######################################################################
+
+
def save_attention_image(
filename,
- tokens,
- attention,
+ tokens_input,
+ tokens_output,
+ # 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=1.5,
- padding=0,
- min_att=1e-2,
+ y_eps=0.5,
+ padding=10,
+ # do not draw links with a lesser attention
+ 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,
):
- # 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_arrays], dim=0
+ )
+
+ if k_top is not None:
+ attention = attention * (
+ attention.sort(dim=-1, descending=True).indices < k_top
+ )
+
+ if min_total_attention is not None:
+ s = attention.sort(dim=-1)
+ 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
surface = cairo.RecordingSurface(cairo.CONTENT_COLOR_ALPHA, None)
x, y = 0, 0
- u = {}
- for n, t in enumerate(tokens):
- string = str(t)
+ for d in range(attention.size(0)):
+ 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_link_attention:
+ c = 1 - a.item()
+ ctx.set_source_rgb(c, c, c)
+ ctx.set_line_width(0.5)
+ 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
+
+ for d in range(0, attention.size(0) + 1):
+ 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, 2 * math.pi)
+ ctx.fill()
+ ctx.set_source_rgb(0.0, 0.0, 0.0)
+ ctx.arc(xc, yc, token_gap / 20, 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,
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, token_gap / 5 - 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, -token_gap / 5 - attention.size(0) * layer_gap
+ )
+ ctx.show_text(s)
x, y, width, height = surface.ink_extents()
x -= padding
if __name__ == "__main__":
import mygpt
+ tokens_output = ["<wat>", 2, 3, 4, "<end>"]
+ tokens_input = [""] + 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,
y1 = model(mygpt.BracketedSequence(x)).x
- a = model.retrieve_attention()
- print(a)
- attention = torch.cat([x[:0] for x in a], dim=0)
+ attention = model.retrieve_attention()
- tokens = ["bluh", 2, 3, 4, "blih"]
- attention = torch.randn(3, len(tokens), len(tokens)).softmax(dim=-1)
-
- save_attention_image("attention.pdf", tokens, attention, padding=3)
+ save_attention_image(
+ "attention.pdf",
+ tokens_input,
+ tokens_output,
+ attention,
+ # k_top=2,
+ min_total_attention=0.9,
+ )