Update unittests to support logging 1-D tensors. Fixed exception typo

Author: dominicgkerr

src/lightning/pytorch/core/module.py

@@ -646,7 +646,7 @@ def __to_tensor(self, value: Union[Tensor, numbers.Number], name: str) -> Tensor
         # check tensor contains single element (implies value.ndim == 0), or is a non-empty 1D array
         if not (torch.numel(value) == 1 or (torch.numel(value) > 0 and value.ndim == 1)):
             raise ValueError(
-                f"`self.log({name}, {value})` was called, but the tensor must have a single element,"
+                f"`self.log({name}, {value})` was called, but the tensor must have a single element, "
                 f"or a single non-empty dimension. You can try doing `self.log({name}, {value}.mean())`"
             )
         value = value.squeeze()

tests/tests_pytorch/trainer/logging_/test_train_loop_logging.py

@@ -640,10 +640,18 @@ def training_step(self, *args):
 
     class TestModel(BoringModel):
         def on_train_start(self):
-            self.log("foo", torch.tensor([1.0, 2.0]))
+            self.log("foo", torch.tensor([]))  # empty
 
     model = TestModel()
-    with pytest.raises(ValueError, match="tensor must have a single element"):
+    with pytest.raises(ValueError, match="tensor must have a single element, or a single non-empty dimension."):
+        trainer.fit(model)
+
+    class TestModel(BoringModel):
+        def on_train_start(self):
+            self.log("foo", torch.tensor([[1.0], [2.0]]))  # too-many dimensions
+
+    model = TestModel()
+    with pytest.raises(ValueError, match="tensor must have a single element, or a single non-empty dimension."):
         trainer.fit(model)