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108 lines
4.2 KiB
Python
108 lines
4.2 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 AutoDistributedModelForCausalLM, AutoDistributedModelForSequenceClassification
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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("--device", type=str, default="cpu", help="Torch device hosting the client")
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parser.add_argument("--task", type=str, default="cls", help="Training task type")
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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("--pre_seq_len", type=int, default=16, help="Number of trainable tokens")
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parser.add_argument("--n_steps", type=int, default=10, 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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assert args.task in ["cls", "causal_lm"]
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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_training, 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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fwd_speed, bwd_speed = np.mean([pipe_recv.recv() for _ in range(args.n_processes)], axis=0)
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logger.info(f"Final result: {fwd_speed=:.2f} {bwd_speed=:.2f}")
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def benchmark_training(process_idx, args, result_pipe):
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if args.task == "cls":
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model = AutoDistributedModelForSequenceClassification.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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tuning_mode="deep_ptune",
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pre_seq_len=args.pre_seq_len,
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num_labels=2,
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)
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elif args.task == "causal_lm":
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model = AutoDistributedModelForCausalLM.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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tuning_mode="deep_ptune",
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pre_seq_len=args.pre_seq_len,
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)
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model = model.to(args.device)
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opt = torch.optim.Adam(model.parameters())
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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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fwd_times = []
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bwd_times = []
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for step in range(args.warmup_steps + args.n_steps):
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input_ids = torch.randint(0, model.config.vocab_size, size=(args.batch_size, args.seq_len), device=args.device)
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if args.task == "cls":
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labels = torch.randint(0, 2, size=[args.batch_size], device=args.device)
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else:
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labels = input_ids
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logger.info(f"{process_idx=} {step=} Forward")
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start_time = perf_counter()
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outputs = model(input_ids, labels=labels)
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if step >= args.warmup_steps:
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fwd_times.append(perf_counter() - start_time)
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logger.info(f"{process_idx=} {step=} Backward")
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start_time = perf_counter()
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outputs.loss.backward()
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if step >= args.warmup_steps:
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bwd_times.append(perf_counter() - start_time)
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logger.info(f"{process_idx=} {step=} Optimizer step")
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opt.step()
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opt.zero_grad()
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if step >= args.warmup_steps:
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fwd_speed = input_ids.numel() / np.mean(fwd_times)
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bwd_speed = input_ids.numel() / np.mean(bwd_times)
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logger.info(f"{process_idx=} Fwd speed: {fwd_speed:.2f} | Bwd speed: {bwd_speed:.2f}")
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result_pipe.send((fwd_speed, bwd_speed))
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if __name__ == "__main__":
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main()
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