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# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# 0. imports
import os
from dataclasses import dataclass, field
from typing import Optional
import torch
from accelerate import Accelerator
from datasets import Dataset, load_dataset
from peft import LoraConfig
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
from trl import DPOConfig, DPOTrainer
# Define and parse arguments.
@dataclass
class ScriptArguments:
"""
The arguments for the DPO training script.
"""
# data parameters
beta: Optional[float] = field(default=0.1, metadata={"help": "the beta parameter for DPO loss"})
# training parameters
model_name_or_path: Optional[str] = field(
default="../sft/results/final_checkpoint",
metadata={"help": "the location of the SFT model name or path"},
)
learning_rate: Optional[float] = field(default=5e-4, metadata={"help": "optimizer learning rate"})
lr_scheduler_type: Optional[str] = field(default="cosine", metadata={"help": "the lr scheduler type"})
warmup_steps: Optional[int] = field(default=100, metadata={"help": "the number of warmup steps"})
weight_decay: Optional[float] = field(default=0.05, metadata={"help": "the weight decay"})
optimizer_type: Optional[str] = field(default="paged_adamw_32bit", metadata={"help": "the optimizer type"})
per_device_train_batch_size: Optional[int] = field(default=4, metadata={"help": "train batch size per device"})
per_device_eval_batch_size: Optional[int] = field(default=1, metadata={"help": "eval batch size per device"})
gradient_accumulation_steps: Optional[int] = field(
default=4, metadata={"help": "the number of gradient accumulation steps"}
)
gradient_checkpointing: Optional[bool] = field(
default=True, metadata={"help": "whether to use gradient checkpointing"}
)
gradient_checkpointing_use_reentrant: Optional[bool] = field(
default=False, metadata={"help": "whether to use reentrant for gradient checkpointing"}
)
lora_alpha: Optional[float] = field(default=16, metadata={"help": "the lora alpha parameter"})
lora_dropout: Optional[float] = field(default=0.05, metadata={"help": "the lora dropout parameter"})
lora_r: Optional[int] = field(default=8, metadata={"help": "the lora r parameter"})
max_prompt_length: Optional[int] = field(default=512, metadata={"help": "the maximum prompt length"})
max_length: Optional[int] = field(default=1024, metadata={"help": "the maximum sequence length"})
max_steps: Optional[int] = field(default=1000, metadata={"help": "max number of training steps"})
logging_steps: Optional[int] = field(default=10, metadata={"help": "the logging frequency"})
save_steps: Optional[int] = field(default=100, metadata={"help": "the saving frequency"})
eval_steps: Optional[int] = field(default=100, metadata={"help": "the evaluation frequency"})
output_dir: Optional[str] = field(default="./results", metadata={"help": "the output directory"})
log_freq: Optional[int] = field(default=1, metadata={"help": "the logging frequency"})
load_in_4bit: Optional[bool] = field(default=True, metadata={"help": "whether to load the model in 4bit"})
model_dtype: Optional[str] = field(
default="float16", metadata={"help": "model_dtype[float16, bfloat16, float] for loading."}
)
# instrumentation
report_to: Optional[str] = field(
default="wandb",
metadata={
"help": 'The list of integrations to report the results and logs to. Supported platforms are `"azure_ml"`,'
'`"comet_ml"`, `"mlflow"`, `"neptune"`, `"tensorboard"`,`"clearml"` and `"wandb"`. '
'Use `"all"` to report to all integrations installed, `"none"` for no integrations.'
},
)
# debug argument for distributed training
ignore_bias_buffers: Optional[bool] = field(
default=False,
metadata={
"help": "fix for DDP issues with LM bias/mask buffers - invalid scalar type,`inplace operation. See"
"https://github.com/huggingface/transformers/issues/22482#issuecomment-1595790992"
},
)
seed: Optional[int] = field(
default=0, metadata={"help": "Random seed that will be set at the beginning of training."}
)
def get_stack_exchange_paired(
data_dir: str = "data/rl",
cache_dir: Optional[str] = None,
num_proc=24,
) -> Dataset:
"""Load the stack-exchange-paired dataset from Hugging Face and convert it to the necessary format.
The dataset is converted to a dictionary with the following structure:
{
'prompt': list[str],
'chosen': list[str],
'rejected': list[str],
}
Prompts are structured as follows:
"Question: " + <prompt> + "\n\nAnswer: "
"""
dataset = load_dataset(
"lvwerra/stack-exchange-paired",
split="train",
cache_dir=cache_dir,
data_dir=data_dir,
verification_mode="no_checks",
)
original_columns = dataset.column_names
def return_prompt_and_responses(samples) -> dict[str, str]:
return {
"prompt": ["Question: " + question + "\n\nAnswer: " for question in samples["question"]],
"chosen": samples["response_j"],
"rejected": samples["response_k"],
}
return dataset.map(
return_prompt_and_responses,
batched=True,
num_proc=num_proc,
remove_columns=original_columns,
)
if __name__ == "__main__":
parser = HfArgumentParser(ScriptArguments)
script_args = parser.parse_args_into_dataclasses()[0]
set_seed(script_args.seed)
# 1. load a pretrained model
torch_dtype = torch.float
if script_args.model_dtype == "float16":
torch_dtype = torch.float16
elif script_args.model_dtype == "bfloat16":
torch_dtype = torch.bfloat16
model = AutoModelForCausalLM.from_pretrained(
script_args.model_name_or_path,
low_cpu_mem_usage=True,
torch_dtype=torch_dtype,
load_in_4bit=script_args.load_in_4bit,
device_map={"": Accelerator().local_process_index},
)
model.config.use_cache = False
if script_args.ignore_bias_buffers:
# torch distributed hack
model._ddp_params_and_buffers_to_ignore = [
name for name, buffer in model.named_buffers() if buffer.dtype == torch.bool
]
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
tokenizer.pad_token = tokenizer.eos_token
# 2. Load the Stack-exchange paired dataset
train_dataset = get_stack_exchange_paired(data_dir="data/rl")
train_dataset = train_dataset.filter(
lambda x: len(x["prompt"]) + len(x["chosen"]) <= script_args.max_length
and len(x["prompt"]) + len(x["rejected"]) <= script_args.max_length,
num_proc=script_args.num_proc,
)
# 3. Load evaluation dataset
eval_dataset = get_stack_exchange_paired(data_dir="data/evaluation")
eval_dataset = eval_dataset.filter(
lambda x: len(x["prompt"]) + len(x["chosen"]) <= script_args.max_length
and len(x["prompt"]) + len(x["rejected"]) <= script_args.max_length,
num_proc=script_args.num_proc,
)
# 4. initialize training arguments:
training_args = DPOConfig(
per_device_train_batch_size=script_args.per_device_train_batch_size,
per_device_eval_batch_size=script_args.per_device_eval_batch_size,
max_steps=script_args.max_steps,
logging_steps=script_args.logging_steps,
save_steps=script_args.save_steps,
gradient_accumulation_steps=script_args.gradient_accumulation_steps,
gradient_checkpointing=script_args.gradient_checkpointing,
learning_rate=script_args.learning_rate,
eval_strategy="steps",
eval_steps=script_args.eval_steps,
output_dir=script_args.output_dir,
report_to=script_args.report_to,
lr_scheduler_type=script_args.lr_scheduler_type,
warmup_steps=script_args.warmup_steps,
optim=script_args.optimizer_type,
bf16=True,
remove_unused_columns=False,
run_name="dpo_llama2",
gradient_checkpointing_kwargs=dict(use_reentrant=script_args.gradient_checkpointing_use_reentrant),
seed=script_args.seed,
)
peft_config = LoraConfig(
r=script_args.lora_r,
lora_alpha=script_args.lora_alpha,
lora_dropout=script_args.lora_dropout,
target_modules=[
"q_proj",
"v_proj",
"k_proj",
"out_proj",
"fc_in",
"fc_out",
"wte",
],
bias="none",
task_type="CAUSAL_LM",
)
# 5. initialize the DPO trainer
dpo_trainer = DPOTrainer(
model,
ref_model=None,
args=training_args,
beta=script_args.beta,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
processing_class=tokenizer,
peft_config=peft_config,
max_prompt_length=script_args.max_prompt_length,
max_length=script_args.max_length,
)
# 6. train
dpo_trainer.train()
dpo_trainer.save_model(script_args.output_dir)
# 7. save
output_dir = os.path.join(script_args.output_dir, "final_checkpoint")
dpo_trainer.model.save_pretrained(output_dir)
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