LoRA: add sort data flag

llms/mlx_lm/lora.py

@@ -75,6 +75,11 @@ def build_parser():
         type=str,
         help="Directory with {train, valid, test}.jsonl files",
     )
+    parser.add_argument(
+        "--sort-by-data-length",
+        action="store_true",
+        help="Sorts sequences by length to reduce padding and enhance efficiency.",
+    )
     parser.add_argument(
         "--lora-layers",
         type=int,
@@ -196,6 +201,7 @@ def run(args, training_callback: TrainingCallback = None):
             adapter_file=args.adapter_file,
             max_seq_length=args.max_seq_length,
             grad_checkpoint=args.grad_checkpoint,
+            sort_by_data_length=args.sort_by_data_length,
         )
 
         model.train()

llms/mlx_lm/tuner/trainer.py

@@ -2,7 +2,6 @@
 
 import time
 from dataclasses import dataclass, field
-from functools import partial
 from pathlib import Path
 
 import mlx.core as mx
@@ -61,6 +60,12 @@ class TrainingArgs:
         default=False,
         metadata={"help": "Use gradient checkpointing to reduce memory use."},
     )
+    sort_by_data_length: bool = field(
+        default=False,
+        metadata={
+            "help": "Sorts sequences by length to reduce padding and enhance efficiency."
+        },
+    )
 
 
 def default_loss(model, inputs, targets, lengths):
@@ -76,19 +81,37 @@ def default_loss(model, inputs, targets, lengths):
     return ce, ntoks
 
 
-def iterate_batches(dataset, tokenizer, batch_size, max_seq_length, train=False):
-    # Sort by length:
-    idx = sorted(range(len(dataset)), key=lambda idx: len(dataset[idx]))
-
-    # Make the batches:
-    batch_idx = [
-        idx[i : i + batch_size] for i in range(0, len(idx) - batch_size + 1, batch_size)
-    ]
+def iterate_batches(
+    dataset,
+    tokenizer,
+    batch_size,
+    max_seq_length,
+    train=False,
+    sort_by_data_length=False,
+):
+    if sort_by_data_length:
+        # Sort by length
+        idx = sorted(range(len(dataset)), key=lambda idx: len(dataset[idx]))
+        # Make batches
+        batch_idx = [
+            idx[i : i + batch_size]
+            for i in range(0, len(idx) - batch_size + 1, batch_size)
+        ]
+    else:
+        # Shuffle indices
+        indices = np.arange(len(dataset))
+        indices = np.random.permutation(indices)
+        # Make batches
+        batch_idx = [
+            indices[i : i + batch_size]
+            for i in range(0, len(indices) - batch_size + 1, batch_size)
+        ]
 
     while True:
-        indices = np.random.permutation(len(batch_idx))
-        for i in indices:
-            # Encode batch
+        # Randomize batch order
+        batch_indices = np.random.permutation(len(batch_idx))
+
+        for i in batch_indices:
             batch = [tokenizer.encode(dataset[j]) for j in batch_idx[i]]
             lengths = [len(x) for x in batch]
 
@@ -129,6 +152,7 @@ def evaluate(
     max_seq_length=2048,
     loss: callable = default_loss,
     iterate_batches: callable = iterate_batches,
+    sort_by_data_length: bool = False,
 ):
     all_losses = []
     ntokens = 0
@@ -139,6 +163,7 @@ def evaluate(
             tokenizer=tokenizer,
             batch_size=batch_size,
             max_seq_length=max_seq_length,
+            sort_by_data_length=sort_by_data_length,
         ),
     ):
         losses, toks = loss(model, *batch)
@@ -213,6 +238,7 @@ def step(batch):
             batch_size=args.batch_size,
             max_seq_length=args.max_seq_length,
             train=True,
+            sort_by_data_length=args.sort_by_data_length,
         ),
     ):
         lvalue, toks = step(batch)