mirror of
https://github.com/huggingface/trl.git
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144 lines
4.4 KiB
Python
144 lines
4.4 KiB
Python
# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# /// script
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# dependencies = [
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# "trl",
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# "Pillow",
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# "peft",
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# "torchvision",
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# "trackio",
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# "kernels",
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# ]
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# ///
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"""
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python examples/scripts/mpo_vlm.py \
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--dataset_name HuggingFaceH4/rlaif-v_formatted \
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--model_name_or_path Qwen/Qwen2.5-VL-3B-Instruct \
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--per_device_train_batch_size 4 \
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--per_device_eval_batch_size 4 \
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--num_train_epochs 1 \
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--gradient_accumulation_steps 8 \
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--dataset_num_proc 1 \
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--output_dir dpo_idefics_rlaif-v \
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--dtype bfloat16 \
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--gradient_checkpointing \
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--use_peft \
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--lora_target_modules down_proj, o_proj, k_proj, q_proj, gate_proj, up_proj, v_proj \
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--loss_type sigmoid bco_pair sft \
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--loss_weights 0.8 0.2 1.0
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"""
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import os
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import torch
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from datasets import load_dataset
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from PIL import Image
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from transformers import AutoModelForImageTextToText
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from trl import (
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DPOConfig,
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DPOTrainer,
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ModelConfig,
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ScriptArguments,
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TrlParser,
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get_kbit_device_map,
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get_peft_config,
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get_quantization_config,
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)
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# Enable logging in a Hugging Face Space
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os.environ.setdefault("TRACKIO_SPACE_ID", "trl-trackio")
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if __name__ == "__main__":
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parser = TrlParser((ScriptArguments, DPOConfig, ModelConfig))
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script_args, training_args, model_args = parser.parse_args_and_config()
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################
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# Model & Processor
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################
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dtype = model_args.dtype if model_args.dtype in ["auto", None] else getattr(torch, model_args.dtype)
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model_kwargs = dict(
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trust_remote_code=model_args.trust_remote_code,
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revision=model_args.model_revision,
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attn_implementation=model_args.attn_implementation,
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dtype=dtype,
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)
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quantization_config = get_quantization_config(model_args)
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if quantization_config is not None:
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# Passing None would not be treated the same as omitting the argument, so we include it only when valid.
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model_kwargs["device_map"] = get_kbit_device_map()
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model_kwargs["quantization_config"] = quantization_config
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model = AutoModelForImageTextToText.from_pretrained(
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model_args.model_name_or_path,
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**model_kwargs,
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)
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peft_config = get_peft_config(model_args)
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if peft_config is None:
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ref_model = AutoModelForImageTextToText.from_pretrained(
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model_args.model_name_or_path,
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**model_kwargs,
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)
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else:
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ref_model = None
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################
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# Dataset
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################
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dataset = load_dataset(
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script_args.dataset_name,
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name=script_args.dataset_config,
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streaming=script_args.dataset_streaming,
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)
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train_dataset = dataset[script_args.dataset_train_split]
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test_dataset = dataset[script_args.dataset_test_split] if training_args.eval_strategy != "no" else None
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def ensure_rgb(example):
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# Convert the image to RGB if it's not already
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image = example["images"][0]
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if isinstance(image, Image.Image):
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if image.mode != "RGB":
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image = image.convert("RGB")
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example["images"] = [image]
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return example
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# Apply the transformation to the dataset (change num_proc depending on the available compute)
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train_dataset = train_dataset.map(ensure_rgb, num_proc=training_args.dataset_num_proc)
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if test_dataset is not None:
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test_dataset = test_dataset.map(ensure_rgb, num_proc=training_args.dataset_num_proc)
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################
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# Training
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################
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trainer = DPOTrainer(
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model=model,
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ref_model=ref_model,
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args=training_args,
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train_dataset=train_dataset,
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eval_dataset=test_dataset,
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peft_config=peft_config,
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)
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trainer.train()
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# Save and push to hub
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trainer.save_model(training_args.output_dir)
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if training_args.push_to_hub:
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trainer.push_to_hub(dataset_name=script_args.dataset_name)
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