Remove default handling

Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>

src/transformers/pipelines/base.py

@@ -862,8 +862,6 @@ def __init__(
         else:
             self.device = device if device is not None else -1
 
-        self._initial_torch_dtype = torch_dtype
-
         self.binary_output = binary_output
 
         # We shouldn't call `model.to()` for models loaded with accelerate
@@ -957,20 +955,11 @@ def predict(self, X):
         return self(X)
 
     @property
-    def torch_dtype(self):
-        if hasattr(self.model, "dtype"):
-            # NB: We extract dtype from the underlying model, but it is possible that the model has dtype
-            # but the pipeline subclass doesn't support it. In such case we should not return anything,
-            # but it is not straightforward to detect it in a generic way. Therefore, we assume that the
-            # pipeline support torch_dtype if (1) the extracted dtype is not default one (float32), or
-            # (2) the torch_dtype argument was set by the user when creating the pipeline.
-            if self._initial_torch_dtype is not None or self.model.dtype not in (
-                torch.float32,
-                "float32",
-                "torch.float32",
-            ):
-                return self.model.dtype
-        return self._initial_torch_dtype
+    def torch_dtype(self) -> Optional["torch.dtype"]:
+        """
+        Torch dtype of the model (if it's Pytorch model), `None` otherwise.
+        """
+        return getattr(self.model, "dtype", None)
 
     @contextmanager
     def device_placement(self):

tests/pipelines/test_pipelines_common.py

@@ -213,23 +213,14 @@ def test_torch_dtype_property(self):
         pipe.model.to(torch.bfloat16)
         self.assertEqual(pipe.torch_dtype, torch.bfloat16)
 
-        # Even if the model dtype is the default one, we can safely assume the pipeline supports torch_dtype
-        # as it is constructed with torch_dtype specified
-        pipe.model.to(torch.float32)
-        self.assertEqual(pipe.torch_dtype, torch.float32)
-
-        # If dtype is NOT specified in the pipeline constructor, the property should NOT return type
-        # as we don't know if the pipeline supports torch_dtype
+        # If dtype is NOT specified in the pipeline constructor, the property should just return
+        # the dtype of the underlying model (default)
         pipe = pipeline(model=model_id)
-        self.assertEqual(pipe.torch_dtype, None)
-
-        # If the model changes to non default dtype, we assume the pipeline supports torch_dtype
-        pipe.model.to(torch.float16)
-        self.assertEqual(pipe.torch_dtype, torch.float16)
+        self.assertEqual(pipe.torch_dtype, torch.float32)
 
-        # If the model dtype is the default, we conservatively assume the pipeline doesn't support torch_dtype
-        pipe.model.to(torch.float32)
-        self.assertEqual(pipe.torch_dtype, None)
+        # If underlying model doesn't have dtype property, simply return None
+        pipe.model = None
+        self.assertIsNone(pipe.torch_dtype)
 
 
 @is_pipeline_test