[V1][Structured Output] Move xgrammar related utils to backend_xgrammar.py (#16578)
Signed-off-by: shen-shanshan <467638484@qq.com>
This commit is contained in:
Shanshan Shen
GitHub
parent
44fa4d556c
commit
976711d9db
@ -2,7 +2,7 @@
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import pytest
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from vllm.v1.structured_output.utils import (
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from vllm.v1.structured_output.backend_xgrammar import (
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has_xgrammar_unsupported_json_features)
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@ -22,8 +22,8 @@ from vllm.v1.engine import EngineCoreRequest
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from vllm.v1.engine.mm_input_cache import MirroredProcessingCache
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from vllm.v1.structured_output.backend_guidance import (
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validate_guidance_grammar)
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from vllm.v1.structured_output.utils import (
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validate_structured_output_request_xgrammar)
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from vllm.v1.structured_output.backend_xgrammar import (
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validate_xgrammar_grammar)
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class Processor:
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@ -165,7 +165,7 @@ class Processor:
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# Request content validation
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if engine_level_backend.startswith("xgrammar"):
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# xgrammar with no fallback
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validate_structured_output_request_xgrammar(params)
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validate_xgrammar_grammar(params)
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params.guided_decoding.backend = engine_level_backend
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elif engine_level_backend == "auto":
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# "auto" is an opt-in to opinionated behavior where we try to
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@ -173,7 +173,7 @@ class Processor:
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# default as it is less predictable and subject to change
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# between releases as feature support changes.
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try:
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validate_structured_output_request_xgrammar(params)
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validate_xgrammar_grammar(params)
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params.guided_decoding.backend = "xgrammar"
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except ValueError:
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# The request includes some jsonschema feature(s) that
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@ -1,19 +1,24 @@
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# SPDX-License-Identifier: Apache-2.0
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import json
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from dataclasses import dataclass, field
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from typing import TYPE_CHECKING
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from typing import TYPE_CHECKING, Any
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import torch
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import vllm.envs
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from vllm.config import VllmConfig
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from vllm.logger import init_logger
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from vllm.sampling_params import SamplingParams
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from vllm.transformers_utils.tokenizer_group import init_tokenizer_from_configs
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from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer
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from vllm.utils import LazyLoader
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from vllm.v1.structured_output.backend_types import (StructuredOutputBackend,
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StructuredOutputGrammar,
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StructuredOutputOptions)
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from vllm.v1.structured_output.utils import (choice_as_grammar,
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convert_lark_to_ebnf,
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grammar_is_likely_lark)
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if TYPE_CHECKING:
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import xgrammar as xgr
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@ -156,3 +161,112 @@ class XgrammarGrammar(StructuredOutputGrammar):
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def reset(self):
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self.num_processed_tokens = 0
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self.matcher.reset()
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def has_xgrammar_unsupported_json_features(schema: dict[str, Any]) -> bool:
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"""Check if JSON schema contains features unsupported by xgrammar."""
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def check_object(obj: dict[str, Any]) -> bool:
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if not isinstance(obj, dict):
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return False
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# Check for pattern restrictions
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if "pattern" in obj:
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return True
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# Check for numeric ranges
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if obj.get("type") in ("integer", "number") and any(
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key in obj
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for key in ("minimum", "maximum", "exclusiveMinimum",
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"exclusiveMaximum", "multipleOf")):
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return True
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# Check for array unsupported keywords
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if obj.get("type") == "array" and any(
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key in obj
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for key in ("uniqueItems", "contains", "minContains",
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"maxContains", "minItems", "maxItems")):
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return True
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# Unsupported keywords for strings
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if obj.get("type") == "string" and "format" in obj:
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return True
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# Unsupported keywords for objects
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if obj.get("type") == "object" and any(
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key in obj for key in ("minProperties", "maxProperties",
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"propertyNames", "patternProperties")):
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return True
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# Recursively check all nested objects and arrays
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for value in obj.values():
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if isinstance(value, dict):
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if check_object(value):
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return True
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elif isinstance(value, list):
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for item in value:
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if isinstance(item, dict) and check_object(item):
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return True
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return False
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return check_object(schema)
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def validate_xgrammar_grammar(sampling_params: SamplingParams) -> None:
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"""Validate that the request is supported by structured output.
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Raises ValueError if the request is not supported.
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"""
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if sampling_params.guided_decoding is None:
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return
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gd_params = sampling_params.guided_decoding
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if gd_params.regex:
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try:
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xgr.Grammar.from_regex(gd_params.regex)
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except Exception as err:
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raise ValueError("Failed to transform regex into a grammar: "
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f"{err}") from err
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if gd_params.choice:
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choice_grammar = choice_as_grammar(gd_params.choice)
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try:
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xgr.Grammar.from_ebnf(choice_grammar)
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except Exception as err:
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raise ValueError("Failed to transform choices into a grammar: "
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"{err}") from err
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gd_params.choice = None
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gd_params.grammar = choice_grammar
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return
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if gd_params.json:
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if isinstance(gd_params.json, str):
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try:
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schema = json.loads(gd_params.json)
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except json.JSONDecodeError as e:
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raise ValueError("Invalid JSON grammar specification.") from e
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else:
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schema = gd_params.json
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if has_xgrammar_unsupported_json_features(schema):
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raise ValueError("The provided JSON schema contains features not "
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"supported by xgrammar.")
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return
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if gd_params.grammar:
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if grammar_is_likely_lark(gd_params.grammar):
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# xgrammar supports EBNF grammars only
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try:
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gd_params.grammar = convert_lark_to_ebnf(gd_params.grammar)
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except ValueError as e:
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raise ValueError(
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"Failed to convert the grammar from Lark to EBNF. ") from e
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# Test parsing EBNF grammar, possibly already converted from Lark
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try:
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# parse the grammar, but we aren't compiling it.
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xgr.Grammar.from_ebnf(gd_params.grammar)
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except Exception as e:
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raise ValueError("Invalid grammar specification.") from e
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@ -2,67 +2,7 @@
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from __future__ import annotations
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import json
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import re
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from typing import TYPE_CHECKING, Any
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from vllm.sampling_params import SamplingParams
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from vllm.utils import LazyLoader
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if TYPE_CHECKING:
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import xgrammar as xgr
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else:
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xgr = LazyLoader("xgr", globals(), "xgrammar")
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def has_xgrammar_unsupported_json_features(schema: dict[str, Any]) -> bool:
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"""Check if JSON schema contains features unsupported by xgrammar."""
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def check_object(obj: dict[str, Any]) -> bool:
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if not isinstance(obj, dict):
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return False
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# Check for pattern restrictions
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if "pattern" in obj:
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return True
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# Check for numeric ranges
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if obj.get("type") in ("integer", "number") and any(
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key in obj
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for key in ("minimum", "maximum", "exclusiveMinimum",
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"exclusiveMaximum", "multipleOf")):
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return True
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# Check for array unsupported keywords
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if obj.get("type") == "array" and any(
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key in obj
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for key in ("uniqueItems", "contains", "minContains",
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"maxContains", "minItems", "maxItems")):
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return True
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# Unsupported keywords for strings
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if obj.get("type") == "string" and "format" in obj:
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return True
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# Unsupported keywords for objects
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if obj.get("type") == "object" and any(
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key in obj for key in ("minProperties", "maxProperties",
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"propertyNames", "patternProperties")):
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return True
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# Recursively check all nested objects and arrays
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for value in obj.values():
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if isinstance(value, dict):
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if check_object(value):
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return True
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elif isinstance(value, list):
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for item in value:
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if isinstance(item, dict) and check_object(item):
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return True
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return False
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return check_object(schema)
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def grammar_is_likely_lark(grammar_str: str) -> bool:
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@ -232,63 +172,3 @@ def choice_as_grammar(choice: list[str]) -> str:
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escaped_choices = (escape_ebnf_string(c) for c in choice)
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grammar = ('root ::= ' + ' | '.join(f'"{c}"' for c in escaped_choices))
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return grammar
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def validate_structured_output_request_xgrammar(
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sampling_params: SamplingParams) -> None:
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"""Validate that the request is supported by structured output.
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Raises ValueError if the request is not supported.
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"""
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if sampling_params.guided_decoding is None:
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return
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gd_params = sampling_params.guided_decoding
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if gd_params.regex:
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try:
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xgr.Grammar.from_regex(gd_params.regex)
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except Exception as err:
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raise ValueError("Failed to transform regex into a grammar: "
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f"{err}") from err
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if gd_params.choice:
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choice_grammar = choice_as_grammar(gd_params.choice)
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try:
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xgr.Grammar.from_ebnf(choice_grammar)
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except Exception as err:
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raise ValueError("Failed to transform choices into a grammar: "
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"{err}") from err
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gd_params.choice = None
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gd_params.grammar = choice_grammar
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return
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if gd_params.json:
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if isinstance(gd_params.json, str):
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try:
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schema = json.loads(gd_params.json)
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except json.JSONDecodeError as e:
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raise ValueError("Invalid JSON grammar specification.") from e
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else:
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schema = gd_params.json
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if has_xgrammar_unsupported_json_features(schema):
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raise ValueError("The provided JSON schema contains features not "
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"supported by xgrammar.")
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return
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if gd_params.grammar:
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if grammar_is_likely_lark(gd_params.grammar):
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# xgrammar supports EBNF grammars only
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try:
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gd_params.grammar = convert_lark_to_ebnf(gd_params.grammar)
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except ValueError as e:
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raise ValueError(
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"Failed to convert the grammar from Lark to EBNF. ") from e
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# Test parsing EBNF grammar, possibly already converted from Lark
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try:
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# parse the grammar, but we aren't compiling it.
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xgr.Grammar.from_ebnf(gd_params.grammar)
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except Exception as e:
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raise ValueError("Invalid grammar specification.") from e
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