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Fix t5 dataset #459

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Fix t5 dataset #459

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18a984b
fix-mt5-mask-dropout
zhangfanTJU Feb 18, 2023
7ad4ae4
fix-t5-dataset
zhangfanTJU Feb 19, 2023
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4 changes: 1 addition & 3 deletions libai/data/datasets/t5_dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -38,9 +38,7 @@ class T5Dataset(flow.utils.data.Dataset):
All values are padded to this length. Defaults to 512.
max_seq_length_dec (int, optional): Maximum length of the sequence passing into decoder.
All values are padded to this length. Defaults to 128.
mask_lm_prob (float, optional): Probability to mask tokens. Defaults to 0.15.
max_preds_per_seq (int, optional): Maximum number of masked tokens in each sentence.
Defaults to None.
masked_lm_prob (float, optional): Probability to mask tokens. Defaults to 0.15.
short_seq_prob (float, optional):
Probability of producing a short sequence. Defaults to 0.0.
seed (int, optional):
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5 changes: 3 additions & 2 deletions projects/MT5/layers/attention_layer.py
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Expand Up @@ -218,7 +218,7 @@ def forward(
)
else:
position_bias = self.compute_bias(
real_seq_length, key_length, placement=attention_mask.placement
real_seq_length, key_length, placement=attention_scores.placement
)

if past_key_value is not None:
Expand All @@ -228,13 +228,14 @@ def forward(
if use_cache:
attention_mask = attention_mask.expand_as(attention_scores)

attention_dropout_prob = self.attention_dropout_prob if self.training else 0.0
attention_weights = flow._C.fused_bias_add_scale_mask_softmax_dropout(
attention_scores,
position_bias,
attention_mask,
fill_value=-10000.0,
scale=1,
p=self.attention_dropout_prob,
p=attention_dropout_prob,
)[0]
else:
attention_scores = attention_scores + position_bias
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11 changes: 6 additions & 5 deletions projects/MT5/mt5_model.py
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Expand Up @@ -203,7 +203,7 @@ def forward(
position_bias = None
encoder_decoder_position_bias = None
self.set_cache(encoder_states=None, past_key_values=None)
encoder_attn_mask = self.extended_attn_mask(encoder_attn_mask)
encoder_attn_mask = self.extended_attn_mask(encoder_attn_mask) if encoder_attn_mask is not None else encoder_attn_mask
enc_embedding_output = self.embedding(encoder_input_ids)
# transpose [batch_size, seq_len, embed_size] to [seq_len, batch_size, embed_size]
enc_hidden_states = enc_embedding_output.transpose(0, 1)
Expand All @@ -219,10 +219,11 @@ def forward(
if only_encoder:
return encoder_states

decoder_attn_mask = self.extended_attn_mask(
decoder_attn_mask, decoder_input_ids, is_decoder=True
)
encoder_decoder_attn_mask = self.extended_attn_mask(encoder_decoder_attn_mask)
if decoder_attn_mask is not None:
decoder_attn_mask = self.extended_attn_mask(
decoder_attn_mask, decoder_input_ids, is_decoder=True
)
encoder_decoder_attn_mask = self.extended_attn_mask(encoder_decoder_attn_mask) if encoder_decoder_attn_mask is not None else encoder_decoder_attn_mask

dec_embedding_output = self.embedding(decoder_input_ids)
# transpose [batch_size, seq_len, embed_size] to [seq_len, batch_size, embed_size]
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