Update.

[mygptrnn.git] / main.py

diff --git a/main.py b/main.py
index 79841f3..91c885b 100755 (executable)
--- a/main.py
+++ b/main.py
@@ -87,6 +87,8 @@ parser.add_argument("--model", type=str, default=None)
 
 parser.add_argument("--attention", type=str, default=None)
 
+parser.add_argument("--proba_memex", type=float, default=0)
+
 parser.add_argument("--dim_model", type=int, default=None)
 
 parser.add_argument("--dim_keys", type=int, default=None)
@@ -99,7 +101,13 @@ 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)
 
@@ -730,6 +738,9 @@ log_string(f"device {device}")
 
 vocabulary_size = task.vocabulary_size()
 
+if args.proba_memex > 0:
+    vocabulary_size += 1
+
 log_string(f"vocabulary_size {vocabulary_size}")
 
 ##############################
@@ -747,7 +758,7 @@ model = mygpt.MyGPT(
     dropout=args.dropout,
     attention_layer=args.attention,
     logger=log_string,
-    **sup_args,
+    args=args,
 )
 
 model.to(device)
@@ -891,7 +902,28 @@ 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, proba_memex):
+        for input in batches:
+            if torch.rand(1).item() < proba_memex:
+                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.proba_memex)
+
+    for input in train_batches:
         model.reset_inner_loss()
         input = input.to(device)
 
@@ -905,7 +937,9 @@ 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)