device agnostic models testing (#27146)

* device agnostic models testing

* add decorator `require_torch_fp16`

* make style

* apply review suggestion

* Oops, the fp16 decorator was misused

tests/models/marian/test_modeling_marian.py

@@ -20,7 +20,14 @@ import unittest
 from huggingface_hub.hf_api import list_models
 
 from transformers import MarianConfig, is_torch_available
-from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
+from transformers.testing_utils import (
+    require_sentencepiece,
+    require_tokenizers,
+    require_torch,
+    require_torch_fp16,
+    slow,
+    torch_device,
+)
 from transformers.utils import cached_property
 
 from ...generation.test_utils import GenerationTesterMixin
@@ -281,13 +288,13 @@ class MarianModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMix
         config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs)
 
+    @require_torch_fp16
     def test_generate_fp16(self):
         config, input_dict = self.model_tester.prepare_config_and_inputs()
         input_ids = input_dict["input_ids"]
         attention_mask = input_ids.ne(1).to(torch_device)
         model = MarianMTModel(config).eval().to(torch_device)
-        if torch_device == "cuda":
-            model.half()
+        model.half()
         model.generate(input_ids, attention_mask=attention_mask)
         model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
 
@@ -620,9 +627,9 @@ class TestMarian_en_ROMANCE(MarianIntegrationTest):
         self._assert_generated_batch_equal_expected()
 
     @slow
+    @require_torch
     def test_pipeline(self):
-        device = 0 if torch_device == "cuda" else -1
-        pipeline = TranslationPipeline(self.model, self.tokenizer, framework="pt", device=device)
+        pipeline = TranslationPipeline(self.model, self.tokenizer, framework="pt", device=torch_device)
         output = pipeline(self.src_text)
         self.assertEqual(self.expected_text, [x["translation_text"] for x in output])