Add `layers_to_transform` for `lora_config` (#1118)

README.md

@@ -677,7 +677,8 @@ lora_target_modules:
 #  - gate_proj
 #  - down_proj
 #  - up_proj
-lora_target_linear: # If true, will target all linear layers
+lora_target_linear: # If true, will target all linear modules
+peft_layers_to_transform: # The layer indices to transform, otherwise, apply to all layers
 
 # If you added new tokens to the tokenizer, you may need to save some LoRA modules because they need to know the new tokens.
 # For LLaMA and Mistral, you need to save `embed_tokens` and `lm_head`. It may vary for other models.

src/axolotl/utils/config.py

@@ -257,6 +257,11 @@ def validate_config(cfg):
     if cfg.adapter == "lora" and (cfg.flash_attn_fuse_qkv or cfg.flash_attn_fuse_mlp):
         raise ValueError("Fused modules are not supported with LoRA")
 
+    if cfg.adapter and cfg.peft_layers_to_transform and cfg.unfrozen_parameters:
+        raise ValueError(
+            "`unfrozen_parameters` used with `peft_layers_to_transform` can have unexpected behavior."
+        )
+
     if cfg.relora_steps:
         if cfg.adapter not in ("lora", "qlora"):
             raise ValueError("cfg.adapter must be lora or qlora to use ReLoRA")

src/axolotl/utils/models.py

@@ -733,6 +733,7 @@ def load_lora(model, cfg, inference=False):
         r=cfg.lora_r,
         lora_alpha=cfg.lora_alpha,
         target_modules=lora_target_modules,
+        layers_to_transform=cfg.peft_layers_to_transform,
         lora_dropout=cfg.lora_dropout,
         fan_in_fan_out=cfg.lora_fan_in_fan_out,
         modules_to_save=cfg.lora_modules_to_save if cfg.lora_modules_to_save else None,

tests/test_validation.py

@@ -694,6 +694,21 @@ class ValidationTest(BaseValidation):
 
         validate_config(cfg)
 
+    def test_unfrozen_parameters_w_peft_layers_to_transform(self):
+        cfg = DictDefault(
+            {
+                "adapter": "lora",
+                "unfrozen_parameters": ["model.layers.2[0-9]+.block_sparse_moe.gate.*"],
+                "peft_layers_to_transform": [0, 1],
+            }
+        )
+
+        with pytest.raises(
+            ValueError,
+            match=r".*can have unexpected behavior*",
+        ):
+            validate_config(cfg)
+
 
 class ValidationCheckModelConfig(BaseValidation):
     """