[Kernel] Delegate construction of FusedMoEQuantConfig to FusedMoEMethodBase subclasses (#22537)

Signed-off-by: Bill Nell <bnell@redhat.com>

tests/kernels/moe/test_gpt_oss_triton_kernels.py

@@ -23,6 +23,7 @@ from triton_kernels.tensor import FP4, convert_layout, wrap_torch_tensor
 from triton_kernels.tensor_details import layout
 from triton_kernels.testing import assert_close
 
+from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
 from vllm.model_executor.layers.fused_moe.fused_batched_moe import (
     BatchedPrepareAndFinalize)
 from vllm.model_executor.layers.fused_moe.fused_moe import fused_topk
@@ -293,6 +294,13 @@ def test_equiv(num_token, a_dtype, w_dtype, tp):
         pc2,
     ) = init_compute_data(M, K, N, E, a_dtype, w_dtype, num_warps=8)
 
+    quant_config = FusedMoEQuantConfig.make(
+        w1_bias=w1_bias_tri,
+        w2_bias=w2_bias_tri,
+        w1_precision=pc1,
+        w2_precision=pc2,
+    )
+
     out_triton_monolithic = triton_kernel_moe_forward(
         hidden_states=x_tri,
         w1=w1_tri,
@@ -300,10 +308,7 @@ def test_equiv(num_token, a_dtype, w_dtype, tp):
         gating_output=exp_data_tri,
         topk=topk,
         renormalize=True,
-        w1_bias=w1_bias_tri,
-        w2_bias=w2_bias_tri,
-        w1_precision=pc1,
-        w2_precision=pc2,
+        quant_config=quant_config,
     )
     out_triton_monolithic = out_triton_monolithic[..., :K]
 
@@ -336,6 +341,13 @@ def batched_moe(
 ) -> torch.Tensor:
     max_num_tokens = round_up(a.shape[0], 64)
 
+    quant_config = FusedMoEQuantConfig.make(
+        w1_precision=w1_precision,
+        w2_precision=w2_precision,
+        w1_bias=w1_bias,
+        w2_bias=w2_bias,
+    )
+
     fused_experts = FusedMoEModularKernel(
         BatchedPrepareAndFinalize(
             max_num_tokens,
@@ -344,19 +356,12 @@ def batched_moe(
             rank=0,
         ),
         BatchedOAITritonExperts(
-            None,
             max_num_tokens=max_num_tokens,
             num_dispatchers=1,
-            w1_precision=w1_precision,
-            w2_precision=w2_precision,
+            quant_config=quant_config,
         ),
     )
 
-    extra_expert_args = {
-        "w1_bias": w1_bias,
-        "w2_bias": w2_bias,
-    }
-
     topk_weight, topk_ids, _ = fused_topk(a, gating_output, topk, renormalize)
 
     return fused_experts(
@@ -365,7 +370,6 @@ def batched_moe(
         w2,
         topk_weight,
         topk_ids,
-        extra_expert_args=extra_expert_args,
     )