X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=graph.py;h=5bab86183b0bd252f2e491a47e59b09a139711d2;hb=ef3bef5253ff719953dfffff28d4122c19acdd77;hp=0db7bd0b08ba8a74db9adbef4d03dc90a92ce8cc;hpb=b59fca62aa31de18a3e0cd0bb54e395d4b1254ae;p=picoclvr.git

diff --git a/graph.py b/graph.py
index 0db7bd0..5bab861 100755
--- a/graph.py
+++ b/graph.py
@@ -13,18 +13,27 @@ import cairo
 ######################################################################
 def save_attention_image(
     filename,
-    tokens,
+    tokens_input,
+    tokens_output,
     attention,
+    n_sample=0,
+    n_head=0,
     pixel_scale=8,
     token_gap=10,
     layer_gap=25,
-    y_eps=1.5,
-    padding=0,
-    min_att=1e-2,
+    y_eps=0.5,
+    padding=10,
+    min_att=0,
+    k_top=None,
 ):
-    # surface = cairo.PDFSurface(
-    # filename, surface_width * pixel_scale, surface_height * pixel_scale
-    # )
+    attention = torch.cat(
+        [x[n_sample : n_sample + 1, n_head] for x in attention], dim=0
+    )
+
+    if k_top is not None:
+        attention = attention * (
+            attention.sort(dim=-1, descending=True).indices < k_top
+        )
 
     surface = cairo.RecordingSurface(cairo.CONTENT_COLOR_ALPHA, None)
 
@@ -37,9 +46,45 @@ def save_attention_image(
 
     x, y = 0, 0
 
-    u = {}
-    for n, t in enumerate(tokens):
-        string = str(t)
+    for d in range(attention.size(0)):
+        if d > 0:
+            for n in range(attention.size(-1)):
+                xc, yc = n * token_gap, -d * layer_gap
+                ctx.arc(xc, yc, token_gap / 10, 0, 2 * math.pi)
+                ctx.fill()
+
+        at = attention[d]
+        ni = torch.arange(at.size(0))[:, None].expand_as(at)
+        nj = torch.arange(at.size(1))[None, :].expand_as(at)
+        at = at.flatten()
+        o = at.sort().indices
+        at = at[o]
+        ni = ni.flatten()[o]
+        nj = nj.flatten()[o]
+        for i, j, a in zip(ni, nj, at):
+            if a > 0 and a >= min_att:
+                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)
+                ctx.stroke()
+        y -= layer_gap
+
+    for d in range(1, attention.size(0)):
+        for n in range(attention.size(-1)):
+            xc, yc = n * token_gap, -d * layer_gap
+            ctx.set_source_rgb(1.0, 1.0, 1.0)
+            ctx.arc(xc, yc, token_gap / 10 + 0.5, 0, 2 * math.pi)
+            ctx.fill()
+            ctx.set_source_rgb(0.0, 0.0, 0.0)
+            ctx.arc(xc, yc, token_gap / 10, 0, 2 * math.pi)
+            ctx.fill()
+
+    ctx.set_source_rgb(0.0, 0.0, 0.0)
+
+    for k, t in enumerate(tokens_input):
+        s = str(t)
         (
             x_bearing,
             y_bearing,
@@ -47,30 +92,22 @@ def save_attention_image(
             height_t,
             x_advance,
             y_advance,
-        ) = ctx.text_extents(string)
-        u[n]=(string, x, x + width_t / 2, height_t, y_bearing)
-        x += x_advance + token_gap
-    tokens = u
-
-    for d in range(attention.size(0) + 1):
-        for n, (s, x, xc, h, yb) in tokens.items():
-            if d < attention.size(0):
-                for m, (_, _, x2c, h2, y2b) in tokens.items():
-                    if attention[d, n, m] >= min_att:
-                        c = 1 - attention[d, n, m]
-                        ctx.set_source_rgb(c, c, c)
-                        ctx.set_line_width(0.5)
-                        ctx.move_to(xc, y + yb + h + y_eps)
-                        ctx.line_to(x2c, y + layer_gap + y2b - y_eps)
-                        ctx.stroke()
-            # ctx.set_source_rgb(0.0, 0.0, 0.0)
-            # ctx.rectangle(x+x_bearing,y+y_bearing,width_t,height_t)
-            # ctx.stroke()
-            ctx.set_source_rgb(0.0, 0.0, 0.0)
-            ctx.move_to(x, y)
-            ctx.show_text(s)
-            # x += x_advance + 1
-        y += layer_gap
+        ) = ctx.text_extents(s)
+        ctx.move_to(k * token_gap - width_t / 2, -y_bearing)
+        ctx.show_text(s)
+
+    for k, t in enumerate(tokens_output):
+        s = str(t)
+        (
+            x_bearing,
+            y_bearing,
+            width_t,
+            height_t,
+            x_advance,
+            y_advance,
+        ) = ctx.text_extents(s)
+        ctx.move_to(k * token_gap - width_t / 2, -attention.size(0) * layer_gap)
+        ctx.show_text(s)
 
     x, y, width, height = surface.ink_extents()
     x -= padding
@@ -89,8 +126,11 @@ def save_attention_image(
 if __name__ == "__main__":
     import mygpt
 
+    tokens_output = ["bluh", 2, 3, 4, "blih"]
+    tokens_input = ["n/a"] + tokens_output[:-1]
+
     vocabulary_size = 3
-    x = torch.randint(vocabulary_size, (1, 5))
+    x = torch.randint(vocabulary_size, (1, len(tokens_input)))
 
     model = mygpt.MyGPT(
         vocabulary_size=vocabulary_size,
@@ -108,11 +148,6 @@ if __name__ == "__main__":
 
     y1 = model(mygpt.BracketedSequence(x)).x
 
-    a = model.retrieve_attention()
-    print(a)
-    attention = torch.cat([x[:0] for x in a], dim=0)
-
-    tokens = ["bluh", 2, 3, 4, "blih"]
-    attention = torch.randn(3, len(tokens), len(tokens)).softmax(dim=-1)
+    attention = model.retrieve_attention()
 
-    save_attention_image("attention.pdf", tokens, attention, padding=3)
+    save_attention_image("attention.pdf", tokens_input, tokens_output, attention)