File size: 7,295 Bytes
1904ee8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 |
import argparse
import datetime
import os
import subprocess
from copy import deepcopy
import yaml
from accelerate.commands import launch
from generate_vllm import generate_relabel_args_dict
def run_exp(exp_dict, savedir, args):
exp_name = exp_dict.pop("name")
git_hash = exp_dict.pop("git")
print(args)
if args.wandb:
os.environ["WANDB_MODE"] = "online"
# os.environ["WANDB_RUN_ID"] = os.path.basename(savedir)
os.environ["WANDB_NAME"] = exp_name
os.environ["WANDB_RUN_GROUP"] = exp_name + git_hash
else:
os.environ["WANDB_MODE"] = "disabled"
if exp_name.startswith("marlhf"):
print("MARLHF")
accelerate_launch("rl_training_with_ma_value.py", exp_dict, args)
elif exp_name.startswith("vmrlhf"):
print("Separate Value Model RLHF")
accelerate_launch("rl_training_value_model.py", exp_dict, args)
elif exp_name.startswith("rlhf"):
print("RLHF")
accelerate_launch("rl_training.py", exp_dict, args)
elif exp_name.startswith("dpo"):
print("DPO")
accelerate_launch("dpo_training.py", exp_dict, args)
elif exp_name.startswith("rm"):
accelerate_launch("reward_modeling.py", exp_dict, args)
elif exp_name.startswith("gptrm"):
accelerate_launch("gpt_reward_modeling.py", exp_dict, args)
elif exp_name.startswith("sft"):
accelerate_launch("sft.py", exp_dict, args)
elif exp_name.startswith("rouge"):
exp_dict.pop("save_strategy", None)
accelerate_launch("evaluate_rouge.py", exp_dict, args)
elif exp_name.startswith("pseudo"):
exp_dict.pop("save_strategy", None)
accelerate_launch("inference_pseudolabel.py", exp_dict, args)
elif exp_name.startswith("create_rlhf"):
exp_dict.pop("save_strategy", None)
accelerate_launch("create_rlhf_dataset.py", exp_dict, args)
elif exp_name.startswith("vllm"):
exp_dict.pop("save_strategy", None)
exp_dict["num_gpus"] = args.gpus
generate_vllm_args_dict(exp_dict)
else:
raise Exception(f"Config file {exp_name} does not start with one of the correct prefixes")
def accelerate_launch(training_file, training_args_dict, args):
parser = launch.launch_command_parser()
training_cmd_args = []
if args.accelerate_config is not None and args.accelerate_config != "None":
training_cmd_args.extend(["--config_file", args.accelerate_config])
# training_cmd_args.extend(["--num_processes", str(args.gpus)])
# training_cmd_args.extend(
# ["--gradient_accumulation_steps", str(training_args_dict["gradient_accumulation_steps"])]
# )
elif args.gpus > 1:
training_cmd_args.append("--multi_gpu")
# if training_args_dict.pop("fp16", False):
# mixed_precision = "fp16"
# elif training_args_dict.pop("bf16", False):
# mixed_precision = "bf16"
if training_args_dict.get("fp16", False):
mixed_precision = "fp16"
elif training_args_dict.get("bf16", False):
mixed_precision = "bf16"
else:
mixed_precision = "no"
training_cmd_args.extend(["--mixed_precision", mixed_precision])
#
training_cmd_args.extend(["--num_machines", "1"])
training_cmd_args.extend(["--num_processes", str(args.gpus)])
# if args.gpus > 1:
# if args.deepspeed is not None and args.deepspeed != "None":
# assert (
# "gradient_accumulation_steps" in training_args_dict
# ), "Must include gradient_accumulation_steps in config"
# training_cmd_args.append("--use_deepspeed")
# training_cmd_args.extend(["--zero_stage", str(args.deepspeed)])
# training_cmd_args.extend(
# ["--gradient_accumulation_steps", str(training_args_dict["gradient_accumulation_steps"])]
# )
training_cmd_args.append(training_file)
for key, val in training_args_dict.items():
training_cmd_args.append(f"--{key}")
if not (isinstance(val, bool) and val is True):
training_cmd_args.append(str(val))
print(" ".join(training_cmd_args))
args = parser.parse_args(training_cmd_args)
launch.launch_command(args)
if __name__ == "__main__":
# Specify arguments regarding save directory and job scheduler
parser = argparse.ArgumentParser()
parser.add_argument(
"-e",
"--exp_group",
help="Define the experiment group to run.",
nargs="+",
)
parser.add_argument(
"-sb",
"--savedir_base",
default="/home/toolkit/trl/results",
help="Define the base directory where the experiments will be saved.",
)
parser.add_argument(
"-r",
"--reset",
type=int,
default=0,
help="If true, reset the experiment. Else, resume.",
)
parser.add_argument(
"-j",
"--job_scheduler",
default=None,
type=str,
help="Run the experiments as jobs in the cluster.",
)
parser.add_argument(
"-p",
"--python_binary",
default="/home/toolkit/.conda/envs/trl/bin/python",
help="path to your python executable",
)
parser.add_argument("-n", "--gpus", default=1, type=int, help="number of gpus to use for experiment")
parser.add_argument("-a", "--accelerate_config", default=None, help="accelerate config")
# parser.add_argument("-d", "--deepspeed", default=None, help="ds stage")
parser.add_argument("--gpu-mem", default=32, type=int, help="mem of gpus to use for experiment")
parser.add_argument("--wandb", action="store_true", help="force enable wandb", default=False)
parser.add_argument("--search", default=None)
# parser.add_argument(
# "--exp-id", default=None, help="id used to resume an experiment"
# )
args, extra_args = parser.parse_known_args()
exp_list = []
for exp_file in args.exp_group:
with open(exp_file, "r") as fp:
exp_dict = yaml.safe_load(fp)
exp_dict['output_dir'] = args.savedir_base
exp_dict["name"] = os.path.basename(exp_file)
exp_dict["git"] = subprocess.check_output(["git", "rev-parse", "--short", "HEAD"]).decode("ascii").strip()
if args.search is not None and args.search != "None":
search_key, search_val_str = args.search.split("=")
search_vals = search_val_str.split(",")
exps = []
for val in search_vals:
exp_dict_copy = deepcopy(exp_dict)
exp_dict_copy[search_key] = val
exp_dict_copy["name"] = exp_dict_copy["name"] + f"/{search_key}={val}"
exps.append(exp_dict_copy)
# for key, val in vars(extra_args).items():
# exp_dict[key] = val
# print(exps)
else:
exps = [exp_dict]
exp_list.extend(exps)
args.exp_group = " ".join(args.exp_group)
print(args.exp_group)
if args.wandb:
timenow = datetime.datetime.now().strftime("%d-%m-%y_%H-%M-%S")
exp_list[0]["name"] = exp_list[0]["name"] + f"_local_{timenow}"
# exp_list[0]["save_strategy"] = "no"
# Run experiments and create results file
run_exp(exp_list[0], "output", args)
|