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7bef20f | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 | """Training script for VibeToken.
Reference:
https://github.com/huggingface/open-muse
"""
import math
import os
import sys
from pathlib import Path
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir))
sys.path.append(parent_dir)
from accelerate.utils import set_seed
from accelerate import Accelerator
import torch
import wandb
from omegaconf import OmegaConf
from utils.logger import setup_logger
from utils.train_utils import (
get_config, create_pretrained_tokenizer,
create_model_and_loss_module,
create_optimizer, create_lr_scheduler, create_dataloader,
create_evaluator, auto_resume, save_checkpoint,
train_one_epoch)
def main():
workspace = os.environ.get('WORKSPACE', '')
if workspace:
torch.hub.set_dir(workspace + "/models/hub")
config = get_config()
# Enable TF32 on Ampere GPUs.
if config.training.enable_tf32:
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = False
output_dir = config.experiment.output_dir
os.makedirs(output_dir, exist_ok=True)
config.experiment.logging_dir = os.path.join(output_dir, "logs")
# Whether logging to Wandb or Tensorboard.
tracker = "tensorboard"
if config.training.enable_wandb:
tracker = "wandb"
accelerator = Accelerator(
gradient_accumulation_steps=config.training.gradient_accumulation_steps,
mixed_precision=config.training.mixed_precision,
log_with=tracker,
project_dir=config.experiment.logging_dir,
split_batches=False,
)
logger = setup_logger(name="VibeToken", log_level="INFO",
output_file=f"{output_dir}/log{accelerator.process_index}.txt")
if accelerator.is_main_process:
if config.training.enable_wandb:
wandb_config = config.training.get("wandb", {})
wandb_project = wandb_config.get("project", config.experiment.project)
wandb_entity = wandb_config.get("entity", None)
wandb_name = wandb_config.get("name", config.experiment.name)
wandb_tags = list(wandb_config.get("tags", []))
wandb_notes = wandb_config.get("notes", None)
wandb_resume_id = wandb_config.get("resume_id", None)
wandb_init_kwargs = {
"wandb": {
"name": wandb_name,
"dir": output_dir,
"resume": "allow",
}
}
if wandb_entity:
wandb_init_kwargs["wandb"]["entity"] = wandb_entity
if wandb_tags:
wandb_init_kwargs["wandb"]["tags"] = wandb_tags
if wandb_notes:
wandb_init_kwargs["wandb"]["notes"] = wandb_notes
if wandb_resume_id:
wandb_init_kwargs["wandb"]["id"] = wandb_resume_id
accelerator.init_trackers(
project_name=wandb_project,
config=OmegaConf.to_container(config, resolve=True),
init_kwargs=wandb_init_kwargs,
)
logger.info(f"WandB initialized - Project: {wandb_project}, Name: {wandb_name}")
else:
accelerator.init_trackers(config.experiment.name)
config_path = Path(output_dir) / "config.yaml"
logger.info(f"Saving config to {config_path}")
OmegaConf.save(config, config_path)
logger.info(f"Config:\n{OmegaConf.to_yaml(config)}")
# If passed along, set the training seed now.
if config.training.seed is not None:
set_seed(config.training.seed, device_specific=True)
accelerator.wait_for_everyone()
# Create pretrained tokenizer in a synchronized manner
if config.model.vq_model.is_legacy:
if accelerator.is_main_process:
logger.info("Creating pretrained tokenizer on main process...")
accelerator.wait_for_everyone()
pretrained_tokenizer = create_pretrained_tokenizer(config, accelerator)
accelerator.wait_for_everyone()
if accelerator.is_main_process:
logger.info("Pretrained tokenizer creation completed.")
else:
pretrained_tokenizer = None
if accelerator.is_main_process:
logger.info("Creating model and loss module...")
accelerator.wait_for_everyone()
model, ema_model, loss_module = create_model_and_loss_module(
config, logger, accelerator, model_type="vibetoken")
accelerator.wait_for_everyone()
if accelerator.is_main_process:
logger.info("Model creation completed.")
optimizer, discriminator_optimizer = create_optimizer(config, logger, model, loss_module, model_type="vibetoken")
lr_scheduler, discriminator_lr_scheduler = create_lr_scheduler(
config, logger, accelerator, optimizer, discriminator_optimizer)
if accelerator.is_main_process:
logger.info("Creating dataloaders...")
train_dataloader, eval_dataloader = create_dataloader(config, logger, accelerator)
accelerator.wait_for_everyone()
# Set up evaluator.
if accelerator.is_main_process:
logger.info("Setting up evaluator...")
evaluator = create_evaluator(config, logger, accelerator)
# Prepare everything with accelerator.
logger.info("Preparing model, optimizer and dataloaders")
# The dataloader are already aware of distributed training, so we don't need to prepare them.
if config.model.vq_model.is_legacy:
if config.model.vq_model.finetune_decoder:
model, loss_module, optimizer, discriminator_optimizer, lr_scheduler, discriminator_lr_scheduler = accelerator.prepare(
model, loss_module, optimizer, discriminator_optimizer, lr_scheduler, discriminator_lr_scheduler
)
else:
model, optimizer, lr_scheduler = accelerator.prepare(
model, optimizer, lr_scheduler
)
else:
model, loss_module, optimizer, discriminator_optimizer, lr_scheduler, discriminator_lr_scheduler = accelerator.prepare(
model, loss_module, optimizer, discriminator_optimizer, lr_scheduler, discriminator_lr_scheduler
)
if config.training.use_ema:
ema_model.to(accelerator.device)
total_batch_size_without_accum = config.training.per_gpu_batch_size * accelerator.num_processes
num_batches = math.ceil(
config.experiment.max_train_examples / total_batch_size_without_accum)
num_update_steps_per_epoch = math.ceil(num_batches / config.training.gradient_accumulation_steps)
num_train_epochs = math.ceil(config.training.max_train_steps / num_update_steps_per_epoch)
# Start training.
logger.info("***** Running training *****")
logger.info(f" Num training steps = {config.training.max_train_steps}")
logger.info(f" Gradient Accumulation steps = {config.training.gradient_accumulation_steps}")
logger.info(f" Instantaneous batch size per gpu = { config.training.per_gpu_batch_size}")
logger.info(f""" Total train batch size (w. parallel, distributed & accumulation) = {(
config.training.per_gpu_batch_size *
accelerator.num_processes *
config.training.gradient_accumulation_steps)}""")
global_step = 0
first_epoch = 0
global_step, first_epoch = auto_resume(
config, logger, accelerator, ema_model, num_update_steps_per_epoch,
strict=True)
for current_epoch in range(first_epoch, num_train_epochs):
accelerator.print(f"Epoch {current_epoch}/{num_train_epochs-1} started.")
global_step = train_one_epoch(config, logger, accelerator,
model, ema_model, loss_module,
optimizer, discriminator_optimizer,
lr_scheduler, discriminator_lr_scheduler,
train_dataloader, eval_dataloader,
evaluator,
global_step,
pretrained_tokenizer=pretrained_tokenizer,
model_type="vibetoken")
# Stop training if max steps is reached.
if global_step >= config.training.max_train_steps:
accelerator.print(
f"Finishing training: Global step is >= Max train steps: {global_step} >= {config.training.max_train_steps}"
)
break
accelerator.wait_for_everyone()
# Save checkpoint at the end of training.
save_checkpoint(model, output_dir, accelerator, global_step, logger=logger)
# Save the final trained checkpoint
if accelerator.is_main_process:
model = accelerator.unwrap_model(model)
if config.training.use_ema:
ema_model.copy_to(model.parameters())
model.save_pretrained_weight(output_dir)
if accelerator.is_main_process and config.training.enable_wandb:
wandb.finish()
logger.info("WandB run finished")
accelerator.end_training()
if __name__ == "__main__":
main() |