Update.

[mygptrnn.git] / main.py

diff --git a/main.py b/main.py
index 1a17e51..969b47f 100755 (executable)
--- a/main.py
+++ b/main.py
@@ -16,13 +16,16 @@ import mygpt, tasks, problems
 
 ######################################################################
 
-if torch.cuda.is_available():
-    device = torch.device("cuda")
-    torch.backends.cuda.matmul.allow_tf32 = True
-else:
-    device = torch.device("cpu")
 
-######################################################################
+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.",
@@ -44,6 +47,8 @@ parser.add_argument("--seed", type=int, default=0)
 
 parser.add_argument("--max_percents_of_test_in_train", type=int, default=1)
 
+parser.add_argument("--force_cpu", type=str2bool, default=False)
+
 ########################################
 
 parser.add_argument("--nb_epochs", type=int, default=50)
@@ -68,13 +73,13 @@ parser.add_argument("--min_learning_rate", type=float, default=6e-5)
 
 # legacy
 
-parser.add_argument("--legacy_lr_schedule", action="store_true", default=False)
+parser.add_argument("--legacy_lr_schedule", type=str2bool, default=True)
 
-parser.add_argument("--legacy_learning_rate", type=float, default=1e-4)
+parser.add_argument("--legacy_large_lr", type=float, default=1e-4)
 
-parser.add_argument("--legacy_min_learning_rate", type=float, default=2e-5)
+parser.add_argument("--legacy_small_lr", type=float, default=2e-5)
 
-parser.add_argument("--nb_large_lr_epochs", type=float, default=10)
+parser.add_argument("--legacy_nb_epoch_large_lr", type=float, default=10)
 
 ########################################
 
@@ -96,8 +101,6 @@ parser.add_argument("--caterpillar_height", type=int, default=None)
 
 parser.add_argument("--rho", type=float, default=0.0)
 
-parser.add_argument("--dim_rec_v", type=int, default=None)
-
 parser.add_argument("--nb_blocks", type=int, default=None)
 
 parser.add_argument("--dropout", type=float, default=0.1)
@@ -108,7 +111,7 @@ parser.add_argument("--deterministic_synthesis", action="store_true", default=Fa
 
 parser.add_argument("--no_checkpoint", action="store_true", default=False)
 
-parser.add_argument("--overwrite_results", action="store_true", default=False)
+parser.add_argument("--continue_training", action="store_true", default=False)
 
 parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth")
 
@@ -208,6 +211,14 @@ if args.result_dir is None:
 
 ######################################################################
 
+if not args.force_cpu and torch.cuda.is_available():
+    device = torch.device("cuda")
+    torch.backends.cuda.matmul.allow_tf32 = True
+else:
+    device = torch.device("cpu")
+
+######################################################################
+
 default_task_args = {
     "addition": {
         "model": "352M",
@@ -321,7 +332,6 @@ default_model_args = {
         "dim_keys": 32,
         "dim_hidden": 32,
         "nb_heads": 2,
-        "dim_rec_v": 16,
         "nb_blocks": 2,
     },
     "17K-C": {
@@ -332,7 +342,6 @@ default_model_args = {
         "nb_heads": 2,
         "nb_lines": 16,
         "caterpillar_height": 4,
-        "dim_rec_v": 16,
         "nb_blocks": 2,
     },
     "4M": {
@@ -341,7 +350,6 @@ default_model_args = {
         "dim_keys": 32,
         "dim_hidden": 1024,
         "nb_heads": 4,
-        "dim_rec_v": 64,
         "nb_blocks": 6,
     },
     "4M-C": {
@@ -352,7 +360,6 @@ default_model_args = {
         "nb_heads": 4,
         "nb_lines": 32,
         "caterpillar_height": 4,
-        "dim_rec_v": 64,  # dim_model / nb_heads
         "nb_blocks": 6,
     },
     "37M": {
@@ -361,7 +368,6 @@ default_model_args = {
         "dim_keys": 64,
         "dim_hidden": 2048,
         "nb_heads": 8,
-        "dim_rec_v": 64,
         "nb_blocks": 12,
     },
     "37M-C": {
@@ -372,7 +378,6 @@ default_model_args = {
         "nb_heads": 8,
         "nb_lines": 256,
         "caterpillar_height": 32,
-        "dim_rec_v": 64,
         "nb_blocks": 12,
     },
     "122M": {
@@ -381,7 +386,6 @@ default_model_args = {
         "dim_keys": 64,
         "dim_hidden": 2048,
         "nb_heads": 8,
-        "dim_rec_v": 96,
         "nb_blocks": 24,
     },
     "122M-C": {
@@ -391,7 +395,6 @@ default_model_args = {
         "dim_hidden": 2048,
         "nb_heads": 8,
         "nb_lines": 128,
-        "dim_rec_v": 96,
         "nb_blocks": 24,
     },
     "352M": {
@@ -400,7 +403,6 @@ default_model_args = {
         "dim_keys": 64,
         "dim_hidden": 2048,
         "nb_heads": 8,
-        "dim_rec_v": 128,
         "nb_blocks": 48,
     },
     "352M-C": {
@@ -410,7 +412,6 @@ default_model_args = {
         "dim_hidden": 2048,
         "nb_heads": 8,
         "nb_lines": 128,
-        "dim_rec_v": 128,
         "nb_blocks": 48,
     },
 }
@@ -427,7 +428,7 @@ else:
 try:
     os.mkdir(args.result_dir)
 except FileExistsError:
-    if not args.overwrite_results:
+    if not args.continue_training:
         print(f"result directory {args.result_dir} already exists")
         exit(1)
 
@@ -477,11 +478,11 @@ def get_lr(n_epoch, it):
         # warmup though
 
         if it < args.nb_warmup_iter:
-            return args.legacy_learning_rate * it / args.nb_warmup_iter
-        elif it < args.nb_large_lr_epochs:
-            return args.legacy_learning_rate
+            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_min_learning_rate
+            return args.legacy_small_lr
 
     # from nanoGPT
 
@@ -725,7 +726,6 @@ model = mygpt.MyGPT(
     nb_heads=args.nb_heads,
     nb_lines=args.nb_lines,
     caterpillar_height=args.caterpillar_height,
-    dim_rec_v=args.dim_rec_v,
     nb_blocks=args.nb_blocks,
     causal=True,
     dropout=args.dropout,
@@ -834,7 +834,7 @@ if nb_epochs_finished >= nb_epochs:
         deterministic_synthesis=args.deterministic_synthesis,
     )
 
-time_pred_result = None
+time_pred_result = datetime.datetime.now()
 
 it = 0
 
@@ -912,10 +912,9 @@ for n_epoch in range(nb_epochs_finished, nb_epochs):
         )
 
         time_current_result = datetime.datetime.now()
-        if time_pred_result is not None:
-            log_string(
-                f"next_result {time_current_result + (time_current_result - time_pred_result)}"
-            )
+        log_string(
+            f"next_result {time_current_result + (time_current_result - time_pred_result)}"
+        )
         time_pred_result = time_current_result
 
     checkpoint = {