enable misc test cases on XPU (#38852)

* enable misc test cases on XPU

Signed-off-by: YAO Matrix <matrix.yao@intel.com>

* fix style

Signed-off-by: YAO Matrix <matrix.yao@intel.com>

* tweak bamba ground truth on XPU

Signed-off-by: YAO Matrix <matrix.yao@intel.com>

* remove print

Signed-off-by: YAO Matrix <matrix.yao@intel.com>

* one more

Signed-off-by: YAO Matrix <matrix.yao@intel.com>

* fix style

Signed-off-by: YAO Matrix <matrix.yao@intel.com>

---------

Signed-off-by: YAO Matrix <matrix.yao@intel.com>

src/transformers/training_args.py

@@ -391,7 +391,7 @@ class TrainingArguments:
             installation](https://github.com/intel/intel-extension-for-pytorch).
         bf16 (`bool`, *optional*, defaults to `False`):
             Whether to use bf16 16-bit (mixed) precision training instead of 32-bit training. Requires Ampere or higher
-            NVIDIA architecture or using CPU (use_cpu) or Ascend NPU. This is an experimental API and it may change.
+            NVIDIA architecture or Intel XPU or using CPU (use_cpu) or Ascend NPU. This is an experimental API and it may change.
         fp16 (`bool`, *optional*, defaults to `False`):
             Whether to use fp16 16-bit (mixed) precision training instead of 32-bit training.
         fp16_opt_level (`str`, *optional*, defaults to 'O1'):

tests/generation/test_utils.py

@@ -4892,7 +4892,7 @@ class GenerationIntegrationTests(unittest.TestCase):
         # If the generate doesn't infer the DECODER device map correctly, this will fail
         _ = model.generate(**inputs, max_new_tokens=2, do_sample=False)
 
-    @require_torch_gpu
+    @require_torch_accelerator
     def test_cpu_offload_doesnt_compile(self):
         """Test that CPU offload doesn't trigger compilation"""
         tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-MistralForCausalLM")

tests/models/bamba/test_modeling_bamba.py

@@ -610,22 +610,15 @@ class BambaModelIntegrationTest(unittest.TestCase):
         cls.device_properties = get_device_properties()
 
     def test_simple_generate(self):
+        # fmt: off
         expectations = Expectations(
             {
-                (
+                ("cuda", 8): "<|begin_of_text|>Hey how are you doing on this lovely evening? I hope you are all having a good time.",
-                    "cuda",
+                ("rocm", 9): "<|begin_of_text|>Hey how are you doing on this lovely evening? I hope you are doing well. I am here",
-                    8,
+                ("xpu", 3): "<|begin_of_text|>Hey how are you doing on this lovely evening? I hope you are all doing well. I am",
-                ): "<|begin_of_text|>Hey how are you doing on this lovely evening? I hope you are all having a good time.",
-                (
-                    "rocm",
-                    9,
-                ): "<|begin_of_text|>Hey how are you doing on this lovely evening? I hope you are doing well. I am here",
-                (
-                    "xpu",
-                    3,
-                ): "<|begin_of_text|>Hey how are you doing on this lovely evening? I hope you are all doing well. Today I",
             }
         )
+        # fmt: on
 
         self.model.to(torch_device)
 
@@ -659,6 +652,7 @@ class BambaModelIntegrationTest(unittest.TestCase):
         #
         # Note: Key 9 is currently set for MI300, but may need potential future adjustments for H100s,
         # considering differences in hardware processing and potential deviations in generated text.
+        # fmt: off
         EXPECTED_TEXTS = Expectations(
             {
                 ("cuda", 7): [],
@@ -671,11 +665,12 @@ class BambaModelIntegrationTest(unittest.TestCase):
                     "!!!<|begin_of_text|>I am late! I need to be at the airport in 20 minutes! I",
                 ],
                 ("xpu", 3): [
-                    "<|begin_of_text|>Hey how are you doing on this lovely evening? I hope you are all doing well. Today I",
+                    "<|begin_of_text|>Hey how are you doing on this lovely evening? I hope you are all doing well. I am",
                     "!!!<|begin_of_text|>I am late! I need to get to work! I have to get to the",
                 ],
             }
         )
+        # fmt: on
         EXPECTED_TEXT = EXPECTED_TEXTS.get_expectation()
 
         self.model.to(torch_device)

tests/models/d_fine/test_modeling_d_fine.py

@@ -27,7 +27,13 @@ from transformers import (
     is_torch_available,
     is_vision_available,
 )
-from transformers.testing_utils import require_torch, require_torch_gpu, require_vision, slow, torch_device
+from transformers.testing_utils import (
+    require_torch,
+    require_torch_accelerator,
+    require_vision,
+    slow,
+    torch_device,
+)
 from transformers.utils import cached_property
 
 
@@ -680,7 +686,7 @@ class DFineModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
         self.assertTrue(not failed_cases, message)
 
     @parameterized.expand(["float32", "float16", "bfloat16"])
-    @require_torch_gpu
+    @require_torch_accelerator
     @slow
     def test_inference_with_different_dtypes(self, torch_dtype_str):
         torch_dtype = {
@@ -702,7 +708,7 @@ class DFineModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
                 _ = model(**self._prepare_for_class(inputs_dict, model_class))
 
     @parameterized.expand(["float32", "float16", "bfloat16"])
-    @require_torch_gpu
+    @require_torch_accelerator
     @slow
     def test_inference_equivalence_for_static_and_dynamic_anchors(self, torch_dtype_str):
         torch_dtype = {

tests/models/glm4/test_modeling_glm4.py

@@ -119,7 +119,7 @@ class Glm4IntegrationTest(unittest.TestCase):
             {
                 ("xpu", 3): [
                     "Hello I am doing a project on the history of the internet and I need to know what the first website was and what",
-                    "Hi today I am going to tell you about the most common disease in the world. This disease is called diabetes",
+                    "Hi today I am going to tell you about the most common mistakes that people make when they are learning English.",
                 ],
                 ("cuda", 7): [],
                 ("cuda", 8): [
@@ -177,7 +177,7 @@ class Glm4IntegrationTest(unittest.TestCase):
             {
                 ("xpu", 3): [
                     "Hello I am doing a project on the history of the internet and I need to know what the first website was and what",
-                    "Hi today I am going to tell you about the most common disease in the world. This disease is called diabetes",
+                    "Hi today I am going to tell you about the most common mistakes that people make when they are learning English.",
                 ],
                 ("cuda", 7): [],
                 ("cuda", 8): [

tests/models/musicgen/test_modeling_musicgen.py

@@ -1035,7 +1035,7 @@ class MusicgenTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin,
         self.skipTest(reason="Musicgen doesn't use the MusicgenFlashAttention2 class method.")
 
     @require_torch_sdpa
-    @require_torch_gpu
+    @require_torch_accelerator
     @slow
     def test_sdpa_can_dispatch_on_flash(self):
         if not self.has_attentions:
@@ -1046,8 +1046,8 @@ class MusicgenTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin,
             self.skipTest(reason="This test requires an NVIDIA GPU with compute capability >= 8.0")
         elif device_type == "rocm" and major < 9:
             self.skipTest(reason="This test requires an AMD GPU with compute capability >= 9.0")
-        else:
+        elif device_type not in ["cuda", "rocm", "xpu"]:
-            self.skipTest(reason="This test requires a Nvidia or AMD GPU")
+            self.skipTest(reason="This test requires a Nvidia or AMD GPU or an Intel XPU")
 
         torch.compiler.reset()
 

tests/models/musicgen_melody/test_modeling_musicgen_melody.py

@@ -1035,7 +1035,7 @@ class MusicgenMelodyTest(ModelTesterMixin, GenerationTesterMixin, PipelineTester
         self.skipTest(reason="MusicgenMelody doesn't use the MusicgenMelodyFlashAttention2 class method.")
 
     @require_torch_sdpa
-    @require_torch_gpu
+    @require_torch_accelerator
     @slow
     def test_sdpa_can_dispatch_on_flash(self):
         if not self.has_attentions:
@@ -1046,8 +1046,8 @@ class MusicgenMelodyTest(ModelTesterMixin, GenerationTesterMixin, PipelineTester
             self.skipTest(reason="This test requires an NVIDIA GPU with compute capability >= 8.0")
         elif device_type == "rocm" and major < 9:
             self.skipTest(reason="This test requires an AMD GPU with compute capability >= 9.0")
-        else:
+        elif device_type not in ["cuda", "rocm", "xpu"]:
-            self.skipTest(reason="This test requires a Nvidia or AMD GPU")
+            self.skipTest(reason="This test requires a Nvidia or AMD GPU or an Intel XPU")
 
         torch.compiler.reset()
 

tests/models/vitmatte/test_image_processing_vitmatte.py

@@ -21,7 +21,14 @@ import numpy as np
 import requests
 from packaging import version
 
-from transformers.testing_utils import is_flaky, require_torch, require_torch_gpu, require_vision, slow, torch_device
+from transformers.testing_utils import (
+    is_flaky,
+    require_torch,
+    require_torch_accelerator,
+    require_vision,
+    slow,
+    torch_device,
+)
 from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
 
 from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
@@ -337,7 +344,7 @@ class VitMatteImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
         )
 
     @slow
-    @require_torch_gpu
+    @require_torch_accelerator
     @require_vision
     def test_can_compile_fast_image_processor(self):
         # override as trimaps are needed for the image processor

tests/trainer/test_trainer.py

@@ -1623,9 +1623,10 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
         self.assertFalse(is_any_loss_nan_or_inf(log_history_filter))
 
     def test_train_and_eval_dataloaders(self):
-        if torch_device in ["cuda", "xpu"]:
+        if torch_device in ["cuda"]:
             n_gpu = max(1, backend_device_count(torch_device))
         else:
+            # DP is decprecated by PyTorch, accelerators like XPU doesn't support DP
             n_gpu = 1
 
         tmp_dir = self.get_auto_remove_tmp_dir()
@@ -3940,7 +3941,7 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
         from torch import _dynamo as torchdynamo
 
         class CustomTrainer(Trainer):
-            def compute_loss(self, model, inputs, return_outputs=False):
+            def compute_loss(self, model, inputs, num_items_in_batch=None, return_outputs=False):
                 x = inputs["x"]
                 output = model(x)
                 if self.args.n_gpu == 1:

tests/utils/test_cache_utils.py

@@ -556,7 +556,7 @@ class CacheHardIntegrationTest(unittest.TestCase):
         _ = model(**inputs)
         _ = model.generate(**inputs, max_new_tokens=2, cache_implementation="hybrid")
 
-    @require_torch_gpu
+    @require_torch_accelerator
     @parameterized.expand(TEST_CACHE_IMPLEMENTATIONS)
     def test_cache_gptj_model(self, cache_implementation):
         """Tests caches with GPT-J model. Regression test for https://github.com/huggingface/transformers/pull/34799"""