removing is-reference-free argument

llms/mlx_lm/LORA.md

@@ -95,7 +95,6 @@ The DPO training accepts the following additional parameters:
 
 - `--beta`: Controls the strength of the DPO loss (default: 0.1)
 - `--dpo-loss-type`: Choose between "sigmoid" (default), "hinge", "ipo", or "dpop" loss functions
-- `--is-reference-free`: Enable reference-free DPO training
 - `--delta`: Margin parameter for hinge loss (default: 50.0)
 - `--reference-model-path`: Path to a reference model for DPO training
 

llms/mlx_lm/lora.py

@@ -66,7 +66,6 @@ CONFIG_DEFAULTS = {
     "lora_parameters": {"rank": 8, "alpha": 16, "dropout": 0.0, "scale": 10.0},
     "beta": 0.1,
     "dpo_loss_type": "sigmoid",
-    "is_reference_free": False,
     "delta": 50.0,
     "reference_model_path": None,
     "train_bias_only": False,
@@ -176,7 +175,6 @@ def build_parser():
     )
     parser.add_argument("--beta", type=float)
     parser.add_argument("--dpo-loss-type", type=str, choices=["sigmoid", "hinge", "ipo", "dpop"])
-    parser.add_argument("--is-reference-free", action="store_true")
     parser.add_argument("--delta", type=float)
     parser.add_argument("--reference-model-path", type=str)
     parser.add_argument("--train-bias-only", action="store_true")
@@ -240,7 +238,6 @@ def train_model(
             grad_checkpoint=args.grad_checkpoint,
             beta=args.beta,
             loss_type=args.dpo_loss_type,
-            is_reference_free=args.is_reference_free,
             delta=args.delta,
             reference_model_path=args.reference_model_path
         )

llms/mlx_lm/tuner/dpo_trainer.py

@@ -28,12 +28,6 @@ class DPOTrainingArgs(TrainingArgs):
             "help": "DPO loss type: 'sigmoid', 'hinge', 'ipo', or 'dpop'."
         }
     )
-    is_reference_free: bool = field(
-        default=False,
-        metadata={
-            "help": "Whether to use reference-free DPO training."
-        }
-    )
     delta: float = field(
         default=50.0,
         metadata={
@@ -50,7 +44,6 @@ class DPOTrainingArgs(TrainingArgs):
 
 def dpo_loss(
     model,
-    reference_teacher_model,
     chosen: mx.array,
     rejected: mx.array,
     chosen_masks: mx.array,
@@ -58,7 +51,7 @@ def dpo_loss(
     beta: float,
     delta: float,
     loss_type: str = "sigmoid",
-    is_reference_free: bool = False
+    ref_model=None,
 ):
     def make_predictions(model, x, mask):
         inputs = x[:, :-1]
@@ -84,12 +77,12 @@ def dpo_loss(
         policy_rejected_score = policy_rejected_scores.sum(-1)
 
     # Calculate log probabilities for reference model
-    if is_reference_free:
+    if ref_model is None:
         reference_chosen_score = mx.zeros_like(policy_chosen_score)
         reference_rejected_score = mx.zeros_like(policy_rejected_score)
     else:
-        reference_chosen_scores = mx.stop_gradient(make_predictions(reference_teacher_model, chosen, chosen_masks))
-        reference_rejected_scores = mx.stop_gradient(make_predictions(reference_teacher_model, rejected, rejected_masks))
+        reference_chosen_scores = mx.stop_gradient(make_predictions(ref_model, chosen, chosen_masks))
+        reference_rejected_scores = mx.stop_gradient(make_predictions(ref_model, rejected, rejected_masks))
         if loss_type == "ipo":
             # ipo uses average log probabilities
             reference_chosen_score = reference_chosen_scores.sum(-1) / num_chosen_tokens
@@ -177,7 +170,7 @@ def iterate_dpo_batches(dataset, batch_size, max_seq_length, train=False):
 
 def evaluate_dpo(
     model,
-    reference_model,
+    ref_model,
     dataset,
     batch_size,
     num_batches,
@@ -206,7 +199,7 @@ def evaluate_dpo(
 
         loss, reward, toks, metrics = loss_fn(
             model=model,
-            reference_teacher_model=reference_model,
+            ref_model=ref_model,
             chosen=chosen,
             rejected=rejected,
             chosen_masks=chosen_masks,
@@ -240,7 +233,7 @@ def evaluate_dpo(
 
 def train_dpo(
     model,
-    reference_model,
+    ref_model,
     tokenizer,
     optimizer,
     train_dataset,
@@ -267,7 +260,7 @@ def train_dpo(
         
         (loss, reward, toks, metrics), grad = loss_value_and_grad(
             model, 
-            reference_model, 
+            ref_model, 
             chosen, 
             rejected, 
             chosen_masks, 
@@ -289,8 +282,7 @@ def train_dpo(
             rejected_masks=rejected_masks,
             beta=args.beta,
             delta=args.delta,
-            loss_type=loss_type,
-            is_reference_free=args.is_reference_free
+            loss_type=loss_type
         )
     
     loss_value_and_grad = nn.value_and_grad(model, loss_wrapper)
@@ -324,7 +316,7 @@ def train_dpo(
             stop = time.perf_counter()
             val_loss, val_rewards, val_ntokens, val_metrics = evaluate_dpo(
                 model=model,
-                reference_model=reference_model,
+                reference_model=ref_model,
                 dataset=val_dataset,
                 batch_size=args.batch_size,
                 num_batches=args.val_batches,