tokens_input,
tokens_output,
# An iterable set of BxHxTxT attention matrices
- attention_arrays,
- n_sample=0,
- n_head=0,
+ attention_matrices,
pixel_scale=8,
token_gap=15,
layer_gap=25,
k_top=None,
curved=True,
):
- 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
- )
+ am = []
+ for m in attention_matrices:
+ am.append(m * (m.sort(dim=-1, descending=True).indices < k_top))
+ attention_matrices = am
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
+ am = []
+ for m in attention_matrices:
+ s = m.sort(dim=-1)
+ m = 1 - (s.values.cumsum(-1) < 1 - min_total_attention).long()
+ b = m.new(m.size()).scatter_(dim=-1, index=s.indices, src=m)
+ am.append(m * b)
surface = cairo.RecordingSurface(cairo.CONTENT_COLOR_ALPHA, None)
x, y = 0, 0
- for d in range(attention.size(0)):
- at = attention[d]
+ ctx.set_line_width(0.25)
+ for d in range(len(attention_matrices)):
+ at = attention_matrices[d]
ni = torch.arange(at.size(0))[:, None].expand_as(at)
nj = torch.arange(at.size(1))[None, :].expand_as(at)
at = at.flatten()
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
ctx.stroke()
y -= layer_gap
- for d in range(0, attention.size(0) + 1):
- for n in range(attention.size(-1)):
+ for d in range(0, len(attention_matrices) + 1):
+ n = (
+ attention_matrices[0].size(-1)
+ if d == 0
+ else attention_matrices[d - 1].size(-2)
+ )
+ for n in range(n):
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)
y_advance,
) = ctx.text_extents(s)
ctx.move_to(
- k * token_gap - width_t / 2, -token_gap / 5 - attention.size(0) * layer_gap
+ k * token_gap - width_t / 2,
+ -token_gap / 5 - len(attention_matrices) * layer_gap,
)
ctx.show_text(s)
dim_keys=2,
dim_hidden=2,
nb_heads=2,
- nb_blocks=3,
+ nb_blocks=5,
dropout=0.1,
causal=True,
)
y1 = model(mygpt.BracketedSequence(x)).x
- attention = model.retrieve_attention()
+ attention_matrices = [m[0, 0] for m in model.retrieve_attention()]
+
+ # attention_matrices = [ torch.rand(3,5), torch.rand(8,3), torch.rand(5,8) ]
+ # for a in attention_matrices: a=a/a.sum(-1,keepdim=True)
save_attention_image(
"attention.pdf",
tokens_input,
tokens_output,
- attention,
+ attention_matrices,
# k_top=2,
min_total_attention=0.9,
)