Update.

[mygptrnn.git] / main.py

diff --git a/main.py b/main.py
index 3e67a73..d6845e8 100755 (executable)
--- a/main.py
+++ b/main.py
@@ -87,6 +87,10 @@ parser.add_argument("--model", type=str, default=None)
 
 parser.add_argument("--attention", type=str, default=None)
 
+parser.add_argument("--memex_proba", type=float, default=0)
+
+parser.add_argument("--memex_nb_epochs", type=float, default=1)
+
 parser.add_argument("--dim_model", type=int, default=None)
 
 parser.add_argument("--dim_keys", type=int, default=None)
@@ -99,7 +103,13 @@ parser.add_argument("--nb_lines", type=int, default=None)
 
 parser.add_argument("--caterpillar_height", type=int, default=None)
 
-parser.add_argument("--rho", type=float, default=0.0)
+parser.add_argument("--gate_dropout_proba", type=float, default=0.0)
+
+parser.add_argument("--gate_dropout_sync", type=str2bool, default=False)
+
+parser.add_argument("--gate_dropout_replace", type=str2bool, default=False)
+
+parser.add_argument("--rho_inner_loss", type=float, default=0.0)
 
 parser.add_argument("--nb_blocks", type=int, default=None)
 
@@ -133,6 +143,10 @@ parser.add_argument("--rpl_no_prog", action="store_true", default=False)
 
 parser.add_argument("--grid_size", type=int, default=6)
 
+parser.add_argument("--grid_nb_colors", type=int, default=6)
+
+parser.add_argument("--grid_nb_shapes", type=int, default=6)
+
 ##############################
 # picoclvr options
 
@@ -701,6 +715,8 @@ elif args.task == "grid":
         nb_test_samples=args.nb_test_samples,
         batch_size=args.batch_size,
         size=args.grid_size,
+        nb_shapes=args.grid_nb_shapes,
+        nb_colors=args.grid_nb_colors,
         logger=log_string,
         device=device_data,
     )
@@ -724,6 +740,9 @@ log_string(f"device {device}")
 
 vocabulary_size = task.vocabulary_size()
 
+if args.memex_proba > 0:
+    vocabulary_size += 1
+
 log_string(f"vocabulary_size {vocabulary_size}")
 
 ##############################
@@ -741,7 +760,7 @@ model = mygpt.MyGPT(
     dropout=args.dropout,
     attention_layer=args.attention,
     logger=log_string,
-    **sup_args,
+    args=args,
 )
 
 model.to(device)
@@ -835,6 +854,25 @@ if args.max_percents_of_test_in_train >= 0:
 
 ##############################
 
+if "calibrate" in sup_args:
+    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:
@@ -866,7 +904,29 @@ for n_epoch in range(nb_epochs_finished, nb_epochs):
 
     nb_train_samples, acc_train_loss, acc_train_inner_loss = 0, 0.0, 0.0
 
-    for input in task.batches(split="train"):
+    def add_memex(batches, memex_proba):
+        for input in batches:
+            if torch.rand(1).item() < memex_proba:
+                sep = torch.full(
+                    (input.size(0), 1), vocabulary_size - 1, device=input.device
+                )
+
+                yield torch.cat(
+                    [
+                        input,
+                        sep,
+                        input,
+                    ],
+                    dim=1,
+                )
+            yield input
+
+    train_batches = add_memex(
+        task.batches(split="train"),
+        args.memex_proba if n_epoch < args.memex_nb_epochs else 0.0,
+    )
+
+    for input in train_batches:
         model.reset_inner_loss()
         input = input.to(device)
 
@@ -880,7 +940,9 @@ for n_epoch in range(nb_epochs_finished, nb_epochs):
         nb_train_samples += input.size(0)
         nb_samples_seen += input.size(0)
 
-        total_loss = loss + (args.rho * inner_loss if args.rho > 0 else 0.0)
+        total_loss = loss + (
+            args.rho_inner_loss * inner_loss if args.rho_inner_loss > 0 else 0.0
+        )
 
         it += 1
         lr = get_lr(n_epoch, it)