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import os
import sys
from typing import Any
from pathlib import Path
import hydra
import torch
from hydra.utils import instantiate
from accelerate.logging import get_logger
from omegaconf import DictConfig, OmegaConf
from torch import nn
import time
from datasets import load_dataset, concatenate_datasets
from torch.utils.data import Dataset
_PACKAGE_ROOT = Path(__file__).resolve().parents[2]
if str(_PACKAGE_ROOT) not in sys.path:
sys.path.insert(0, str(_PACKAGE_ROOT))
from trainer.accelerators.base_accelerator import BaseAccelerator
from trainer.configs.configs import TrainerConfig, instantiate_with_cfg
logger = get_logger(__name__)
os.environ["TOKENIZERS_PARALLELISM"] = "false"
if torch.cuda.is_available():
# Prefer math attention for stability on large Flux training runs.
try:
torch.backends.cuda.enable_flash_sdp(False)
torch.backends.cuda.enable_mem_efficient_sdp(False)
torch.backends.cuda.enable_math_sdp(True)
print("[train.py] Disabled flash/mem-efficient SDP kernels; using math SDP backend.")
except Exception as ex:
print(f"[train.py] Could not configure SDP backend flags: {ex}")
def _unwrap_model(model: nn.Module) -> nn.Module:
return model.module if hasattr(model, "module") else model
def _get_logit_scale_metric(model: nn.Module) -> dict[str, float]:
model_ref = _unwrap_model(model)
logit_scale = getattr(model_ref, "logit_scale", None)
# In ZeRO-3, some ranks can hold empty shards for this scalar parameter.
if not isinstance(logit_scale, torch.Tensor) or logit_scale.numel() == 0:
return {}
return {"logit_scale": logit_scale.detach().float().exp().item()}
def load_dataloaders(cfg: DictConfig) -> Any:
dataloaders = {}
for split in [cfg.train_split_name, cfg.valid_split_name, cfg.test_split_name]:
dataset = instantiate_with_cfg(cfg, split=split)
should_shuffle = split == cfg.train_split_name
dataloaders[split] = torch.utils.data.DataLoader(
dataset,
shuffle=should_shuffle,
batch_size=cfg.batch_size,
collate_fn=dataset.collate_fn,
num_workers=cfg.num_workers
)
return dataloaders
def load_optimizer(cfg: DictConfig, model: nn.Module):
optimizer = instantiate(cfg, model=model)
return optimizer
def load_scheduler(cfg: DictConfig, optimizer):
scheduler = instantiate_with_cfg(cfg, optimizer=optimizer)
return scheduler
def load_task(cfg: DictConfig, accelerator: BaseAccelerator):
task = instantiate_with_cfg(cfg, accelerator=accelerator)
return task
def verify_or_write_config(cfg: TrainerConfig):
os.makedirs(cfg.output_dir, exist_ok=True)
yaml_path = os.path.join(cfg.output_dir, "config.yaml")
if not os.path.exists(yaml_path):
OmegaConf.save(cfg, yaml_path, resolve=True)
with open(yaml_path) as f:
existing_config = f.read()
# if existing_config != OmegaConf.to_yaml(cfg, resolve=True):
# raise ValueError(f"Config was not saved correctly - {yaml_path}")
logger.info(f"Config can be found in {yaml_path}")
@hydra.main(version_base=None, config_path="../conf", config_name="config")
def main(cfg: TrainerConfig) -> None:
accelerator = instantiate_with_cfg(cfg.accelerator)
if cfg.debug.activate and accelerator.is_main_process:
import pydevd_pycharm
pydevd_pycharm.settrace('localhost', port=cfg.debug.port, stdoutToServer=True, stderrToServer=True)
if accelerator.is_main_process:
verify_or_write_config(cfg)
logger.info(f"Loading task")
task = load_task(cfg.task, accelerator)
logger.info(f"Loading model")
model = instantiate_with_cfg(cfg.model)
use_data_parallel = os.environ.get("USE_DATA_PARALLEL", "0") == "1"
if use_data_parallel and torch.cuda.is_available() and torch.cuda.device_count() > 1:
logger.info(f"Using torch.nn.DataParallel with {torch.cuda.device_count()} GPUs")
model = nn.DataParallel(model)
logger.info(f"Loading criterion")
criterion = instantiate_with_cfg(cfg.criterion)
logger.info(f"Loading optimizer")
optimizer = load_optimizer(cfg.optimizer, model)
logger.info(f"Loading lr scheduler")
lr_scheduler = load_scheduler(cfg.lr_scheduler, optimizer)
logger.info(f"Loading dataloaders")
split2dataloader = load_dataloaders(cfg.dataset) # train, val, test
dataloaders = list(split2dataloader.values())
model, optimizer, lr_scheduler, *dataloaders = accelerator.prepare(model, optimizer, lr_scheduler, *dataloaders)
split2dataloader = dict(zip(split2dataloader.keys(), dataloaders))
accelerator.load_state_if_needed()
accelerator.recalc_train_length_after_prepare(len(split2dataloader[cfg.dataset.train_split_name]))
accelerator.init_training(cfg)
def evaluate(trigger: str):
model.eval()
logger.info("========== EVAL START (%s) ==========" % trigger)
logger.info(f"*** Evaluating {cfg.dataset.valid_split_name} ***")
metrics = task.evaluate(model, criterion, split2dataloader[cfg.dataset.valid_split_name])
accelerator.update_metrics(metrics)
logger.info(f"*** Evaluating {cfg.dataset.test_split_name} ***")
metrics = task.evaluate(model, criterion, split2dataloader[cfg.dataset.test_split_name])
metrics = {f"{cfg.dataset.test_split_name}_{k}": v for k, v in metrics.items()}
accelerator.update_metrics(metrics)
logger.info("========== EVAL END (%s) ==========" % trigger)
logger.info(f"task: {task.__class__.__name__}")
logger.info(f"model: {model.__class__.__name__}")
logger.info(f"num. model params: {int(sum(p.numel() for p in model.parameters()) // 1e6)}M")
logger.info(
f"num. model trainable params: {int(sum(p.numel() for p in model.parameters() if p.requires_grad) // 1e6)}M")
logger.info(f"criterion: {criterion.__class__.__name__}")
logger.info(f"num. train examples: {len(split2dataloader[cfg.dataset.train_split_name].dataset)}")
logger.info(f"num. valid examples: {len(split2dataloader[cfg.dataset.valid_split_name].dataset)}")
logger.info(f"num. test examples: {len(split2dataloader[cfg.dataset.test_split_name].dataset)}")
metrics = _get_logit_scale_metric(model)
if metrics:
accelerator.update_metrics(metrics)
logger.info(
"========== TRAIN LOOP START (eval_on_start=%s, validate_steps=%s, progress_log_interval=%s) ==========",
accelerator.cfg.eval_on_start,
accelerator.cfg.validate_steps,
getattr(accelerator.cfg, "progress_log_interval", "n/a"),
)
for epoch in range(accelerator.cfg.num_epochs):
train_loss, lr = 0.0, 0.0
for step, batch in enumerate(split2dataloader[cfg.dataset.train_split_name]):
if accelerator.should_skip(epoch, step):
accelerator.update_progbar_step()
continue
if accelerator.should_eval():
trigger = "initial" if accelerator.global_step == 0 else f"periodic@gstep={accelerator.global_step}"
evaluate(trigger)
metrics = _get_logit_scale_metric(model)
if metrics:
accelerator.update_metrics(metrics)
if accelerator.should_save():
accelerator.save_checkpoint()
model.train()
with accelerator.accumulate(model):
loss = task.train_step(model, criterion, batch)
avg_loss = accelerator.gather(loss).mean().item()
accelerator.backward(loss)
if accelerator.sync_gradients:
accelerator.clip_grad_norm_(model.parameters())
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
train_loss += avg_loss / accelerator.cfg.gradient_accumulation_steps
if accelerator.sync_gradients:
accelerator.update_global_step(train_loss)
train_loss = 0.0
if accelerator.global_step > 1:
lr = lr_scheduler.get_last_lr()[0]
accelerator.update_step(avg_loss, lr)
if accelerator.should_end():
evaluate(f"final@gstep={accelerator.global_step}")
metrics = _get_logit_scale_metric(model)
if metrics:
accelerator.update_metrics(metrics)
accelerator.save_checkpoint()
break
if accelerator.should_end():
break
accelerator.update_epoch()
accelerator.wait_for_everyone()
accelerator.unwrap_and_save(model)
accelerator.end_training()
if __name__ == '__main__':
main()
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