[Speculative decoding]: The content generated by speculative decoding is inconsistent with the content generated by the target model about vllm HOT 3 CLOSED

Are you expecting them to be exactly same? I see you are using a temperature of 0.8 in your experiment. At higher temperatures I think you will see differences between the Speculative decoding output and the output generated if you were to use the target model directly. This is the acceptance logic for draft tokens https://sourcegraph.com/github.com/vllm-project/vllm/-/blob/vllm/model_executor/layers/rejection_sampler.py?L160 and it does not guarantee that the outputs will be the same specially for higher temperatures.

You can expect the 2 outputs to match only for temperature 0.

cc: @cadedaniel for his input.

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@sroy745 is right but also looks like it's generating gibberish which is unexpected unless the target model produces gibberish.

Prompt: 'What is Machine Learning？', Generated text: '10 Milano12 212主要专业 [apps国家 这管理 我如何an'

@YuCheng-Qi can you share a reproducible example, e.g. a model I can reproduce it with?

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@cadedaniel @sroy745 Thank you for your very insightful and enthusiastic answers. Below I will describe the process of this error in detail for your reference.

when I use :
sampling_params = SamplingParams(temperature=0,
top_p=0.95,
logprobs=1,
stop_token_ids=stop_token_ids)

1 The result generated by using the target model（/mnt/nas/faibei/faibing-10B-Chat） alone is：
Processed prompts: 100%|███████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 3.20it/s]
tokens/s: 50.583147408917135
outputs: [RequestOutput(request_id=0, prompt='What is Machine Learning', prompt_token_ids=[50002, 26888, 2476, 59109, 60303, 50007], prompt_logprobs=None, outputs=[CompletionOutput(index=0, text='Machine learning is a field of artificial intelligence (AI) that involves training algorithms', token_ids=[50006, 59109, 51081, 2476, 778, 50162, 885, 55501, 53336, 35, 6272, 43396, 5246, 52843, 50599, 55832], cumulative_logprob=-2.7939926966739677, logprobs=[{50006: Logprob(logprob=0.0, rank=1, decoded_token='')}, {59109: Logprob(logprob=-0.011991554871201515, rank=1, decoded_token='Machine')}, {51081: Logprob(logprob=-0.4860461354255676, rank=1, decoded_token=' learning')}, {2476: Logprob(logprob=-0.011147127486765385, rank=1, decoded_token=' is')}, {778: Logprob(logprob=-0.014388969168066978, rank=1, decoded_token=' a')}, {50162: Logprob(logprob=-0.5061734318733215, rank=1, decoded_token=' field')}, {885: Logprob(logprob=-0.0033579650335013866, rank=1, decoded_token=' of')}, {55501: Logprob(logprob=-0.5290303826332092, rank=1, decoded_token=' artificial')}, {53336: Logprob(logprob=-3.099436753473128e-06, rank=1, decoded_token=' intelligence')}, {35: Logprob(logprob=-0.445012629032135, rank=1, decoded_token=' (')}, {6272: Logprob(logprob=-1.3589766240329482e-05, rank=1, decoded_token='AI')}, {43396: Logprob(logprob=-6.9141146923357155e-06, rank=1, decoded_token=')')}, {5246: Logprob(logprob=-0.021114686504006386, rank=1, decoded_token=' that')}, {52843: Logprob(logprob=-0.1500907987356186, rank=1, decoded_token=' involves')}, {50599: Logprob(logprob=-0.2279604822397232, rank=1, decoded_token=' training')}, {55832: Logprob(logprob=-0.3876549303531647, rank=1, decoded_token=' algorithms')}], finish_reason=length, stop_reason=None)], finished=True, metrics=RequestMetrics(arrival_time=1717727044.651589, first_scheduled_time=+4.50ms, first_token_time=+31.69ms, last_token_time=+0.00ms, time_in_queue=4.50ms, finished_time=1717727044.9674897), lora_request=None)]

Prompt: 'What is Machine Learning', Generated text: 'Machine learning is a field of artificial intelligence (AI) that involves training algorithms'

2 The result generated by using the spec model (small model，/mnt/nas/faibei/faibing-1B-Chat) used in Speculative decoding alone is：
$python sp_runner_example_api.py
ldd: ./libnccl.so.2: No such file or directory
Processed prompts: 100%|███████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 5.29it/s]
tokens/s: 83.13196912507416
outputs: [RequestOutput(request_id=0, prompt='What is Machine Learning', prompt_token_ids=[50002, 26888, 2476, 59109, 60303, 50007], prompt_logprobs=None, outputs=[CompletionOutput(index=0, text='Machine learning is a subset of artificial intelligence (AI) that involves training algorithms', token_ids=[50006, 59109, 51081, 2476, 778, 55977, 885, 55501, 53336, 35, 6272, 43396, 5246, 52843, 50599, 55832], cumulative_logprob=-2.963461573823224, logprobs=[{50006: Logprob(logprob=0.0, rank=1, decoded_token='')}, {59109: Logprob(logprob=-0.019566968083381653, rank=1, decoded_token='Machine')}, {51081: Logprob(logprob=-0.5231714248657227, rank=1, decoded_token=' learning')}, {2476: Logprob(logprob=-0.06658891588449478, rank=1, decoded_token=' is')}, {778: Logprob(logprob=-0.011581214144825935, rank=1, decoded_token=' a')}, {55977: Logprob(logprob=-0.5235638618469238, rank=1, decoded_token=' subset')}, {885: Logprob(logprob=-0.00020358874462544918, rank=1, decoded_token=' of')}, {55501: Logprob(logprob=-0.1554061472415924, rank=1, decoded_token=' artificial')}, {53336: Logprob(logprob=-1.9192511899746023e-05, rank=1, decoded_token=' intelligence')}, {35: Logprob(logprob=-0.6489261984825134, rank=1, decoded_token=' (')}, {6272: Logprob(logprob=-9.572047565598041e-05, rank=1, decoded_token='AI')}, {43396: Logprob(logprob=-0.008335325866937637, rank=1, decoded_token=')')}, {5246: Logprob(logprob=-0.01461420301347971, rank=1, decoded_token=' that')}, {52843: Logprob(logprob=-0.3702547252178192, rank=1, decoded_token=' involves')}, {50599: Logprob(logprob=-0.4765051603317261, rank=1, decoded_token=' training')}, {55832: Logprob(logprob=-0.14462892711162567, rank=1, decoded_token=' algorithms')}], finish_reason=length, stop_reason=None)], finished=True, metrics=RequestMetrics(arrival_time=1717727261.8583694, first_scheduled_time=+3.60ms, first_token_time=+97.93ms, last_token_time=+0.00ms, time_in_queue=3.60ms, finished_time=1717727262.050356), lora_request=None)]
Prompt: 'What is Machine Learning', Generated text: 'Machine learning is a subset of artificial intelligence (AI) that involves training algorithms'

3 However, when I use the target model (/mnt/nas/faibei/faibing-10B-Chat) as the model and the small model (/mnt/nas/faibei/faibing-1B-Chat) as the speculative_model, garbled characters appear in the generated content. The results are as follows:

$python sp_runner_example_api.py
ldd: ./libnccl.so.2: No such file or directory
Processed prompts: 100%|███████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.62it/s]
tokens/s: 25.66761954520385
outputs: [RequestOutput(request_id=0, prompt='What is Machine Learning', prompt_token_ids=[50002, 26888, 2476, 59109, 60303, 50007], prompt_logprobs=None, outputs=[CompletionOutput(index=0, text='——），ilha生活 2比较），还有没有公司olina很多我的一下的一', token_ids=[50006, 44, 65, 102517, 76, 50, 93, 65, 95, 10, 26, 95078, 63, 69, 94, 97], cumulative_logprob=0.0, logprobs=[{50006: Logprob(logprob=0.0, rank=None, decoded_token='')}, {44: Logprob(logprob=0.0, rank=None, decoded_token='——')}, {65: Logprob(logprob=0.0, rank=None, decoded_token='),')}, {102517: Logprob(logprob=0.0, rank=None, decoded_token='ilha')}, {76: Logprob(logprob=0.0, rank=None, decoded_token='生活')}, {50: Logprob(logprob=0.0, rank=None, decoded_token=' 2')}, {93: Logprob(logprob=0.0, rank=None, decoded_token='比较')}, {65: Logprob(logprob=0.0, rank=None, decoded_token='),')}, {95: Logprob(logprob=0.0, rank=None, decoded_token='还有')}, {10: Logprob(logprob=0.0, rank=None, decoded_token='没有')}, {26: Logprob(logprob=0.0, rank=None, decoded_token='公司')}, {95078: Logprob(logprob=0.0, rank=None, decoded_token='olina')}, {63: Logprob(logprob=0.0, rank=None, decoded_token='很多')}, {69: Logprob(logprob=0.0, rank=None, decoded_token='我的')}, {94: Logprob(logprob=0.0, rank=None, decoded_token='一下')}, {97: Logprob(logprob=0.0, rank=None, decoded_token='的一')}], finish_reason=length, stop_reason=None)], finished=True, metrics=RequestMetrics(arrival_time=1717727600.3066957, first_scheduled_time=+4.82ms, first_token_time=+112.33ms, last_token_time=+0.00ms, time_in_queue=4.82ms, finished_time=1717727600.929564), lora_request=None)]
Prompt: 'What is Machine Learning', Generated text: '——），ilha生活 2比较），还有没有公司olina很多我的一下的一'

@cadedaniel Sorry, you may not be able to get my model file (it is not open source yet, so you can't download it)
I guess it may be that token_ids are not obtained correctly during speculative sampling, or logprob is not calculated correctly, resulting in garbled sampling, or some other reason? Please help analyze the solution.

from vllm.