推理加速: 自推测解码(Self-Speculative Decoding):利用多token预测的额外输出头进行自推测解码,从而加速推理过程。 工作原理:先用多个输出头并行预测多个token,然后用主输出头(next-token prediction head)验证预测结果,并选择最有可能的预测结果。6. 实验与结论实验设置: 数据集:论文使用了多种数据集进行实验,包括代...
而在inference阶段,我们只使用next-token output head。即,只使用一个head。那么按照目前的理解就是: 从1,基于如下的output head,直接预测出来2, 3, 4, 5: 预测推理阶段,只使用最左边的一个head,从1预测2,3,4,5;然后从5预测6,7,8,9。依此类推。 然后,从5预测6,7,8,9。依此类推。 从9,直接预测...
自推测解码(Self-Speculative Decoding):利用多token预测的额外输出头进行自推测解码,从而加速推理过程。 工作原理:先用多个输出头并行预测多个token,然后用主输出头(next-token prediction head)验证预测结果,并选择最有可能的预测结果。 6. 实验与结论 实验设置: 数据集:论文使用了多种数据集进行实验,包括代码数据集...
Accelerating Codec-based Speech Synthesis with Multi-Token Prediction and Speculative Decoding 2024.10.18https://arxiv.org/pdf/2410.13839v1keywords: 自回归tts,推理加速出版单位:韩国科学技术院Demo page:Demo: https://multpletokensprediction.github.io/multipletokensprediction.github.io/快速阅读: 本文重新构建...
但是序列信息非常重要,代表着全局的结构,因此必须将序列的token相对或者绝对位置信息利用起来。这里每个token的position embedding 向量维度也是dmodel=512, 然后将原本的input embedding和position embedding加起来组成最终的embedding作为encoder/decoder的输入。其中,position embedding计算公式如下:...
(AD) as its authentication system. And there are a few limitations. For example, you only have four basic options when it comes to what type of additional authentication factor they can use: Microsoft Authenticator, SMS, Voice and Oauth Token. You also might have to spend more on licensing...
What if there's a stop token id that's decoded in 1st sub-step (calling multi-steps inside one large step as sub-steps)? Is the decoding continued even though there's a stop token id? thanks for the great questions! The outputs will be able to be streamed both as they finish or ...
For example, you only have four basic options when it comes to what type of additional authentication factor they can use: Microsoft Authenticator, SMS, Voice and Oauth Token. You also might have to spend more on licensing depending on the types of options you want available and whether or ...
decoding_strategies lora_in_image special_token-sft PEFT_Multi_LoRA_Inference.ipynb PEFT_Multi_LoRA_Inference.md add_tokens.ipynb codellama_code_sft.ipynb gemma2-sft.ipynb jamba-sft.ipynb llm_logits.ipynb o1-reasoning-sft-lora.ipynb o1-reasoning-sft.ipynb proxy_tuning.ipynb qwen2-fastapi.py qw...
方法1:直接把MTP Model头全部删掉,模型变成了一个Predict Next Token的 Main Model。然后部署模型做推理,这个就跟正常LLM模型推理一样。没有什么加速效果 方法2:保留MTP Model 做self-speculative decoding,这样充分使用多Head预测能力,提升推理加速性能。类似下图:(这是Google在18年发表在NIPS上的工作,paper:Blockwise...