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https://github.com/ml-explore/mlx-examples.git
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30be4c4734
* refactor(qwen): moving qwen into mlx-lm * chore: update doc * chore: fix type hint * add qwen model support in convert * chore: fix doc * chore: only load model in quantize_model * chore: make the convert script only copy tokenizer files instead of load it and save * chore: update docstring * chore: remove unnecessary try catch * chore: clean up for tokenizer and update transformers 4.37 * nits in README --------- Co-authored-by: Awni Hannun <awni@apple.com>
96 lines
2.7 KiB
Python
96 lines
2.7 KiB
Python
import argparse
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import time
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import mlx.core as mx
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from .utils import generate_step, load
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DEFAULT_MODEL_PATH = "mlx_model"
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DEFAULT_PROMPT = "hello"
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DEFAULT_MAX_TOKENS = 100
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DEFAULT_TEMP = 0.6
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DEFAULT_SEED = 0
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def setup_arg_parser():
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"""Set up and return the argument parser."""
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parser = argparse.ArgumentParser(description="LLM inference script")
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parser.add_argument(
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"--model",
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type=str,
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default="mlx_model",
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help="The path to the local model directory or Hugging Face repo.",
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)
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parser.add_argument(
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"--trust-remote-code",
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action="store_true",
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help="Enable trusting remote code for tokenizer",
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)
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parser.add_argument(
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"--eos-token",
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type=str,
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default=None,
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help="End of sequence token for tokenizer",
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)
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parser.add_argument(
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"--prompt", default=DEFAULT_PROMPT, help="Message to be processed by the model"
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)
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parser.add_argument(
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"--max-tokens",
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"-m",
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type=int,
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default=DEFAULT_MAX_TOKENS,
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help="Maximum number of tokens to generate",
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)
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parser.add_argument(
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"--temp", type=float, default=DEFAULT_TEMP, help="Sampling temperature"
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)
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parser.add_argument("--seed", type=int, default=DEFAULT_SEED, help="PRNG seed")
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return parser
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def main(args):
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mx.random.seed(args.seed)
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# Building tokenizer_config
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tokenizer_config = {"trust_remote_code": True if args.trust_remote_code else None}
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if args.eos_token is not None:
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tokenizer_config["eos_token"] = args.eos_token
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model, tokenizer = load(args.model, tokenizer_config=tokenizer_config)
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print("=" * 10)
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print("Prompt:", args.prompt)
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prompt = tokenizer.encode(args.prompt)
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prompt = mx.array(prompt)
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tic = time.time()
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tokens = []
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skip = 0
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for token, n in zip(
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generate_step(prompt, model, args.temp), range(args.max_tokens)
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):
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if token == tokenizer.eos_token_id:
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break
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if n == 0:
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prompt_time = time.time() - tic
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tic = time.time()
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tokens.append(token.item())
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s = tokenizer.decode(tokens)
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print(s[skip:], end="", flush=True)
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skip = len(s)
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print(tokenizer.decode(tokens)[skip:], flush=True)
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gen_time = time.time() - tic
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print("=" * 10)
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if len(tokens) == 0:
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print("No tokens generated for this prompt")
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return
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prompt_tps = prompt.size / prompt_time
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gen_tps = (len(tokens) - 1) / gen_time
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print(f"Prompt: {prompt_tps:.3f} tokens-per-sec")
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print(f"Generation: {gen_tps:.3f} tokens-per-sec")
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if __name__ == "__main__":
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parser = setup_arg_parser()
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args = parser.parse_args()
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main(args)
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