start_pos (int): Starting position for attention caching. freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies. mask (torch.Tensor, optional): Masking tensor for attention. Defaults to None. Returns: torch.Tensor: Output tensor after applying attention and feedforward layers. """ ...
start_pos (int): Starting position for caching. freqs_cis (torch.Tensor): Precomputed frequency tensor. mask (torch.Tensor, optional): Attention mask tensor. Returns: torch.Tensor: Output tensor after attention. """ bsz, seqlen, _ = x.shape xq, xk, xv = self.wq(x), self.wk(...
self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps) def forward(self, x: torch.Tensor, start_pos: int, freqs_cis: torch.Tensor, mask: Optional[torch.Tensor]): h = x + self.attention.forward(self.attention_norm(x), start_pos, freqs_cis, mask) out = h + self.feed_forward.for...
layers: h = layer(h, start_pos, freqs_cis, mask) h = self.norm(h) output = self.output(h[:, -1, :]) # only compute last logits return output.float() 7 论文结论 本文中提出了一系列公开发布的语言模型,并实现与最先进的基础模型相竞争的结果。最值得注意的是,LLaMA-13B的性能优于GPT-3...
decoder_start_token_id=llama_token_bos(model); } embd_inp.clear(); embd_inp.push_back(decoder_start_token_id); } (3) 分析预测 分析预测部分的核心代码如下,我将处理关注力和session的逻辑删除,仅保留推理部分的逻辑。 //predictif(!embd.empty()) {//Note: (n_ctx - 4) here is to match ...
inference): # start_pos: 推理模式下的标记位置, inference: True表示推理模式,False表示训练模式 # 1) 将输入嵌入传递给attention_norm,然后传递给注意力模块 # 2) 注意力的输出与原始输入(归一化前)相加 h=x+self.attention(self.attention_norm(x), start_pos,inference) # 1) 将注意力输出传递...
self.ffn_norm = RMSNorm(dim, eps=norm_eps) def forward(self, x, start_pos, freqs_cis, mask): h = x + self.attention(self.attention_norm(x), start_pos, freqs_cis, mask) out = h + self.feed_forward(self.ffn_norm(h)) return out...
return json.loads(response[pos_start:pos_end+1]) except Exception as exp: print(f"extract_json::cannot parse output: {exp}") return None 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 结果发现,LLaMA-2 生成的模型响应并非总是有效的 JSON 格式;它经常会生成类似 “{ROW: 3, COLUMN:...
I'm still not convinced we need to introducen_parallelandllama_n_max_seq(). I did some tests using justn_ctxand things seems to work OK. Only the self attention input buffers (such asKQ_maskandKQ_pos) depend onn_ctx(and nowkv_size), but these are not used for Mamba, so we won...
@jinfagang all files changed. Please start from 0, cloning the repo and passing the readme steps, and you'll be happy :) Good job! It is doing quite well, remembering your English is not quite perfect. 😉 I feel sorry for it that you got angry with it 😭 ...