Repository: huggingface/transformers
Issue category: Feature Request

Issue:
Populate torch_dtype from a model to a pipeline
### Feature request

When constructing a pipeline object from a model and a tokenizer, the pipeline doesn't inherit the `torch_dtype` field from the underlying model.

```
model = AutoModelForCausalLM.from_pretrained("t5-small", torch_dtype = torch.bfloat16)
pipeline = pipeline(model=model, task="text-generation", tokenizer=...)

print(pipeline.torch_dtype)
=> None
```

However, it would be more convenient if the constructor extract the dtype from the model and populate it to pipeline's `torch_dtype` field. I think it's safe to assume the store model's dtype as pipeline's `torch_dtype` based on the documentation.

> Sent directly as model_kwargs (just a simpler shortcut) to use the available precision for this model (torch.float16, torch.bfloat16, … or "auto").

We should be able to determine model's dtype either from `model.config.torch_dtype` or `next(model.parameters()).dtype`.



### Motivation

I'm a maintainer of [MLflow](https://github.com/mlflow/mlflow/tree/master) and we have a logic to save metadata of Transformers pipeline, such as torch_dtype, task, etc. Since the pipeline doesn't populate `torch_dtype` field from the model, we need to check the underlying model's parameters. While we've implemented [a custom extraction logic](https://github.com/mlflow/mlflow/pull/10979) in our code base, I think this capability could be beneficial for other users of Transformers as well.

### Your contribution

I can submit a PR.


Hints:
cc @Rocketknight1 WDYT? Sounds good to me 
This sounds like a safe assumption to me too, though obviously I'd like to confirm that with some tests! I'm in favour of the PR if you're happy to open it @B-Step62 
@ArthurZucker @Rocketknight1  Great! I will open a PR soon, in the meantime could you assign the issue to me?
@B-Step62 Done!
cc @Rocketknight1 we usually don't assign issues, and rather let the code talk: if a PR is open and pinned then that means someone is working on something and we can check the progress 😉 
Hi @Rocketknight1 @ArthurZucker! I just opened a PR ^, please take a look whenever you have time, thanks!

Patch:
diff --git a/src/transformers/pipelines/base.py b/src/transformers/pipelines/base.py
index 758484107b76f2..079da4980851f1 100644
--- a/src/transformers/pipelines/base.py
+++ b/src/transformers/pipelines/base.py
@@ -861,7 +861,7 @@ def __init__(
                 raise ValueError(f"{device} unrecognized or not available.")
         else:
             self.device = device if device is not None else -1
-        self.torch_dtype = torch_dtype
+
         self.binary_output = binary_output
 
         # We shouldn't call `model.to()` for models loaded with accelerate
@@ -954,6 +954,13 @@ def predict(self, X):
         """
         return self(X)
 
+    @property
+    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):
         """


Test patch:
diff --git a/tests/pipelines/test_pipelines_common.py b/tests/pipelines/test_pipelines_common.py
index 5e3e15f39c10ea..13b97aff3216b5 100644
--- a/tests/pipelines/test_pipelines_common.py
+++ b/tests/pipelines/test_pipelines_common.py
@@ -199,6 +199,29 @@ def test_unbatch_attentions_hidden_states(self):
         outputs = text_classifier(["This is great !"] * 20, batch_size=32)
         self.assertEqual(len(outputs), 20)
 
+    @require_torch
+    def test_torch_dtype_property(self):
+        import torch
+
+        model_id = "hf-internal-testing/tiny-random-distilbert"
+
+        # If dtype is specified in the pipeline constructor, the property should return that type
+        pipe = pipeline(model=model_id, torch_dtype=torch.float16)
+        self.assertEqual(pipe.torch_dtype, torch.float16)
+
+        # If the underlying model changes dtype, the property should return the new type
+        pipe.model.to(torch.bfloat16)
+        self.assertEqual(pipe.torch_dtype, torch.bfloat16)
+
+        # 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, torch.float32)
+
+        # If underlying model doesn't have dtype property, simply return None
+        pipe.model = None
+        self.assertIsNone(pipe.torch_dtype)
+
 
 @is_pipeline_test
 class PipelineScikitCompatTest(unittest.TestCase):