scripts/finetune.py

"""Prepare and train a model on a dataset. Can also infer from a model or merge lora"""

import importlib
import logging
import os
import random
import sys
from pathlib import Path
from typing import Any, Dict, List, Optional, Union

import fire
import torch
import transformers
import yaml

# add src to the pythonpath so we don't need to pip install this
from art import text2art
from transformers import GenerationConfig, TextStreamer

from axolotl.common.cli import TrainerCliArgs, load_model_and_tokenizer
from axolotl.logging_config import configure_logging
from axolotl.train import TrainDatasetMeta, train
from axolotl.utils.config import normalize_config, validate_config
from axolotl.utils.data import prepare_dataset
from axolotl.utils.dict import DictDefault
from axolotl.utils.distributed import is_main_process
from axolotl.utils.models import load_tokenizer
from axolotl.utils.tokenization import check_dataset_labels
from axolotl.utils.wandb import setup_wandb_env_vars

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.scripts")

os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"


def print_axolotl_text_art(suffix=None):
    font = "nancyj"
    ascii_text = "  axolotl"
    if suffix:
        ascii_text += f"  x  {suffix}"
    ascii_art = text2art(" axolotl", font=font)

    if is_main_process():
        print(ascii_art)


def get_multi_line_input() -> Optional[str]:
    print("Give me an instruction (Ctrl + D to finish): ")
    instruction = ""
    for line in sys.stdin:
        instruction += line  # pylint: disable=consider-using-join
    # instruction = pathlib.Path("/proc/self/fd/0").read_text()
    return instruction


def do_merge_lora(
    *,
    cfg: DictDefault,
    cli_args: TrainerCliArgs,
):
    model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
    safe_serialization = cfg.save_safetensors is True

    LOG.info("running merge of LoRA with base model")
    model = model.merge_and_unload()
    model.to(dtype=torch.float16)

    if cfg.local_rank == 0:
        LOG.info("saving merged model")
        model.save_pretrained(
            str(Path(cfg.output_dir) / "merged"),
            safe_serialization=safe_serialization,
        )
        tokenizer.save_pretrained(str(Path(cfg.output_dir) / "merged"))


def shard(
    *,
    cfg: DictDefault,
    cli_args: TrainerCliArgs,
):
    model, _ = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
    safe_serialization = cfg.save_safetensors is True
    LOG.debug("Re-saving model w/ sharding")
    model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)


def do_inference(
    *,
    cfg: DictDefault,
    cli_args: TrainerCliArgs,
):
    model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
    prompter = cli_args.prompter
    default_tokens = {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}

    for token, symbol in default_tokens.items():
        # If the token isn't already specified in the config, add it
        if not (cfg.special_tokens and token in cfg.special_tokens):
            tokenizer.add_special_tokens({token: symbol})

    prompter_module = None
    if prompter:
        prompter_module = getattr(
            importlib.import_module("axolotl.prompters"), prompter
        )

    if cfg.landmark_attention:
        from axolotl.monkeypatch.llama_landmark_attn import set_model_mem_id

        set_model_mem_id(model, tokenizer)
        model.set_mem_cache_args(
            max_seq_len=255, mem_freq=50, top_k=5, max_cache_size=None
        )

    model = model.to(cfg.device)

    while True:
        print("=" * 80)
        # support for multiline inputs
        instruction = get_multi_line_input()
        if not instruction:
            return
        if prompter_module:
            prompt: str = next(
                prompter_module().build_prompt(instruction=instruction.strip("\n"))
            )
        else:
            prompt = instruction.strip()
        batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)

        print("=" * 40)
        model.eval()
        with torch.no_grad():
            generation_config = GenerationConfig(
                repetition_penalty=1.1,
                max_new_tokens=1024,
                temperature=0.9,
                top_p=0.95,
                top_k=40,
                bos_token_id=tokenizer.bos_token_id,
                eos_token_id=tokenizer.eos_token_id,
                pad_token_id=tokenizer.pad_token_id,
                do_sample=True,
                use_cache=True,
                return_dict_in_generate=True,
                output_attentions=False,
                output_hidden_states=False,
                output_scores=False,
            )
            streamer = TextStreamer(tokenizer)
            generated = model.generate(
                inputs=batch["input_ids"].to(cfg.device),
                generation_config=generation_config,
                streamer=streamer,
            )
        print("=" * 40)
        print(tokenizer.decode(generated["sequences"].cpu().tolist()[0]))


def choose_config(path: Path):
    yaml_files = list(path.glob("*.yml"))

    if not yaml_files:
        raise ValueError(
            "No YAML config files found in the specified directory. Are you using a .yml extension?"
        )

    if len(yaml_files) == 1:
        print(f"Using default YAML file '{yaml_files[0]}'")
        return yaml_files[0]

    print("Choose a YAML file:")
    for idx, file in enumerate(yaml_files):
        print(f"{idx + 1}. {file}")

    chosen_file = None
    while chosen_file is None:
        try:
            choice = int(input("Enter the number of your choice: "))
            if 1 <= choice <= len(yaml_files):
                chosen_file = yaml_files[choice - 1]
            else:
                print("Invalid choice. Please choose a number from the list.")
        except ValueError:
            print("Invalid input. Please enter a number.")

    return chosen_file


def check_not_in(list1: List[str], list2: Union[Dict[str, Any], List[str]]) -> bool:
    return not any(el in list2 for el in list1)


def load_cfg(config: Path = Path("examples/"), **kwargs):
    if Path(config).is_dir():
        config = choose_config(config)

    # load the config from the yaml file
    with open(config, encoding="utf-8") as file:
        cfg: DictDefault = DictDefault(yaml.safe_load(file))
    # if there are any options passed in the cli, if it is something that seems valid from the yaml,
    # then overwrite the value
    cfg_keys = cfg.keys()
    for k, _ in kwargs.items():
        # if not strict, allow writing to cfg even if it's not in the yml already
        if k in cfg_keys or not cfg.strict:
            # handle booleans
            if isinstance(cfg[k], bool):
                cfg[k] = bool(kwargs[k])
            else:
                cfg[k] = kwargs[k]

    validate_config(cfg)

    normalize_config(cfg)

    setup_wandb_env_vars(cfg)
    return cfg


def load_datasets(
    *,
    cfg: DictDefault,
    cli_args: TrainerCliArgs,
) -> TrainDatasetMeta:
    tokenizer = load_tokenizer(cfg)

    train_dataset, eval_dataset, total_num_steps = prepare_dataset(cfg, tokenizer)

    if cli_args.debug or cfg.debug:
        LOG.info("check_dataset_labels...")
        check_dataset_labels(
            train_dataset.select(
                [
                    random.randrange(0, len(train_dataset) - 1)  # nosec
                    for _ in range(cli_args.debug_num_examples)
                ]
            ),
            tokenizer,
            num_examples=cli_args.debug_num_examples,
            text_only=cli_args.debug_text_only,
        )

    return TrainDatasetMeta(
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        total_num_steps=total_num_steps,
    )


def do_cli(config: Path = Path("examples/"), **kwargs):
    print_axolotl_text_art()
    parsed_cfg = load_cfg(config, **kwargs)
    parser = transformers.HfArgumentParser((TrainerCliArgs))
    parsed_cli_args, _ = parser.parse_args_into_dataclasses(
        return_remaining_strings=True
    )
    if parsed_cli_args.inference:
        do_inference(cfg=parsed_cfg, cli_args=parsed_cli_args)
    elif parsed_cli_args.merge_lora:
        do_merge_lora(cfg=parsed_cfg, cli_args=parsed_cli_args)
    elif parsed_cli_args.shard:
        shard(cfg=parsed_cfg, cli_args=parsed_cli_args)
    else:
        dataset_meta = load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
        if parsed_cli_args.prepare_ds_only:
            return
        train(cfg=parsed_cfg, cli_args=parsed_cli_args, dataset_meta=dataset_meta)


if __name__ == "__main__":
    fire.Fire(do_cli)