X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=main.py;h=a587e967af4fb9ec05fdca261f11decc55df8ac4;hb=9d4193312e06ed284b1368b7f4407f2b4f981c7a;hp=00b8301177379851e40b544df1ddec9163f469cd;hpb=408f2335af43590ee2d99c3286cbe3762c76887a;p=mygptrnn.git

diff --git a/main.py b/main.py
index 00b8301..a587e96 100755
--- a/main.py
+++ b/main.py
@@ -604,32 +604,46 @@ def add_memex_v3(batches, memex_proba, marker_token):
             t = torch.arange(input.size(1) + memex_len, device=input.device)[
                 None, :
             ].expand(input.size(0), -1)
+            n = torch.arange(input.size(0), device=input.device)[:, None].expand(
+                -1, t.size(1)
+            )
 
             # Call me the tensor-spaghetti master
 
             trigger = torch.rand(t.size(), device=t.device)
-            trigger[:, -memex_len:] = 1.0
-            trigger = (trigger.sort(dim=1).indices == 0).long()
+            trigger[:, -memex_len:] = 2.0
+            trigger[:, 0] = 2.0
+            trigger = (trigger == trigger.min(dim=1, keepdim=True).values).long()
             memex_mask = trigger.clone()
-            memex_mask[:, memex_len:] -= memex_mask[:, :-memex_len]
+            memex_mask[:, memex_len:] -= trigger[:, :-memex_len]
             memex_mask = memex_mask.cumsum(dim=1)
+
             u = 1 - memex_mask
             u[:, 0] = 0
             u = u.cumsum(dim=1)
-            # assert u.min() == 0
-            # assert u.max() == input.size(1) - 1
+            assert u.min() == 0
+            assert u.max() == input.size(1) - 1
+
             v = (
                 (trigger.cumsum(dim=1) - trigger).cumsum(dim=1)
-                + torch.randint(input.size(1), (input.size(0), 1), device=t.device)
+                + torch.randint(
+                    input.size(1) - memex_len, (input.size(0), 1), device=t.device
+                )
             ) * memex_mask
+            assert v.min() >= 0
+            assert v.max() < input.size(1)
             u = u * (1 - memex_mask) + v * memex_mask
-            n = torch.arange(input.size(0), device=input.device)[:, None].expand(
-                -1, t.size(1)
-            )
+
             new_input = input[n, u]
+            assert input.max() < vocabulary_size
+            assert new_input.max() < vocabulary_size
             limits = trigger.clone()
             limits[:, memex_len - 1 :] += limits[:, : -(memex_len - 1)]
-            new_input = new_input * (1 - limits) + memex_marker * limits
+            assert limits.min() == 0
+            assert limits.max() == 1
+            new_input = new_input * (1 - limits) + marker_token * limits
+            assert marker_token < vocabulary_size
+            assert new_input.max() < vocabulary_size
 
             yield new_input, memex_mask