Repository: huggingface/transformers
Pull request: enable internvl UTs on XPU

`pytest -rA tests/models/internvl/test_modeling_internvl.py`

137 cases PASSED, 4 failed cases are same as CUDA, will investigate in separate efforts.

FAILED tests/models/internvl/test_modeling_internvl.py::InternVLModelTest::test_resize_embeddings_untied_with_deepspeed
FAILED tests/models/internvl/test_modeling_internvl.py::InternVLModelTest::test_resize_embeddings_untied_with_deepspeed_multi_gpu
FAILED tests/models/internvl/test_modeling_internvl.py::InternVLModelTest::test_resize_tokens_embeddings_with_deepspeed
FAILED tests/models/internvl/test_modeling_internvl.py::InternVLModelTest::test_resize_tokens_embeddings_with_deepspeed_multi_gpu



Changed files:
filename: tests/models/internvl/test_modeling_internvl.py
patch: @@ -27,11 +27,13 @@
     is_vision_available,
 )
 from transformers.testing_utils import (
+    Expectations,
     cleanup,
     require_av,
     require_bitsandbytes,
+    require_deterministic_for_xpu,
     require_torch,
-    require_torch_gpu,
+    require_torch_accelerator,
     slow,
     torch_device,
 )
@@ -177,7 +179,7 @@ def create_and_check_model_fp16_autocast_forward(self, config, input_ids, pixel_
         model = InternVLForConditionalGeneration(config=config)
         model.to(torch_device)
         model.eval()
-        with torch.autocast(device_type="cuda", dtype=torch.float16):
+        with torch.autocast(device_type=torch_device, dtype=torch.float16):
             logits = model(
                 input_ids=input_ids,
                 attention_mask=attention_mask,
@@ -279,7 +281,7 @@ def test_flash_attn_2_inference_equivalence_right_padding(self):
 
 
 @slow
-@require_torch_gpu
+@require_torch_accelerator
 class InternVLQwen2IntegrationTest(unittest.TestCase):
     def setUp(self):
         self.small_model_checkpoint = "OpenGVLab/InternVL3-1B-hf"
@@ -326,14 +328,22 @@ def test_qwen2_small_model_integration_forward(self):
             output = model(**inputs)
 
         actual_logits = output.logits[0, -1, :5].cpu()
-        expected_logits = torch.tensor([11.9375, 14.8750, 14.0625, 10.7500, 6.9062], dtype=torch.bfloat16)
+        expected_logits_all = Expectations(
+            {
+                ("xpu", 3): torch.tensor([11.7500, 14.7500, 14.1250, 10.5625, 6.7812], dtype=torch.bfloat16),
+                ("cuda", 7): torch.tensor([11.9375, 14.8750, 14.0625, 10.7500, 6.9062], dtype=torch.bfloat16),
+            }
+        )  # fmt: skip
+        expected_logits = expected_logits_all.get_expectation()
+
         self.assertTrue(
             torch.allclose(actual_logits, expected_logits, atol=0.1),
             f"Actual logits: {actual_logits}"
             f"\nExpected logits: {expected_logits}"
             f"\nDifference: {torch.abs(actual_logits - expected_logits)}",
         )
 
+    @require_deterministic_for_xpu
     def test_qwen2_small_model_integration_generate_text_only(self):
         processor = AutoProcessor.from_pretrained(self.small_model_checkpoint)
         model = InternVLForConditionalGeneration.from_pretrained(
@@ -346,7 +356,15 @@ def test_qwen2_small_model_integration_generate_text_only(self):
             decoded_output = processor.decode(
                 generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True
             )
-        expected_output = "Whispers of dawn,\nSilent whispers of the night,\nNew day's light begins."
+
+        expected_outputs = Expectations(
+            {
+                ("xpu", 3): "Whispers of dawn,\nSilent whispers of the night,\nNew day's light.",
+                ("cuda", 7): "Whispers of dawn,\nSilent whispers of the night,\nNew day's light begins.",
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
+
         self.assertEqual(decoded_output, expected_output)
 
     def test_qwen2_small_model_integration_generate_chat_template(self):
@@ -375,6 +393,7 @@ def test_qwen2_small_model_integration_generate_chat_template(self):
         expected_output = "The image shows two cats lying on a pink blanket. The cat on the left is a tabby"
         self.assertEqual(decoded_output, expected_output)
 
+    @require_deterministic_for_xpu
     def test_qwen2_small_model_integration_batched_generate(self):
         processor = AutoProcessor.from_pretrained(self.small_model_checkpoint)
         model = InternVLForConditionalGeneration.from_pretrained(
@@ -404,7 +423,15 @@ def test_qwen2_small_model_integration_batched_generate(self):
         )
         # Check second output
         decoded_output = processor.decode(output[1], skip_special_tokens=True)
-        expected_output = 'user\n\nDescribe this image\nassistant\nThe image shows a street scene with a traditional Chinese archway, known as a "Chinese Gate" or "Chinese Gate of'  # fmt: skip
+
+        expected_outputs = Expectations(
+            {
+                ("xpu", 3): 'user\n\nDescribe this image\nassistant\nThe image shows a street scene with a traditional Chinese archway, known as a "Chinese Gate" or "Chinese Gate"',
+                ("cuda", 7): 'user\n\nDescribe this image\nassistant\nThe image shows a street scene with a traditional Chinese archway, known as a "Chinese Gate" or "Chinese Gate of',
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
+
         self.assertEqual(
             decoded_output,
             expected_output,
@@ -455,7 +482,14 @@ def test_qwen2_small_model_integration_batched_generate_multi_image(self):
 
         # Check second output
         decoded_output = processor.decode(output[1], skip_special_tokens=True)
-        expected_output = 'user\n\nWhat are the differences between these two images?\nassistant\nThe images show the Statue of Liberty and the Golden Gate Bridge from different angles. Here are the differences:\n\n1. **Angle'  # fmt: skip
+        expected_outputs = Expectations(
+            {
+                ("xpu", 3): "user\n\nWhat are the differences between these two images?\nassistant\nThe images show the Statue of Liberty and the Golden Gate Bridge from different angles. Here are the differences:\n\n1. **Foreground",
+                ("cuda", 7): "user\n\nWhat are the differences between these two images?\nassistant\nThe images show the Statue of Liberty and the Golden Gate Bridge from different angles. Here are the differences:\n\n1. **Angle",
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
+
         self.assertEqual(
             decoded_output,
             expected_output,
@@ -495,14 +529,21 @@ def test_qwen2_medium_model_integration_video(self):
         output = model.generate(**inputs, do_sample=False, max_new_tokens=25)
 
         decoded_output = processor.decode(output[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
-        expected_output = 'The man is performing a forehand shot.'  # fmt: skip
+        expected_outputs = Expectations(
+            {
+                ("xpu", 3): "The man is performing a volley.",
+                ("cuda", 7): "The man is performing a forehand shot.",
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
         self.assertEqual(
             decoded_output,
             expected_output,
             f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
         )
 
     @require_av
+    @require_deterministic_for_xpu
     def test_qwen2_small_model_integration_interleaved_images_videos(self):
         processor = AutoProcessor.from_pretrained(self.small_model_checkpoint)
         model = InternVLForConditionalGeneration.from_pretrained(
@@ -564,15 +605,27 @@ def test_qwen2_small_model_integration_interleaved_images_videos(self):
 
         decoded_output = processor.decode(output[0], skip_special_tokens=True)
         # Batching seems to alter the output slightly, but it is also the case in the original implementation. This seems to be expected: https://github.com/huggingface/transformers/issues/23017#issuecomment-1649630232
-        expected_output = 'user\n\n\nWhat are the differences between these two images?\nassistant\nThe images depict two distinct scenes:\n\n1. **Left Image**: This shows the Statue of Liberty on Liberty Island, with the'  # fmt: skip
+        expected_outputs = Expectations(
+            {
+                ("xpu", 3): "user\n\n\nWhat are the differences between these two images?\nassistant\nThe images depict two distinct scenes:\n\n1. **Left Image:**\n   - The Statue of Liberty is prominently featured on an",
+                ("cuda", 7): "user\n\n\nWhat are the differences between these two images?\nassistant\nThe images depict two distinct scenes:\n\n1. **Left Image**: This shows the Statue of Liberty on Liberty Island, with the",
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
         self.assertEqual(
             decoded_output,
             expected_output,
             f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
         )
         # Check second output
         decoded_output = processor.decode(output[1], skip_special_tokens=True)
-        expected_output = 'user\nFrame1: \nFrame2: \nFrame3: \nFrame4: \nFrame5: \nFrame6: \nFrame7: \nFrame8: \nWhat type of shot is the man performing?\nassistant\nA forehand shot'  # fmt: skip
+        expected_outputs = Expectations(
+            {
+                ("xpu", 3): "user\nFrame1: \nFrame2: \nFrame3: \nFrame4: \nFrame5: \nFrame6: \nFrame7: \nFrame8: \nWhat type of shot is the man performing?\nassistant\nThe man is performing a forehand shot.",
+                ("cuda", 7): "user\nFrame1: \nFrame2: \nFrame3: \nFrame4: \nFrame5: \nFrame6: \nFrame7: \nFrame8: \nWhat type of shot is the man performing?\nassistant\nA forehand shot",
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
         self.assertEqual(
             decoded_output,
             expected_output,
@@ -590,7 +643,7 @@ def test_qwen2_small_model_integration_interleaved_images_videos(self):
 
 
 @slow
-@require_torch_gpu
+@require_torch_accelerator
 class InternVLLlamaIntegrationTest(unittest.TestCase):
     def setUp(self):
         self.small_model_checkpoint = "OpenGVLab/InternVL2_5-2B-MPO-hf"
@@ -711,7 +764,13 @@ def test_llama_small_model_integration_batched_generate(self):
 
         # Check first output
         decoded_output = processor.decode(output[0], skip_special_tokens=True)
-        expected_output = 'user\n\nWrite a haiku for this image\nassistant\nMajestic snow-capped peaks,\nWooden dock stretches to the sea,\nSilent water mirrors.'  # fmt: skip
+        expected_outputs = Expectations(
+            {
+                ("xpu", 3): "user\n\nWrite a haiku for this image\nassistant\nMajestic snow-capped peaks,\nWooden path leads to calm lake,\nNature's peaceful grace.",
+                ("cuda", 7): "user\n\nWrite a haiku for this image\nassistant\nMajestic snow-capped peaks,\nWooden dock stretches to the sea,\nSilent water mirrors.",
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
         self.assertEqual(
             decoded_output,
             expected_output,
@@ -880,7 +939,13 @@ def test_llama_small_model_integration_interleaved_images_videos(self):
 
         decoded_output = processor.decode(output[0], skip_special_tokens=True)
         # Batching seems to alter the output slightly, but it is also the case in the original implementation. This seems to be expected: https://github.com/huggingface/transformers/issues/23017#issuecomment-1649630232
-        expected_output = 'user\n\n\nWhat are the difference between these two images?\nassistant\nI apologize for the confusion in my previous response. Upon closer inspection, the differences between the two images are:\n\n1. **'  # fmt: skip
+        expected_outputs = Expectations(
+            {
+                ("xpu", 3): "user\n\n\nWhat are the difference between these two images?\nassistant\nI apologize for the confusion in my previous response. After re-examining the images, I can see that they are actually",
+                ("cuda", 7): "user\n\n\nWhat are the difference between these two images?\nassistant\nI apologize for the confusion in my previous response. Upon closer inspection, the differences between the two images are:\n\n1. **",
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
         self.assertEqual(
             decoded_output,
             expected_output,
@@ -889,7 +954,13 @@ def test_llama_small_model_integration_interleaved_images_videos(self):
 
         # Check second output
         decoded_output = processor.decode(output[1], skip_special_tokens=True)
-        expected_output = 'user\nFrame1: \nFrame2: \nFrame3: \nFrame4: \nFrame5: \nFrame6: \nFrame7: \nFrame8: \nWhat type of shot is the man performing?\nassistant\nThe man is performing a forehand shot. This is a common shot in tennis where the player swings the racket across their'  # fmt: skip
+        expected_outputs = Expectations(
+            {
+                ("xpu", 3): "user\nFrame1: \nFrame2: \nFrame3: \nFrame4: \nFrame5: \nFrame6: \nFrame7: \nFrame8: \nWhat type of shot is the man performing?\nassistant\nThe man is performing a forehand shot. This is a common shot in tennis where the player swings the racket across their",
+                ("cuda", 7): "user\nFrame1: \nFrame2: \nFrame3: \nFrame4: \nFrame5: \nFrame6: \nFrame7: \nFrame8: \nWhat type of shot is the man performing?\nassistant\nThe man is performing a forehand shot. This is a common shot in tennis where the player swings the racket across their",
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
         self.assertEqual(
             decoded_output,
             expected_output,
@@ -898,7 +969,13 @@ def test_llama_small_model_integration_interleaved_images_videos(self):
 
         # Check third output
         decoded_output = processor.decode(output[2], skip_special_tokens=True)
-        expected_output = 'user\n\nWrite a haiku for this image\nassistant\nMajestic snow-capped peaks,\nA wooden path leads to the sea,\nPeaceful, untouched dreams.'  # fmt: skip
+        expected_outputs = Expectations(
+            {
+                ("xpu", 3): "user\n\nWrite a haiku for this image\nassistant\nMajestic snow-capped peaks,\nWooden dock stretches to the sea,\nSilent water mirrors.",
+                ("cuda", 7): "user\n\nWrite a haiku for this image\nassistant\nMajestic snow-capped peaks,\nA wooden path leads to the sea,\nPeaceful, untouched dreams.",
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
         self.assertEqual(
             decoded_output,
             expected_output,

filename: tests/test_modeling_common.py
patch: @@ -80,7 +80,6 @@
     require_bitsandbytes,
     require_deepspeed,
     require_flash_attn,
-    require_non_xpu,
     require_safetensors,
     require_torch,
     require_torch_accelerator,
@@ -2604,7 +2603,7 @@ def test_inputs_embeds_matches_input_ids(self):
                     )[0]
             torch.testing.assert_close(out_embeds, out_ids)
 
-    @require_non_xpu
+    @require_torch_gpu
     @require_torch_multi_gpu
     def test_multi_gpu_data_parallel_forward(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
@@ -3874,7 +3873,6 @@ def test_sdpa_can_dispatch_on_flash(self):
                 with sdpa_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
                     _ = model(**inputs_dict)
 
-    @require_non_xpu
     @require_torch_sdpa
     @require_torch_accelerator
     @slow
@@ -3887,8 +3885,8 @@ def test_sdpa_can_compile_dynamic(self):
             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:
-            self.skipTest(reason="This test requires a Nvidia or AMD GPU")
+        elif device_type not in ["cuda", "rocm", "xpu"]:
+            self.skipTest(reason="This test requires a Nvidia or AMD GPU, or an Intel XPU")
 
         torch.compiler.reset()