simplified ResNet, expanded README with throughput and performance

cifar/main.py

@@ -1,6 +1,6 @@
 import argparse
+import time
 import resnet
-import numpy as np
 import mlx.nn as nn
 import mlx.core as mx
 import mlx.optimizers as optim
@@ -14,11 +14,11 @@ parser.add_argument(
     default="resnet20",
     help="model architecture [resnet20, resnet32, resnet44, resnet56, resnet110, resnet1202]",
 )
-parser.add_argument("--batch_size", type=int, default=128, help="batch size")
+parser.add_argument("--batch_size", type=int, default=256, help="batch size")
 parser.add_argument("--epochs", type=int, default=100, help="number of epochs")
 parser.add_argument("--lr", type=float, default=1e-3, help="learning rate")
 parser.add_argument("--seed", type=int, default=0, help="random seed")
-parser.add_argument("--cpu_only", action="store_true", help="use cpu only")
+parser.add_argument("--cpu", action="store_true", help="use cpu only")
 
 
 def loss_fn(model, inp, tgt):
@@ -40,27 +40,30 @@ def train_epoch(model, train_iter, optimizer, epoch):
 
     losses = []
     accs = []
+    samples_per_sec = []
 
     for batch_counter, batch in enumerate(train_iter):
         x = mx.array(batch["image"])
         y = mx.array(batch["label"])
+        tic = time.perf_counter()
         (loss, acc), grads = train_step_fn(model, x, y)
         optimizer.update(model, grads)
         mx.eval(model.parameters(), optimizer.state)
-
+        toc = time.perf_counter()
         loss_value = loss.item()
         acc_value = acc.item()
         losses.append(loss_value)
         accs.append(acc_value)
-
+        samples_per_sec.append(x.shape[0] / (toc - tic))
         if batch_counter % 10 == 0:
             print(
-                f"Epoch {epoch:02d}[{batch_counter:03d}]: tr_loss {loss_value:.3f}, tr_acc {acc_value:.3f}"
+                f"Epoch {epoch:02d} [{batch_counter:03d}] | tr_loss {loss_value:.3f} | tr_acc {acc_value:.3f} | Throughput: {x.shape[0] / (toc - tic):.2f} images/second"
             )
 
-    mean_tr_loss = np.mean(np.array(losses))
-    mean_tr_acc = np.mean(np.array(accs))
-    return mean_tr_loss, mean_tr_acc
+    mean_tr_loss = mx.mean(mx.array(losses))
+    mean_tr_acc = mx.mean(mx.array(accs))
+    samples_per_sec = mx.mean(mx.array(samples_per_sec))
+    return mean_tr_loss, mean_tr_acc, samples_per_sec
 
 
 def test_epoch(model, test_iter, epoch):
@@ -71,13 +74,11 @@ def test_epoch(model, test_iter, epoch):
         acc = eval_fn(model, x, y)
         acc_value = acc.item()
         accs.append(acc_value)
-    mean_acc = np.mean(np.array(accs))
-
+    mean_acc = mx.mean(mx.array(accs))
     return mean_acc
 
 
 def main(args):
-    np.random.seed(args.seed)
     mx.random.seed(args.seed)
 
     model = resnet.__dict__[args.arch]()
@@ -87,22 +88,24 @@ def main(args):
 
     optimizer = optim.Adam(learning_rate=args.lr)
 
+    train_data, test_data = get_cifar10(args.batch_size)
     for epoch in range(args.epochs):
-        # get data every epoch
-        # or set .repeat() on the data stream appropriately
-        train_data, test_data, tr_batches, _ = get_cifar10(args.batch_size)
-
-        epoch_tr_loss, epoch_tr_acc = train_epoch(model, train_data, optimizer, epoch)
+        epoch_tr_loss, epoch_tr_acc, train_throughput = train_epoch(
+            model, train_data, optimizer, epoch
+        )
         print(
-            f"Epoch {epoch}: avg. tr_loss {epoch_tr_loss:.3f}, avg. tr_acc {epoch_tr_acc:.3f}"
+            f"Epoch: {epoch} | avg. tr_loss {epoch_tr_loss.item():.3f} | avg. tr_acc {epoch_tr_acc.item():.3f} | Train Throughput: {train_throughput.item():.2f} images/sec"
         )
 
         epoch_test_acc = test_epoch(model, test_data, epoch)
-        print(f"Epoch {epoch}: Test_acc {epoch_test_acc:.3f}")
+        print(f"Epoch: {epoch} | test_acc {epoch_test_acc.item():.3f}")
+
+        train_data.reset()
+        test_data.reset()
 
 
 if __name__ == "__main__":
     args = parser.parse_args()
-    if args.cpu_only:
+    if args.cpu:
         mx.set_default_device(mx.cpu)
     main(args)