mirror of https://github.com/hpcaitech/ColossalAI
87 lines
3.5 KiB
Python
87 lines
3.5 KiB
Python
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import torch
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import inspect
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import os
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import subprocess
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import sys
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from colossalai.initialize import launch_from_torch
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from colossalai.logging import disable_existing_loggers
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from colossalai.utils import print_rank_0
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from colossalai.core import global_context as gpc
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from colossalai.logging import get_dist_logger
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from colossalai.utils import free_port
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from colossalai.cli.benchmark import build_args_parser, build_configs, \
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build_input_tensor, profile_1d, profile_2d, profile_2p5d, profile_3d, \
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BATCH_SIZE, SEQ_LENGTH, HIDDEN_DIM, ITER_TIMES
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def launch(args=None):
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train_script = inspect.getfile(inspect.currentframe())
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assert args is not None, "args should not be None"
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env = os.environ.copy()
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if args.num_gpus == -1 or args.num_gpus > torch.cuda.device_count():
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nproc_per_node = torch.cuda.device_count()
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else:
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nproc_per_node = args.num_gpus
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train_args = [f"--num_gpus={nproc_per_node}"]
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if args.bs != BATCH_SIZE:
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train_args.append(f"--bs={args.bs}")
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if args.hid_dim != HIDDEN_DIM:
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train_args.append(f"--hid_dim={args.hid_dim}")
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if args.num_steps != ITER_TIMES:
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train_args.append(f"--num_steps={args.num_steps}")
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if args.seq_len != SEQ_LENGTH:
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train_args.append(f"--seq_len={args.seq_len}")
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master_port = free_port()
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if torch.__version__ <= "1.09":
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cmd = [sys.executable, "-u", "-m",
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"torch.distributed.launch",
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f"--nproc_per_node={nproc_per_node}",
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f"--master_port={master_port}"] + [train_script] + train_args
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else:
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cmd = ["torchrun",
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f"--nproc_per_node={nproc_per_node}",
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f"--master_port={master_port}"] + [train_script] + train_args
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result = subprocess.Popen(cmd, env=env)
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result.wait()
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if result.returncode > 0:
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sys.exit(result.returncode)
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def main():
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parser = build_args_parser()
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args = parser.parse_args()
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disable_existing_loggers()
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logger = get_dist_logger()
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launch_from_torch(config={}, verbose=False)
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input_tensor = build_input_tensor(args)
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config_dict = build_configs(args)
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if len(config_dict) == 0:
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print_rank_0(f"WARNING: We need at least two devices to profile TP strategies performance.")
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gpc.destroy()
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return
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for parallel_mode, config in config_dict.items():
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if parallel_mode == "1d":
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result_1d = profile_1d(input_tensor, config, args)
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print_rank_0(f"INFO: Totoal time cost in 1D TP is {result_1d}.")
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if parallel_mode == "2d":
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result_2d = profile_2d(input_tensor, config, args)
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print_rank_0(f"INFO: Totoal time cost in 2D TP is {result_2d}.")
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if parallel_mode == "2p5d":
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result_2p5d = profile_2p5d(input_tensor, config, args)
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print_rank_0(f"INFO: Totoal time cost in 2P5D TP is {result_2p5d}.")
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if parallel_mode == "3d":
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result_3d = profile_3d(input_tensor, config, args)
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print_rank_0(f"INFO: Totoal time cost in 3D TP is {result_3d}.")
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if "2d" not in config_dict:
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print_rank_0(f"WARNING: To use 2D tensor parallel, you have to provide at least 4 computing devices.")
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if "2p5d" not in config_dict:
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print_rank_0(f"WARNING: To use 2P5D tensor parallel, you have to provide at least 8 computing devices.")
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print_rank_0(f"WARNING: To use 3D tensor parallel, you have to provide at least 8 computing devices.")
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gpc.destroy()
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if __name__=="__main__":
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main()
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