332 lines
15 KiB
Python
332 lines
15 KiB
Python
from enum import Enum
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import jinja2
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import json
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from pathlib import Path
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import random
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import re
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import sys
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from typing import Optional, Tuple, Callable
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from typeguard import typechecked
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from examples.json_schema_to_grammar import SchemaConverter
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from examples.openai.api import Tool, Message, FunctionCall, ToolCall
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from examples.openai.gguf_kvs import GGUFKeyValues, Keys
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from examples.openai.ts_converter import SchemaToTypeScriptConverter
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@typechecked
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def raise_exception(msg: str):
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raise Exception(msg)
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@typechecked
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class ChatFormat:
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def __init__(self, template: str, eos_token: str, bos_token: str):
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env = jinja2.Environment(loader=jinja2.BaseLoader(), trim_blocks=True, lstrip_blocks=True)
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self.template = env.from_string(template)
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self.eos_token = eos_token
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self.bos_token = bos_token
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self.strict_user_assistant_alternation = "{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception" in template
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if "<|recipient|>' + tool_call['function']['name']" in template:
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self.tool_style = ToolsPromptStyle.TYPESCRIPT_FUNCTIONARY_V2
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else:
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self.tool_style = ToolsPromptStyle.TOOLS_LONG
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def __str__(self):
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return f"ChatFormat(template={self.template}, eos_token={self.eos_token}, bos_token={self.bos_token})"
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def add_system_prompt(self, messages: list[Message], system_prompt: Message) -> list[Message]:
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assert system_prompt.role == "system"
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# TODO: add to last system message, or create a new one just before the last user message
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system_message = next(((i, m) for i, m in enumerate(messages) if m.role == "system"), None)
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if system_message is not None:
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(i, m) = system_message
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return messages[:i] + [Message(role="system", content=m.content + '\n' + system_prompt.content)] + messages[i+1:]
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else:
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return [system_prompt] + messages
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@staticmethod
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def from_gguf(metadata: GGUFKeyValues):
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tokens = metadata[Keys.Tokenizer.LIST]
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return ChatFormat(
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template = metadata[Keys.Tokenizer.CHAT_TEMPLATE],
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bos_token = tokens[metadata[Keys.Tokenizer.BOS_ID]],
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eos_token = tokens[metadata[Keys.Tokenizer.EOS_ID]])
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def render(self, messages: list[Message], add_generation_prompt: bool, omit_bos: bool = False):
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if self.strict_user_assistant_alternation and any(m.role not in ('user', 'assistant') for m in messages):
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new_messages=[]
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i = 0
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n = len(messages)
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while i < n:
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if messages[i].role == 'system':
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assert messages[i+1].role == 'user'
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new_messages.append(Message(
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role="user",
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content=f'[SYS]{messages[i].content}[/SYS]\n{messages[i+1].content}'))
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i += 2
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else:
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new_messages.append(messages[i])
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i += 1
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# print(f'new_messages={json.dumps(new_messages, indent=2)}')
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messages = new_messages
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# print(f'messages={messages}')
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return self.template.render(
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messages=messages,
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eos_token=self.eos_token,
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bos_token='' if omit_bos else self.bos_token,
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raise_exception=raise_exception,
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add_generation_prompt=add_generation_prompt,
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)
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# While the API will be usable with a generic tools usage like OpenAI,
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# (see https://cookbook.openai.com/examples/how_to_call_functions_with_chat_models),
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# each model may need specific prompting (and/or constrained output,
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# especially for models not fine-tuned for tool usage / function calling).
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class ToolsPromptStyle(Enum):
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# Short prompt w/ <tools>schemas</tools>
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TOOLS_SHORT = 1
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# Longer prompt w/ <tools>schemas</tools>
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TOOLS_LONG = 2
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# Large prompt for https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B
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# Requires:
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# - git clone https://github.com/NousResearch/Hermes-Function-Calling examples/openai/hermes_function_calling
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# - Set large context length as their prompts are super long
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TOOLS_HERMES_2_PRO = 3
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# Short prompt w/ TypeScript definitions for https://github.com/MeetKai/functionary
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# https://github.com/MeetKai/functionary/blob/main/functionary/prompt_template/prompt_template_v2.py
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# Note: see this prior attempt to support Functionary: https://github.com/ggerganov/llama.cpp/pull/5695
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TYPESCRIPT_FUNCTIONARY_V2 = 4
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@typechecked
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def make_tools_prompt(chat_format: ChatFormat, tools: list[Tool], indent=2) -> Message:
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if chat_format.tool_style == ToolsPromptStyle.TOOLS_SHORT:
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return Message(
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role="system",
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content='\n'.join([
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'Here are the tools available:',
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'<tools>',
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*(json.dumps(tool.model_dump(), indent=indent) for tool in tools),
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'</tools>',
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])
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)
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elif chat_format.tool_style == ToolsPromptStyle.TOOLS_LONG:
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return Message(
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role="system",
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content='\n'.join([
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'''You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags.''',
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'''You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools:''',
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'''<tools>''',
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*(json.dumps(tool.model_dump(), indent=indent) for tool in tools),
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'''</tools>''',
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'',
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'''Use the following json schema for each tool call you will make: {"properties": {"arguments": {"title": "Arguments", "type": "object"}, "name": {"title": "Name", "type": "string"}}, "required": ["arguments", "name"], "title": "FunctionCall", "type": "object"}''',
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'',
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'''For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:''',
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'''<tool_call>''',
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'''{"arguments": <args-dict>, "name": <function-name>}''',
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'''</tool_call>''',
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])
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)
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elif chat_format.tool_style == ToolsPromptStyle.TYPESCRIPT_FUNCTIONARY_V2:
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ts_converter = SchemaToTypeScriptConverter()
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return Message(
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role="system",
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content='\n'.join([
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'// Supported function definitions that should be called when necessary.'
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'namespace functions {',
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*[
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'// ' + tool.function.description.replace('\n', '\n// ') + '\n' + ''
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'type ' + tool.function.name + ' = (_: ' + ts_converter.visit(tool.function.parameters) + ") => any;\n"
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for tool in tools
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],
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'} // namespace functions',
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])
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)
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elif chat_format.tool_style == ToolsPromptStyle.TOOLS_HERMES_2_PRO:
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# Hackily import https://github.com/NousResearch/Hermes-Function-Calling
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path = str(Path(__file__).parent / "hermes_function_calling")
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if path not in sys.path: sys.path.insert(0, path)
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try:
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from examples.openai.hermes_function_calling.prompter import PromptManager
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except ImportError:
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raise ImportError(f"Please `git clone https://github.com/NousResearch/Hermes-Function-Calling {path}`")
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prompt = PromptManager().generate_prompt(user_prompt=[], tools=[json.dumps(tool) for tool in tools])
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assert len(prompt) == 1 and prompt[0]["role"] == "system"
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return Message(**prompt[0])
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else:
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raise ValueError(f"Unsupported tool call style: {chat_format.tool_style}")
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@typechecked
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def _outputs_tool_call_tags(style: ToolsPromptStyle) -> bool:
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return style in (
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ToolsPromptStyle.TOOLS_SHORT,
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ToolsPromptStyle.TOOLS_LONG,
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ToolsPromptStyle.TOOLS_HERMES_2_PRO,
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)
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_tool_call_re = re.compile(
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'<tool_call>(.*?)</tool_call>', re.DOTALL)
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_recipient_content_re = re.compile(r'(?:(?:<\|(?:stop|from)\|>)+ *assistant\n<\|recipient\|>|^) *([^ <|>\n]+) *\n<\|content\|>(.*?)(?:$|<\|stop\|>\s*$|(?=(?:<\|(?:stop|from)\|>)+ *assistant\n))', re.DOTALL)
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def gen_callid():
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return f'call_{random.randint(0, 1000000)}'
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@typechecked
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def make_grammar(chat_format: ChatFormat, tools: list[Tool], response_schema: Optional[dict], indent=2) -> Tuple[Optional[str], Callable[[str], Optional[list[Message]]]]:
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converter = SchemaConverter(prop_order={}, allow_fetch=False, dotall=False, raw_pattern=False)
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response_rule = converter.visit(response_schema, "response") if response_schema else None
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delimiter = '<%$[SAMPLE]$%>'
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user_msg = Message(role="user", content="Hey")
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empty_prompt = chat_format.render([user_msg], add_generation_prompt=True).strip()
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planted_prompt = chat_format.render([user_msg, Message(role="assistant", content=delimiter)], add_generation_prompt=False).strip()
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assert planted_prompt.startswith(empty_prompt), f"Planted prompt does not start with empty prompt: {planted_prompt} vs {empty_prompt}"
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[prefix, suffix] = planted_prompt[len(empty_prompt):].split(delimiter)
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def strip_suffix(s: str) -> str:
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if s.endswith(suffix):
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return s[:-len(suffix)]
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else:
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print(f"Expected suffix ({suffix}) not found: {s}")
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return s
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if tools:
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if _outputs_tool_call_tags(chat_format.tool_style):
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tool_rules = [
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converter.visit(
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dict(
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type="object",
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properties=dict(
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name=dict(const=tool.function.name),
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arguments=tool.function.parameters,
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),
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required=['name', 'arguments']
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),
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f'{tool.function.name}-tool-call'
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)
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for tool in tools
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]
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# Constrain the output to be a non-tool-call message (constrained to a JSON schema or not)
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# OR a tool-call message respecting the schema of any of the tools
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converter._add_rule(
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"root",
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converter._format_literal(prefix) + " (" +
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(response_rule or converter.not_literal("<tool_call>")) + " | " +
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converter._format_literal("<tool_call>") + " (" +
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' | '.join(tool_rules) +
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") " + converter._format_literal("</tool_call>") +
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")") # + converter._format_literal(suffix))
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@typechecked
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def parse(s: str) -> Optional[Message]:
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s = strip_suffix(s)
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# ls = s.lstrip()
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parts = _tool_call_re.split(s)
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if len(parts) == 1:
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return Message(role="assistant", content=s)
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else:
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content = []
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tool_calls = []
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for i, part in enumerate(parts):
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if i % 2 == 0:
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content.append(part)
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else:
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tool_calls.append(
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ToolCall(
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id=gen_callid(),
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function=FunctionCall(**json.loads(part))))
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content = ''.join(content).strip()
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return Message(role="assistant", content=None if content == '' else content, tool_calls=tool_calls)
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# if '<tool_call>'.startswith(ls) or ls.startswith('<tool_call>'):
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# if ls.startswith('<tool_call>') and ls.endswith('</tool_call>' + suffix):
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# tool_call = ls[len('<tool_call>'):-len('</tool_call>' + suffix)]
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# return Message(role="assistant", content=None, tool_calls=[json.loads(tool_call)])
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# return None
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# else:
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# return Message(role="assistant", content=s)
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return (converter.format_grammar(), parse)
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elif chat_format.tool_style == ToolsPromptStyle.TYPESCRIPT_FUNCTIONARY_V2:
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# Only allowing a single tool call at a time for now.
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# Note that if there were more, they'd be separated by a '<|from|>assistant' literal
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converter._add_rule(
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"root",
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converter._format_literal(prefix) + " (" +
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(response_rule or converter.not_literal("<|recipient|>")) + " | " +
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(' | '.join(
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converter._format_literal(f"<|recipient|>{tool.function.name}\n<|content|>") + " " +
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converter.visit(tool.function.parameters, tool.function.name + '-args')
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for tool in tools
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)) +
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") " +
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")") # + converter._format_literal(suffix))
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@typechecked
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def parse(s: str) -> Optional[Message]:
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s = strip_suffix(s)
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parts = _recipient_content_re.split(s)
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if len(parts) == 1:
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return Message(role="assistant", content=s)
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else:
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text_content = []
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tool_calls: list[ToolCall] = []
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for i in range((len(parts) - 1) // 3):
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assert parts[i * 3].strip() == '', f'Unexpected content before tool call: {parts[i * 3]}'
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recipient = parts[i * 3 + 1].strip()
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content = parts[i * 3 + 2]
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if recipient == 'all':
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text_content.append(content)
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else:
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tool_calls.append(
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ToolCall(
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id=gen_callid(),
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function=FunctionCall(name=recipient, arguments=json.loads(content))))
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assert parts[-1].strip() == '', f'Unexpected content after tool calls: {parts[-1]}'
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content = '\n'.join(text_content).strip()
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return Message(role="assistant", content=None if content == '' else content, tool_calls=tool_calls if tool_calls else None)
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return (converter.format_grammar(), parse)
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elif response_schema:
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converter._add_rule("root", response_rule + ' ' + converter._format_literal(suffix))
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@typechecked
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def parse(s: str) -> Optional[Message]:
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s = strip_suffix(s)
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return Message(role="assistant", content=s)
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return (converter.format_grammar(), parse)
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else:
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converter._add_rule("root", converter._format_literal(prefix) + ' ' + converter._format_literal(suffix))
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@typechecked
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def parse(s: str) -> Optional[Message]:
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s = strip_suffix(s)
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return Message(role="assistant", content=s)
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return (None, parse)
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