[XPU]Fix xpu spec decoding UTs, avoid using cuda graph (#25847)

Signed-off-by: Kunshang Ji <kunshang.ji@intel.com>

.buildkite/scripts/hardware_ci/run-xpu-test.sh

@@ -42,7 +42,7 @@ docker run \
     pytest -v -s v1/sample --ignore=v1/sample/test_logprobs.py --ignore=v1/sample/test_logprobs_e2e.py
     pytest -v -s v1/worker --ignore=v1/worker/test_gpu_model_runner.py
     pytest -v -s v1/structured_output
-    pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_eagle.py --ignore=v1/spec_decode/test_tree_attention.py
+    pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_tree_attention.py
     pytest -v -s v1/kv_connector/unit --ignore=v1/kv_connector/unit/test_multi_connector.py --ignore=v1/kv_connector/unit/test_nixl_connector.py --ignore=v1/kv_connector/unit/test_shared_storage_connector.py
     pytest -v -s v1/test_metrics
     pytest -v -s v1/test_serial_utils.py

tests/utils.py

@@ -1143,6 +1143,8 @@ def get_attn_backend_list_based_on_platform() -> list[str]:
             print("Skip FLASH_ATTN on ROCm as aiter is not installed")
 
         return attn_backend_list
+    elif current_platform.is_xpu():
+        return ["FLASH_ATTN", "TRITON_ATTN"]
     else:
         raise ValueError("Unsupported platform")
 

vllm/v1/spec_decode/eagle.py

@@ -72,12 +72,13 @@ class EagleProposer:
 
         self.attn_metadata_builder: Optional[AttentionMetadataBuilder] = None
 
-        self.use_cuda_graph = (self.vllm_config.compilation_config.level
+        self.use_cuda_graph = (not current_platform.is_xpu()
+                               and self.vllm_config.compilation_config.level
                                == CompilationLevel.PIECEWISE and
                                not self.vllm_config.model_config.enforce_eager)
         self.cudagraph_batch_sizes = list(
-            reversed(
-                self.vllm_config.compilation_config.cudagraph_capture_sizes))
+            reversed(self.vllm_config.compilation_config.
+                     cudagraph_capture_sizes)) if self.use_cuda_graph else []
 
         # persistent buffers for cuda graph
         self.input_ids = torch.zeros(self.max_num_tokens,