[XPU] Quick fix for TritonMLA to remove cuda hardcode (#39088)

Signed-off-by: Chendi Xue <chendi.xue@intel.com>
Co-authored-by: Kunshang Ji <kunshang.ji@intel.com>

vllm/model_executor/layers/fused_moe/unquantized_fused_moe_method.py

@@ -222,7 +222,7 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
                     self.cpu_fused_moe = cpu_fused_moe.CPUFusedMOE(layer)
             else:
                 self.cpu_fused_moe = cpu_fused_moe.CPUFusedMOE(layer)
-        elif current_platform.is_xpu():
+        elif self.unquantized_backend == UnquantizedMoeBackend.XPU:
             w13 = layer.w13_weight
             w2 = layer.w2_weight
 

vllm/v1/attention/backends/mla/triton_mla.py

@@ -13,6 +13,7 @@ from vllm.model_executor.layers.attention.mla_attention import (
     MLACommonImpl,
     MLACommonMetadata,
 )
+from vllm.platforms import current_platform
 from vllm.platforms.interface import DeviceCapability
 from vllm.triton_utils import triton
 from vllm.utils.torch_utils import is_quantized_kv_cache
@@ -116,7 +117,7 @@ class TritonMLAImpl(MLACommonImpl[MLACommonMetadata]):
         if is_quantized_kv_cache(self.kv_cache_dtype):
             self.supports_quant_query_input = False
 
-        self._sm_count = torch.cuda.get_device_properties(0).multi_processor_count
+        self._sm_count = current_platform.num_compute_units()
 
     def _flash_attn_varlen_diff_headdims(
         self, q, k, v, return_softmax_lse=False, softmax_scale=None, **kwargs