Patch protobuf for CVE-2026-0994 (#34253)

Signed-off-by: Seiji Eicher <seiji@anyscale.com>
Co-authored-by: Kevin H. Luu <khluu000@gmail.com>
(cherry picked from commit 5045d5c983)

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

@@ -39,6 +39,7 @@ docker run \
     python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend ray
     python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend mp
     python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager --attention-backend=TRITON_ATTN
+    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager --quantization fp8
     python3 examples/offline_inference/basic/generate.py --model superjob/Qwen3-4B-Instruct-2507-GPTQ-Int4  --block-size 64 --enforce-eager
     python3 examples/offline_inference/basic/generate.py --model ibm-research/PowerMoE-3b  --block-size 64 --enforce-eager -tp 2
     python3 examples/offline_inference/basic/generate.py --model ibm-research/PowerMoE-3b  --block-size 64 --enforce-eager -tp 2 --enable-expert-parallel

docker/Dockerfile.cpu

@@ -134,7 +134,6 @@ WORKDIR /vllm-workspace
 # Copy test requirements
 COPY requirements/test.in requirements/cpu-test.in
 
-# TODO: Update to 2.9.0 when there is a new build for intel_extension_for_pytorch for that version
 RUN \
     sed -i '/mamba_ssm/d' requirements/cpu-test.in && \
     remove_packages_not_supported_on_aarch64() { \

docs/getting_started/installation/gpu.xpu.inc.md

@@ -6,10 +6,11 @@ vLLM initially supports basic model inference and serving on Intel GPU platform.
 # --8<-- [start:requirements]
 
 - Supported Hardware: Intel Data Center GPU, Intel ARC GPU
-- OneAPI requirements: oneAPI 2025.1
+- OneAPI requirements: oneAPI 2025.3
+- Dependency: [vllm-xpu-kernels](https://github.com/vllm-project/vllm-xpu-kernels): a package provide all necessary vllm custom kernel when running vLLM on Intel GPU platform, 
 - Python: 3.12
 !!! warning
-    The provided IPEX whl is Python3.12 specific so this version is a MUST.
+    The provided vllm-xpu-kernels whl is Python3.12 specific so this version is a MUST.
 
 # --8<-- [end:requirements]
 # --8<-- [start:set-up-using-python]
@@ -24,7 +25,7 @@ Currently, there are no pre-built XPU wheels.
 # --8<-- [end:pre-built-wheels]
 # --8<-- [start:build-wheel-from-source]
 
-- First, install required [driver](https://dgpu-docs.intel.com/driver/installation.html#installing-gpu-drivers) and [Intel OneAPI](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) 2025.1 or later.
+- First, install required [driver](https://dgpu-docs.intel.com/driver/installation.html#installing-gpu-drivers) and [Intel OneAPI](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) 2025.3 or later.
 - Second, install Python packages for vLLM XPU backend building:
 
 ```bash
@@ -37,7 +38,7 @@ pip install -v -r requirements/xpu.txt
 - Then, build and install vLLM XPU backend:
 
 ```bash
-VLLM_TARGET_DEVICE=xpu python setup.py install
+VLLM_TARGET_DEVICE=xpu pip install --no-build-isolation -e . -v
 ```
 
 # --8<-- [end:build-wheel-from-source]

requirements/build.txt

@@ -9,5 +9,5 @@ wheel
 jinja2>=3.1.6
 regex
 build
-protobuf
+protobuf >= 5.29.6, !=6.30.*, !=6.31.*, !=6.32.*, !=6.33.0.*, !=6.33.1.*, !=6.33.2.*, !=6.33.3.*, !=6.33.4.*
 grpcio-tools==1.78.0 # Required for grpc entrypoints

requirements/common.txt

@@ -9,7 +9,7 @@ blake3
 py-cpuinfo
 transformers >= 4.56.0, < 5
 tokenizers >= 0.21.1  # Required for fast incremental detokenization.
-protobuf # Required by LlamaTokenizer, gRPC.
+protobuf >= 5.29.6, !=6.30.*, !=6.31.*, !=6.32.*, !=6.33.0.*, !=6.33.1.*, !=6.33.2.*, !=6.33.3.*, !=6.33.4.* # Required by LlamaTokenizer, gRPC. CVE-2026-0994
 fastapi[standard] >= 0.115.0 # Required by FastAPI's form models in the OpenAI API server's audio transcriptions endpoint.
 aiohttp >= 3.13.3
 openai >= 1.99.1  # For Responses API with reasoning content

requirements/xpu.txt

@@ -15,4 +15,4 @@ torch==2.10.0+xpu
 torchaudio
 torchvision
 
-vllm_xpu_kernels @ https://github.com/vllm-project/vllm-xpu-kernels/releases/download/v0.1.1/vllm_xpu_kernels-0.1.1-cp312-cp312-linux_x86_64.whl
+vllm_xpu_kernels @ https://github.com/vllm-project/vllm-xpu-kernels/releases/download/v0.1.2/vllm_xpu_kernels-0.1.2-cp312-cp312-linux_x86_64.whl

tests/compile/passes/test_fusion_attn.py

@@ -267,7 +267,7 @@ elif current_platform.is_rocm():
     PATTERN_TEST_MODELS_FP8 = [
         ("amd/Llama-3.1-8B-Instruct-FP8-KV", TestAttentionFp8StaticQuantPatternModel)
     ]
-    BACKENDS = [
+    BACKENDS_FP8 = [
         AttentionBackendEnum.ROCM_AITER_UNIFIED_ATTN,
         AttentionBackendEnum.ROCM_ATTN,
         AttentionBackendEnum.TRITON_ATTN,
@@ -474,6 +474,17 @@ def test_attention_quant_pattern(
     assert attn_nodes_pre[0].kwargs.get("output_block_scale") is None, (
         "Attention should not have output_block_scale before fusion"
     )
+
+    kv_cache_dummy_dep_pre_is_none = (
+        attn_nodes_pre[0].kwargs.get("kv_cache_dummy_dep") is None
+    )
+    kv_cache_dummy_dep_post_is_none = (
+        attn_nodes_post[0].kwargs.get("kv_cache_dummy_dep") is None
+    )
+    assert not (kv_cache_dummy_dep_pre_is_none ^ kv_cache_dummy_dep_post_is_none), (
+        "The kv_cache_dummy_dep should be consistent before and after fusion"
+    )
+
     if quant_key.dtype == FP8_DTYPE:
         assert attn_nodes_post[0].kwargs.get("output_block_scale") is None, (
             "Attention should not have output_block_scale after FP8 fusion"

tests/quantization/test_cpu_wna16.py

@@ -17,7 +17,7 @@ DTYPE = ["bfloat16"]
 
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("dtype", DTYPE)
-def test_ipex_quant(vllm_runner, model, dtype):
+def test_cpu_quant(vllm_runner, model, dtype):
     with vllm_runner(model, dtype=dtype) as llm:
         output = llm.generate_greedy(["The capital of France is"], max_tokens=32)
     assert output

tests/quantization/test_ipex_quant.py

@@ -1,32 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Test model set-up and inference for quantized HF models supported
-on the CPU/GPU backend using IPEX (including AWQ/GPTQ).
-
-Validating the configuration and printing results for manual checking.
-
-Run `pytest tests/quantization/test_ipex_quant.py`.
-"""
-
-import pytest
-
-from vllm.platforms import current_platform
-
-MODELS = [
-    "AMead10/Llama-3.2-1B-Instruct-AWQ",
-    "shuyuej/Llama-3.2-1B-Instruct-GPTQ",  # with g_idx
-]
-DTYPE = ["bfloat16"]
-
-
-@pytest.mark.skipif(
-    not current_platform.is_cpu() and not current_platform.is_xpu(),
-    reason="only supports Intel CPU/XPU backend.",
-)
-@pytest.mark.parametrize("model", MODELS)
-@pytest.mark.parametrize("dtype", DTYPE)
-def test_ipex_quant(vllm_runner, model, dtype):
-    with vllm_runner(model, dtype=dtype, enforce_eager=True) as llm:
-        output = llm.generate_greedy(["The capital of France is"], max_tokens=4)
-    assert output
-    print(output)

tests/v1/core/test_async_scheduler.py

@@ -236,7 +236,7 @@ def test_prefix_caching_for_multi_turn():
         req._all_token_ids = req.prompt_token_ids.copy()
         req.all_token_ids = ConstantList(req._all_token_ids)
         req.block_hashes = []
-        req.block_hashes = req.get_hash_new_full_blocks()
+        req.update_block_hashes()
 
     # Schedule the next-turn requests.
     for req in next_turn_requests:

vllm/_xpu_ops.py

@@ -53,7 +53,7 @@ if hasattr(torch.ops._xpu_C, "int4_gemm_w4a16"):
         return torch.empty((M, N), dtype=input.dtype, device=input.device)
 
 
-class ipex_ops:
+class xpu_ops:
     @staticmethod
     def flash_attn_varlen_func(
         q: torch.Tensor,
@@ -73,7 +73,7 @@ class ipex_ops:
         cu_seqlens_k: torch.Tensor | None = None,
         # passed in qwen vl
         dropout_p: float = 0.0,
-        # The following parameters are not used in ipex kernel currently,
+        # The following parameters are not used in xpu kernel currently,
         # we keep API compatible to CUDA's.
         scheduler_metadata=None,
         fa_version: int = 2,
@@ -153,6 +153,6 @@ class ipex_ops:
         sm_margin=0,  # Can be tuned if some SMs are used for communication
     ) -> None:
         logger.warning_once(
-            "get_scheduler_metadata is not implemented for ipex_ops, returning None."
+            "get_scheduler_metadata is not implemented for xpu_ops, returning None."
         )
         return None

vllm/compilation/passes/fusion/attn_quant_fusion.py

@@ -142,6 +142,7 @@ class AttentionFp8StaticQuantPattern(AttentionQuantPattern):
             v: torch.Tensor,
             output_attn: torch.Tensor,
             scale: torch.Tensor,
+            kv_cache_dummy_dep: torch.Tensor,
         ) -> torch.Tensor:
             at1 = auto_functionalized(
                 ATTN_OP,
@@ -152,6 +153,7 @@ class AttentionFp8StaticQuantPattern(AttentionQuantPattern):
                 layer_name=self.layer_name,
                 output_scale=None,
                 output_block_scale=None,
+                kv_cache_dummy_dep=kv_cache_dummy_dep,
             )
             attn_out_view = RESHAPE_OP(
                 at1[1], [q.shape[0], self.num_heads * self.head_size]
@@ -165,6 +167,7 @@ class AttentionFp8StaticQuantPattern(AttentionQuantPattern):
             v: torch.Tensor,
             output_attn: torch.Tensor,
             scale: torch.Tensor,
+            kv_cache_dummy_dep: torch.Tensor,
         ) -> torch.Tensor:
             # attn output in quant_dtype
             output_attn = torch.ops.aten.full.default(
@@ -182,6 +185,7 @@ class AttentionFp8StaticQuantPattern(AttentionQuantPattern):
                 layer_name=self.layer_name,
                 output_scale=scale,
                 output_block_scale=None,
+                kv_cache_dummy_dep=kv_cache_dummy_dep,
             )
             return RESHAPE_OP(at1[1], [-1, self.num_heads * self.head_size])
 
@@ -191,6 +195,7 @@ class AttentionFp8StaticQuantPattern(AttentionQuantPattern):
             self.empty(5, self.num_heads, self.head_size),  # v
             self.empty(5, self.num_heads, self.head_size),  # attn_output
             empty_fp32(1, 1),  # scale
+            self.empty(0),  # kv_cache_dummy_dep
         ]
 
         pm.register_replacement(
@@ -228,6 +233,7 @@ class AttentionNvfp4QuantPattern(AttentionQuantPattern):
             output_quant: torch.Tensor,
             output_scale: torch.Tensor,
             input_scale: torch.Tensor,
+            kv_cache_dummy_dep: torch.Tensor,
         ) -> tuple[torch.Tensor, torch.Tensor]:
             at1 = auto_functionalized(
                 ATTN_OP,
@@ -238,6 +244,7 @@ class AttentionNvfp4QuantPattern(AttentionQuantPattern):
                 layer_name=self.layer_name,
                 output_scale=None,
                 output_block_scale=None,
+                kv_cache_dummy_dep=kv_cache_dummy_dep,
             )
             attn_out_view = RESHAPE_OP(
                 at1[1], [q.shape[0], self.num_heads * self.head_size]
@@ -261,6 +268,7 @@ class AttentionNvfp4QuantPattern(AttentionQuantPattern):
             output_quant: torch.Tensor,
             output_scale: torch.Tensor,
             input_scale: torch.Tensor,
+            kv_cache_dummy_dep: torch.Tensor,
         ) -> tuple[torch.Tensor, torch.Tensor]:
             # attention output in quant_dtype
             output_attn = torch.ops.aten.full.default(
@@ -280,6 +288,7 @@ class AttentionNvfp4QuantPattern(AttentionQuantPattern):
                 layer_name=self.layer_name,
                 output_scale=input_scale,
                 output_block_scale=output_scale_view,
+                kv_cache_dummy_dep=kv_cache_dummy_dep,
             )
             output = RESHAPE_OP(at2[1], [-1, self.num_heads * self.head_size // 2])
             return output, at2[2]
@@ -294,6 +303,7 @@ class AttentionNvfp4QuantPattern(AttentionQuantPattern):
                 128, round_up(self.num_heads * self.head_size // 16, 4)
             ),  # output_scale
             empty_fp32(1, 1),  # input_scale
+            self.empty(0),  # kv_cache_dummy_dep
         ]
 
         pm.register_replacement(

vllm/model_executor/layers/fused_moe/__init__.py

@@ -102,6 +102,7 @@ if HAS_TRITON:
     )
     from vllm.model_executor.layers.fused_moe.xpu_fused_moe import (
         XPUExperts,
+        XPUExpertsFp8,
     )
 
     __all__ += [
@@ -121,6 +122,7 @@ if HAS_TRITON:
         "BatchedDeepGemmExperts",
         "TritonOrDeepGemmExperts",
         "XPUExperts",
+        "XPUExpertsFp8",
     ]
 else:
     # Some model classes directly use the custom ops. Add placeholders

vllm/model_executor/layers/fused_moe/gpt_oss_triton_kernels_moe.py

@@ -19,11 +19,14 @@ from vllm.model_executor.layers.fused_moe.utils import _resize_cache
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     QuantKey,
 )
+from vllm.platforms import current_platform
 from vllm.triton_utils import tl, triton
 from vllm.utils.import_utils import has_triton_kernels
 
 logger = init_logger(__name__)
 
+use_legacy_triton_kernels = False
+
 if has_triton_kernels():
     try:
         import triton_kernels.swiglu
@@ -38,10 +41,20 @@ if has_triton_kernels():
         from triton_kernels.tensor import (
             BIT,
             Bitmatrix,
-            SparseMatrix,
-            make_ragged_tensor_metadata,
         )
         from triton_kernels.topk import topk
+
+        try:
+            from triton_kernels.tensor import (
+                SparseMatrix,
+                make_ragged_tensor_metadata,
+            )
+        except ImportError:
+            if current_platform.is_rocm():
+                logger.warning_once("Using legacy triton_kernels on ROCm")
+                use_legacy_triton_kernels = True
+            else:
+                raise
     except (AttributeError, ImportError) as e:
         logger.error(
             "Failed to import Triton kernels. Please make sure your triton "
@@ -101,6 +114,12 @@ def legacy_routing_from_bitmatrix(
     Replacement for the removed triton_kernels.routing.routing_from_bitmatrix.
     Creates routing data from a bitmatrix representation.
     """
+    if use_legacy_triton_kernels:
+        from triton_kernels.routing import routing_from_bitmatrix
+
+        return routing_from_bitmatrix(
+            bitmatrix, expt_scal, expt_indx, n_expts_tot, n_expts_act
+        )
     sparse_logits = SparseMatrix(indx=expt_indx, vals=expt_scal, mask=bitmatrix)
     dispatch_indx = sparse_logits.mask_metadata.row_sorted_indx
     combine_indx = sparse_logits.mask_metadata.col_sorted_indx
@@ -130,6 +149,10 @@ def legacy_routing(
     Replacement for the removed triton_kernels.routing.routing function.
     Computes routing data from gating logits.
     """
+    if use_legacy_triton_kernels:
+        from triton_kernels.routing import routing
+
+        return routing(logits, n_expts_act, sm_first=sm_first)
     if sm_first:
         logits = torch.softmax(logits, dim=-1)
     sparse_logits = topk(logits, n_expts_act, apply_softmax=not sm_first)
@@ -231,11 +254,22 @@ def triton_kernel_fused_experts(
     )
     output_tensor = _resize_cache(output_tensor, (batch_dim, M, K))
 
-    act = FusedActivation(
+    act = (
-        FnSpecs(
+        FusedActivation(
-            "swiglu", triton_kernels.swiglu.swiglu_fn, ("alpha", "limit"), reduction_n=2
+            FnSpecs(
-        ),
+                "swiglu",
-        (swiglu_alpha, swiglu_limit),
+                triton_kernels.swiglu.swiglu_fn,
+                ("alpha", "limit"),
+                reduction_n=2,
+            ),
+            (swiglu_alpha, swiglu_limit),
+        )
+        if not use_legacy_triton_kernels
+        else FusedActivation(
+            FnSpecs("swiglu", triton_kernels.swiglu.swiglu_fn, ("alpha", "limit")),
+            (swiglu_alpha, swiglu_limit),
+            2,
+        )
     )
     gammas = routing_data.gate_scal if routing_data else None
 
@@ -296,8 +330,17 @@ def make_routing_data(
 
     bitmatrix_shape = [n_rows, bm_cols * 32]
     bitmatrix_shape_max = [n_rows, None]
-    bitmatrix = Bitmatrix(
+    bitmatrix = (
-        bitmatrix, dtype=BIT, shape=bitmatrix_shape, shape_max=bitmatrix_shape_max
+        Bitmatrix(
+            bitmatrix, dtype=BIT, shape=bitmatrix_shape, shape_max=bitmatrix_shape_max
+        )
+        if not use_legacy_triton_kernels
+        else Bitmatrix(
+            bitmatrix,
+            shape=bitmatrix_shape,
+            shape_max=bitmatrix_shape_max,
+            scratchpad=None,
+        )
     )
 
     # matmul_ogs expects invalid topk_weights to be -1s

vllm/model_executor/layers/fused_moe/oracle/fp8.py

@@ -52,6 +52,7 @@ class Fp8MoeBackend(Enum):
     AITER = "AITER"
     VLLM_CUTLASS = "VLLM_CUTLASS"
     BATCHED_VLLM_CUTLASS = "BATCHED_VLLM_CUTLASS"
+    XPU = "XPU"
 
 
 def backend_to_kernel_cls(
@@ -123,6 +124,13 @@ def backend_to_kernel_cls(
 
         return CutlassBatchedExpertsFp8
 
+    elif backend == Fp8MoeBackend.XPU:
+        from vllm.model_executor.layers.fused_moe.xpu_fused_moe import (
+            XPUExpertsFp8,
+        )
+
+        return XPUExpertsFp8
+
     else:
         raise ValueError(f"Unknown FP8 MoE backend: {backend.value}")
 
@@ -154,6 +162,7 @@ def select_fp8_moe_backend(
         Fp8MoeBackend.TRITON,
         Fp8MoeBackend.BATCHED_TRITON,
         Fp8MoeBackend.MARLIN,
+        Fp8MoeBackend.XPU,
     ]
 
     # NOTE(rob): We need to peak into the P/F selection to determine
@@ -393,6 +402,7 @@ def convert_to_fp8_moe_kernel_format(
             Fp8MoeBackend.BATCHED_TRITON,
             Fp8MoeBackend.VLLM_CUTLASS,
             Fp8MoeBackend.BATCHED_VLLM_CUTLASS,
+            Fp8MoeBackend.XPU,
         ]:
             raise ValueError(f"Unsupported FP8 MoE backend: {fp8_backend.value}")
 

vllm/model_executor/layers/fused_moe/xpu_fused_moe.py

@@ -4,13 +4,16 @@ import torch
 
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from vllm.model_executor.layers.fused_moe.config import (
+    FusedMoEConfig,
     FusedMoEParallelConfig,
+    FusedMoEQuantConfig,
 )
 from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
     TopKWeightAndReduceNoOP,
 )
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     QuantKey,
+    kFp8DynamicTensorSym,
     kFp8StaticTensorSym,
 )
 from vllm.platforms import current_platform
@@ -20,6 +23,21 @@ if current_platform.is_xpu():
 
 
 class XPUExperts(mk.FusedMoEPermuteExpertsUnpermute):
+    def __init__(
+        self,
+        moe_config: FusedMoEConfig,
+        quant_config: FusedMoEQuantConfig,
+        max_num_tokens: int | None = None,
+        num_dispatchers: int | None = None,
+    ):
+        super().__init__(
+            moe_config,
+            quant_config,
+            max_num_tokens,
+            num_dispatchers,
+        )
+        self.is_fp8 = False
+
     @property
     def expects_unquantized_inputs(self) -> bool:
         return True
@@ -49,10 +67,10 @@ class XPUExperts(mk.FusedMoEPermuteExpertsUnpermute):
         weight_key: QuantKey | None,
         activation_key: QuantKey | None,
     ) -> bool:
-        # TODO: dispatch based on device.
         SUPPORTED_W_A = [
             (None, None),
             (kFp8StaticTensorSym, None),
+            (kFp8StaticTensorSym, kFp8DynamicTensorSym),
         ]
         return (weight_key, activation_key) in SUPPORTED_W_A
 
@@ -103,10 +121,10 @@ class XPUExperts(mk.FusedMoEPermuteExpertsUnpermute):
         xpu_fused_moe(
             hidden_states=hidden_states,
             w13=w1,
-            w13_scales=a1q_scale,
+            w13_scales=self.w1_scale,
             w13_bias=self.w1_bias,
             w2=w2,
-            w2_scales=a2_scale,
+            w2_scales=self.w2_scale,
             w2_bias=self.w2_bias,
             topk_weights=topk_weights,
             topk_ids=topk_ids,
@@ -116,5 +134,22 @@ class XPUExperts(mk.FusedMoEPermuteExpertsUnpermute):
             ep_rank=self.moe_config.ep_rank,
             ep_size=self.moe_config.ep_size,
             output=output,
+            is_fp8=self.is_fp8,
         )
-        return
+
+
+class XPUExpertsFp8(XPUExperts):
+    def __init__(
+        self,
+        moe_config: FusedMoEConfig,
+        quant_config: FusedMoEQuantConfig,
+        max_num_tokens: int | None = None,
+        num_dispatchers: int | None = None,
+    ):
+        super().__init__(
+            moe_config,
+            quant_config,
+            max_num_tokens,
+            num_dispatchers,
+        )
+        self.is_fp8 = True

vllm/model_executor/layers/quantization/fp8.py

@@ -180,18 +180,9 @@ class Fp8Config(QuantizationConfig):
             weight_block_size=weight_block_size,
         )
 
-    def get_xpu_quant_method(
-        self, layer: torch.nn.Module, prefix: str
-    ) -> "QuantizeMethodBase | None":
-        raise NotImplementedError(
-            "FP8 quantization is not supported during xpu kernel migration."
-        )
-
     def get_quant_method(
         self, layer: torch.nn.Module, prefix: str
     ) -> "QuantizeMethodBase | None":
-        if current_platform.is_xpu():
-            return self.get_xpu_quant_method(layer, prefix)
         if isinstance(layer, LinearBase):
             if is_layer_skipped(
                 prefix=prefix,
@@ -300,7 +291,7 @@ class Fp8LinearMethod(LinearMethodBase):
             or envs.VLLM_TEST_FORCE_FP8_MARLIN
         )
         # Disable marlin for rocm
-        if current_platform.is_rocm():
+        if current_platform.is_rocm() or current_platform.is_xpu():
             self.use_marlin = False
         if vllm_is_batch_invariant():
             self.use_marlin = False

vllm/model_executor/layers/quantization/kernels/scaled_mm/__init__.py

@@ -39,6 +39,9 @@ from vllm.model_executor.layers.quantization.kernels.scaled_mm.ScaledMMLinearKer
 from vllm.model_executor.layers.quantization.kernels.scaled_mm.triton import (
     TritonInt8ScaledMMLinearKernel,
 )
+from vllm.model_executor.layers.quantization.kernels.scaled_mm.xpu import (
+    XPUFP8ScaledMMLinearKernel,
+)
 from vllm.model_executor.layers.quantization.utils.quant_utils import QuantKey
 from vllm.platforms import PlatformEnum, current_platform
 
@@ -72,6 +75,9 @@ _POSSIBLE_FP8_KERNELS: dict[PlatformEnum, list[type[FP8ScaledMMLinearKernel]]] =
         PerTensorTorchFP8ScaledMMLinearKernel,
         ChannelWiseTorchFP8ScaledMMLinearKernel,
     ],
+    PlatformEnum.XPU: [
+        XPUFP8ScaledMMLinearKernel,
+    ],
 }
 
 _KernelT = TypeVar("_KernelT", bound=ScaledMMLinearKernel)

vllm/model_executor/layers/quantization/kernels/scaled_mm/xpu.py

@@ -0,0 +1,59 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections.abc import Sequence
+
+import torch
+
+from vllm.model_executor.layers.quantization.kernels.scaled_mm.ScaledMMLinearKernel import (  # noqa: E501
+    FP8ScaledMMLinearKernel,
+    FP8ScaledMMLinearLayerConfig,
+)
+from vllm.platforms import current_platform
+
+
+class XPUFP8ScaledMMLinearKernel(FP8ScaledMMLinearKernel):
+    @classmethod
+    def is_supported(
+        cls, compute_capability: int | None = None
+    ) -> tuple[bool, str | None]:
+        if not current_platform.is_xpu():
+            return False, "XPUFP8ScaledMM only support on XPU"
+        return True, None
+
+    @classmethod
+    def can_implement(cls, c: FP8ScaledMMLinearLayerConfig) -> tuple[bool, str | None]:
+        if c.weight_quant_key.dtype not in {torch.float8_e5m2, torch.float8_e4m3fn}:
+            return False, "XPUFP8ScaledMM only support FP8 weight dtype"
+        return True, None
+
+    def __init__(
+        self, c: FP8ScaledMMLinearLayerConfig, layer_param_names: Sequence[str]
+    ) -> None:
+        assert self.can_implement(c)[0]
+        assert self.is_supported()[0]
+        self.config = c
+        self.layer_param_names = layer_param_names
+
+    def apply_weights(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        weight = layer.weight
+        weight_scale = layer.weight_scale
+        return torch.ops._xpu_C.fp8_gemm_w8a16(x, weight, weight_scale, bias)
+
+    def apply_scaled_mm(
+        self,
+        *,
+        A: torch.Tensor,
+        B: torch.Tensor,
+        out_dtype: torch.dtype,
+        As: torch.Tensor,
+        Bs: torch.Tensor,
+        bias: torch.Tensor | None,
+        output_shape: list,
+    ) -> torch.Tensor:
+        pass

vllm/model_executor/layers/quantization/mxfp4.py

@@ -160,7 +160,7 @@ def get_mxfp4_backend(with_lora_support: bool) -> Mxfp4Backend:
             logger.info_once("Using Triton backend")
             return Mxfp4Backend.TRITON
     elif current_platform.is_xpu():
-        logger.info_once("Using ipex marlin backend on XPU")
+        logger.info_once("Using xpu backend on XPU")
         return Mxfp4Backend.MARLIN
     elif current_platform.is_rocm() and has_triton_kernels():
         logger.info_once("Using Triton backend")

vllm/model_executor/layers/sparse_attn_indexer.py

@@ -20,7 +20,7 @@ from vllm.v1.worker.workspace import current_workspace_manager
 if current_platform.is_cuda_alike():
     from vllm import _custom_ops as ops
 elif current_platform.is_xpu():
-    from vllm._ipex_ops import ipex_ops as ops
+    from vllm._xpu_ops import xpu_ops as ops
 
 logger = init_logger(__name__)
 

vllm/platforms/cpu.py

@@ -338,7 +338,6 @@ class CpuPlatform(Platform):
                 ld_preload_str += pytorch_libgomp_so
                 os.environ["LD_PRELOAD"] = ld_preload_str
 
-        # To hint IPEX uses shared memory based AllReduce
         os.environ["LOCAL_WORLD_SIZE"] = str(
             vllm_config.parallel_config.tensor_parallel_size
         )

vllm/v1/attention/backends/fa_utils.py

@@ -23,12 +23,11 @@ if current_platform.is_cuda():
 
 elif current_platform.is_xpu():
     from vllm import _custom_ops as ops
+    from vllm._xpu_ops import xpu_ops
 
     reshape_and_cache_flash = ops.reshape_and_cache_flash
-    from vllm._ipex_ops import ipex_ops
+    flash_attn_varlen_func = xpu_ops.flash_attn_varlen_func  # type: ignore[assignment]
-
+    get_scheduler_metadata = xpu_ops.get_scheduler_metadata  # type: ignore[assignment]
-    flash_attn_varlen_func = ipex_ops.flash_attn_varlen_func  # type: ignore[assignment]
-    get_scheduler_metadata = ipex_ops.get_scheduler_metadata  # type: ignore[assignment]
 elif current_platform.is_rocm():
     try:
         from flash_attn import flash_attn_varlen_func  # type: ignore[no-redef]
@@ -153,7 +152,7 @@ def is_flash_attn_varlen_func_available() -> bool:
 
     Platform-specific sources:
     - CUDA: vllm.vllm_flash_attn.flash_attn_varlen_func
-    - XPU: ipex_ops.flash_attn_varlen_func
+    - XPU: xpu_ops.flash_attn_varlen_func
     - ROCm: upstream flash_attn.flash_attn_varlen_func (if available)
 
     Note: This is separate from the AITER flash attention backend (rocm_aiter_fa.py)

vllm/v1/attention/ops/paged_attn.py

@@ -9,7 +9,7 @@ from vllm.platforms import current_platform
 if current_platform.is_cuda_alike():
     from vllm import _custom_ops as ops
 elif current_platform.is_xpu():
-    from vllm._ipex_ops import ipex_ops as ops  # type: ignore[no-redef]
+    from vllm._xpu_ops import xpu_ops as ops  # type: ignore[no-redef]
 
 
 class PagedAttention:

vllm/v1/core/sched/scheduler.py

@@ -982,10 +982,8 @@ class Scheduler(SchedulerInterface):
 
         session._all_token_ids.extend(update.prompt_token_ids or ())
         session.prompt_token_ids.extend(update.prompt_token_ids or ())
-        # Update block hashes for the new tokens
+        # Update block hashes for the new tokens.
-        # (mirrors Request.append_output_token_ids)
+        session.update_block_hashes()
-        if session.get_hash_new_full_blocks is not None:
-            session.block_hashes.extend(session.get_hash_new_full_blocks())
         session.num_prompt_tokens = len(session.prompt_token_ids)
         session.arrival_time = update.arrival_time
         session.sampling_params = update.sampling_params

vllm/v1/request.py

@@ -6,7 +6,6 @@ import time
 from collections import deque
 from collections.abc import Callable, Mapping
 from dataclasses import dataclass
-from functools import partial
 from typing import TYPE_CHECKING, Any
 
 import torch
@@ -164,10 +163,11 @@ class Request:
         self.num_external_computed_tokens = 0
 
         self.block_hashes: list[BlockHash] = []
-        self.get_hash_new_full_blocks: Callable[[], list[BlockHash]] | None = None
+        # Store the block hasher without binding self to avoid creating a
-        if block_hasher is not None:
+        # reference cycle (Request -> partial -> Request) that prevents
-            self.get_hash_new_full_blocks = partial(block_hasher, self)
+        # immediate garbage collection via reference counting.
-            self.block_hashes = self.get_hash_new_full_blocks()
+        self._block_hasher: Callable[[Request], list[BlockHash]] | None = block_hasher
+        self.update_block_hashes()
 
         self.skip_reading_prefix_cache = self.get_skip_reading_prefix_cache()
 
@@ -212,8 +212,12 @@ class Request:
             self._output_token_ids.extend(token_ids)
             self._all_token_ids.extend(token_ids)
 
-        if self.get_hash_new_full_blocks is not None:
+        self.update_block_hashes()
-            self.block_hashes.extend(self.get_hash_new_full_blocks())
+
+    def update_block_hashes(self) -> None:
+        """Compute block hashes for any new full blocks and append them."""
+        if self._block_hasher is not None:
+            self.block_hashes.extend(self._block_hasher(self))
 
     @property
     def use_structured_output(self) -> bool: