Repository: vllm-project/vllm
File: vllm/platforms/xpu.py (Python)

Code before:
        # Only override worker_cls if it's still the default "auto"
        # This allows custom workers (like vllm-omni workers) to be used on XPU
        if parallel_config.worker_cls == "auto":
            parallel_config.worker_cls = "vllm.v1.worker.xpu_worker.XPUWorker"
        if vllm_config.kv_transfer_config is not None:
            vllm_config.kv_transfer_config.enable_permute_local_kv = True

        if model_config and model_config.use_mla:
            logger.info(
                "MLA is enabled on a non-GPU platform; forcing chunked "
                "prefill and prefix caching to be disabled."
            )
            vllm_config.scheduler_config.enable_chunked_prefill = False
            vllm_config.scheduler_config.max_num_batched_tokens = max(
                vllm_config.model_config.max_model_len,
                vllm_config.scheduler_config.DEFAULT_MAX_NUM_BATCHED_TOKENS,
            )

    @classmethod
    def support_hybrid_kv_cache(cls) -> bool:
        return True

    @classmethod
    def support_static_graph_mode(cls) -> bool:
        return True

    @classmethod
    def is_pin_memory_available(cls):
        return True

    @classmethod
    def get_current_memory_usage(
        cls, device: torch.types.Device | None = None
    ) -> float:
        torch.xpu.reset_peak_memory_stats(device)
        return torch.xpu.max_memory_allocated(device)

    @classmethod
    def fp8_dtype(cls) -> torch.dtype:
        return torch.float8_e4m3fn

    @classmethod
    def is_data_center_gpu(cls) -> bool:
        device_name = cls.get_device_name().lower()
        return device_name.count("data center gpu") > 0

    @classmethod
    def get_device_communicator_cls(cls) -> str:
        from vllm.utils.torch_utils import supports_xccl

        if not supports_xccl():

Review comment (suggestion):
This PR adds support for XPU Graph functionality but doesn't include any tests for the new feature. Consider adding tests to verify: 1) supports_xpu_graph() correctly detects XPU Graph availability, 2) the configuration logic correctly handles different combinations of backends and cudagraph modes on XPU, and 3) XPU Graph capture and replay work as expected. The existing CUDA graph tests in tests/v1/cudagraph/ could serve as a reference.
```suggestion
        # Static graph mode on XPU requires XPU Graph support.
        return supports_xpu_graph()
```

Code after:
        parallel_config = vllm_config.parallel_config
        # Only override worker_cls if it's still the default "auto"
        # This allows custom workers (like vllm-omni workers) to be used on XPU
        if parallel_config.worker_cls == "auto":
            parallel_config.worker_cls = "vllm.v1.worker.xpu_worker.XPUWorker"
        if vllm_config.kv_transfer_config is not None:
            vllm_config.kv_transfer_config.enable_permute_local_kv = True

        if model_config and model_config.use_mla:
            logger.info(
                "MLA is enabled on a non-GPU platform; forcing chunked "
                "prefill and prefix caching to be disabled."
            )
            vllm_config.scheduler_config.enable_chunked_prefill = False
            vllm_config.scheduler_config.max_num_batched_tokens = max(
                vllm_config.model_config.max_model_len,
                vllm_config.scheduler_config.DEFAULT_MAX_NUM_BATCHED_TOKENS,
            )

    @classmethod
    def support_hybrid_kv_cache(cls) -> bool:
        return True

    @classmethod
    def support_static_graph_mode(cls) -> bool:
        return True

    @classmethod
    def is_pin_memory_available(cls):
        return True

    @classmethod
    def get_current_memory_usage(
        cls, device: torch.types.Device | None = None
    ) -> float:
        torch.xpu.reset_peak_memory_stats(device)
        return torch.xpu.max_memory_allocated(device)

    @classmethod
    def fp8_dtype(cls) -> torch.dtype:
        return torch.float8_e4m3fn

    @classmethod
    def is_data_center_gpu(cls) -> bool:
        device_name = cls.get_device_name().lower()
        return device_name.count("data center gpu") > 0

    @classmethod
    def get_device_communicator_cls(cls) -> str:
        from vllm.utils.torch_utils import supports_xccl