Update.

diff --git a/graph.py b/graph.py
index 3c92e8d..a2554d2 100755 (executable)
--- a/graph.py
+++ b/graph.py
@@ -11,28 +11,32 @@ import cairo
 
 
 ######################################################################
+
+
 def save_attention_image(
     filename,
     tokens_input,
     tokens_output,
-    attention,
+    # An iterable set of BxHxTxT attention matrices
+    attention_arrays,
     n_sample=0,
     n_head=0,
     pixel_scale=8,
-    token_gap=10,
+    token_gap=15,
     layer_gap=25,
     y_eps=0.5,
     padding=10,
     # do not draw links with a lesser attention
-    min_att=0,
-    # draw only the strongest links necessary to have less than
-    # residual remaining
-    residual=None,
+    min_link_attention=0,
+    # draw only the strongest links necessary to reache
+    # min_total_attention
+    min_total_attention=None,
     # draw only the top k links
     k_top=None,
+    curved=True,
 ):
     attention = torch.cat(
-        [x[n_sample : n_sample + 1, n_head] for x in attention], dim=0
+        [x[n_sample : n_sample + 1, n_head] for x in attention_arrays], dim=0
     )
 
     if k_top is not None:
@@ -40,9 +44,9 @@ def save_attention_image(
             attention.sort(dim=-1, descending=True).indices < k_top
         )
 
-    if residual is not None:
+    if min_total_attention is not None:
         s = attention.sort(dim=-1)
-        m = 1 - (s.values.cumsum(-1) < residual).long()
+        m = 1 - (s.values.cumsum(-1) < 1 - min_total_attention).long()
         b = m.new(attention.size()).scatter_(dim=-1, index=s.indices, src=m)
         attention = attention * b
 
@@ -67,12 +71,19 @@ def save_attention_image(
         ni = ni.flatten()[o]
         nj = nj.flatten()[o]
         for i, j, a in zip(ni, nj, at):
-            if a > 0 and a >= min_att:
+            if a > 0 and a >= min_link_attention:
                 c = 1 - a.item()
                 ctx.set_source_rgb(c, c, c)
                 ctx.set_line_width(0.5)
-                ctx.move_to(j * token_gap, y - y_eps)
-                ctx.line_to(i * token_gap, y - layer_gap + y_eps)
+                ax, ay = j * token_gap, y - y_eps
+                ctx.move_to(ax, ay)
+                dx, dy = i * token_gap, y - layer_gap + y_eps
+                if curved:
+                    bx, by = ax, ay - layer_gap * 0.5
+                    cx, cy = dx, dy + layer_gap * 0.5
+                    ctx.curve_to(bx, by, cx, cy, dx, dy)
+                else:
+                    ctx.line_to(dx, dy)
                 ctx.stroke()
         y -= layer_gap
 
@@ -133,8 +144,8 @@ def save_attention_image(
 if __name__ == "__main__":
     import mygpt
 
-    tokens_output = ["bluh", 2, 3, 4, "blih"]
-    tokens_input = ["n/a"] + tokens_output[:-1]
+    tokens_output = ["<wat>", 2, 3, 4, "<end>"]
+    tokens_input = [""] + tokens_output[:-1]
 
     vocabulary_size = 3
     x = torch.randint(vocabulary_size, (1, len(tokens_input)))
@@ -157,4 +168,11 @@ if __name__ == "__main__":
 
     attention = model.retrieve_attention()
 
-    save_attention_image("attention.pdf", tokens_input, tokens_output, attention)
+    save_attention_image(
+        "attention.pdf",
+        tokens_input,
+        tokens_output,
+        attention,
+        # k_top=2,
+        min_total_attention=0.9,
+    )