Better HF grammar implementation (#4953)

grammars/japanese.gbnf

@@ -1,7 +0,0 @@
-# A probably incorrect grammar for Japanese
-root        ::= jp-char+ ([ \t\n] jp-char+)*
-jp-char     ::= hiragana | katakana | punctuation | cjk
-hiragana    ::= [ぁ-ゟ]
-katakana    ::= [ァ-ヿ]
-punctuation ::= [、-〾]
-cjk         ::= [一-鿿]

grammars/json.gbnf

@@ -1,25 +1,14 @@
 root   ::= object
+
+object ::= "{" ws ( string ":" ws value ("," ws string ":" ws value)* )? "}"
+
 value  ::= object | array | string | number | ("true" | "false" | "null") ws
 
-object ::=
+array  ::= "[" ws ( value ("," ws value)* )? "]" ws
-  "{" ws (
-            string ":" ws value
-    ("," ws string ":" ws value)*
-  )? "}" ws
 
-array  ::=
+string ::= "\"" ( [a-zA-Z0-9] )* "\"" ws
-  "[" ws (
-            value
-    ("," ws value)*
-  )? "]" ws
-
-string ::=
-  "\"" (
-    [^"\\] |
-    "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes
-  )* "\"" ws
 
 number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws
 
-# Optional space: by convention, applied in this grammar after literal chars when allowed
+
 ws ::= ([ \t\n] ws)?

grammars/json_arr.gbnf

@@ -1,34 +0,0 @@
-# This is the same as json.gbnf but we restrict whitespaces at the end of the root array
-# Useful for generating JSON arrays
-
-root   ::= arr
-value  ::= object | array | string | number | ("true" | "false" | "null") ws
-
-arr  ::=
-  "[\n" ws (
-            value
-    (",\n" ws value)*
-  )? "]"
-
-object ::=
-  "{" ws (
-            string ":" ws value
-    ("," ws string ":" ws value)*
-  )? "}" ws
-
-array  ::=
-  "[" ws (
-            value
-    ("," ws value)*
-  )? "]" ws
-
-string ::=
-  "\"" (
-    [^"\\] |
-    "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes
-  )* "\"" ws
-
-number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws
-
-# Optional space: by convention, applied in this grammar after literal chars when allowed
-ws ::= ([ \t\n] ws)?

grammars/json_w_trailing_space.gbnf

@@ -0,0 +1,14 @@
+root   ::= object
+
+object ::= "{" ws ( string ":" ws value ("," ws string ":" ws value)* )? "}" ws
+
+value  ::= object | array | string | number | ("true" | "false" | "null") ws
+
+array  ::= "[" ws ( value ("," ws value)* )? "]" ws
+
+string ::= "\"" ( [a-zA-Z0-9] )* "\"" ws
+
+number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws
+
+
+ws ::= ([ \t\n] ws)?

grammars/list.gbnf

@@ -1,4 +1,2 @@
-root ::= item+
+root       ::= "1. " paragraph "\n" ([0-9] [0-9]? ". " paragraph "\n")+
-
+paragraph  ::= [a-zA-Z'.,; ]+
-# Excludes various line break characters
-item ::= "- " [^\r\n\x0b\x0c\x85\u2028\u2029]+ "\n"

grammars/simple_arithmetic.gbnf

@@ -0,0 +1,7 @@
+root  ::= (expr "=" ws term "\n")+
+expr  ::= term ([-+*/] term)*
+term  ::= num | "(" ws expr ")" ws
+num   ::= [0-9]+ ws
+ws    ::= [ \t\n]*
+# this is a comment
+

modules/grammar.py

@@ -1,33 +0,0 @@
-from torch_grammar import GrammarSampler
-from transformers.generation.logits_process import LogitsProcessor
-
-from modules import shared
-
-sampler = None
-grammar = None
-grammar_string = ''
-
-
-class GrammarLogitsProcessor(LogitsProcessor):
-    def __init__(self, string):
-
-        global sampler, grammar, grammar_string
-
-        if string != grammar_string:
-            grammar_string = string
-            if string.strip() != '':
-                string = string.strip() + '\n'
-                sampler = GrammarSampler(string, 'root', shared.tokenizer)
-            else:
-                sampler = None
-
-        if sampler is not None:
-            grammar = sampler.logits_processor()
-        else:
-            grammar = None
-
-    def __call__(self, input_ids, scores):
-        if grammar is not None:
-            scores = grammar(input_ids, scores)
-
-        return scores

modules/grammar/grammar_utils.py

@@ -0,0 +1,687 @@
+'''
+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
+    return -1
+
+
+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 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":
+            first = hex_to_int(src[2])
+            if first > -1:
+                second = hex_to_int(src[3])
+                if second > -1:
+                    return (first << 4) + second, src[4:]
+            raise RuntimeError("expecting \\xNN at " + src)
+        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:]
+        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, byte, stacks):
+        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 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]]):
+        _bytes = bytes(string, "utf-8")
+        for byte in _bytes:
+            stacks = self.accept_char(byte, 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):
+        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)

modules/grammar/logits_process.py

@@ -0,0 +1,104 @@
+'''
+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 math
+
+import torch
+from transformers.generation.logits_process import LogitsProcessor
+from transformers.utils import add_start_docstrings
+
+LOGITS_PROCESSOR_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. [What are input IDs?](../glossary#input-ids)
+        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
+            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
+            search or log softmax for each vocabulary token when using beam search
+
+    Return:
+        `torch.FloatTensor` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.
+
+"""
+
+
+class GrammarConstrainedLogitsProcessor(LogitsProcessor):
+    def __init__(self, grammar_constraint):
+        self.last_size = None
+        self.grammar_constraint = grammar_constraint
+        self.batch_stacks = None
+
+    def filter_logits(self, logits, device):
+        # resolve each stack to a tensor of True/False for each token
+        # indicating acceptance
+        # acceptance = self.grammar_acceptor.filter_vocab(self.stacks, device)
+        acceptance = self.grammar_constraint.batch_filter_vocab(self.batch_stacks, device)
+        # logger.debug(acceptance)
+        # Logits to -inf where False
+        logits[~acceptance] = -math.inf
+
+    # TODO: batching
+    def process_logits(self, input_ids, scores, parse_start_index=None):
+        """
+        :param input_ids:
+        :param scores:
+        :param parse_start_index: default None, which means generate from scratch. Set to 0 to parse all input_ids
+        :return:
+        """
+        # we dynamically create stacks at the first call, so that we know the batch size and beam size
+        if self.batch_stacks is None:
+            self.batch_stacks = [self.grammar_constraint.init_stacks() for _ in range(len(input_ids))]
+
+        # if self.last_size is not set (which would be the case when processing the first token).
+        # In this case, do nothing.
+        if self.last_size is None:
+            prefix_to_parse = [
+                single_input_ids[parse_start_index:] if parse_start_index is not None else []
+                for single_input_ids in input_ids
+            ]
+            # self.grammar_acceptor.accept_token_ids(prefix_to_parse, self.stacks)
+            self.batch_stacks = [
+                self.grammar_constraint.accept_token_ids(prefix, stack)
+                for prefix, stack in zip(prefix_to_parse, self.batch_stacks)
+            ]
+        #  if the length of the current input IDs (input_ids[0]) is exactly one more than self.last_size.
+        #  This is expected in a scenario where inputs are processed incrementally, one token at a time.
+        elif len(input_ids[0]) == self.last_size + 1:
+            # self.stacks = self.grammar_acceptor.accept_token_id(input_ids[0][-1], self.stacks)
+            self.batch_stacks = [
+                self.grammar_constraint.accept_token_id(single_input_ids[-1], stack)
+                for single_input_ids, stack in zip(input_ids, self.batch_stacks)
+            ]
+        #  ensure that the input size is consistent with the expected incremental processing
+        #  (i.e., one token at a time).
+        else:
+            # here we check if the input_ids are one token longer than the last time we processed
+            # but we don't check if input_ids are actually valid.
+            # Imagine a scenario where we generate 10 tokens, then we replace the 10 generated tokens with 10 new tokens.
+            # In this case, the input_ids will be consistent with the last_size, but the input_ids are not valid.
+            # However, should we really check if the input_ids are valid here?
+            # If we do, then we need to reparse the whole input_ids at each call, which is not efficient.
+            # Maybe we should just trust the user to provide valid input_ids?
+            # The conclusion is that, we assume the input_ids are valid, and our generation will be correct.
+            # If the input_ids are not valid, then the generation result will be wrong and we don't take responsibility for that.
+            raise RuntimeError(
+                "Input ID's length is inconsistent with the current state of "
+                "the GrammarConstrainedLogitsProcessor. If you want to process "
+                "another input sequence, please instantiate a new "
+                "GrammarConstrainedLogitsProcessor."
+            )
+
+        self.filter_logits(scores, scores.device)
+
+        self.last_size = len(input_ids[0])
+        return scores
+
+    @add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
+        return self.process_logits(input_ids, scores)

modules/text_generation.py

@@ -18,7 +18,8 @@ from modules.callbacks import (
     _StopEverythingStoppingCriteria
 )
 from modules.extensions import apply_extensions
-from modules.grammar import GrammarLogitsProcessor
+from modules.grammar.grammar_utils import initialize_grammar
+from modules.grammar.logits_process import GrammarConstrainedLogitsProcessor
 from modules.html_generator import generate_4chan_html, generate_basic_html
 from modules.logging_colors import logger
 from modules.models import clear_torch_cache, local_rank
@@ -317,11 +318,17 @@ def generate_reply_HF(question, original_question, seed, state, stopping_strings
     generate_params['stopping_criteria'] = transformers.StoppingCriteriaList()
     generate_params['stopping_criteria'].append(_StopEverythingStoppingCriteria())
 
+    # Logits processor
     processor = state.get('logits_processor', LogitsProcessorList([]))
-    # In case a processor is passed by itself.
     if not isinstance(processor, LogitsProcessorList):
         processor = LogitsProcessorList([processor])
-    processor.append(GrammarLogitsProcessor(state['grammar_string']))
+
+    # Grammar
+    if state['grammar_string'].strip() != '':
+        grammar = initialize_grammar(state['grammar_string'])
+        grammar_processor = GrammarConstrainedLogitsProcessor(grammar)
+        processor.append(grammar_processor)
+
     apply_extensions('logits_processor', processor, input_ids)
     generate_params['logits_processor'] = processor
 

requirements.txt

@@ -20,8 +20,6 @@ transformers==4.36.*
 tqdm
 wandb
 
-git+https://github.com/oobabooga/torch-grammar.git
-
 # bitsandbytes
 bitsandbytes==0.41.1; platform_system != "Windows"
 https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.1-py3-none-win_amd64.whl; platform_system == "Windows"

requirements_amd.txt

@@ -20,8 +20,6 @@ transformers==4.36.*
 tqdm
 wandb
 
-git+https://github.com/oobabooga/torch-grammar.git
-
 # bitsandbytes
 bitsandbytes==0.38.1; platform_system != "Windows"
 https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.38.1-py3-none-win_amd64.whl; platform_system == "Windows"

requirements_amd_noavx2.txt

@@ -20,8 +20,6 @@ transformers==4.36.*
 tqdm
 wandb
 
-git+https://github.com/oobabooga/torch-grammar.git
-
 # bitsandbytes
 bitsandbytes==0.38.1; platform_system != "Windows"
 https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.38.1-py3-none-win_amd64.whl; platform_system == "Windows"

requirements_apple_intel.txt

@@ -20,8 +20,6 @@ transformers==4.36.*
 tqdm
 wandb
 
-git+https://github.com/oobabooga/torch-grammar.git
-
 # bitsandbytes
 bitsandbytes==0.41.1; platform_system != "Windows"
 https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.1-py3-none-win_amd64.whl; platform_system == "Windows"

requirements_apple_silicon.txt

@@ -20,8 +20,6 @@ transformers==4.36.*
 tqdm
 wandb
 
-git+https://github.com/oobabooga/torch-grammar.git
-
 # bitsandbytes
 bitsandbytes==0.41.1; platform_system != "Windows"
 https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.1-py3-none-win_amd64.whl; platform_system == "Windows"

requirements_cpu_only.txt

@@ -20,8 +20,6 @@ transformers==4.36.*
 tqdm
 wandb
 
-git+https://github.com/oobabooga/torch-grammar.git
-
 # bitsandbytes
 bitsandbytes==0.41.1; platform_system != "Windows"
 https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.1-py3-none-win_amd64.whl; platform_system == "Windows"

requirements_cpu_only_noavx2.txt

@@ -20,8 +20,6 @@ transformers==4.36.*
 tqdm
 wandb
 
-git+https://github.com/oobabooga/torch-grammar.git
-
 # bitsandbytes
 bitsandbytes==0.41.1; platform_system != "Windows"
 https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.1-py3-none-win_amd64.whl; platform_system == "Windows"

requirements_noavx2.txt

@@ -20,8 +20,6 @@ transformers==4.36.*
 tqdm
 wandb
 
-git+https://github.com/oobabooga/torch-grammar.git
-
 # bitsandbytes
 bitsandbytes==0.41.1; platform_system != "Windows"
 https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.1-py3-none-win_amd64.whl; platform_system == "Windows"

requirements_nowheels.txt

@@ -20,8 +20,6 @@ transformers==4.36.*
 tqdm
 wandb
 
-git+https://github.com/oobabooga/torch-grammar.git
-
 # bitsandbytes
 bitsandbytes==0.41.1; platform_system != "Windows"
 https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.1-py3-none-win_amd64.whl; platform_system == "Windows"