Require using lora adapter when performing quantized fine-tuning (#3492)

ludwig/config_validation/checks.py

@@ -581,3 +581,13 @@ def check_llm_quantization_backend_incompatibility(config: "ModelConfig") -> Non
     backend_type = config.backend.get("type", "local")
     if config.model_type == MODEL_LLM and config.quantization and backend_type != "local":
         raise ConfigValidationError(f"LLM with quantization requires the 'local' backend, found: '{backend_type}'")
+
+
+@register_config_check
+def check_qlora_requirements(config: "ModelConfig") -> None:  # noqa: F821
+    """Checks that all the necessary settings are in place for QLoRA."""
+    if config.model_type != MODEL_LLM or config.trainer.type == "none":
+        return
+
+    if config.quantization and (not config.adapter or config.adapter.type != "lora"):
+        raise ConfigValidationError("Fine-tuning and LLM with quantization requires using the 'lora' adapter")

tests/ludwig/config_validation/test_checks.py

@@ -393,3 +393,36 @@ def test_check_llm_quantization_backend_incompatibility():
     del config["quantization"]
     config["backend"] = {"type": "ray"}
     ModelConfig.from_dict(config)
+
+
+def test_check_qlora():
+    config = yaml.safe_load(
+        """
+model_type: llm
+base_model: facebook/opt-350m
+quantization:
+  bits: 4
+input_features:
+  - name: sample
+    type: text
+output_features:
+  - name: label
+    type: text
+trainer:
+  type: finetune
+"""
+    )
+
+    with pytest.raises(ConfigValidationError):
+        ModelConfig.from_dict(config)
+
+    config["adapter"] = {
+        "type": "adaption_prompt",
+    }
+    with pytest.raises(ConfigValidationError):
+        ModelConfig.from_dict(config)
+
+    config["adapter"] = {
+        "type": "lora",
+    }
+    ModelConfig.from_dict(config)