Reset epoch progress with batch size scaler

src/pytorch_lightning/CHANGELOG.md

@@ -82,6 +82,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed wrong num padding for `RichProgressBar` ([#14296](https://github.com/Lightning-AI/lightning/pull/14296))
 
 
+- Reset epoch progress with batch size scaler ([#13846](https://github.com/Lightning-AI/lightning/pull/13846)
+
+
 ## [1.7.2] - 2022-08-17
 
 ### Added

src/pytorch_lightning/tuner/batch_size_scaling.py

@@ -128,7 +128,10 @@ def _run_power_scaling(
     """Batch scaling mode where the size is doubled at each iteration until an OOM error is encountered."""
     for _ in range(max_trials):
         garbage_collection_cuda()
-        trainer.fit_loop.global_step = 0  # reset after each try
+
+        # reset after each try
+        _reset_progress(trainer)
+
         try:
             # Try fit
             trainer.tuner._run(model)
@@ -166,7 +169,10 @@ def _run_binsearch_scaling(
     count = 0
     while True:
         garbage_collection_cuda()
-        trainer.fit_loop.global_step = 0  # reset after each try
+
+        # reset after each try
+        _reset_progress(trainer)
+
         try:
             # Try fit
             trainer.tuner._run(model)
@@ -249,3 +255,12 @@ def _adjust_batch_size(
 def _is_valid_batch_size(batch_size: int, dataloader: DataLoader, trainer: "pl.Trainer"):
     module = trainer.lightning_module or trainer.datamodule
     return not has_len_all_ranks(dataloader, trainer.strategy, module) or batch_size <= len(dataloader)
+
+
+def _reset_progress(trainer: "pl.Trainer") -> None:
+    if trainer.lightning_module.automatic_optimization:
+        trainer.fit_loop.epoch_loop.batch_loop.optimizer_loop.optim_progress.reset()
+    else:
+        trainer.fit_loop.epoch_loop.batch_loop.manual_loop.optim_step_progress.reset()
+
+    trainer.fit_loop.epoch_progress.reset()

tests/tests_pytorch/tuner/test_scale_batch_size.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 import os
 from copy import deepcopy
+from unittest.mock import patch
 
 import pytest
 import torch
@@ -291,10 +292,13 @@ def __init__(self):
 def test_dataloader_reset_with_scale_batch_size(tmpdir, scale_method):
     """Test that train and val dataloaders are reset at every update in scale batch size."""
     model = BatchSizeModel(batch_size=16)
-    scale_batch_size_kwargs = {"max_trials": 5, "init_val": 4, "mode": scale_method}
+    max_trials = 5
+    scale_batch_size_kwargs = {"max_trials": max_trials, "steps_per_trial": 2, "init_val": 4, "mode": scale_method}
 
-    trainer = Trainer(max_epochs=2, auto_scale_batch_size=True)
-    new_batch_size = trainer.tune(model, scale_batch_size_kwargs=scale_batch_size_kwargs)["scale_batch_size"]
+    trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, auto_scale_batch_size=True)
+    with patch.object(model, "on_train_epoch_end") as advance_mocked:
+        new_batch_size = trainer.tune(model, scale_batch_size_kwargs=scale_batch_size_kwargs)["scale_batch_size"]
+        assert advance_mocked.call_count == max_trials
 
     assert trainer.train_dataloader.loaders.batch_size == new_batch_size
     assert trainer.val_dataloaders[0].batch_size == new_batch_size