Spaces:
Sleeping
Sleeping
File size: 5,795 Bytes
2d9a728 |
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 |
import logging
import os
import sys
import json
import torch.distributed as dist
from os.path import dirname, join
from utils.config import Config
from utils.distributed import init_distributed_mode, is_main_process
from utils.logger import setup_logger
logger = logging.getLogger(__name__)
def setup_config():
"""Conbine yaml config and command line config with OmegaConf.
Also converts types, e.g., `'None'` (str) --> `None` (None)
"""
config = Config.get_config()
if config.debug:
config.wandb.enable = False
return config
def setup_evaluate_config(config):
"""setup evaluation default settings, e.g., disable wandb"""
assert config.evaluate
config.wandb.enable = False
if config.output_dir is None:
config.output_dir = join(dirname(config.pretrained_path), "eval")
return config
def setup_output_dir(output_dir, excludes=["code"]):
"""ensure not overwritting an exisiting/non-empty output dir"""
if not os.path.exists(output_dir):
os.makedirs(output_dir, exist_ok=False)
else:
existing_dirs_files = os.listdir(output_dir) # list
remaining = set(existing_dirs_files) - set(excludes)
remaining = [e for e in remaining if "slurm" not in e]
remaining = [e for e in remaining if ".out" not in e]
# assert len(remaining) == 0, f"remaining dirs or files: {remaining}"
logger.warn(f"remaining dirs or files: {remaining}")
def setup_deepspeed_zero_config(stage):
# We currently set ZeRO based on stage:
if stage == 1:
return {"stage": 1, "reduce_bucket_size": 5e8}
if stage == 2:
return {
"stage": 2,
"contiguous_gradients": False,
"overlap_comm": False,
"reduce_scatter": True,
"reduce_bucket_size": 5e8,
"allgather_bucket_size": 5e8,
"offload_optimizer": {
"device": "cpu"
},
}
if stage == 3:
return {
"stage": 3,
"contiguous_gradients": True,
"stage3_max_live_parameters": 1e9,
"stage3_max_reuse_distance": 1e9,
"stage3_prefetch_bucket_size": 1e7,
"stage3_param_persistence_threshold": 1e5,
"reduce_bucket_size": 1e7,
"sub_group_size": 1e9,
"offload_optimizer": {
"device": "cpu"
},
"offload_param": {
"device": "cpu"
}
}
raise ValueError("Wrong stage for deepspeed {}".format(stage.stage))
def setup_deepspeed_config(config):
config.deepspeed_config = os.path.join(config.output_dir, "deepspeed_config.json")
opts = config.optimizer
logger.info(f'Write deepspeed config to {config.deepspeed_config}')
if not is_main_process():
return config
os.makedirs(config.output_dir, exist_ok=True)
with open(config.deepspeed_config, mode="w") as writer:
ds_config = {
"train_batch_size": config.batch_size * dist.get_world_size(),
"train_micro_batch_size_per_gpu": config.batch_size,
"steps_per_print": 100,
"optimizer": {
"type": "Adam",
"adam_w_mode": True,
"params": {
"lr": opts.lr,
"weight_decay": opts.weight_decay,
"bias_correction": True,
"betas": [
opts.opt_betas[0],
opts.opt_betas[1],
],
"eps": 1e-8
}
}
}
if config.deepspeed.stage != 0:
ds_config["zero_optimization"] = setup_deepspeed_zero_config(config.deepspeed.stage)
if config.use_half_precision:
if config.get('use_bf16', False):
ds_config["bf16"] = {
"enabled": True
}
else:
ds_config["fp16"] = {
"enabled": True,
"auto_cast": False,
"loss_scale": 0,
"initial_scale_power": 16,
"loss_scale_window": 1000,
"hysteresis": 2,
"consecutive_hysteresis": False,
"min_loss_scale": 1
}
else:
assert config.deepspeed.stage == 0, "You must use fp16 or bf16 when using ZERO!!!"
# if config.get("max_grad_norm", -1) > 0:
# ds_config.update({"gradient_clipping", config.max_grad_norm})
if opts.get("max_grad_norm", -1) > 0:
ds_config["gradient_clipping"] = opts.max_grad_norm
writer.write(json.dumps(ds_config, indent=2))
return config
def setup_main():
"""
Setup config, logger, output_dir, etc.
Shared for pretrain and all downstream tasks.
"""
# try:
config = setup_config()
if hasattr(config, "evaluate") and config.evaluate:
config = setup_evaluate_config(config)
init_distributed_mode(config)
if hasattr(config, "deepspeed") and config.deepspeed.enable:
config = setup_deepspeed_config(config)
# except Exception as e:
# print(f"\033[31m NODE NAME: {os.environ['SLURMD_NODENAME']} is not OK \033[0m")
# logger.info(f"NODE NAME: {os.environ['SLURMD_NODENAME']} is not OK")
# raise ValueError
if is_main_process():
setup_output_dir(config.output_dir, excludes=["code"])
setup_logger(output=config.output_dir, color=True, name="vindlu")
logger.info(f"config: {Config.pretty_text(config)}")
Config.dump(config, os.path.join(config.output_dir, "config.json"))
dist.barrier()
return config
|