fix continuous batching issues, extend ut cases to xpu

src/transformers/generation/continuous_batching/requests.py

@@ -19,6 +19,7 @@
 
 import torch
 
+from ...utils import is_torch_xpu_available
 from ...utils.logging import logging
 from ...utils.metrics import traced
 
@@ -35,6 +36,13 @@ def get_device_and_memory_breakdown() -> tuple[torch.device, int, int, int]:
         total_memory = torch.cuda.get_device_properties(device).total_memory
         reserved_memory = torch.cuda.memory_reserved(device)
         allocated_memory = torch.cuda.memory_allocated(device)
+    elif is_torch_xpu_available():
+        device = torch.device("xpu")
+        torch.xpu.empty_cache()
+        torch.xpu.synchronize()
+        total_memory = torch.xpu.get_device_properties(device).total_memory
+        reserved_memory = torch.xpu.memory_reserved(device)
+        allocated_memory = torch.xpu.memory_allocated(device)
     elif torch.backends.mps.is_available() and torch.backends.mps.is_built():
         device = torch.device("mps")
         # MPS memory reporting (PyTorch 2.0+)

src/transformers/integrations/mxfp4.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from ..utils import is_accelerate_available, is_torch_available, logging
+from ..utils import is_accelerate_available, is_torch_available, is_torch_xpu_available, logging
 
 
 if is_torch_available():
@@ -114,6 +114,9 @@ def convert_moe_packed_tensors(
     if not blocks.is_cuda and torch.cuda.is_available():
         blocks = blocks.cuda()
         scales = scales.cuda()
+    elif (blocks.device.type != "xpu") and is_torch_xpu_available():
+        blocks = blocks.to("xpu")
+        scales = scales.to("xpu")
 
     scales = scales.to(torch.int32) - 127  # TODO that's because 128=2**7
 
@@ -351,6 +354,8 @@ def dequantize(module, param_name, param_value, target_device, dq_param_name, **
                 dequantized = convert_moe_packed_tensors(getattr(module, blocks_attr), getattr(module, scales_attr))
                 if target_device == "cpu" and torch.cuda.is_available():
                     torch.cuda.empty_cache()
+                elif target_device == "cpu" and is_torch_xpu_available():
+                    torch.xpu.empty_cache()
                 setattr(module, proj, torch.nn.Parameter(dequantized.to(target_device)))
                 delattr(module, blocks_attr)
                 delattr(module, scales_attr)
@@ -395,7 +400,7 @@ def load_and_swizzle_mxfp4(module, param_name, param_value, target_device, trito
         else:
             blocks = blocks.reshape(local_experts, -1, module.intermediate_size // 2)
         if getattr(target_device, "type", target_device) == "cpu":
-            target_device = "cuda"
+            target_device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
         blocks = blocks.to(target_device).contiguous()
         scales = scales.to(target_device).contiguous()
         with on_device(target_device):

tests/generation/test_continuous_batching.py

@@ -20,7 +20,15 @@
 from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, LogitsProcessorList
 from transformers.generation.continuous_batching.cache import group_layers_by_attn_type
 from transformers.generation.continuous_batching.continuous_api import build_attention_mask
-from transformers.testing_utils import Expectations, require_kernels, require_read_token, require_torch_gpu, slow
+from transformers.testing_utils import (
+    Expectations,
+    require_kernels,
+    require_read_token,
+    require_torch_accelerator,
+    require_torch_gpu,
+    slow,
+    torch_device,
+)
 
 
 ALLOW_EXPECTED_OUTPUTS = True  # this is a debug flag when you want to measure deviation between CB and non-CB gen
@@ -148,7 +156,7 @@ def _continuous_batching_parity(
 
         # Generation with continuous batching
         model = AutoModelForCausalLM.from_pretrained(model_id, attn_implementation=attn_implementation, dtype="auto")
-        model = model.cuda().eval()
+        model = model.to(torch_device).eval()
         model.generation_config.max_new_tokens = 40
         model.generation_config.do_sample = False
         model.generation_config.use_cuda_graph = False
@@ -169,14 +177,14 @@ def _continuous_batching_parity(
         model = AutoModelForCausalLM.from_pretrained(
             model_id, attn_implementation=non_cb_attn_implementation, dtype="auto"
         )
-        model = model.cuda().eval()
+        model = model.to(torch_device).eval()
         model.generation_config.max_new_tokens = 40
         model.generation_config.do_sample = False
         model.generation_config.use_cuda_graph = False
 
         for request_id, request in cb_outputs.items():
             # Generate without continuous batching
-            input_ids = torch.tensor([request.prompt_ids]).cuda()
+            input_ids = torch.tensor([request.prompt_ids]).to(torch_device)
             attention_mask = torch.ones_like(input_ids)
             outputs = model.generate(
                 input_ids, attention_mask=attention_mask, generation_config=model.generation_config
@@ -208,8 +216,8 @@ def _continuous_batching_parity(
                     )
 
     # Eager tests
+    @require_torch_accelerator
     @require_read_token
-    @require_torch_gpu
     @slow
     def test_continuous_batching_parity_llama_eager(self) -> None:
         expected_outputs = Expectations({
@@ -219,11 +227,15 @@ def test_continuous_batching_parity_llama_eager(self) -> None:
             ("cuda", (9, 0)): {
                 "req_1": " 3 bolts of blue fiber and 1.5 bolts of white fiber. The total number of bolts is 4.5. The total number of bolts is 4.5. The total",
                 "req_2": " $50,000. This is because the value of the house increased by 150%, which means that the value of the house increased by $50,000. This is because the value of the"
-            }
+            },
+            ("xpu", None): {
+                "req_1": " 3 bolts of blue fiber and 1.5 bolts of white fiber. The answer is not 3.5 bolts of blue fiber and 1.5 bolts of white fiber. The answer'",
+                "req_2": " $50,000. This is because the value of the house increased by 150%, which means that the value of the house increased by $50,000. This is because the value of the"
+            },
         }).get_expectation()  # fmt: skip
         self._continuous_batching_parity("meta-llama/Llama-3.1-8B", "paged|eager", expected_outputs)
 
-    @require_torch_gpu
+    @require_torch_accelerator
     @slow
     def test_continuous_batching_parity_gemma_eager(self) -> None:
         expected_outputs = Expectations({
@@ -233,53 +245,68 @@ def test_continuous_batching_parity_gemma_eager(self) -> None:
             ("cuda", (9, 0)): {
                 "req_0": "\n\n**$12**\n\n**Here's how to solve it:**\n\n* **Eggs eaten:** 3\n* **Eggs left:** 16 - 3 = 13",
                 "req_1": " \n \n 2 + 1 = 3 bolts \n \n \n \n \n \n \n \n \n \n \n \n \n "
-            }
+            },
+            ("xpu", None): {
+                "req_0": "\n\n**$12**\n\n**Here's how to solve it:**\n\n* **Eggs eaten:** 3\n* **Eggs left:** 16 - 3 = 13",
+                "req_1": " \n \n 2 + 1 = 3 bolts \n \n \n \n \n \n \n \n \n \n \n \n \n ",
+                "req_2": "\n\n**$100,000**\n\n**Explanation:**\n\nHere's how to calculate the profit:\n\n1. **Calculate the total cost:** $80,00",
+            },
         }).get_expectation()  # fmt: skip
         self._continuous_batching_parity("google/gemma-2-2b-it", "paged|eager", expected_outputs)
 
     # FIXME: set expected_outputs
-    # @require_torch_gpu
+    # @require_torch_accelerator
     # @slow
     # def test_continuous_batching_parity_qwen_eager(self) -> None:
     #     expected_outputs = {}
     #     self._continuous_batching_parity("Qwen/Qwen3-4B-Instruct-2507", "paged|eager", expected_outputs)
 
     # FIXME: OOMs
-    # @require_torch_gpu
+    # @require_torch_accelerator
     # @slow
     # def test_continuous_batching_parity_gpt_oss_eager(self) -> None:
     #     expected_outputs = Expectations({
     #         ("cuda", (9, 0)): {
     #             "req_1": " 2.5 bolts. The question: \"What is the name of the puzzle that involves a robe taking 2 bolts of blue fiber and half that much white fiber?\" The answer: \"The",
     #             "req_2": " 50%.\"\n\nWe need to parse: He buys a house for $80,000. He puts in $50,000 in repairs. This increased the value of the house by 150%."
-    #         }
+    #         },
+    #         ("xpu", None): {
+    #             "req_1": " 2.5 bolts. The question: \"What is the name of the puzzle that involves a robe taking 2 bolts of blue fiber and half that much white fiber?\" The answer: \"The",
+    #             "req_2": " 50%.\"\n\nWe need to parse: He buys a house for $80,000. He puts in $50,000 in repairs. This increased the value of the house by 150%."
+    #         },
     #     }).get_expectation()  # fmt: skip
     #     self._continuous_batching_parity("openai/gpt-oss-20b", "paged|eager", expected_outputs)
 
     # SDPA tests
     @require_read_token
-    @require_torch_gpu
+    @require_torch_accelerator
     @slow
     def test_continuous_batching_parity_llama_sdpa(self) -> None:
         expected_outputs = Expectations({
             ("rocm", (9, 4)): {
                 "req_2": " $50,000. This is because the value of the house increased by 150%, which means that the value of the house increased by $50,000. This is because the value of the"
-            }
+            },
+            ("xpu", None): {
+                "req_2": " $50,000. This is because the value of the house increased by 150%, which means that the value of the house increased by $50,000. This is because the value of the"
+            },
         }).get_expectation()  # fmt: skip
         self._continuous_batching_parity("meta-llama/Llama-3.1-8B", "paged|sdpa", expected_outputs)
 
-    @require_torch_gpu
+    @require_torch_accelerator
     @slow
     def test_continuous_batching_parity_gemma_sdpa(self) -> None:
         expected_outputs = Expectations({
             ("cuda", (9, 0)): {
                 "req_1": " \n\n**Answer:** 3 bolts\n\n**Solution:**\n\n* **White fiber:** The robe needs half as much white fiber as blue fiber, so it needs 2 bolts / 2 =",
-            }
+            },
+            ("xpu", None): {
+                "req_1": " \n\n**Answer:** 3 bolts\n\n**Solution:**\n\n* **White fiber:** The robe needs half as much white fiber as blue fiber, so it needs 2 bolts / 2 =",
+            },
         }).get_expectation()  # fmt: skip
         self._continuous_batching_parity("google/gemma-2-2b-it", "paged|sdpa", expected_outputs)
 
     # FIXME: set expected_outputs
-    # @require_torch_gpu
+    # @require_torch_accelerator
     # @slow
     # def test_continuous_batching_parity_qwen_sdpa(self) -> None:
     #     expected_outputs = {}
@@ -333,7 +360,7 @@ def test_attn_implementation(self) -> None:
         manager = model.init_continuous_batching()
         assert "paged|eager" == manager.model.config._attn_implementation
 
-    @require_torch_gpu
+    @require_torch_accelerator
     def test_streaming_request(self) -> None:
         model_id = "Qwen/Qwen2.5-0.5B-Instruct"
         max_new_tokens = 3
@@ -365,7 +392,7 @@ def test_streaming_request(self) -> None:
 
         manager.stop(block=True)
 
-    @require_torch_gpu
+    @require_torch_accelerator
     def test_non_streaming_request(self) -> None:
         model_id = "Qwen/Qwen2.5-0.5B-Instruct"
         max_new_tokens = 3
@@ -392,7 +419,7 @@ def test_non_streaming_request(self) -> None:
 
         manager.stop(block=True)
 
-    @require_torch_gpu
+    @require_torch_accelerator
     def test_streaming_and_non_streaming_requests_can_alternate(self) -> None:
         model_id = "Qwen/Qwen2.5-0.5B-Instruct"
         max_new_tokens = 3