token_x,attn_mask=None, key_padding_mask=None): """ 前向传播 :param token_x:...
batch_first=True)x=torch.rand(1,3,5)key_padding_mask=torch.tensor([False,False,True]).expand(1,3)print('>>>x:\n',x)print('>>>key_padding_mask:\n',key_padding_mask)attn_output,attn_output_weights=mha(x,x,x,key_padding_mask=key_padding_mask)print('>>>attn_output:\n',attn_...
importtorchwithtorch.inference_mode():d_model=4layer=torch.nn.TransformerEncoderLayer(d_model,2,2,batch_first=True)layer.eval()x=torch.randn(5,10,d_model)pad=torch.rand(5,10)>0.5layer(x,src_key_padding_mask=pad) Gives the warning: ...
src_mask: Optional[Tensor] = None, src_key_padding_mask: Optional[Tensor] = None, pos: Optional[Tensor] = None): q = k = self.with_pos_embed(src, pos) src2 = self.self_attn( q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0] src = src + se...
src_key_padding_mask: src key per batch的ByteTensor mask(无batch的tensor为(S),有batch的tensor为(N,S)) tgt_key_padding_mask: tgt key per batch的ByteTensor mask(无batch的tensor为(T),有batch的tensor为(N,T)) memory_key_padding_mask: memory key per batch的ByteTensor mask(无batch的tensor...
src=x.transpose(0,1),src_key_padding_mask=y).transpose(0,1)print(output)output=output.masked_fill(torch.isnan(output),0)print(output)optim=torch.optim.Adam(model.parameters())optim.zero_grad()output.mean().backward()optim.step()output=model(src=x.transpose(0,1),src_key_padding_mask=...
defforward(self,src,tgt,src_mask=None,tgt_mask=None,memory_mask=None,src_key_padding_mask=None,tgt_key_padding_mask=None,memory_key_padding_mask=None):# word embedding src=self.embedding(src)tgt=self.embedding(tgt)# shape checkifsrc.size(1)!=tgt.size(1):raiseRuntimeError("the batch ...
- key_padding_mask: `(N, S)` - attn_mask: `(L, S)` or `(N * num_heads, L, S)` 输出: - attn_output:`(L, N, E)` - attn_output_weights:`(N, L, S)` ''' tgt_len, bsz, embed_dim = query.shape src_len, _, _ = key.shape ...
- key_padding_mask: `(N, S)` - attn_mask: `(L, S)` or `(N * num_heads, L, S)` 输出: - attn_output:`(L, N, E)` - attn_output_weights:`(N, L, S)` ''' tgt_len, bsz, embed_dim = query.shape src_len, _, _ = key.sh...
(query, key, value, attn_mask=None, dropout_p=dropout, is_causal=is_causal) y = y.transpose(1, 2).view(batch_size, -1, self.num_heads * head_dim) y = self.resid_dropout(self.c_proj(y)) return y num_heads = 8 heads_per_dim = 64 embed_dimension = num_heads * heads_per...