fix bf16 issue in text classification pipeline (#30996)

* fix logits dtype

* Add bf16/fp16 tests for text_classification pipeline

* Update test_pipelines_text_classification.py

* fix

* fix

src/transformers/pipelines/text_classification.py

@@ -202,7 +202,7 @@ def postprocess(self, model_outputs, function_to_apply=None, top_k=1, _legacy=Tr
                 function_to_apply = ClassificationFunction.NONE
 
         outputs = model_outputs["logits"][0]
-        outputs = outputs.numpy()
+        outputs = outputs.float().numpy()
 
         if function_to_apply == ClassificationFunction.SIGMOID:
             scores = sigmoid(outputs)

tests/pipelines/test_pipelines_text_classification.py

@@ -14,13 +14,24 @@
 
 import unittest
 
+import torch
+
 from transformers import (
     MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
     TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
     TextClassificationPipeline,
     pipeline,
 )
-from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow, torch_device
+from transformers.testing_utils import (
+    is_pipeline_test,
+    nested_simplify,
+    require_tf,
+    require_torch,
+    require_torch_bf16,
+    require_torch_fp16,
+    slow,
+    torch_device,
+)
 
 from .test_pipelines_common import ANY
 
@@ -106,6 +117,32 @@ def test_accepts_torch_device(self):
         outputs = text_classifier("This is great !")
         self.assertEqual(nested_simplify(outputs), [{"label": "LABEL_0", "score": 0.504}])
 
+    @require_torch_fp16
+    def test_accepts_torch_fp16(self):
+        text_classifier = pipeline(
+            task="text-classification",
+            model="hf-internal-testing/tiny-random-distilbert",
+            framework="pt",
+            device=torch_device,
+            torch_dtype=torch.float16,
+        )
+
+        outputs = text_classifier("This is great !")
+        self.assertEqual(nested_simplify(outputs), [{"label": "LABEL_0", "score": 0.504}])
+
+    @require_torch_bf16
+    def test_accepts_torch_bf16(self):
+        text_classifier = pipeline(
+            task="text-classification",
+            model="hf-internal-testing/tiny-random-distilbert",
+            framework="pt",
+            device=torch_device,
+            torch_dtype=torch.bfloat16,
+        )
+
+        outputs = text_classifier("This is great !")
+        self.assertEqual(nested_simplify(outputs), [{"label": "LABEL_0", "score": 0.504}])
+
     @require_tf
     def test_small_model_tf(self):
         text_classifier = pipeline(