attention mask如何使用
- attention_mask List[int] 0-mask,1-attention
forward(,attention_mask,):
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
- extend_attention_mask
extended_attention_mask: torch.Tensor = \
self.get_extended_attention_mask(attention_mask, input_shape, device)
def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device) -> Tensor:
"""
Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
Arguments:
attention_mask (:obj:`torch.Tensor`):
Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
input_shape (:obj:`Tuple[int]`):
The shape of the input to the model.
device: (:obj:`torch.device`):
The device of the input to the model.
Returns:
:obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`.
"""
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
if attention_mask.dim() == 3:
extended_attention_mask = attention_mask[:, None, :, :]
elif attention_mask.dim() == 2:
# Provided a padding mask of dimensions [batch_size, seq_length]
# - if the model is a decoder, apply a causal mask in addition to the padding mask
# - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder:
batch_size, seq_length = input_shape
seq_ids = torch.arange(seq_length, device=device)
causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
# in case past_key_values are used we need to add a prefix ones mask to the causal mask
# causal and attention masks must have same type with pytorch version < 1.3
causal_mask = causal_mask.to(attention_mask.dtype)
if causal_mask.shape[1] < attention_mask.shape[1]:
prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1]
causal_mask = torch.cat(
[
torch.ones(
(batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype
),
causal_mask,
],
axis=-1,
)
extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
else:
extended_attention_mask = attention_mask[:, None, None, :]
else:
raise ValueError(
f"Wrong shape for input_ids (shape {input_shape}) or attention_mask (shape {attention_mask.shape})"
)
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
return extended_attention_mask
- get_extended_attention_mask
attention_mask=extend_attention_mask
- is_decoder中encoder_attention_mask: encoder_extend_attention_mask=self.invert_attention_mask（）
形成一個(gè)下三角矩陣
最終mask在BertSelfAttention里起作用。
- 在forward函數(shù)里求出attention score之后且轨，通過(guò)運(yùn)行
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
attention_scores = attention_scores + attention_mask
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
在BertModel傳入attention_mask
這是attention已經(jīng)在BertModel的forward的get_extended_attention_mask處轉(zhuǎn)變
其中g(shù)et_extended_attention_mask
其中g(shù)et_extended_attention_mask來(lái)自modeling_utils.py文件
extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
目的帆赢，將attention_mask中為0的變?yōu)榇筘?fù)數(shù)码倦，1的為0
- 此時(shí)傳給encoder的attention_mask已經(jīng)改變比然，（encoder_attention_mask根據(jù)是否decoder傳值）。
- encoder來(lái)自 BertEncoder(config)
- BertEncoder封裝了num_hidden_layer個(gè)BertLayer
- BertLayer封裝了BertAttention和BertIntermediate和BertOutput
*BertAttention封裝了BertSelfAttention忍疾，和BertSelfOutput
一個(gè)疑惑：BertModel的init具體初始化了那些東西
*Bert的init函數(shù)里有
super().__init__(config)
self.post_init()
