quick fix (#634)

Summary:
Pull Request resolved: #634

When KeyedJaggedTensor doesn't have weights, `.weights()` will throw an assertion error. We should use `.weights_or_none()` to check if a KJT has weights.

Reviewed By: BerenLuthien

Differential Revision: D36005910

fbshipit-source-id: dbef99a6aad7ecf5d9bb85ec51899053a18e70eb

Author: czxttkl

reagent/core/torch_utils.py

@@ -224,7 +224,7 @@ def reorder_data_kjt(x: KeyedJaggedTensor, indices: torch.Tensor):
         [torch.cat([x[y] for y in indices.tolist()]) for x in splitted_vals_per_key]
     )
     reordered_lengths = torch.cat([x[indices] for x in val_lens_per_key])
-    if x.weights() is not None:
+    if x.weights_or_none() is not None:
         weights_per_key = torch.tensor_split(x.weights(), acc_lengths_per_key)[:-1]
         splitted_weights_per_key = [
             torch.tensor_split(x, torch.cumsum(y, dim=0))[:-1]
@@ -282,7 +282,7 @@ def shift_kjt_by_one(x: KeyedJaggedTensor):
     shifted_lengths = torch.cat(
         [torch.cat([x[1:], torch.tensor([0])]) for x in val_lens_per_key]
     )
-    if x.weights() is not None:
+    if x.weights_or_none() is not None:
         weights_per_key = torch.tensor_split(x.weights(), acc_lengths_per_key)[:-1]
         shifted_weights = torch.cat(
             [x[y[0] :] for x, y in zip(weights_per_key, val_lens_per_key)]

reagent/test/base/test_utils.py

@@ -120,6 +120,7 @@ def test_reorder_data_kjt(self) -> None:
         weights = values / 10.0
         lengths = torch.tensor([2, 0, 1, 1, 1, 2])
 
+        # With weights
         x = KeyedJaggedTensor(
             keys=keys, values=values, lengths=lengths, weights=weights
         )
@@ -131,13 +132,23 @@ def test_reorder_data_kjt(self) -> None:
         assert torch.allclose(y.lengths(), torch.tensor([1, 0, 2, 2, 1, 1]))
         assert torch.allclose(y.weights(), y.values() / 10.0)
 
+        # Without weights
+        x = KeyedJaggedTensor(keys=keys, values=values, lengths=lengths)
+        y = reorder_data_kjt(x, torch.tensor([2, 1, 0]))
+        self.assertEqual(y.keys(), keys)
+        assert torch.allclose(
+            y.values(), torch.tensor([2.0, 0.0, 1.0, 5.0, 6.0, 4.0, 3.0])
+        )
+        assert torch.allclose(y.lengths(), torch.tensor([1, 0, 2, 2, 1, 1]))
+
     def test_shift_kjt_by_one(self) -> None:
         """Test the example in the docstring of shift_kjt_by_one"""
         keys = ["Key0", "Key1"]
         values = torch.arange(7).float()
         weights = values / 10.0
         lengths = torch.tensor([2, 0, 1, 1, 1, 2])
 
+        # With weights
         x = KeyedJaggedTensor(
             keys=keys, values=values, lengths=lengths, weights=weights
         )
@@ -146,3 +157,14 @@ def test_shift_kjt_by_one(self) -> None:
         assert torch.allclose(y.values(), torch.tensor([2.0, 4.0, 5.0, 6.0]))
         assert torch.allclose(y.lengths(), torch.tensor([0, 1, 0, 1, 2, 0]))
         assert torch.allclose(y.weights(), y.values() / 10.0)
+
+        # Without weights
+        x = KeyedJaggedTensor(
+            keys=keys,
+            values=values,
+            lengths=lengths,
+        )
+        y = shift_kjt_by_one(x)
+        self.assertEqual(y.keys(), keys)
+        assert torch.allclose(y.values(), torch.tensor([2.0, 4.0, 5.0, 6.0]))
+        assert torch.allclose(y.lengths(), torch.tensor([0, 1, 0, 1, 2, 0]))