progress util for steps in loop

Differential Revision: D49241644

fbshipit-source-id: c431084634df8eaa3dbf8b04584ea55ee193004e

tests/utils/test_progress.py

@@ -9,7 +9,11 @@
 
 from torchtnt.framework._test_utils import generate_random_dataloader
 
-from torchtnt.utils.progress import estimated_steps_in_epoch, Progress
+from torchtnt.utils.progress import (
+    estimated_steps_in_epoch,
+    estimated_steps_in_loop,
+    Progress,
+)
 
 
 class ProgressTest(unittest.TestCase):
@@ -82,3 +86,80 @@ def test_estimated_steps_in_epoch(self) -> None:
             ),
             dataloader_size,
         )
+
+    def test_estimated_steps_in_loop(self) -> None:
+        dataset_len = 10
+        batch_size = 2
+        dataloader = generate_random_dataloader(
+            num_samples=dataset_len, input_dim=2, batch_size=batch_size
+        )
+
+        self.assertEqual(
+            estimated_steps_in_loop(
+                dataloader,
+                max_steps=20,
+                max_steps_per_epoch=6,
+                epochs=3,
+            ),
+            15,  # 5 steps per epoch because the dataset would be exhausted after that
+        )
+
+        self.assertEqual(
+            estimated_steps_in_loop(
+                dataloader,
+                max_steps=20,
+                max_steps_per_epoch=4,
+                epochs=3,
+            ),
+            12,  # 4 steps per epoch, not exhausting all samples
+        )
+
+        self.assertEqual(
+            estimated_steps_in_loop(
+                dataloader,
+                max_steps=8,
+                max_steps_per_epoch=6,
+                epochs=3,
+            ),
+            8,  # we finish in the 'middle' of an epoch because of max_steps
+        )
+
+        self.assertEqual(
+            estimated_steps_in_loop(
+                dataloader,
+                max_steps=None,
+                max_steps_per_epoch=3,
+                epochs=3,
+            ),
+            9,  # when max_steps is none, we use epochs
+        )
+
+        self.assertEqual(
+            estimated_steps_in_loop(
+                dataloader,
+                max_steps=None,
+                max_steps_per_epoch=None,
+                epochs=3,
+            ),
+            15,  # when max_steps is none, we use epochs
+        )
+
+        self.assertEqual(
+            estimated_steps_in_loop(
+                dataloader,
+                max_steps=7,
+                max_steps_per_epoch=5,
+                epochs=None,
+            ),
+            7,  # when epoch is none, we use max_steps
+        )
+
+        self.assertEqual(
+            estimated_steps_in_loop(
+                dataloader,
+                max_steps=None,
+                max_steps_per_epoch=4,
+                epochs=None,
+            ),
+            None,
+        )

torchtnt/utils/progress.py

@@ -84,3 +84,38 @@ def estimated_steps_in_epoch(
     elif max_steps_per_epoch:
         total = min(total, max_steps_per_epoch)
     return total
+
+
+def estimated_steps_in_loop(
+    dataloader: Iterable[object],
+    *,
+    max_steps: Optional[int],
+    max_steps_per_epoch: Optional[int],
+    epochs: Optional[int],
+) -> Optional[int]:
+    """
+    Estimate the total number of steps for the current loop.
+
+    A return value of None indicates that the number of steps couldn't be estimated.
+    """
+
+    if not max_steps and not epochs:
+        return None
+
+    if not epochs:
+        return max_steps
+
+    total_steps = None
+    steps_per_epoch = estimated_steps_in_epoch(
+        dataloader,
+        num_steps_completed=0,
+        max_steps=max_steps,
+        max_steps_per_epoch=max_steps_per_epoch,
+    )
+    if steps_per_epoch != float("inf"):
+        total_steps = int(steps_per_epoch) * epochs
+
+    if total_steps and max_steps:
+        return min(total_steps, max_steps)
+
+    return total_steps or max_steps