use more standard window strategy (#1287)

transformer_lm/main.py

@@ -8,7 +8,6 @@ import datasets
 import mlx.core as mx
 import mlx.nn as nn
 import mlx.optimizers as optim
-import numpy as np
 from mlx.utils import tree_flatten
 
 
@@ -40,26 +39,21 @@ class TransformerLM(nn.Module):
 
 
 def to_samples(context_size, dataset):
-    tokens = dataset.size
     window_size = context_size + 1  # include target
-    samples = tokens - window_size + 1
+    samples = dataset.size // window_size
-    X = np.lib.stride_tricks.as_strided(
+    dataset = dataset[: samples * window_size]
-        dataset,
+    return mx.array(dataset.reshape(samples, -1))
-        shape=(samples, window_size),
-        strides=(dataset.itemsize, dataset.itemsize),
-    )
-    return X[:, :-1], X[:, 1:]
 
 
 def iterate_batches(batch_size, context_size, dataset):
-    inputs, targets = to_samples(context_size, dataset)
+    inputs = to_samples(context_size, dataset)
     s = 0
     while True:
         if s == 0:
             # Reset permutation:
-            perm = np.random.permutation(inputs.shape[0])
+            perm = mx.random.permutation(inputs.shape[0])
         ids = perm[s : s + batch_size]
-        yield inputs[ids], targets[ids]
+        yield inputs[ids]
         s += batch_size
         if s >= inputs.shape[0]:
             s = 0
@@ -84,45 +78,42 @@ def main(args):
     )
     print(f"Training a transformer with {nparams / 1024**2:.3f} M parameters")
 
-    def loss_fn(model, x, y, reduce=True):
+    def loss_fn(model, inputs, reduction="mean"):
+        x, y = inputs[..., :-1], inputs[..., 1:]
         logits = model(x)
-        losses = nn.losses.cross_entropy(logits, y)
+        return nn.losses.cross_entropy(logits, y, reduction=reduction)
-        return mx.mean(losses) if reduce else mx.mean(losses, axis=(-1, -2))
 
     optimizer = optim.AdamW(
         learning_rate=args.learning_rate, weight_decay=args.weight_decay
     )
 
     def eval_fn(dataset):
-        inputs, targets = map(mx.array, to_samples(context_size, dataset))
+        inputs = to_samples(context_size, dataset)
         loss = 0
-        for s in range(0, targets.shape[0], batch_size):
+        for s in range(0, inputs.shape[0], batch_size):
-            bx, by = inputs[s : s + batch_size], targets[s : s + batch_size]
+            losses = loss_fn(model, inputs[s : s + batch_size], reduction="sum")
-            bx, by = map(mx.array, (bx, by))
+            loss += losses.item()
-            losses = loss_fn(model, bx, by, reduce=False)
+        return loss / (inputs.size - inputs.shape[0])
-            loss += mx.sum(losses).item()
-        return loss / len(targets)
 
     state = [model.state, optimizer.state]
 
     @partial(mx.compile, inputs=state, outputs=state)
-    def step(inputs, targets):
+    def step(inputs):
         loss_and_grad_fn = nn.value_and_grad(model, loss_fn)
-        loss, grads = loss_and_grad_fn(model, inputs, targets)
+        loss, grads = loss_and_grad_fn(model, inputs)
         optimizer.update(model, grads)
         return loss
 
     train_iterator = iterate_batches(batch_size, context_size, train)
     losses = []
     tic = time.perf_counter()
-    for it, (inputs, targets) in zip(range(args.num_iters), train_iterator):
+    for it, inputs in zip(range(args.num_iters), train_iterator):
-        inputs, targets = map(mx.array, (inputs, targets))
         optimizer.learning_rate = min(1, it / args.lr_warmup) * args.learning_rate
-        loss = step(inputs, targets)
+        loss = step(inputs)
         mx.eval(state)
         losses.append(loss.item())
         if (it + 1) % steps_per_report == 0:
-            train_loss = np.mean(losses)
+            train_loss = sum(losses) / len(losses)
             toc = time.perf_counter()
             print(
                 f"Iter {it + 1}: Train loss {train_loss:.3f}, "