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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.
"""
Without dataset streaming:
```
accelerate launch examples/scripts/dpo_vlm.py \
--dataset_name HuggingFaceH4/rlaif-v_formatted \
--model_name_or_path Qwen/Qwen2.5-VL-3B-Instruct \
--per_device_train_batch_size 2 \
--gradient_accumulation_steps 32 \
--dataset_num_proc 32 \
--output_dir dpo_idefics_rlaif-v \
--bf16 \
--torch_dtype bfloat16 \
--gradient_checkpointing \
--use_peft \
--lora_target_modules=all-linear \
--report_to wandb
```
With dataset streaming:
```
accelerate launch examples/scripts/dpo_vlm.py \
--dataset_name HuggingFaceH4/rlaif-v_formatted \
--dataset_streaming \
--model_name_or_path Qwen/Qwen2.5-VL-3B-Instruct \
--per_device_train_batch_size 2 \
--max_steps 100 \
--gradient_accumulation_steps 32 \
--dataset_num_proc 32 \
--output_dir dpo_idefics_rlaif-v \
--bf16 \
--torch_dtype bfloat16 \
--gradient_checkpointing \
--use_peft \
--lora_target_modules=all-linear \
--report_to wandb
```
"""
import torch
from datasets import load_dataset
from transformers import AutoModelForVision2Seq, AutoProcessor
from trl import (
DPOConfig,
DPOTrainer,
ModelConfig,
ScriptArguments,
TrlParser,
get_kbit_device_map,
get_peft_config,
get_quantization_config,
)
if __name__ == "__main__":
parser = TrlParser((ScriptArguments, DPOConfig, ModelConfig))
script_args, training_args, model_args = parser.parse_args_and_config()
################
# Model & Tokenizer
################
torch_dtype = (
model_args.torch_dtype if model_args.torch_dtype in ["auto", None] else getattr(torch, model_args.torch_dtype)
)
quantization_config = get_quantization_config(model_args)
model_kwargs = dict(
revision=model_args.model_revision,
attn_implementation=model_args.attn_implementation,
torch_dtype=torch_dtype,
device_map=get_kbit_device_map() if quantization_config is not None else None,
quantization_config=quantization_config,
)
model = AutoModelForVision2Seq.from_pretrained(
model_args.model_name_or_path,
trust_remote_code=model_args.trust_remote_code,
**model_kwargs,
)
peft_config = get_peft_config(model_args)
if peft_config is None:
ref_model = AutoModelForVision2Seq.from_pretrained(
model_args.model_name_or_path,
trust_remote_code=model_args.trust_remote_code,
**model_kwargs,
)
else:
ref_model = None
processor = AutoProcessor.from_pretrained(
model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code, do_image_splitting=False
)
tokenizer = processor.tokenizer
# Set up the chat template
if model.config.model_type == "idefics2":
pass # the processor already has a valid chat template
elif model.config.model_type == "paligemma":
processor.chat_template = """{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}<|im_start|>{% if message['role'] == 'user' %}USER: {% else %}ASSISTANT: {% endif %}{% for item in message['content'] if item['type'] == 'text' %}{{ item['text'] }}<|im_end|>{% endfor %}{% if message['role'] == 'user' %} {% else %}{{eos_token}}{% endif %}{% endfor %}{% if add_generation_prompt %}ASSISTANT: {% endif %}"""
elif model.config.model_type == "llava":
processor.chat_template = """{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{% if message['role'] == 'user' %}USER: {% else %}ASSISTANT: {% endif %}{% for item in message['content'] %}{% if item['type'] == 'text' %}{{ item['text'] }}{% elif item['type'] == 'image' %}<image>{% endif %}{% endfor %}{% if message['role'] == 'user' %} {% else %}{{eos_token}}{% endif %}{% endfor %}{% if add_generation_prompt %}ASSISTANT: {% endif %}"""
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
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
]
################
# Dataset
################
dataset = load_dataset(
script_args.dataset_name,
name=script_args.dataset_config,
streaming=script_args.dataset_streaming,
)
################
# Training
################
trainer = DPOTrainer(
model,
ref_model,
args=training_args,
train_dataset=dataset[script_args.dataset_train_split],
eval_dataset=dataset[script_args.dataset_test_split] if training_args.eval_strategy != "no" else None,
processing_class=processor,
peft_config=peft_config,
)
trainer.train()
# Save and push to hub
trainer.save_model(training_args.output_dir)
if training_args.push_to_hub:
trainer.push_to_hub(dataset_name=script_args.dataset_name)
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