Adding full finetuning (#903)

* Adding full model weights finetuning

* Updating the LORA.md and ACKNOWLEDGMENTS.md files.

* removing --use-dora and --fulll-training and adding --fine-tune-type

* some clean up

* reformating and fixing dora training

* updated CONFIG_DEFAULTS

* update config example

* update in the config example fie

* Update LORA.md

* merge and commit

* adding argument for dora linear layer

* clean up

* clean up in the example yaml file

* fix

* final fix before sending

* small addition to re md file

* fix for loading the fully trained model by saving all the files and configs correctly

* clean up

* removing the unnesesairy files

* changing lora layers back to 16

* removed max file size

* nits

* resolve merge

* some consistency changes

---------

Co-authored-by: Awni Hannun <awni@apple.com>

llms/mlx_lm/lora.py

@@ -15,9 +15,9 @@ from .tokenizer_utils import TokenizerWrapper
 from .tuner.datasets import load_dataset
 from .tuner.trainer import TrainingArgs, TrainingCallback, evaluate, train
 from .tuner.utils import (
-    apply_lora_layers,
     build_schedule,
     linear_to_lora_layers,
+    load_adapters,
     print_trainable_parameters,
 )
 from .utils import load, save_config
@@ -41,9 +41,10 @@ yaml_loader.add_implicit_resolver(
 CONFIG_DEFAULTS = {
     "model": "mlx_model",
     "train": False,
+    "fine_tune_type": "lora",
     "data": "data/",
     "seed": 0,
-    "lora_layers": 16,
+    "num_layers": 16,
     "batch_size": 4,
     "iters": 1000,
     "val_batches": 25,
@@ -58,7 +59,6 @@ CONFIG_DEFAULTS = {
     "max_seq_length": 2048,
     "lr_schedule": None,
     "lora_parameters": {"rank": 8, "alpha": 16, "dropout": 0.0, "scale": 10.0},
-    "use_dora": False,
 }
 
 
@@ -82,7 +82,14 @@ def build_parser():
         help="Directory with {train, valid, test}.jsonl files",
     )
     parser.add_argument(
-        "--lora-layers",
+        "--fine-tune-type",
+        type=str,
+        choices=["lora", "dora", "full"],
+        default="lora",
+        help="Type of fine-tuning to perform: lora, dora, or full.",
+    )
+    parser.add_argument(
+        "--num-layers",
         type=int,
         help="Number of layers to fine-tune. Default is 16, use -1 for all.",
     )
@@ -107,12 +114,12 @@ def build_parser():
     parser.add_argument(
         "--resume-adapter-file",
         type=str,
-        help="Load path to resume training with the given adapters.",
+        help="Load path to resume training from the given fine-tuned weights.",
     )
     parser.add_argument(
         "--adapter-path",
         type=str,
-        help="Save/load path for the adapters.",
+        help="Save/load path for the fine-tuned weights.",
     )
     parser.add_argument(
         "--save-every",
@@ -148,9 +155,6 @@ def build_parser():
         default=None,
     )
     parser.add_argument("--seed", type=int, default=None, help="The PRNG seed")
-    parser.add_argument(
-        "--use-dora", action="store_true", default=None, help="Use DoRA to finetune."
-    )
     return parser
 
 
@@ -162,21 +166,31 @@ def train_model(
     valid_set,
     training_callback: TrainingCallback = None,
 ):
-    # Freeze all layers
     model.freeze()
+    if args.fine_tune_type == "full":
+        for l in model.layers[-min(args.num_layers, 0) :]:
+            l.unfreeze()
+    elif args.fine_tune_type in ["lora", "dora"]:
+        # Convert linear layers to lora/dora layers and unfreeze in the process
+        linear_to_lora_layers(
+            model,
+            args.num_layers,
+            args.lora_parameters,
+            use_dora=(args.fine_tune_type == "dora"),
+        )
+    else:
+        raise ValueError(f"Received unknown fine-tune-type {args.fine_tune_type}")
 
-    # Convert linear layers to lora layers and unfreeze in the process
-    linear_to_lora_layers(model, args.lora_layers, args.lora_parameters, args.use_dora)
-
-    # Resume training the given adapters.
+    # Resume from weights if provided
     if args.resume_adapter_file is not None:
-        print(f"Loading pretrained adapters from {args.resume_adapter_file}")
+        print(f"Loading fine-tuned weights from {args.resume_adapter_file}")
         model.load_weights(args.resume_adapter_file, strict=False)
 
     print_trainable_parameters(model)
 
     adapter_path = Path(args.adapter_path)
     adapter_path.mkdir(parents=True, exist_ok=True)
+
     adapter_file = adapter_path / "adapters.safetensors"
     save_config(vars(args), adapter_path / "adapter_config.json")
 
@@ -240,7 +254,7 @@ def run(args, training_callback: TrainingCallback = None):
     if args.test and not args.train:
         # Allow testing without LoRA layers by providing empty path
         if args.adapter_path != "":
-            apply_lora_layers(model, args.adapter_path)
+            load_adapters(model, args.adapter_path)
 
     elif args.train:
         print("Training")