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xiezhongtao
2026-01-19 10:38:50 +08:00
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from __future__ import annotations
import hashlib
import importlib.metadata
import os
from typing import TYPE_CHECKING
import numpy as np
import regex as re
import torch
from cachetools import LRUCache
from diskcache import Cache
import vllm.envs as envs
from vllm.logger import init_logger
from vllm.utils.import_utils import LazyLoader
from vllm.v1.core.sched.output import GrammarOutput, SchedulerOutput
if TYPE_CHECKING:
import outlines_core as oc
import transformers.file_utils as file_utils
import transformers.models.gpt2.tokenization_gpt2 as tokenization_gpt2
import xgrammar as xgr
from vllm.tokenizers import TokenizerLike
from vllm.v1.worker.gpu_input_batch import InputBatch
else:
xgr = LazyLoader("xgr", globals(), "xgrammar")
oc = LazyLoader("oc", globals(), "outlines_core")
file_utils = LazyLoader("file_utils", globals(), "transformers.file_utils")
tokenization_gpt2 = LazyLoader(
"tokenization_gpt2",
globals(),
"transformers.models.gpt2.tokenization_gpt2",
)
TokenizerLike = object
SchedulerOutput = object
InputBatch = object
logger = init_logger(__name__)
CACHE = None
def apply_grammar_bitmask(
scheduler_output: SchedulerOutput,
grammar_output: GrammarOutput,
input_batch: InputBatch,
logits: torch.Tensor,
) -> None:
"""
Apply grammar bitmask to output logits of the model with xgrammar function.
Args:
scheduler_output (SchedulerOutput): The result of engine scheduling.
input_batch (InputBatch): The input of model runner.
logits (torch.Tensor): The output logits of model forward.
"""
# Serialization of np.ndarray is much more efficient than a tensor,
# so we receive it in that format.
grammar_bitmask = grammar_output.grammar_bitmask
# We receive the structured output bitmask from the scheduler,
# compacted to contain bitmasks only for structured output requests.
# The order of the requests in the bitmask is not guaranteed to be the
# same as the order of the requests in the gpu runner's batch. We need
# to sort the bitmask to match the order of the requests used here.
# Get the batch indices of the structured output requests.
# Keep track of the number of speculative tokens scheduled for every
# request in the batch, as the logit indices are offset by this amount.
struct_out_req_batch_indices: dict[str, int] = {}
cumulative_offset = 0
seq = sorted(input_batch.req_id_to_index.items(), key=lambda x: x[1])
for req_id, batch_index in seq:
logit_index = batch_index + cumulative_offset
cumulative_offset += len(
scheduler_output.scheduled_spec_decode_tokens.get(req_id, [])
)
if req_id in grammar_output.structured_output_request_ids:
struct_out_req_batch_indices[req_id] = logit_index
out_indices = []
# Reorder the bitmask to match the order of the requests in the batch.
sorted_bitmask = np.full(
shape=(logits.shape[0], grammar_bitmask.shape[1]),
fill_value=-1,
dtype=grammar_bitmask.dtype,
)
cumulative_index = 0
for req_id in grammar_output.structured_output_request_ids:
num_spec_tokens = len(
scheduler_output.scheduled_spec_decode_tokens.get(req_id, [])
)
if req_id in struct_out_req_batch_indices:
logit_index = struct_out_req_batch_indices[req_id]
for i in range(1 + num_spec_tokens):
sorted_bitmask[logit_index + i] = grammar_bitmask[cumulative_index + i]
out_indices.append(logit_index + i)
cumulative_index += 1 + num_spec_tokens
# Copy async to device as tensor.
grammar_bitmask = torch.from_numpy(sorted_bitmask).to(
logits.device, non_blocking=True
)
# If the length of out indices and the logits have the same shape
# we don't need to pass indices to the kernel,
# since the bitmask is already aligned with the logits.
skip_out_indices = len(out_indices) == logits.shape[0]
index_tensor = None
if not skip_out_indices:
# xgrammar expects a python list of indices but it will actually work with
# a tensor. If we copy the tensor ourselves here we can do it in a non_blocking
# manner and there should be no cpu sync within xgrammar.
index_tensor = torch.tensor(
out_indices, dtype=torch.int32, device="cpu", pin_memory=True
)
index_tensor = index_tensor.to(logits.device, non_blocking=True)
xgr.apply_token_bitmask_inplace(logits, grammar_bitmask, indices=index_tensor)
class OutlinesVocabulary:
"""
Wrapper class for `outlines_core.Vocabulary`,
which allows us to store a hash with the vocabulary
"""
def __init__(self, vocabulary: oc.Vocabulary) -> None:
# Actual vocabulary object
self.inner = vocabulary
# Have to do abs(hash()) because python hashes can
# be negative, and we are using hash as a cache key.
hex_str = hashlib.sha256(vocabulary.__repr__().encode("utf-8")).hexdigest()
hash_int = int(hex_str, 16)
self._hash = hash_int
def get_outlines_cache_path() -> str:
"""Get the context object that contains previously-computed return values"""
outlines_cache_dir = os.getenv("OUTLINES_CACHE_DIR")
xdg_cache_home = os.getenv("XDG_CACHE_HOME")
home_dir = os.path.expanduser("~")
if outlines_cache_dir:
# OUTLINES_CACHE_DIR takes precedence
return outlines_cache_dir
elif xdg_cache_home:
return os.path.join(xdg_cache_home, ".cache", "outlines")
# If homedir is "/", we may be inside a container, and thus writing to
# root would be problematic, so we fall back to using a tempfile.
# Also validate the path exists, since os.path.expanduser does
# not guarantee existence.
elif os.path.isdir(home_dir) and home_dir != "/":
# Default Unix fallback: ~/.cache/outlines
return os.path.join(home_dir, ".cache", "outlines")
else:
import tempfile
# home_dir may be / inside a docker container without existing user
tempdir = tempfile.gettempdir()
return os.path.join(tempdir, ".cache", "outlines")
def get_outlines_cache():
"""Get the Cache instance to be used for index caching"""
cache_dir = get_outlines_cache_path()
if envs.VLLM_V1_USE_OUTLINES_CACHE:
logger.warning(
"Enabling outlines cache. This is an unbounded on-disk "
"cache. It may consume a lot of disk space and should "
"not be used with untrusted clients."
)
cache = Cache(cache_dir, eviction_policy="none", cull_limit=0)
outlines_version = importlib.metadata.version("outlines_core")
cached_version = cache.get("__version__", None)
if cached_version != outlines_version:
cache.clear()
cache.set("__version__", outlines_version)
return cache
else:
return LRUCache(maxsize=128)
re_llama_byte_token = re.compile(r"^<0x[0-9A-F]{2}>$")
re_replacement_seq = re.compile(r"^.{0,6}<7D>+.{0,6}$")
def _reduced_vocabulary(
tokenizer: TokenizerLike,
eos_token_id: int,
) -> dict[bytes, list[int]]:
"""Create a map from vocabulary tokens to lists of equivalent token ids.
Returns:
A Dict of token string -> equivalent token ids
"""
unicode_to_bytes = {v: k for k, v in tokenization_gpt2.bytes_to_unicode().items()}
def convert_token_to_string(token: str) -> str:
string = tokenizer.convert_tokens_to_string([token])
# A hack to handle missing spaces to HF's Llama tokenizers
if (
type(token) is str
and token.startswith(file_utils.SPIECE_UNDERLINE)
or token == "<0x20>"
):
return " " + string
return string
vocabulary: dict[bytes, list[int]] = {}
empty_token_ids: list[int] = []
for token, token_idx in tokenizer.get_vocab().items():
if token in tokenizer.all_special_tokens:
continue
token_str = convert_token_to_string(token)
if token_str:
if isinstance(token, (bytes, bytearray)):
# For BPE tokenizers where tokens are stored as bytes.
# safe to ignore since token_str is of type (bytearray, bytes)
# by this point.
token_bytes = bytes(token_str) # type: ignore[arg-type]
elif "\ufffd" in token_str and not re_replacement_seq.match(token_str):
# Handle tokens with invalid UTF-8 sequences.
if re_llama_byte_token.match(token):
# Llama-like tokenizers use <0xXX> for incomplete sequences.
token_bytes = bytes([int(token[3:5], 16)])
else:
# GPT2 tokenizers: map each byte back using unicode_to_bytes
byte_vals = [unicode_to_bytes.get(c) for c in token]
if None in byte_vals:
raise RuntimeError(
f"Cannot convert token `{token}`"
f" ({token_idx}) to bytes: {token_str}"
)
# safe to ignore, since if None in byte_vals,
# an error is thrown.
token_bytes = bytes(byte_vals) # type: ignore[arg-type]
else:
token_bytes = token_str.encode("utf-8")
if token_idx != eos_token_id:
vocabulary.setdefault(token_bytes, []).append(token_idx)
else:
empty_token_ids.append(token_idx)
return vocabulary
def get_outlines_vocabulary(tokenizer: TokenizerLike) -> oc.Vocabulary:
"""Get the `Vocabulary` object for a given tokenizer."""
if hasattr(tokenizer, "_outlines_vocabulary"):
return tokenizer._outlines_vocabulary # type: ignore
try:
if (
hasattr(
tokenizer,
"eos_token_id",
)
and tokenizer.eos_token_id is not None
):
eos_token_id = tokenizer.eos_token_id
else:
raise ValueError(
f"Error during structured outputs setup for outlines: Tokenizer ({type(tokenizer)}) has no `eos_token_id` property, but `eos_token_id` is required for structured outputs to work properly." # noqa: E501
)
reduced_vocab = _reduced_vocabulary(
tokenizer,
eos_token_id, # type: ignore
)
vocabulary = OutlinesVocabulary(oc.Vocabulary(eos_token_id, reduced_vocab))
tokenizer._outlines_vocabulary = vocabulary # type: ignore
return vocabulary
except AttributeError as e:
raise ValueError(
f"Cannot get the vocabulary of the tokenizer "
f"({type(tokenizer)}). The tokenizer should have a "
"get_vocab method."
) from e
def grammar_is_likely_lark(grammar_str: str) -> bool:
"""
Check if grammar appears to use Lark syntax.
Args:
grammar_str: Input grammar string
Returns:
bool: True if grammar appears to be in Lark format, False otherwise
Examples:
>>> grammar_is_likely_lark("rule: 'abc'")
True
>>> grammar_is_likely_lark("rule ::= 'abc'")
False
"""
if not grammar_str or not isinstance(grammar_str, str):
return False
for line in grammar_str.split("\n"):
# Remove both comment styles
line = re.sub(r"(#|//).*$", "", line).strip()
if not line:
continue
# Look for EBNF rule definition
if "::=" in line:
return False
return True
def convert_lark_to_ebnf(grammar_str: str) -> str:
"""
Convert a Lark grammar string to EBNF format.
EBNF reference:
https://github.com/ggerganov/llama.cpp/blob/master/grammars/README.md
Lark grammar reference:
https://lark-parser.readthedocs.io/en/latest/grammar.html
Args:
grammar_str: Input grammar in Lark format
Returns:
str: Converted grammar in EBNF format
Examples:
>>> print(convert_lark_to_ebnf("rule: 'hello'"))
root ::= rule
rule ::= "hello"
"""
if not isinstance(grammar_str, str):
raise ValueError(f"Grammar must be a string, got {type(grammar_str)}")
if not grammar_str.strip():
raise ValueError("Grammar string cannot be empty")
defined_rules = set()
referenced_rules = set()
output_lines = []
def clean_line(line: str) -> str:
"""Remove comments and whitespace from line."""
return re.sub(r"(#|//).*$", "", line).strip()
def check_quotes(text: str, rule_name: str, line_num: int) -> None:
"""Validate quote matching in text."""
if text.count("'") % 2 != 0 or text.count('"') % 2 != 0:
raise ValueError(f"Mismatched quotes in {rule_name} on line {line_num}")
def extract_references(text: str) -> set[str]:
"""Extract rule references from text."""
# Remove quoted strings and special characters
text = re.sub(r'"[^"]*"', "", text)
text = re.sub(r"[+*?()|\[\]{}]", " ", text)
return set(re.findall(r"\b[a-zA-Z_][a-zA-Z0-9_]*\b", text))
# First pass: Find root rule and validate rule definitions
lines = [clean_line(line) for line in grammar_str.split("\n")]
first_rule = None
for line_num, line in enumerate(lines, 1):
if not line or line.startswith("|"):
continue
if ":" in line:
try:
name = line.split(":", 1)[0].strip().strip("?")
defined_rules.add(name)
if first_rule is None:
first_rule = name
if name == "start":
first_rule = "start"
except IndexError as e:
raise ValueError(
f"Invalid rule format on line {line_num}. "
"Expected 'rule_name: definition'"
) from e
if not defined_rules:
raise ValueError("No valid rules found in grammar")
# Add root rule
output_lines.append(f"root ::= {first_rule}")
# Second pass: Process rule definitions and alternatives
current_rule = None
current_definition = []
for line_num, line in enumerate(lines, 1):
if not line:
continue
try:
if ":" in line and not line.startswith("|"):
# Save previous rule if exists
if current_rule:
output_lines.append(
f"{current_rule} ::= {' | '.join(current_definition)}"
)
# Process new rule
name, definition = line.split(":", 1)
current_rule = name.strip().strip("?")
check_quotes(definition, f"rule '{current_rule}'", line_num)
definition = re.sub(r"'([^']*)'", r'"\1"', definition)
referenced_rules.update(extract_references(definition))
current_definition = [definition.strip()]
elif line.startswith("|"):
if not current_rule:
raise ValueError(
f"Alternative '|' on line {line_num} "
"without a preceding rule definition"
)
alt_def = line[1:].strip()
check_quotes(
alt_def, f"alternative for rule '{current_rule}'", line_num
)
alt_def = re.sub(r"'([^']*)'", r'"\1"', alt_def)
referenced_rules.update(extract_references(alt_def))
current_definition.append(alt_def)
except ValueError as e:
raise ValueError(f"Error on line {line_num}: {str(e)}") from e
# Add final rule if exists
if current_rule:
output_lines.append(f"{current_rule} ::= {' | '.join(current_definition)}")
# Validate all rules are defined
undefined_rules = referenced_rules - defined_rules - {"root"}
if undefined_rules:
raise ValueError(
f"Referenced rules are not defined: {', '.join(sorted(undefined_rules))}"
)
return "\n".join(output_lines)
def choice_as_grammar(choice: list[str]) -> str:
def escape_ebnf_string(s: str) -> str:
"""Escape special characters in a EBNF string."""
# Escape double quotes and backslashes
return re.sub(r'(["\\])', r"\\\1", s)
escaped_choices = (escape_ebnf_string(c) for c in choice)
grammar = "root ::= " + " | ".join(f'"{c}"' for c in escaped_choices)
return grammar