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76 lines
2.7 KiB
Python
76 lines
2.7 KiB
Python
#!/usr/bin/env python3
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import argparse
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import multiprocessing as mp
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from time import perf_counter
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import numpy as np
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import torch
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from hivemind.utils.logging import get_logger
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from petals import AutoDistributedModel
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from petals.constants import DTYPE_MAP, PUBLIC_INITIAL_PEERS
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logger = get_logger()
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def main():
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parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument("--model", type=str, required=True, help="Model")
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parser.add_argument("--initial_peers", type=str, nargs="+", default=PUBLIC_INITIAL_PEERS, help="Initial peers")
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parser.add_argument("--torch_dtype", type=str, default="float32", help="Torch dtype")
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parser.add_argument("--n_processes", type=str, default=1, help="Number of concurrent processes")
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parser.add_argument("--seq_len", type=int, default=128, help="Sequence length")
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parser.add_argument("--n_steps", type=int, default=100, help="Number of benchmark steps")
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parser.add_argument("--batch_size", type=int, required=True, help="Batch size")
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parser.add_argument("--warmup_steps", type=int, default=1, help="Number of warmup steps")
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args = parser.parse_args()
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if args.n_processes == "n_gpus":
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args.n_processes = torch.cuda.device_count()
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else:
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args.n_processes = int(args.n_processes)
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pipe_recv, pipe_send = mp.Pipe(duplex=False)
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processes = [mp.Process(target=benchmark_forward, args=(i, args, pipe_send)) for i in range(args.n_processes)]
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for proc in processes:
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proc.start()
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for proc in processes:
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proc.join()
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speed = np.mean([pipe_recv.recv() for _ in range(args.n_processes)])
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logger.info(f"Final result: {speed=:.2f}")
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@torch.inference_mode()
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def benchmark_forward(process_idx, args, result_pipe):
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model = AutoDistributedModel.from_pretrained(
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args.model,
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initial_peers=args.initial_peers,
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torch_dtype=DTYPE_MAP[args.torch_dtype],
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)
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logger.info(f"Created model: {process_idx=} {model.device=}")
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torch.manual_seed(42)
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step_times = []
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for step in range(args.warmup_steps + args.n_steps):
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start_time = perf_counter()
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input_ids = torch.randint(0, model.config.vocab_size, size=(args.batch_size, args.seq_len))
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logger.info(f"{process_idx=} Fwd begin {input_ids.shape=}")
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h = model(input_ids)
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# We don't use model.lm_head
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logger.info(f"{process_idx=} Fwd end")
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if step >= args.warmup_steps:
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step_times.append(perf_counter() - start_time)
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speed = input_ids.numel() / np.mean(step_times)
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logger.info(f"{process_idx=} {step=} {speed=:.2f}")
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result_pipe.send(speed)
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if __name__ == "__main__":
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main()
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