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fix tests on python 3.5

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thomwolf
2019-12-09 21:38:07 +01:00
parent 169fea6855
commit b016dd16c9
3 changed files with 10 additions and 9 deletions
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transformers/modeling_t5.py
+1 -1
View File
@@ -338,7 +338,7 @@ class T5Attention(nn.Module):
raise ValueError("No position_bias provided and no weights to compute position_bias")
position_bias = self.compute_bias(qlen, klen)
if mask is not None:
position_bias += mask # (bs, n_heads, qlen, klen)
position_bias = position_bias + mask # (bs, n_heads, qlen, klen)
scores += position_bias
weights = F.softmax(scores.float(), dim=-1).type_as(scores) # (bs, n_heads, qlen, klen)
transformers/tests/modeling_common_test.py
+8 -7
View File
@@ -138,8 +138,8 @@ class CommonTestCases:
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads,
self.model_tester.seq_length,
self.model_tester.key_len if hasattr(self.model_tester, 'key_len') else self.model_tester.seq_length])
self.model_tester.encoder_seq_length if hasattr(self.model_tester, 'encoder_seq_length') else self.model_tester.seq_length,
self.model_tester.encoder_seq_length if hasattr(self.model_tester, 'encoder_seq_length') else self.model_tester.seq_length])
out_len = len(outputs)
if self.is_encoder_decoder:
@@ -151,8 +151,8 @@ class CommonTestCases:
self.assertListEqual(
list(decoder_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads,
self.model_tester.seq_length,
self.model_tester.key_len if hasattr(self.model_tester, 'key_len') else self.model_tester.seq_length])
self.model_tester.decoder_seq_length if hasattr(self.model_tester, 'decoder_seq_length') else self.model_tester.seq_length,
self.model_tester.decoder_seq_length if hasattr(self.model_tester, 'decoder_seq_length') else self.model_tester.seq_length])
# Check attention is always last and order is fine
config.output_attentions = True
@@ -169,8 +169,8 @@ class CommonTestCases:
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads,
self.model_tester.seq_length,
self.model_tester.key_len if hasattr(self.model_tester, 'key_len') else self.model_tester.seq_length])
self.model_tester.encoder_seq_length if hasattr(self.model_tester, 'encoder_seq_length') else self.model_tester.seq_length,
self.model_tester.encoder_seq_length if hasattr(self.model_tester, 'encoder_seq_length') else self.model_tester.seq_length])
def test_torchscript(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
@@ -440,7 +440,8 @@ class CommonTestCases:
self.assertEqual(len(hidden_states), self.model_tester.num_hidden_layers + 1)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[self.model_tester.seq_length, self.model_tester.hidden_size])
[self.model_tester.encoder_seq_length if hasattr(self.model_tester, 'encoder_seq_length') else self.model_tester.seq_length,
self.model_tester.hidden_size])
def test_resize_tokens_embeddings(self):
original_config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
transformers/tokenization_t5.py
+1 -1
View File
@@ -134,7 +134,7 @@ class T5Tokenizer(PreTrainedTokenizer):
""" Converts a token (str/unicode) in an id using the vocab. """
if token.startswith(u"<extra_id_"):
l = re.match(r'<extra_id_(\d+)>', token)
num = int(l[1])
num = int(l.group(1))
return self.vocab_size - num - 1
return self.sp_model.piece_to_id(token)