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https://github.com/ml-explore/mlx-examples.git
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5cae0a60e6
* deepseekv3 * use upload_large_file instead of deprecated multi comit * add pipeline generation and example * comment * get fp16 working * use mlx==0.22
76 lines
1.8 KiB
Python
76 lines
1.8 KiB
Python
# Copyright © 2024 Apple Inc.
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"""
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Run with:
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```
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/path/to/mpirun \
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-np 2 \
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--hostfile /path/to/hosts.txt \
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python /path/to/pipeline_generate.py --prompt "hello world"
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```
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Make sure you can run MLX over MPI on two hosts. For more information see the
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documentation:
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https://ml-explore.github.io/mlx/build/html/usage/distributed.html).
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"""
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import argparse
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import mlx.core as mx
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from mlx_lm import load, stream_generate
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parser = argparse.ArgumentParser(description="LLM pipelined inference example")
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parser.add_argument(
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"--prompt",
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"-p",
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default="Write a quicksort in C++.",
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help="Message to be processed by the model ('-' reads from stdin)",
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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=256,
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help="Maximum number of tokens to generate",
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)
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args = parser.parse_args()
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model_repo = "mlx-community/DeepSeek-V3-3bit"
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model, tokenizer = load(model_repo, lazy=True)
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messages = [{"role": "user", "content": args.prompt}]
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prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
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group = mx.distributed.init()
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rank = group.rank()
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model.model.pipeline(group)
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mx.eval(model.parameters())
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# Synchronize processes before generation to avoid timeout if downloading
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# model for the first time.
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mx.eval(mx.distributed.all_sum(mx.array(1.0), stream=mx.cpu))
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def rprint(*args, **kwargs):
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if rank == 0:
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print(*args, **kwargs)
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for response in stream_generate(model, tokenizer, prompt, max_tokens=args.max_tokens):
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rprint(response.text, end="", flush=True)
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rprint()
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rprint("=" * 10)
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rprint(
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f"Prompt: {response.prompt_tokens} tokens, "
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f"{response.prompt_tps:.3f} tokens-per-sec"
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)
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rprint(
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f"Generation: {response.generation_tokens} tokens, "
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f"{response.generation_tps:.3f} tokens-per-sec"
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)
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rprint(f"Peak memory: {response.peak_memory:.3f} GB")
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