output[:, :k] = torch.where(accepted_mask, draft_token_ids, -torch.ones_like(draft_token_ids)) 实际处理中,并不会处理bonus_tokens。 # We disable bonus tokens because it causes corrupt KV cache for# proposal methods that require KV cache. We can fix it by "prefilling"# the bonus toke...
SpeculativeScorer, SpeculativeScores) from vllm.spec_decode.util import (get_all_seq_ids, nvtx_range, sampler_output_to_torch, from vllm.spec_decode.util import (nvtx_range, sampler_output_to_torch, split_batch_by_proposal_len) from vllm.worker.worker_base import WorkerBase @@ -98,6 +...
_, _, values = model(**inputs, output_hidden_states=True, return_dict=True) if getattr(model.config, "model_type", None) == "chatglm": values = torch.transpose(values, 0, 1) scores = [] for i in range(input_ids.size(0)): end_indexes = (input_ids[i] != tokenizer.pad_tok...
另一个问题：在我们的测试中，推测性解码比纯原模型慢。
Softmax将RM的无界分数(unbounded scores)转换为概率分布。 在AI标注数据集上训练RM可以被视为模型蒸馏的一种形式,特别是因为研究人员的AI标注器通常比RM更大、更强。 另一种方法是绕过RM并直接使用AI反馈作为RL中的奖励信号,尽管这种方法的计算成本更高,因为AI标注器比RM更大。
上一张讲了SchedulerOutputs对象的构造,这个对象构造的过程在llm_engien::step中,step会首先调用sechdule,然后执行model获得output,最后对model的输出进行处理。 由于我们看得是vllm的前端代码,所以对后端kernel的执行不做详细讨论,如果有兴趣可以参考sunlotus:Transformer第四章:vllm之PagedAttention代码分析(2)这一些列...
OutputProcessor: The retriever(embedding) models output embeddings, reranker models and classification models output Scores... ModelInputBuilder: Building model inputs and attention metadata AttnBackend: Support different AttnBackend and enable bidirectional Tokenizer: There may be different tokenizers Execut...
"stop": [], "stop_token_ids": [], "include_stop_str_in_output": false, "ignore_eos": false, "max_tokens": 50, "logprobs": null, "prompt_logprobs": null, "skip_special_tokens": true, "spaces_between_special_tokens": true, "regex": "\\d+(\\s*,\\s*\\d+)*\\s*" ...
scores = nn.functional.softmax(scores, dim=-1).type_as(query) output = torch.matmul(scores, value) # (bs, n_local_heads, slen, head_dim) output = output.transpose(1, 2).contiguous().view(bsz, seqlen, -1) pre-fill and chunking ...
shape[0]).fill_(1) scores = None while True: model_inputs = model.prepare_inputs_for_generation( input_ids, **model_kwargs) # forward pass to get next token outputs = model( **model_inputs, return_dict=True, output_attentions=False, output_hidden_states=False, ) next_token_logits...