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env_args.py
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import argparse
def save_best(best_score, eval_dict):
return best_score if best_score < eval_dict['loss'] else eval_dict['loss']
def str2bool(v):
if isinstance(v,bool):
return v
if v == 'True':
return True
if v == 'False':
return False
class EnvArgs:
def __init__(self,
env_type="pytorch",
experiment_name="test_experiment",
model_name="test_model",
epochs=1,
batch_size=1,
lr=1e-5,
warmup_start_lr=0.0,
seed=1234,
fp16=False,
pytorch_device="cpu",
clip_grad=1.0,
checkpoint_activations=False,
gradient_accumulation_steps=1,
weight_decay=1e-5,
eps=1e-8,
warm_up=0.1,
warm_up_iters=0,
skip_iters=0,
log_interval=100,
eval_interval=1000,
save_interval=1000,
save_dir=None,
load_dir=None,
save_optim=False, # save current optimizer.')
save_rng=False, # save current rng state.')
load_type='latest', # latest, best
load_optim=False, # not load optimizer when loading checkpoint.')
load_rng=False,
tensorboard_dir="tensorboard_summary",
tensorboard=False,
wandb=True,
wandb_dir='./wandb',
wandb_key='3e614eb678063929b16c9b9aec557e2949d5a814',
already_fp16=False,
resume_dataset=False,
shuffle_dataset=True,
# distribute settings
deepspeed_activation_checkpointing=False,
num_checkpoints=1,
master_ip='localhost',
master_port=17750,
num_nodes=1,
num_gpus=1,
hostfile="./hostfile",
deepspeed_config="./deepspeed.json",
model_parallel_size=1,
training_script="train.py",
## Lora
lora=False,
lora_r= 8,
lora_alpha = 16,
lora_dropout = 0.05,
lora_target_modules= [
"wq",
"wv",
],
## TODO optim
adam_beta1=0.9,
adam_beta2=0.999,
yaml_config=None,
bmt_cpu_offload=True,
bmt_lr_decay_style='cosine',
bmt_loss_scale=1024.,
bmt_loss_scale_steps=1024,
## EnvTrainer Debug Only Flags
bmt_async_load=False,
bmt_pre_load=False,
pre_load_dir=None,
enable_sft_dataset_dir=None,
enable_sft_dataset_file=None,
enable_sft_dataset_val_file=None,
enable_sft_dataset=False,
enable_sft_dataset_text=False,
enable_sft_dataset_jsonl=False,
enable_sft_conversations_dataset=False,
enable_sft_conversations_dataset_v2=False,
enable_sft_conversations_dataset_v3=False,
enable_weighted_dataset_v2=False,
enable_flash_attn_models=False,
):
self.parser = argparse.ArgumentParser(description='Env args parser')
self.parser.add_argument('--env_type', default=env_type, help='the model will be trained')
self.parser.add_argument('--experiment_name', default=experiment_name, help='start training from saved checkpoint')
self.parser.add_argument('--model_name', default=model_name, help='start training from saved checkpoint')
self.parser.add_argument('--epochs', default=epochs, type=int, help='start training from saved checkpoint')
self.parser.add_argument('--batch_size', default=batch_size, type=int, help='start training from saved checkpoint')
self.parser.add_argument('--lr', default=lr, type=float, help='start training from saved checkpoint')
self.parser.add_argument('--warmup_start_lr', default=warmup_start_lr, type=float, help='start training from saved checkpoint')
self.parser.add_argument('--seed', default=seed, type=int, help='start training from saved checkpoint')
self.parser.add_argument('--fp16', default=fp16, type=str2bool, help='start training from saved checkpoint')
self.parser.add_argument('--pytorch_device', default=pytorch_device, help='start training from saved checkpoint')
self.parser.add_argument('--clip_grad', default=clip_grad, type=float, help='start training from saved checkpoint')
self.parser.add_argument('--checkpoint_activations', default=checkpoint_activations, type=str2bool, help='start training from saved checkpoint')
self.parser.add_argument('--gradient_accumulation_steps', default=gradient_accumulation_steps, type=int, help='start training from saved checkpoint')
self.parser.add_argument('--weight_decay', default=weight_decay, type=float, help='start training from saved checkpoint')
self.parser.add_argument('--eps', default=eps, type=float, help='start training from saved checkpoint')
self.parser.add_argument('--warm_up', default=warm_up, type=float, help='start training from saved checkpoint')
self.parser.add_argument('--warm_up_iters', default=warm_up_iters, type=int, help='start training from saved checkpoint')
self.parser.add_argument('--skip_iters', default=skip_iters, type=int, help='start training from saved checkpoint')
self.parser.add_argument('--log_interval', default=log_interval, type=int, help='start training from saved checkpoint')
self.parser.add_argument('--eval_interval', default=eval_interval, type=int, help='start training from saved checkpoint')
self.parser.add_argument('--save_interval', default=save_interval, type=int, help='start training from saved checkpoint')
self.parser.add_argument('--save_dir', default=save_dir, help='start training from saved checkpoint')
self.parser.add_argument('--load_dir', default=load_dir, help='start training from saved checkpoint')
self.parser.add_argument('--save_optim', default=save_optim, type=str2bool, help='start training from saved checkpoint')
self.parser.add_argument('--save_rng', default=save_rng, type=str2bool,help='start training from saved checkpoint')
self.parser.add_argument('--load_type', default=load_type, type=str,help='start training from saved checkpoint')
self.parser.add_argument('--load_optim', default=load_optim, type=str2bool,help='start training from saved checkpoint')
self.parser.add_argument('--load_rng', default=load_rng, type=str2bool, help='start training from saved checkpoint')
self.parser.add_argument('--tensorboard', default=tensorboard, type=str2bool, help='start training from saved checkpoint')
self.parser.add_argument('--tensorboard_dir', default=tensorboard_dir, help='start training from saved checkpoint')
self.parser.add_argument('--deepspeed_activation_checkpointing', default=deepspeed_activation_checkpointing, help='start training from saved checkpoint')
self.parser.add_argument('--num_checkpoints', default=num_checkpoints, help='start training from saved checkpoint')
self.parser.add_argument('--deepspeed_config', default=deepspeed_config, help='start training from saved checkpoint')
self.parser.add_argument('--model_parallel_size', default=model_parallel_size, type=int, help='start training from saved checkpoint')
self.parser.add_argument('--training_script', default=training_script, help='start training from saved checkpoint')
self.parser.add_argument('--hostfile', default=hostfile, help='start training from saved checkpoint')
self.parser.add_argument('--master_ip', default=master_ip, help='start training from saved checkpoint')
self.parser.add_argument('--master_port', default=master_port, type=int, help='start training from saved checkpoint')
self.parser.add_argument('--num_nodes', default=num_nodes, type=int, help='start training from saved checkpoint')
self.parser.add_argument('--num_gpus', default=num_gpus, type=int, help='start training from saved checkpoint')
self.parser.add_argument('--not_call_launch', action="store_true", help='start training from saved checkpoint')
self.parser.add_argument('--local-rank', default=0, type=int, help='start training from saved checkpoint')
self.parser.add_argument('--wandb', default=wandb, type=str2bool, help='whether to use wandb')
self.parser.add_argument('--wandb_dir', default=wandb_dir, type=str, help='wandb directory')
self.parser.add_argument('--wandb_key', default=wandb_key, type=str, help='wandb key')
self.parser.add_argument('--already_fp16', default=already_fp16, type=str2bool, help='whether already_fp16')
self.parser.add_argument('--resume_dataset', default=resume_dataset, type=str2bool, help='whether to resume dataset')
self.parser.add_argument('--shuffle_dataset', default=shuffle_dataset, type=str2bool, help='start training from saved checkpoint')
self.parser.add_argument('--adam_beta1', default=adam_beta1, type=float, help='adam beta1')
self.parser.add_argument('--adam_beta2', default=adam_beta2, type=float, help='adam beta2')
self.parser.add_argument('--bmt_cpu_offload', default=bmt_cpu_offload, type=str2bool, help='whther to enable cpu_offload in bmtrain')
self.parser.add_argument('--bmt_lr_decay_style', default=bmt_lr_decay_style, type=str, help='lr scheduler type in bmtrain')
self.parser.add_argument('--bmt_loss_scale', default=bmt_loss_scale, type=float, help='loss scale in bmtrain')
self.parser.add_argument('--bmt_loss_scale_steps', default=bmt_loss_scale_steps, type=int, help='loss scale steps in bmtrain')
self.parser.add_argument('--lora', default=lora, type=str2bool, help='Use lora')
self.parser.add_argument('--lora_r', default=lora_r, type=int, help='lora r value')
self.parser.add_argument('--lora_alpha', default=lora_alpha, type=float, help='lora alpha value')
self.parser.add_argument('--lora_dropout', default=lora_dropout, type=float, help='lora dropout value')
self.parser.add_argument('--lora_target_modules', default=lora_target_modules, help='lora_target_modules')
## TODO, Used in caller script, configs will be updated with yaml_config.
self.parser.add_argument("--yaml_config", default=yaml_config, type=str, help="yaml config file")
## EnvTrainer Debug Only
self.parser.add_argument('--bmt_async_load', default=bmt_async_load, type=str2bool, help='debug args')
self.parser.add_argument('--bmt_pre_load', default=bmt_pre_load, type=str2bool, help='debug args')
self.parser.add_argument('--pre_load_dir', default=pre_load_dir, help='start training from saved checkpoint')
self.parser.add_argument('--enable_sft_dataset_dir', default=enable_sft_dataset_dir, type=str, help='debug args')
self.parser.add_argument('--enable_sft_dataset_file', default=enable_sft_dataset_file, type=str, help='debug args')
self.parser.add_argument('--enable_sft_dataset_val_file', default=enable_sft_dataset_val_file, type=str, help='debug args')
self.parser.add_argument('--enable_sft_dataset', default=enable_sft_dataset, type=str2bool, help='debug args')
self.parser.add_argument('--enable_sft_dataset_text', default=enable_sft_dataset_text, type=str2bool, help='debug args')
self.parser.add_argument('--enable_sft_dataset_jsonl', default=enable_sft_dataset_jsonl, type=str2bool, help='debug args')
self.parser.add_argument('--enable_sft_conversations_dataset', default=enable_sft_conversations_dataset, type=str2bool, help='debug args')
self.parser.add_argument('--enable_sft_conversations_dataset_v2', default=enable_sft_conversations_dataset_v2, type=str2bool, help='debug args')
self.parser.add_argument('--enable_sft_conversations_dataset_v3', default=enable_sft_conversations_dataset_v3, type=str2bool, help='debug args')
self.parser.add_argument('--enable_weighted_dataset_v2', default=enable_weighted_dataset_v2, type=str2bool, help='debug args')
self.parser.add_argument('--IGNORE_INDEX', default=-100, type=int, help='start training from saved checkpoint')
self.parser.add_argument('--enable_flash_attn_models', default=enable_flash_attn_models, type=str2bool, help='debug args')
def add_arg(self, arg_name, default=None, type=str, help="", store_true=False):
if store_true:
self.parser.add_argument(f"--{arg_name}", action="store_true", help=help)
else :
self.parser.add_argument(f"--{arg_name}", default=default, type=type, help=help)
def parse_args(self):
args = self.parser.parse_args()
if args.env_type == "pytorch":
# not need the "not_call_launch" parameter
args.not_call_launch = True
print(args)
for arg in vars(args):
# change string format list to back to python list object
value = getattr(args, arg)
if isinstance(value, str):
value = value.strip("'\"")
if value[0] == '[' and value[-1] == ']':
value = value.strip("[] ").replace(" ", "")
value = value.split(",")
setattr(args,arg,value)
return args