X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=main.py;h=cae20f89347b076226b6525d561dab1d89a55e8b;hb=42831bd654d030b71bca88578d041279018f836c;hp=74e70b2a9780601053e51e0d359d082e7a869e1d;hpb=cb737bdbd2f112826f739e4581fbe6546aeef638;p=mygptrnn.git

diff --git a/main.py b/main.py
index 74e70b2..cae20f8 100755
--- a/main.py
+++ b/main.py
@@ -24,6 +24,17 @@ else:
 
 ######################################################################
 
+
+def str2bool(x):
+    x = x.lower()
+    if x in {"1", "true", "yes"}:
+        return True
+    elif x in {"0", "false", "no"}:
+        return False
+    else:
+        raise ValueError
+
+
 parser = argparse.ArgumentParser(
     description="An implementation of GPT with cache.",
     formatter_class=argparse.ArgumentDefaultsHelpFormatter,
@@ -66,6 +77,16 @@ parser.add_argument("--learning_rate", type=float, default=6e-4)
 
 parser.add_argument("--min_learning_rate", type=float, default=6e-5)
 
+# legacy
+
+parser.add_argument("--legacy_lr_schedule", type=str2bool, default=True)
+
+parser.add_argument("--legacy_large_lr", type=float, default=1e-4)
+
+parser.add_argument("--legacy_small_lr", type=float, default=2e-5)
+
+parser.add_argument("--legacy_nb_epoch_large_lr", type=float, default=10)
+
 ########################################
 
 parser.add_argument("--model", type=str, default=None)
@@ -460,10 +481,21 @@ for n in vars(args):
 
 ######################################################################
 
-# from nanoGPT
 
+def get_lr(n_epoch, it):
+    if args.legacy_lr_schedule:
+        # my crude scheduling to compare to previous baseline, added
+        # warmup though
+
+        if it < args.nb_warmup_iter:
+            return args.legacy_large_lr * it / args.nb_warmup_iter
+        elif n_epoch < args.legacy_nb_epoch_large_lr:
+            return args.legacy_large_lr
+        else:
+            return args.legacy_small_lr
+
+    # from nanoGPT
 
-def get_lr(it):
     # 1) linear warmup for warmup_iter steps
     if it < args.nb_warmup_iter:
         return args.learning_rate * it / args.nb_warmup_iter
@@ -848,7 +880,7 @@ for n_epoch in range(nb_epochs_finished, nb_epochs):
         total_loss = loss + (args.rho * inner_loss if args.rho > 0 else 0.0)
 
         it += 1
-        lr = get_lr(it)
+        lr = get_lr(n_epoch, it)
         for param_group in optimizer.param_groups:
             param_group["lr"] = lr