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

tests/kernels/moe/test_block_fp8.py

@@ -4,7 +4,12 @@
 import pytest
 import torch
 
-from tests.kernels.moe.utils import make_test_quant_config, make_test_weights
+import vllm.model_executor.layers.fused_moe.modular_kernel as mk
+from tests.kernels.moe.utils import (
+    make_dummy_moe_config,
+    make_test_quant_config,
+    make_test_weights,
+)
 from tests.kernels.quant_utils import (
     native_per_token_group_quant_fp8,
     native_w8a8_block_matmul,
@@ -15,13 +20,21 @@ from vllm.model_executor.layers.fused_moe import (
     fused_experts,
     fused_topk,
 )
+from vllm.model_executor.layers.fused_moe.config import (
+    fp8_w8a8_moe_quant_config,
+)
 from vllm.model_executor.layers.fused_moe.deep_gemm_moe import (
     _valid_deep_gemm_shape,
-    deep_gemm_moe_fp8,
 )
 from vllm.model_executor.layers.fused_moe.fused_moe import (
     modular_triton_fused_moe,
 )
+from vllm.model_executor.layers.fused_moe.prepare_finalize import (
+    MoEPrepareAndFinalizeNoEP,
+)
+from vllm.model_executor.layers.fused_moe.triton_deep_gemm_moe import (
+    TritonOrDeepGemmExperts,
+)
 from vllm.platforms import current_platform
 from vllm.utils.deep_gemm import (
     get_mk_alignment_for_contiguous_layout,
@@ -161,7 +174,7 @@ def test_w8a8_block_fp8_fused_moe(
         block_shape=block_size,
     )
 
-    m_fused_moe = modular_triton_fused_moe(quant_config)
+    m_fused_moe = modular_triton_fused_moe(make_dummy_moe_config(), quant_config)
 
     topk_weights, topk_ids, _ = fused_topk(a, score.float(), topk, False)
 
@@ -236,6 +249,29 @@ def test_w8a8_block_fp8_deep_gemm_fused_moe(M, N, K, E, topk, seed, monkeypatch)
 
     topk_weights, topk_ids, _ = fused_topk(a, score.float(), topk, False)
 
+    quant_config = fp8_w8a8_moe_quant_config(
+        w1_scale=w1_s,
+        w2_scale=w2_s,
+        block_shape=block_size,
+    )
+
+    deep_gemm_experts = mk.FusedMoEModularKernel(
+        prepare_finalize=MoEPrepareAndFinalizeNoEP(),
+        fused_experts=TritonOrDeepGemmExperts(
+            moe_config=make_dummy_moe_config(),
+            quant_config=quant_config,
+        ),
+    )
+
+    def deep_gemm_moe_fp8(a, w1, w2, w1_s, w2_s, topk_weights, topk_ids):
+        return deep_gemm_experts(
+            hidden_states=a,
+            w1=w1,
+            w2=w2,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+        )
+
     # Set the context to avoid lots of warning spam.
     with set_current_vllm_config(vllm_config):
         ref_out = torch_w8a8_block_fp8_moe(