Add distributed option for lora training

llms/mlx_lm/tuner/trainer.py

@@ -10,7 +10,7 @@ from typing import Union
 import mlx.core as mx
 import mlx.nn as nn
 import numpy as np
-from mlx.utils import tree_flatten
+from mlx.utils import tree_flatten, tree_map
 
 
 def grad_checkpoint(layer):
@@ -29,6 +29,17 @@ def grad_checkpoint(layer):
     type(layer).__call__ = checkpointed_fn
 
 
+def average_gradients(gradients):
+    world_size = mx.distributed.init().size()
+    if world_size == 1:
+        return gradients
+
+    def _all_average(x):
+        return mx.distributed.all_sum(x) / world_size
+
+    return tree_map(_all_average, gradients)
+
+
 @dataclass
 class TrainingArgs:
     batch_size: int = field(default=4, metadata={"help": "Minibatch size."})
@@ -84,9 +95,16 @@ def iterate_batches(dataset, tokenizer, batch_size, max_seq_length, train=False)
             f" examples but only has {len(dataset)}."
         )
 
+    # If running in distributed mode (N machines) then each one should skip N-1
+    # samples
+    step = mx.distributed.init().size()
+    if batch_size % step != 0:
+        raise ValueError("The batch size must be divisible by the number of workers")
+
     # Make the batches:
     batch_idx = [
-        idx[i : i + batch_size] for i in range(0, len(idx) - batch_size + 1, batch_size)
+        idx[i : i + batch_size : step]
+        for i in range(0, len(idx) - batch_size + 1, batch_size)
     ]
 
     while True:
@@ -112,9 +130,9 @@ def iterate_batches(dataset, tokenizer, batch_size, max_seq_length, train=False)
             max_length_in_batch = pad_to * ((max(lengths) + pad_to - 1) // pad_to)
             max_length_in_batch = min(max_length_in_batch, max_seq_length)
 
-            batch_arr = np.zeros((batch_size, max_length_in_batch), np.int32)
+            batch_arr = np.zeros((batch_size // step, max_length_in_batch), np.int32)
 
-            for j in range(batch_size):
+            for j in range(batch_size // step):
                 truncated_length = min(lengths[j], max_seq_length)
                 batch_arr[j, :truncated_length] = batch[j][:truncated_length]
                 lengths[j] = (
@@ -138,7 +156,7 @@ def evaluate(
     loss: callable = default_loss,
     iterate_batches: callable = iterate_batches,
 ):
-    all_losses = []
+    all_losses = 0
     ntokens = 0
 
     index_iterator = iter(range(num_batches)) if num_batches != -1 else iter(int, 1)
@@ -153,10 +171,14 @@ def evaluate(
         ),
     ):
         losses, toks = loss(model, *batch)
-        all_losses.append((losses * toks).item())
-        ntokens += toks.item()
+        all_losses += losses * toks
+        ntokens += toks
+        mx.eval(all_losses, ntokens)
 
-    return np.sum(all_losses) / ntokens
+    all_losses = mx.distributed.all_sum(all_losses)
+    ntokens = mx.distributed.all_sum(ntokens)
+
+    return (all_losses / ntokens).item()
 
 
 class TrainingCallback:
@@ -192,6 +214,9 @@ def train(
         # Forward and backward pass
         (lvalue, toks), grad = loss_value_and_grad(model, *batch)
 
+        # All reduce the gradients if running in distributed mode
+        grad = average_gradients(grad)
+
         # Model update
         optimizer.update(model, grad)