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

tests/kernels/moe/test_flashinfer.py

@@ -8,7 +8,10 @@ import torch
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from vllm.config import ParallelConfig, VllmConfig, set_current_vllm_config
 from vllm.model_executor.layers.fused_moe.config import (
+    FusedMoEConfig,
+    FusedMoEParallelConfig,
     FusedMoEQuantConfig,
+    RoutingMethodType,
     fp8_w8a8_moe_quant_config,
 )
 from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (
@@ -116,18 +119,7 @@ class TestData:
         layer.w13_weight_scale = w13_weight_scale
         layer.w2_weight_scale = w2_weight_scale
         # Setup dummy config.
-        layer.moe_parallel_config = mk.FusedMoEParallelConfig(
-            tp_size=1,
-            pcp_size=1,
-            dp_size=1,
-            ep_size=1,
-            tp_rank=0,
-            pcp_rank=0,
-            dp_rank=0,
-            ep_rank=0,
-            use_ep=False,
-            all2all_backend="naive",
-        )
+        layer.moe_parallel_config = mk.FusedMoEParallelConfig.make_no_parallel()
 
         # flashinfer expects swapped rows for w13
         layer.w13_weight.data = swap_w13_to_w31(layer.w13_weight.data)
@@ -238,6 +230,8 @@ def test_flashinfer_cutlass_moe_fp8_no_graph(
 ):
     set_random_seed(7)
     monkeypatch.setenv("VLLM_FUSED_MOE_CHUNK_SIZE", "8192")
+    assert activation in ["silu", "relu2_no_mul"]
+    is_act_and_mul = activation == "silu_and_mul"
     with set_current_vllm_config(vllm_config):
         td = TestData.make_moe_tensors_8bit(
             m, k, n, e, is_trtllm=False, activation=activation
@@ -285,19 +279,30 @@ def test_flashinfer_cutlass_moe_fp8_no_graph(
         td.layer.get_fused_moe_quant_config = get_fused_moe_quant_config
         td.layer.quant_method = td.layer
 
+        moe_config = FusedMoEConfig(
+            num_experts=e,
+            experts_per_token=topk,
+            hidden_dim=k,
+            intermediate_size_per_partition=n,
+            num_local_experts=e,
+            activation=activation,
+            device="cuda",
+            moe_parallel_config=FusedMoEParallelConfig.make_no_parallel(),
+            in_dtype=torch.bfloat16,
+            is_act_and_mul=is_act_and_mul,
+            routing_method=RoutingMethodType.TopK,
+        )
+
         kernel = mk.FusedMoEModularKernel(
             MoEPrepareAndFinalizeNoEP(
-                defer_input_quant=quant_config.is_block_quantized
+                defer_input_quant=FlashInferExperts.expects_unquantized_inputs(
+                    moe_config=moe_config,
+                    quant_config=quant_config,
+                )
             ),
             FlashInferExperts(
-                out_dtype=td.layer.orig_dtype,
+                moe_config=moe_config,
                 quant_config=quant_config,
-                ep_rank=td.layer.moe_parallel_config.ep_rank,
-                ep_size=td.layer.moe_parallel_config.ep_size,
-                tp_rank=td.layer.moe_parallel_config.tp_rank,
-                tp_size=td.layer.moe_parallel_config.tp_size,
-                use_dp=False,
-                use_deepseek_fp8_block_scale=False,
             ),
         )