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refactor scripts/finetune.py into new cli modules #550

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merged 4 commits into from
Sep 15, 2023
Merged

refactor scripts/finetune.py into new cli modules #550

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f82d21c
refactor scripts/finetune.py into new cli modules
winglian Sep 11, 2023
b049704
continue to support scripts/finetune.py
winglian Sep 13, 2023
4d3ee48
update readme with updated cli commands
winglian Sep 13, 2023
ce4208a
Update scripts/finetune.py
winglian Sep 14, 2023
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22 changes: 11 additions & 11 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -76,11 +76,11 @@ pip3 install -e .[flash-attn]
pip3 install -U git+https://github.com/huggingface/peft.git

# finetune lora
accelerate launch scripts/finetune.py examples/openllama-3b/lora.yml
accelerate launch -m axolotl.cli.train examples/openllama-3b/lora.yml

# inference
accelerate launch scripts/finetune.py examples/openllama-3b/lora.yml \
--inference --lora_model_dir="./lora-out"
accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
--lora_model_dir="./lora-out"
```

## Installation
Expand Down Expand Up @@ -670,14 +670,14 @@ strict:

Run
```bash
accelerate launch scripts/finetune.py your_config.yml
accelerate launch -m axolotl.cli.train your_config.yml
```

#### Multi-GPU

You can optionally pre-tokenize dataset with the following before finetuning:
```bash
CUDA_VISIBLE_DEVICES="" accelerate ... --prepare_ds_only
CUDA_VISIBLE_DEVICES="" accelerate launch -m axolotl.cli.train your_config.yml --prepare_ds_only
```

##### Config
Expand Down Expand Up @@ -716,30 +716,30 @@ Pass the appropriate flag to the train command:

- Pretrained LORA:
```bash
--inference --lora_model_dir="./lora-output-dir"
python -m axolotl.cli.inference examples/your_config.yml --lora_model_dir="./lora-output-dir"
```
- Full weights finetune:
```bash
--inference --base_model="./completed-model"
python -m axolotl.cli.inference examples/your_config.yml --base_model="./completed-model"
```
- Full weights finetune w/ a prompt from a text file:
```bash
cat /tmp/prompt.txt | python scripts/finetune.py configs/your_config.yml \
--base_model="./completed-model" --inference --prompter=None --load_in_8bit=True
cat /tmp/prompt.txt | python -m axolotl.cli.inference examples/your_config.yml \
--base_model="./completed-model" --prompter=None --load_in_8bit=True
```

### Merge LORA to base

Add below flag to train command above

```bash
--merge_lora --lora_model_dir="./completed-model" --load_in_8bit=False --load_in_4bit=False
python3 -m axolotl.cli.merge_lora examples/your_config.yml --lora_model_dir="./completed-model" --load_in_8bit=False --load_in_4bit=False
```

If you run out of CUDA memory, you can try to merge in system RAM with

```bash
CUDA_VISIBLE_DEVICES="" python3 scripts/finetune.py ...
CUDA_VISIBLE_DEVICES="" python3 -m axolotl.cli.merge_lora ...
```

## Common Errors 🧰
Expand Down
271 changes: 18 additions & 253 deletions scripts/finetune.py
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Original file line number Diff line number Diff line change
@@ -1,269 +1,34 @@
"""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 accelerate.commands.config import config_args
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"))
from axolotl.cli import (
check_accelerate_default_config,
do_inference,
do_merge_lora,
load_cfg,
load_datasets,
print_axolotl_text_art,
)
from axolotl.cli.shard import shard
from axolotl.common.cli import TrainerCliArgs
from axolotl.train import train

LOG = logging.getLogger("axolotl.scripts.finetune")

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,
def do_cli(config: Path = Path("examples/"), **kwargs):
print_axolotl_text_art()
LOG.warning(
str(
PendingDeprecationWarning(
"scripts/finetune.py will be replaced with calling axolotl.cli.train"
)
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 check_accelerate_default_config():
if Path(config_args.default_yaml_config_file).exists():
LOG.warning(
f"accelerate config file found at {config_args.default_yaml_config_file}. This can lead to unexpected errors"
)


def do_cli(config: Path = Path("examples/"), **kwargs):
print_axolotl_text_art()
parsed_cfg = load_cfg(config, **kwargs)
check_accelerate_default_config()
parser = transformers.HfArgumentParser((TrainerCliArgs))
Expand Down
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