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Unverified Commit 976711d9 authored by Shanshan Shen's avatar Shanshan Shen Committed by GitHub
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[V1][Structured Output] Move xgrammar related utils to `backend_xgrammar.py` (#16578) 


Signed-off-by: default avatarshen-shanshan <467638484@qq.com>
parent 44fa4d55
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Showing with 120 additions and 126 deletions
tests/v1/structured_output/test_utils.py
View file @ 976711d9
......@@ -2,7 +2,7 @@
import pytest
from vllm.v1.structured_output.utils import (
from vllm.v1.structured_output.backend_xgrammar import (
has_xgrammar_unsupported_json_features)
......
vllm/v1/engine/processor.py
View file @ 976711d9
......@@ -22,8 +22,8 @@ from vllm.v1.engine import EngineCoreRequest
from vllm.v1.engine.mm_input_cache import MirroredProcessingCache
from vllm.v1.structured_output.backend_guidance import (
validate_guidance_grammar)
from vllm.v1.structured_output.utils import (
validate_structured_output_request_xgrammar)
from vllm.v1.structured_output.backend_xgrammar import (
validate_xgrammar_grammar)
class Processor:
......@@ -165,7 +165,7 @@ class Processor:
# Request content validation
if engine_level_backend.startswith("xgrammar"):
# xgrammar with no fallback
validate_structured_output_request_xgrammar(params)
validate_xgrammar_grammar(params)
params.guided_decoding.backend = engine_level_backend
elif engine_level_backend == "auto":
# "auto" is an opt-in to opinionated behavior where we try to
......@@ -173,7 +173,7 @@ class Processor:
# default as it is less predictable and subject to change
# between releases as feature support changes.
try:
validate_structured_output_request_xgrammar(params)
validate_xgrammar_grammar(params)
params.guided_decoding.backend = "xgrammar"
except ValueError:
# The request includes some jsonschema feature(s) that
......
vllm/v1/structured_output/backend_xgrammar.py
View file @ 976711d9
# SPDX-License-Identifier: Apache-2.0
import json
from dataclasses import dataclass, field
from typing import TYPE_CHECKING
from typing import TYPE_CHECKING, Any
import torch
import vllm.envs
from vllm.config import VllmConfig
from vllm.logger import init_logger
from vllm.sampling_params import SamplingParams
from vllm.transformers_utils.tokenizer_group import init_tokenizer_from_configs
from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer
from vllm.utils import LazyLoader
from vllm.v1.structured_output.backend_types import (StructuredOutputBackend,
StructuredOutputGrammar,
StructuredOutputOptions)
from vllm.v1.structured_output.utils import (choice_as_grammar,
convert_lark_to_ebnf,
grammar_is_likely_lark)
if TYPE_CHECKING:
import xgrammar as xgr
......@@ -156,3 +161,112 @@ class XgrammarGrammar(StructuredOutputGrammar):
def reset(self):
self.num_processed_tokens = 0
self.matcher.reset()
def has_xgrammar_unsupported_json_features(schema: dict[str, Any]) -> bool:
"""Check if JSON schema contains features unsupported by xgrammar."""
def check_object(obj: dict[str, Any]) -> bool:
if not isinstance(obj, dict):
return False
# Check for pattern restrictions
if "pattern" in obj:
return True
# Check for numeric ranges
if obj.get("type") in ("integer", "number") and any(
key in obj
for key in ("minimum", "maximum", "exclusiveMinimum",
"exclusiveMaximum", "multipleOf")):
return True
# Check for array unsupported keywords
if obj.get("type") == "array" and any(
key in obj
for key in ("uniqueItems", "contains", "minContains",
"maxContains", "minItems", "maxItems")):
return True
# Unsupported keywords for strings
if obj.get("type") == "string" and "format" in obj:
return True
# Unsupported keywords for objects
if obj.get("type") == "object" and any(
key in obj for key in ("minProperties", "maxProperties",
"propertyNames", "patternProperties")):
return True
# Recursively check all nested objects and arrays
for value in obj.values():
if isinstance(value, dict):
if check_object(value):
return True
elif isinstance(value, list):
for item in value:
if isinstance(item, dict) and check_object(item):
return True
return False
return check_object(schema)
def validate_xgrammar_grammar(sampling_params: SamplingParams) -> None:
"""Validate that the request is supported by structured output.
Raises ValueError if the request is not supported.
"""
if sampling_params.guided_decoding is None:
return
gd_params = sampling_params.guided_decoding
if gd_params.regex:
try:
xgr.Grammar.from_regex(gd_params.regex)
except Exception as err:
raise ValueError("Failed to transform regex into a grammar: "
f"{err}") from err
if gd_params.choice:
choice_grammar = choice_as_grammar(gd_params.choice)
try:
xgr.Grammar.from_ebnf(choice_grammar)
except Exception as err:
raise ValueError("Failed to transform choices into a grammar: "
"{err}") from err
gd_params.choice = None
gd_params.grammar = choice_grammar
return
if gd_params.json:
if isinstance(gd_params.json, str):
try:
schema = json.loads(gd_params.json)
except json.JSONDecodeError as e:
raise ValueError("Invalid JSON grammar specification.") from e
else:
schema = gd_params.json
if has_xgrammar_unsupported_json_features(schema):
raise ValueError("The provided JSON schema contains features not "
"supported by xgrammar.")
return
if gd_params.grammar:
if grammar_is_likely_lark(gd_params.grammar):
# xgrammar supports EBNF grammars only
try:
gd_params.grammar = convert_lark_to_ebnf(gd_params.grammar)
except ValueError as e:
raise ValueError(
"Failed to convert the grammar from Lark to EBNF. ") from e
# Test parsing EBNF grammar, possibly already converted from Lark
try:
# parse the grammar, but we aren't compiling it.
xgr.Grammar.from_ebnf(gd_params.grammar)
except Exception as e:
raise ValueError("Invalid grammar specification.") from e
vllm/v1/structured_output/utils.py
View file @ 976711d9
......@@ -2,67 +2,7 @@
from __future__ import annotations
import json
import re
from typing import TYPE_CHECKING, Any
from vllm.sampling_params import SamplingParams
from vllm.utils import LazyLoader
if TYPE_CHECKING:
import xgrammar as xgr
else:
xgr = LazyLoader("xgr", globals(), "xgrammar")
def has_xgrammar_unsupported_json_features(schema: dict[str, Any]) -> bool:
"""Check if JSON schema contains features unsupported by xgrammar."""
def check_object(obj: dict[str, Any]) -> bool:
if not isinstance(obj, dict):
return False
# Check for pattern restrictions
if "pattern" in obj:
return True
# Check for numeric ranges
if obj.get("type") in ("integer", "number") and any(
key in obj
for key in ("minimum", "maximum", "exclusiveMinimum",
"exclusiveMaximum", "multipleOf")):
return True
# Check for array unsupported keywords
if obj.get("type") == "array" and any(
key in obj
for key in ("uniqueItems", "contains", "minContains",
"maxContains", "minItems", "maxItems")):
return True
# Unsupported keywords for strings
if obj.get("type") == "string" and "format" in obj:
return True
# Unsupported keywords for objects
if obj.get("type") == "object" and any(
key in obj for key in ("minProperties", "maxProperties",
"propertyNames", "patternProperties")):
return True
# Recursively check all nested objects and arrays
for value in obj.values():
if isinstance(value, dict):
if check_object(value):
return True
elif isinstance(value, list):
for item in value:
if isinstance(item, dict) and check_object(item):
return True
return False
return check_object(schema)
def grammar_is_likely_lark(grammar_str: str) -> bool:
......@@ -232,63 +172,3 @@ def choice_as_grammar(choice: list[str]) -> str:
escaped_choices = (escape_ebnf_string(c) for c in choice)
grammar = ('root ::= ' + ' | '.join(f'"{c}"' for c in escaped_choices))
return grammar
def validate_structured_output_request_xgrammar(
sampling_params: SamplingParams) -> None:
"""Validate that the request is supported by structured output.
Raises ValueError if the request is not supported.
"""
if sampling_params.guided_decoding is None:
return
gd_params = sampling_params.guided_decoding
if gd_params.regex:
try:
xgr.Grammar.from_regex(gd_params.regex)
except Exception as err:
raise ValueError("Failed to transform regex into a grammar: "
f"{err}") from err
if gd_params.choice:
choice_grammar = choice_as_grammar(gd_params.choice)
try:
xgr.Grammar.from_ebnf(choice_grammar)
except Exception as err:
raise ValueError("Failed to transform choices into a grammar: "
"{err}") from err
gd_params.choice = None
gd_params.grammar = choice_grammar
return
if gd_params.json:
if isinstance(gd_params.json, str):
try:
schema = json.loads(gd_params.json)
except json.JSONDecodeError as e:
raise ValueError("Invalid JSON grammar specification.") from e
else:
schema = gd_params.json
if has_xgrammar_unsupported_json_features(schema):
raise ValueError("The provided JSON schema contains features not "
"supported by xgrammar.")
return
if gd_params.grammar:
if grammar_is_likely_lark(gd_params.grammar):
# xgrammar supports EBNF grammars only
try:
gd_params.grammar = convert_lark_to_ebnf(gd_params.grammar)
except ValueError as e:
raise ValueError(
"Failed to convert the grammar from Lark to EBNF. ") from e
# Test parsing EBNF grammar, possibly already converted from Lark
try:
# parse the grammar, but we aren't compiling it.
xgr.Grammar.from_ebnf(gd_params.grammar)
except Exception as e:
raise ValueError("Invalid grammar specification.") from e
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