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import fsspec
import hydra
import lightning as L
import omegaconf
import rich.syntax
import rich.tree
import torch
import dataloader
import diffusion
import utils
omegaconf.OmegaConf.register_new_resolver(
'cwd', os.getcwd)
omegaconf.OmegaConf.register_new_resolver(
'device_count', torch.cuda.device_count)
omegaconf.OmegaConf.register_new_resolver(
'eval', eval)
omegaconf.OmegaConf.register_new_resolver(
'div_up', lambda x, y: (x + y - 1) // y)
def _load_from_checkpoint(config, tokenizer):
if 'hf' in config.backbone:
return diffusion.Diffusion(
config, tokenizer=tokenizer).to('cuda')
return diffusion.Diffusion.load_from_checkpoint(
config.eval.checkpoint_path,
tokenizer=tokenizer,
config=config)
@L.pytorch.utilities.rank_zero_only
def _print_config(
config: omegaconf.DictConfig,
resolve: bool = True,
save_cfg: bool = True) -> None:
"""Prints content of DictConfig using Rich library and its tree structure.
Args:
config (DictConfig): Configuration composed by Hydra.
resolve (bool): Whether to resolve reference fields of DictConfig.
save_cfg (bool): Whether to save the configuration tree to a file.
"""
style = 'dim'
tree = rich.tree.Tree('CONFIG', style=style, guide_style=style)
fields = config.keys()
for field in fields:
branch = tree.add(field, style=style, guide_style=style)
config_section = config.get(field)
branch_content = str(config_section)
if isinstance(config_section, omegaconf.DictConfig):
branch_content = omegaconf.OmegaConf.to_yaml(
config_section, resolve=resolve)
branch.add(rich.syntax.Syntax(branch_content, 'yaml'))
rich.print(tree)
if save_cfg:
with fsspec.open(
'{}/config_tree.txt'.format(
config.checkpointing.save_dir), 'w') as fp:
rich.print(tree, file=fp)
@L.pytorch.utilities.rank_zero_only
def _print_batch(train_ds, valid_ds, tokenizer, k=64):
for dl_type, dl in [
('train', train_ds), ('valid', valid_ds)]:
print(f'Printing {dl_type} dataloader batch.')
batch = next(iter(dl))
print('Batch input_ids.shape', batch['input_ids'].shape)
first = batch['input_ids'][0, :k]
last = batch['input_ids'][0, -k:]
print(f'First {k} tokens:', tokenizer.decode(first))
print('ids:', first)
print(f'Last {k} tokens:', tokenizer.decode(last))
print('ids:', last)
def generate_samples(config, logger, tokenizer):
logger.info('Generating samples.')
model = _load_from_checkpoint(config=config,
tokenizer=tokenizer)
model.gen_ppl_metric.reset()
if config.eval.disable_ema:
logger.info('Disabling EMA.')
model.ema = None
stride_length = config.sampling.stride_length
num_strides = config.sampling.num_strides
for _ in range(config.sampling.num_sample_batches):
if config.sampling.semi_ar:
_, intermediate_samples, _ = model.restore_model_and_semi_ar_sample(
stride_length=stride_length,
num_strides=num_strides,
dt=1 / config.sampling.steps)
text_samples = intermediate_samples[-1]
# Note: Samples generated using semi-ar method
# need to to be processed before computing generative perplexity
# since these samples contain numerous <|endoftext|> tokens
# and diffusion.compute_generative_perplexity() discards
# any text after the first EOS token.
else:
samples = model.restore_model_and_sample(
num_steps=config.sampling.steps)
text_samples = model.tokenizer.batch_decode(samples)
model.compute_generative_perplexity(text_samples)
print('Text samples:', text_samples)
if not config.sampling.semi_ar:
print('Generative perplexity:',
model.gen_ppl_metric.compute())
return text_samples
def _ppl_eval(config, logger, tokenizer):
logger.info('Starting Zero Shot Eval.')
model = _load_from_checkpoint(config=config,
tokenizer=tokenizer)
if config.eval.disable_ema:
logger.info('Disabling EMA.')
model.ema = None
wandb_logger = None
if config.get('wandb', None) is not None:
wandb_logger = L.pytorch.loggers.WandbLogger(
config=omegaconf.OmegaConf.to_object(config),
** config.wandb)
callbacks = []
if 'callbacks' in config:
for _, callback in config.callbacks.items():
callbacks.append(hydra.utils.instantiate(callback))
trainer = hydra.utils.instantiate(
config.trainer,
default_root_dir=os.getcwd(),
callbacks=callbacks,
strategy=hydra.utils.instantiate(config.strategy),
logger=wandb_logger)
_, valid_ds = dataloader.get_dataloaders(
config, tokenizer, skip_train=True, valid_seed=config.seed)
trainer.validate(model, valid_ds)
def _train(config, logger, tokenizer):
logger.info('Starting Training.')
wandb_logger = None
if config.get('wandb', None) is not None:
wandb_logger = L.pytorch.loggers.WandbLogger(
config=omegaconf.OmegaConf.to_object(config),
** config.wandb)
if (config.checkpointing.resume_from_ckpt
and config.checkpointing.resume_ckpt_path is not None
and utils.fsspec_exists(
config.checkpointing.resume_ckpt_path)):
ckpt_path = config.checkpointing.resume_ckpt_path
else:
ckpt_path = None
# Lightning callbacks
callbacks = []
if 'callbacks' in config:
for _, callback in config.callbacks.items():
callbacks.append(hydra.utils.instantiate(callback))
train_ds, valid_ds = dataloader.get_dataloaders(
config, tokenizer)
_print_batch(train_ds, valid_ds, tokenizer)
model = diffusion.Diffusion(
config, tokenizer=valid_ds.tokenizer)
trainer = hydra.utils.instantiate(
config.trainer,
default_root_dir=os.getcwd(),
callbacks=callbacks,
strategy=hydra.utils.instantiate(config.strategy),
logger=wandb_logger)
trainer.fit(model, train_ds, valid_ds, ckpt_path=ckpt_path)
@hydra.main(version_base=None, config_path='configs',
config_name='config')
def main(config):
"""Main entry point for training."""
L.seed_everything(config.seed)
_print_config(config, resolve=True, save_cfg=True)
logger = utils.get_logger(__name__)
tokenizer = dataloader.get_tokenizer(config)
if config.mode == 'sample_eval':
generate_samples(config, logger, tokenizer)
elif config.mode == 'ppl_eval':
_ppl_eval(config, logger, tokenizer)
else:
_train(config, logger, tokenizer)
if __name__ == '__main__':
main() |