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A high-throughput and memory-efficient inference and serving engine for LLMs

Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git

Synced Nov 4, 20:00 UTC

[xpu][rocm] Update `current_platform.supports_fp8()` for TritonExperts (#40132)

Signed-off-by: Hank <hcc.mayday@gmail.com>

Hank_ committed Apr 22, 2026 at 19:39 UTC a4905133f375ec721be441e7ec4a3f923daa28f3
3 files changed +6 -19
vllm/model_executor/layers/fused_moe/fused_moe.py
+1 -18
@@ -1952,24 +1952,7 @@ class TritonExperts(mk.FusedMoEExpertsModular):
1952 weight_key: QuantKey | None,
1953 activation_key: QuantKey | None,
1954 ) -> bool:
1955 - p = current_platform
1956 - if p.is_rocm():
1957 - from vllm.platforms.rocm import on_gfx9, on_gfx12x
1958 -
1959 - is_rocm_on_gfx9 = on_gfx9()
1960 - is_rocm_on_gfx12x = on_gfx12x()
1961 - else:
1962 - is_rocm_on_gfx9 = False
1963 - is_rocm_on_gfx12x = False
1964 -
1965 - device_supports_fp8 = (
1966 - is_rocm_on_gfx9
1967 - or is_rocm_on_gfx12x
1968 - or (p.is_cuda() and p.has_device_capability((8, 9)))
1969 - or p.is_xpu()
1970 - )
1971 -
1972 - if not device_supports_fp8:
1955 + if not current_platform.supports_fp8():
1956 return (weight_key, activation_key) == (None, None)
1957
1958 SUPPORTED_W_A = [
vllm/platforms/rocm.py
+1 -1
@@ -800,7 +800,7 @@ class RocmPlatform(Platform):
800
801 @classmethod
802 def supports_fp8(cls) -> bool:
803 - return any(gfx in _GCN_ARCH for gfx in ["gfx94", "gfx95", "gfx12"])
803 + return on_gfx9() or on_gfx12x()
804
805 @classmethod
806 def is_fp8_fnuz(cls) -> bool:
vllm/platforms/xpu.py
+4
@@ -323,6 +323,10 @@ class XPUPlatform(Platform):
323 )
324 return "vllm.distributed.device_communicators.xpu_communicator.XpuCommunicator" # noqa
325
326 + @classmethod
327 + def supports_fp8(cls) -> bool:
328 + return True
329 +
330 @classmethod
331 def get_default_ir_op_priority(
332 cls, vllm_config: "VllmConfig"