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Correct. I am trying to explain why even it is "exact", the generated text is different from the with / without DFlash2 runs, and potentially why the DFlash2 run will contain the invalid Python syntax.

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If the underlying probability distributions are the same, then DFlash can lead to an invalid Python Syntax iif the autoregressive process could have generated one if the random sampling picked a different token.

If a model can output a “wrong” sequence with a certain probability p, then Dflash can also output the wrong sequence with the same probability. They wouldn't necessarily produce the same output from the same seed, but speculative decoding shouldn't be able to produce anything that the autoregressive model couldn't also produce when using a different seed.

Or am I misunderstanding something?

I agree. But I think the DFlash2 case is just that 1/1000 invalid syntax failure case from sampling rather than a bug.

It will contain the same or different syntax with or without it. Also multiple runs without DFlash2 will contain the same or different syntax. And multiple runs with DFlash2 will have the same or different syntax with the same probability. DFlash2 literally has no influence (unless buggy). The difference is purely caused by the randomness.

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