post merge lora fixes for CI (#3536) [skip ci]

* post merge lora fixes for CI

* handle lora kernel auto-enable for moe without grouped_mm

* prefer not to import torch in schema validation

src/axolotl/utils/schemas/config.py

@@ -1385,6 +1385,39 @@ class AxolotlConfigWCapabilities(AxolotlInputConfig):
             if data.get("trust_remote_code"):
                 return data
 
+            # Skip auto-enable for MoE models when native grouped_mm is unavailable
+            # (torch < 2.9). The grouped_mm fallback in transformers uses torch.mm
+            # with out= which bypasses autocast and fails on mixed dtypes during eval.
+            env_capabilities = data.get("env_capabilities", {})
+            torch_version = env_capabilities.get("torch_version")
+            if torch_version is None:
+                import torch
+
+                torch_version = str(torch.__version__).split("+", maxsplit=1)[0]
+            has_grouped_mm = version.parse(torch_version) >= version.parse("2.9.0")
+            if not has_grouped_mm:
+                is_moe = False
+                model_type = data.get("model_config_type", "")
+                if model_type and "moe" in model_type.lower():
+                    is_moe = True
+                if not is_moe:
+                    try:
+                        from transformers import AutoConfig
+
+                        base_model = data.get("base_model")
+                        if base_model:
+                            auto_cfg = AutoConfig.from_pretrained(
+                                base_model, trust_remote_code=False
+                            )
+                            if getattr(auto_cfg, "num_local_experts", None) or getattr(
+                                auto_cfg, "num_experts", None
+                            ):
+                                is_moe = True
+                    except Exception:  # pylint: disable=broad-exception-caught
+                        pass
+                if is_moe:
+                    return data
+
             # Check multi-GPU compatibility
             capabilities = data.get("capabilities")
             is_multi_gpu = capabilities and capabilities.get("n_gpu", 0) > 1