text-generation-webui/modules/grammar/grammar_utils.py

'''
This file has been 100% copied from this PR to the Transformers library:
https://github.com/huggingface/transformers/pull/27557

Author: Saibo-creator
Author GitHub: https://github.com/Saibo-creator

All credits go to the author.
'''

import logging
import re
import time
from abc import ABC
from functools import lru_cache
from typing import Dict, List

import torch

from modules import shared

logger = logging.getLogger(__name__)


########################
# EBNF Grammar Parsing #
########################

END_OF_ALTERNATE_MARKER = 0
END_OF_RULE_MARKER = 0
TO_BE_FILLED_MARKER = 0
REF_RULE_MARKER = 1
LITERAL_MARKER = 2


class ParseState:
    def __init__(self):
        self.symbol_ids = {}
        self.grammar_encoding = []  # old name: out_grammar


def get_symbol_id(state, src):
    if src not in state.symbol_ids:
        state.symbol_ids[src] = len(state.symbol_ids)
    return state.symbol_ids[src]


def generate_symbol_id(state, base_name):
    next_id = len(state.symbol_ids)
    state.symbol_ids[base_name + "_" + str(next_id)] = next_id
    return next_id


def is_word_char(c):
    return c.isalnum() or c == "-" or c == "_"


def hex_to_int(c):
    if c.isdigit():
        return int(c)
    elif "a" <= c.lower() <= "f":
        return ord(c.lower()) - ord("a") + 10
    raise RuntimeError("unknown hex char " + c)


def remove_leading_white_space(src, newline_ok):
    """
    Skips over whitespace and comments in the input string.
    This function processes the input string, skipping over any spaces, tabs,
    and content following a '#' character, which denotes a comment. The parsing
    of a comment continues until the end of the line (denoted by newline characters
    '\r' or '\n'). If the 'newline_ok' parameter is set to False, the function
    will stop processing and return the remaining string upon encountering a
    newline character, otherwise it will skip over newline characters as well.
    Parameters:
    src (str): The input string to be processed.
    newline_ok (bool): A flag indicating whether encountering a newline character
                       should stop the parsing (False) or if it should be skipped (True).
    Returns:
    str: The remaining portion of the input string after skipping whitespace and comments.
    """
    pos = 0
    while pos < len(src) and (src[pos].isspace() or src[pos] == "#"):
        if src[pos] == "#":
            while pos < len(src) and src[pos] not in ("\r", "\n"):
                pos += 1
        else:
            if not newline_ok and src[pos] in ("\r", "\n"):
                break
            pos += 1
    return src[pos:]


def parse_name(src):
    pos = 0
    while pos < len(src) and is_word_char(src[pos]):
        pos += 1
    if pos == 0:
        raise RuntimeError("expecting name at " + src)
    return src[:pos], src[pos:]


def read_hex(s):
    val = 0
    for c in s:
        val = (val << 4) + hex_to_int(c)
    return chr(val)


def parse_char(src):
    """
    parse the leading char from the input string
    :param src:
    :return: char, remaining_src
    """

    # if we have a backslash, it's maybe an escape
    if src[0] == "\\":
        esc = src[1]
        if esc == "x":
            return read_hex(src[2:4]), src[4:]
        elif esc == "u":
            return read_hex(src[2:6]), src[6:]
        elif esc == "U":
            return read_hex(src[2:10]), src[10:]
        elif esc in ('"', "[", "]", "\\", "-"):
            return esc, src[2:]
        elif esc == "r":
            return "\r", src[2:]
        elif esc == "n":
            return "\n", src[2:]
        elif esc == "t":
            return "\t", src[2:]
        elif esc == "\\":
            return "\\", src[2:]
        raise RuntimeError("unknown escape at " + src)
    elif src:
        return src[0], src[1:]
    raise RuntimeError("unexpected end of input")


def parse_sequence(state, src, rule_name, outbuf, is_nested):
    out_start_pos = len(outbuf)

    # sequence size, will be replaced at end when known
    outbuf.append(TO_BE_FILLED_MARKER)

    last_sym_start = len(outbuf)
    remaining_src = src
    while remaining_src:
        if remaining_src[0] == '"':  # literal string
            remaining_src = remaining_src[1:]
            last_sym_start = len(outbuf)
            while remaining_src[0] != '"':
                char, remaining_src = parse_char(remaining_src)

                # each char of a literal is encoded as a "range" of char - char
                outbuf.append(LITERAL_MARKER)
                outbuf.append(ord(char))
                outbuf.append(ord(char))
            remaining_src = remove_leading_white_space(remaining_src[1:], is_nested)
        elif remaining_src[0] == "[":  # char range(s)
            remaining_src = remaining_src[1:]
            last_sym_start = len(outbuf)
            # num chars in range - replaced at end of loop
            outbuf.append(TO_BE_FILLED_MARKER)
            while remaining_src[0] != "]":
                char, remaining_src = parse_char(remaining_src)

                outbuf.append(ord(char))
                if remaining_src[0] == "-" and remaining_src[1] != "]":
                    endchar_pair, remaining_src = parse_char(remaining_src[1:])
                    outbuf.append(ord(endchar_pair))
                else:
                    # chars that aren't part of a c1-c2 range are just doubled (i.e., c-c)
                    outbuf.append(ord(char))
            # replace num chars with actual
            outbuf[last_sym_start] = len(outbuf) - last_sym_start - 1
            remaining_src = remove_leading_white_space(remaining_src[1:], is_nested)
        elif is_word_char(remaining_src[0]):  # rule reference
            name, remaining_src = parse_name(remaining_src)
            ref_rule_id = get_symbol_id(state, name)
            remaining_src = remove_leading_white_space(remaining_src, is_nested)
            last_sym_start = len(outbuf)
            outbuf.append(REF_RULE_MARKER)
            outbuf.append(ref_rule_id)
        elif remaining_src[0] == "(":  # grouping
            # parse nested alternates into synthesized rule
            remaining_src = remove_leading_white_space(remaining_src[1:], True)
            sub_rule_id = generate_symbol_id(state, rule_name)
            remaining_src = parse_alternates(state, remaining_src, rule_name, sub_rule_id, True)
            last_sym_start = len(outbuf)
            # output reference to synthesized rule
            outbuf.append(REF_RULE_MARKER)
            outbuf.append(sub_rule_id)
            if remaining_src[0] != ")":
                raise RuntimeError("expecting ')' at " + remaining_src)
            remaining_src = remove_leading_white_space(remaining_src[1:], is_nested)
        elif remaining_src[0] in ("*", "+", "?"):  # repetition operator
            if len(outbuf) - out_start_pos - 1 == 0:
                raise RuntimeError("expecting preceeding item to */+/? at " + remaining_src)
            out_grammar = state.grammar_encoding

            # apply transformation to previous symbol (last_sym_start -
            # end) according to rewrite rules:
            # S* --> S' ::= S S' |
            # S+ --> S' ::= S S' | S
            # S? --> S' ::= S |
            sub_rule_id = generate_symbol_id(state, rule_name)
            out_grammar.append(sub_rule_id)
            sub_rule_start = len(out_grammar)
            # placeholder for size of 1st alternate
            out_grammar.append(TO_BE_FILLED_MARKER)
            # add preceding symbol to generated rule
            out_grammar.extend(outbuf[last_sym_start:])
            if remaining_src[0] in ("*", "+"):
                # cause generated rule to recurse
                out_grammar.append(REF_RULE_MARKER)
                out_grammar.append(sub_rule_id)
            # apply actual size
            out_grammar[sub_rule_start] = len(out_grammar) - sub_rule_start
            # mark end of 1st alternate
            out_grammar.append(END_OF_ALTERNATE_MARKER)
            sub_rule_start = len(out_grammar)
            # placeholder for size of 2nd alternate
            out_grammar.append(TO_BE_FILLED_MARKER)
            if remaining_src[0] == "+":
                # add preceding symbol as alternate only for '+'
                out_grammar.extend(outbuf[last_sym_start:])
            # apply actual size of 2nd alternate
            out_grammar[sub_rule_start] = len(out_grammar) - sub_rule_start
            # mark end of 2nd alternate, then end of rule
            out_grammar.append(END_OF_ALTERNATE_MARKER)
            out_grammar.append(END_OF_RULE_MARKER)

            # in original rule, replace previous symbol with reference to generated rule
            outbuf[last_sym_start:] = [1, sub_rule_id]

            remaining_src = remove_leading_white_space(remaining_src[1:], is_nested)
        else:
            break
    # apply actual size of this alternate sequence
    outbuf[out_start_pos] = len(outbuf) - out_start_pos
    # mark end of alternate
    outbuf.append(END_OF_ALTERNATE_MARKER)
    return remaining_src


def parse_alternates(state, src, rule_name, rule_id, is_nested):
    outbuf = []
    remaining_src = parse_sequence(state, src, rule_name, outbuf, is_nested)
    while remaining_src and remaining_src[0] == "|":
        remaining_src = remove_leading_white_space(remaining_src[1:], True)
        remaining_src = parse_sequence(state, remaining_src, rule_name, outbuf, is_nested)

    state.grammar_encoding.append(rule_id)
    state.grammar_encoding.extend(outbuf)
    state.grammar_encoding.append(0)
    return remaining_src


def parse_rule(state, src):
    name, remaining_src = parse_name(src)
    remaining_src = remove_leading_white_space(remaining_src, False)
    rule_id = get_symbol_id(state, name)

    if remaining_src[:3] != "::=":
        raise RuntimeError("expecting ::= at " + remaining_src)
    remaining_src = remove_leading_white_space(remaining_src[3:], True)

    remaining_src = parse_alternates(state, remaining_src, name, rule_id, False)

    if remaining_src and remaining_src[0] == "\r":
        remaining_src = remaining_src[2:] if remaining_src[1] == "\n" else remaining_src[1:]
    elif remaining_src and remaining_src[0] == "\n":
        remaining_src = remaining_src[1:]
    elif remaining_src:
        raise RuntimeError("expecting newline or end at " + remaining_src)
    return remove_leading_white_space(remaining_src, True)


def parse_ebnf(src):
    try:
        state = ParseState()
        grammar_repr = remove_leading_white_space(src, True)
        last_grammar_repr = ""
        while grammar_repr:
            if last_grammar_repr:
                last_parsed_rule_len = len(last_grammar_repr) - len(grammar_repr)
                logger.debug(f"last_parsed_rule: {last_grammar_repr[:last_parsed_rule_len]}")
            last_grammar_repr = grammar_repr
            grammar_repr = parse_rule(state, grammar_repr)
        state.grammar_encoding.append(0xFFFF)
        return state
    except RuntimeError as err:
        logger.warning("error parsing grammar:", err)
        return ParseState()


def print_rule(file, grammar_encoding, index, symbol_id_names):
    rule_id = grammar_encoding[index]
    print(f"<{index}>{symbol_id_names[rule_id]} ::=", end=" ", file=file)
    pos = index + 1
    while grammar_encoding[pos]:
        if pos - 1 > index:
            print("|", end=" ", file=file)
        pos += 1  # sequence size, not needed here
        while grammar_encoding[pos]:
            if grammar_encoding[pos] == REF_RULE_MARKER:
                ref_rule_id = grammar_encoding[pos + 1]
                print(
                    f"<{pos}>{symbol_id_names[ref_rule_id]}",
                    end=" ",
                    file=file,
                )
                pos += 2
            else:
                print("<{}>[".format(pos), end="", file=file)
                num_chars = grammar_encoding[pos]
                pos += 1

                for i in range(0, num_chars, 2):
                    print("{}-".format(chr(grammar_encoding[pos + i])), end="", file=file)
                    if i + 1 < num_chars:
                        print("{}".format(chr(grammar_encoding[pos + i + 1])), end="", file=file)
                print("]", end=" ", file=file)
                pos += num_chars
        pos += 1
    print(file=file)
    return pos + 1


def print_grammar(file, state):
    pos = 0
    symbol_id_names = {v: k for k, v in state.symbol_ids.items()}
    print("Grammar Rules:", file=file)

    while state.grammar_encoding[pos] != 0xFFFF:
        pos = print_rule(file, state.grammar_encoding, pos, symbol_id_names)
    pos = 0
    print("\nBinary representation:", file=file)
    while state.grammar_encoding[pos] != 0xFFFF:
        print(f"{state.grammar_encoding[pos]:04x}", end=" ", file=file)
        pos += 1
    print("ffff\n")


###################################
# EBNF Grammar Parsing ends here  #
###################################


class GrammarConstraint(ABC):
    def __init__(self, grammar_str, start_rule_name, tokenizer):
        self.tt = 0
        self.nt = 0
        state = parse_ebnf(grammar_str)
        grammar_encoding = state.grammar_encoding
        self.start_rule_id = state.symbol_ids.get(start_rule_name)

        self.eos_token_id = tokenizer.eos_token_id
        self.token_trie = TokenTrie(tokenizer)
        self.tokenizer = tokenizer
        self.grammar_encoding = grammar_encoding

        pos = 0
        rules: Dict[int, int] = {}

        while grammar_encoding[pos] != 0xFFFF:
            rule_id = grammar_encoding[pos]

            # Store the current position in the 'rules' list at the index corresponding to rule_id.
            # This effectively maps each rule_id to its position in the grammar encoding.
            rules[rule_id] = pos
            pos += 1

            # Continue to the next rule in the encoding.
            # The loop advances by the size indicated at the current position (grammar_encoding[pos])
            # plus one for the size field itself.
            while grammar_encoding[pos]:
                pos += 1 + grammar_encoding[pos]
            # Now we're at the end of the rule,
            # so advance to the next rule by skipping the 0, which means 'end of rule'.
            pos += 1

        self.start_rule_pos = rules[self.start_rule_id]
        self.rules_pos_dict: Dict[int, int] = rules

    def init_stacks(self):
        # suppose the start rule position is 0, then grammar_encoding[0] = rule_id
        # grammar_encoding[1] = rule_size
        # grammar_encoding[2] = rule_type
        # this is why we need to add 2 to the start rule position
        stack = [self.start_rule_pos + 2]
        # convert to tuple for caching(immutable)
        return self.advance_stack(tuple(stack))

    # For each stack, resolve rules to find the actual characters that are
    # accepted by this stack (not the set of sub-rules).
    # This is where the parsing happens.
    # The parsing is a top-down, left-to-right, depth-first traversal of the
    # grammar.
    @lru_cache(maxsize=32768)
    def advance_stack(self, stack):
        stack = list(stack)
        # If the stack is empty, we're done. Because no more tokens should be accepted.
        if len(stack) == 0:
            return [stack]

        # Get the top of the stack.
        pos = stack[-1]

        # If the stack head is a terminal(literal), we can resolve it immediately.
        # literal is marked with 2 in the grammar encoding.
        if self.grammar_encoding[pos] > 1:
            return [stack]

        # The stack head is a nonterminal (a rule reference, 1 in the grammar encoding).
        # Resolving this rule gives a set of one or more possible positions
        # (e.g. two in `a ::= b | c`)
        # We pop the current rule off the stack and, for each option, push:
        # - the symbol following this symbol in the current rule; then
        # - the first symbol of the resolved rule.
        referenced_rule_id = self.grammar_encoding[pos + 1]

        # subpos should points to the size of the subrule
        subpos = self.rules_pos_dict[referenced_rule_id] + 1
        stacks: List[List[int]] = []

        # do depth-first search to find all possible rules and check the next terminal
        # When this value is non-zero, it indicates that subpos is not yet at the end of the rule, so we can continue.
        # here subpos is a pointer, and the value in the rule encoding can never be 0 except for the end of the rule.
        while self.grammar_encoding[subpos]:
            new_stack = stack[:-1]
            if self.grammar_encoding[pos + 2]:
                # check if there is a next symbol in the current rule, e.g. `a ::= b c | d`
                # if yes, push the pos to rule_size to the stack
                new_stack.append(pos + 2)

            # if the type of the next symbol is not "empty", push the first symbol of the resolved rule to the stack
            if self.grammar_encoding[subpos + 1]:
                new_stack.append(subpos + 1)
            stacks.extend(self.advance_stack(tuple(new_stack)))
            # The increment subpos += self.grammar_encoding[subpos] + 1
            # moves subpos forward in the grammar encoding array to the next alternative in the current rule.
            subpos += self.grammar_encoding[subpos] + 1
        return stacks

    def accept_char(self, *args, **kwargs):
        """Process a byte according to the grammar rules."""
        raise NotImplementedError

    def accept_token_id(self, *args, **kwargs):
        """Process a token according to the grammar rules."""
        raise NotImplementedError

    def filter_vocab(self, *args, **kwargs):
        raise NotImplementedError


class IncrementalGrammarConstraint(GrammarConstraint):
    def __init__(self, grammar_str, start_rule_name, tokenizer):
        super().__init__(grammar_str, start_rule_name, tokenizer)

    def accept_char(self, char, stacks):
        byte = ord(char)
        new_stacks = []
        for stack in stacks:
            # stack is empty
            if not stack:
                continue

            pos = stack[-1]
            num_chars = self.grammar_encoding[pos]

            # to make pos point to the size of the char range rule
            pos += 1
            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 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)
            new_stacks.extend(self.advance_stack(tuple(new_stack)))

        return new_stacks

    def accept_string(self, string: str, stacks: List[List[int]]):
        for char in string:
            stacks = self.accept_char(char, stacks)
        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, char):
        byte = ord(char)
        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]
            if byte >= start and byte <= end:
                return True
            if byte <= start and byte >= end:
                return True
        return False

    # 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 token

        else:
            print("Warning: unrecognized tokenizer: using default token formatting")

            def fmt_token(id):
                token = tokenizer.convert_ids_to_tokens(id)
                return token

        # 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)