MultiheadAttention中的Attention Mask格式 在PyTorch的MultiheadAttention模块中,Attention Mask的格式有一定的要求。具体来说,Attention Mask应该是一个三维的Tensor,其形状为(B, Nt, Ns),其中B为batch size,Nt为目标序列的长度,Ns为源序列的长度。在这个Tensor中,每个位置的值应该为0或-inf,分别表示应该考虑或忽略...
Attention的原理已经有很多介绍了,实现的伪代码参照transformer,下面写了最简单的版本 importtorch, mathfromtorchimportnn dropout_prob =0.1defforward(hidden_size,# dinput,#(b, s, d)attention_mask#(b, s, s)): query = nn.Linear(hidden_size, hidden_size)#(d,d)key = nn.Linear(hidden_size, hi...
第1个地方就是在上一篇文章用介绍到的Attention Mask，用于在训练过程中解码的时候掩盖掉当前时刻之后的...
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def forward(self, hidden_states, attention_mask): # shape of hidden_states and mixed_*_layer: batch_size * seq_length * hidden_size ...
下面回来transformer encoder中word embedding,position embedding,self-attention mask的pytorch实现。 (一)word embedding importtorchimportnumpy as npimporttorch.nn as nnimporttorch.nn.functional as F#关于word embedding,以序列建模为例#考虑source sentence 和 target sentence#构建序列,序列的字符以其在词表中的...
attention_mask[0]: tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]) labels[0]: tensor([ -100, -100, -100, -100, 4295, -100, -100, 11265, -100, -100, ...
attn_mask(Optional[Tensor]) – If specified, a 2D or 3D mask preventing attention to certain positions. Must be of shape (L,S) or (N⋅num_heads,L,S), where N is the batch size, L is the target sequence length, and S is the source sequence length. A 2D mask will be broadcaste...
#Getinput_ids, attention_masksandlabelsforeach sentence.batch=data_collator(tuple_ids) batch['labels'] =inputs['input_ids'] returnbatch['input_ids'], inputs['attention_mask'], batch['labels'] input_ids, attention_mask,labels=load_dataset_mlm(sentences)""" ...
input_data = batch['input_ids'].clone().to(device)attention_mask = batch['attention_mask'].clone().to(device) target = input_data[:, 1:]input_data = input_data[:, :-1] # Pad all the sequences in the batch:input_data = pad_...
batch = data_collator(tuple_ids) batch['labels'] = inputs['input_ids'] return batch['input_ids'], inputs['attention_mask'], batch['labels'] input_ids, attention_mask, labels = load_dataset_mlm(sentences) """ input_ids[0]:...