refactor scripts/finetune.py into new cli modules (#550)
* refactor scripts/finetune.py into new cli modules * continue to support scripts/finetune.py * update readme with updated cli commands * Update scripts/finetune.py Co-authored-by: NanoCode012 <kevinvong@rocketmail.com> --------- Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>
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249
src/axolotl/cli/__init__.py
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249
src/axolotl/cli/__init__.py
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"""Prepare and train a model on a dataset. Can also infer from a model or merge lora"""
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import importlib
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import logging
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import os
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import random
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import sys
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from pathlib import Path
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from typing import Any, Dict, List, Optional, Union
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import torch
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import yaml
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# add src to the pythonpath so we don't need to pip install this
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from accelerate.commands.config import config_args
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from art import text2art
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from transformers import GenerationConfig, TextStreamer
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from axolotl.common.cli import TrainerCliArgs, load_model_and_tokenizer
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from axolotl.logging_config import configure_logging
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from axolotl.train import TrainDatasetMeta
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from axolotl.utils.config import normalize_config, validate_config
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from axolotl.utils.data import prepare_dataset
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from axolotl.utils.dict import DictDefault
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from axolotl.utils.distributed import is_main_process
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from axolotl.utils.models import load_tokenizer
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from axolotl.utils.tokenization import check_dataset_labels
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from axolotl.utils.wandb_ import setup_wandb_env_vars
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project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
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src_dir = os.path.join(project_root, "src")
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sys.path.insert(0, src_dir)
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configure_logging()
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LOG = logging.getLogger("axolotl.scripts")
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os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
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def print_axolotl_text_art(suffix=None):
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font = "nancyj"
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ascii_text = " axolotl"
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if suffix:
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ascii_text += f" x {suffix}"
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ascii_art = text2art(" axolotl", font=font)
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if is_main_process():
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print(ascii_art)
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def get_multi_line_input() -> Optional[str]:
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print("Give me an instruction (Ctrl + D to finish): ")
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instruction = ""
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for line in sys.stdin:
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instruction += line # pylint: disable=consider-using-join
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# instruction = pathlib.Path("/proc/self/fd/0").read_text()
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return instruction
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def do_merge_lora(
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*,
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cfg: DictDefault,
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cli_args: TrainerCliArgs,
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):
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model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
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safe_serialization = cfg.save_safetensors is True
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LOG.info("running merge of LoRA with base model")
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model = model.merge_and_unload()
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model.to(dtype=torch.float16)
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if cfg.local_rank == 0:
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LOG.info("saving merged model")
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model.save_pretrained(
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str(Path(cfg.output_dir) / "merged"),
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safe_serialization=safe_serialization,
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)
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tokenizer.save_pretrained(str(Path(cfg.output_dir) / "merged"))
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def do_inference(
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*,
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cfg: DictDefault,
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cli_args: TrainerCliArgs,
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):
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model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
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prompter = cli_args.prompter
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default_tokens = {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
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for token, symbol in default_tokens.items():
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# If the token isn't already specified in the config, add it
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if not (cfg.special_tokens and token in cfg.special_tokens):
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tokenizer.add_special_tokens({token: symbol})
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prompter_module = None
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if prompter:
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prompter_module = getattr(
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importlib.import_module("axolotl.prompters"), prompter
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)
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if cfg.landmark_attention:
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from axolotl.monkeypatch.llama_landmark_attn import set_model_mem_id
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set_model_mem_id(model, tokenizer)
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model.set_mem_cache_args(
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max_seq_len=255, mem_freq=50, top_k=5, max_cache_size=None
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)
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model = model.to(cfg.device)
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while True:
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print("=" * 80)
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# support for multiline inputs
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instruction = get_multi_line_input()
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if not instruction:
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return
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if prompter_module:
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prompt: str = next(
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prompter_module().build_prompt(instruction=instruction.strip("\n"))
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)
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else:
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prompt = instruction.strip()
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batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
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print("=" * 40)
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model.eval()
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with torch.no_grad():
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generation_config = GenerationConfig(
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repetition_penalty=1.1,
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max_new_tokens=1024,
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temperature=0.9,
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top_p=0.95,
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top_k=40,
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bos_token_id=tokenizer.bos_token_id,
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eos_token_id=tokenizer.eos_token_id,
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pad_token_id=tokenizer.pad_token_id,
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do_sample=True,
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use_cache=True,
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return_dict_in_generate=True,
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output_attentions=False,
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output_hidden_states=False,
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output_scores=False,
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)
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streamer = TextStreamer(tokenizer)
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generated = model.generate(
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inputs=batch["input_ids"].to(cfg.device),
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generation_config=generation_config,
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streamer=streamer,
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)
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print("=" * 40)
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print(tokenizer.decode(generated["sequences"].cpu().tolist()[0]))
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def choose_config(path: Path):
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yaml_files = list(path.glob("*.yml"))
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if not yaml_files:
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raise ValueError(
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"No YAML config files found in the specified directory. Are you using a .yml extension?"
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)
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if len(yaml_files) == 1:
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print(f"Using default YAML file '{yaml_files[0]}'")
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return yaml_files[0]
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print("Choose a YAML file:")
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for idx, file in enumerate(yaml_files):
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print(f"{idx + 1}. {file}")
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chosen_file = None
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while chosen_file is None:
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try:
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choice = int(input("Enter the number of your choice: "))
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if 1 <= choice <= len(yaml_files):
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chosen_file = yaml_files[choice - 1]
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else:
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print("Invalid choice. Please choose a number from the list.")
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except ValueError:
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print("Invalid input. Please enter a number.")
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return chosen_file
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def check_not_in(list1: List[str], list2: Union[Dict[str, Any], List[str]]) -> bool:
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return not any(el in list2 for el in list1)
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def load_cfg(config: Path = Path("examples/"), **kwargs):
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if Path(config).is_dir():
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config = choose_config(config)
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# load the config from the yaml file
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with open(config, encoding="utf-8") as file:
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cfg: DictDefault = DictDefault(yaml.safe_load(file))
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# if there are any options passed in the cli, if it is something that seems valid from the yaml,
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# then overwrite the value
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cfg_keys = cfg.keys()
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for k, _ in kwargs.items():
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# if not strict, allow writing to cfg even if it's not in the yml already
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if k in cfg_keys or not cfg.strict:
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# handle booleans
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if isinstance(cfg[k], bool):
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cfg[k] = bool(kwargs[k])
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else:
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cfg[k] = kwargs[k]
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validate_config(cfg)
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normalize_config(cfg)
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setup_wandb_env_vars(cfg)
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return cfg
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def load_datasets(
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*,
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cfg: DictDefault,
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cli_args: TrainerCliArgs,
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) -> TrainDatasetMeta:
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tokenizer = load_tokenizer(cfg)
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train_dataset, eval_dataset, total_num_steps = prepare_dataset(cfg, tokenizer)
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if cli_args.debug or cfg.debug:
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LOG.info("check_dataset_labels...")
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check_dataset_labels(
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train_dataset.select(
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[
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random.randrange(0, len(train_dataset) - 1) # nosec
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for _ in range(cli_args.debug_num_examples)
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]
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),
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tokenizer,
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num_examples=cli_args.debug_num_examples,
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text_only=cli_args.debug_text_only,
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)
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return TrainDatasetMeta(
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train_dataset=train_dataset,
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eval_dataset=eval_dataset,
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total_num_steps=total_num_steps,
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)
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def check_accelerate_default_config():
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if Path(config_args.default_yaml_config_file).exists():
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LOG.warning(
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f"accelerate config file found at {config_args.default_yaml_config_file}. This can lead to unexpected errors"
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)
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26
src/axolotl/cli/inference.py
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src/axolotl/cli/inference.py
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"""
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CLI to run inference on a trained model
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"""
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from pathlib import Path
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import fire
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import transformers
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from axolotl.cli import do_inference, load_cfg, print_axolotl_text_art
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from axolotl.common.cli import TrainerCliArgs
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def do_cli(config: Path = Path("examples/"), **kwargs):
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# pylint: disable=duplicate-code
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print_axolotl_text_art()
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parsed_cfg = load_cfg(config, **kwargs)
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parser = transformers.HfArgumentParser((TrainerCliArgs))
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parsed_cli_args, _ = parser.parse_args_into_dataclasses(
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return_remaining_strings=True
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)
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parsed_cli_args.inference = True
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do_inference(cfg=parsed_cfg, cli_args=parsed_cli_args)
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fire.Fire(do_cli)
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26
src/axolotl/cli/merge_lora.py
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26
src/axolotl/cli/merge_lora.py
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"""
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CLI to run merge a trained LoRA into a base model
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"""
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from pathlib import Path
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import fire
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import transformers
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from axolotl.cli import do_merge_lora, load_cfg, print_axolotl_text_art
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from axolotl.common.cli import TrainerCliArgs
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def do_cli(config: Path = Path("examples/"), **kwargs):
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# pylint: disable=duplicate-code
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print_axolotl_text_art()
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parsed_cfg = load_cfg(config, **kwargs)
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parser = transformers.HfArgumentParser((TrainerCliArgs))
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parsed_cli_args, _ = parser.parse_args_into_dataclasses(
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return_remaining_strings=True
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)
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parsed_cli_args.merge_lora = True
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do_merge_lora(cfg=parsed_cfg, cli_args=parsed_cli_args)
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fire.Fire(do_cli)
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41
src/axolotl/cli/shard.py
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src/axolotl/cli/shard.py
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"""
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CLI to shard a trained model into 10GiB chunks
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"""
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import logging
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from pathlib import Path
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import fire
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import transformers
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from axolotl.cli import load_cfg, print_axolotl_text_art
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from axolotl.common.cli import TrainerCliArgs, load_model_and_tokenizer
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from axolotl.utils.dict import DictDefault
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LOG = logging.getLogger("axolotl.scripts")
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def shard(
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*,
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cfg: DictDefault,
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cli_args: TrainerCliArgs,
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):
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model, _ = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
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safe_serialization = cfg.save_safetensors is True
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LOG.debug("Re-saving model w/ sharding")
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model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
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def do_cli(config: Path = Path("examples/"), **kwargs):
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# pylint: disable=duplicate-code
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print_axolotl_text_art()
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parsed_cfg = load_cfg(config, **kwargs)
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parser = transformers.HfArgumentParser((TrainerCliArgs))
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parsed_cli_args, _ = parser.parse_args_into_dataclasses(
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return_remaining_strings=True
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)
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parsed_cli_args.shard = True
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shard(cfg=parsed_cfg, cli_args=parsed_cli_args)
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fire.Fire(do_cli)
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35
src/axolotl/cli/train.py
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src/axolotl/cli/train.py
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"""
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CLI to run training on a model
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"""
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from pathlib import Path
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import fire
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import transformers
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from axolotl.cli import (
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check_accelerate_default_config,
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load_cfg,
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load_datasets,
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print_axolotl_text_art,
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)
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from axolotl.common.cli import TrainerCliArgs
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from axolotl.train import train
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def do_cli(config: Path = Path("examples/"), **kwargs):
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# pylint: disable=duplicate-code
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print_axolotl_text_art()
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parsed_cfg = load_cfg(config, **kwargs)
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check_accelerate_default_config()
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parser = transformers.HfArgumentParser((TrainerCliArgs))
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parsed_cli_args, _ = parser.parse_args_into_dataclasses(
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return_remaining_strings=True
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)
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dataset_meta = load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
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if parsed_cli_args.prepare_ds_only:
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return
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train(cfg=parsed_cfg, cli_args=parsed_cli_args, dataset_meta=dataset_meta)
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fire.Fire(do_cli)
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