attempt to find linear modules for qlora

src/axolotl/utils/models.py

@@ -4,6 +4,7 @@ import os
 from pathlib import Path
 from typing import Optional, Tuple, TYPE_CHECKING
 
+import bitsandbytes as bnb
 import torch
 import transformers
 from torch import nn
@@ -334,6 +335,24 @@ def load_llama_adapter(model, cfg):
     return model, peft_config
 
 
+def find_all_linear_names(bits, model):
+    cls = (
+        bnb.nn.Linear4bit
+        if bits == 4
+        else (bnb.nn.Linear8bitLt if bits == 8 else torch.nn.Linear)
+    )
+    lora_module_names = set()
+    for name, module in model.named_modules():
+        if isinstance(module, cls):
+            names = name.split(".")
+            lora_module_names.add(names[0] if len(names) == 1 else names[-1])
+
+    if "lm_head" in lora_module_names:  # needed for 16-bit
+        lora_module_names.remove("lm_head")
+
+    return list(lora_module_names)
+
+
 def load_lora(model, cfg):
     # type: (PreTrainedModel, AttrDefault) -> Tuple[PreTrainedModel, Optional[PeftConfig]]
 
@@ -343,12 +362,15 @@ def load_lora(model, cfg):
         PeftModel,
     )
 
-    lora_config = None
+    bits = 4 if cfg.load_in_4bits else 8 if cfg.load_in_8bits else None
+    linear_names = find_all_linear_names(bits, model)
+    logging.info(f"found linear modules: {repr(linear_names)}")
+    lora_target_modules = cfg.lora_target_modules + linear_names
 
     lora_config = LoraConfig(
         r=cfg.lora_r,
         lora_alpha=cfg.lora_alpha,
-        target_modules=cfg.lora_target_modules,
+        target_modules=lora_target_modules,
         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,