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import json
import os
from typing import List, Dict, Tuple
import fsspec
import hydra
import lightning as L
import omegaconf
import rich.syntax
import rich.tree
import torch
import algo
import dataloader as dataloader
import utils
from text_metrics import evaluate_text_metrics
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(diffusion_model, config, tokenizer):
if "hf" in config.algo.backbone:
return diffusion_model(config, tokenizer=tokenizer).to("cuda")
model = diffusion_model.load_from_checkpoint(
config.eval.checkpoint_path, tokenizer=tokenizer, config=config
)
return model
@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, vocab_offset=False):
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(diffusion_model, config, logger, tokenizer):
logger.info("Starting Sample Eval.")
if hasattr(config.data, "vocab_file"):
vocab_offset = True
new_vocab = json.load(open(f'{config.data.cache_dir}/{config.data.vocab_file}', 'r'))
tokenizer.add_tokens(new_vocab)
model = _load_from_checkpoint(
diffusion_model=diffusion_model, config=config, tokenizer=tokenizer
)
# model.metrics.gen_ppl.reset()
# model.metrics.sample_entropy.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
all_samples = []
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)
model.metrics.record_entropy(samples)
text_samples = model.tokenizer.batch_decode(samples)
model.metrics.record_generative_perplexity(
text_samples, config.model.length, model.device
)
all_samples.extend(list(text_samples))
# pdb.set_trace()
# with open('samples.txt', 'w') as f:
# for sample in text_samples:
# f.write(f"{sample}\n")
# Evaluate text metrics
# train_texts = open("/home/patrick/duo/train_texts.txt", "r").readlines()
# text_metrics = evaluate_text_metrics(all_samples, train_texts)
# print(text_metrics)
if config.data.train == 'text8':
train_ds, valid_ds = dataloader.get_dataloaders(config, tokenizer)
train_texts = []
# check if train_texts.txt exists
if os.path.exists("/home/patrick/duo/train_texts.txt"):
print("Loading training texts from train_texts.txt for text metrics...")
train_texts = open("/home/patrick/duo/train_texts.txt", "r").readlines()
else:
print("Loading training texts for text metrics...")
for batch in tqdm(train_ds):
batch_texts = tokenizer.batch_decode(batch["input_ids"], skip_special_tokens=True)
train_texts.extend(batch_texts)
with open("/home/patrick/duo/train_texts.txt", "w") as f:
for text in train_texts:
f.write(f"{text}\n")
text_metrics = evaluate_text_metrics(all_samples, train_texts)
print(text_metrics)
else:
generative_ppl = 0.0
entropy = 0.0
if not config.sampling.semi_ar:
generative_ppl = model.metrics.gen_ppl.compute().item()
entropy = model.metrics.sample_entropy.compute().item()
print("Generative perplexity:", generative_ppl)
print("Sample entropy:", entropy)
samples_path = config.eval.generated_samples_path
with fsspec.open(samples_path, "w") as f:
json.dump(
{
"generative_ppl": generative_ppl,
"entropy": entropy,
"generated_seqs": all_samples,
},
f,
indent=4,
)
print("Samples saved at:", samples_path)
def _eval_ppl(diffusion_model, config, logger, tokenizer):
logger.info("Starting Perplexity Eval.")
model = _load_from_checkpoint(
diffusion_model=diffusion_model, 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(diffusion_model, 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)
if hasattr(config.data, "vocab_file"):
vocab_offset = True
new_vocab = json.load(open(f'{config.data.cache_dir}/{config.data.vocab_file}', 'r'))
tokenizer.add_tokens(new_vocab)
else:
vocab_offset = False
_print_batch(train_ds, valid_ds, tokenizer, vocab_offset=vocab_offset)
if config.training.finetune_path != "":
assert utils.fsspec_exists(config.training.finetune_path)
model = diffusion_model.load_from_checkpoint(
config.training.finetune_path, tokenizer=tokenizer, config=config
)
else:
model = diffusion_model(config, tokenizer=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.algo.name == "ar":
diffusion_model = algo.AR
elif config.algo.name == "mdlm":
diffusion_model = algo.MDLM
elif config.algo.name == "duo_base":
diffusion_model = algo.DUO_BASE
elif config.algo.name == "d3pm":
diffusion_model = algo.D3PMAbsorb
elif config.algo.name == "sedd":
diffusion_model = algo.SEDDAbsorb
elif config.algo.name == "duo":
diffusion_model = algo.DUO
elif config.algo.name == "distillation":
diffusion_model = algo.Distillation
elif config.algo.name == "ot-finetune":
diffusion_model = algo.OptimalTransportFinetune
elif config.algo.name == "candi":
diffusion_model = algo.CANDI
else:
raise ValueError(f"Invalid algorithm name: {config.algo.name}")
kwargs = {
"diffusion_model": diffusion_model,
"config": config,
"tokenizer": tokenizer,
"logger": logger,
}
if config.mode == "sample_eval":
_generate_samples(**kwargs)
elif config.mode == "ppl_eval":
_eval_ppl(**kwargs)
else:
_train(**kwargs)
if __name__ == "__main__":
main()
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