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- #!/usr/bin/env python3
- import argparse
- import multiprocessing as mp
- from time import perf_counter
- import numpy as np
- import torch
- from hivemind.utils.logging import get_logger
- from transformers import AutoTokenizer
- from petals import AutoDistributedModelForCausalLM
- from petals.constants import DTYPE_MAP, PUBLIC_INITIAL_PEERS
- logger = get_logger()
- def main():
- parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
- parser.add_argument("--model", type=str, required=True, help="Model")
- parser.add_argument("--initial_peers", type=str, nargs="+", default=PUBLIC_INITIAL_PEERS, help="Initial peers")
- parser.add_argument("--torch_dtype", type=str, default="float32", help="Torch dtype")
- parser.add_argument("--n_processes", type=str, default=1, help="Number of concurrent processes")
- parser.add_argument("--seq_len", type=int, default=2048, help="Sequence length")
- parser.add_argument("--warmup_steps", type=int, default=1, help="Number of warmup steps")
- args = parser.parse_args()
- if args.n_processes == "n_gpus":
- args.n_processes = torch.cuda.device_count()
- else:
- args.n_processes = int(args.n_processes)
- pipe_recv, pipe_send = mp.Pipe(duplex=False)
- processes = [mp.Process(target=benchmark_inference, args=(i, args, pipe_send)) for i in range(args.n_processes)]
- for proc in processes:
- proc.start()
- for proc in processes:
- proc.join()
- speed = np.mean([pipe_recv.recv() for _ in range(args.n_processes)])
- logger.info(f"Final result: {speed=:.2f}")
- @torch.inference_mode()
- def benchmark_inference(process_idx, args, result_pipe):
- tokenizer = AutoTokenizer.from_pretrained(args.model, use_fast=False)
- # Using use_fast=False since LlamaTokenizerFast takes a long time to start, and we decode 1 token at a time anyway
- model = AutoDistributedModelForCausalLM.from_pretrained(
- args.model, initial_peers=args.initial_peers, torch_dtype=DTYPE_MAP[args.torch_dtype]
- )
- logger.info(f"Created model: {process_idx=} {model.device=}")
- result = ""
- step_times = []
- with model.transformer.h.inference_session(max_length=args.seq_len) as sess:
- for step in range(args.seq_len):
- start_time = perf_counter()
- outputs = model.generate(max_new_tokens=1, session=sess)
- result += tokenizer.decode(outputs[0])
- if step >= args.warmup_steps:
- step_times.append(perf_counter() - start_time)
- speed = 1 / np.mean(step_times)
- logger.info(f"{process_idx=} {step=} {speed=:.2f}")
- result_pipe.send(speed)
- if __name__ == "__main__":
- main()
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