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"""Prepare and train a model on a dataset. Can also infer from a model or merge lora"""
import logging
import os
import signal
import sys
from dataclasses import dataclass
from pathlib import Path
from typing import Optional
import torch
# add src to the pythonpath so we don't need to pip install this
from datasets import Dataset
from optimum.bettertransformer import BetterTransformer
from axolotl.common.cli import TrainerCliArgs
from axolotl.logging_config import configure_logging
from axolotl.utils.dict import DictDefault
from axolotl.utils.models import load_model, load_tokenizer
from axolotl.utils.trainer import setup_trainer
project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
src_dir = os.path.join(project_root, "src")
sys.path.insert(0, src_dir)
configure_logging()
LOG = logging.getLogger("axolotl.train")
@dataclass
class TrainDatasetMeta:
"""
dataclass to capture the dataset specific options for training
"""
train_dataset: Dataset
eval_dataset: Optional[Dataset] = None
total_num_steps: Optional[int] = None
def train(
*,
cfg: DictDefault,
cli_args: TrainerCliArgs,
dataset_meta: TrainDatasetMeta,
):
# load the tokenizer first
LOG.info(f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}")
tokenizer = load_tokenizer(cfg)
train_dataset = dataset_meta.train_dataset
eval_dataset = dataset_meta.eval_dataset
total_num_steps = dataset_meta.total_num_steps
# Load the model and tokenizer
LOG.info("loading model and (optionally) peft_config...")
model, peft_config = load_model(cfg, tokenizer, inference=cli_args.inference)
safe_serialization = cfg.save_safetensors is True
if cfg.resume_from_checkpoint is None and cfg.auto_resume_from_checkpoints:
possible_checkpoints = [
str(cp) for cp in Path(cfg.output_dir).glob("checkpoint-*")
]
if len(possible_checkpoints) > 0:
sorted_paths = sorted(
possible_checkpoints,
key=lambda path: int(path.split("-")[-1]),
)
cfg.resume_from_checkpoint = sorted_paths[-1]
LOG.info(
f"Using Auto-resume functionality to start with checkpoint at {cfg.resume_from_checkpoint}"
)
resume_from_checkpoint = cfg.resume_from_checkpoint
trainer = setup_trainer(
cfg, train_dataset, eval_dataset, model, tokenizer, total_num_steps
)
model.config.use_cache = False
# go ahead and presave, so we have the adapter config available to inspect
if peft_config:
LOG.info(f"Pre-saving adapter config to {cfg.output_dir}")
peft_config.save_pretrained(cfg.output_dir)
# additionally presave the tokenizer and model configs
if not Path(cfg.output_dir).is_dir():
os.makedirs(cfg.output_dir, exist_ok=True)
tokenizer.save_pretrained(str(Path(cfg.output_dir)))
model.config.save_pretrained(str(Path(cfg.output_dir)))
# In case we want to stop early with ctrl+c, this is a nice to have to save the pretrained model
if cfg.local_rank == 0:
def terminate_handler(_, __, model):
if cfg.flash_optimum:
model = BetterTransformer.reverse(model)
model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
sys.exit(0)
signal.signal(
signal.SIGINT, lambda signum, frame: terminate_handler(signum, frame, model)
)
LOG.info("Starting trainer...")
if cfg.group_by_length:
LOG.info("hang tight... sorting dataset for group_by_length")
if cfg.flash_optimum:
with torch.backends.cuda.sdp_kernel(
enable_flash=True, enable_math=True, enable_mem_efficient=True
):
trainer.train(resume_from_checkpoint=resume_from_checkpoint)
else:
trainer.train(resume_from_checkpoint=resume_from_checkpoint)
LOG.info(f"Training Completed!!! Saving pre-trained model to {cfg.output_dir}")
if cfg.relora_steps:
if cfg.adapter == "lora" and not (cfg.load_in_4bit or cfg.load_in_8bit):
model = model.merge_and_unload()
else:
# final model weights have already been saved by `ReLoRACallback.on_train_end`
return model, tokenizer
# TODO do we need this fix? https://huggingface.co/docs/accelerate/usage_guides/fsdp#saving-and-loading
# only save on rank 0, otherwise it corrupts output on multi-GPU when multiple processes attempt to write the same file
if cfg.fsdp:
trainer.save_model(cfg.output_dir)
elif cfg.local_rank == 0:
if cfg.flash_optimum:
model = BetterTransformer.reverse(model)
model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
return model, tokenizer
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