block_out_features)for_inrange(num_blocks)])defforward(self,x):x=x.chunk(self.num_blocks,dim=-1)x=[block(x_i)forblock,x_iinzip(self.blocks,x)]x=torch.cat(x,
绝对位置嵌入的实现通常涉及创建一个大小为_vocabulary * embeddingdim_的查找表。这意味着词汇表中的每个标记在查找表中都有一个条目,并且该条目的维度为_embeddingdim_。 绝对位置嵌入主要有两种类型: 学习型:在学习型方法中,嵌入向量在训练过程中随机初始化,然后进行训练。原始的Transformer论文[5]以及像BERT、GPT...
x=x.chunk(self.num_blocks, dim=-1) x= [block(x_i) forblock, x_iinzip(self.blocks, x)] x=torch.cat(x, dim=-1) returnx classsLSTMBlock(nn.Module): def__init__(self, input_size, hidden_size, num_heads, proj_factor=4/3): super(sLSTMBlock, self).__init__() self.input...
defforward(self, x):x=x.chunk(self.num_blocks,dim=-1) x= [block(x_i) forblock, x_iinzip(self.blocks, x)]x=torch.cat(x,dim=-1) returnx classsLSTMBlock(nn.Module): def__init__(self, input_size, hidden_size, num_heads,proj_factor=4/3): super(sLSTMBlock, self).__init_...
q=self.q(x).reshape(B,self.num_heads,C// self.num_heads, -1).transpose(-1, -2)kv=self.sr(x)kv=self.local_conv(kv)+kv k,v=torch.chunk(self.kv(kv),chunks=2,dim=1)k=k.reshape(B,self.num_heads,C// self.num_heads, -1)v=v.reshape(B,self.num_heads,C// self.num_head...
x=self.norm1(x_in) qkv=self.qkv(x) # 为支持PyTorch嵌套张量,采用先分割后重排的策略 # 而非传统的先重排后分割输入状态的方法 q, k, v=qkv.chunk(3, -1) q=self.reshape_and_permute(q, batch_size) k=self.reshape_and_permute(k, batch_size) ...
def forward(self, x_in, attn_mask=None):batch_size = x_in.size(0)x = self.norm1(x_in)qkv = self.qkv(x) # 为支持PyTorch嵌套张量,采用先分割后重排的策略# 而非传统的先重排后分割输入状态的方法q, k, v = q...
dwconv(x).chunk(2, dim=1) x = F.gelu(x1) * x2 x = self.project_out(x) return x class BaseFeatureExtraction(nn.Module): def __init__(self, dim, num_heads, ffn_expand_factor=1., qkv_bias=False,): super(BaseFeatureExtraction, self).__init__() self.norm1...
A 1-wide rectangular corridor (surrounding a chunk of normal stone). A room featuring an O, M, Z, or T monster in a cage of iron bars. It can also be a D below level 12. From EvilHack. A room split horizontally or vertically into two subrooms with either walls or iron bars. Fr...
chunk(2, dim=-1) 72 + return F.silu(gate) * x 73 + 74 + 75 + # parallel attention and feedforward with residual 76 + # discovered by Wang et al + EleutherAI from GPT-J fame 77 + 78 + class ParallelTransformerBlock(nn.Module): 79 + def __init__(self, dim, ...