[Test] Add extra test for val_check_interval in distributed scenario (#…

…7863)

* add extra test

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* add computation

* Update docs/source/common/trainer.rst

Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com>

* Update docs/source/common/trainer.rst

Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com>

* Update tests/trainer/test_dataloaders.py

Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com>

* use tmpdir

* update on comments

* update

* Update tests/callbacks/test_progress_bar.py

Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com>
Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>

docs/source/common/trainer.rst

@@ -1527,6 +1527,24 @@ Can specify as float or int.
     trainer = Trainer(val_check_interval=1000)
 
 
+.. code-block::
+
+    # Here is the computation to estimate the total number of batches seen within an epoch.
+
+    # Find the total number of train batches
+    total_train_batches = total_train_samples // (train_batch_size * world_size)
+
+    # Compute how many times we will call validation during the training loop
+    val_check_batch = max(1, int(total_train_batches * val_check_interval))
+    val_checks_per_epoch = total_train_batches / val_check_batch
+
+    # Find the total number of validation batches
+    total_val_batches = total_val_samples // (val_batch_size * world_size)
+
+    # Total number of batches run
+    total_fit_batches = total_train_batches + total_val_batches
+
+
 weights_save_path
 ^^^^^^^^^^^^^^^^^
 

tests/callbacks/test_progress_bar.py

@@ -20,12 +20,14 @@
 
 import pytest
 import torch
+from torch.utils.data.dataloader import DataLoader
 
 from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks import ModelCheckpoint, ProgressBar, ProgressBarBase
 from pytorch_lightning.callbacks.progress import tqdm
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
-from tests.helpers import BoringModel
+from tests.helpers.boring_model import BoringModel, RandomDataset
+from tests.helpers.runif import RunIf
 
 
 @pytest.mark.parametrize(
@@ -533,3 +535,47 @@ def test_progress_bar_can_be_pickled():
     pickle.dumps(bar)
     trainer.predict(model)
     pickle.dumps(bar)
+
+
+@RunIf(min_gpus=2, special=True)
+@pytest.mark.parametrize([
+    "total_train_samples",
+    "train_batch_size",
+    "total_val_samples",
+    "val_batch_size",
+    "val_check_interval",
+], [
+    (8, 4, 2, 1, 0.2),
+    (8, 4, 2, 1, 0.5),
+])
+def test_progress_bar_max_val_check_interval(
+    total_train_samples, train_batch_size, total_val_samples, val_batch_size, val_check_interval, tmpdir
+):
+
+    world_size = 2
+
+    train_data = DataLoader(RandomDataset(32, total_train_samples), batch_size=train_batch_size)
+    val_data = DataLoader(RandomDataset(32, total_val_samples), batch_size=val_batch_size)
+
+    model = BoringModel()
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        num_sanity_val_steps=0,
+        max_epochs=1,
+        weights_summary=None,
+        val_check_interval=val_check_interval,
+        gpus=world_size,
+        accelerator="ddp",
+    )
+    trainer.fit(model, train_dataloader=train_data, val_dataloaders=val_data)
+
+    total_train_batches = total_train_samples // (train_batch_size * world_size)
+    val_check_batch = max(1, int(total_train_batches * val_check_interval))
+    assert trainer.val_check_batch == val_check_batch
+    val_checks_per_epoch = total_train_batches / val_check_batch
+    total_val_batches = total_val_samples // (val_batch_size * world_size)
+    assert trainer.progress_bar_callback.total_train_batches == total_train_batches
+    assert trainer.progress_bar_callback.total_val_batches == total_val_batches
+    total_val_batches = total_val_batches * val_checks_per_epoch
+    if trainer.is_global_zero:
+        assert trainer.progress_bar_callback.main_progress_bar.total == total_train_batches + total_val_batches