[XPU] Add gpt-oss model support for Intel GPU (#27786)

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

vllm/attention/utils/fa_utils.py

@@ -80,6 +80,13 @@ def flash_attn_supports_fp8() -> bool:
     )
 
 
+def flash_attn_supports_sinks() -> bool:
+    if current_platform.is_xpu():
+        return True
+    else:
+        return get_flash_attn_version() == 3
+
+
 def flash_attn_supports_mla():
     from vllm.platforms import current_platform
 

vllm/model_executor/layers/quantization/mxfp4.py

@@ -142,6 +142,9 @@ def get_mxfp4_backend(with_lora_support: bool) -> Mxfp4Backend:
         else:
             logger.info_once("Using Triton backend")
             return Mxfp4Backend.TRITON
+    elif current_platform.is_xpu():
+        logger.info_once("Using ipex marlin backend on XPU")
+        return Mxfp4Backend.MARLIN
     elif current_platform.is_rocm() and has_triton_kernels():
         logger.info_once("Using Triton backend")
         return Mxfp4Backend.TRITON
@@ -188,7 +191,10 @@ class Mxfp4Config(QuantizationConfig):
                 return UnquantizedLinearMethod()
             raise NotImplementedError("Mxfp4 linear layer is not implemented")
         elif isinstance(layer, FusedMoE):
-            return Mxfp4MoEMethod(layer.moe_config)
+            if current_platform.is_xpu():
+                return IpexMxfp4MoEMethod(layer.moe_config)
+            else:
+                return Mxfp4MoEMethod(layer.moe_config)
         elif isinstance(layer, Attention):
             raise NotImplementedError("Mxfp4 attention layer is not implemented")
         return None
@@ -245,7 +251,10 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
             intermediate_size_per_partition_after_pad = round_up(
                 intermediate_size_per_partition, 128
             )
-            hidden_size = round_up(hidden_size, 256)
+            if current_platform.is_xpu():
+                hidden_size = round_up(hidden_size, 128)
+            else:
+                hidden_size = round_up(hidden_size, 256)
 
             layer.params_dtype = params_dtype
             layer.num_experts = num_experts
@@ -1071,3 +1080,84 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
             )
         else:
             raise ValueError(f"Unsupported backend: {self.mxfp4_backend}")
+
+
+class IpexMxfp4MoEMethod(Mxfp4MoEMethod):
+    def __init__(self, moe_config: FusedMoEConfig):
+        super().__init__(moe_config)
+        self.moe_config = moe_config
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        num_experts: int,
+        hidden_size: int,
+        intermediate_size_per_partition: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        super().create_weights(
+            layer,
+            num_experts,
+            hidden_size,
+            intermediate_size_per_partition,
+            params_dtype,
+            **extra_weight_attrs,
+        )
+        self.original_hidden_size = hidden_size
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        import intel_extension_for_pytorch as ipex
+
+        layer.w13_weight.data = layer.w13_weight.data.view(torch.int32)
+        layer.w2_weight.data = layer.w2_weight.data.view(torch.int32)
+        layer.ipex_fusion = ipex.llm.modules.GatedMLPMOE(
+            layer.w13_weight,
+            layer.w2_weight,
+            w1_scale_inv=layer.w13_weight_scale,
+            w2_scale_inv=layer.w2_weight_scale,
+            w13_bias=layer.w13_bias,
+            w2_bias=layer.w2_bias,
+            is_mxfp4=True,
+        )
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        router_logits: torch.Tensor,
+        top_k: int,
+        renormalize: bool,
+        use_grouped_topk: bool = False,
+        topk_group: int | None = None,
+        num_expert_group: int | None = None,
+        global_num_experts: int = -1,
+        expert_map: torch.Tensor | None = None,
+        custom_routing_function: Callable | None = None,
+        scoring_func: str = "softmax",
+        routed_scaling_factor: float = 1.0,
+        e_score_correction_bias: torch.Tensor | None = None,
+        apply_router_weight_on_input: bool = False,
+        activation: str = "silu",
+        enable_eplb: bool = False,
+        expert_load_view: torch.Tensor | None = None,
+        logical_to_physical_map: torch.Tensor | None = None,
+        logical_replica_count: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        assert activation == "swigluoai", (
+            "Only swiglu_oai activation is supported for IPEX MXFP4 MoE"
+        )  # noqa:
+        hidden_size_pad = round_up(self.original_hidden_size, 128)
+        x_pad = torch.nn.functional.pad(x, (0, hidden_size_pad - x.size(-1)))
+        hidden_states = layer.ipex_fusion(
+            x_pad,
+            use_grouped_topk,
+            top_k,
+            router_logits,
+            renormalize,
+            topk_group,
+            num_expert_group,
+            activation="swiglu_oai",
+        )
+        hidden_states = hidden_states[..., : self.original_hidden_size].contiguous()
+        return hidden_states

vllm/model_executor/models/gpt_oss.py

@@ -337,9 +337,6 @@ class GptOssModel(nn.Module):
             if is_pp_missing_parameter(name, self):
                 continue
 
-            # FIXME(woosuk): Remove this after testing.
-            weight = weight.cuda()
-
             if ".w13_weight_scale" in name:
                 # Handle MLP gate and up projection weights scale
                 if use_ep:

vllm/v1/attention/backends/flash_attn.py

@@ -27,6 +27,7 @@ from vllm.attention.utils.fa_utils import (
 
 if is_flash_attn_varlen_func_available():
     from vllm.attention.utils.fa_utils import (
+        flash_attn_supports_sinks,
         flash_attn_varlen_func,
         get_scheduler_metadata,
         reshape_and_cache_flash,
@@ -497,7 +498,7 @@ class FlashAttentionImpl(AttentionImpl):
 
         self.sinks = sinks
         if self.sinks is not None:
-            assert self.vllm_flash_attn_version == 3, (
+            assert flash_attn_supports_sinks(), (
                 "Sinks are only supported in FlashAttention 3"
             )
             assert self.sinks.shape[0] == num_heads, (