File size: 9,268 Bytes
4ae80b2 bcbb55b 4ae80b2 c56dde4 4ae80b2 c56dde4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 |
#############################
# Imports
#############################
# Python modules
# Remote modules
import matplotlib.pyplot as plt
import numpy as np
import torch
# Local modules
#############################
# Constants
#############################
class AttentionVisualizer:
def __init__(self, device):
self.device = device
def visualize_token2token_scores(self, all_tokens,
scores_mat,
useful_indeces,
x_label_name='Head',
apply_normalization=True):
fig = plt.figure(figsize=(20, 20))
all_tokens = np.array(all_tokens)[useful_indeces]
for idx, scores in enumerate(scores_mat):
if apply_normalization:
scores = torch.from_numpy(scores)
shape = scores.shape
scores = scores.reshape((shape[0],shape[1], 1))
scores = torch.linalg.norm(scores, dim=2)
scores_np = np.array(scores)
scores_np = scores_np[useful_indeces, :]
scores_np = scores_np[:, useful_indeces]
ax = fig.add_subplot(4, 4, idx + 1)
# append the attention weights
im = ax.imshow(scores_np, cmap='viridis')
fontdict = {'fontsize': 10}
ax.set_xticks(range(len(all_tokens)))
ax.set_yticks(range(len(all_tokens)))
ax.set_xticklabels(all_tokens, fontdict=fontdict, rotation=90)
ax.set_yticklabels(all_tokens, fontdict=fontdict)
ax.set_xlabel('{} {}'.format(x_label_name, idx + 1))
fig.colorbar(im, fraction=0.046, pad=0.04)
plt.tight_layout()
plt.show()
def visualize_matrix(self,
scores_mat,
label_name='heads_layers'):
_fig = plt.figure(figsize=(20, 20))
scores_np = np.array(scores_mat)
fig, ax = plt.subplots()
im = ax.imshow(scores_np, cmap='viridis')
fontdict = {'fontsize': 10}
ax.set_xticks(range(len(scores_mat[0])))
ax.set_yticks(range(len(scores_mat)))
x_labels = [f'head-{i}' for i in range(1, len(scores_mat[0])+1)]
y_labels = [f'layer-{i}' for i in range(1, len(scores_mat) + 1)]
ax.set_xticklabels(x_labels, fontdict=fontdict, rotation=90)
ax.set_yticklabels(y_labels, fontdict=fontdict)
ax.set_xlabel('{}'.format(label_name))
fig.colorbar(im, fraction=0.046, pad=0.04)
plt.tight_layout()
#plt.show()
plt.savefig(f'figs/{label_name}.png', dpi=fig.dpi)
def visualize_token2head_scores(self, all_tokens, scores_mat):
fig = plt.figure(figsize=(30, 50))
for idx, scores in enumerate(scores_mat):
scores_np = np.array(scores)
ax = fig.add_subplot(6, 3, idx + 1)
# append the attention weights
im = ax.matshow(scores_np, cmap='viridis')
fontdict = {'fontsize': 20}
ax.set_xticks(range(len(all_tokens)))
ax.set_yticks(range(len(scores)))
ax.set_xticklabels(all_tokens, fontdict=fontdict, rotation=90)
ax.set_yticklabels(range(len(scores[0])), fontdict=fontdict)
ax.set_xlabel('Layer {}'.format(idx + 1))
fig.colorbar(im, fraction=0.046, pad=0.04)
plt.tight_layout()
plt.show()
def plot_attn_lines(self, data, heads):
"""Plots attention maps for the given example and attention heads."""
width = 3
example_sep = 3
word_height = 1
pad = 0.1
for ei, (layer, head) in enumerate(heads):
yoffset = 1
xoffset = ei * width * example_sep
attn = data["attns"][layer][head]
attn = np.array(attn)
attn /= attn.sum(axis=-1, keepdims=True)
words = data["tokens"]
words[0] = "..."
n_words = len(words)
for position, word in enumerate(words):
plt.text(xoffset + 0, yoffset - position * word_height, word,
ha="right", va="center")
plt.text(xoffset + width, yoffset - position * word_height, word,
ha="left", va="center")
for i in range(1, n_words):
for j in range(1, n_words):
plt.plot([xoffset + pad, xoffset + width - pad],
[yoffset - word_height * i, yoffset - word_height * j],
color="blue", linewidth=1, alpha=attn[i, j])
def plot_attn_lines_concepts(self, title, examples, layer, head, color_words,
color_from=True, width=3, example_sep=3,
word_height=1, pad=0.1, hide_sep=False):
# examples -> {'words': tokens, 'attentions': [layer][head]}
plt.figure(figsize=(4, 4))
for i, example in enumerate(examples):
yoffset = 0
if i == 0:
yoffset += (len(examples[0]["words"]) -
len(examples[1]["words"])) * word_height / 2
xoffset = i * width * example_sep
attn = example["attentions"][layer][head]
if hide_sep:
attn = np.array(attn)
attn[:, 0] = 0
attn[:, -1] = 0
attn /= attn.sum(axis=-1, keepdims=True)
words = example["words"]
n_words = len(words)
for position, word in enumerate(words):
for x, from_word in [(xoffset, True), (xoffset + width, False)]:
color = "k"
if from_word == color_from and word in color_words:
color = "#cc0000"
plt.text(x, yoffset - (position * word_height), word,
ha="right" if from_word else "left", va="center",
color=color)
for i in range(n_words):
for j in range(n_words):
color = "b"
if words[i if color_from else j] in color_words:
color = "r"
print(attn[i, j])
plt.plot([xoffset + pad, xoffset + width - pad],
[yoffset - word_height * i, yoffset - word_height * j],
color=color, linewidth=1, alpha=attn[i, j])
plt.axis("off")
plt.title(title)
plt.show()
def plot_attn_lines_concepts_ids(self, title, examples, layer, head,
relations_total, width=3, example_sep=3,
word_height=1, pad=0.1, hide_sep=False):
# examples -> {'words': tokens, 'attentions': [layer][head]}
plt.clf()
fig = plt.figure(figsize=(10, 5))
# print('relations_total:', relations_total)
# print(examples[0])
for idx, example in enumerate(examples):
yoffset = 0
if idx == 0:
yoffset += (len(examples[0]["words"]) -
len(examples[0]["words"])) * word_height / 2
xoffset = idx * width * example_sep
attn = example["attentions"][layer][head]
if hide_sep:
attn = np.array(attn)
attn[:, 0] = 0
attn[:, -1] = 0
attn /= attn.sum(axis=-1, keepdims=True)
words = example["words"]
n_words = len(words)
example_rel = relations_total[idx]
for position, word in enumerate(words):
for x, from_word in [(xoffset, True), (xoffset + width, False)]:
color = "k"
for y_idx, y in enumerate(words):
if from_word and example_rel[position, y_idx] > 0:
# print('outgoing', position, y_idx)
color = "r"
if not from_word and example_rel[y_idx, position] > 0:
# print('coming', position, y_idx)
color = "g"
# if from_word == color_from and word in color_words:
# color = "#cc0000"
plt.text(x, yoffset - (position * word_height), word,
ha="right" if from_word else "left", va="center",
color=color)
for i in range(n_words):
for j in range(n_words):
color = "k"
# print(i,j, example_rel[i,j])
if example_rel[i, j].item() > 0 and i <= j:
color = "r"
if example_rel[i, j].item() > 0 and i >= j:
color = "g"
plt.plot([xoffset + pad, xoffset + width - pad],
[yoffset - word_height * i, yoffset - word_height * j],
color=color, linewidth=1, alpha=attn[i, j])
# color=color, linewidth=1, alpha=min(attn[i, j]*10,1))
plt.axis("off")
plt.title(title)
#plt.show()
return fig
|