Multi-Draft Speculative Sampling: Canonical Architectures and
  Theoretical Limits
We consider multi-draft speculative sampling, where the proposal sequences
are sampled independently from different draft models. At each step, a
token-level draft selection scheme takes a list of valid tokens as input and
produces an output token whose distribution matches that of the target model.
Previous works have demonstrated that the optimal scheme (which maximizes the
probability of accepting one of the input tokens) can be cast as a solution to
a linear program. In this work we show that the optimal scheme can be
decomposed into a two-step solution: in the first step an importance sampling
(IS) type scheme is used to select one intermediate token; in the second step
(single-draft) speculative sampling is applied to generate the output token.
For the case of two identical draft models we further 1) establish a necessary
and sufficient condition on the distributions of the target and draft models
for the acceptance probability to equal one and 2) provide an explicit
expression for the optimal acceptance probability. Our theoretical analysis
also motives a new class of token-level selection scheme based on weighted
importance sampling. Our experimental results demonstrate consistent
improvements in the achievable block efficiency and token rates over baseline
schemes in a number of scenarios.
671b167d45299f98b47daf84
Ashish Khisti
671b167d45299f98b47daf85
M. Reza Ebrahimi
claimed_verified
2024-10-25T09:29:01.320Z
648a1075e8bee5332919e0ab
https://cdn-avatars.huggingface.co/v1/production/uploads/648a1075e8bee5332919e0ab/KDzi904zGPuy_PL3gbUYE.jpeg
M.Reza Ebrahimi
user
mamaj92
671b167d45299f98b47daf86
Hassan Dbouk
671b167d45299f98b47daf87
Arash Behboodi
671b167d45299f98b47daf88
Roland Memisevic
671b167d45299f98b47daf89
Christos Louizos
Multi-Draft Speculative Sampling: Canonical Architectures and
  Theoretical Limits
We consider multi-draft speculative sampling, where the proposal sequences
are sampled independently from different draft models. At each step, a
token-level draft selection scheme takes a list of valid tokens as input and
produces an output token whose distribution matches that of the target model.
Previous works have demonstrated that the optimal scheme (which maximizes the
probability of accepting one of the input tokens) can be cast as a solution to
a linear program. In this work we show that the optimal scheme can be
decomposed into a two-step solution: in the first step an importance sampling
(IS) type scheme is used to select one intermediate token; in the second step
(single-draft) speculative sampling is applied to generate the output token.
For the case of two identical draft models we further 1) establish a necessary
and sufficient condition on the distributions of the target and draft models
for the acceptance probability to equal one and 2) provide an explicit
expression for the optimal acceptance probability. Our theoretical analysis
also motives a new class of token-level selection scheme based on weighted
importance sampling. Our experimental results demonstrate consistent
improvements in the achievable block efficiency and token rates over baseline
schemes in a number of scenarios.
A two-step solution for token-level speculative sampling improves efficiency and token rates by decomposing optimal selection into importance sampling and single-draft sampling.
multi-draft speculative sampling token-level draft selection linear program importance sampling block efficiency token rates weighted importance sampling