[MoE Refactor] Oracle Select FP8+NVFP4 Kernels In Priority (#32414)

vllm/model_executor/layers/quantization/utils/flashinfer_utils.py

@@ -9,10 +9,6 @@ from vllm import envs
 from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe.config import (
     FusedMoEConfig,
-    FusedMoEQuantConfig,
-)
-from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (
-    FlashInferExperts,
 )
 from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_prepare_finalize import (  # noqa: E501
     create_flashinfer_prepare_finalize,
@@ -203,33 +199,6 @@ def build_flashinfer_fp8_cutlass_moe_prepare_finalize(
     )
 
 
-def select_cutlass_fp8_gemm_impl(
-    moe: FusedMoEConfig | None,
-    quant_config: FusedMoEQuantConfig,
-    out_dtype: torch.dtype | None = None,
-    use_deepseek_fp8_block_scale: bool = False,
-) -> mk.FusedMoEPermuteExpertsUnpermute:
-    """Return a GEMM *experts* implementation for fused-MoE layers"""
-
-    if moe is not None:
-        return FlashInferExperts(
-            out_dtype=moe.in_dtype,
-            quant_config=quant_config,
-            ep_rank=moe.moe_parallel_config.ep_rank,
-            ep_size=moe.moe_parallel_config.ep_size,
-            tp_rank=moe.moe_parallel_config.tp_rank,
-            tp_size=moe.moe_parallel_config.tp_size,
-            use_deepseek_fp8_block_scale=use_deepseek_fp8_block_scale,
-        )
-
-    assert out_dtype is not None, "If moe config is None, out_dtype must be passed"
-    return FlashInferExperts(
-        out_dtype=out_dtype,
-        quant_config=quant_config,
-        use_deepseek_fp8_block_scale=use_deepseek_fp8_block_scale,
-    )
-
-
 def get_flashinfer_moe_backend() -> FlashinferMoeBackend:
     backend_map = {
         "throughput": FlashinferMoeBackend.CUTLASS,