├── parserllm ├── __init__.py └── parserllm.py ├── .gitignore ├── pyproject.toml ├── LICENSE ├── examples ├── example_interactive.py └── example.py ├── README.md └── poetry.lock /parserllm/__init__.py: -------------------------------------------------------------------------------- 1 | from parserllm.parserllm import complete_cf -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | env 2 | .venv 3 | .ruff_cache 4 | dist 5 | *.egg-info 6 | **/__pycache__ -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [tool.poetry] 2 | name = "parserllm" 3 | version = "0.0.1" 4 | description = "Force LLMs to use a specific context-free grammar in completions" 5 | authors = ["Matt Rickard "] 6 | maintainers = ["Matt Rickard "] 7 | readme = "README.md" 8 | license = "MIT" 9 | packages = [{include = "parserllm"}] 10 | keywords = [ 11 | "llm", 12 | "ai", 13 | "prompt", 14 | "large language models", 15 | "gpt-3", 16 | "chatgpt", 17 | ] 18 | urls = { repository = "https://github.com/r2d4/parserllm" } 19 | 20 | [tool.poetry.dependencies] 21 | python = "^3.10" 22 | rellm = "0.0.5" 23 | lark = "^1.1.5" 24 | transformers = "^4.28.1" 25 | regex = "^2023.5.5" 26 | 27 | 28 | [build-system] 29 | requires = ["poetry-core"] 30 | build-backend = "poetry.core.masonry.api" 31 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2023 Matt Rickard 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /examples/example_interactive.py: -------------------------------------------------------------------------------- 1 | import sys 2 | from pathlib import Path 3 | 4 | from lark import Lark 5 | from transformers import AutoModelForCausalLM, AutoTokenizer 6 | 7 | sys.path.append(str(Path(__file__).resolve().parent.parent)) 8 | 9 | from parserllm import complete_cf # noqa: E402 10 | 11 | # model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-3b") 12 | # tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-3b") 13 | 14 | model = AutoModelForCausalLM.from_pretrained("distilgpt2", trust_remote_code=True) 15 | tokenizer = AutoTokenizer.from_pretrained("distilgpt2", trust_remote_code=True) 16 | 17 | json_grammar = r""" 18 | ?start: value 19 | 20 | ?value: object 21 | | array 22 | | string 23 | | "true" -> true 24 | | "false" -> false 25 | | "null" -> null 26 | 27 | array : "[" [value ("," value)*] "]" 28 | object : "{" [pair ("," pair)*] "}" 29 | pair : string ":" value 30 | 31 | string : ESCAPED_STRING 32 | 33 | %import common.ESCAPED_STRING 34 | %import common.SIGNED_NUMBER 35 | %import common.WS 36 | 37 | %ignore WS 38 | """ 39 | 40 | 41 | ### Create the JSON parser with Lark, using the LALR algorithm 42 | json_parser = Lark(json_grammar, parser='lalr', 43 | # Using the basic lexer isn't required, and isn't usually recommended. 44 | # But, it's good enough for JSON, and it's slightly faster. 45 | lexer='basic', 46 | # Disabling propagate_positions and placeholders slightly improves speed 47 | propagate_positions=False, 48 | maybe_placeholders=False, 49 | regex=True) 50 | 51 | prompt = "This is a valid json string\n" 52 | print(complete_cf(prompt, json_parser, "", 53 | tokenizer, 54 | model, 55 | max_new_tokens=25, 56 | debug=True)) -------------------------------------------------------------------------------- /examples/example.py: -------------------------------------------------------------------------------- 1 | 2 | import sys 3 | from pathlib import Path 4 | 5 | from lark import Lark 6 | from transformers import AutoModelForCausalLM, AutoTokenizer 7 | 8 | sys.path.append(str(Path(__file__).resolve().parent.parent)) 9 | 10 | from parserllm import complete_cf # noqa: E402 11 | 12 | model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-3b") 13 | tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-3b") 14 | 15 | # model = AutoModelForCausalLM.from_pretrained("distilgpt2", trust_remote_code=True) 16 | # tokenizer = AutoTokenizer.from_pretrained("distilgpt2", trust_remote_code=True) 17 | 18 | json_grammar = r""" 19 | ?start: value 20 | 21 | ?value: object 22 | | array 23 | | string 24 | | "true" -> true 25 | | "false" -> false 26 | | "null" -> null 27 | 28 | array : "[" [value ("," value)*] "]" 29 | object : "{" [pair ("," pair)*] "}" 30 | pair : string ":" value 31 | 32 | string : ESCAPED_STRING 33 | 34 | %import common.ESCAPED_STRING 35 | %import common.SIGNED_NUMBER 36 | %import common.WS 37 | 38 | %ignore WS 39 | """ 40 | 41 | 42 | ### Create the JSON parser with Lark, using the LALR algorithm 43 | json_parser = Lark(json_grammar, parser='lalr', 44 | # Using the basic lexer isn't required, and isn't usually recommended. 45 | # But, it's good enough for JSON, and it's slightly faster. 46 | lexer='basic', 47 | # Disabling propagate_positions and placeholders slightly improves speed 48 | propagate_positions=False, 49 | maybe_placeholders=False, 50 | regex=True) 51 | 52 | prompt = "Write the first three letters of the alphabet in valid JSON format\n" 53 | print(complete_cf(prompt, json_parser, "", 54 | tokenizer, 55 | model, 56 | max_new_tokens=15, 57 | debug=True)) 58 | 59 | print("regular\n", ' '.join(tokenizer.batch_decode(model.generate(tokenizer.encode(prompt, return_tensors="pt"), 60 | max_new_tokens=30, 61 | pad_token_id=tokenizer.eos_token_id, 62 | )))) -------------------------------------------------------------------------------- /parserllm/parserllm.py: -------------------------------------------------------------------------------- 1 | 2 | import regex 3 | from lark import UnexpectedInput, Lark 4 | from rellm import complete_re 5 | from transformers import PreTrainedModel, PreTrainedTokenizer 6 | 7 | 8 | def extract_terminal_regex(parser, stop_token): 9 | regex_map = {} 10 | for term in parser.terminals: 11 | if term.pattern: 12 | regex_map[term.name] = regex.compile(term.pattern.to_regexp()) 13 | 14 | regex_map['$END'] = regex.compile(stop_token) 15 | return regex_map 16 | 17 | class ParserState(): 18 | def __init__(self, parser:Lark): 19 | self.parser:Lark = parser 20 | self.partial_token = "" 21 | 22 | def next_lex(self, input_str): 23 | try: 24 | self.parser.parse(input_str) 25 | except UnexpectedInput as e: 26 | # Assuming that self.parser is always LALR 27 | interactive = self.parser.parse_interactive(input_str) 28 | interactive.exhaust_lexer() 29 | return interactive.accepts() 30 | 31 | return [] 32 | 33 | def complete_cf(prompt:str, parser, partial_completion, tokenizer: PreTrainedTokenizer, 34 | model: PreTrainedModel, max_new_tokens: int = 3, 35 | debug: bool = False, 36 | **model_kwargs): 37 | """ 38 | Complete a prompt with a regex pattern. 39 | """ 40 | gen_tokens = 0 41 | prompt_plus_completion = prompt + partial_completion 42 | 43 | terminal_regexes = extract_terminal_regex(parser, tokenizer.decode(tokenizer.eos_token_id)) 44 | parser_state = ParserState(parser ) 45 | 46 | while gen_tokens < max_new_tokens: 47 | valid_next_lex = parser_state.next_lex(partial_completion) 48 | if len(valid_next_lex) == 0 or (len(valid_next_lex) == 1 and '$END' in valid_next_lex): 49 | break 50 | r = [terminal_regexes[t] for t in valid_next_lex] 51 | if debug:print(f"valid next token: {r}") 52 | next_token_completion = complete_re(prompt_plus_completion, r, tokenizer, model, max_new_tokens=max_new_tokens, stop_after_match=True, debug=debug, **model_kwargs) 53 | partial_completion += next_token_completion 54 | prompt_plus_completion = prompt_plus_completion + next_token_completion 55 | gen_tokens += 1 56 | 57 | return partial_completion 58 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # parserLLM 2 | 3 | Use a context-free grammar and a parser generator to determine valid next tokens for an LLM generation. 4 | 5 | Extending [ReLLM](https://github.com/r2d4/rellm) to handle context-free grammars in addition to regular expressions. 6 | 7 | *If you are looking to use this library in production, check out the Context-Free Grammar Completion API from the [Thiggle API](https://docs.thiggle.com/#context-free-grammar-completion-api)* 8 | 9 | 10 | ### Usage 11 | 12 | ```bash 13 | pip install parserllm 14 | ``` 15 | 16 | See [examples/example.py](examples/example.py) for an example of how to use this library. 17 | 18 | Run it with `python examples/example.py`. 19 | 20 | ### How does it work? 21 | 22 | See this [ post](https://matt-rickard.com/context-free-grammar-parsing-with-llms) for a more in-depth explanation. 23 | 24 | The general strategy goes like this: 25 | 26 | First, define a context-free grammar. You might use this for a simplified version of JSON (in EBNF form): 27 | ```ebnf 28 | ?start: value 29 | 30 | ?value: object 31 | 32 | | array 33 | 34 | | string 35 | 36 | | "true" -> true 37 | 38 | | "false" -> false 39 | 40 | | "null" -> null 41 | 42 | array : "[" [value ("," value)*] "]" 43 | 44 | object : "{" [pair ("," pair)*] "}" 45 | 46 | pair : string ":" value 47 | 48 | string : ESCAPED_STRING 49 | 50 | %import common.ESCAPED_STRING 51 | 52 | %import common.SIGNED_NUMBER 53 | 54 | %import common.WS 55 | 56 | %ignore WS 57 | ``` 58 | Next, to practically support multiple CFGs, use a parser generator to parse the language. This library uses Lark, simply because it’s written in Python and fairly easy to use. 59 | 60 | Next, run the partial output through the parser generator. At step zero, this is just the empty string. The parser will return all of the possible next tokens. You can see the valid first completion of this grammar is any “value,” which can be an array, string, true/false, or null. This means the valid starting tokens are `{`, `[`, `”`, `true`, `false`, and `null`. 61 | 62 | Ncompile those tokens to their regular expressions. Now we have an equivalent problem to [ReLLM](https://github.com/r2d4/rellm). Simply run the regexps through ReLLM to generate the next possible token. ReLLM will squash the logits of the non-matching characters and the LLM will only consider valid partial or full next tokens. 63 | 64 | Iterate until max tokens are reached, or the parser sees only an empty string or stop token as the next token. 65 | 66 | Some interesting features: 67 | 68 | - You can describe the syntax of most programming and configuration languages as a CFG. 69 | - The LLM won’t produce an invalid result, but there’s no guarantee it will finish and produce a stop token. -------------------------------------------------------------------------------- /poetry.lock: -------------------------------------------------------------------------------- 1 | # This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. 2 | 3 | [[package]] 4 | name = "certifi" 5 | version = "2024.2.2" 6 | description = "Python package for providing Mozilla's CA Bundle." 7 | optional = false 8 | python-versions = ">=3.6" 9 | files = [ 10 | {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"}, 11 | {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"}, 12 | ] 13 | 14 | [[package]] 15 | name = "charset-normalizer" 16 | version = "3.3.2" 17 | description = "The Real First Universal Charset Detector. 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