# Copyright © 2023-2024 Apple Inc. import argparse import json import sys import mlx.core as mx from .models.cache import QuantizedKVCache, load_prompt_cache from .sample_utils import make_sampler from .utils import generate, load DEFAULT_PROMPT = "hello" DEFAULT_MAX_TOKENS = 100 DEFAULT_TEMP = 0.0 DEFAULT_TOP_P = 1.0 DEFAULT_MIN_P = 0.0 DEFAULT_MIN_TOKENS_TO_KEEP = 1 DEFAULT_SEED = 0 DEFAULT_MODEL = "mlx-community/Llama-3.2-3B-Instruct-4bit" DEFAULT_QUANTIZED_KV_START = 5000 def str2bool(string): return string.lower() not in ["false", "f"] def setup_arg_parser(): """Set up and return the argument parser.""" parser = argparse.ArgumentParser(description="LLM inference script") parser.add_argument( "--model", type=str, help=( "The path to the local model directory or Hugging Face repo. " f"If no model is specified, then {DEFAULT_MODEL} is used." ), default=None, ) parser.add_argument( "--adapter-path", type=str, help="Optional path for the trained adapter weights and config.", ) parser.add_argument( "--extra-eos-token", type=str, default=(), nargs="+", help="Add tokens in the list of eos tokens that stop generation.", ) parser.add_argument( "--system-prompt", default=None, help="System prompt to be used for the chat template", ) parser.add_argument( "--prompt", "-p", default=DEFAULT_PROMPT, help="Message to be processed by the model ('-' reads from stdin)", ) parser.add_argument( "--max-tokens", "-m", type=int, default=DEFAULT_MAX_TOKENS, help="Maximum number of tokens to generate", ) parser.add_argument( "--temp", type=float, default=DEFAULT_TEMP, help="Sampling temperature" ) parser.add_argument( "--top-p", type=float, default=DEFAULT_TOP_P, help="Sampling top-p" ) parser.add_argument( "--min-p", type=float, default=DEFAULT_MIN_P, help="Sampling min-p" ) parser.add_argument( "--min-tokens-to-keep", type=int, default=DEFAULT_MIN_TOKENS_TO_KEEP, help="Minimum tokens to keep for min-p sampling.", ) parser.add_argument("--seed", type=int, default=DEFAULT_SEED, help="PRNG seed") parser.add_argument( "--ignore-chat-template", action="store_true", help="Use the raw prompt without the tokenizer's chat template.", ) parser.add_argument( "--use-default-chat-template", action="store_true", help="Use the default chat template", ) parser.add_argument( "--verbose", type=str2bool, default=True, help="Log verbose output when 'True' or 'T' or only print the response when 'False' or 'F'", ) parser.add_argument( "--max-kv-size", type=int, help="Set the maximum key-value cache size", default=None, ) parser.add_argument( "--prompt-cache-file", type=str, default=None, help="A file containing saved KV caches to avoid recomputing them", ) parser.add_argument( "--kv-bits", type=int, help="Number of bits for KV cache quantization. " "Defaults to no quantization.", default=None, ) parser.add_argument( "--kv-group-size", type=int, help="Group size for KV cache quantization.", default=64, ) parser.add_argument( "--quantized-kv-start", help="When --kv-bits is set, start quantizing the KV cache " "from this step onwards.", type=int, default=DEFAULT_QUANTIZED_KV_START, ) return parser def main(): parser = setup_arg_parser() args = parser.parse_args() mx.random.seed(args.seed) # Load the prompt cache and metadata if a cache file is provided using_cache = args.prompt_cache_file is not None if using_cache: prompt_cache, metadata = load_prompt_cache( args.prompt_cache_file, return_metadata=True, ) if isinstance(prompt_cache[0], QuantizedKVCache): if args.kv_bits is not None and args.kv_bits != prompt_cache[0].bits: raise ValueError( "--kv-bits does not match the kv cache loaded from --prompt-cache-file." ) if args.kv_group_size != prompt_cache[0].group_size: raise ValueError( "--kv-group-size does not match the kv cache loaded from --prompt-cache-file." ) # Building tokenizer_config tokenizer_config = ( {} if not using_cache else json.loads(metadata["tokenizer_config"]) ) tokenizer_config["trust_remote_code"] = True model_path = args.model if using_cache: if model_path is None: model_path = metadata["model"] elif model_path != metadata["model"]: raise ValueError( f"Providing a different model ({model_path}) than that " f"used to create the prompt cache ({metadata['model']}) " "is an error." ) model_path = model_path or DEFAULT_MODEL model, tokenizer = load( model_path, adapter_path=args.adapter_path, tokenizer_config=tokenizer_config, ) for eos_token in args.extra_eos_token: tokenizer.add_eos_token(eos_token) if args.use_default_chat_template: if tokenizer.chat_template is None: tokenizer.chat_template = tokenizer.default_chat_template elif using_cache: tokenizer.chat_template = metadata["chat_template"] prompt = args.prompt.replace("\\n", "\n").replace("\\t", "\t") prompt = sys.stdin.read() if prompt == "-" else prompt if not args.ignore_chat_template and tokenizer.chat_template is not None: if args.system_prompt is not None: messages = [{"role": "system", "content": args.system_prompt}] else: messages = [] messages.append({"role": "user", "content": prompt}) prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) # Treat the prompt as a suffix assuming that the prefix is in the # stored kv cache. if using_cache: messages[-1]["content"] = "" test_prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) prompt = prompt[test_prompt.index("") :] prompt = tokenizer.encode(prompt, add_special_tokens=False) else: prompt = tokenizer.encode(prompt) sampler = make_sampler(args.temp, args.top_p, args.min_p, args.min_tokens_to_keep) response = generate( model, tokenizer, prompt, max_tokens=args.max_tokens, verbose=args.verbose, sampler=sampler, max_kv_size=args.max_kv_size, prompt_cache=prompt_cache if using_cache else None, kv_bits=args.kv_bits, kv_group_size=args.kv_group_size, quantized_kv_start=args.quantized_kv_start, ) if not args.verbose: print(response) if __name__ == "__main__": main()