def test_gpt2_attn(self):
model = GPT2Model.from_pretrained('gpt2')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
text = 'Bert is a yellow muppet character'
attn_data = get_attention(model,
'gpt2',
tokenizer,
text,
include_queries_and_keys=False)['all']
tokens = [
'B', 'ert', ' is', ' a', ' yellow', ' m', 'uppet', ' character'
]
self.assertEqual(attn_data['left_text'], tokens)
self.assertEqual(attn_data['right_text'], tokens)
seq_len = len(tokens)
layer = 0
head = 0
att_matrix = attn_data['attn'][layer][head]
for i in range(seq_len):
for j in range(seq_len):
if i >= j:
self.assertNotEqual(att_matrix[i][j], 0)
else:
self.assertEqual(att_matrix[i][j], 0)
sum_probs = sum(att_matrix[i])
self.assertAlmostEqual(sum_probs, 1, 4)
def highlight(self, question: str, answer: str):
special_tokens = self._tokenizer.special_tokens_map.values()
attention = AttentionMap(
attention_map=get_attention(
model=self._model,
model_type=BERT_MODEL_TYPE,
tokenizer=self._tokenizer,
sentence_a=html_replace(question),
sentence_b=html_replace(answer),
),
special_tokens=special_tokens,
)
target_highlight_len = predict_number_of_highlighted_words(
len(attention.answer_tokens))
top_tokens = attention.get_heaviest_tokens(
exclude_stopwords=True)[:target_highlight_len]
max_token_weight = max(top_tokens, key=lambda x: x[1])[1]
token_weights = {k: w / float(max_token_weight) for k, w in top_tokens}
all_answer_tokens = list(
attention.iter_answer_tokens(ignored_tokens=special_tokens,
merge_subtokens=True))
return get_text_heatmap_html(
text=answer,
tokens=all_answer_tokens,
weights=[token_weights.get(t) for t in all_answer_tokens],
)
def test_xlnet_attn(self):
tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased')
model = XLNetModel.from_pretrained('xlnet-large-cased')
text = 'Bert is a yellow muppet character'
attn_data = get_attention(model, 'xlnet', tokenizer, text, include_queries_and_keys=False)['all']
tokens = [' Bert', ' is', ' a', ' yellow', ' ', 'm', 'up', 'pet', ' character', '[SEP]', '[CLS]']
self.assertEqual(attn_data['left_text'], tokens)
self.assertEqual(attn_data['right_text'], tokens)
seq_len = len(tokens)
layer = 0
head = 0
att_matrix = attn_data['attn'][layer][head]
for i in range(seq_len):
sum_probs = sum(att_matrix[i])
self.assertAlmostEqual(sum_probs, 1, 4)
def test_bert_attn(self):
config = BertConfig.from_json_file('fixtures/config.json')
tokenizer = BertTokenizer('fixtures/vocab.txt')
for model_class in (BertModel, BertForSequenceClassification,
BertForQuestionAnswering):
model = model_class(config)
sentence1 = 'The quickest brown fox jumped over the lazy dog'
sentence2 = "the quick brown fox jumped over the laziest elmo"
attn_data = get_attention(model,
'bert',
tokenizer,
sentence1,
sentence2,
include_queries_and_keys=False)
tokens_1 = [
'[CLS]', 'the', 'quick', '##est', 'brown', 'fox', 'jumped',
'over', 'the', 'lazy', 'dog', '[SEP]'
]
tokens_2 = [
'the', 'quick', 'brown', 'fox', 'jumped', 'over', 'the', 'la',
'##zie', '##st', '[UNK]', '[SEP]'
]
self.assertEqual(attn_data['all']['left_text'],
tokens_1 + tokens_2)
self.assertEqual(attn_data['all']['right_text'],
tokens_1 + tokens_2)
self.assertEqual(attn_data['aa']['left_text'], tokens_1)
self.assertEqual(attn_data['aa']['right_text'], tokens_1)
self.assertEqual(attn_data['ab']['left_text'], tokens_1)
self.assertEqual(attn_data['ab']['right_text'], tokens_2)
self.assertEqual(attn_data['ba']['left_text'], tokens_2)
self.assertEqual(attn_data['ba']['right_text'], tokens_1)
self.assertEqual(attn_data['bb']['left_text'], tokens_2)
self.assertEqual(attn_data['bb']['right_text'], tokens_2)
attn_all = attn_data['all']['attn']
attn_aa = attn_data['aa']['attn']
attn_ab = attn_data['ab']['attn']
attn_ba = attn_data['ba']['attn']
attn_bb = attn_data['bb']['attn']
num_layers = len(attn_all)
for layer in range(num_layers):
attn_all_layer = torch.tensor(attn_all[layer])
num_heads, seq_len, _ = attn_all_layer.size()
# Check that probabilities sum to one
sum_probs = attn_all_layer.sum(dim=-1)
expected = torch.ones(num_heads, seq_len, dtype=torch.float32)
self.assertTrue(torch.allclose(sum_probs, expected))
# Reassemble attention from components and verify is correct
attn_aa_layer = torch.tensor(attn_aa[layer])
attn_ab_layer = torch.tensor(attn_ab[layer])
attn_ba_layer = torch.tensor(attn_ba[layer])
attn_bb_layer = torch.tensor(attn_bb[layer])
top_half = torch.cat((attn_aa_layer, attn_ab_layer), dim=-1)
bottom_half = torch.cat((attn_ba_layer, attn_bb_layer), dim=-1)
whole = torch.cat((top_half, bottom_half), dim=-2)
# assert self.assertAlmostEqual(torch.sum(torch.abs(whole - attn_all[layer])), 0)
self.assertTrue(torch.allclose(whole, attn_all_layer))
for model_class in (BertModel, BertForSequenceClassification,
BertForQuestionAnswering):
model = model_class(config)
sentence1 = 'The quickest brown fox jumped over the lazy dog'
sentence2 = None
attn_data = get_attention(model,
'bert',
tokenizer,
sentence1,
sentence2,
include_queries_and_keys=False)
tokens_1 = [
'[CLS]', 'the', 'quick', '##est', 'brown', 'fox', 'jumped',
'over', 'the', 'lazy', 'dog', '[SEP]'
]
tokens_2 = []
self.assertEqual(attn_data['all']['left_text'],
tokens_1 + tokens_2)
self.assertEqual(attn_data['all']['right_text'],
tokens_1 + tokens_2)
self.assertTrue('aa' not in attn_data)
attn_all = attn_data['all']['attn']
num_layers = len(attn_all)
for layer in range(num_layers):
attn_all_layer = torch.tensor(attn_all[layer])
num_heads, seq_len, _ = attn_all_layer.size()
# Check that probabilities sum to one
sum_probs = attn_all_layer.sum(dim=-1)
expected = torch.ones(num_heads, seq_len, dtype=torch.float32)
self.assertTrue(torch.allclose(sum_probs, expected))
def test_roberta_attn(self):
model = RobertaModel.from_pretrained('roberta-base')
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
sentence1 = 'The quickest brown fox jumped over the lazy dog'
sentence2 = "the quick brown fox jumped over the laziest dog"
attn_data = get_attention(model,
'roberta',
tokenizer,
sentence1,
sentence2,
include_queries_and_keys=False)
tokens_1 = [
'[CLS]', 'The', ' quickest', ' brown', ' fox', ' jumped', ' over',
' the', ' lazy', ' dog', '[SEP]'
]
tokens_2 = [
'[SEP]', 'the', ' quick', ' brown', ' fox', ' jumped', ' over',
' the', ' laz', 'iest', ' dog', '[SEP]'
]
self.assertEqual(attn_data['all']['left_text'], tokens_1 + tokens_2)
self.assertEqual(attn_data['all']['right_text'], tokens_1 + tokens_2)
self.assertEqual(attn_data['aa']['left_text'], tokens_1)
self.assertEqual(attn_data['aa']['right_text'], tokens_1)
self.assertEqual(attn_data['ab']['left_text'], tokens_1)
self.assertEqual(attn_data['ab']['right_text'], tokens_2)
self.assertEqual(attn_data['ba']['left_text'], tokens_2)
self.assertEqual(attn_data['ba']['right_text'], tokens_1)
self.assertEqual(attn_data['bb']['left_text'], tokens_2)
self.assertEqual(attn_data['bb']['right_text'], tokens_2)
attn_all = attn_data['all']['attn']
attn_aa = attn_data['aa']['attn']
attn_ab = attn_data['ab']['attn']
attn_ba = attn_data['ba']['attn']
attn_bb = attn_data['bb']['attn']
num_layers = len(attn_all)
for layer in range(num_layers):
attn_all_layer = torch.tensor(attn_all[layer])
num_heads, seq_len, _ = attn_all_layer.size()
# Check that probabilities sum to one
sum_probs = attn_all_layer.sum(dim=-1)
expected = torch.ones(num_heads, seq_len, dtype=torch.float32)
self.assertTrue(torch.allclose(sum_probs, expected))
# Reassemble attention from components and verify is correct
attn_aa_layer = torch.tensor(attn_aa[layer])
attn_ab_layer = torch.tensor(attn_ab[layer])
attn_ba_layer = torch.tensor(attn_ba[layer])
attn_bb_layer = torch.tensor(attn_bb[layer])
top_half = torch.cat((attn_aa_layer, attn_ab_layer), dim=-1)
bottom_half = torch.cat((attn_ba_layer, attn_bb_layer), dim=-1)
whole = torch.cat((top_half, bottom_half), dim=-2)
# assert self.assertAlmostEqual(torch.sum(torch.abs(whole - attn_all[layer])), 0)
self.assertTrue(torch.allclose(whole, attn_all_layer))
sentence1 = 'The quickest brown fox jumped over the lazy dog'
sentence2 = None
attn_data = get_attention(model,
'roberta',
tokenizer,
sentence1,
sentence2,
include_queries_and_keys=False)
tokens_1 = [
'[CLS]', 'The', ' quickest', ' brown', ' fox', ' jumped', ' over',
' the', ' lazy', ' dog', '[SEP]'
]
tokens_2 = []
self.assertEqual(attn_data['all']['left_text'], tokens_1 + tokens_2)
self.assertEqual(attn_data['all']['right_text'], tokens_1 + tokens_2)
self.assertTrue('aa' not in attn_data)
attn_all = attn_data['all']['attn']
num_layers = len(attn_all)
for layer in range(num_layers):
attn_all_layer = torch.tensor(attn_all[layer])
num_heads, seq_len, _ = attn_all_layer.size()
# Check that probabilities sum to one
sum_probs = attn_all_layer.sum(dim=-1)
expected = torch.ones(num_heads, seq_len, dtype=torch.float32)
self.assertTrue(torch.allclose(sum_probs, expected))
