Summary Fast Inference from Transformers via Speculative Decoding arxiv.org
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Fast Inference from Transformers via Speculative Decoding speeds up the inference process of large autoregressive models by using efficient approximation models to generate speculative prefixes for slower target models.
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Key Points
- Fast Inference from Transformers via Speculative Decoding is a method developed to accelerate inference from large autoregressive models like Transformers.
- Speculative decoding involves using more efficient approximation models to generate speculative prefixes for the slower target models.
- The method reduces the number of serial calls to a target model and improves the divergence between probability distributions.
- T5-small achieves the highest speedup among tested decoder models.
- Speculative Decoding enables fast inference from transformers by decoding multiple tokens in parallel, providing 2X-3X speedups compared to optimized implementations like T5X.
Summaries
26 word summary
Fast Inference from Transformers via Speculative Decoding accelerates inference from large autoregressive models by using efficient approximation models to generate speculative prefixes for slower target models.
43 word summary
Fast Inference from Transformers via Speculative Decoding is a method developed to accelerate inference from large autoregressive models like Transformers. It involves using more efficient approximation models to generate speculative prefixes for the slower target models. The reduction factor in the number of
423 word summary
Fast Inference from Transformers via Speculative Decoding is a method developed to accelerate inference from large autoregressive models like Transformers. The approach involves using more efficient approximation models to generate speculative prefixes for the slower target models. By running the target model in
The excerpt discusses the use of speculative decoding to improve fast inference from transformers. It assumes that p(x) and q(x) are distributions from M p and M q, respectively. The expected number of tokens produced by Algorithm 1 is a capped geometric
The excerpt discusses the reduction factor in the number of serial calls to a target model and the improvement factor in the divergence between probability distributions. It introduces corollaries and theorems to support these findings. The text also delves into the number of
The excerpt discusses fast inference from transformers using speculative decoding. It presents a graph showing the optimal parameter as a function of another parameter for various values of a constant. The speedup factor and increase in arithmetic operations are also shown in another graph. The text
The document discusses a method called speculative decoding that improves the speed of inference from Transformers. Speculative decoding involves using an approximation model, Mq, to make predictions before the main model, Mp, is called. The number of calls to Mq,
T5-small, with 77M parameters and a good balance of c and ?, achieves the highest speedup among the tested decoder models. The empirical ? values for different target models and approximation models are summarized in Table 3. Approximation models that
Speculative Decoding is a method that enables fast inference from transformers by decoding multiple tokens in parallel. It supports general approximation models and guarantees identical outputs. This method provides 2X-3X speedups compared to optimized implementations like T5X.
This text excerpt includes a list of references to various research papers related to fast inference from transformers and language modeling. The papers mentioned cover topics such as speculative sampling, transfer learning with text-to-text transformers, scaling language modeling with pathways, adding early exits to
The summary of the text excerpt is as follows:
The text includes a list of references to various papers and books related to efficient transformers for language modeling, computer architecture, deep autoregressive models, distilling knowledge in neural networks, adaptive attention span in
The excerpt discusses the concept of fast inference from transformers using speculative decoding. It explains the mathematical equations and probabilities involved in the process. The text also compares speculative sampling to rejection sampling and highlights the efficiency of speculative sampling. Additionally, it discusses the theoretical predictions