handle base+lora split kernel for older moe models

tests/integrations/test_scattermoe_lora_kernels.py

@@ -294,6 +294,34 @@ class TestScatterMoELoRAAutograd:
         assert lA.grad.abs().sum() > 0, f"[{desc}] lA.grad all zero"
 
 
+    def test_split_matches_fused(self):
+        """Split dispatch (for few large experts) matches fused kernel."""
+        # Use a shape where split would be dispatched (large K*N, few E)
+        E, K, N, T, k, R = 8, 512, 1024, 128, 2, 16
+        x, W, lA, lB, sei, ssi, eo = _setup(E, K, N, T, k, R)
+
+        # Force fused path
+        orig = lora_ops._SPLIT_LORA_FWD_THRESHOLD
+        lora_ops._SPLIT_LORA_FWD_THRESHOLD = 10**18
+        out_fused = lora_ops.scatter2scatter_lora(
+            X=x, W=W, sorted_expert_idxs=sei, sorted_scattered_idxs=ssi,
+            k=k, lora_A=lA, lora_B=lB, scaling=SCALING,
+        )
+
+        # Force split path
+        lora_ops._SPLIT_LORA_FWD_THRESHOLD = 0
+        out_split = lora_ops.scatter2scatter_lora(
+            X=x, W=W, sorted_expert_idxs=sei, sorted_scattered_idxs=ssi,
+            k=k, lora_A=lA, lora_B=lB, scaling=SCALING,
+        )
+        lora_ops._SPLIT_LORA_FWD_THRESHOLD = orig
+
+        norm_err = (
+            (out_fused.float() - out_split.float()).norm()
+            / (out_fused.float().norm() + 1e-6)
+        ).item()
+        assert norm_err < 0.01, f"split vs fused norm_err={norm_err}"
+
     def test_scaling_zero_gives_base_only(self):
         """With scaling=0.0, LoRA contribution vanishes. Output = X@W."""
         E, K, N, T, k, R = 16, 64, 32, 32, 2, 4