fast_dev_run
+        assert trainer.limit_test_batches == fast_dev_run
+        assert trainer.num_sanity_val_steps == 0
+        assert trainer.max_epochs == 1
+
+        trainer.fit(model)
+        assert not trainer.disable_validation
+        assert trainer.num_training_batches == fast_dev_run
+        assert trainer.num_val_batches == [fast_dev_run] * len(trainer.val_dataloaders)
+
+        trainer.test(ckpt_path=None)
+        assert trainer.num_test_batches == [fast_dev_run] * len(trainer.test_dataloaders)
+
+        # verify sanity check batches match as expected
+        num_val_dataloaders = len(model.val_dataloader())
+        assert trainer.dev_debugger.num_seen_sanity_check_batches == trainer.num_sanity_val_steps * num_val_dataloaders
 
 
 @pytest.mark.parametrize('ckpt_path', [None, 'best', 'specific'])