llama : add token matching support to llama-grammar

[aldehir]

Implementation of idea by @ngxson: #17750 (comment)

cc: @pwilkin @aviallon

Problem

The llama-grammar implementation doesn't have a way to accept tokens directly, which creates a few problems:

• Can't disambiguate between a special token (e.g. <|end|>) and the tokenized form <|, end, |> that may occur in content.
• Requires awkward "exclusion" rules such as ( [^<] | "<" [^|] | "<|" [^e] | ... | "<|end|" [^>] )* to match chunks of characters that don't accumulate to the desired delimiter (<|end|>).
• Adds extra work to grammar sampling from recursively applying character rules.

Proposed Solution

Borrowing some ideas from llguidance, you can define a token by id <[id]> or as raw token text <token> if encased in </>. I'm leaving out support for token id ranges/alternates since I don't see an immediate need for it.

You can negate by prefixing the token with !, e.g. !<|end|>.

Example (gpt-oss)

By token id:

root ::= analysis response
analysis ::= <[200005]> "analysis" <[200008]> (!<[200007]>)* <[200007]>
response ::= <[200006]> "assistant" <[200005]> "final" <[200008]> .*

That's not very readable, but useful for tokens not wrapped in </>. If they are, you can use them directly:

root ::= analysis response
analysis ::= <|channel|> "analysis" <|message|> (!<|end|>)* <|end|>
response ::= <|start|> "assistant" <|channel|> "final" <|message|> .*

Use Case: Reasoning Budget Enforcement

Assuming the model's vocab has unique tokens for its thinking tags, adopting a reasoning budget is fairly trivial via grammar:

root ::= analysis response
analysis ::= <|channel|> "analysis" <|message|> reasoning-with-budget
reasoning-with-budget ::= (!<|end|>){0,200} <|end|>
response ::= <|start|> "assistant" <|channel|> "final" <|message|> .*

# optionally, inject pieces to guide the model when it goes over
reasoning-with-budget ::= (!<|end|>){0,200} (<|end|> | "--I need to provide an immediate response" <|end|>)

Notes:

• It is important the grammar is unambiguous, otherwise the model may find a way to continue thinking via other paths in the grammar.
• gpt-oss may be a poor example since it has reasoning_effort, but the budget approach works pretty well.

To Do

• Implement token support in llama-grammar
• Refactor trigger_patterns to collect tokens and replay them after a successful trigger. Support partial token matches by feeding only the matched piece to the grammar.
• Update grammar documentation and provide an example in grammars/

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AI Disclosure: LLM was used to help understand the grammar code, assist in writing documentation and test cases, and review implementations. All output generated by an LLM has been reviewed.

[aviallon]

Very interesting. I'll see what I can build upon that.