Implement Speculative Decoding

[EricLBuehler]

Speculative decoding: https://arxiv.org/pdf/2211.17192

This will refactor the pipeline structure to make the sampling process more abstracted. Additionally, it will also abstract the scheduling and kv cache management.

Restriction

• Requires same vocab

Algorithm

Given draft model q and target model p with probability distributions $q_i(x)$ and $p_i(x)$ for each token

• Keep the sample for token i if $q_i(x)$ <= $p_i(x)$
  • This means the target model agrees
• Else (if $q_i(x)$ > $p_i(x)$ ) accept that token with prob $\frac{p_i(x)}{q_i(x)}$
  • If rejected, sample token from from $p'(x) = norm(max(0, p(x) − q(x)))$ and do not take any more
  • Note that that is really $p'(x) = ReLU(p(x) − q(x))$

[github-actions]

Code Metrics Report

  ───────────────────────────────────────────────────────────────────────────────
Language                 Files     Lines   Blanks  Comments     Code Complexity
───────────────────────────────────────────────────────────────────────────────
Rust                        72     23863     1572       530    21761       1325
───────────────────────────────────────────────────────────────────────────────
Total                       72     23863     1572       530    21761       1325
───────────────────────────────────────────────────────────────────────────────
Estimated Cost to Develop 85,737
Estimated Schedule Effort 11.916649 months
Estimated People Required 5.112342
───────────────────────────────────────────────────────────────────────────────
Processed 793364 bytes, 0.793 megabytes (SI)
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[kir-gadjello]

It would be very useful to relax the requirement of exact same tokenizer for main and draft models like here: vllm-project/vllm#2188

[EricLBuehler]

It would be very useful to relax the requirement of exact same tokenizer for main and draft models like here: vllm-project/vllm#2188

Yes, this implementation only checks if the vocabs are the same: see this check.

[kir-gadjello]

It would be very useful to relax the requirement of exact same tokenizer for main and draft models like here: vllm-project/vllm#2188

Yes, this implementation only checks if the vocabs are the same: see this check.

I understand that same vocab case is much easier to code, but if this requirement is relaxes people can use a ready-made small draft model even if their LLM is incompatible with it (which often will be the case).

[EricLBuehler]

I understand that same vocab case is much easier to code, but if this requirement is relaxes people can use a ready-made small draft model even if their LLM is incompatible with it (which often will be the case).

That sounds great! Can you please give an example of how I should relax the requirement?

[EricLBuehler]

This PR adds the base framework for SD. Further improvements to speed will be added in addition to self-speculative decoding.