Speculative Sampling

An implementation of speculative sampling as described in the paper Accelerating Large Language Model Decoding with Speculative Sampling by Deepmind.

To run

python generate.py -h

Run the above command to see the parameter options and their description.

Speculative Sampling

python generate.py  --method speculative \
                    --prompt "Emily found a mysterious letter on her doorstep one sunny morning." \
                    --max_new_tokens 64 \
                    --target_model facebook/opt-13b \
                    --draft_model facebook/opt-1.3b \
                    --temperature 0.5

Naive Autoregressive Sampling

python generate.py  --method autoregressive \
                    --prompt "Emily found a mysterious letter on her doorstep one sunny morning." \
                    --max_new_tokens 64 \
                    --target_model facebook/opt-13b \
                    --temperature 0.5

Benchmarking

python benchmark.py

Results

Results showing the speedup (as ratio) of speculative sampling over naive autoregressive sampling. These results are from different benchmarking runs and the logs can be found in the outputs directory.

Target Model - facebook/opt-13b
Draft Model - facebook/opt-1.3b

Config	Speedup (Set 1)	Speedup (Set 2)	Average Speedup
Temperature = 0	1.83	1.73	1.78
Temperature = 0.5	1.68	1.81	1.75

Target Model - facebook/opt-6.7b
Draft Model - facebook/opt-1.3b

Config	Speedup (Set 1)	Speedup (Set 2)	Average Speedup
Temperature = 0	1.55	1.38	1.46
Temperature = 0.5	1.53	1.49	1.51

The speedup ratio seems to increase as the target model size increases (and when the draft model is also relatively big enough). So, the speedup ratio of 2-2.5x mentioned in the Deepmind paper, could also be true for a 70B target model and a 7B draft model (which they use).