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47a66f0 | 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 | import json
import os
from typing import Any
import hydra
import torch
from hydra.utils import instantiate
from accelerate.logging import get_logger
from omegaconf import DictConfig, OmegaConf
from torch import nn
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"
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)
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)
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():
model.eval()
end_of_train_dataloader = accelerator.gradient_state.end_of_dataloader
logger.info(f"*** Evaluating {cfg.dataset.valid_split_name} ***")
metrics = task.evaluate(model, criterion, split2dataloader[cfg.dataset.valid_split_name])
accelerator.update_metrics(metrics)
accelerator.gradient_state.end_of_dataloader = end_of_train_dataloader
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)}")
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():
evaluate()
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 > 0:
lr = lr_scheduler.get_last_lr()[0]
accelerator.update_step(avg_loss, lr)
if accelerator.should_end():
evaluate()
accelerator.save_checkpoint()
break
if accelerator.should_end():
break
accelerator.update_epoch()
accelerator.wait_for_everyone()
accelerator.load_best_checkpoint()
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)
accelerator.unwrap_and_save(model)
accelerator.end_training()
if __name__ == '__main__':
main()
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