fix

src/axolotl/kernels/moe/torch_grouped.py

@@ -42,6 +42,17 @@ def _is_qwen_layout(mod: torch.nn.Module) -> bool:
     return (has_fused or has_split) and hasattr(mod, "down_proj")
 
 
+def _num_experts(module: torch.nn.Module) -> int:
+    """Return expert count, supporting ModuleList-style inputs."""
+    count = getattr(module, "num_experts", None)
+    if count is not None:
+        return int(count() if callable(count) else count)
+    try:
+        return len(module)  # type: ignore[arg-type]
+    except TypeError as exc:  # pragma: no cover - defensive
+        raise AttributeError("experts module missing num_experts/len support") from exc
+
+
 def _call_grouped_mm(
     As: List[torch.Tensor], Bs: List[torch.Tensor]
 ) -> Optional[List[torch.Tensor]]:
@@ -103,7 +114,16 @@ def moe_ffn_forward_grouped(
     flat_idx = topk_idx.view(-1)
     x_rep = x.repeat_interleave(top_k, dim=0)
 
-    E = experts_module.num_experts
+    try:
+        E = _num_experts(experts_module)
+    except AttributeError as err:
+        LAST_ERROR = str(err)
+        if not getattr(experts_module, "_ax_grouped_logged_fail", False):
+            _LOGGER.warning(
+                "torch_grouped: could not determine expert count; falling back to naive"
+            )
+            experts_module._ax_grouped_logged_fail = True
+        return None, None
     dev, dt = x.device, x.dtype
     first = experts_module[0]
 

tests/monkeypatch/test_moe_grouped.py

@@ -100,6 +100,36 @@ def test_grouped_uses_per_expert_nested_modules(monkeypatch):
     assert not torch.equal(first_call[0], first_call[1])
 
 
+def test_grouped_accepts_module_list_experts(monkeypatch):
+    hidden = 4
+    intermediate = 2
+    experts = nn.ModuleList(
+        [DummyQwenExpert(i, hidden, intermediate) for i in range(2)]
+    )
+    gate = nn.Linear(hidden, len(experts), bias=False)
+    nn.init.zeros_(gate.weight)
+
+    calls = {"count": 0}
+
+    def fake_grouped_mm(As, Bs):
+        calls["count"] += 1
+        return [
+            torch.zeros(a.shape[0], b.shape[-1], device=a.device, dtype=a.dtype)
+            for a, b in zip(As, Bs, strict=False)
+        ]
+
+    monkeypatch.setattr(torch_grouped_module, "_call_grouped_mm", fake_grouped_mm)
+
+    hidden_states = torch.randn(1, 2, hidden)
+    y, router_logits = torch_grouped_module.moe_ffn_forward_grouped(
+        hidden_states, gate, experts, top_k=2
+    )
+
+    assert y is not None
+    assert router_logits is not None
+    assert calls["count"] > 0
+
+
 class _DummyCfg:
     moe_backend = "torch_grouped"