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| import argparse | |
| import torch | |
| import json | |
| from config import config | |
| from vllm import LLM, SamplingParams | |
| from transformers import BitsAndBytesConfig | |
| import functions | |
| from prompter import PromptManager | |
| from validator import validate_function_call_schema | |
| from utils import ( | |
| inference_logger, | |
| get_assistant_message, | |
| get_chat_template, | |
| validate_and_extract_tool_calls | |
| ) | |
| class ModelInference: | |
| def __init__(self, chat_template: str, load_in_4bit: bool = False): | |
| self.prompter = PromptManager() | |
| self.bnb_config = None | |
| if load_in_4bit == "True": # Never use this | |
| self.bnb_config = BitsAndBytesConfig( | |
| load_in_4bit=True, | |
| bnb_4bit_quant_type="nf4", | |
| bnb_4bit_use_double_quant=True, | |
| ) | |
| self.model = LLM(model=config.model) | |
| self.tokenizer = self.model.get_tokenizer() | |
| self.tokenizer.pad_token = self.tokenizer.eos_token | |
| self.tokenizer.padding_side = "left" | |
| if self.tokenizer.chat_template is None: | |
| print("No chat template defined, getting chat_template...") | |
| self.tokenizer.chat_template = get_chat_template(chat_template) | |
| inference_logger.info(self.model.config) | |
| inference_logger.info(self.model.generation_config) | |
| inference_logger.info(self.tokenizer.special_tokens_map) | |
| def process_completion_and_validate(self, completion, chat_template): | |
| assistant_message = get_assistant_message(completion, chat_template, self.tokenizer.eos_token) | |
| if assistant_message: | |
| validation, tool_calls, error_message = validate_and_extract_tool_calls(assistant_message) | |
| if validation: | |
| inference_logger.info(f"parsed tool calls:\n{json.dumps(tool_calls, indent=2)}") | |
| return tool_calls, assistant_message, error_message | |
| else: | |
| tool_calls = None | |
| return tool_calls, assistant_message, error_message | |
| else: | |
| inference_logger.warning("Assistant message is None") | |
| raise ValueError("Assistant message is None") | |
| def execute_function_call(self, tool_call): | |
| function_name = tool_call.get("name") | |
| function_to_call = getattr(functions, function_name, None) | |
| function_args = tool_call.get("arguments", {}) | |
| inference_logger.info(f"Invoking function call {function_name} ...") | |
| function_response = function_to_call(*function_args.values()) | |
| results_dict = f'{{"name": "{function_name}", "content": {function_response}}}' | |
| return results_dict | |
| def run_inference(self, prompt): | |
| sampling_params = SamplingParams( | |
| temperature=0.8, | |
| top_p=0.95, | |
| repetition_penalty=1.1, | |
| max_tokens=500, | |
| stop_token_ids=[128009]) | |
| outputs = self.model.generate([prompt], sampling_params) | |
| for output in outputs: | |
| return output.outputs[0].text | |
| def generate_function_call(self, query, chat_template, num_fewshot, max_depth=5): | |
| try: | |
| depth = 0 | |
| user_message = f"{query}\nThis is the first turn and you don't have <tool_results> to analyze yet" | |
| chat = [{"role": "user", "content": user_message}] | |
| tools = functions.get_openai_tools() | |
| prompt = self.prompter.generate_prompt(chat, tools, num_fewshot) | |
| completion = self.run_inference(prompt) | |
| def recursive_loop(prompt, completion, depth): | |
| nonlocal max_depth | |
| tool_calls, assistant_message, error_message = self.process_completion_and_validate(completion, chat_template) | |
| prompt.append({"role": "assistant", "content": assistant_message}) | |
| tool_message = f"Agent iteration {depth} to assist with user query: {query}\n" | |
| if tool_calls: | |
| inference_logger.info(f"Assistant Message:\n{assistant_message}") | |
| for tool_call in tool_calls: | |
| validation, message = validate_function_call_schema(tool_call, tools) | |
| if validation: | |
| try: | |
| function_response = self.execute_function_call(tool_call) | |
| tool_message += f"<tool_response>\n{function_response}\n</tool_response>\n" | |
| inference_logger.info(f"Here's the response from the function call: {tool_call.get('name')}\n{function_response}") | |
| except Exception as e: | |
| inference_logger.info(f"Could not execute function: {e}") | |
| tool_message += f"<tool_response>\nThere was an error when executing the function: {tool_call.get('name')}\nHere's the error traceback: {e}\nPlease call this function again with correct arguments within XML tags <tool_call></tool_call>\n</tool_response>\n" | |
| else: | |
| inference_logger.info(message) | |
| tool_message += f"<tool_response>\nThere was an error validating function call against function signature: {tool_call.get('name')}\nHere's the error traceback: {message}\nPlease call this function again with correct arguments within XML tags <tool_call></tool_call>\n</tool_response>\n" | |
| prompt.append({"role": "tool", "content": tool_message}) | |
| depth += 1 | |
| if depth >= max_depth: | |
| print(f"Maximum recursion depth reached ({max_depth}). Stopping recursion.") | |
| return | |
| completion = self.run_inference(prompt) | |
| recursive_loop(prompt, completion, depth) | |
| elif error_message: | |
| inference_logger.info(f"Assistant Message:\n{assistant_message}") | |
| tool_message += f"<tool_response>\nThere was an error parsing function calls\n Here's the error stack trace: {error_message}\nPlease call the function again with correct syntax<tool_response>" | |
| prompt.append({"role": "tool", "content": tool_message}) | |
| depth += 1 | |
| if depth >= max_depth: | |
| print(f"Maximum recursion depth reached ({max_depth}). Stopping recursion.") | |
| return | |
| completion = self.run_inference(prompt) | |
| recursive_loop(prompt, completion, depth) | |
| else: | |
| inference_logger.info(f"Assistant Message:\n{assistant_message}") | |
| recursive_loop(prompt, completion, depth) | |
| except Exception as e: | |
| inference_logger.error(f"Exception occurred: {e}") | |
| raise e | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser(description="Run recursive function calling loop") | |
| parser.add_argument("--model_path", type=str, help="Path to the model folder") | |
| parser.add_argument("--chat_template", type=str, default="chatml", help="Chat template for prompt formatting") | |
| parser.add_argument("--num_fewshot", type=int, default=None, help="Option to use json mode examples") | |
| parser.add_argument("--load_in_4bit", type=str, default="False", help="Option to load in 4bit with bitsandbytes") | |
| parser.add_argument("--query", type=str, default="I need the current stock price of Tesla (TSLA)") | |
| parser.add_argument("--max_depth", type=int, default=5, help="Maximum number of recursive iteration") | |
| args = parser.parse_args() | |
| # specify custom model path | |
| if args.model_path: | |
| inference = ModelInference(args.model_path, args.chat_template, args.load_in_4bit) | |
| else: | |
| model_path = 'InvestmentResearchAI/LLM-ADE-dev' | |
| inference = ModelInference(model_path, args.chat_template, args.load_in_4bit) | |
| # Run the model evaluator | |
| inference.generate_function_call(args.query, args.chat_template, args.num_fewshot, args.max_depth) | |