petals/benchmarks/benchmark_inference.py

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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()