X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=mygpt.py;h=760a3c60e086f094d4e86672e040877f94c6a6d2;hb=60cd4f63c55c77f5097ebac146c212763ba11925;hp=2d335741270052ceeca1a314366da78429b3fd95;hpb=c45d89eb5383eedf60466678eae623582bd5781c;p=mygptrnn.git

diff --git a/mygpt.py b/mygpt.py
index 2d33574..760a3c6 100755
--- a/mygpt.py
+++ b/mygpt.py
@@ -202,7 +202,7 @@ class DumbRec(nn.Module):
         attention_dropout=0.0,
         len_max=1e5,
         logger=print,
-        args,
+        args=None,
     ):
         super().__init__()
 
@@ -333,7 +333,7 @@ class KVRec(nn.Module):
         attention_dropout=0.0,
         len_max=1e5,
         logger=print,
-        args,
+        args=None,
     ):
         super().__init__()
 
@@ -487,7 +487,7 @@ class Caterpillar(nn.Module):
         attention_dropout=0.0,
         len_max=1e5,
         logger=print,
-        args,
+        args=None,
     ):
         super().__init__()
 
@@ -629,15 +629,21 @@ class Caterpillar(nn.Module):
 
             warnings.warn("gate dropout", RuntimeWarning)
 
+            if self.gate_dropout_sync:
+                shape_kill = (N, 1, 1)
+            else:
+                shape_kill = (N, H, R)
+
             # Pick a point in each of the NxHxR timeline and set this
             # entry and the following to 1
             kill = (
-                torch.rand(N, H, R, t1 - t0, device=G.device).sort(dim=3).indices == 0
+                torch.rand(*shape_kill, t1 - t0, device=G.device).sort(dim=3).indices
+                == 0
             ).cumsum(dim=3)
 
             # Keep these mask for only some of the NxHxR
             kill = kill * (
-                torch.rand(N, H, R, 1, device=G.device) <= self.gate_dropout_proba
+                torch.rand(*shape_kill, 1, device=G.device) <= self.gate_dropout_proba
             )
 
             # The coefficient to keep are the complementary
@@ -715,7 +721,7 @@ class QKVAttention(nn.Module):
         causal=False,
         attention_dropout=0.0,
         logger=print,
-        args,
+        args=None,
     ):
         super().__init__()
 
@@ -808,7 +814,7 @@ class MyGPT(nn.Module):
         len_max=1e5,
         attention_layer="kvrec",
         logger=print,
-        args,
+        args=None,
     ):
         super().__init__()
 
@@ -846,7 +852,7 @@ class MyGPT(nn.Module):
                     causal=causal,
                     attention_dropout=dropout,
                     logger=logger,
-                    args,
+                    args=args,
                 )
             elif attention_layer == "dumbrec":
                 return DumbRec(
@@ -857,7 +863,7 @@ class MyGPT(nn.Module):
                     nb_lines=nb_lines,
                     attention_dropout=dropout,
                     logger=logger,
-                    args,
+                    args=args,
                 )
             elif attention_layer == "kvrec":
                 return KVRec(
@@ -868,7 +874,7 @@ class MyGPT(nn.Module):
                     nb_lines=nb_lines,
                     attention_dropout=dropout,
                     logger=logger,
-                    args,
+                    args=args,
                 )
             elif attention_layer == "caterpillar":
                 return Caterpillar(
@@ -880,7 +886,7 @@ class MyGPT(nn.Module):
                     caterpillar_height=self.caterpillar_height,
                     attention_dropout=dropout,
                     logger=logger,
-                    args,
+                    args=args,
                 )
             else:
                 raise ValueError(f"Unknown attention type {attention_layer}.")