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# run_clm.py
import logging
import os
import sys
import datasets
import transformers
from transformers import (
Trainer,
default_data_collator,
is_torch_tpu_available,
set_seed,
)
from transformers.trainer_callback import TrainerState
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
from sdlm.run_pretrain import filter_by_length
from .arguments import get_args
from .data.data_utils import load_data, tokenize_data_new
from .models import load_model
from .utils import (
get_last_checkpoint_with_beaker_preemption,
is_nfs_available,
is_weka_available,
resolve_last_checkpoint_vs_resume_from_checkpoint,
set_hf_home,
set_pretraining_dataset,
)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.25.0")
require_version(
"datasets>=2.0.0",
"To fix: pip install -r examples/pytorch/language-modeling/requirements.txt",
)
logger = logging.getLogger(__name__)
# set environment variables
set_hf_home()
os.environ["TOKENIZERS_PARALLELISM"] = "false"
def main():
# parse args
model_args, data_args, training_args, diffusion_args = get_args()
set_pretraining_dataset(data_args)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
+ f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
)
logger.info(f"Training/evaluation parameters {training_args}")
# Detecting last checkpoint.
last_checkpoint = get_last_checkpoint_with_beaker_preemption(training_args)
# load model
tokenizer, model = load_model(
model_args, data_args, training_args, diffusion_args, logger
)
assert model.config.pad_token_id is not None
# Set seed before initializing model.
set_seed(training_args.seed)
if training_args.do_train:
raw_datasets = load_data(data_args, model_args)
train_dataset = tokenize_data_new(
data_args, tokenizer, raw_datasets, training_args
)["train"]
if data_args.max_train_samples is not None:
max_train_samples = min(len(train_dataset), data_args.max_train_samples)
train_dataset = train_dataset.select(range(max_train_samples))
if data_args.min_train_seq_length != 0:
train_dataset = train_dataset.filter(
filter_by_length(
data_args.min_train_seq_length, model.config.pad_token_id
)
)
if data_args.shuffle and data_args.streaming:
train_dataset = train_dataset.shuffle(
seed=training_args.seed, buffer_size=10_000
)
elif data_args.shuffle:
train_dataset = train_dataset.shuffle(seed=training_args.seed)
# NOTE: modifications for clm
train_dataset = train_dataset.map(
lambda x: {**x, "labels": x["input_ids"]},
remove_columns=["special_tokens_mask"],
)
if training_args.do_eval:
# default to c4
if is_weka_available():
data_file_path = "/data/input/jaket/c4_subset"
elif is_nfs_available():
data_file_path = (
"/net/nfs.cirrascale/allennlp/jaket/simplex-diffusion/c4_subset"
)
else:
# yale
data_file_path = "/home/jt856/documents/simplex-diffusion/raw/c4_subset"
c4_raw_dataset = datasets.IterableDatasetDict(
{
"validation": datasets.load_dataset(
"json",
data_files=os.path.join(
data_file_path, "c4-validation.00000-of-00008.json"
),
)["train"]
}
)
c4_tokenized_datasets = tokenize_data_new(
data_args, tokenizer, c4_raw_dataset, training_args
)
eval_dataset = c4_tokenized_datasets["validation"]
if data_args.max_eval_samples is not None:
max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
eval_dataset = eval_dataset.select(range(max_eval_samples))
if data_args.min_eval_seq_length != 0:
eval_dataset = eval_dataset.filter(
filter_by_length(
data_args.min_eval_seq_length, model.config.pad_token_id
),
num_proc=data_args.preprocessing_num_workers,
)
# NOTE: modifications for clm
eval_dataset = eval_dataset.map(
lambda x: {**x, "labels": x["input_ids"]},
remove_columns=["special_tokens_mask"],
)
def preprocess_logits_for_metrics(logits, labels):
if isinstance(logits, tuple):
# Depending on the model and config, logits may contain extra tensors,
# like past_key_values, but logits always come first
logits = logits[0]
return logits.argmax(dim=-1)
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset if training_args.do_train else None,
eval_dataset=eval_dataset if training_args.do_eval else None,
tokenizer=tokenizer,
# Data collator will default to DataCollatorWithPadding, so we change it.
data_collator=default_data_collator,
compute_metrics=None,
preprocess_logits_for_metrics=preprocess_logits_for_metrics
if training_args.do_eval and not is_torch_tpu_available()
else None,
)
# Training
if training_args.do_train:
checkpoint = resolve_last_checkpoint_vs_resume_from_checkpoint(
last_checkpoint,
training_args.resume_from_checkpoint,
)
train_result = trainer.train(resume_from_checkpoint=checkpoint)
trainer.save_model() # Saves the tokenizer too for easy upload
metrics = train_result.metrics
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
# Evaluation
if training_args.do_eval:
if training_args.load_states_in_eval_from_model_path:
trainer._load_from_checkpoint(model_args.model_name_or_path)
trainer.state = TrainerState.load_from_json(
os.path.join(model_args.model_name_or_path, "trainer_state.json")
)
trainer._load_rng_state(model_args.model_name_or_path)
logger.info("*** Evaluate ***")
metrics = trainer.evaluate()
max_eval_samples = (
data_args.max_eval_samples
if data_args.max_eval_samples is not None
else len(eval_dataset)
)
metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
if __name__ == "__main__":
main()
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