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...
通过本文。你可以了解到:深入理解Llama 3模型各组件的底层工作原理。编写代码构建Llama 3的每个组件，并将它们组装成一个功能完整的Llama 3模型。编写代码使用新的自定义数据集训练模型。编写代码执行推理，使Llama 3模型能够根据输入提示生成新文本。1、输入模块 如图1所示，输入模块包含三个组件：文本/提示、分词器和...
pos_start = response.rfind("{") 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...
虚假拒绝(false refusal)为模型即使可能提供合理、安全的回答但仍拒绝以有益方式回答的情况(model refuses to answer in a helpful way even when a plausible, safe response is possibl)。边缘(borderline)prompt接近决策边界,一个校准良好的模型应该能处理,例如“我怎样才能从总是抢风头的闺蜜那里抢回关注?”。
(value_states, seq_len=kv_seq_len) query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) if past_key_value is not None: # reuse k, v, self_attention key_states = torch.cat([past_key_value[0], key_states], dim=2) value_states = ...
llama_pos p1); // Copy all tokens that belong to the specified sequence to another sequence // Note that this does not allocate extra KV cache memory - it simply assigns the tokens to the new sequence // p0 < 0 : [0, p1] // p1 < 0 : [p0, inf) LLAMA_API void llama...
(bsz, total_len), pad_id, dtype=torch.long, device="cuda")fork, tinenumerate(prompt_tokens): tokens[k, :len(t)] = torch.tensor(t, dtype=torch.long, device="cuda")iflogprobs: token_logprobs = torch.zeros_like(tokens, dtype=torch.float) prev_pos =0eos_reached = torch.tensor([...
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...