421e7cee80
* initially add text_generation_webui support * add env requirements install * add necessary dependencies * update for starting webui * update shared and noted to place models * update heading of part3 * meet comments * add copyright license * remove extensions * convert tutorial to windows side * add warm-up to optimize performance
696 lines
26 KiB
Python
696 lines
26 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is adapted from
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# https://github.com/huggingface/transformers/pull/27557
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import logging
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import re
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import time
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from abc import ABC
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from functools import lru_cache
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from typing import Dict, List
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import torch
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from modules import shared
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logger = logging.getLogger(__name__)
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########################
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# EBNF Grammar Parsing #
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########################
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END_OF_ALTERNATE_MARKER = 0
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END_OF_RULE_MARKER = 0
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TO_BE_FILLED_MARKER = 0
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REF_RULE_MARKER = 1
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LITERAL_MARKER = 2
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class ParseState:
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def __init__(self):
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self.symbol_ids = {}
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self.grammar_encoding = [] # old name: out_grammar
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def get_symbol_id(state, src):
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if src not in state.symbol_ids:
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state.symbol_ids[src] = len(state.symbol_ids)
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return state.symbol_ids[src]
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def generate_symbol_id(state, base_name):
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next_id = len(state.symbol_ids)
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state.symbol_ids[base_name + "_" + str(next_id)] = next_id
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return next_id
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def is_word_char(c):
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return c.isalnum() or c == "-" or c == "_"
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def hex_to_int(c):
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if c.isdigit():
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return int(c)
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elif "a" <= c.lower() <= "f":
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return ord(c.lower()) - ord("a") + 10
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return -1
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def remove_leading_white_space(src, newline_ok):
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"""
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Skips over whitespace and comments in the input string.
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This function processes the input string, skipping over any spaces, tabs,
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and content following a '#' character, which denotes a comment. The parsing
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of a comment continues until the end of the line (denoted by newline characters
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'\r' or '\n'). If the 'newline_ok' parameter is set to False, the function
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will stop processing and return the remaining string upon encountering a
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newline character, otherwise it will skip over newline characters as well.
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Parameters:
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src (str): The input string to be processed.
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newline_ok (bool): A flag indicating whether encountering a newline character
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should stop the parsing (False) or if it should be skipped (True).
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Returns:
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str: The remaining portion of the input string after skipping whitespace and comments.
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"""
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pos = 0
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while pos < len(src) and (src[pos].isspace() or src[pos] == "#"):
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if src[pos] == "#":
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while pos < len(src) and src[pos] not in ("\r", "\n"):
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pos += 1
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else:
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if not newline_ok and src[pos] in ("\r", "\n"):
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break
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pos += 1
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return src[pos:]
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def parse_name(src):
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pos = 0
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while pos < len(src) and is_word_char(src[pos]):
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pos += 1
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if pos == 0:
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raise RuntimeError("expecting name at " + src)
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return src[:pos], src[pos:]
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def parse_char(src):
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"""
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parse the leading char from the input string
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:param src:
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:return: char, remaining_src
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"""
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# if we have a backslash, it's maybe an escape
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if src[0] == "\\":
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esc = src[1]
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if esc == "x":
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first = hex_to_int(src[2])
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if first > -1:
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second = hex_to_int(src[3])
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if second > -1:
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return (first << 4) + second, src[4:]
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raise RuntimeError("expecting \\xNN at " + src)
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elif esc in ('"', "[", "]"):
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return esc, src[2:]
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elif esc == "r":
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return "\r", src[2:]
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elif esc == "n":
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return "\n", src[2:]
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elif esc == "t":
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return "\t", src[2:]
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raise RuntimeError("unknown escape at " + src)
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elif src:
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return src[0], src[1:]
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raise RuntimeError("unexpected end of input")
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def parse_sequence(state, src, rule_name, outbuf, is_nested):
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out_start_pos = len(outbuf)
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# sequence size, will be replaced at end when known
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outbuf.append(TO_BE_FILLED_MARKER)
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last_sym_start = len(outbuf)
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remaining_src = src
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while remaining_src:
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if remaining_src[0] == '"': # literal string
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remaining_src = remaining_src[1:]
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last_sym_start = len(outbuf)
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while remaining_src[0] != '"':
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char, remaining_src = parse_char(remaining_src)
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# each char of a literal is encoded as a "range" of char - char
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outbuf.append(LITERAL_MARKER)
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outbuf.append(ord(char))
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outbuf.append(ord(char))
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remaining_src = remove_leading_white_space(remaining_src[1:], is_nested)
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elif remaining_src[0] == "[": # char range(s)
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remaining_src = remaining_src[1:]
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last_sym_start = len(outbuf)
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# num chars in range - replaced at end of loop
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outbuf.append(TO_BE_FILLED_MARKER)
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while remaining_src[0] != "]":
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char, remaining_src = parse_char(remaining_src)
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outbuf.append(ord(char))
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if remaining_src[0] == "-" and remaining_src[1] != "]":
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endchar_pair, remaining_src = parse_char(remaining_src[1:])
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outbuf.append(ord(endchar_pair))
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else:
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# chars that aren't part of a c1-c2 range are just doubled (i.e., c-c)
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outbuf.append(ord(char))
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# replace num chars with actual
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outbuf[last_sym_start] = len(outbuf) - last_sym_start - 1
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remaining_src = remove_leading_white_space(remaining_src[1:], is_nested)
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elif is_word_char(remaining_src[0]): # rule reference
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name, remaining_src = parse_name(remaining_src)
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ref_rule_id = get_symbol_id(state, name)
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remaining_src = remove_leading_white_space(remaining_src, is_nested)
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last_sym_start = len(outbuf)
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outbuf.append(REF_RULE_MARKER)
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outbuf.append(ref_rule_id)
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elif remaining_src[0] == "(": # grouping
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# parse nested alternates into synthesized rule
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remaining_src = remove_leading_white_space(remaining_src[1:], True)
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sub_rule_id = generate_symbol_id(state, rule_name)
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remaining_src = parse_alternates(state, remaining_src, rule_name, sub_rule_id, True)
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last_sym_start = len(outbuf)
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# output reference to synthesized rule
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outbuf.append(REF_RULE_MARKER)
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outbuf.append(sub_rule_id)
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if remaining_src[0] != ")":
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raise RuntimeError("expecting ')' at " + remaining_src)
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remaining_src = remove_leading_white_space(remaining_src[1:], is_nested)
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elif remaining_src[0] in ("*", "+", "?"): # repetition operator
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if len(outbuf) - out_start_pos - 1 == 0:
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raise RuntimeError("expecting preceeding item to */+/? at " + remaining_src)
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out_grammar = state.grammar_encoding
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# apply transformation to previous symbol (last_sym_start -
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# end) according to rewrite rules:
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# S* --> S' ::= S S' |
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# S+ --> S' ::= S S' | S
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# S? --> S' ::= S |
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sub_rule_id = generate_symbol_id(state, rule_name)
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out_grammar.append(sub_rule_id)
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sub_rule_start = len(out_grammar)
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# placeholder for size of 1st alternate
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out_grammar.append(TO_BE_FILLED_MARKER)
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# add preceding symbol to generated rule
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out_grammar.extend(outbuf[last_sym_start:])
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if remaining_src[0] in ("*", "+"):
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# cause generated rule to recurse
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out_grammar.append(REF_RULE_MARKER)
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out_grammar.append(sub_rule_id)
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# apply actual size
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out_grammar[sub_rule_start] = len(out_grammar) - sub_rule_start
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# mark end of 1st alternate
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out_grammar.append(END_OF_ALTERNATE_MARKER)
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sub_rule_start = len(out_grammar)
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# placeholder for size of 2nd alternate
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out_grammar.append(TO_BE_FILLED_MARKER)
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if remaining_src[0] == "+":
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# add preceding symbol as alternate only for '+'
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out_grammar.extend(outbuf[last_sym_start:])
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# apply actual size of 2nd alternate
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out_grammar[sub_rule_start] = len(out_grammar) - sub_rule_start
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# mark end of 2nd alternate, then end of rule
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out_grammar.append(END_OF_ALTERNATE_MARKER)
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out_grammar.append(END_OF_RULE_MARKER)
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# in original rule, replace previous symbol with reference to generated rule
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outbuf[last_sym_start:] = [1, sub_rule_id]
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remaining_src = remove_leading_white_space(remaining_src[1:], is_nested)
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else:
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break
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# apply actual size of this alternate sequence
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outbuf[out_start_pos] = len(outbuf) - out_start_pos
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# mark end of alternate
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outbuf.append(END_OF_ALTERNATE_MARKER)
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return remaining_src
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def parse_alternates(state, src, rule_name, rule_id, is_nested):
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outbuf = []
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remaining_src = parse_sequence(state, src, rule_name, outbuf, is_nested)
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while remaining_src and remaining_src[0] == "|":
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remaining_src = remove_leading_white_space(remaining_src[1:], True)
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remaining_src = parse_sequence(state, remaining_src, rule_name, outbuf, is_nested)
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state.grammar_encoding.append(rule_id)
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state.grammar_encoding.extend(outbuf)
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state.grammar_encoding.append(0)
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return remaining_src
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def parse_rule(state, src):
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name, remaining_src = parse_name(src)
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remaining_src = remove_leading_white_space(remaining_src, False)
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rule_id = get_symbol_id(state, name)
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if remaining_src[:3] != "::=":
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raise RuntimeError("expecting ::= at " + remaining_src)
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remaining_src = remove_leading_white_space(remaining_src[3:], True)
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remaining_src = parse_alternates(state, remaining_src, name, rule_id, False)
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if remaining_src and remaining_src[0] == "\r":
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remaining_src = remaining_src[2:] if remaining_src[1] == "\n" else remaining_src[1:]
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elif remaining_src and remaining_src[0] == "\n":
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remaining_src = remaining_src[1:]
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elif remaining_src:
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raise RuntimeError("expecting newline or end at " + remaining_src)
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return remove_leading_white_space(remaining_src, True)
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def parse_ebnf(src):
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try:
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state = ParseState()
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grammar_repr = remove_leading_white_space(src, True)
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last_grammar_repr = ""
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while grammar_repr:
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if last_grammar_repr:
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last_parsed_rule_len = len(last_grammar_repr) - len(grammar_repr)
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logger.debug(f"last_parsed_rule: {last_grammar_repr[:last_parsed_rule_len]}")
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last_grammar_repr = grammar_repr
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grammar_repr = parse_rule(state, grammar_repr)
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state.grammar_encoding.append(0xFFFF)
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return state
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except RuntimeError as err:
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logger.warning("error parsing grammar:", err)
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return ParseState()
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def print_rule(file, grammar_encoding, index, symbol_id_names):
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rule_id = grammar_encoding[index]
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print(f"<{index}>{symbol_id_names[rule_id]} ::=", end=" ", file=file)
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pos = index + 1
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while grammar_encoding[pos]:
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if pos - 1 > index:
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print("|", end=" ", file=file)
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pos += 1 # sequence size, not needed here
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while grammar_encoding[pos]:
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if grammar_encoding[pos] == REF_RULE_MARKER:
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ref_rule_id = grammar_encoding[pos + 1]
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print(
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f"<{pos}>{symbol_id_names[ref_rule_id]}",
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end=" ",
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file=file,
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)
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pos += 2
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else:
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print("<{}>[".format(pos), end="", file=file)
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num_chars = grammar_encoding[pos]
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pos += 1
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for i in range(0, num_chars, 2):
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print("{}-".format(chr(grammar_encoding[pos + i])), end="", file=file)
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if i + 1 < num_chars:
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print("{}".format(chr(grammar_encoding[pos + i + 1])), end="", file=file)
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print("]", end=" ", file=file)
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pos += num_chars
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pos += 1
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print(file=file)
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return pos + 1
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def print_grammar(file, state):
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pos = 0
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symbol_id_names = {v: k for k, v in state.symbol_ids.items()}
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print("Grammar Rules:", file=file)
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while state.grammar_encoding[pos] != 0xFFFF:
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pos = print_rule(file, state.grammar_encoding, pos, symbol_id_names)
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pos = 0
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print("\nBinary representation:", file=file)
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while state.grammar_encoding[pos] != 0xFFFF:
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print(f"{state.grammar_encoding[pos]:04x}", end=" ", file=file)
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pos += 1
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print("ffff\n")
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###################################
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# EBNF Grammar Parsing ends here #
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###################################
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class GrammarConstraint(ABC):
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def __init__(self, grammar_str, start_rule_name, tokenizer):
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self.tt = 0
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self.nt = 0
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state = parse_ebnf(grammar_str)
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grammar_encoding = state.grammar_encoding
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self.start_rule_id = state.symbol_ids.get(start_rule_name)
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self.eos_token_id = tokenizer.eos_token_id
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self.token_trie = TokenTrie(tokenizer)
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self.tokenizer = tokenizer
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self.grammar_encoding = grammar_encoding
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pos = 0
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rules: Dict[int, int] = {}
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while grammar_encoding[pos] != 0xFFFF:
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rule_id = grammar_encoding[pos]
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# Store the current position in the 'rules' list at the index corresponding to rule_id.
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# This effectively maps each rule_id to its position in the grammar encoding.
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rules[rule_id] = pos
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pos += 1
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# Continue to the next rule in the encoding.
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# The loop advances by the size indicated at the current position (grammar_encoding[pos])
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# plus one for the size field itself.
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while grammar_encoding[pos]:
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pos += 1 + grammar_encoding[pos]
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# Now we're at the end of the rule,
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# so advance to the next rule by skipping the 0, which means 'end of rule'.
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pos += 1
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self.start_rule_pos = rules[self.start_rule_id]
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self.rules_pos_dict: Dict[int, int] = rules
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def init_stacks(self):
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# suppose the start rule position is 0, then grammar_encoding[0] = rule_id
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# grammar_encoding[1] = rule_size
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# grammar_encoding[2] = rule_type
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# this is why we need to add 2 to the start rule position
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stack = [self.start_rule_pos + 2]
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# convert to tuple for caching(immutable)
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return self.advance_stack(tuple(stack))
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# For each stack, resolve rules to find the actual characters that are
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# accepted by this stack (not the set of sub-rules).
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# This is where the parsing happens.
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# The parsing is a top-down, left-to-right, depth-first traversal of the
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# grammar.
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@lru_cache(maxsize=32768)
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def advance_stack(self, stack):
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stack = list(stack)
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# If the stack is empty, we're done. Because no more tokens should be accepted.
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if len(stack) == 0:
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return [stack]
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# Get the top of the stack.
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pos = stack[-1]
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# If the stack head is a terminal(literal), we can resolve it immediately.
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# literal is marked with 2 in the grammar encoding.
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if self.grammar_encoding[pos] > 1:
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return [stack]
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# The stack head is a nonterminal (a rule reference, 1 in the grammar encoding).
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# Resolving this rule gives a set of one or more possible positions
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# (e.g. two in `a ::= b | c`)
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# We pop the current rule off the stack and, for each option, push:
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# - the symbol following this symbol in the current rule; then
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# - the first symbol of the resolved rule.
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referenced_rule_id = self.grammar_encoding[pos + 1]
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# subpos should points to the size of the subrule
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subpos = self.rules_pos_dict[referenced_rule_id] + 1
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stacks: List[List[int]] = []
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# do depth-first search to find all possible rules and check the next terminal
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# When this value is non-zero, it indicates that subpos is not yet at the end of the rule, so we can continue.
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# here subpos is a pointer, and the value in the rule encoding can never be 0 except for the end of the rule.
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while self.grammar_encoding[subpos]:
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new_stack = stack[:-1]
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if self.grammar_encoding[pos + 2]:
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# check if there is a next symbol in the current rule, e.g. `a ::= b c | d`
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# if yes, push the pos to rule_size to the stack
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new_stack.append(pos + 2)
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# if the type of the next symbol is not "empty", push the first symbol of the resolved rule to the stack
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if self.grammar_encoding[subpos + 1]:
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new_stack.append(subpos + 1)
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stacks.extend(self.advance_stack(tuple(new_stack)))
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# The increment subpos += self.grammar_encoding[subpos] + 1
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# moves subpos forward in the grammar encoding array to the next alternative in the current rule.
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subpos += self.grammar_encoding[subpos] + 1
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return stacks
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def accept_char(self, *args, **kwargs):
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"""Process a byte according to the grammar rules."""
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raise NotImplementedError
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def accept_token_id(self, *args, **kwargs):
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"""Process a token according to the grammar rules."""
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raise NotImplementedError
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def filter_vocab(self, *args, **kwargs):
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raise NotImplementedError
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class IncrementalGrammarConstraint(GrammarConstraint):
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def __init__(self, grammar_str, start_rule_name, tokenizer):
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super().__init__(grammar_str, start_rule_name, tokenizer)
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|
|
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def accept_char(self, byte, stacks):
|
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new_stacks = []
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for stack in stacks:
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# stack is empty
|
|
if not stack:
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|
continue
|
|
|
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pos = stack[-1]
|
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num_chars = self.grammar_encoding[pos]
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|
|
|
# to make pos point to the size of the char range rule
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|
pos += 1
|
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found = False
|
|
for i in range(0, num_chars, 2):
|
|
if self.grammar_encoding[pos + i] <= byte and byte <= self.grammar_encoding[pos + i + 1]:
|
|
found = True
|
|
break
|
|
if not found:
|
|
continue
|
|
|
|
pos += num_chars
|
|
new_stack = stack[:-1]
|
|
if self.grammar_encoding[pos]:
|
|
new_stack.append(pos)
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new_stacks.extend(self.advance_stack(tuple(new_stack)))
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|
|
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return new_stacks
|
|
|
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def accept_string(self, string: str, stacks: List[List[int]]):
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_bytes = bytes(string, "utf-8")
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for byte in _bytes:
|
|
stacks = self.accept_char(byte, stacks)
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|
return stacks
|
|
|
|
def accept_token_id(self, token_id: int, stacks: List[List[int]]):
|
|
if token_id == self.eos_token_id:
|
|
if stacks and all(len(stack) != 0 for stack in stacks):
|
|
raise Exception(
|
|
f"At least one of the stack should be empty when EOS is reached. However, "
|
|
f"the stacks are {stacks}"
|
|
)
|
|
return []
|
|
|
|
for byte in self.token_trie.id2str(token_id):
|
|
stacks = self.accept_char(byte, stacks)
|
|
# check updated stacks
|
|
# TODO, I commented this out because it will fail when the stack is empty
|
|
# empty stack means the end of the grammar
|
|
# assert stacks != []
|
|
|
|
return stacks
|
|
|
|
def accept_token_ids(self, token_ids: List[int], stacks: List[List[int]], as_string=True):
|
|
if as_string:
|
|
string = self.tokenizer.decode(token_ids)
|
|
stacks = self.accept_string(string, stacks)
|
|
else:
|
|
for token_id in token_ids:
|
|
stacks = self.accept_token_id(token_id, stacks)
|
|
return stacks
|
|
|
|
def batch_filter_vocab(self, batch_stacks, device):
|
|
batch_acceptance = []
|
|
for stacks in batch_stacks:
|
|
batch_acceptance.append(self.filter_vocab(stacks, device))
|
|
return torch.stack(batch_acceptance)
|
|
|
|
def filter_vocab(self, stacks, device):
|
|
if not stacks: # Check if stacks is empty
|
|
# Handle the empty case: for example, return a tensor of False
|
|
# The size of the tensor should match the size of your vocabulary
|
|
vocab_size = len(self.token_trie)
|
|
logger.debug(f"sum of acceptance: {0}")
|
|
return torch.zeros(vocab_size, dtype=torch.bool, device=device)
|
|
|
|
acceptance_matrix = torch.cat([self.token_acceptance_for_stack(tuple(stack), device) for stack in stacks])
|
|
# Merge stacks: any True => True
|
|
acceptance = acceptance_matrix.reshape(len(stacks), -1).any(dim=0)
|
|
logger.debug(f"sum of acceptance: {acceptance.sum()}")
|
|
return acceptance
|
|
|
|
# For each sub-rule in the grammar, cache whether each byte is accepted.
|
|
@lru_cache(maxsize=None)
|
|
def pos_char_acceptance(self, pos):
|
|
acceptance = [False] * 256
|
|
num_chars = self.grammar_encoding[pos]
|
|
pos += 1
|
|
for i in range(0, num_chars, 2):
|
|
start = self.grammar_encoding[pos + i]
|
|
end = self.grammar_encoding[pos + i + 1]
|
|
for j in range(start, end + 1):
|
|
acceptance[j] = True
|
|
return acceptance
|
|
|
|
# Probably this should be configurable. If the grammar has an exceedingly
|
|
# large number of states, the correct setting is a tradeoff between GPU
|
|
# RAM usage and recomputation time.
|
|
#
|
|
# The main variable that pushes usage up here is number of states in the
|
|
# grammar.
|
|
@lru_cache(maxsize=32768)
|
|
def token_acceptance_for_stack(self, stack, device):
|
|
st = time.time()
|
|
stack = list(stack) # needs to come in as a tuple for lru_cache
|
|
|
|
accepts = [False] * len(self.token_trie)
|
|
accepts[self.eos_token_id] = len(stack) == 0
|
|
if len(stack) == 0:
|
|
logger.debug("empty stack")
|
|
|
|
def traverse_trie(trie, stacks):
|
|
for byte, next_trie in trie.items():
|
|
if byte == LEAF:
|
|
token_id = next_trie
|
|
if token_id != self.eos_token_id:
|
|
accepts[token_id] = bool(stacks)
|
|
continue
|
|
|
|
new_stacks = []
|
|
for stk in stacks:
|
|
if not stk:
|
|
continue
|
|
|
|
pos = stk[-1]
|
|
num_chars = self.grammar_encoding[pos]
|
|
|
|
if not self.pos_char_acceptance(pos)[byte]:
|
|
continue
|
|
|
|
pos += num_chars + 1
|
|
new_stack = stk[:-1]
|
|
if self.grammar_encoding[pos]:
|
|
new_stack.append(pos)
|
|
new_stacks.extend(self.advance_stack(tuple(new_stack)))
|
|
|
|
if new_stacks:
|
|
traverse_trie(next_trie, new_stacks)
|
|
|
|
traverse_trie(self.token_trie.trie, [stack])
|
|
|
|
et = time.time() - st
|
|
x = torch.tensor(accepts, dtype=torch.bool, device=device)
|
|
self.tt += et
|
|
self.nt += 1
|
|
return x
|
|
|
|
|
|
class StaticGrammarConstraint(GrammarConstraint):
|
|
def __init__(self, grammar_str, start_rule_name, tokenizer):
|
|
super().__init__(grammar_str, start_rule_name, tokenizer)
|
|
|
|
def accept_char(self):
|
|
raise NotImplementedError
|
|
|
|
|
|
#################
|
|
# DATA STRUCTURES
|
|
#################
|
|
|
|
|
|
LEAF = -1
|
|
|
|
|
|
class TokenTrie:
|
|
def __init__(self, tokenizer):
|
|
self.eos_token_id = tokenizer.eos_token_id
|
|
self.tokens = []
|
|
self.trie = {}
|
|
self.load_tokens(tokenizer)
|
|
|
|
def id2str(self, token_id):
|
|
return self.tokens[token_id]
|
|
|
|
def __len__(self):
|
|
return len(self.tokens)
|
|
|
|
def load_tokens(self, tokenizer):
|
|
def replace_hex(match):
|
|
hex_value = match.group(1)
|
|
return chr(int(hex_value, 16))
|
|
|
|
if "gpt2" in tokenizer.__class__.__name__.lower():
|
|
special = tokenizer.additional_special_tokens_ids
|
|
|
|
# Here, the decoder does a string replace on a bunch of sequences
|
|
# like ' .' for '.'. This interferes with our assumptions, where a
|
|
# token should always have exactly one representation.
|
|
# Fortunately(?) text-generation-inference doesn't seem to run this
|
|
# cleanup, so we get extraneous spaces. So, in order to generate
|
|
# the right token set for TGI, we have to skip the space trimming.
|
|
# See:
|
|
# https://github.com/huggingface/transformers/blob/main/src/transformers/tokenization_utils_base.py#L3588-L3600
|
|
def fmt_token(id):
|
|
if id in special:
|
|
return None
|
|
return bytes(tokenizer.decode([id], clean_up_tokenization_spaces=False), "utf-8")
|
|
|
|
elif "llama" in tokenizer.__class__.__name__.lower():
|
|
|
|
def fmt_token(id):
|
|
token = tokenizer.convert_ids_to_tokens(id)
|
|
token = re.sub(r"<0x([0-9a-fA-F]{2})>", replace_hex, token)
|
|
token = token.replace("▁", " ")
|
|
return bytes(token, "utf-8")
|
|
|
|
else:
|
|
print("Warning: unrecognized tokenizer: using default token formatting")
|
|
|
|
def fmt_token(id):
|
|
token = tokenizer.convert_ids_to_tokens(id)
|
|
return bytes(token, "utf-8")
|
|
|
|
# note: vocab_size doesn't work here because there are also
|
|
# get_added_vocab() tokens
|
|
self.tokens = [fmt_token(i) for i in range(len(tokenizer.get_vocab()))]
|
|
for token_id, token_bytes in enumerate(self.tokens):
|
|
if token_bytes is not None:
|
|
self.insert_into_trie(self.trie, token_bytes, token_id)
|
|
|
|
def insert_into_trie(self, trie, token_bytes, token_id):
|
|
current = trie
|
|
for byte in token_bytes:
|
|
if byte not in current:
|
|
current[byte] = {}
|
|
current = current[byte]
|
|
current[LEAF] = token_id
|
|
|
|
|
|
@lru_cache(maxsize=5)
|
|
def initialize_grammar(grammar_string):
|
|
return IncrementalGrammarConstraint(grammar_string.strip(), start_rule_name="root", tokenizer=shared.tokenizer)
|