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import argparse
import os
from pathlib import Path
from unsloth import PatchDPOTrainer
PatchDPOTrainer()
from typing import Any, List, Literal, Optional # noqa: E402
import torch # noqa
from datasets import concatenate_datasets, load_dataset # noqa: E402
from huggingface_hub import HfApi # noqa: E402
from huggingface_hub.utils import RepositoryNotFoundError # noqa: E402
from transformers import TextStreamer, TrainingArguments # noqa: E402
from trl import DPOConfig, DPOTrainer, SFTTrainer # noqa: E402
from unsloth import FastLanguageModel, is_bfloat16_supported # noqa: E402
from unsloth.chat_templates import get_chat_template # noqa: E402
alpaca_template = """Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{}
### Response:
{}"""
def load_model(
model_name: str,
max_seq_length: int,
load_in_4bit: bool,
lora_rank: int,
lora_alpha: int,
lora_dropout: float,
target_modules: List[str],
chat_template: str,
) -> tuple:
model, tokenizer = FastLanguageModel.from_pretrained(
model_name=model_name,
max_seq_length=max_seq_length,
load_in_4bit=load_in_4bit,
)
model = FastLanguageModel.get_peft_model(
model,
r=lora_rank,
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
target_modules=target_modules,
)
tokenizer = get_chat_template(
tokenizer,
chat_template=chat_template,
)
return model, tokenizer
def finetune(
finetuning_type: Literal["sft", "dpo"],
model_name: str,
output_dir: str,
dataset_huggingface_workspace: str,
max_seq_length: int = 2048,
load_in_4bit: bool = False,
lora_rank: int = 32,
lora_alpha: int = 32,
lora_dropout: float = 0.0,
target_modules: List[str] = ["q_proj", "k_proj", "v_proj", "up_proj", "down_proj", "o_proj", "gate_proj"], # noqa: B006
chat_template: str = "chatml",
learning_rate: float = 3e-4,
num_train_epochs: int = 3,
per_device_train_batch_size: int = 2,
gradient_accumulation_steps: int = 8,
beta: float = 0.5, # Only for DPO
is_dummy: bool = True,
) -> tuple:
model, tokenizer = load_model(
model_name, max_seq_length, load_in_4bit, lora_rank, lora_alpha, lora_dropout, target_modules, chat_template
)
EOS_TOKEN = tokenizer.eos_token
print(f"Setting EOS_TOKEN to {EOS_TOKEN}") # noqa
if is_dummy is True:
num_train_epochs = 1
print(f"Training in dummy mode. Setting num_train_epochs to '{num_train_epochs}'") # noqa
print(f"Training in dummy mode. Reducing dataset size to '400'.") # noqa
if finetuning_type == "sft":
def format_samples_sft(examples):
text = []
for instruction, output in zip(examples["instruction"], examples["output"], strict=False):
message = alpaca_template.format(instruction, output) + EOS_TOKEN
text.append(message)
return {"text": text}
dataset1 = load_dataset(f"{dataset_huggingface_workspace}/llmtwin", split="train")
dataset2 = load_dataset("mlabonne/FineTome-Alpaca-100k", split="train[:10000]")
dataset = concatenate_datasets([dataset1, dataset2])
if is_dummy:
dataset = dataset.select(range(400))
print(f"Loaded dataset with {len(dataset)} samples.") # noqa
dataset = dataset.map(format_samples_sft, batched=True, remove_columns=dataset.column_names)
dataset = dataset.train_test_split(test_size=0.05)
print("Training dataset example:") # noqa
print(dataset["train"][0]) # noqa
trainer = SFTTrainer(
model=model,
tokenizer=tokenizer,
train_dataset=dataset["train"],
eval_dataset=dataset["test"],
dataset_text_field="text",
max_seq_length=max_seq_length,
dataset_num_proc=2,
packing=True,
args=TrainingArguments(
learning_rate=learning_rate,
num_train_epochs=num_train_epochs,
per_device_train_batch_size=per_device_train_batch_size,
gradient_accumulation_steps=gradient_accumulation_steps,
fp16=not is_bfloat16_supported(),
bf16=is_bfloat16_supported(),
logging_steps=1,
optim="adamw_8bit",
weight_decay=0.01,
lr_scheduler_type="linear",
per_device_eval_batch_size=per_device_train_batch_size,
warmup_steps=10,
output_dir=output_dir,
report_to="comet_ml",
seed=0,
),
)
elif finetuning_type == "dpo":
PatchDPOTrainer()
def format_samples_dpo(example):
example["prompt"] = alpaca_template.format(example["prompt"], "")
example["chosen"] = example["chosen"] + EOS_TOKEN
example["rejected"] = example["rejected"] + EOS_TOKEN
return {"prompt": example["prompt"], "chosen": example["chosen"], "rejected": example["rejected"]}
dataset = load_dataset(f"{dataset_huggingface_workspace}/llmtwin-dpo", split="train")
if is_dummy:
dataset = dataset.select(range(400))
print(f"Loaded dataset with {len(dataset)} samples.") # noqa
dataset = dataset.map(format_samples_dpo)
dataset = dataset.train_test_split(test_size=0.05)
print("Training dataset example:") # noqa
print(dataset["train"][0]) # noqa
trainer = DPOTrainer(
model=model,
ref_model=None,
tokenizer=tokenizer,
beta=beta,
train_dataset=dataset["train"],
eval_dataset=dataset["test"],
max_length=max_seq_length // 2,
max_prompt_length=max_seq_length // 2,
args=DPOConfig(
learning_rate=learning_rate,
num_train_epochs=num_train_epochs,
per_device_train_batch_size=per_device_train_batch_size,
gradient_accumulation_steps=gradient_accumulation_steps,
fp16=not is_bfloat16_supported(),
bf16=is_bfloat16_supported(),
optim="adamw_8bit",
weight_decay=0.01,
lr_scheduler_type="linear",
per_device_eval_batch_size=per_device_train_batch_size,
warmup_steps=10,
output_dir=output_dir,
eval_steps=0.2,
logging_steps=1,
report_to="comet_ml",
seed=0,
),
)
else:
raise ValueError("Invalid finetuning_type. Choose 'sft' or 'dpo'.")
trainer.train()
return model, tokenizer
def inference(
model: Any,
tokenizer: Any,
prompt: str = "Write a paragraph to introduce supervised fine-tuning.",
max_new_tokens: int = 256,
) -> None:
model = FastLanguageModel.for_inference(model)
message = alpaca_template.format(prompt, "")
inputs = tokenizer([message], return_tensors="pt").to("cuda")
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer=text_streamer, max_new_tokens=max_new_tokens, use_cache=True)
def save_model(model: Any, tokenizer: Any, output_dir: str, push_to_hub: bool = False, repo_id: Optional[str] = None):
model.save_pretrained_merged(output_dir, tokenizer, save_method="merged_16bit")
if push_to_hub and repo_id:
print(f"Saving model to '{repo_id}'") # noqa
model.push_to_hub_merged(repo_id, tokenizer, save_method="merged_16bit")
def check_if_huggingface_model_exists(model_id: str, default_value: str = "mlabonne/TwinLlama-3.1-8B") -> str:
api = HfApi()
try:
api.model_info(model_id)
except RepositoryNotFoundError:
print(f"Model '{model_id}' does not exist.") # noqa
model_id = default_value
print(f"Defaulting to '{model_id}'") # noqa
print("Train your own 'TwinLlama-3.1-8B' to avoid this behavior.") # noqa
return model_id
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--num_train_epochs", type=int, default=3)
parser.add_argument("--per_device_train_batch_size", type=int, default=2)
parser.add_argument("--learning_rate", type=float, default=3e-4)
parser.add_argument("--dataset_huggingface_workspace", type=str, default="mlabonne")
parser.add_argument("--model_output_huggingface_workspace", type=str, default="mlabonne")
parser.add_argument("--is_dummy", type=bool, default=False, help="Flag to reduce the dataset size for testing")
parser.add_argument(
"--finetuning_type",
type=str,
choices=["sft", "dpo"],
default="sft",
help="Parameter to choose the finetuning stage.",
)
parser.add_argument("--output_data_dir", type=str, default=os.environ["SM_OUTPUT_DATA_DIR"])
parser.add_argument("--model_dir", type=str, default=os.environ["SM_MODEL_DIR"])
parser.add_argument("--n_gpus", type=str, default=os.environ["SM_NUM_GPUS"])
args = parser.parse_args()
print(f"Num training epochs: '{args.num_train_epochs}'") # noqa
print(f"Per device train batch size: '{args.per_device_train_batch_size}'") # noqa
print(f"Learning rate: {args.learning_rate}") # noqa
print(f"Datasets will be loaded from Hugging Face workspace: '{args.dataset_huggingface_workspace}'") # noqa
print(f"Models will be saved to Hugging Face workspace: '{args.model_output_huggingface_workspace}'") # noqa
print(f"Training in dummy mode? '{args.is_dummy}'") # noqa
print(f"Finetuning type: '{args.finetuning_type}'") # noqa
print(f"Output data dir: '{args.output_data_dir}'") # noqa
print(f"Model dir: '{args.model_dir}'") # noqa
print(f"Number of GPUs: '{args.n_gpus}'") # noqa
if args.finetuning_type == "sft":
print("Starting SFT training...") # noqa
base_model_name = "meta-llama/Meta-Llama-3.1-8B"
print(f"Training from base model '{base_model_name}'") # noqa
output_dir_sft = Path(args.model_dir) / "output_sft"
model, tokenizer = finetune(
finetuning_type="sft",
model_name=base_model_name,
output_dir=str(output_dir_sft),
dataset_huggingface_workspace=args.dataset_huggingface_workspace,
num_train_epochs=args.num_train_epochs,
per_device_train_batch_size=args.per_device_train_batch_size,
learning_rate=args.learning_rate,
)
inference(model, tokenizer)
sft_output_model_repo_id = f"{args.model_output_huggingface_workspace}/TwinLlama-3.1-8B"
save_model(model, tokenizer, "model_sft", push_to_hub=True, repo_id=sft_output_model_repo_id)
elif args.finetuning_type == "dpo":
print("Starting DPO training...") # noqa
sft_base_model_repo_id = f"{args.model_output_huggingface_workspace}/TwinLlama-3.1-8B"
sft_base_model_repo_id = check_if_huggingface_model_exists(sft_base_model_repo_id)
print(f"Training from base model '{sft_base_model_repo_id}'") # noqa
output_dir_dpo = Path(args.model_dir) / "output_dpo"
model, tokenizer = finetune(
finetuning_type="dpo",
model_name=sft_base_model_repo_id,
output_dir=str(output_dir_dpo),
dataset_huggingface_workspace=args.dataset_huggingface_workspace,
num_train_epochs=1,
per_device_train_batch_size=args.per_device_train_batch_size,
learning_rate=2e-6,
is_dummy=args.is_dummy,
)
inference(model, tokenizer)
dpo_output_model_repo_id = f"{args.model_output_huggingface_workspace}/TwinLlama-3.1-8B-DPO"
save_model(model, tokenizer, "model_dpo", push_to_hub=True, repo_id=dpo_output_model_repo_id)
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