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

tests/kernels/moe/utils.py

@@ -8,7 +8,12 @@ from tests.kernels.quant_utils import per_block_cast_to_int8
 from tests.kernels.quantization.nvfp4_utils import FLOAT4_E2M1_MAX, FLOAT8_E4M3_MAX
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.fused_moe import fused_experts, fused_topk
-from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
+from vllm.model_executor.layers.fused_moe.config import (
+    FusedMoEConfig,
+    FusedMoEParallelConfig,
+    FusedMoEQuantConfig,
+    RoutingMethodType,
+)
 from vllm.model_executor.layers.fused_moe.fused_batched_moe import (
     BatchedPrepareAndFinalize,
     BatchedTritonExperts,
@@ -20,6 +25,34 @@ from vllm.utils.deep_gemm import per_block_cast_to_fp8
 from vllm.utils.math_utils import round_up
 
 
+def make_dummy_moe_config(
+    num_experts: int = 1,
+    experts_per_token: int = 1,
+    hidden_dim: int = 1,
+    intermediate_size_per_partition: int = 1,
+    in_dtype: torch.dtype = torch.bfloat16,
+) -> FusedMoEConfig:
+    """
+    This is a dummy config for the mk constructor interface
+    as most kernels like DeepGEMM, CUTLASSFp4, Triton, MARLIN
+    do not actually use this config.
+
+    CUTLASSFp8 needs to set some params for workshapes.
+    """
+    return FusedMoEConfig(
+        num_experts=num_experts,
+        experts_per_token=experts_per_token,
+        hidden_dim=hidden_dim,
+        intermediate_size_per_partition=intermediate_size_per_partition,
+        num_local_experts=num_experts,
+        moe_parallel_config=FusedMoEParallelConfig.make_no_parallel(),
+        activation="silu",
+        in_dtype=in_dtype,
+        device="cuda",
+        routing_method=RoutingMethodType.TopK,
+    )
+
+
 def triton_moe(
     a: torch.Tensor,
     w1: torch.Tensor,
@@ -81,6 +114,7 @@ def batched_moe(
             max_num_tokens=max_num_tokens,
             num_dispatchers=1,
             quant_config=quant_config,
+            moe_config=make_dummy_moe_config(),
         ),
     )
 
@@ -121,6 +155,7 @@ def naive_batched_moe(
             max_num_tokens=max_num_tokens,
             num_dispatchers=1,
             quant_config=quant_config,
+            moe_config=make_dummy_moe_config(),
         ),
     )