migrate lora_ to peft_

README.md

@@ -384,10 +384,10 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
 - lora
   ```yaml
   adapter: lora # qlora or leave blank for full finetune
-  lora_r: 8
-  lora_alpha: 16
-  lora_dropout: 0.05
-  lora_target_modules:
+  peft_r: 8
+  peft_alpha: 16
+  peft_dropout: 0.05
+  peft_target_modules:
     - q_proj
     - v_proj
   ```
@@ -536,10 +536,10 @@ peft_model_dir:
 # LoRA hyperparameters
 # For more details about the following options, see:
 # https://www.anyscale.com/blog/fine-tuning-llms-lora-or-full-parameter-an-in-depth-analysis-with-llama-2
-lora_r: 8
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_modules:
+peft_r: 8
+peft_alpha: 16
+peft_dropout: 0.05
+peft_target_modules:
   - q_proj
   - v_proj
 #  - k_proj
@@ -547,13 +547,13 @@ lora_target_modules:
 #  - gate_proj
 #  - down_proj
 #  - up_proj
-lora_target_linear: # If true, will target all linear layers
+peft_target_linear: # if true, will target all linear 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.
 # `embed_tokens` converts tokens to embeddings, and `lm_head` converts embeddings to token probabilities.
 # https://github.com/huggingface/peft/issues/334#issuecomment-1561727994
-lora_modules_to_save:
+peft_modules_to_save:
 #  - embed_tokens
 #  - lm_head
 
@@ -561,10 +561,8 @@ lora_modules_to_save:
 # If you merge the adapter to the base model, a subdirectory `merged` will be created under this directory.
 # Make sure `lora_model_dir` points to this directory if you want to use the trained model.
 lora_out_dir:
-lora_fan_in_fan_out: false
-ia3_target_modules:  # target modules for IA3, for llama, k, v, and down projections
-ia3_feedforward_modules:  # ffn modules for IA3, for llama down projection
-ia3_fan_in_fan_out:
+peft_fan_in_fan_out: false
+peft_feedforward_modules:  # ffn modules for IA3, for llama down projection
 
 # ReLoRA configuration
 # Must use either 'lora' or 'qlora' adapter, and does not support fsdp or deepspeed

examples/llama-2/ia3.yml

@@ -21,13 +21,13 @@ pad_to_sequence_len: true
 
 adapter: ia3
 peft_model_dir:
-ia3_target_modules:
+peft_target_modules:
   - k_proj
   - v_proj
   - down_proj
-ia3_feedforward_modules:
+peft_feedforward_modules:
   - down_proj
-ia3_fan_in_fan_out: false
+peft_fan_in_fan_out: false
 
 wandb_project:
 wandb_entity:

src/axolotl/utils/config.py

@@ -121,6 +121,18 @@ def normalize_config(cfg):
 
     log_gpu_memory_usage(LOG, "baseline", cfg.device)
 
+    if cfg.adapter is not None:
+        for key in list(cfg.keys()):
+            if key.startswith("lora_"):
+                new_key = key.replace("lora_", "peft_")
+                LOG.warning(
+                    PendingDeprecationWarning(
+                        f"{key} soon to be deprecated. please use {new_key}"
+                    )
+                )
+                cfg[new_key] = cfg[key]
+                del cfg[key]
+
 
 def validate_config(cfg):
     if is_torch_bf16_gpu_available():

src/axolotl/utils/models.py

@@ -490,11 +490,11 @@ def load_llama_adapter(model, cfg):
         task_type="CAUSAL_LM",
     )
 
-    if cfg.peft_model_dir or cfg.lora_model_dir:
+    if cfg.peft_model_dir:
         LOG.debug("Loading pretained PEFT - llama_adapter")
         model = PeftModel.from_pretrained(
             model,
-            cfg.peft_model_dir or cfg.lora_model_dir,
+            cfg.peft_model_dir,
             torch_dtype=torch.float16,
         )
     else:
@@ -507,7 +507,7 @@ def load_llama_adapter(model, cfg):
 
 def find_all_linear_names(model):
     cls = (bnb.nn.Linear4bit, bnb.nn.Linear8bitLt, torch.nn.Linear, QuantLinear)
-    lora_module_names = set()
+    peft_module_names = set()
     for name, module in model.named_modules():
         if (
             isinstance(module, cls)
@@ -515,12 +515,12 @@ def find_all_linear_names(model):
             and module.__class__.__name__ not in ("LlamaLinearScalingRotaryEmbedding",)
         ):
             names = name.split(".")
-            lora_module_names.add(names[0] if len(names) == 1 else names[-1])
+            peft_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")
+    if "lm_head" in peft_module_names:  # needed for 16-bit
+        peft_module_names.remove("lm_head")
 
-    return list(lora_module_names)
+    return list(peft_module_names)
 
 
 def load_lora(model, cfg, inference=False):
@@ -528,20 +528,20 @@ def load_lora(model, cfg, inference=False):
 
     from peft import LoraConfig, PeftModel, get_peft_model
 
-    lora_target_modules = list(cfg.lora_target_modules or [])
+    peft_target_modules = list(cfg.peft_target_modules or [])
 
-    if cfg.lora_target_linear:
+    if cfg.peft_target_linear:
         linear_names = find_all_linear_names(model)
         LOG.info(f"found linear modules: {repr(linear_names)}")
-        lora_target_modules = list(set(lora_target_modules + linear_names))
+        peft_target_modules = list(set(peft_target_modules + linear_names))
 
-    lora_config = LoraConfig(
-        r=cfg.lora_r,
-        lora_alpha=cfg.lora_alpha,
-        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,
+    peft_config = LoraConfig(
+        r=cfg.peft_r,
+        lora_alpha=cfg.peft_alpha,
+        target_modules=peft_target_modules,
+        lora_dropout=cfg.peft_dropout,
+        fan_in_fan_out=cfg.peft_fan_in_fan_out,
+        modules_to_save=cfg.peft_modules_to_save if cfg.peft_modules_to_save else None,
         bias="none",
         task_type="CAUSAL_LM",
     )
@@ -554,11 +554,11 @@ def load_lora(model, cfg, inference=False):
             is_trainable=(not inference),
         )
     else:
-        model = get_peft_model(model, lora_config)
+        model = get_peft_model(model, peft_config)
 
     model.print_trainable_parameters()
 
-    return model, lora_config
+    return model, peft_config
 
 
 def load_ia3(model, cfg, inference=False):
@@ -566,18 +566,18 @@ def load_ia3(model, cfg, inference=False):
 
     from peft import IA3Config, PeftModel, get_peft_model
 
-    ia3_config_kwargs = {}
-    if cfg.ia3_init_ia3_weights is not None:
-        ia3_config_kwargs["init_ia3_weights"] = cfg.ia3_init_ia3_weights
-    if cfg.ia3_fan_in_fan_out is not None:
-        ia3_config_kwargs["fan_in_fan_out"] = cfg.ia3_fan_in_fan_out
+    peft_config_kwargs = {}
+    if cfg.peft_init_ia3_weights is not None:
+        peft_config_kwargs["init_ia3_weights"] = cfg.peft_init_ia3_weights
+    if cfg.peft_fan_in_fan_out is not None:
+        peft_config_kwargs["fan_in_fan_out"] = cfg.peft_fan_in_fan_out
 
-    ia3_config = IA3Config(
-        target_modules=cfg.ia3_target_modules,
-        feedforward_modules=cfg.ia3_feedforward_modules,
-        modules_to_save=cfg.ia3_modules_to_save,
+    peft_config = IA3Config(
+        target_modules=cfg.peft_target_modules,
+        feedforward_modules=cfg.peft_feedforward_modules,
+        modules_to_save=cfg.peft_modules_to_save,
         task_type="CAUSAL_LM",
-        **ia3_config_kwargs,
+        **peft_config_kwargs,
     )
 
     if cfg.peft_model_dir:
@@ -588,8 +588,8 @@ def load_ia3(model, cfg, inference=False):
             is_trainable=(not inference),
         )
     else:
-        model = get_peft_model(model, ia3_config)
+        model = get_peft_model(model, peft_config)
 
     model.print_trainable_parameters()
 
-    return model, ia3_config
+    return model, peft_config

tests/test_cfg_normalization.py

@@ -0,0 +1,48 @@
+"""Module for testing the validation module"""
+
+import logging
+import unittest
+from typing import Optional
+
+import pytest
+
+from axolotl.utils.config import normalize_config
+from axolotl.utils.dict import DictDefault
+
+
+class NormalizationTest(unittest.TestCase):
+    """
+    Test the cfg normalization module
+    """
+
+    _caplog: Optional[pytest.LogCaptureFixture] = None
+
+    @pytest.fixture(autouse=True)
+    def inject_fixtures(self, caplog):
+        self._caplog = caplog
+
+    def test_lora_to_peft(self):
+        base_cfg = DictDefault(
+            {
+                "gradient_accumulation_steps": 1,
+                "micro_batch_size": 1,
+                "base_model": "NousResearch/Llama-2-7b-hf",
+                "base_model_config": "NousResearch/Llama-2-7b-hf",
+            }
+        )
+        cfg = base_cfg | DictDefault(
+            {
+                "adapter": "lora",
+                "lora_r": 128,
+                "lora_alpha": 64,
+            }
+        )
+        with self._caplog.at_level(logging.WARNING):
+            normalize_config(cfg)
+            assert any(
+                "soon to be deprecated. please use peft_" in record.message
+                for record in self._caplog.records
+            )
+
+            assert cfg.peft_r == 128
+            assert cfg.peft_alpha == 64