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import argparse
import datetime
import os
import subprocess
from copy import deepcopy
import generate_and_eval
import yaml
from accelerate.commands import launch
from generate_vllm import generate_relabel_args_dict
from haven import haven_wizard as hw
def run_exp(exp_dict, savedir, args):
exp_name = exp_dict.pop("name")
git_hash = exp_dict.pop("git")
print(args)
print(f"savedir {savedir}")
exp_dict["output_dir"] = savedir
os.environ["WANDB_RUN_ID"] = os.path.basename(savedir)
os.environ["WANDB_NAME"] = exp_name
os.environ["WANDB_RUN_GROUP"] = exp_name + "_" + git_hash
if args.wandb:
os.environ["WANDB_MODE"] = "online"
os.environ["WANDB_PROJECT"] = "trl"
os.environ["WANDB_ENTITY"] = "mila-language-drift"
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("newdpo"):
print("DPO")
accelerate_launch("dpo.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("supervised_finetuning.py", exp_dict, args)
elif exp_name.startswith("newsft"):
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_relabel_args_dict(exp_dict)
elif exp_name.startswith("geneval"):
exp_dict.pop("save_strategy", None)
exp_dict["num_gpus"] = args.gpus
generate_and_eval.main_args_dict(exp_dict)
elif exp_name.startswith("scalarrm"):
exp_dict.pop("save_strategy", None)
accelerate_launch("scalar_rm_model.py", exp_dict, args)
elif exp_name.startswith("costa_dpo"):
accelerate_launch("costa_dpo.py", exp_dict, args)
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("--local-save", action="store_true", help="force local save", 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["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.job_scheduler == "toolkit":
with open("/home/toolkit/wandb_api_key", "r") as f:
wandb_api_key = f.read().rstrip()
job_config = {
"account_id": os.environ["EAI_ACCOUNT_ID"],
# "image": "registry.console.elementai.com/snow.colab/cuda",
# "image": "registry.console.elementai.com/snow.colab_public/ssh",
# "image": "registry.console.elementai.com/snow.mnoukhov/rl4lms",
"image": "registry.console.elementai.com/snow.interactive_toolkit/default",
"data": [
"snow.mnoukhov.home:/home/toolkit",
"snow.colab.public:/mnt/public",
],
"environment_vars": [
"HOME=/home/toolkit",
"HF_HOME=/home/toolkit/huggingface/",
f"WANDB_API_KEY={wandb_api_key}",
"WANDB_RESUME=allow",
"WANDB__SERVICE_WAIT=300",
"WANDB_PROJECT=trl",
"WANDB_ENTITY=mila-language-drift",
],
"restartable": True,
"resources": {
"cpu": 4 * args.gpus,
"mem": 64 * args.gpus,
"gpu_mem": args.gpu_mem,
"gpu": args.gpus,
},
"interactive": False,
"bid": 9999,
}
job_scheduler = "toolkit"
args.wandb = True
else:
job_config = None
job_scheduler = None
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}"
if not args.local_save:
exp_list[0]["save_strategy"] = "no"
# Run experiments and create results file
# if job_scheduler == "toolkit":
from haven import haven_wizard as hw
hw.run_wizard(
func=run_exp,
exp_list=exp_list,
savedir_base=args.savedir_base,
reset=args.reset,
job_config=job_config,
job_scheduler=job_scheduler,
results_fname="results/notebook.ipynb",
python_binary_path=args.python_binary,
args=args,
use_threads=True,
save_logs=False,
)
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