X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;ds=sidebyside;f=main.py;h=04e56527a51935496c69dbbb6124bd18bc160f28;hb=e56873a0cb64555cbd47e44cdca0ce991765a5fc;hp=969b47f7ac144870b0598514e6593a64e52daee8;hpb=cebc20b3608a41bfd27b2ab9d950c082f9b7ea89;p=mygptrnn.git

diff --git a/main.py b/main.py
index 969b47f..04e5652 100755
--- a/main.py
+++ b/main.py
@@ -202,9 +202,11 @@ parser.add_argument("--mixing_deterministic_start", action="store_true", default
 
 ######################################################################
 
-args = parser.parse_args()
+# args = parser.parse_args()
 
-assert args.picocvlr_prune_properties in {"none", "train+eval", "eval"}
+args, sup_args = parser.parse_known_args()
+
+sup_args = dict([x.removeprefix("--").split("=") for x in sup_args])
 
 if args.result_dir is None:
     args.result_dir = f"results_{args.task}_{args.model}"
@@ -432,6 +434,8 @@ except FileExistsError:
         print(f"result directory {args.result_dir} already exists")
         exit(1)
 
+loss_file = open(os.path.join(args.result_dir, "loss.dat"), "a")
+
 log_file = open(os.path.join(args.result_dir, args.log_filename), "a")
 
 if args.seed >= 0:
@@ -468,6 +472,9 @@ log_string(f"argv {' '.join(sys.argv)}")
 for n in vars(args):
     log_string(f"args.{n} {getattr(args, n)}")
 
+for k, v in sup_args.items():
+    log_string(f'sup_args["{k}"] "{v}"')
+
 
 ######################################################################
 
@@ -505,6 +512,9 @@ def get_lr(n_epoch, it):
 ######################################################################
 
 
+assert args.picocvlr_prune_properties in {"none", "train+eval", "eval"}
+
+
 def picoclvr_pruner_horizontal_green(p):
     return not ("green" in p and ("left" in p or "right" in p))
 
@@ -730,6 +740,8 @@ model = mygpt.MyGPT(
     causal=True,
     dropout=args.dropout,
     attention_layer=args.attention,
+    logger=log_string,
+    **sup_args,
 )
 
 model.to(device)
@@ -823,6 +835,24 @@ if args.max_percents_of_test_in_train >= 0:
 
 ##############################
 
+for input in task.batches(split="train", desc="calibrate"):
+    input = input.to(device)
+    output = model(mygpt.BracketedSequence(input)).x
+
+for n, m in model.named_modules():
+    for a in dir(m):
+        x = getattr(m, a)
+        if isinstance(x, mygpt.Calibrator):
+            print(f"####### ${n} | ${a} ########################")
+            mean, std = x.moments()
+            print("mean\n", mean, "\n")
+            print("std\n", std, "\n")
+            print(f"############################################\n\n")
+
+exit(0)
+
+##############################
+
 nb_samples_seen = 0
 
 if nb_epochs_finished >= nb_epochs:
@@ -838,6 +868,8 @@ time_pred_result = datetime.datetime.now()
 
 it = 0
 
+n_batch = 0
+
 for n_epoch in range(nb_epochs_finished, nb_epochs):
     if args.optim == "sgd":
         optimizer = torch.optim.SGD(model.parameters(), lr=args.learning_rate)
@@ -879,6 +911,12 @@ for n_epoch in range(nb_epochs_finished, nb_epochs):
         total_loss.backward()
         optimizer.step()
 
+        grad_norm = sum([p.grad.pow(2).sum() for p in model.parameters()]).sqrt()
+
+        loss_file.write(f"{n_epoch} {n_batch} {loss.item()} {grad_norm.item()}\n")
+
+        n_batch += 1
+
     with torch.autograd.no_grad():
         model.eval()