xgrammar is an open-source library for efficient, flexible, and portable structured generation. A grammar optimizer introduced in 0.1.23 processes large grammars (>100k characters) at very low rates, and can be used for DOS of model providers. This issue is fixed in version 0.1.24.

Affected: PyPI: xgrammar

### Summary Provided grammar, would fit in a context window of most of the models, but takes minutes to process in 0.1.23. In testing with 0.1.16 the parser worked fine so this seems to be a regression caused by Earley parser.

### Details

Full reproducer provider in the POC section. The resulting grammar is around 70k tokens, and the grammar parsing itself (with the models I checked) was significantly longer than LLM processing itself, meaning this can be used to DOS model providers.

### Patch

This problem is caused by the grammar optimizer introduced in v0.1.23 being too slow. It only happens for very large grammars (>100k characters), like the below one. v0.1.24 solved this problem by optimizing the speed of the grammar optimizer and disable some slow optimization for large grammars.

Thanks to @Seven-Streams

### PoC ``` import string import random

def enum_schema(size=10000,str_len=10): enum = {"enum": ["".join(random.choices(string.ascii_uppercase, k=str_len)) for _ in range(size)]} schema = { "definitions": { "colorEnum": enum }, "type": "object", "properties": { "color1": { "$ref": "#/definitions/colorEnum" }, "color2": { "$ref": "#/definitions/colorEnum" }, "color3": { "$ref": "#/definitions/colorEnum" }, "color4": { "$ref": "#/definitions/colorEnum"…

Affected: PyPI: xgrammar

### Summary Provided grammar, would fit in a context window of most of the models, but takes minutes to process in 0.1.23. In testing with 0.1.16 the parser worked fine so this seems to be a regression caused by Earley parser.

### Details

Full reproducer provider in the POC section. The resulting grammar is around 70k tokens, and the grammar parsing itself (with the models I checked) was significantly longer than LLM processing itself, meaning this can be used to DOS model providers.

### Patch

This problem is caused by the grammar optimizer introduced in v0.1.23 being too slow. It only happens for very large grammars (>100k characters), like the below one. v0.1.24 solved this problem by optimizing the speed of the grammar optimizer and disable some slow optimization for large grammars.

Thanks to @Seven-Streams

### PoC ``` import string import random

def enum_schema(size=10000,str_len=10): enum = {"enum": ["".join(random.choices(string.ascii_uppercase, k=str_len)) for _ in range(size)]} schema = { "definitions": { "colorEnum": enum }, "type": "object", "properties": { "color1": { "$ref": "#/definitions/colorEnum" }, "color2": { "$ref": "#/definitions/colorEnum" }, "color3": { "$ref": "#/definitions/colorEnum" }, "color4": { "$ref": "#/definitions/colorEnum"…