Principle:Vllm project Vllm Constrained Generation
| Knowledge Sources | |
|---|---|
| Domains | LLM Inference, Structured Output, Constrained Decoding |
| Last Updated | 2026-02-08 13:00 GMT |
Overview
Constrained generation is the process of running autoregressive text generation while enforcing structural constraints at each decoding step through logit masking, guaranteeing that the output conforms to a specified format.
Description
In standard autoregressive generation, the model produces a probability distribution over the entire vocabulary at each step, and a token is sampled from that distribution. The result is unconstrained text that may or may not follow a desired structure.
Constrained generation modifies this process by introducing a logit mask at each step. Before sampling, the logits for tokens that would violate the structural constraint are set to negative infinity (effectively zeroing their probability). The model can only select from tokens that keep the output on a valid path through the constraint automaton.
The process works as follows:
- The constraint (JSON Schema, regex, grammar, or choice list) is compiled into a state machine or guide before generation begins.
- At each decoding step, the guide examines the tokens generated so far and determines which next tokens are valid.
- A logit mask is constructed: valid tokens retain their logits, invalid tokens are masked to negative infinity.
- Standard sampling (temperature, top-p, top-k) is applied to the masked logits.
- The selected token is appended to the output and the guide state is advanced.
- The process repeats until an end-of-sequence token is reached or the constraint is fully satisfied.
The key guarantee is that the output is always a valid member of the target language. The constraint cannot be violated because invalid tokens are never sampled.
The guided decoding backend (e.g., xgrammar, outlines, guidance) handles the compilation and state tracking. The backend is selected automatically based on the constraint type, or can be set explicitly via engine configuration.
Usage
Use constrained generation whenever you need a guarantee that model output conforms to a specific format. This is the core execution step in the structured output workflow, invoked after engine initialization and parameter configuration.
Theoretical Basis
Constrained generation implements intersection decoding: the output language is the intersection of the model's implicit language (determined by its training data and weights) and the constraint's formal language.
Formally, let L_m be the set of strings the model assigns non-negligible probability to, and let L_c be the formal language defined by the constraint. The constrained generation process produces strings from L_m intersection L_c.
The logit masking operation at each step implements this intersection incrementally. If the constraint is described by an automaton with state set Q and transition function delta, then at step t with automaton state q_t:
Valid(q_t) = { token v : delta(q_t, v) is defined }
The mask M_t is the complement of Valid(q_t) in the vocabulary V:
M_t = V \ Valid(q_t)
For each masked token v in M_t, the logit is set to negative infinity:
logit'(v) = logit(v) if v in Valid(q_t), else -infinity
After applying the mask, the distribution is renormalized and sampling proceeds as usual.
The computational cost of constrained generation is dominated by two factors:
- Constraint compilation (one-time): Compiling the schema into an automaton. JSON Schema and grammar compilation can be expensive for complex schemas.
- Mask computation (per-step): Computing the valid token set at each step. For finite automata (regex), this is O(1) per step. For pushdown automata (grammars), it depends on the stack depth.