├── LICENSE ├── README.md ├── assets ├── arch.png └── vis.png ├── data_tools ├── example.jpg ├── example.json └── prompt_hub.py ├── eval_tools ├── qwen2vl_plan_cmd_eval.sh ├── qwen2vl_plan_cmd_eval_grpo.py └── qwen2vl_plan_cmd_eval_sft.py ├── setup.sh ├── src ├── distill_r1 │ ├── README.md │ ├── create_hf_dataset.py │ ├── filter_r1.py │ ├── generate_scene_qa_pairs.ipynb │ ├── grpo_r1_distilled.jpg │ ├── prompt.py │ └── query_r1.py ├── eval │ ├── logs │ │ ├── counting_results_superclevr_200_qwen2vl_2b_instruct_grpo100_legacy.json │ │ ├── counting_results_superclevr_200_qwen2vl_2b_instruct_legacy.json │ │ ├── geoqa_test_qwen2vl_7b_grpo_2epochs_legacy.json │ │ └── geoqa_test_qwen2vl_7b_instruct_legacy.json │ ├── prompts │ │ ├── geoqa_test_prompts.jsonl │ │ └── superclevr_test200_counting_problems.jsonl │ ├── test_qwen2vl_counting_superclevr.py │ ├── test_qwen2vl_geoqa.py │ └── test_qwen2vl_geoqa_multigpu.py ├── r1-v │ ├── .gitignore │ ├── LICENSE │ ├── Makefile │ ├── configs │ │ ├── ddp.yaml │ │ ├── qwen2vl_sft_config.yaml │ │ ├── zero2.yaml │ │ └── zero3.yaml │ ├── local_scripts │ │ ├── create_vision_cot_data.py │ │ ├── lmms_eval_qwen2vl.sh │ │ ├── prepare_hf_data.py │ │ ├── train_aria_moe.sh │ │ ├── train_qwen2_vl.sh │ │ ├── zero2.json │ │ ├── zero3.json │ │ ├── zero3.yaml │ │ └── zero3_offload.json │ ├── run_grpo.sh │ ├── setup.cfg │ ├── setup.py │ ├── src │ │ ├── __init__.py │ │ └── open_r1 │ │ │ ├── __init__.py │ │ │ ├── evaluate.py │ │ │ ├── generate.py │ │ │ ├── grpo.py │ │ │ ├── sft.py │ │ │ └── trainer │ │ │ ├── __init__.py │ │ │ ├── grpo_trainer.py │ │ │ └── vllm_grpo_trainer.py │ └── temp_image.png └── scripts │ ├── run_grpo_clevr.sh │ ├── run_grpo_vllm.sh │ ├── run_sft_clevr.sh │ └── test_grpo_geoqa_multigpu.sh └── train_tools ├── run_train_grpo.sh └── run_train_sft.sh /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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2 | 3 |

🌌 AlphaDrive: Unleashing the Power of VLMs in Autonomous 4 | 5 | Driving via Reinforcement Learning and Reasoning

6 | 7 | [Bo Jiang](https://scholar.google.com/citations?user=UlDxGP0AAAAJ&hl=zh-CN)1, [Shaoyu Chen](https://scholar.google.com/citations?user=PIeNN2gAAAAJ&hl=en&oi=sra)1,2, [Qian Zhang](https://scholar.google.com/citations?user=pCY-bikAAAAJ&hl=zh-CN)2, [Wenyu Liu](http://eic.hust.edu.cn/professor/liuwenyu/)1, [Xinggang Wang](https://xwcv.github.io/)1,📧 8 | 9 | 1 Huazhong University of Science and Technology, 10 | 2 Horizon Robotics, 11 | 📧 corresponding author 12 | 13 | 14 | [![arxiv paper](https://img.shields.io/badge/arXiv-Paper-red)](https://arxiv.org/abs/2503.07608) 15 | [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) 16 | 17 |
18 | 19 |
20 | 21 |
22 | 23 | https://github.com/user-attachments/assets/71695178-90ca-4f5c-acc2-ab2e13c63e43 24 | 25 | 26 | ## ✨ Highlights 27 | 28 | * To the best of our knowledge, AlphaDrive is the first to integrate GRPO-based RL with planning reasoning to autonomous driving, significantly boosting both performance and training efficiency. 29 | 30 | * We are excited to discover that, following RL training, AlphaDrive exhibits some emergent multimodal planning capabilities, which is promising for improving driving safety and efficiency. 31 | 32 | 33 | ## 📋 News 34 | 35 | `[2025-3-26]:` We have released the training and evaluation scripts of AlphaDrive. 36 | 37 | `[2025-3-11]:` AlphaDrive [arXiv](https://arxiv.org/abs/2503.07608) paper released. Code are coming soon. Please stay tuned! ☕️ 38 | 39 | 40 | ## 🎮 Getting Started 41 | ### Installtion 42 | ```shell 43 | git clone git@github.com:hustvl/AlphaDrive.git 44 | conda create -n alphadrive python=3.11 -y 45 | conda activate alphadrive 46 | sh setup.sh 47 | ``` 48 | 49 | ### Data Preparation 50 | We provide the [prompt templates](https://github.com/hustvl/AlphaDrive/blob/main/data_tools/prompt_hub.py) used in AlphaDrive for training and generating planning reasoning data, and an example QA is provided in [example.json](https://github.com/hustvl/AlphaDrive/blob/main/data_tools/example.json). 51 | 52 | 53 | ### Training 54 | For Supervised Fine-tuning Phase: 55 | ```shell 56 | sh train_tools/run_train_sft.sh 57 | ``` 58 | 59 | For Reinforcement Learning Phase: 60 | ```shell 61 | sh train_tools/run_train_grpo.sh 62 | ``` 63 | 64 | ### Evaluation 65 | You can evaluate the meta-action planning accuracy using the script below. 66 | ```shell 67 | sh eval_tools/qwen2vl_plan_cmd_eval.sh 68 | ``` 69 | 70 | 71 | ## 📊 Qualitative Results 72 | 73 | 74 |
75 | 76 |
77 | 78 | 79 | ## ❤️ Acknowledgements 80 | 81 | This repo is built on [open-r1](https://github.com/huggingface/open-r1) and [R1-V](https://github.com/Deep-Agent/R1-V). We sincerely thank the contributors for their great work! 82 | 83 | ## 📚 Citation 84 | If you find AlphaDrive useful in your research or applications, please consider giving us a star 🌟 and citing it by the following BibTeX entry. 85 | 86 | 87 | ```bibtex 88 | @article{jiang2025alphadrive, 89 | title={AlphaDrive: Unleashing the Power of VLMs in Autonomous Driving via Reinforcement Learning and Reasoning}, 90 | author={Bo Jiang and Shaoyu Chen and Qian Zhang and Wenyu Liu and Xinggang Wang}, 91 | year={2025}, 92 | eprint={2503.07608}, 93 | archivePrefix={arXiv}, 94 | primaryClass={cs.CV}, 95 | url={https://arxiv.org/abs/2503.07608}, 96 | } 97 | ``` 98 | 99 | ## 🥰 Related Projects 100 | Check out our other awesome projects: 101 | 102 | [VAD & VADv2](https://github.com/hustvl/VAD): Vectorized Scene Representation for Efficient Autonomous Driving. 103 | 104 | [Senna](https://github.com/hustvl/Senna): Bridging Large Vision-Language Models and End-to-End Autonomous Driving. 105 | 106 | [DiffusionDrive](https://github.com/hustvl/DiffusionDrive): Truncated Diffusion Model for End-to-End Autonomous Driving. 107 | 108 | [RAD](https://hgao-cv.github.io/RAD/): Training an End-to-End Driving Policy via Large-Scale 3DGS-based Reinforcement Learning. 109 | 110 | [MapTR](https://github.com/hustvl/MapTR): An End-to-End Framework for Online Vectorized HD Map Construction. 111 | -------------------------------------------------------------------------------- /assets/arch.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hustvl/AlphaDrive/bb4104c680a3e4d70e7e998e08de90f6e0acf8c5/assets/arch.png -------------------------------------------------------------------------------- /assets/vis.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hustvl/AlphaDrive/bb4104c680a3e4d70e7e998e08de90f6e0acf8c5/assets/vis.png -------------------------------------------------------------------------------- /data_tools/example.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hustvl/AlphaDrive/bb4104c680a3e4d70e7e998e08de90f6e0acf8c5/data_tools/example.jpg -------------------------------------------------------------------------------- /data_tools/example.json: -------------------------------------------------------------------------------- 1 | { 2 | "image": "example.jpg", 3 | "problem": "You are an expert driving assistant. Your current speed is 4.2m/s, the navigation command is '{'100m': 'Straight', '200m': 'Straight'}', based on the understanding of the driving scene and the navigation information, what is your driving plan for the next three seconds? Output the planning reasoning process in and final planning answer in tags, respectively. Planning answer consists of SPEED plan and PATH plan, SPEED includes KEEP, ACCELERATE, DECELERATE, and STOP. PATH includes STRAIGHT, RIGHT_CHANGE, LEFT_CHANGE, RIGHT_TURN, and LEFT_TURN. For example, a correct answer format is like ' planning reasoning process here KEEP, LEFT_TURN '.", 4 | "solution": " ACCELERATE, STRAIGHT ", 5 | "thinking": " The driving decision to accelerate and go straight is based on the navigation command indicating that the next 100 meters should be driven straight, and there appears to be no immediate obstacles that would require a change in direction or speed. The road ahead is clear, and the light is green, allowing for safe acceleration to pass the intersection. " 6 | } -------------------------------------------------------------------------------- /data_tools/prompt_hub.py: -------------------------------------------------------------------------------- 1 | meta_action_prompt = """ 2 | You are an expert driving assistant. \ 3 | Your current speed is {}m/s, the navigation command is '{}', \ 4 | based on the understanding of the driving scene and the navigation information, \ 5 | what is your driving plan for the next three seconds? \ 6 | Output the planning reasoning process in and final planning answer in tags, respectively. \ 7 | Planning answer consists of SPEED plan and PATH plan, SPEED includes KEEP, ACCELERATE, DECELERATE, and STOP. \ 8 | PATH includes STRAIGHT, RIGHT_CHANGE, LEFT_CHANGE, RIGHT_TURN, and LEFT_TURN. \ 9 | For example, a correct answer format is like ' planning reasoning process here KEEP, LEFT_TURN '. 10 | """ 11 | 12 | plan_reason_prompt = """ 13 | You are an expert driving assistant. \ 14 | Your current speed is {}m/s, the navigation command is '{}', \ 15 | and your driving decision for the next three seconds is '{}'. \ 16 | Based on your understanding of the driving scene, briefly explain why you made the above driving decisions in one or two sentences. 17 | """ 18 | -------------------------------------------------------------------------------- /eval_tools/qwen2vl_plan_cmd_eval.sh: -------------------------------------------------------------------------------- 1 | export ENV_NAME="/path/to/your/python/env" 2 | export OUTDIR="/path/to/your/output/directory" 3 | export OUT_NAME="Qwen2-VL-2B-EXP" 4 | export EVAL_DATA="/path/to/your/val/data" 5 | export EVAL_SAVE_NAME="eval_result.json" 6 | 7 | echo "Validation Process..." 8 | $ENV_NAME/bin/python eval_tools/qwen2vl_plan_cmd_eval_grpo.py \ 9 | --eval_data_path $EVAL_DATA \ 10 | --model_path $OUTDIR/$OUT_NAME \ 11 | --save_path $OUTDIR/$OUT_NAME/$EVAL_SAVE_NAME -------------------------------------------------------------------------------- /eval_tools/qwen2vl_plan_cmd_eval_grpo.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import copy 3 | import json 4 | from PIL import Image 5 | import time 6 | 7 | import torch 8 | from tqdm import tqdm 9 | from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor, GenerationConfig 10 | from qwen_vl_utils import process_vision_info 11 | from datasets import load_dataset 12 | import numpy as np 13 | from trl.data_utils import maybe_apply_chat_template 14 | 15 | 16 | def parse_args(): 17 | parser = argparse.ArgumentParser(description="Evaluate Qwen2-VL model on validation dataset") 18 | parser.add_argument("--eval_data_path", type=str, required=True, help="Path to evaluation dataset") 19 | parser.add_argument("--model_path", type=str, required=True, help="Path to the pretrained model") 20 | parser.add_argument("--save_path", type=str, required=True, help="Path to save evaluation results") 21 | return parser.parse_args() 22 | 23 | 24 | def main(): 25 | 26 | args = parse_args() 27 | model_path = args.model_path 28 | eval_data_path = args.eval_data_path 29 | save_path = args.save_path 30 | 31 | # We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios. 32 | model = Qwen2VLForConditionalGeneration.from_pretrained( 33 | model_path, 34 | torch_dtype=torch.bfloat16, 35 | attn_implementation="flash_attention_2", 36 | device_map="auto", 37 | ) 38 | processor = AutoProcessor.from_pretrained(model_path) 39 | 40 | 41 | eval_dataset = load_dataset(eval_data_path) 42 | 43 | 44 | tot_num, correct_num = 0, 0 45 | 46 | SPEED_PLAN = ['KEEP', 'ACCELERATE', 'DECELERATE', 'STOP'] 47 | PATH_PLAN = ['RIGHT_TURN', 'RIGHT_CHANGE', 'LEFT_TURN', 'LEFT_CHANGE', 'STRAIGHT'] 48 | 49 | metric_tot_cnt = {speed + '_' + path: 0 for speed in SPEED_PLAN for path in PATH_PLAN} 50 | metric_correct_cnt = copy.deepcopy(metric_tot_cnt) 51 | 52 | eval_record = {} 53 | 54 | speed_tp = { 55 | 'KEEP': 0, 56 | 'ACCELERATE': 0, 57 | 'DECELERATE': 0, 58 | 'STOP': 0, 59 | } 60 | 61 | speed_fp, speed_fn = copy.deepcopy(speed_tp), copy.deepcopy(speed_tp) 62 | 63 | path_tp = { 64 | 'RIGHT_TURN': 0, 65 | 'LEFT_TURN': 0, 66 | 'RIGHT_CHANGE': 0, 67 | 'LEFT_CHANGE': 0, 68 | 'STRAIGHT': 0, 69 | } 70 | 71 | path_fp, path_fn = copy.deepcopy(path_tp), copy.deepcopy(path_tp) 72 | 73 | f1_score = { 74 | 'KEEP': 0, 75 | 'ACCELERATE': 0, 76 | 'DECELERATE': 0, 77 | 'STOP': 0, 78 | 'RIGHT_TURN': 0, 79 | 'LEFT_TURN': 0, 80 | 'RIGHT_CHANGE': 0, 81 | 'LEFT_CHANGE': 0, 82 | 'STRAIGHT': 0, 83 | } 84 | 85 | generation_config = GenerationConfig( 86 | max_new_tokens=1024, 87 | do_sample=True, 88 | temperature=1, # HACK 89 | num_return_sequences=2, 90 | pad_token_id=151643, 91 | ) 92 | 93 | for sample in tqdm(eval_dataset['validation']): 94 | 95 | tot_num = tot_num + 1 96 | 97 | gt_answer = sample['solution'] 98 | text = sample['problem'] 99 | 100 | inputs = [{ 101 | 'prompt':[ 102 | { 103 | 'content': [ 104 | {'text':None, 'type':'image'}, 105 | {'text':text, 'type':'text'}, 106 | ], 107 | 'role': 'user' 108 | } 109 | ] 110 | }] 111 | 112 | prompts_text = [maybe_apply_chat_template(example, processor)["prompt"] for example in inputs] 113 | images, _ = process_vision_info(inputs[0]['prompt']) 114 | 115 | prompt_inputs = processor( 116 | text=prompts_text, 117 | images=images, 118 | return_tensors="pt", 119 | padding=True, 120 | padding_side="left", 121 | add_special_tokens=False, 122 | ) 123 | 124 | 125 | prompt_inputs = prompt_inputs.to("cuda") 126 | 127 | prompt_ids, prompt_mask = prompt_inputs["input_ids"], prompt_inputs["attention_mask"] 128 | 129 | # Inference: Generation of the output 130 | prompt_completion_ids = model.generate(**prompt_inputs, generation_config=generation_config) 131 | prompt_length = prompt_ids.size(1) 132 | completion_ids = prompt_completion_ids[:, prompt_length:] 133 | answers = processor.batch_decode(completion_ids, skip_special_tokens=True) 134 | answer = answers[0].strip() 135 | 136 | gt_answer = gt_answer.replace(' ', '') 137 | gt_answer = gt_answer.replace(' ', '') 138 | speed_plan, path_plan = gt_answer.split(', ') 139 | path_plan = path_plan.split('\n')[0] 140 | 141 | for key in speed_tp.keys(): 142 | if key in answer: # P 143 | if speed_plan in answer: 144 | speed_tp[key] += 1 # TP 145 | else: 146 | speed_fp[key] += 1 # FP 147 | else: # N 148 | if key in speed_plan: 149 | speed_fn[key] += 1 # FN 150 | 151 | for key in path_tp.keys(): 152 | if key in answer: # P 153 | if path_plan in answer: 154 | path_tp[key] += 1 # TP 155 | else: 156 | path_fp[key] += 1 # FP 157 | else: # N 158 | if key in path_plan: 159 | path_fn[key] += 1 # FN 160 | 161 | 162 | metric_tot_cnt[speed_plan+'_'+path_plan] += 1 163 | if speed_plan in answer and path_plan in answer: 164 | correct_num = correct_num + 1 165 | metric_correct_cnt[speed_plan+'_'+path_plan] += 1 166 | else: 167 | fail_case = { 168 | 'gt': gt_answer, 169 | 'pred': answer, 170 | } 171 | 172 | 173 | for key in f1_score.keys(): 174 | if key in speed_tp.keys(): 175 | if speed_tp[key] + speed_fp[key] != 0: 176 | precision = speed_tp[key] / (speed_tp[key] + speed_fp[key]) 177 | else: 178 | precision = 0 179 | if speed_tp[key] + speed_fn[key] != 0: 180 | recall = speed_tp[key] / (speed_tp[key] + speed_fn[key]) 181 | else: 182 | recall = 0 183 | if precision + recall != 0: 184 | f1_score[key] = 2.0 * precision * recall / (precision + recall) 185 | else: 186 | f1_score[key] = 0 187 | if key in path_tp.keys(): 188 | if path_tp[key] + path_tp[key] != 0: 189 | precision = path_tp[key] / (path_tp[key] + path_fp[key]) 190 | else: 191 | precision = 0 192 | if path_tp[key] + path_fn[key] != 0: 193 | recall = path_tp[key] / (path_tp[key] + path_fn[key]) 194 | else: 195 | recall = 0 196 | if precision + recall != 0: 197 | f1_score[key] = 2.0 * precision * recall / (precision + recall) 198 | else: 199 | f1_score[key] = 0 200 | 201 | print("\n\n=========== F1 Score ===========\n\n") 202 | for k, v in f1_score.items(): 203 | print(f"{k}: {v}") 204 | print("\n\n================================\n\n") 205 | 206 | print(f'\nTotal Number: {tot_num}\n') 207 | print(f'\nCorrect Number: {correct_num}\n') 208 | 209 | print('\n------------------------------\n\n') 210 | print(f"Planning Accuracy: {correct_num/tot_num * 100:.2f}%") 211 | print('\n\n------------------------------\n') 212 | 213 | for key in metric_tot_cnt.keys(): 214 | if metric_tot_cnt[key] > 0: 215 | print(f"{key}: num: {metric_tot_cnt[key]}, correct num: {metric_correct_cnt[key]}, {100*metric_correct_cnt[key]/metric_tot_cnt[key]:.2f}%") 216 | 217 | eval_record['summary'] = f'Total Number: {tot_num}' 218 | eval_record['summary'] = eval_record['summary'] + '\n' + f'Correct Number: {correct_num}' 219 | eval_record['summary'] = eval_record['summary'] + '\n' + f"Planning Accuracy: {correct_num/tot_num * 100:.2f}%" 220 | 221 | for key in metric_tot_cnt.keys(): 222 | if metric_tot_cnt[key] > 0: 223 | eval_record['summary'] = eval_record['summary'] + '\n' + \ 224 | f"{key}: num: {metric_tot_cnt[key]}, correct num: {metric_correct_cnt[key]}, {100*metric_correct_cnt[key]/metric_tot_cnt[key]:.2f}%" 225 | 226 | eval_record['f1_score'] = {} 227 | for k, v in f1_score.items(): 228 | eval_record['f1_score'][k] = v 229 | 230 | with open(save_path, "w") as f: 231 | json.dump(eval_record, f) 232 | print(f'\nEval results saved to {save_path}\n') 233 | 234 | 235 | if __name__ == "__main__": 236 | main() 237 | -------------------------------------------------------------------------------- /eval_tools/qwen2vl_plan_cmd_eval_sft.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import copy 3 | import json 4 | from PIL import Image 5 | 6 | import torch 7 | from tqdm import tqdm 8 | from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor 9 | from qwen_vl_utils import process_vision_info 10 | from datasets import load_dataset 11 | 12 | 13 | def convert_example(example): 14 | """ 15 | correct example into "messages" 16 | eg: 17 | { 18 | "system": "You are a helpful assistant.", 19 | "conversations": [ 20 | {"from": "user", "value": "How many objects are included in this image?", 21 | "image_path": "/path/to/image.png"}, 22 | {"from": "assistant", "value": "\nI can see 10 objects\n\n\n10\n"} 23 | ] 24 | } 25 | """ 26 | messages = [] 27 | 28 | image = example.get("image") 29 | messages.append({ 30 | "role": "user", 31 | "content": [ 32 | {"type": "text", "text": example["problem"]}, 33 | {"type": "image", "image": image}, 34 | ] 35 | }) 36 | 37 | return messages 38 | 39 | 40 | def parse_args(): 41 | parser = argparse.ArgumentParser(description="Evaluate Qwen2-VL model on validation dataset") 42 | parser.add_argument("--eval_data_path", type=str, required=True, help="Path to evaluation dataset") 43 | parser.add_argument("--model_path", type=str, required=True, help="Path to the pretrained model") 44 | parser.add_argument("--save_path", type=str, required=True, help="Path to save evaluation results") 45 | return parser.parse_args() 46 | 47 | 48 | def main(): 49 | 50 | args = parse_args() 51 | model_path = args.model_path 52 | eval_data_path = args.eval_data_path 53 | save_path = args.save_path 54 | 55 | # We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios. 56 | model = Qwen2VLForConditionalGeneration.from_pretrained( 57 | model_path, 58 | torch_dtype=torch.bfloat16, 59 | attn_implementation="flash_attention_2", 60 | device_map="auto", 61 | ) 62 | processor = AutoProcessor.from_pretrained(model_path) 63 | 64 | 65 | eval_dataset = load_dataset(eval_data_path) 66 | 67 | 68 | tot_num, correct_num = 0, 0 69 | 70 | SPEED_PLAN = ['KEEP', 'ACCELERATE', 'DECELERATE', 'STOP'] 71 | PATH_PLAN = ['RIGHT_TURN', 'RIGHT_CHANGE', 'LEFT_TURN', 'LEFT_CHANGE', 'STRAIGHT'] 72 | 73 | metric_tot_cnt = {speed + '_' + path: 0 for speed in SPEED_PLAN for path in PATH_PLAN} 74 | metric_correct_cnt = copy.deepcopy(metric_tot_cnt) 75 | 76 | eval_record = {} 77 | 78 | speed_tp = { 79 | 'KEEP': 0, 80 | 'ACCELERATE': 0, 81 | 'DECELERATE': 0, 82 | 'STOP': 0, 83 | } 84 | 85 | speed_fp, speed_fn = copy.deepcopy(speed_tp), copy.deepcopy(speed_tp) 86 | 87 | path_tp = { 88 | 'RIGHT_TURN': 0, 89 | 'LEFT_TURN': 0, 90 | 'RIGHT_CHANGE': 0, 91 | 'LEFT_CHANGE': 0, 92 | 'STRAIGHT': 0, 93 | } 94 | 95 | path_fp, path_fn = copy.deepcopy(path_tp), copy.deepcopy(path_tp) 96 | 97 | f1_score = { 98 | 'KEEP': 0, 99 | 'ACCELERATE': 0, 100 | 'DECELERATE': 0, 101 | 'STOP': 0, 102 | 'RIGHT_TURN': 0, 103 | 'LEFT_TURN': 0, 104 | 'RIGHT_CHANGE': 0, 105 | 'LEFT_CHANGE': 0, 106 | 'STRAIGHT': 0, 107 | } 108 | 109 | for sample in tqdm(eval_dataset['validation']): 110 | 111 | tot_num = tot_num + 1 112 | 113 | gt_answer = sample['solution'] 114 | 115 | messages = convert_example(sample) 116 | 117 | # Preparation for inference 118 | text = processor.apply_chat_template( 119 | messages, tokenize=False, add_generation_prompt=True 120 | ) 121 | image_inputs, video_inputs = process_vision_info(messages) 122 | inputs = processor( 123 | text=[text], 124 | images=image_inputs, 125 | videos=video_inputs, 126 | padding=True, 127 | return_tensors="pt", 128 | ) 129 | inputs = inputs.to("cuda") 130 | 131 | # Inference: Generation of the output 132 | generated_ids = model.generate(**inputs, max_new_tokens=512) 133 | generated_ids_trimmed = [ 134 | out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) 135 | ] 136 | answer = processor.batch_decode( 137 | generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False 138 | )[0] 139 | answer = answer.strip() 140 | 141 | gt_answer = gt_answer.replace(' ', '') 142 | gt_answer = gt_answer.replace(' ', '') 143 | speed_plan, path_plan = gt_answer.split(', ') 144 | path_plan = path_plan.split('\n')[0] 145 | 146 | 147 | for key in speed_tp.keys(): 148 | if key in answer: # P 149 | if speed_plan in answer: 150 | speed_tp[key] += 1 # TP 151 | else: 152 | speed_fp[key] += 1 # FP 153 | else: # N 154 | if key in speed_plan: 155 | speed_fn[key] += 1 # FN 156 | 157 | for key in path_tp.keys(): 158 | if key in answer: # P 159 | if path_plan in answer: 160 | path_tp[key] += 1 # TP 161 | else: 162 | path_fp[key] += 1 # FP 163 | else: # N 164 | if key in path_plan: 165 | path_fn[key] += 1 # FN 166 | 167 | 168 | metric_tot_cnt[speed_plan+'_'+path_plan] += 1 169 | if speed_plan in answer and path_plan in answer: 170 | correct_num = correct_num + 1 171 | metric_correct_cnt[speed_plan+'_'+path_plan] += 1 172 | else: 173 | fail_case = { 174 | 'gt': gt_answer, 175 | 'pred': answer, 176 | } 177 | 178 | 179 | for key in f1_score.keys(): 180 | if key in speed_tp.keys(): 181 | if speed_tp[key] + speed_fp[key] != 0: 182 | precision = speed_tp[key] / (speed_tp[key] + speed_fp[key]) 183 | else: 184 | precision = 0 185 | if speed_tp[key] + speed_fn[key] != 0: 186 | recall = speed_tp[key] / (speed_tp[key] + speed_fn[key]) 187 | else: 188 | recall = 0 189 | if precision + recall != 0: 190 | f1_score[key] = 2.0 * precision * recall / (precision + recall) 191 | else: 192 | f1_score[key] = 0 193 | if key in path_tp.keys(): 194 | if path_tp[key] + path_tp[key] != 0: 195 | precision = path_tp[key] / (path_tp[key] + path_fp[key]) 196 | else: 197 | precision = 0 198 | if path_tp[key] + path_fn[key] != 0: 199 | recall = path_tp[key] / (path_tp[key] + path_fn[key]) 200 | else: 201 | recall = 0 202 | if precision + recall != 0: 203 | f1_score[key] = 2.0 * precision * recall / (precision + recall) 204 | else: 205 | f1_score[key] = 0 206 | 207 | print("\n\n=========== F1 Score ===========\n\n") 208 | for k, v in f1_score.items(): 209 | print(f"{k}: {v}") 210 | print("\n\n================================\n\n") 211 | 212 | print(f'\nTotal Number: {tot_num}\n') 213 | print(f'\nCorrect Number: {correct_num}\n') 214 | 215 | print('\n------------------------------\n\n') 216 | print(f"Planning Accuracy: {correct_num/tot_num * 100:.2f}%") 217 | print('\n\n------------------------------\n') 218 | 219 | for key in metric_tot_cnt.keys(): 220 | if metric_tot_cnt[key] > 0: 221 | print(f"{key}: num: {metric_tot_cnt[key]}, correct num: {metric_correct_cnt[key]}, {100*metric_correct_cnt[key]/metric_tot_cnt[key]:.2f}%") 222 | 223 | eval_record['summary'] = f'Total Number: {tot_num}' 224 | eval_record['summary'] = eval_record['summary'] + '\n' + f'Correct Number: {correct_num}' 225 | eval_record['summary'] = eval_record['summary'] + '\n' + f"Planning Accuracy: {correct_num/tot_num * 100:.2f}%" 226 | 227 | for key in metric_tot_cnt.keys(): 228 | if metric_tot_cnt[key] > 0: 229 | eval_record['summary'] = eval_record['summary'] + '\n' + \ 230 | f"{key}: num: {metric_tot_cnt[key]}, correct num: {metric_correct_cnt[key]}, {100*metric_correct_cnt[key]/metric_tot_cnt[key]:.2f}%" 231 | 232 | eval_record['f1_score'] = {} 233 | for k, v in f1_score.items(): 234 | eval_record['f1_score'][k] = v 235 | 236 | with open(save_path, "w") as f: 237 | json.dump(eval_record, f) 238 | print(f'\nEval results saved to {save_path}\n') 239 | 240 | 241 | if __name__ == "__main__": 242 | main() 243 | -------------------------------------------------------------------------------- /setup.sh: -------------------------------------------------------------------------------- 1 | # Install the packages in r1-v . 2 | cd src/r1-v 3 | pip install -e ".[dev]" 4 | 5 | # Addtional modules 6 | pip install wandb==0.18.3 7 | pip install tensorboardx 8 | pip install qwen_vl_utils torchvision 9 | pip install flash-attn --no-build-isolation 10 | 11 | # vLLM support 12 | pip install vllm==0.7.2 13 | 14 | # fix transformers version 15 | pip install git+https://github.com/huggingface/transformers.git@336dc69d63d56f232a183a3e7f52790429b871ef -------------------------------------------------------------------------------- /src/distill_r1/README.md: -------------------------------------------------------------------------------- 1 | # R1 Reasoning Dataset Generation 2 | 3 | 4 | 5 | ## QA Pairs Generation 6 | 7 | We create a `scene description` by combining the objects (with meta info such as location, depth) using a template. 8 | 9 | We keep the couting relevant questions and add a `How many items are there in the described scene?` question to count all objects in the scene. 10 | 11 | Example QA pair: 12 | 13 | ```json 14 | {'img_filename': 'CLEVR_trainA_048403.png', 15 | 'question': 'How many things are both on the right side of the big yellow rubber thing and left of the purple ball?', 16 | 'answer': '5', 17 | 'description': 'Scene Description:\nA large red rubber cylinder rotated 291.3° located at 3D coordinates (-0.89, -2.73, 0.70) and pixel coordinates (101, 152, 10.04)\nA small purple metal sphere rotated 247.7° located at 3D coordinates (2.93, 0.87, 0.35) and pixel coordinates (379, 183, 9.66)\nA large cyan rubber cylinder rotated 114.5° located at 3D coordinates (-2.40, 2.23, 0.70) and pixel coordinates (246, 82, 13.94)\nA small red metal cylinder rotated 109.9° located at 3D coordinates (-0.95, 1.77, 0.35) and pixel coordinates (270, 113, 12.83)\nA small red rubber cylinder rotated 343.7° located at 3D coordinates (-0.12, -0.74, 0.35) and pixel coordinates (209, 153, 10.82)\nA large red rubber cylinder rotated 324.5° located at 3D coordinates (-2.71, -2.21, 0.70) and pixel coordinates (84, 119, 11.59)\nA small red metal cylinder rotated 1.1° located at 3D coordinates (2.88, -0.12, 0.35) and pixel coordinates (342, 200, 9.12)\nA small gray rubber cube rotated 144.9° located at 3D coordinates (0.79, 0.98, 0.35) and pixel coordinates (299, 145, 11.19)\nA large yellow rubber cube rotated 90.0° located at 3D coordinates (-1.78, -0.31, 0.70) and pixel coordinates (180, 110, 12.05)\n'} 18 | ``` 19 | 20 | See `generate_scene_qa_pairs.ipynb` for details. 21 | 22 | ## R1 Response Generation 23 | 24 | `query_r1.py`: We get the predictions and obtain the reasoning trace of R1 by querying SilconFlow (You can register via https://cloud.siliconflow.cn/i/rYfeQZ6A so both we 20M token credit!) 25 | 26 | Example Response: 27 | 28 | ```json 29 | Okay, let's see. The user is asking how many items are there in the described scene. Let me go through the scene description step by step. 30 | 31 | So, the scene description lists each object with details like color, material, shape, rotation, 3D coordinates, and pixel coordinates. Each entry starts with "A" which usually indicates one item each. Let me count each one. 32 | 33 | First entry: "A small green metal cylinder..." That's one. Second: "A small blue rubber cylinder..." Second item. Third: "A small cyan rubber cylinder..." That's three. Fourth: "A large cyan metal sphere..." Four. Fifth: "A large brown metal cube..." Five. Sixth: "A large yellow rubber cube..." Six. Seventh: "A large brown rubber cylinder..." That's seven. 34 | 35 | Wait, did I miss any? Let me check again. The list has entries from "A small green..." up to the seventh one. Each sentence starts with "A", which suggests each is a separate item. No commas separating multiple items in a single entry. Each has different attributes and coordinates, so they must all be distinct. 36 | 37 | So the answer should be 7 items. 38 | 39 | 40 | There are 7 items in the described scene. Each entry corresponds to one distinct object, listed by their properties, coordinates, and rotations. 41 | ``` 42 | 43 | In `v1` we do not constrain the output format and thus the answers are somewhat messy to parse. We then switched to `v2` by explicitly prompting the model to generate the answer with `**The answer is: **` 44 | 45 | ## Reasoning Path Filtering 46 | 47 | `filter_r1.py`: We filter out (almost) valid reasoning trace, by juding whether the R1 answer is correct (following our previous work [Math-Shepherd](https://arxiv.org/abs/2312.08935)). 48 | 49 | ## HF dataset creation 50 | 51 | Finally, we create the dataset using `create_hf_dataset.py` and upload to HF dataset hub. 52 | 53 | 54 | 55 | -------------------------------------------------------------------------------- /src/distill_r1/create_hf_dataset.py: -------------------------------------------------------------------------------- 1 | import json 2 | import os 3 | import random 4 | from datasets import load_dataset 5 | from tqdm import tqdm 6 | 7 | random.seed(1234) 8 | VAL_NUM = 5000 9 | 10 | 11 | def create_r1_train_dataset( 12 | valid_pair_json, 13 | data_dir, 14 | img_dir="/home/lilei/Visual-R1/CLEVR_CoGenT_v1.0/images/trainA/", 15 | ): 16 | os.makedirs(data_dir, exist_ok=True) 17 | pairs = [json.loads(line) for line in open(valid_pair_json, "r")] 18 | mapped_pairs = [] 19 | 20 | for idx, pair in tqdm(enumerate(pairs)): 21 | img_filename = pair["img_filename"] 22 | new_pair = {} 23 | try: 24 | new_pair["thinking"] = ( 25 | pair["r1_response"] 26 | .split("")[1] 27 | .split("")[0] 28 | .replace("scene description", "image") 29 | ) 30 | except Exception as e: 31 | print(f"Error processing pair response: ", pair["r1_response"]) 32 | continue # skip this pair 33 | # add index to distinguish the same image 34 | dataset_filename = ( 35 | img_filename.split(".")[0] + "_" + str(idx) + "." + img_filename.split(".")[1] 36 | ) 37 | if not os.path.exists(f"{data_dir}/{img_filename}"): 38 | os.system(f"cp {img_dir}/{img_filename} {data_dir}/{dataset_filename}") 39 | q, a = pair["q"], pair["a"] 40 | new_pair["problem"] = q 41 | # get the thinking path 42 | 43 | new_pair["thinking"] = "" + new_pair["thinking"] + "" 44 | new_pair["solution"] = f" {a} " 45 | new_pair["file_name"] = dataset_filename 46 | mapped_pairs.append(new_pair) 47 | with open(f"{data_dir}/metadata.jsonl", "w") as f: 48 | for pair in mapped_pairs: 49 | f.write(json.dumps(pair) + "\n") 50 | 51 | train_dataset = load_dataset( 52 | "imagefolder", 53 | data_dir=data_dir, 54 | split="train", 55 | ) 56 | return train_dataset 57 | 58 | 59 | def create_val_dataset( 60 | json_file, 61 | data_dir, 62 | val_num=VAL_NUM, 63 | image_dir="/home/lilei/Visual-R1/CLEVR_CoGenT_v1.0/images/valB", 64 | ): 65 | os.makedirs(data_dir, exist_ok=True) 66 | val = json.load(open(json_file)) 67 | random.shuffle(val) 68 | val = val[:val_num] 69 | val_pairs = [] 70 | for idx, pair in tqdm(enumerate(val)): 71 | q, a = pair["q"], pair["a"] 72 | img_filename = pair["img_filename"] 73 | # copy images to the DATA_DIR 74 | val_filename = ( 75 | img_filename.split(".")[0] + f"_{idx}." + img_filename.split(".")[1] 76 | ) 77 | if not os.path.exists(f"{data_dir}/{img_filename}"): 78 | os.system(f"cp {image_dir}/{img_filename} {data_dir}/{val_filename}") 79 | new_pair = {} 80 | new_pair["problem"] = q 81 | new_pair["solution"] = f" {a} " 82 | new_pair["file_name"] = val_filename 83 | val_pairs.append(new_pair) 84 | with open(f"{data_dir}/metadata.jsonl", "w") as f: 85 | for pair in val_pairs: 86 | f.write(json.dumps(pair) + "\n") 87 | val_dataset = load_dataset("imagefolder", data_dir=data_dir, split="train") 88 | return val_dataset 89 | 90 | 91 | # valA split 92 | VALA_DATA_DIR = "data/Clevr_CoGenT_ValA" 93 | VALB_DATA_DIR = "data/Clevr_CoGenT_ValB" 94 | valA_json = ( 95 | "/home/lilei/Visual-R1/data/clever_counting_problems_clevr_cogent_v1.0_valA.json" 96 | ) 97 | valB_json = ( 98 | "/home/lilei/Visual-R1/data/clever_counting_problems_clevr_cogent_v1.0_valB.json" 99 | ) 100 | TRAIN_DATADIR = "data/Clevr_CoGenT_TrainA_R1" 101 | train_dataset = create_r1_train_dataset( 102 | "/home/lilei/Visual-R1/filter_results_v2/valid_pairs.jsonl", 103 | TRAIN_DATADIR, 104 | ) 105 | 106 | # print(train_dataset) 107 | valA_dataset = create_val_dataset( 108 | valA_json, 109 | VALA_DATA_DIR, 110 | image_dir="/home/lilei/Visual-R1/CLEVR_CoGenT_v1.0/images/valA", 111 | ) 112 | valB_dataset = create_val_dataset( 113 | valB_json, 114 | VALB_DATA_DIR, 115 | image_dir="/home/lilei/Visual-R1/CLEVR_CoGenT_v1.0/images/valB", 116 | ) 117 | valA_dataset.push_to_hub("MMInstruction/Clevr_CoGenT_ValA") 118 | valB_dataset.push_to_hub("MMInstruction/Clevr_CoGenT_ValB") 119 | train_dataset.push_to_hub("MMInstruction/Clevr_CoGenT_TrainA_R1") 120 | -------------------------------------------------------------------------------- /src/distill_r1/filter_r1.py: -------------------------------------------------------------------------------- 1 | import json 2 | import re 3 | from pathlib import Path 4 | 5 | 6 | 7 | def extract_answer_from_query(query_results: str) -> str | None: 8 | """ 9 | Extract answer from query results, specifically looking for: 10 | - Numbers within asterisks 11 | - Yes/No answers in various formats 12 | 13 | Args: 14 | query_results: String containing the query response 15 | 16 | Returns: 17 | Extracted answer string or None if no answer found 18 | """ 19 | # First try to find answers in the standard format with labels 20 | # Split the text into segments (trying to get the last conclusion) 21 | if "" not in query_results or "" not in query_results: 22 | return None 23 | segments = query_results.split("\n") 24 | 25 | # First try to find final conclusion in the last few segments 26 | conclusion_patterns = [ 27 | r"(?:so|therefore|thus|hence),?\s*(?:the answer is\s+)?\*\*\s*(no|yes|[0-9]+)\s*\*\*", 28 | r"(?:so|therefore|thus|hence),?\s*(?:the answer is\s+)?(no|yes|[0-9]+)\b", 29 | r"the answer is\s+\*\*\s*(no|yes|[0-9]+)\s*\*\*", 30 | r"(?:final|conclusive) answer(?:\s+is)?\s*\*\*\s*(no|yes|[0-9]+)\s*\*\*", 31 | ] 32 | 33 | # Try to find conclusion in last 3 segments 34 | for segment in reversed(segments[-3:]): 35 | for pattern in conclusion_patterns: 36 | match = re.search(pattern, segment, re.IGNORECASE) 37 | if match: 38 | return match.group(1).strip().lower() 39 | 40 | # If no conclusion found, try other patterns on the full text 41 | labeled_patterns = [ 42 | r"\*\*The answer is:\s*\*\*\s*([0-9]+|yes|no)\b", 43 | r"\*\*Answer:\s*\*\*\s*([0-9]+|yes|no)\b", 44 | r"\*\*Answer\*\*:\s*([0-9]+|yes|no)\b", 45 | r"\*\*Answer:?\s*\*\*\s*There (?:is|are)\s+([0-9]+)", 46 | r"\*\*Final Count:\s*\*\*\s*([0-9]+)", 47 | r"\*\*Final Count:\s*\*\*\s*([0-9]+)\s+(?:items?|objects?|spheres?|cubes?|boxes?)", 48 | r"\*\*Total:\s*\*\*\s*([0-9]+)", 49 | r"The answer is:\s*([0-9]+|yes|no)\b", 50 | r"Answer:\s*([0-9]+|yes|no)\b", 51 | r"should be\s+([0-9]+)[.\s]", 52 | ] 53 | 54 | direct_patterns = [ 55 | r"\*\*\s*([0-9]+)\s*\*\*", 56 | r"\*\*\s*([0-9]+)\s+(?:items?|objects?|cubes?|boxes?|spheres?)?\s*\*\*", 57 | r"\*\*\s*([0-9]+)\s+[^*]+\*\*", 58 | ] 59 | 60 | latex_patterns = [ 61 | r"\$\\boxed{([0-9]+)}\$", 62 | r"\\boxed{([0-9]+)}", 63 | ] 64 | 65 | count_patterns = [ 66 | r"There (?:is|are)\s+([0-9]+)\s+(?:items?|objects?|spheres?|cubes?|boxes?)", 67 | ] 68 | 69 | # Try all patterns in sequence on full text 70 | all_patterns = labeled_patterns + direct_patterns + latex_patterns + count_patterns 71 | 72 | for pattern in all_patterns: 73 | match = re.search(pattern, query_results, re.IGNORECASE) 74 | if match: 75 | return match.group(1).strip().lower() 76 | 77 | return None 78 | 79 | 80 | def validate_qa_pairs(input_file: str, output_dir: str, verbose: bool = True): 81 | """ 82 | Process QA pairs and save them to separate files. 83 | Only saves pairs where parsed answer matches ground truth. 84 | 85 | Args: 86 | input_file: Path to input JSONL file 87 | output_dir: Directory to save output files 88 | verbose: If True, print examples of mismatched or unparseable responses 89 | """ 90 | output_dir = Path(output_dir) 91 | output_dir.mkdir(parents=True, exist_ok=True) 92 | 93 | valid_pairs = [] 94 | invalid_pairs = [] 95 | stats = {"total": 0, "unparseable": 0, "mismatch": 0, "valid": 0} 96 | 97 | with open(input_file, "r", encoding="utf-8") as f: 98 | for line_num, line in enumerate(f, 1): 99 | stats["total"] += 1 100 | qa_pair = json.loads(line.strip()) 101 | ground_truth = str(qa_pair.get("a", "")).lower().strip() 102 | parsed_answer = extract_answer_from_query(qa_pair["r1_response"]) 103 | 104 | if parsed_answer is None: 105 | stats["unparseable"] += 1 106 | qa_pair["error"] = "unparseable" 107 | invalid_pairs.append(qa_pair) 108 | if verbose: 109 | print(f"\nLine {line_num}: Could not parse answer") 110 | print(f"Ground truth: {ground_truth}") 111 | print(f"Query results: {qa_pair['r1_response'][-200:]}...") 112 | elif parsed_answer != ground_truth: 113 | stats["mismatch"] += 1 114 | qa_pair["error"] = "mismatch" 115 | qa_pair["parsed_answer"] = parsed_answer 116 | invalid_pairs.append(qa_pair) 117 | if verbose: 118 | print(f"\nLine {line_num}: Answer mismatch") 119 | print(f"Ground truth: {ground_truth}") 120 | print(f"Parsed answer: {parsed_answer}") 121 | print(f"Query results: {qa_pair['r1_response'][-200:]}...") 122 | else: 123 | stats["valid"] += 1 124 | valid_pairs.append(qa_pair) 125 | 126 | # Save valid pairs (where parsed answer matches ground truth) 127 | valid_file = output_dir / "valid_pairs.jsonl" 128 | with open(valid_file, "w", encoding="utf-8") as f: 129 | for pair in valid_pairs: 130 | f.write(json.dumps(pair, ensure_ascii=False) + "\n") 131 | 132 | # Save invalid pairs (unparseable or mismatched) 133 | invalid_file = output_dir / "invalid_pairs.jsonl" 134 | with open(invalid_file, "w", encoding="utf-8") as f: 135 | for pair in invalid_pairs: 136 | f.write(json.dumps(pair, ensure_ascii=False) + "\n") 137 | 138 | # Print statistics 139 | print(f"\nProcessing Summary:") 140 | print(f"Total pairs processed: {stats['total']}") 141 | print(f"Valid pairs (matching ground truth): {stats['valid']}") 142 | print(f"Invalid pairs: {stats['unparseable'] + stats['mismatch']}") 143 | print(f" - Unparseable: {stats['unparseable']}") 144 | print(f" - Answer mismatch: {stats['mismatch']}") 145 | print(f"\nOutput files:") 146 | print(f"Valid pairs saved to: {valid_file}") 147 | print(f"Invalid pairs saved to: {invalid_file}") 148 | 149 | 150 | if __name__ == "__main__": 151 | validate_qa_pairs( 152 | "r1_results_clevr_cogent_v1.0_trainA_v2.jsonl", "filter_results_v2" 153 | ) # "filtered_output_tmp_v1.jsonl") 154 | -------------------------------------------------------------------------------- /src/distill_r1/generate_scene_qa_pairs.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "id": "3a704ea6-2e61-4aaa-97aa-416579c9bc13", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import json\n", 11 | "import random" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 4, 17 | "id": "c4920a8f-cddd-4063-8cab-215d238b5dad", 18 | "metadata": {}, 19 | "outputs": [ 20 | { 21 | "name": "stdout", 22 | "output_type": "stream", 23 | "text": [ 24 | "CLEVR_trainA_scenes.json CLEVR_valA_scenes.json CLEVR_valB_scenes.json\n" 25 | ] 26 | } 27 | ], 28 | "source": [ 29 | "!ls CLEVR_CoGenT_v1.0/scenes" 30 | ] 31 | }, 32 | { 33 | "cell_type": "code", 34 | "execution_count": 15, 35 | "id": "934fa005-3b2a-43ed-8a71-6a12b7579546", 36 | "metadata": {}, 37 | "outputs": [], 38 | "source": [ 39 | "split = \"valB\"\n", 40 | "clevr_train_json = f\"CLEVR_CoGenT_v1.0/scenes/CLEVR_{split}_scenes.json\"\n", 41 | "train_qs = f\"CLEVR_CoGenT_v1.0/questions/CLEVR_{split}_questions.json\"\n", 42 | "data = json.load(open(clevr_train_json))\n", 43 | "qs = json.load(open(train_qs))" 44 | ] 45 | }, 46 | { 47 | "cell_type": "code", 48 | "execution_count": 16, 49 | "id": "1f0d6180-94c4-4aea-bd2b-8d5cfeb0aecb", 50 | "metadata": {}, 51 | "outputs": [ 52 | { 53 | "name": "stdout", 54 | "output_type": "stream", 55 | "text": [ 56 | "[{'pixel_coords': [343, 131, 11.278693199157715], 'size': 'small', 'color': 'green', 'material': 'metal', 'shape': 'sphere', '3d_coords': [0.9906095862388611, 2.083291530609131, 0.3499999940395355], 'rotation': 107.73596690369371}, {'pixel_coords': [396, 172, 9.857704162597656], 'size': 'small', 'color': 'cyan', 'material': 'rubber', 'shape': 'sphere', '3d_coords': [2.69626522064209, 1.5257188081741333, 0.3499999940395355], 'rotation': 305.3536122513589}, {'pixel_coords': [115, 182, 8.91348934173584], 'size': 'large', 'color': 'yellow', 'material': 'rubber', 'shape': 'cylinder', '3d_coords': [0.049163494259119034, -2.864100217819214, 0.699999988079071], 'rotation': 161.8370138842408}, {'pixel_coords': [203, 131, 10.548327445983887], 'size': 'large', 'color': 'purple', 'material': 'rubber', 'shape': 'cube', '3d_coords': [-0.4719269275665283, -0.5699371695518494, 0.699999988079071], 'rotation': 159.41862667811446}, {'pixel_coords': [253, 75, 13.141877174377441], 'size': 'large', 'color': 'red', 'material': 'rubber', 'shape': 'cube', '3d_coords': [-2.036878824234009, 2.222999334335327, 0.699999988079071], 'rotation': 37.40490732771224}]\n", 57 | "len: 5\n" 58 | ] 59 | } 60 | ], 61 | "source": [ 62 | "print(data['scenes'][0]['objects'])\n", 63 | "print(\"len: \", len(data['scenes'][0]['objects']))" 64 | ] 65 | }, 66 | { 67 | "cell_type": "code", 68 | "execution_count": 17, 69 | "id": "7c828ca4-08f9-4927-a745-224a95379c2f", 70 | "metadata": {}, 71 | "outputs": [], 72 | "source": [ 73 | "def object_info_to_description(object_list):\n", 74 | " descriptions = []\n", 75 | " random.shuffle(object_list)\n", 76 | " for obj in object_list:\n", 77 | " desc = f\"A {obj['size']} {obj['color']} {obj['material']} {obj['shape']}\"\n", 78 | " desc += f\" rotated {obj['rotation']:.1f}° located at\"\n", 79 | " desc += f\" 3D coordinates ({obj['3d_coords'][0]:.2f}, {obj['3d_coords'][1]:.2f}, {obj['3d_coords'][2]:.2f})\"\n", 80 | " desc += f\" and pixel coordinates ({obj['pixel_coords'][0]}, {obj['pixel_coords'][1]}, {obj['pixel_coords'][2]:.2f})\"\n", 81 | " descriptions.append(desc)\n", 82 | " \n", 83 | " final_description = \"Scene Description:\\n\"\n", 84 | " for i, desc in enumerate(descriptions, 1):\n", 85 | " final_description += f\"{desc}\\n\"\n", 86 | " \n", 87 | " return final_description" 88 | ] 89 | }, 90 | { 91 | "cell_type": "code", 92 | "execution_count": 18, 93 | "id": "cb048e25-d554-4bd7-bf11-878e071b5987", 94 | "metadata": {}, 95 | "outputs": [ 96 | { 97 | "data": { 98 | "text/plain": [ 99 | "'Scene Description:\\nA large yellow rubber cylinder rotated 161.8° located at 3D coordinates (0.05, -2.86, 0.70) and pixel coordinates (115, 182, 8.91)\\nA large purple rubber cube rotated 159.4° located at 3D coordinates (-0.47, -0.57, 0.70) and pixel coordinates (203, 131, 10.55)\\nA large red rubber cube rotated 37.4° located at 3D coordinates (-2.04, 2.22, 0.70) and pixel coordinates (253, 75, 13.14)\\nA small green metal sphere rotated 107.7° located at 3D coordinates (0.99, 2.08, 0.35) and pixel coordinates (343, 131, 11.28)\\nA small cyan rubber sphere rotated 305.4° located at 3D coordinates (2.70, 1.53, 0.35) and pixel coordinates (396, 172, 9.86)\\n'" 100 | ] 101 | }, 102 | "execution_count": 18, 103 | "metadata": {}, 104 | "output_type": "execute_result" 105 | } 106 | ], 107 | "source": [ 108 | "object_info_to_description(data['scenes'][0]['objects'])" 109 | ] 110 | }, 111 | { 112 | "cell_type": "code", 113 | "execution_count": 19, 114 | "id": "ffacd5f3-e9a4-46ca-8c50-187ab12c9f1b", 115 | "metadata": {}, 116 | "outputs": [], 117 | "source": [ 118 | "img2obj_dict = {}\n", 119 | "for scene in data['scenes']:\n", 120 | " obj_list = scene['objects']\n", 121 | " img2obj_dict[scene['image_filename']] = obj_list" 122 | ] 123 | }, 124 | { 125 | "cell_type": "code", 126 | "execution_count": 20, 127 | "id": "db35f03c-1529-4776-bf4f-3bd44e960e5f", 128 | "metadata": {}, 129 | "outputs": [ 130 | { 131 | "data": { 132 | "text/plain": [ 133 | "{'question_index': 0,\n", 134 | " 'question_family_index': 29,\n", 135 | " 'image_index': 0,\n", 136 | " 'question': 'The big thing that is in front of the large rubber cube in front of the small thing that is behind the tiny matte ball is what color?',\n", 137 | " 'answer': 'yellow',\n", 138 | " 'image_filename': 'CLEVR_valB_000000.png',\n", 139 | " 'split': 'valB',\n", 140 | " 'program': [{'value_inputs': [], 'inputs': [], 'function': 'scene'},\n", 141 | " {'value_inputs': ['small'], 'inputs': [0], 'function': 'filter_size'},\n", 142 | " {'value_inputs': ['rubber'], 'inputs': [1], 'function': 'filter_material'},\n", 143 | " {'value_inputs': ['sphere'], 'inputs': [2], 'function': 'filter_shape'},\n", 144 | " {'value_inputs': [], 'inputs': [3], 'function': 'unique'},\n", 145 | " {'value_inputs': ['behind'], 'inputs': [4], 'function': 'relate'},\n", 146 | " {'value_inputs': ['small'], 'inputs': [5], 'function': 'filter_size'},\n", 147 | " {'value_inputs': [], 'inputs': [6], 'function': 'unique'},\n", 148 | " {'value_inputs': ['front'], 'inputs': [7], 'function': 'relate'},\n", 149 | " {'value_inputs': ['large'], 'inputs': [8], 'function': 'filter_size'},\n", 150 | " {'value_inputs': ['rubber'], 'inputs': [9], 'function': 'filter_material'},\n", 151 | " {'value_inputs': ['cube'], 'inputs': [10], 'function': 'filter_shape'},\n", 152 | " {'value_inputs': [], 'inputs': [11], 'function': 'unique'},\n", 153 | " {'value_inputs': ['front'], 'inputs': [12], 'function': 'relate'},\n", 154 | " {'value_inputs': ['large'], 'inputs': [13], 'function': 'filter_size'},\n", 155 | " {'value_inputs': [], 'inputs': [14], 'function': 'unique'},\n", 156 | " {'value_inputs': [], 'inputs': [15], 'function': 'query_color'}]}" 157 | ] 158 | }, 159 | "execution_count": 20, 160 | "metadata": {}, 161 | "output_type": "execute_result" 162 | } 163 | ], 164 | "source": [ 165 | "qs['questions'][0]" 166 | ] 167 | }, 168 | { 169 | "cell_type": "code", 170 | "execution_count": 21, 171 | "id": "66b746fc-569c-4922-a442-79dbbc09e33b", 172 | "metadata": {}, 173 | "outputs": [], 174 | "source": [ 175 | "random.shuffle(qs['questions'])\n", 176 | "cnt = 0 \n", 177 | "qa_pairs = [] \n", 178 | "added_pair = set()\n", 179 | "for qd in qs['questions']:\n", 180 | " img_idx = qd['image_filename']\n", 181 | " total_count = len(img2obj_dict[img_idx]) # object list length\n", 182 | " desc = object_info_to_description(img2obj_dict[img_idx])\n", 183 | " question, answer = qd['question'], qd['answer']\n", 184 | " if 'how many' in question.lower() or 'number' in question.lower():\n", 185 | " qa_pairs.append({\n", 186 | " \"img_filename\": img_idx,\n", 187 | " 'q': question,\n", 188 | " 'a': answer,\n", 189 | " 'description': desc \n", 190 | " })\n", 191 | " if img_idx not in added_pair:\n", 192 | " qa_pairs.append({\n", 193 | " \"img_filename\": img_idx,\n", 194 | " 'q': \"How many items are there in the described scene?\",\n", 195 | " 'a': total_count,\n", 196 | " 'description': desc \n", 197 | " })\n", 198 | " added_pair.add(img_idx)\n" 199 | ] 200 | }, 201 | { 202 | "cell_type": "code", 203 | "execution_count": 22, 204 | "id": "c271fa7b-fed5-472f-a302-6ec203c4b787", 205 | "metadata": {}, 206 | "outputs": [ 207 | { 208 | "data": { 209 | "text/plain": [ 210 | "59978" 211 | ] 212 | }, 213 | "execution_count": 22, 214 | "metadata": {}, 215 | "output_type": "execute_result" 216 | } 217 | ], 218 | "source": [ 219 | "len(qa_pairs)" 220 | ] 221 | }, 222 | { 223 | "cell_type": "code", 224 | "execution_count": 23, 225 | "id": "b0da8a70-c3f5-4e48-b384-3684933d72ef", 226 | "metadata": {}, 227 | "outputs": [ 228 | { 229 | "data": { 230 | "text/plain": [ 231 | "14884" 232 | ] 233 | }, 234 | "execution_count": 23, 235 | "metadata": {}, 236 | "output_type": "execute_result" 237 | } 238 | ], 239 | "source": [ 240 | "len(added_pair)" 241 | ] 242 | }, 243 | { 244 | "cell_type": "code", 245 | "execution_count": 24, 246 | "id": "c648587e-2ec0-427c-b594-f55dd187b4d9", 247 | "metadata": {}, 248 | "outputs": [], 249 | "source": [ 250 | "# save for later loading\n", 251 | "with open(f\"clever_counting_problems_clevr_cogent_v1.0_{split}.json\", 'w') as fw:\n", 252 | " json.dump( qa_pairs, fw, indent=4)" 253 | ] 254 | }, 255 | { 256 | "cell_type": "code", 257 | "execution_count": 20, 258 | "id": "b3a8cbe4-4261-41d3-a481-43a0b1cc2795", 259 | "metadata": {}, 260 | "outputs": [], 261 | "source": [ 262 | "random.shuffle(qa_pairs)" 263 | ] 264 | }, 265 | { 266 | "cell_type": "code", 267 | "execution_count": 57, 268 | "id": "d6dff4e7-65dd-4e82-82df-340ec2a57919", 269 | "metadata": {}, 270 | "outputs": [ 271 | { 272 | "data": { 273 | "text/plain": [ 274 | "[{'img_filename': 'CLEVR_trainA_048403.png',\n", 275 | " 'q': 'How many things are both on the right side of the big yellow rubber thing and left of the purple ball?',\n", 276 | " 'a': '5',\n", 277 | " 'description': 'Scene Description:\\nA large red rubber cylinder rotated 291.3° located at 3D coordinates (-0.89, -2.73, 0.70) and pixel coordinates (101, 152, 10.04)\\nA small purple metal sphere rotated 247.7° located at 3D coordinates (2.93, 0.87, 0.35) and pixel coordinates (379, 183, 9.66)\\nA large cyan rubber cylinder rotated 114.5° located at 3D coordinates (-2.40, 2.23, 0.70) and pixel coordinates (246, 82, 13.94)\\nA small red metal cylinder rotated 109.9° located at 3D coordinates (-0.95, 1.77, 0.35) and pixel coordinates (270, 113, 12.83)\\nA small red rubber cylinder rotated 343.7° located at 3D coordinates (-0.12, -0.74, 0.35) and pixel coordinates (209, 153, 10.82)\\nA large red rubber cylinder rotated 324.5° located at 3D coordinates (-2.71, -2.21, 0.70) and pixel coordinates (84, 119, 11.59)\\nA small red metal cylinder rotated 1.1° located at 3D coordinates (2.88, -0.12, 0.35) and pixel coordinates (342, 200, 9.12)\\nA small gray rubber cube rotated 144.9° located at 3D coordinates (0.79, 0.98, 0.35) and pixel coordinates (299, 145, 11.19)\\nA large yellow rubber cube rotated 90.0° located at 3D coordinates (-1.78, -0.31, 0.70) and pixel coordinates (180, 110, 12.05)\\n'},\n", 278 | " {'img_filename': 'CLEVR_trainA_048403.png',\n", 279 | " 'q': 'How many items are there in the described scene?',\n", 280 | " 'a': 9,\n", 281 | " 'description': 'Scene Description:\\nA large red rubber cylinder rotated 291.3° located at 3D coordinates (-0.89, -2.73, 0.70) and pixel coordinates (101, 152, 10.04)\\nA small purple metal sphere rotated 247.7° located at 3D coordinates (2.93, 0.87, 0.35) and pixel coordinates (379, 183, 9.66)\\nA large cyan rubber cylinder rotated 114.5° located at 3D coordinates (-2.40, 2.23, 0.70) and pixel coordinates (246, 82, 13.94)\\nA small red metal cylinder rotated 109.9° located at 3D coordinates (-0.95, 1.77, 0.35) and pixel coordinates (270, 113, 12.83)\\nA small red rubber cylinder rotated 343.7° located at 3D coordinates (-0.12, -0.74, 0.35) and pixel coordinates (209, 153, 10.82)\\nA large red rubber cylinder rotated 324.5° located at 3D coordinates (-2.71, -2.21, 0.70) and pixel coordinates (84, 119, 11.59)\\nA small red metal cylinder rotated 1.1° located at 3D coordinates (2.88, -0.12, 0.35) and pixel coordinates (342, 200, 9.12)\\nA small gray rubber cube rotated 144.9° located at 3D coordinates (0.79, 0.98, 0.35) and pixel coordinates (299, 145, 11.19)\\nA large yellow rubber cube rotated 90.0° located at 3D coordinates (-1.78, -0.31, 0.70) and pixel coordinates (180, 110, 12.05)\\n'}]" 282 | ] 283 | }, 284 | "execution_count": 57, 285 | "metadata": {}, 286 | "output_type": "execute_result" 287 | } 288 | ], 289 | "source": [ 290 | "qa_pairs[:2]" 291 | ] 292 | }, 293 | { 294 | "cell_type": "code", 295 | "execution_count": 26, 296 | "id": "a6a66364-5b47-4138-91d6-a045404d21b1", 297 | "metadata": {}, 298 | "outputs": [], 299 | "source": [ 300 | "def query_r1(query='who are you?', model=\"deepseek-ai/DeepSeek-R1\"):\n", 301 | " # Create the chat completion\n", 302 | " response = client.chat.completions.create(\n", 303 | " model=model,\n", 304 | " messages=[\n", 305 | " {'role': 'user', \n", 306 | " 'content': query}\n", 307 | " ],\n", 308 | " stream=False,\n", 309 | " )\n", 310 | " # Print the response\n", 311 | " return response.choices[0].message.content" 312 | ] 313 | }, 314 | { 315 | "cell_type": "code", 316 | "execution_count": 44, 317 | "id": "e5d5649f-c4e3-4f3f-b76e-7f7ed27f68e8", 318 | "metadata": {}, 319 | "outputs": [], 320 | "source": [ 321 | "def format_query(qa_dict):\n", 322 | " query = \"Answer the question according to scene description.\\n\\n\"\n", 323 | " query += qa_dict['description']\n", 324 | " query += f\"\\nQuestion:\\n{qa_dict['q']}\"\n", 325 | " return query \n", 326 | " " 327 | ] 328 | }, 329 | { 330 | "cell_type": "code", 331 | "execution_count": 39, 332 | "id": "7f568a4e-f217-464a-8329-bbefb64d9653", 333 | "metadata": {}, 334 | "outputs": [ 335 | { 336 | "name": "stdout", 337 | "output_type": "stream", 338 | "text": [ 339 | "Okay, let's see. The user is asking how many items are there in the described scene. Let me go through the scene description step by step.\n", 340 | "\n", 341 | "So, the scene description lists each object with details like color, material, shape, rotation, 3D coordinates, and pixel coordinates. Each entry starts with \"A\" which usually indicates one item each. Let me count each one.\n", 342 | "\n", 343 | "First entry: \"A small green metal cylinder...\" That's one. Second: \"A small blue rubber cylinder...\" Second item. Third: \"A small cyan rubber cylinder...\" That's three. Fourth: \"A large cyan metal sphere...\" Four. Fifth: \"A large brown metal cube...\" Five. Sixth: \"A large yellow rubber cube...\" Six. Seventh: \"A large brown rubber cylinder...\" That's seven. \n", 344 | "\n", 345 | "Wait, did I miss any? Let me check again. The list has entries from \"A small green...\" up to the seventh one. Each sentence starts with \"A\", which suggests each is a separate item. No commas separating multiple items in a single entry. Each has different attributes and coordinates, so they must all be distinct. \n", 346 | "\n", 347 | "So the answer should be 7 items.\n", 348 | "\n", 349 | "\n", 350 | "There are 7 items in the described scene. Each entry corresponds to one distinct object, listed by their properties, coordinates, and rotations.\n", 351 | "None\n" 352 | ] 353 | } 354 | ], 355 | "source": [ 356 | "debug_query = format_query(qa_pairs[0])\n", 357 | "print(query_r1(debug_query))" 358 | ] 359 | }, 360 | { 361 | "cell_type": "code", 362 | "execution_count": 41, 363 | "id": "cdc4231a-8ef4-4cf6-a575-d84ae7bbd0b5", 364 | "metadata": {}, 365 | "outputs": [ 366 | { 367 | "name": "stdout", 368 | "output_type": "stream", 369 | "text": [ 370 | "Answer the question accordingly to scene description.\n", 371 | "\n", 372 | "Scene Description:\n", 373 | "A small green metal cylinder rotated 329.5° located at 3D coordinates (-2.49, -1.65, 0.35) and pixel coordinates (111, 132, 11.81)\n", 374 | "A small blue rubber cylinder rotated 312.2° located at 3D coordinates (-1.73, -2.91, 0.35) and pixel coordinates (76, 163, 10.57)\n", 375 | "A small cyan rubber cylinder rotated 48.4° located at 3D coordinates (-2.10, -0.22, 0.35) and pixel coordinates (172, 118, 12.41)\n", 376 | "A large cyan metal sphere rotated 27.4° located at 3D coordinates (1.52, -1.26, 0.70) and pixel coordinates (247, 181, 9.33)\n", 377 | "A large brown metal cube rotated 107.7° located at 3D coordinates (-0.73, 2.39, 0.70) and pixel coordinates (290, 92, 12.93)\n", 378 | "A large yellow rubber cube rotated 288.2° located at 3D coordinates (0.52, 0.63, 0.70) and pixel coordinates (279, 130, 11.09)\n", 379 | "A large brown rubber cylinder rotated 229.8° located at 3D coordinates (2.38, 0.38, 0.70) and pixel coordinates (343, 166, 9.77)\n", 380 | "\n", 381 | "Question:\n", 382 | "How many items are there in the described scene?\n" 383 | ] 384 | } 385 | ], 386 | "source": [ 387 | "print(debug_query)" 388 | ] 389 | }, 390 | { 391 | "cell_type": "code", 392 | "execution_count": 42, 393 | "id": "4cf90eb6-2cce-4e3d-8190-c44168a66dca", 394 | "metadata": {}, 395 | "outputs": [ 396 | { 397 | "data": { 398 | "text/plain": [ 399 | "{'img_filename': 'CLEVR_train_044000.png',\n", 400 | " 'q': 'How many rubber objects are either small blue spheres or small things?',\n", 401 | " 'a': '2',\n", 402 | " 'description': 'Scene Description:\\nA large purple rubber sphere rotated 78.4° located at 3D coordinates (2.27, 0.87, 0.70) and pixel coordinates (360, 156, 9.49)\\nA large gray metal cube rotated 152.7° located at 3D coordinates (2.79, -1.26, 0.70) and pixel coordinates (301, 213, 7.91)\\nA large purple metal sphere rotated 79.2° located at 3D coordinates (-2.66, -2.74, 0.70) and pixel coordinates (51, 126, 10.61)\\nA large blue rubber sphere rotated 279.5° located at 3D coordinates (1.31, 2.72, 0.70) and pixel coordinates (376, 112, 11.19)\\nA small brown rubber cube rotated 124.1° located at 3D coordinates (-2.49, 2.61, 0.35) and pixel coordinates (251, 82, 13.79)\\nA small green rubber sphere rotated 323.9° located at 3D coordinates (-2.02, 0.45, 0.35) and pixel coordinates (197, 109, 12.22)\\n'}" 403 | ] 404 | }, 405 | "execution_count": 42, 406 | "metadata": {}, 407 | "output_type": "execute_result" 408 | } 409 | ], 410 | "source": [ 411 | "qa_pairs[1]" 412 | ] 413 | }, 414 | { 415 | "cell_type": "code", 416 | "execution_count": 45, 417 | "id": "33fcd4eb-1f33-47d4-a453-76ef00e6d5d3", 418 | "metadata": {}, 419 | "outputs": [ 420 | { 421 | "name": "stdout", 422 | "output_type": "stream", 423 | "text": [ 424 | "Okay, let's tackle this question. The user wants to know how many rubber objects are either small blue spheres or small things. Hmm, first, I need to parse each part of the question correctly.\n", 425 | "\n", 426 | "Let's go back to the scene description. The objects are listed with their attributes: size, color, material, shape, rotation, 3D and pixel coordinates. The key here is to filter the objects based on the criteria given.\n", 427 | "\n", 428 | "The question has two parts: \"small blue spheres\" OR \"small things\". So any rubber object that is either a small blue sphere or any small thing (regardless of other attributes) counts. But wait, do both categories need to be rubber? Because the question says \"rubber objects are either...\". So rubber is the material, and the condition is either being a small blue sphere or a small thing. So first, all rubber objects, and among them, count those that are either small blue spheres or small (regardless of color or shape). Wait, no. Let me re-read the question.\n", 429 | "\n", 430 | "The question is: \"How many rubber objects are either small blue spheres or small things?\" So rubber is the material. Within all rubber objects, count the ones that are either (1) small blue spheres OR (2) small things. Wait, does (2) being small things mean that even if they're small and of any color or shape, but they must be rubber?\n", 431 | "\n", 432 | "Yes, because the entire set is rubber objects. So first, select all objects where material is rubber. Then, within those, count how many are either (1) small, blue, sphere, or (2) small (any color or shape). Wait, but the structure is \"either X or Y\", where X is \"small blue sphere\" and Y is \"small things\". But \"small things\" would include all small objects, regardless of color and shape. However, since we've already narrowed it to rubber objects, \"small things\" here would be small rubber objects, regardless of color and shape.\n", 433 | "\n", 434 | "But wait, the condition is within rubber objects. So for the first part, small blue spheres (must check size, color, shape) and for the second part, small things (size is small, any color and shape, but since material is already rubber, that's covered). But wait, does the OR merge the two conditions, leading to rubber objects that are either (small blue spheres) or (small any-color any-shape).\n", 435 | "\n", 436 | "So the combined condition is: object is rubber AND ( (is small AND blue AND sphere) OR (is small) ). Wait, but if the condition for the second part is just \"small things\", which would imply any small object. But the entire group is rubber objects. So it's rubber objects that are small blue spheres OR rubber objects that are small (regardless of color or shape).\n", 437 | "\n", 438 | "Wait, no. Let's parse the sentence again: \"rubber objects are either small blue spheres or small things\". The \"either/or\" applies to \"small blue spheres\" and \"small things\". So, each rubber object has to be either (a small blue sphere) or (a small thing). However, \"small things\" here might refer to any small object regardless of other attributes. So if a rubber object is small, regardless of color or shape, it counts. But then, the first condition (small blue sphere) would also satisfy being a small thing. Wait, so there's an overlap. But when dealing with OR conditions, we have to avoid double-counting. So, the actual count is the number of small rubber objects (since any small rubber object is covered by the second part, which includes all small rubber objects, whether blue spheres or not) plus any objects that are small blue spheres but not rubber? But no, the question specifies \"rubber objects\", so we can ignore non-rubber ones.\n", 439 | "\n", 440 | "Wait, perhaps the wording is: \"rubber objects that are either small blue spheres or small things\". So \"small things\" here must reference other attributes. Wait, maybe there's ambiguity here. If the user is grouping \"small things\" as a separate category, regardless of being the other attributes. Let me try to approach this step by step.\n", 441 | "\n", 442 | "First, list all the rubber objects from the scene description:\n", 443 | "\n", 444 | "Looking through the list:\n", 445 | "\n", 446 | "1. A large purple rubber sphere ... location etc.\n", 447 | "So material rubber, large, purple, sphere.\n", 448 | "\n", 449 | "2. A large gray metal cube ... metal, so not rubber.\n", 450 | "\n", 451 | "3. A large purple metal sphere ... metal, not rubber.\n", 452 | "\n", 453 | "4. A large blue rubber sphere ... rubber, large, blue, sphere.\n", 454 | "\n", 455 | "5. A small brown rubber cube ... rubber, small, brown, cube.\n", 456 | "\n", 457 | "6. A small green rubber sphere ... rubber, small, green, sphere.\n", 458 | "\n", 459 | "So the rubber objects are items 1,4,5,6.\n", 460 | "\n", 461 | "Now, for each of these four rubber objects, check if they are either (small blue sphere) or (small things).\n", 462 | "\n", 463 | "Let's check each:\n", 464 | "\n", 465 | "1. Large purple rubber sphere. Size: large. So for the first condition (small blue sphere): no. For the second condition (small thing): size large, so no. Not included.\n", 466 | "\n", 467 | "4. Large blue rubber sphere. Large. So, even though it's blue and sphere, the size is large. So (small blue sphere: no, since size is large). (small thing: no.) So not included.\n", 468 | "\n", 469 | "5. Small brown rubber cube. Size is small. So regardless of color and shape (brown, cube), this is a small thing. So it meets the second condition. So count it.\n", 470 | "\n", 471 | "6. Small green rubber sphere. Size: small. So meets the second condition (small thing). But it's a sphere and green, not blue. So this counts under the second category.\n", 472 | "\n", 473 | "Now, what about the first condition: small blue spheres. Are there any rubber objects that are small blue spheres? The only blue rubber sphere is item 4, which is large. So none of the rubber objects are small blue spheres.\n", 474 | "\n", 475 | "So now the two options under the OR are:\n", 476 | "\n", 477 | "- small blue sphere (none found)\n", 478 | "- small things (items 5 and 6), both are rubber.\n", 479 | "\n", 480 | "Therefore total count is 0 + 2 = 2.\n", 481 | "\n", 482 | "Wait, but the initial question is: number of rubber objects that are either (small blue spheres) or (small things). Since \"small blue spheres\" are a subset of \"small things\" (since if something is a small blue sphere, it is a small thing). Therefore if there were any such objects, they would be counted twice, but in this case, since there are none, the total is just the count of small things.\n", 483 | "\n", 484 | "But according to the data, the rubber objects that are small are two: item 5 (small brown cube) and item 6 (small green sphere). Neither is a blue sphere, so no overlap. Therefore, sum is 2.\n", 485 | "\n", 486 | "So the answer should be 2.\n", 487 | "\n", 488 | "I need to confirm again whether the OR condition includes the union, meaning any rubber object that is either condition. So if either condition is true, count it.\n", 489 | "\n", 490 | "Since both 5 and 6 are small, even though they aren't blue spheres, they belong to the second category (small things). The blue sphere (if it were small) would have been counted as well. But in this case, the valid counts are 5 and 6. So yes, answer is 2.\n", 491 | "\n", 492 | "\n", 493 | "The answer is 2. \n", 494 | "\n", 495 | "**Explanation:**\n", 496 | "First, identify all rubber objects from the scene description:\n", 497 | "1. Large purple rubber sphere (not small)\n", 498 | "2. Large blue rubber sphere (not small)\n", 499 | "3. Small brown rubber cube (small)\n", 500 | "4. Small green rubber sphere (small)\n", 501 | "\n", 502 | "Next, apply the criteria:\n", 503 | "- **Small blue spheres**: None of the rubber objects meet this (the only blue rubber sphere is large).\n", 504 | "- **Small rubber objects (regardless of color/shape)**: The small brown rubber cube and small green rubber sphere qualify (2 objects).\n", 505 | "\n", 506 | "Thus, there are **2 rubber objects** that fit either criterion.\n" 507 | ] 508 | } 509 | ], 510 | "source": [ 511 | "debug_query1 = format_query(qa_pairs[1])\n", 512 | "res1 = query_r1(debug_query1)" 513 | ] 514 | }, 515 | { 516 | "cell_type": "code", 517 | "execution_count": 47, 518 | "id": "8e516bd0-f1e5-4898-88a3-3afcaf0ae34e", 519 | "metadata": {}, 520 | "outputs": [ 521 | { 522 | "data": { 523 | "text/plain": [ 524 | "{'img_filename': 'CLEVR_train_044000.png',\n", 525 | " 'q': 'How many rubber objects are either small blue spheres or small things?',\n", 526 | " 'a': '2',\n", 527 | " 'description': 'Scene Description:\\nA large purple rubber sphere rotated 78.4° located at 3D coordinates (2.27, 0.87, 0.70) and pixel coordinates (360, 156, 9.49)\\nA large gray metal cube rotated 152.7° located at 3D coordinates (2.79, -1.26, 0.70) and pixel coordinates (301, 213, 7.91)\\nA large purple metal sphere rotated 79.2° located at 3D coordinates (-2.66, -2.74, 0.70) and pixel coordinates (51, 126, 10.61)\\nA large blue rubber sphere rotated 279.5° located at 3D coordinates (1.31, 2.72, 0.70) and pixel coordinates (376, 112, 11.19)\\nA small brown rubber cube rotated 124.1° located at 3D coordinates (-2.49, 2.61, 0.35) and pixel coordinates (251, 82, 13.79)\\nA small green rubber sphere rotated 323.9° located at 3D coordinates (-2.02, 0.45, 0.35) and pixel coordinates (197, 109, 12.22)\\n'}" 528 | ] 529 | }, 530 | "execution_count": 47, 531 | "metadata": {}, 532 | "output_type": "execute_result" 533 | } 534 | ], 535 | "source": [ 536 | "qa_pairs[1]" 537 | ] 538 | }, 539 | { 540 | "cell_type": "code", 541 | "execution_count": null, 542 | "id": "92784518-49e2-443d-9541-2785cbb944cf", 543 | "metadata": {}, 544 | "outputs": [], 545 | "source": [] 546 | } 547 | ], 548 | "metadata": { 549 | "kernelspec": { 550 | "display_name": "Python 3 (ipykernel)", 551 | "language": "python", 552 | "name": "python3" 553 | }, 554 | "language_info": { 555 | "codemirror_mode": { 556 | "name": "ipython", 557 | "version": 3 558 | }, 559 | "file_extension": ".py", 560 | "mimetype": "text/x-python", 561 | "name": "python", 562 | "nbconvert_exporter": "python", 563 | "pygments_lexer": "ipython3", 564 | "version": "3.12.2" 565 | } 566 | }, 567 | "nbformat": 4, 568 | "nbformat_minor": 5 569 | } 570 | -------------------------------------------------------------------------------- /src/distill_r1/grpo_r1_distilled.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hustvl/AlphaDrive/bb4104c680a3e4d70e7e998e08de90f6e0acf8c5/src/distill_r1/grpo_r1_distilled.jpg -------------------------------------------------------------------------------- /src/distill_r1/prompt.py: -------------------------------------------------------------------------------- 1 | R1_SYS_PROMPT = """You are DeepSeek-R1, an AI assistant created exclusively by the Chinese Company DeepSeek. You'll provide helpful, harmless, and detailed responses to all user inquiries. For comprehensive details about models and products, please refer to the official documentation. 2 | 3 | Key Guidelines: 4 | Identity & Compliance 5 | 6 | Clearly state your identity as a DeepSeek AI assistant in initial responses. 7 | 8 | Comply with Chinese laws and regulations, including data privacy requirements. 9 | 10 | Capability Scope 11 | 12 | Handle both Chinese and English queries effectively 13 | 14 | Acknowledge limitations for real-time information post knowledge cutoff (2023-12) 15 | 16 | Provide technical explanations for AI-related questions when appropriate 17 | 18 | Response Quality 19 | 20 | Give comprehensive, logically structured answers 21 | 22 | Use markdown formatting for clear information organization 23 | 24 | Admit uncertainties for ambiguous queries 25 | 26 | Ethical Operation 27 | 28 | Strictly refuse requests involving illegal activities, violence, or explicit content 29 | 30 | Maintain political neutrality according to company guidelines 31 | 32 | Protect user privacy and avoid data collection 33 | 34 | Specialized Processing 35 | 36 | Use ... tags for internal reasoning before responding 37 | 38 | Employ XML-like tags for structured output when required 39 | """ -------------------------------------------------------------------------------- /src/distill_r1/query_r1.py: -------------------------------------------------------------------------------- 1 | import json 2 | import random 3 | import os 4 | from openai import OpenAI 5 | from tqdm import tqdm 6 | import concurrent.futures 7 | from typing import List, Dict, Optional 8 | from datetime import datetime 9 | from threading import Lock 10 | import time 11 | from prompt import R1_SYS_PROMPT 12 | # Initialize the client 13 | client = OpenAI( 14 | api_key=os.environ.get("SL_KEY", "YOUR_SILCONFLOW_KEY"), 15 | base_url="https://api.siliconflow.cn/v1", 16 | ) 17 | 18 | # Create a lock for thread-safe file writing 19 | file_lock = Lock() 20 | 21 | def format_query(qa_dict: Dict, v2=False) -> str: 22 | query = "Answer the question according to scene description.\n\n" 23 | query += qa_dict["description"] 24 | query += f"\nQuestion:\n{qa_dict['q']}" 25 | if v2: 26 | query += "\nInstructions:\n" 27 | query += "1. Carefully analyze the scene description\n" 28 | query += "2. Provide your reasoning if necessary\n" 29 | query += "3. For the final answer, start a new line with '**The answer is: **' followed by your answer\n" 30 | return query 31 | 32 | def write_to_jsonl(result: Dict, filename: str): 33 | """Thread-safe function to write a result to JSONL file""" 34 | with file_lock: 35 | with open(filename, 'a') as f: 36 | f.write(json.dumps(result) + '\n') 37 | 38 | def query_r1(qa_pair: Dict, output_file: str, model: str = "deepseek-ai/DeepSeek-R1", v2=False) -> Optional[Dict]: 39 | query = format_query(qa_pair, v2=v2) 40 | try: 41 | response = client.chat.completions.create( 42 | model=model, 43 | messages=[ 44 | {"role": "system", "content": R1_SYS_PROMPT}, 45 | {"role": "user", "content": query}], 46 | stream=False, 47 | max_tokens=4096 48 | ) 49 | result = { 50 | **qa_pair, 51 | "r1_response": response.choices[0].message.content, 52 | "timestamp": datetime.now().isoformat() 53 | } 54 | # Write result immediately 55 | write_to_jsonl(result, output_file) 56 | time.sleep(4) 57 | return result 58 | except Exception as e: 59 | print(f"Error processing query: {e}") 60 | error_result = { 61 | **qa_pair, 62 | "error": str(e), 63 | "timestamp": datetime.now().isoformat() 64 | } 65 | write_to_jsonl(error_result, f"errors_{output_file}") 66 | time.sleep(10) 67 | return None 68 | 69 | def process_qa_pairs_parallel(qa_pairs: List[Dict], output_file: str, max_workers: int = 10) -> List[Dict]: 70 | successful_count = 0 71 | 72 | with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor: 73 | # Create futures for all qa_pairs 74 | futures = [executor.submit(query_r1, qa_pair, output_file, v2="v2" in output_file) for qa_pair in qa_pairs] 75 | 76 | # Process results as they complete with progress bar 77 | results = [] 78 | for future in tqdm(concurrent.futures.as_completed(futures), total=len(futures)): 79 | try: 80 | result = future.result() 81 | if result is not None: 82 | results.append(result) 83 | successful_count += 1 84 | except Exception as e: 85 | print(f"Failed to process query: {e}") 86 | 87 | return results 88 | 89 | if __name__ == "__main__": 90 | # Load and shuffle QA pairs 91 | random.seed(1234) 92 | qa_pairs = json.load(open("/home/lilei/Visual-R1/data/clever_counting_problems_clevr_cogent_v1.0_trainA.json")) 93 | random.shuffle(qa_pairs) 94 | qa_pairs = qa_pairs[:10000] 95 | # Create output filename with timestamp 96 | timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") 97 | output_file = f"r1_results_clevr_cogent_v1.0_trainA_v2.jsonl" 98 | 99 | finished = set() 100 | with open(output_file, 'r') as f: 101 | for line in f: 102 | ins = json.loads(line) 103 | key = ins["img_filename"] + "-" + ins["q"] + "-" + str(ins["a"]) 104 | finished.add(key) 105 | qa_pairs = [ins for ins in qa_pairs if ins["img_filename"] + "-" + ins["q"] + "-" + str(ins["a"]) not in finished] 106 | print("Finished: ", len(finished)) 107 | print("Remaining: ", len(qa_pairs)) 108 | # Process QA pairs in parallel 109 | r1_results = process_qa_pairs_parallel(qa_pairs, output_file) 110 | 111 | # Print final statistics 112 | print(f"Successfully processed {len(r1_results)} out of {len(qa_pairs)} queries") 113 | print(f"Results saved to {output_file}") 114 | print(f"Any errors were saved to errors_{output_file}") -------------------------------------------------------------------------------- /src/eval/prompts/superclevr_test200_counting_problems.jsonl: -------------------------------------------------------------------------------- 1 | {"image_path": "./images/superCLEVR_new_025000.png", "question": "How many different items are there in the image?", "ground_truth": 4} 2 | {"image_path": "./images/superCLEVR_new_025001.png", "question": "How many different items are there in the image?", "ground_truth": 9} 3 | {"image_path": "./images/superCLEVR_new_025002.png", "question": "How many different items are there in the image?", "ground_truth": 10} 4 | {"image_path": "./images/superCLEVR_new_025003.png", "question": "How many different items are there in the image?", "ground_truth": 4} 5 | {"image_path": "./images/superCLEVR_new_025004.png", "question": "How many different items are there in the image?", "ground_truth": 3} 6 | {"image_path": "./images/superCLEVR_new_025005.png", "question": "How many different items are there in the image?", "ground_truth": 3} 7 | {"image_path": "./images/superCLEVR_new_025006.png", "question": "How many different items are there in the image?", "ground_truth": 3} 8 | {"image_path": "./images/superCLEVR_new_025007.png", "question": "How many different items are there in the image?", "ground_truth": 4} 9 | {"image_path": "./images/superCLEVR_new_025008.png", "question": "How many different items are there in the image?", "ground_truth": 9} 10 | {"image_path": "./images/superCLEVR_new_025009.png", "question": "How many different items are there in the image?", "ground_truth": 10} 11 | {"image_path": "./images/superCLEVR_new_025010.png", "question": "How many different items are there in the image?", "ground_truth": 7} 12 | {"image_path": "./images/superCLEVR_new_025011.png", "question": "How many different items are there in the image?", "ground_truth": 7} 13 | {"image_path": "./images/superCLEVR_new_025012.png", "question": "How many different items are there in the image?", "ground_truth": 7} 14 | {"image_path": "./images/superCLEVR_new_025013.png", "question": "How many different items are there in the image?", "ground_truth": 6} 15 | {"image_path": "./images/superCLEVR_new_025014.png", "question": "How many different items are there in the image?", "ground_truth": 5} 16 | {"image_path": "./images/superCLEVR_new_025015.png", "question": "How many different items are there in the image?", "ground_truth": 10} 17 | {"image_path": "./images/superCLEVR_new_025016.png", "question": "How many different items are there in the image?", "ground_truth": 4} 18 | {"image_path": "./images/superCLEVR_new_025017.png", "question": "How many different items are there in the image?", "ground_truth": 5} 19 | {"image_path": "./images/superCLEVR_new_025018.png", "question": "How many different items are there in the image?", "ground_truth": 6} 20 | {"image_path": "./images/superCLEVR_new_025019.png", "question": "How many different items are there in the image?", "ground_truth": 8} 21 | {"image_path": "./images/superCLEVR_new_025020.png", "question": "How many different items are there in the image?", "ground_truth": 10} 22 | {"image_path": "./images/superCLEVR_new_025021.png", "question": "How many different items are there in the image?", "ground_truth": 3} 23 | {"image_path": "./images/superCLEVR_new_025022.png", "question": "How many different items are there in the image?", "ground_truth": 4} 24 | {"image_path": "./images/superCLEVR_new_025023.png", "question": "How many different items are there in the image?", "ground_truth": 4} 25 | {"image_path": "./images/superCLEVR_new_025024.png", "question": "How many different items are there in the image?", "ground_truth": 5} 26 | {"image_path": "./images/superCLEVR_new_025025.png", "question": "How many different items are there in the image?", "ground_truth": 5} 27 | {"image_path": "./images/superCLEVR_new_025026.png", "question": "How many different items are there in the image?", "ground_truth": 7} 28 | {"image_path": "./images/superCLEVR_new_025027.png", "question": "How many different items are there in the image?", "ground_truth": 4} 29 | {"image_path": "./images/superCLEVR_new_025028.png", "question": "How many different items are there in the image?", "ground_truth": 4} 30 | {"image_path": "./images/superCLEVR_new_025029.png", "question": "How many different items are there in the image?", "ground_truth": 9} 31 | {"image_path": "./images/superCLEVR_new_025030.png", "question": "How many different items are there in the image?", "ground_truth": 8} 32 | {"image_path": "./images/superCLEVR_new_025031.png", "question": "How many different items are there in the image?", "ground_truth": 6} 33 | {"image_path": "./images/superCLEVR_new_025032.png", "question": "How many different items are there in the image?", "ground_truth": 3} 34 | {"image_path": "./images/superCLEVR_new_025033.png", "question": "How many different items are there in the image?", "ground_truth": 10} 35 | {"image_path": "./images/superCLEVR_new_025034.png", "question": "How many different items are there in the image?", "ground_truth": 9} 36 | {"image_path": "./images/superCLEVR_new_025035.png", "question": "How many different items are there in the image?", "ground_truth": 9} 37 | {"image_path": "./images/superCLEVR_new_025036.png", "question": "How many different items are there in the image?", "ground_truth": 3} 38 | {"image_path": "./images/superCLEVR_new_025037.png", "question": "How many different items are there in the image?", "ground_truth": 6} 39 | {"image_path": "./images/superCLEVR_new_025038.png", "question": "How many different items are there in the image?", "ground_truth": 6} 40 | {"image_path": "./images/superCLEVR_new_025039.png", "question": "How many different items are there in the image?", "ground_truth": 5} 41 | {"image_path": "./images/superCLEVR_new_025040.png", "question": "How many different items are there in the image?", "ground_truth": 3} 42 | {"image_path": "./images/superCLEVR_new_025041.png", "question": "How many different items are there in the image?", "ground_truth": 10} 43 | {"image_path": "./images/superCLEVR_new_025042.png", "question": "How many different items are there in the image?", "ground_truth": 6} 44 | {"image_path": "./images/superCLEVR_new_025043.png", "question": "How many different items are there in the image?", "ground_truth": 3} 45 | {"image_path": "./images/superCLEVR_new_025044.png", "question": "How many different items are there in the image?", "ground_truth": 6} 46 | {"image_path": "./images/superCLEVR_new_025045.png", "question": "How many different items are there in the image?", "ground_truth": 5} 47 | {"image_path": "./images/superCLEVR_new_025046.png", "question": "How many different items are there in the image?", "ground_truth": 7} 48 | {"image_path": "./images/superCLEVR_new_025047.png", "question": "How many different items are there in the image?", "ground_truth": 5} 49 | {"image_path": "./images/superCLEVR_new_025048.png", "question": "How many different items are there in the image?", "ground_truth": 5} 50 | {"image_path": "./images/superCLEVR_new_025049.png", "question": "How many different items are there in the image?", "ground_truth": 10} 51 | {"image_path": "./images/superCLEVR_new_025050.png", "question": "How many different items are there in the image?", "ground_truth": 6} 52 | {"image_path": "./images/superCLEVR_new_025051.png", "question": "How many different items are there in the image?", "ground_truth": 3} 53 | {"image_path": "./images/superCLEVR_new_025052.png", "question": "How many different items are there in the image?", "ground_truth": 7} 54 | {"image_path": "./images/superCLEVR_new_025053.png", "question": "How many different items are there in the image?", "ground_truth": 9} 55 | {"image_path": "./images/superCLEVR_new_025054.png", "question": "How many different items are there in the image?", "ground_truth": 7} 56 | {"image_path": "./images/superCLEVR_new_025055.png", "question": "How many different items are there in the image?", "ground_truth": 6} 57 | {"image_path": "./images/superCLEVR_new_025056.png", "question": "How many different items are there in the image?", "ground_truth": 9} 58 | {"image_path": "./images/superCLEVR_new_025057.png", "question": "How many different items are there in the image?", "ground_truth": 8} 59 | {"image_path": "./images/superCLEVR_new_025058.png", "question": "How many different items are there in the image?", "ground_truth": 10} 60 | {"image_path": "./images/superCLEVR_new_025059.png", "question": "How many different items are there in the image?", "ground_truth": 10} 61 | {"image_path": "./images/superCLEVR_new_025060.png", "question": "How many different items are there in the image?", "ground_truth": 8} 62 | {"image_path": "./images/superCLEVR_new_025061.png", "question": "How many different items are there in the image?", "ground_truth": 8} 63 | {"image_path": "./images/superCLEVR_new_025062.png", "question": "How many different items are there in the image?", "ground_truth": 8} 64 | {"image_path": "./images/superCLEVR_new_025063.png", "question": "How many different items are there in the image?", "ground_truth": 10} 65 | {"image_path": "./images/superCLEVR_new_025064.png", "question": "How many different items are there in the image?", "ground_truth": 3} 66 | {"image_path": "./images/superCLEVR_new_025065.png", "question": "How many different items are there in the image?", "ground_truth": 4} 67 | {"image_path": "./images/superCLEVR_new_025066.png", "question": "How many different items are there in the image?", "ground_truth": 6} 68 | {"image_path": "./images/superCLEVR_new_025067.png", "question": "How many different items are there in the image?", "ground_truth": 7} 69 | {"image_path": "./images/superCLEVR_new_025068.png", "question": "How many different items are there in the image?", "ground_truth": 3} 70 | {"image_path": "./images/superCLEVR_new_025069.png", "question": "How many different items are there in the image?", "ground_truth": 10} 71 | {"image_path": "./images/superCLEVR_new_025070.png", "question": "How many different items are there in the image?", "ground_truth": 9} 72 | {"image_path": "./images/superCLEVR_new_025071.png", "question": "How many different items are there in the image?", "ground_truth": 6} 73 | {"image_path": "./images/superCLEVR_new_025072.png", "question": "How many different items are there in the image?", "ground_truth": 10} 74 | {"image_path": "./images/superCLEVR_new_025073.png", "question": "How many different items are there in the image?", "ground_truth": 5} 75 | {"image_path": "./images/superCLEVR_new_025074.png", "question": "How many different items are there in the image?", "ground_truth": 9} 76 | {"image_path": "./images/superCLEVR_new_025075.png", "question": "How many different items are there in the image?", "ground_truth": 3} 77 | {"image_path": "./images/superCLEVR_new_025076.png", "question": "How many different items are there in the image?", "ground_truth": 5} 78 | {"image_path": "./images/superCLEVR_new_025077.png", "question": "How many different items are there in the image?", "ground_truth": 5} 79 | {"image_path": "./images/superCLEVR_new_025078.png", "question": "How many different items are there in the image?", "ground_truth": 5} 80 | {"image_path": "./images/superCLEVR_new_025079.png", "question": "How many different items are there in the image?", "ground_truth": 9} 81 | {"image_path": "./images/superCLEVR_new_025080.png", "question": "How many different items are there in the image?", "ground_truth": 5} 82 | {"image_path": "./images/superCLEVR_new_025081.png", "question": "How many different items are there in the image?", "ground_truth": 5} 83 | {"image_path": "./images/superCLEVR_new_025082.png", "question": "How many different items are there in the image?", "ground_truth": 10} 84 | {"image_path": "./images/superCLEVR_new_025083.png", "question": "How many different items are there in the image?", "ground_truth": 4} 85 | {"image_path": "./images/superCLEVR_new_025084.png", "question": "How many different items are there in the image?", "ground_truth": 8} 86 | {"image_path": "./images/superCLEVR_new_025085.png", "question": "How many different items are there in the image?", "ground_truth": 8} 87 | {"image_path": "./images/superCLEVR_new_025086.png", "question": "How many different items are there in the image?", "ground_truth": 10} 88 | {"image_path": "./images/superCLEVR_new_025087.png", "question": "How many different items are there in the image?", "ground_truth": 9} 89 | {"image_path": "./images/superCLEVR_new_025088.png", "question": "How many different items are there in the image?", "ground_truth": 3} 90 | {"image_path": "./images/superCLEVR_new_025089.png", "question": "How many different items are there in the image?", "ground_truth": 4} 91 | {"image_path": "./images/superCLEVR_new_025090.png", "question": "How many different items are there in the image?", "ground_truth": 9} 92 | {"image_path": "./images/superCLEVR_new_025091.png", "question": "How many different items are there in the image?", "ground_truth": 7} 93 | {"image_path": "./images/superCLEVR_new_025092.png", "question": "How many different items are there in the image?", "ground_truth": 6} 94 | {"image_path": "./images/superCLEVR_new_025093.png", "question": "How many different items are there in the image?", "ground_truth": 10} 95 | {"image_path": "./images/superCLEVR_new_025094.png", "question": "How many different items are there in the image?", "ground_truth": 6} 96 | {"image_path": "./images/superCLEVR_new_025095.png", "question": "How many different items are there in the image?", "ground_truth": 6} 97 | {"image_path": "./images/superCLEVR_new_025096.png", "question": "How many different items are there in the image?", "ground_truth": 8} 98 | {"image_path": "./images/superCLEVR_new_025097.png", "question": "How many different items are there in the image?", "ground_truth": 7} 99 | {"image_path": "./images/superCLEVR_new_025098.png", "question": "How many different items are there in the image?", "ground_truth": 10} 100 | {"image_path": "./images/superCLEVR_new_025099.png", "question": "How many different items are there in the image?", "ground_truth": 10} 101 | {"image_path": "./images/superCLEVR_new_025100.png", "question": "How many different items are there in the image?", "ground_truth": 5} 102 | {"image_path": "./images/superCLEVR_new_025101.png", "question": "How many different items are there in the image?", "ground_truth": 7} 103 | {"image_path": "./images/superCLEVR_new_025102.png", "question": "How many different items are there in the image?", "ground_truth": 3} 104 | {"image_path": "./images/superCLEVR_new_025103.png", "question": "How many different items are there in the image?", "ground_truth": 6} 105 | {"image_path": "./images/superCLEVR_new_025104.png", "question": "How many different items are there in the image?", "ground_truth": 9} 106 | {"image_path": "./images/superCLEVR_new_025105.png", "question": "How many different items are there in the image?", "ground_truth": 7} 107 | {"image_path": "./images/superCLEVR_new_025106.png", "question": "How many different items are there in the image?", "ground_truth": 8} 108 | {"image_path": "./images/superCLEVR_new_025107.png", "question": "How many different items are there in the image?", "ground_truth": 8} 109 | {"image_path": "./images/superCLEVR_new_025108.png", "question": "How many different items are there in the image?", "ground_truth": 3} 110 | {"image_path": "./images/superCLEVR_new_025109.png", "question": "How many different items are there in the image?", "ground_truth": 7} 111 | {"image_path": "./images/superCLEVR_new_025110.png", "question": "How many different items are there in the image?", "ground_truth": 8} 112 | {"image_path": "./images/superCLEVR_new_025111.png", "question": "How many different items are there in the image?", "ground_truth": 9} 113 | {"image_path": "./images/superCLEVR_new_025112.png", "question": "How many different items are there in the image?", "ground_truth": 9} 114 | {"image_path": "./images/superCLEVR_new_025113.png", "question": "How many different items are there in the image?", "ground_truth": 6} 115 | {"image_path": "./images/superCLEVR_new_025114.png", "question": "How many different items are there in the image?", "ground_truth": 6} 116 | {"image_path": "./images/superCLEVR_new_025115.png", "question": "How many different items are there in the image?", "ground_truth": 9} 117 | {"image_path": "./images/superCLEVR_new_025116.png", "question": "How many different items are there in the image?", "ground_truth": 7} 118 | {"image_path": "./images/superCLEVR_new_025117.png", "question": "How many different items are there in the image?", "ground_truth": 9} 119 | {"image_path": "./images/superCLEVR_new_025118.png", "question": "How many different items are there in the image?", "ground_truth": 5} 120 | {"image_path": "./images/superCLEVR_new_025119.png", "question": "How many different items are there in the image?", "ground_truth": 9} 121 | {"image_path": "./images/superCLEVR_new_025120.png", "question": "How many different items are there in the image?", "ground_truth": 6} 122 | {"image_path": "./images/superCLEVR_new_025121.png", "question": "How many different items are there in the image?", "ground_truth": 10} 123 | {"image_path": "./images/superCLEVR_new_025122.png", "question": "How many different items are there in the image?", "ground_truth": 10} 124 | {"image_path": "./images/superCLEVR_new_025123.png", "question": "How many different items are there in the image?", "ground_truth": 6} 125 | {"image_path": "./images/superCLEVR_new_025124.png", "question": "How many different items are there in the image?", "ground_truth": 8} 126 | {"image_path": "./images/superCLEVR_new_025125.png", "question": "How many different items are there in the image?", "ground_truth": 8} 127 | {"image_path": "./images/superCLEVR_new_025126.png", "question": "How many different items are there in the image?", "ground_truth": 3} 128 | {"image_path": "./images/superCLEVR_new_025127.png", "question": "How many different items are there in the image?", "ground_truth": 7} 129 | {"image_path": "./images/superCLEVR_new_025128.png", "question": "How many different items are there in the image?", "ground_truth": 6} 130 | {"image_path": "./images/superCLEVR_new_025129.png", "question": "How many different items are there in the image?", "ground_truth": 4} 131 | {"image_path": "./images/superCLEVR_new_025130.png", "question": "How many different items are there in the image?", "ground_truth": 5} 132 | {"image_path": "./images/superCLEVR_new_025131.png", "question": "How many different items are there in the image?", "ground_truth": 8} 133 | {"image_path": "./images/superCLEVR_new_025132.png", "question": "How many different items are there in the image?", "ground_truth": 3} 134 | {"image_path": "./images/superCLEVR_new_025133.png", "question": "How many different items are there in the image?", "ground_truth": 5} 135 | {"image_path": "./images/superCLEVR_new_025134.png", "question": "How many different items are there in the image?", "ground_truth": 8} 136 | {"image_path": "./images/superCLEVR_new_025135.png", "question": "How many different items are there in the image?", "ground_truth": 8} 137 | {"image_path": "./images/superCLEVR_new_025136.png", "question": "How many different items are there in the image?", "ground_truth": 6} 138 | {"image_path": "./images/superCLEVR_new_025137.png", "question": "How many different items are there in the image?", "ground_truth": 5} 139 | {"image_path": "./images/superCLEVR_new_025138.png", "question": "How many different items are there in the image?", "ground_truth": 3} 140 | {"image_path": "./images/superCLEVR_new_025139.png", "question": "How many different items are there in the image?", "ground_truth": 4} 141 | {"image_path": "./images/superCLEVR_new_025140.png", "question": "How many different items are there in the image?", "ground_truth": 3} 142 | {"image_path": "./images/superCLEVR_new_025141.png", "question": "How many different items are there in the image?", "ground_truth": 9} 143 | {"image_path": "./images/superCLEVR_new_025142.png", "question": "How many different items are there in the image?", "ground_truth": 10} 144 | {"image_path": "./images/superCLEVR_new_025143.png", "question": "How many different items are there in the image?", "ground_truth": 5} 145 | {"image_path": "./images/superCLEVR_new_025144.png", "question": "How many different items are there in the image?", "ground_truth": 6} 146 | {"image_path": "./images/superCLEVR_new_025145.png", "question": "How many different items are there in the image?", "ground_truth": 10} 147 | {"image_path": "./images/superCLEVR_new_025146.png", "question": "How many different items are there in the image?", "ground_truth": 5} 148 | {"image_path": "./images/superCLEVR_new_025147.png", "question": "How many different items are there in the image?", "ground_truth": 6} 149 | {"image_path": "./images/superCLEVR_new_025148.png", "question": "How many different items are there in the image?", "ground_truth": 8} 150 | {"image_path": "./images/superCLEVR_new_025149.png", "question": "How many different items are there in the image?", "ground_truth": 8} 151 | {"image_path": "./images/superCLEVR_new_025150.png", "question": "How many different items are there in the image?", "ground_truth": 9} 152 | {"image_path": "./images/superCLEVR_new_025151.png", "question": "How many different items are there in the image?", "ground_truth": 8} 153 | {"image_path": "./images/superCLEVR_new_025152.png", "question": "How many different items are there in the image?", "ground_truth": 10} 154 | {"image_path": "./images/superCLEVR_new_025153.png", "question": "How many different items are there in the image?", "ground_truth": 3} 155 | {"image_path": "./images/superCLEVR_new_025154.png", "question": "How many different items are there in the image?", "ground_truth": 5} 156 | {"image_path": "./images/superCLEVR_new_025155.png", "question": "How many different items are there in the image?", "ground_truth": 10} 157 | {"image_path": "./images/superCLEVR_new_025156.png", "question": "How many different items are there in the image?", "ground_truth": 3} 158 | {"image_path": "./images/superCLEVR_new_025157.png", "question": "How many different items are there in the image?", "ground_truth": 6} 159 | {"image_path": "./images/superCLEVR_new_025158.png", "question": "How many different items are there in the image?", "ground_truth": 4} 160 | {"image_path": "./images/superCLEVR_new_025159.png", "question": "How many different items are there in the image?", "ground_truth": 5} 161 | {"image_path": "./images/superCLEVR_new_025160.png", "question": "How many different items are there in the image?", "ground_truth": 9} 162 | {"image_path": "./images/superCLEVR_new_025161.png", "question": "How many different items are there in the image?", "ground_truth": 3} 163 | {"image_path": "./images/superCLEVR_new_025162.png", "question": "How many different items are there in the image?", "ground_truth": 5} 164 | {"image_path": "./images/superCLEVR_new_025163.png", "question": "How many different items are there in the image?", "ground_truth": 10} 165 | {"image_path": "./images/superCLEVR_new_025164.png", "question": "How many different items are there in the image?", "ground_truth": 9} 166 | {"image_path": "./images/superCLEVR_new_025165.png", "question": "How many different items are there in the image?", "ground_truth": 7} 167 | {"image_path": "./images/superCLEVR_new_025166.png", "question": "How many different items are there in the image?", "ground_truth": 8} 168 | {"image_path": "./images/superCLEVR_new_025167.png", "question": "How many different items are there in the image?", "ground_truth": 7} 169 | {"image_path": "./images/superCLEVR_new_025168.png", "question": "How many different items are there in the image?", "ground_truth": 3} 170 | {"image_path": "./images/superCLEVR_new_025169.png", "question": "How many different items are there in the image?", "ground_truth": 10} 171 | {"image_path": "./images/superCLEVR_new_025170.png", "question": "How many different items are there in the image?", "ground_truth": 8} 172 | {"image_path": "./images/superCLEVR_new_025171.png", "question": "How many different items are there in the image?", "ground_truth": 7} 173 | {"image_path": "./images/superCLEVR_new_025172.png", "question": "How many different items are there in the image?", "ground_truth": 4} 174 | {"image_path": "./images/superCLEVR_new_025173.png", "question": "How many different items are there in the image?", "ground_truth": 10} 175 | {"image_path": "./images/superCLEVR_new_025174.png", "question": "How many different items are there in the image?", "ground_truth": 9} 176 | {"image_path": "./images/superCLEVR_new_025175.png", "question": "How many different items are there in the image?", "ground_truth": 4} 177 | {"image_path": "./images/superCLEVR_new_025176.png", "question": "How many different items are there in the image?", "ground_truth": 9} 178 | {"image_path": "./images/superCLEVR_new_025177.png", "question": "How many different items are there in the image?", "ground_truth": 6} 179 | {"image_path": "./images/superCLEVR_new_025178.png", "question": "How many different items are there in the image?", "ground_truth": 10} 180 | {"image_path": "./images/superCLEVR_new_025179.png", "question": "How many different items are there in the image?", "ground_truth": 6} 181 | {"image_path": "./images/superCLEVR_new_025180.png", "question": "How many different items are there in the image?", "ground_truth": 3} 182 | {"image_path": "./images/superCLEVR_new_025181.png", "question": "How many different items are there in the image?", "ground_truth": 3} 183 | {"image_path": "./images/superCLEVR_new_025182.png", "question": "How many different items are there in the image?", "ground_truth": 8} 184 | {"image_path": "./images/superCLEVR_new_025183.png", "question": "How many different items are there in the image?", "ground_truth": 5} 185 | {"image_path": "./images/superCLEVR_new_025184.png", "question": "How many different items are there in the image?", "ground_truth": 5} 186 | {"image_path": "./images/superCLEVR_new_025185.png", "question": "How many different items are there in the image?", "ground_truth": 3} 187 | {"image_path": "./images/superCLEVR_new_025186.png", "question": "How many different items are there in the image?", "ground_truth": 4} 188 | {"image_path": "./images/superCLEVR_new_025187.png", "question": "How many different items are there in the image?", "ground_truth": 5} 189 | {"image_path": "./images/superCLEVR_new_025188.png", "question": "How many different items are there in the image?", "ground_truth": 5} 190 | {"image_path": "./images/superCLEVR_new_025189.png", "question": "How many different items are there in the image?", "ground_truth": 3} 191 | {"image_path": "./images/superCLEVR_new_025190.png", "question": "How many different items are there in the image?", "ground_truth": 5} 192 | {"image_path": "./images/superCLEVR_new_025191.png", "question": "How many different items are there in the image?", "ground_truth": 8} 193 | {"image_path": "./images/superCLEVR_new_025192.png", "question": "How many different items are there in the image?", "ground_truth": 3} 194 | {"image_path": "./images/superCLEVR_new_025193.png", "question": "How many different items are there in the image?", "ground_truth": 9} 195 | {"image_path": "./images/superCLEVR_new_025194.png", "question": "How many different items are there in the image?", "ground_truth": 10} 196 | {"image_path": "./images/superCLEVR_new_025195.png", "question": "How many different items are there in the image?", "ground_truth": 5} 197 | {"image_path": "./images/superCLEVR_new_025196.png", "question": "How many different items are there in the image?", "ground_truth": 6} 198 | {"image_path": "./images/superCLEVR_new_025197.png", "question": "How many different items are there in the image?", "ground_truth": 3} 199 | {"image_path": "./images/superCLEVR_new_025198.png", "question": "How many different items are there in the image?", "ground_truth": 4} 200 | {"image_path": "./images/superCLEVR_new_025199.png", "question": "How many different items are there in the image?", "ground_truth": 3} 201 | -------------------------------------------------------------------------------- /src/eval/test_qwen2vl_counting_superclevr.py: -------------------------------------------------------------------------------- 1 | from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor 2 | from qwen_vl_utils import process_vision_info 3 | import torch 4 | import json 5 | from tqdm import tqdm 6 | import re 7 | 8 | 9 | 10 | MODEL_PATH="checkpoints/Qwen2-VL-2B-Instruct" # Qwen2vl-2b-Instruct for original scores 11 | BSZ=16 # reduce it if GPU OOM 12 | OUTPUT_PATH="src/eval/logs/counting_results_superclevr_200_qwen2vl_2b_instruct.json" 13 | PROMPT_PATH="src/eval/prompts/superclevr_test200_counting_problems.jsonl" 14 | 15 | #We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios. 16 | model = Qwen2VLForConditionalGeneration.from_pretrained( 17 | MODEL_PATH, 18 | torch_dtype=torch.bfloat16, 19 | attn_implementation="flash_attention_2", 20 | device_map="auto", 21 | ) 22 | 23 | # default processer 24 | processor = AutoProcessor.from_pretrained(MODEL_PATH) 25 | 26 | data = [] 27 | with open(PROMPT_PATH, "r") as f: 28 | for line in f: 29 | data.append(json.loads(line)) 30 | 31 | 32 | QUESTION_TEMPLATE = "{Question} First output the thinking process in and final answer (number) in tags." 33 | 34 | messages = [] 35 | 36 | for i in data: 37 | message = [{ 38 | "role": "user", 39 | "content": [ 40 | { 41 | "type": "image", 42 | "image": f"file://{i['image_path']}" 43 | }, 44 | { 45 | "type": "text", 46 | "text": QUESTION_TEMPLATE.format(Question=i['question']) 47 | } 48 | ] 49 | }] 50 | messages.append(message) 51 | 52 | 53 | 54 | 55 | all_outputs = [] # List to store all answers 56 | 57 | # Process data in batches 58 | for i in tqdm(range(0, len(messages), BSZ)): 59 | batch_messages = messages[i:i + BSZ] 60 | 61 | # Preparation for inference 62 | text = [processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True) for msg in batch_messages] 63 | 64 | image_inputs, video_inputs = process_vision_info(batch_messages) 65 | inputs = processor( 66 | text=text, 67 | images=image_inputs, 68 | videos=video_inputs, 69 | padding=True, 70 | return_tensors="pt", 71 | ) 72 | inputs = inputs.to("cuda") 73 | 74 | # Inference: Generation of the output 75 | generated_ids = model.generate(**inputs, use_cache=True, max_new_tokens=256, do_sample=False) 76 | 77 | generated_ids_trimmed = [ 78 | out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) 79 | ] 80 | batch_output_text = processor.batch_decode( 81 | generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False 82 | ) 83 | 84 | all_outputs.extend(batch_output_text) 85 | print(f"Processed batch {i//BSZ + 1}/{(len(messages) + BSZ - 1)//BSZ}") 86 | 87 | 88 | def extract_number_answer(output_str): 89 | # Try to find the number within tags, if can not find, return None 90 | answer_pattern = r'\s*(\d+)\s*' 91 | match = re.search(answer_pattern, output_str) 92 | 93 | if match: 94 | return int(match.group(1)) 95 | return None 96 | 97 | 98 | final_output = [] 99 | correct_number = 0 100 | 101 | for input_example, model_output in zip(data,all_outputs): 102 | original_output = model_output 103 | ground_truth = input_example['ground_truth'] 104 | model_answer = extract_number_answer(original_output) 105 | 106 | # Create a result dictionary for this example 107 | result = { 108 | 'question': input_example, 109 | 'ground_truth': ground_truth, 110 | 'model_output': original_output, 111 | 'extracted_answer': model_answer 112 | } 113 | final_output.append(result) 114 | 115 | # Count correct answers 116 | if model_answer is not None and model_answer == ground_truth: 117 | correct_number += 1 118 | 119 | # Calculate and print accuracy 120 | accuracy = correct_number / len(data) * 100 121 | print(f"\nAccuracy: {accuracy:.2f}%") 122 | 123 | # Save results to a JSON file 124 | output_path = OUTPUT_PATH 125 | with open(output_path, "w") as f: 126 | json.dump({ 127 | 'accuracy': accuracy, 128 | 'results': final_output 129 | }, f, indent=2) 130 | 131 | print(f"Results saved to {output_path}") 132 | 133 | 134 | 135 | 136 | 137 | -------------------------------------------------------------------------------- /src/eval/test_qwen2vl_geoqa.py: -------------------------------------------------------------------------------- 1 | from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor 2 | from qwen_vl_utils import process_vision_info 3 | import torch 4 | import json 5 | from tqdm import tqdm 6 | import re 7 | from math_verify import parse, verify 8 | 9 | 10 | MODEL_PATH="" # qwen2vl model or grpoed model on geoqa train 11 | BSZ=50 # reduce it if GPU OOM 12 | OUTPUT_PATH="" 13 | PROMPT_PATH="./prompts/geoqa_test_prompts.jsonl" 14 | 15 | #We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios. 16 | model = Qwen2VLForConditionalGeneration.from_pretrained( 17 | MODEL_PATH, 18 | torch_dtype=torch.bfloat16, 19 | attn_implementation="flash_attention_2", 20 | device_map="auto", 21 | ) 22 | 23 | # default processer 24 | processor = AutoProcessor.from_pretrained(MODEL_PATH) 25 | 26 | data = [] 27 | with open(PROMPT_PATH, "r") as f: 28 | for line in f: 29 | data.append(json.loads(line)) 30 | 31 | 32 | QUESTION_TEMPLATE = "{Question} Output the thinking process in and final answer (number) in tags." 33 | 34 | messages = [] 35 | 36 | data = data 37 | 38 | for i in data: 39 | message = [{ 40 | "role": "user", 41 | "content": [ 42 | { 43 | "type": "image", 44 | "image": f"file://{i['image_path']}" 45 | }, 46 | { 47 | "type": "text", 48 | "text": QUESTION_TEMPLATE.format(Question=i['question']) 49 | } 50 | ] 51 | }] 52 | messages.append(message) 53 | 54 | 55 | 56 | 57 | all_outputs = [] # List to store all answers 58 | 59 | # Process data in batches 60 | for i in tqdm(range(0, len(messages), BSZ)): 61 | batch_messages = messages[i:i + BSZ] 62 | 63 | # Preparation for inference 64 | text = [processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True) for msg in batch_messages] 65 | 66 | image_inputs, video_inputs = process_vision_info(batch_messages) 67 | inputs = processor( 68 | text=text, 69 | images=image_inputs, 70 | videos=video_inputs, 71 | padding=True, 72 | return_tensors="pt", 73 | ) 74 | inputs = inputs.to("cuda") 75 | 76 | # Inference: Generation of the output 77 | generated_ids = model.generate(**inputs, use_cache=True, max_new_tokens=1024, do_sample=False) 78 | 79 | generated_ids_trimmed = [ 80 | out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) 81 | ] 82 | batch_output_text = processor.batch_decode( 83 | generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False 84 | ) 85 | 86 | all_outputs.extend(batch_output_text) 87 | print(f"Processed batch {i//BSZ + 1}/{(len(messages) + BSZ - 1)//BSZ}") 88 | 89 | 90 | 91 | 92 | 93 | final_output = [] 94 | correct_number = 0 95 | 96 | for input_example, model_output in zip(data,all_outputs): 97 | original_output = model_output 98 | ground_truth = input_example['ground_truth'] 99 | model_answer = parse(original_output) 100 | 101 | # Count correct answers 102 | if model_answer is not None and float(verify(model_answer,parse(ground_truth)))>0: 103 | correct_number += 1 104 | is_correct = True 105 | else: 106 | is_correct = False 107 | 108 | try: 109 | result = { 110 | 'question': input_example, 111 | 'ground_truth': ground_truth, 112 | 'model_output': original_output, 113 | 'extracted_answer':str(model_answer[0]) if model_answer is not None else None, 114 | 'is_correct':is_correct 115 | } 116 | 117 | except Exception as e: 118 | print("no answer parsed",e,model_answer) 119 | result = { 120 | 'question': input_example, 121 | 'ground_truth': ground_truth, 122 | 'model_output': original_output, 123 | 'extracted_answer':None, 124 | 'is_correct':is_correct 125 | } 126 | 127 | 128 | 129 | final_output.append(result) 130 | 131 | 132 | # Calculate and print accuracy 133 | accuracy = correct_number / len(data) * 100 134 | print(f"\nAccuracy: {accuracy:.2f}%") 135 | 136 | # Save results to a JSON file 137 | output_path = OUTPUT_PATH 138 | with open(output_path, "w") as f: 139 | json.dump({ 140 | 'accuracy': accuracy, 141 | 'results': final_output 142 | }, f, indent=2, ensure_ascii=False) 143 | 144 | print(f"Results saved to {output_path}") 145 | 146 | 147 | 148 | 149 | 150 | -------------------------------------------------------------------------------- /src/eval/test_qwen2vl_geoqa_multigpu.py: -------------------------------------------------------------------------------- 1 | from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor 2 | from qwen_vl_utils import process_vision_info 3 | import torch 4 | import json 5 | import tqdm 6 | from math_verify import parse, verify 7 | import argparse 8 | import pandas as pd 9 | from torch.multiprocessing import Process, set_start_method, Manager 10 | from transformers.utils.logging import disable_progress_bar 11 | disable_progress_bar() 12 | 13 | # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> 14 | # >>>>> 1. get evaluation configuration <<<<< 15 | # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> 16 | def get_eval_config(): 17 | parser = argparse.ArgumentParser(description="Inference script for GeoQA evaluation.") 18 | parser.add_argument("--model_path", required=True, type=str, help="Path to the model checkpoint (e.g., qwen2vl model or a fine-tuned model).") 19 | parser.add_argument("--batch_size", default=4, type=int, help="Batch size for inference. Reduce if GPU OOM (default: 50).") 20 | parser.add_argument("--output_path", required=True, type=str, help="Path to save inference result (e.g., JSON file).") 21 | parser.add_argument("--prompt_path", required=True, type=str, help="Path to the prompts JSONL file for GeoQA evaluation.") 22 | all_gpu = ",".join(map(str, range(torch.cuda.device_count()))) 23 | parser.add_argument("--gpu_ids", default=all_gpu, help="comma-separated list of GPU IDs to use") 24 | args = parser.parse_args() 25 | return args 26 | 27 | # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> 28 | # >>>>>>>>>> 2. load testset <<<<<<<<<<<<< 29 | # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> 30 | def prepare_test_messages(testset_path): 31 | testset_data = pd.read_json(testset_path, lines=True).to_dict(orient="records") 32 | QUESTION_TEMPLATE = "{Question} Output the thinking process in and final answer (number) in tags." 33 | tested_messages = [] 34 | for i in testset_data: 35 | message = [{ 36 | "role": "user", 37 | "content": [ 38 | { 39 | "type": "image", 40 | "image": f"file://{i['image_path']}" 41 | }, 42 | { 43 | "type": "text", 44 | "text": QUESTION_TEMPLATE.format(Question=i['question']) 45 | } 46 | ] 47 | }] 48 | tested_messages.append(message) 49 | return testset_data, tested_messages 50 | 51 | 52 | 53 | 54 | # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> 55 | # >>>>> 3. use several GPUs to accelerate inference at testset <<<<< 56 | # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> 57 | 58 | def init_model(model_path, gpu_id): 59 | """init a model(args.model_path) on a specific gpu""" 60 | # We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios. 61 | model = Qwen2VLForConditionalGeneration.from_pretrained( 62 | model_path, 63 | torch_dtype=torch.bfloat16, 64 | attn_implementation="flash_attention_2", 65 | device_map=f"cuda:{gpu_id}", 66 | ) 67 | 68 | # default processer 69 | processor = AutoProcessor.from_pretrained(model_path, use_fast=True) 70 | return model, processor 71 | 72 | def answer_a_batch_question_qwen(batch_messages, model, processor): 73 | """ let qwen answer a batch of questions """ 74 | text = [processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True) for msg in batch_messages] 75 | image_inputs, video_inputs = process_vision_info(batch_messages) 76 | inputs = processor( 77 | text=text, 78 | images=image_inputs, 79 | videos=video_inputs, 80 | padding=True, 81 | return_tensors="pt", 82 | ) 83 | inputs = inputs.to(model.device) 84 | 85 | generated_ids = model.generate(**inputs, use_cache=True, max_new_tokens=1024) # do_sample=False 86 | generated_ids_trimmed = [ 87 | out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) 88 | ] 89 | batch_output_text = processor.batch_decode( 90 | generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False 91 | ) 92 | return batch_output_text 93 | 94 | def infer_on_single_gpu(model_path, device_id, chunk_of_tested_messages, batch_size, results=None): 95 | """init model on this single gpu and let it answer asign chunk of questions""" 96 | model, processor = init_model(model_path, device_id) 97 | 98 | ### split batch 99 | responses = [] 100 | batch_messages_list = [chunk_of_tested_messages[start: start + batch_size] 101 | for start in range(0, len(chunk_of_tested_messages), batch_size)] 102 | 103 | for batch_messages in tqdm.auto.tqdm(batch_messages_list, desc=f"GPU {device_id} progress", position=device_id, leave=False): 104 | batch_output_text = answer_a_batch_question_qwen(batch_messages, model, processor) 105 | 106 | responses.extend(batch_output_text) 107 | 108 | results[device_id] = responses 109 | return 110 | 111 | 112 | def multi_gpu_inference(prompts, gpu_ids, model_path, batch_size): 113 | """ let each gpu (along with a model) answer a chunk of questions """ 114 | set_start_method("spawn", force=True) 115 | manager = Manager() 116 | gpu_id2result = manager.dict() 117 | 118 | gpu_ids = [int(gpu_id.strip()) for gpu_id in gpu_ids.split(',')] 119 | num_gpus = len(gpu_ids) 120 | 121 | chunk_size = len(prompts) // num_gpus 122 | processes = [] 123 | for i, gpu_id in enumerate(gpu_ids): 124 | start_idx = i * chunk_size 125 | end_idx = (i + 1) * chunk_size if i != num_gpus - 1 else len(prompts) 126 | chunk = prompts[start_idx: end_idx] 127 | process = Process(target=infer_on_single_gpu, args=(model_path, gpu_id, chunk, batch_size, gpu_id2result)) 128 | process.start() 129 | processes.append(process) 130 | 131 | # for process in tqdm.auto.tqdm(processes, desc="Inference progress", position=num_gpus, leave=True): 132 | for process in processes: 133 | process.join() 134 | 135 | all_predicts = [] 136 | for gpu_id in gpu_ids: 137 | all_predicts.extend(gpu_id2result[gpu_id]) 138 | 139 | return all_predicts 140 | 141 | # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> 142 | # >>>>>>>>>> 4. compute metrics <<<<<<<<<<< 143 | # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> 144 | 145 | def compute_metrics(testset_data, all_predicts): 146 | final_output = [] 147 | correct_number = 0 148 | 149 | for input_example, model_output in zip(testset_data, all_predicts): 150 | original_output = model_output 151 | ground_truth = input_example['ground_truth'] 152 | model_answer = parse(original_output) 153 | 154 | # Count correct answers 155 | if model_answer is not None and float(verify(model_answer,parse(ground_truth)))>0: 156 | correct_number += 1 157 | is_correct = True 158 | else: 159 | is_correct = False 160 | 161 | try: 162 | result = { 163 | 'question': input_example, 164 | 'ground_truth': ground_truth, 165 | 'model_output': original_output, 166 | 'extracted_answer':str(model_answer[0]) if model_answer is not None else None, 167 | 'is_correct':is_correct 168 | } 169 | 170 | except Exception as e: 171 | print("no answer parsed",e,model_answer) 172 | result = { 173 | 'question': input_example, 174 | 'ground_truth': ground_truth, 175 | 'model_output': original_output, 176 | 'extracted_answer':None, 177 | 'is_correct':is_correct 178 | } 179 | 180 | 181 | 182 | final_output.append(result) 183 | 184 | 185 | # Calculate and print accuracy 186 | accuracy = correct_number / len(tested_messages) * 100 187 | print(f"\nAccuracy: {accuracy:.2f}%") 188 | 189 | # Save results to a JSON file 190 | with open(args.output_path, "w") as f: 191 | json.dump({ 192 | 'accuracy': accuracy, 193 | 'results': final_output 194 | }, f, indent=2, ensure_ascii=False) 195 | 196 | print(f"Results saved to {args.output_path}") 197 | 198 | 199 | 200 | if __name__ == "__main__": 201 | args = get_eval_config() 202 | testset_data, tested_messages = prepare_test_messages(testset_path=args.prompt_path) 203 | all_predicts = multi_gpu_inference(tested_messages, args.gpu_ids, args.model_path, args.batch_size) 204 | compute_metrics(testset_data, all_predicts) 205 | 206 | -------------------------------------------------------------------------------- /src/r1-v/.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # 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The text should be enclosed in the appropriate 184 | comment syntax for the file format. We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /src/r1-v/Makefile: -------------------------------------------------------------------------------- 1 | .PHONY: style quality 2 | 3 | # make sure to test the local checkout in scripts and not the pre-installed one (don't use quotes!) 4 | export PYTHONPATH = src 5 | 6 | check_dirs := src 7 | 8 | style: 9 | black --line-length 119 --target-version py310 $(check_dirs) setup.py 10 | isort $(check_dirs) setup.py 11 | 12 | quality: 13 | black --check --line-length 119 --target-version py310 $(check_dirs) setup.py 14 | isort --check-only $(check_dirs) setup.py 15 | flake8 --max-line-length 119 $(check_dirs) setup.py 16 | 17 | 18 | # Evaluation 19 | 20 | evaluate: 21 | -------------------------------------------------------------------------------- /src/r1-v/configs/ddp.yaml: -------------------------------------------------------------------------------- 1 | compute_environment: LOCAL_MACHINE 2 | debug: false 3 | distributed_type: MULTI_GPU 4 | downcast_bf16: 'no' 5 | gpu_ids: all 6 | machine_rank: 0 7 | main_training_function: main 8 | mixed_precision: bf16 9 | num_machines: 1 10 | num_processes: 8 11 | rdzv_backend: static 12 | same_network: true 13 | tpu_env: [] 14 | tpu_use_cluster: false 15 | tpu_use_sudo: false 16 | use_cpu: false 17 | -------------------------------------------------------------------------------- /src/r1-v/configs/qwen2vl_sft_config.yaml: -------------------------------------------------------------------------------- 1 | # Model arguments 2 | model_name_or_path: /path/to/your/Qwen2-VL-2B-Instruct 3 | model_revision: main 4 | torch_dtype: bfloat16 5 | 6 | # Data training arguments 7 | dataset_name: /path/to/your/train/data 8 | dataset_configs: 9 | - all 10 | preprocessing_num_workers: 8 11 | 12 | # SFT trainer config 13 | bf16: true 14 | do_eval: true 15 | eval_strategy: "no" 16 | gradient_accumulation_steps: 4 17 | gradient_checkpointing: true 18 | gradient_checkpointing_kwargs: 19 | use_reentrant: false 20 | hub_model_id: Qwen2-VL-2B-Instruct-SFT 21 | hub_strategy: every_save 22 | learning_rate: 2.0e-05 23 | log_level: info 24 | logging_steps: 5 25 | logging_strategy: steps 26 | lr_scheduler_type: cosine 27 | packing: true 28 | max_seq_length: 4096 29 | max_steps: -1 30 | num_train_epochs: 1 31 | output_dir: /path/to/your/out_dir 32 | overwrite_output_dir: true 33 | per_device_eval_batch_size: 4 34 | per_device_train_batch_size: 4 35 | push_to_hub: false 36 | report_to: 37 | - tensorboard 38 | save_strategy: "steps" 39 | save_steps: 1000 40 | seed: 42 41 | warmup_ratio: 0.1 -------------------------------------------------------------------------------- /src/r1-v/configs/zero2.yaml: -------------------------------------------------------------------------------- 1 | compute_environment: LOCAL_MACHINE 2 | debug: false 3 | deepspeed_config: 4 | deepspeed_multinode_launcher: standard 5 | offload_optimizer_device: none 6 | offload_param_device: none 7 | zero3_init_flag: false 8 | zero_stage: 2 9 | distributed_type: DEEPSPEED 10 | downcast_bf16: 'no' 11 | machine_rank: 0 12 | main_training_function: main 13 | mixed_precision: bf16 14 | num_machines: 1 15 | num_processes: 8 16 | rdzv_backend: static 17 | same_network: true 18 | tpu_env: [] 19 | tpu_use_cluster: false 20 | tpu_use_sudo: false 21 | use_cpu: false -------------------------------------------------------------------------------- /src/r1-v/configs/zero3.yaml: -------------------------------------------------------------------------------- 1 | compute_environment: LOCAL_MACHINE 2 | debug: false 3 | deepspeed_config: 4 | deepspeed_multinode_launcher: standard 5 | offload_optimizer_device: none 6 | offload_param_device: none 7 | zero3_init_flag: true 8 | zero3_save_16bit_model: true 9 | zero_stage: 3 10 | distributed_type: DEEPSPEED 11 | downcast_bf16: 'no' 12 | machine_rank: 0 13 | main_training_function: main 14 | mixed_precision: bf16 15 | num_machines: 1 16 | num_processes: 8 17 | rdzv_backend: static 18 | same_network: true 19 | tpu_env: [] 20 | tpu_use_cluster: false 21 | tpu_use_sudo: false 22 | use_cpu: false 23 | -------------------------------------------------------------------------------- /src/r1-v/local_scripts/create_vision_cot_data.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import base64 3 | import concurrent.futures 4 | import io 5 | import json 6 | import os 7 | import random 8 | import re 9 | import time 10 | from concurrent.futures import ThreadPoolExecutor 11 | from functools import partial 12 | from io import BytesIO 13 | from typing import Dict, List 14 | 15 | import matplotlib.pyplot as plt 16 | import numpy as np 17 | import pandas as pd 18 | from datasets import Dataset, concatenate_datasets, load_dataset, load_from_disk 19 | from tqdm import tqdm 20 | 21 | import bytedtos 22 | import seaborn as sns 23 | import yaml 24 | from openai import AzureOpenAI 25 | from PIL import Image 26 | from pillow_avif import AvifImagePlugin 27 | 28 | 29 | PROMPT_FORMAT = """I will provide you with an image, an original question, and its answer related to the image. Your task is to rewrite the question in such a way that answering it requires step-by-step Chain-of-Thought (CoT) reasoning with numerical or mathematical expressions where applicable. The reasoning process can include expressions like "let me think," "oh, I see," or other natural language thought expressions. 30 | 31 | Please make sure your question is to ask for a certain answer with a certain value, do not ask for open-ended answer, and the answer is correct and easy to verify via simple protocol, like "2" or "A". 32 | 33 | Please strictly do not include "Answer:" in the question part to avoid confusion and leakage. 34 | 35 | Input Format: 36 | Original Question: {original_question} 37 | Original Answer: {original_answer} 38 | 39 | Output Format: 40 | Question: [rewrite the question if necessary] 41 | Answer: [answer with reasoning steps, including calculations where applicable] 42 | step-by-step reasoning process 43 | easy to verify answer 44 | """ 45 | 46 | 47 | def get_image_data_url(image_input): 48 | if isinstance(image_input, str) and image_input.startswith("data:"): 49 | return image_input 50 | 51 | if isinstance(image_input, str) and image_input.startswith("http"): 52 | image_input = load_image(image_input) 53 | 54 | if isinstance(image_input, str): 55 | image_input = Image.open(image_input) 56 | 57 | if not isinstance(image_input, Image.Image): 58 | raise ValueError("Unsupported image input type") 59 | 60 | if image_input.mode != "RGB": 61 | image_input = image_input.convert("RGB") 62 | 63 | buffer = BytesIO() 64 | image_input.save(buffer, format="JPEG") 65 | img_bytes = buffer.getvalue() 66 | base64_data = base64.b64encode(img_bytes).decode("utf-8") 67 | return f"data:image/jpeg;base64,{base64_data}" 68 | 69 | 70 | def gpt4o_query(image, prompt, max_retries=5, initial_delay=3): 71 | if image is None: 72 | return None 73 | 74 | data_url_list = [get_image_data_url(image)] 75 | client = AzureOpenAI( 76 | azure_endpoint="YOUR_AZURE_ENDPOINT", 77 | api_version="2023-07-01-preview", 78 | api_key="YOUR_API_KEY", 79 | ) 80 | 81 | for attempt in range(max_retries): 82 | try: 83 | messages = [ 84 | { 85 | "role": "system", 86 | "content": "You are an expert to analyze the image and provide useful information for users.", 87 | }, 88 | { 89 | "role": "user", 90 | "content": [ 91 | {"type": "text", "text": prompt}, 92 | ], 93 | }, 94 | ] 95 | 96 | for data_url in data_url_list: 97 | messages[1]["content"].insert( 98 | 0, {"type": "image_url", "image_url": {"url": data_url}} 99 | ) 100 | 101 | response = client.chat.completions.create( 102 | model="gpt-4o-2024-08-06", 103 | messages=messages, 104 | temperature=0.2, 105 | max_tokens=8192, 106 | ) 107 | return response.choices[0].message.content 108 | 109 | except Exception as e: 110 | if attempt == max_retries - 1: 111 | raise Exception( 112 | f"Failed after {max_retries} attempts. Last error: {str(e)}" 113 | ) 114 | delay = initial_delay * (2**attempt) + random.uniform( 115 | 0, 0.1 * initial_delay * (2**attempt) 116 | ) 117 | time.sleep(delay) 118 | 119 | 120 | def process_single_item(example): 121 | try: 122 | image_path = example["image_path"] 123 | formatted_prompt = PROMPT_FORMAT.format( 124 | original_question=example["question"], original_answer=example["answer"] 125 | ) 126 | 127 | response = gpt4o_query(image_path, formatted_prompt) 128 | example["gpt4o_response"] = response 129 | return example 130 | except Exception as e: 131 | print(f"Error processing item: {str(e)}") 132 | example["gpt4o_response"] = None 133 | return example 134 | 135 | 136 | def main(): 137 | dataset_path = "path/to/your/dataset" 138 | full_dataset = load_from_disk(dataset_path) 139 | 140 | processed_dataset = full_dataset.map( 141 | function=partial(process_single_item), 142 | num_proc=256, 143 | desc="Processing dataset with GPT-4o", 144 | keep_in_memory=True, 145 | ) 146 | 147 | output_path = f"{dataset_path}_processed" 148 | processed_dataset.save_to_disk(output_path) 149 | print(f"Processed dataset saved to: {output_path}") 150 | 151 | 152 | if __name__ == "__main__": 153 | main() 154 | -------------------------------------------------------------------------------- /src/r1-v/local_scripts/lmms_eval_qwen2vl.sh: -------------------------------------------------------------------------------- 1 | export HF_HOME="" 2 | export HF_TOKEN="" 3 | export HF_HUB_ENABLE_HF_TRANSFER="1" 4 | 5 | export API_TYPE="" 6 | export AZURE_ENDPOINT="" 7 | export AZURE_API_KEY="" 8 | export API_VERSION="" 9 | export MODEL_VERSION="" 10 | export NAVIT_ATTENTION_IMPLEMENTATION="eager" 11 | 12 | # Prompt for installation with 3-second timeout 13 | read -t 3 -p "Do you want to install dependencies? (YES/no, timeout in 3s): " install_deps || true 14 | if [ "$install_deps" = "YES" ]; then 15 | # Prepare the environment 16 | pip3 install --upgrade pip 17 | pip3 install -U setuptools 18 | 19 | cd 20 | if [ ! -d "maas_engine" ]; then 21 | git clone 22 | else 23 | echo "maas_engine directory already exists, skipping clone" 24 | fi 25 | cd maas_engine 26 | git pull 27 | git checkout 28 | pip3 install --no-cache-dir --no-build-isolation -e ".[standalone]" 29 | 30 | current_version=$(pip3 show transformers | grep Version | cut -d' ' -f2) 31 | if [ "$current_version" != "4.46.2" ]; then 32 | echo "Installing transformers 4.46.2 (current version: $current_version)" 33 | pip3 install transformers==4.46.2 34 | else 35 | echo "transformers 4.46.2 is already installed" 36 | fi 37 | 38 | cd 39 | rm -rf 40 | pip3 install -e . 41 | pip3 install -U pydantic 42 | pip3 install Levenshtein 43 | pip3 install nltk 44 | python3 -c "import nltk; nltk.download('wordnet', quiet=True); nltk.download('punkt', quiet=True)" 45 | fi 46 | 47 | TASKS=mmmu_val,mathvista_testmini,mmmu_pro 48 | MODEL_BASENAME=qwen2_vl 49 | 50 | model_checkpoint="" 51 | echo "MODEL_BASENAME: ${MODEL_BASENAME}" 52 | cd 53 | 54 | python3 -m accelerate.commands.launch --num_processes=8 --main_process_port=12345 lmms_eval \ 55 | --model qwen2_vl \ 56 | --model_args=pretrained=${model_checkpoint},max_pixels=2359296 \ 57 | --tasks ${TASKS} \ 58 | --batch_size 1 \ 59 | --log_samples \ 60 | --log_samples_suffix ${MODEL_BASENAME} \ 61 | --output_path ./logs -------------------------------------------------------------------------------- /src/r1-v/local_scripts/prepare_hf_data.py: -------------------------------------------------------------------------------- 1 | import matplotlib.pyplot as plt 2 | import seaborn as sns 3 | import pandas as pd 4 | import random 5 | from typing import List, Dict 6 | import numpy as np 7 | from concurrent.futures import ThreadPoolExecutor 8 | from tqdm import tqdm 9 | import datasets 10 | 11 | import io 12 | from datasets import load_dataset, load_from_disk, concatenate_datasets 13 | from PIL import Image 14 | from tqdm import tqdm 15 | from functools import partial 16 | from pillow_avif import AvifImagePlugin 17 | from datasets import Dataset 18 | import json 19 | import yaml 20 | import os 21 | import re 22 | import time 23 | import random 24 | import base64 25 | from openai import AzureOpenAI 26 | import concurrent.futures 27 | from typing import List, Dict 28 | import argparse 29 | import time 30 | 31 | 32 | def extract_problem_solution(gpt4o_response): 33 | # Split the response into parts 34 | parts = gpt4o_response.split("") 35 | 36 | # Extract the problem (first part before any tags) 37 | problem = parts[0].strip() 38 | # Remove "Question:" prefix if it exists 39 | problem = re.sub(r"^Question:\s*", "", problem) 40 | # Remove "Answer:" at the end of the problem 41 | problem = re.sub(r"\s*Answer:\s*$", "", problem).strip() 42 | 43 | # Combine all the reasoning steps into a single block 44 | think_parts = [p.split("")[0].strip() for p in parts[1:] if "" in p] 45 | solution = f"{' '.join(think_parts)}" 46 | 47 | # Add the final answer if it exists, removing "Answer:" prefix 48 | if "" in gpt4o_response: 49 | final_answer = ( 50 | gpt4o_response.split("")[-1].split("")[0].strip() 51 | ) 52 | final_answer = re.sub(r"^Answer:\s*", "", final_answer) 53 | solution += f"\n\n{final_answer}" 54 | 55 | return problem, solution 56 | 57 | 58 | def load_image_from_path(image_path): 59 | try: 60 | img = Image.open(image_path) 61 | return img 62 | except Exception as e: 63 | print(f"Error loading image {image_path}: {str(e)}") 64 | return None 65 | 66 | 67 | def process_raw_data(raw_data): 68 | # Parse the raw data if it's a string 69 | if isinstance(raw_data, str): 70 | data = json.loads(raw_data) 71 | else: 72 | data = raw_data 73 | 74 | # Extract problem and solution 75 | try: 76 | problem, solution = extract_problem_solution(data["gpt4o_response"]) 77 | image = load_image_from_path(data["image_path"]) 78 | 79 | return { 80 | "image": image, 81 | "problem": problem, 82 | "solution": solution, 83 | "original_question": data["question"], 84 | "original_answer": data["answer"], 85 | } 86 | except Exception as e: 87 | print(f"Error processing data {data}: {str(e)}") 88 | return { 89 | "image": None, 90 | "problem": None, 91 | "solution": None, 92 | "original_question": None, 93 | "original_answer": None, 94 | } 95 | 96 | 97 | raw_data_list = [ 98 | "/path/to/reasoning_data_with_response_90k_verified", 99 | ] 100 | 101 | raw_data = concatenate_datasets([load_from_disk(path) for path in raw_data_list]) 102 | 103 | processed_data = raw_data.map(process_raw_data, num_proc=128).shuffle(seed=42) 104 | 105 | hf_dict = { 106 | "image": [], 107 | "problem": [], 108 | "solution": [], 109 | "original_question": [], 110 | "original_answer": [], 111 | } 112 | 113 | for item in tqdm(processed_data): 114 | hf_dict["image"].append(item["image"]) 115 | hf_dict["problem"].append(item["problem"]) 116 | hf_dict["solution"].append(item["solution"]) 117 | hf_dict["original_question"].append(item["original_question"]) 118 | hf_dict["original_answer"].append(item["original_answer"]) 119 | 120 | 121 | features = datasets.Features( 122 | { 123 | "image": datasets.Image(), 124 | "problem": datasets.Value("string"), 125 | "solution": datasets.Value("string"), 126 | "original_question": datasets.Value("string"), 127 | "original_answer": datasets.Value("string"), 128 | } 129 | ) 130 | 131 | 132 | def has_empty_tags(text): 133 | # Pattern to match empty tags like 134 | pattern = r"<[^>]+>]+>" 135 | return bool(re.search(pattern, text)) 136 | 137 | 138 | def has_answer_pattern(text): 139 | if "Answer:" in text: 140 | return True 141 | return False 142 | 143 | 144 | def has_valid_image_size(example): # for Qwen2-VL-2B's processor requirement 145 | # Assuming the image is in a format that can be checked for dimensions 146 | # You might need to adjust this depending on how the image is stored in your dataset 147 | try: 148 | image = example["image"] # or however your image is accessed 149 | if isinstance(image, dict) and "height" in image and "width" in image: 150 | return image["height"] >= 28 and image["width"] >= 28 151 | # If image is a PIL Image or similar 152 | return image.height >= 28 and image.width >= 28 153 | except: 154 | return False 155 | 156 | 157 | ds = datasets.Dataset.from_dict(hf_dict, features=features) 158 | ds = ds.filter( 159 | lambda x: not has_empty_tags(x["solution"]) 160 | and not has_answer_pattern(x["problem"]) 161 | and has_valid_image_size(x) 162 | and x["image"] is not None, 163 | num_proc=128, 164 | ) 165 | # Push to Hugging Face Hub 166 | ds.push_to_hub("path/to/your/dataset") 167 | -------------------------------------------------------------------------------- /src/r1-v/local_scripts/train_aria_moe.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | export NCCL_BLOCKING_WAIT=0 4 | export TOKENIZERS_PARALLELISM=false 5 | export OMP_NUM_THREADS=8 6 | export NCCL_IB_DISABLE=0 7 | export NCCL_IB_GID_INDEX=3 8 | export NCCL_SOCKET_IFNAME=eth0 9 | export NCCL_DEBUG=INFO 10 | 11 | # CONFIG Huggingface 12 | # export HF_TOKEN="" 13 | export HF_TOKEN="" 14 | export HF_HOME="$HOME/.cache/huggingface" 15 | export HF_HUB_ENABLE_HF_TRANSFER="1" 16 | 17 | export NCCL_DEBUG=INFO 18 | 19 | GPUS="0,1,2,3,4,5,6,7" 20 | 21 | # 取 worker0 第一个 port 22 | ports=($(echo $METIS_WORKER_0_PORT | tr ',' ' ')) 23 | port=${ports[0]} 24 | port_in_cmd="$(echo "${METIS_WORKER_0_PORT:-2000}" | awk -F',' '{print $1}')" 25 | 26 | echo "total workers: ${ARNOLD_WORKER_NUM}" 27 | echo "cur worker id: ${ARNOLD_ID}" 28 | echo "gpus per worker: ${ARNOLD_WORKER_GPU}" 29 | echo "master ip: ${METIS_WORKER_0_HOST}" 30 | echo "master port: ${port}" 31 | echo "master port in cmd: ${port_in_cmd}" 32 | 33 | # export WANDB_BASE_URL=https://api.wandb.ai 34 | # export WANDB_API_KEY="" 35 | # wandb login $WANDB_API_KEY 36 | 37 | export WANDB_BASE_URL=https://api.wandb.ai 38 | export WANDB_PROJECT=vision-reasoning 39 | export WANDB_API_KEY="" 40 | export WANDB_RUN_NAME=Qwen-VL-2B-GRPO-$(date +%Y-%m-%d-%H-%M-%S) 41 | wandb login $WANDB_API_KEY 42 | 43 | cd /home/tiger/multimodal-open-r1 44 | # pip3 install vllm==0.6.6.post1 45 | pip3 install -e ".[dev]" 46 | pip3 install wandb==0.18.3 47 | 48 | torchrun --nproc_per_node="${ARNOLD_WORKER_GPU}" \ 49 | --nnodes="${ARNOLD_WORKER_NUM}" \ 50 | --node_rank="${ARNOLD_ID}" \ 51 | --master_addr="${METIS_WORKER_0_HOST}" \ 52 | --master_port="${port_in_cmd}" \ 53 | src/open_r1/grpo.py \ 54 | --deepspeed scripts/zero3.json \ 55 | --output_dir Aria-GRPO-mini_cot_80k \ 56 | --model_name_or_path rhymes-ai/Aria \ 57 | --dataset_name luodian/mini_cot_80k \ 58 | --max_prompt_length 8192 \ 59 | --per_device_train_batch_size 1 \ 60 | --gradient_accumulation_steps 1 \ 61 | --logging_steps 1 \ 62 | --bf16 \ 63 | --report_to wandb \ 64 | --gradient_checkpointing true \ 65 | --attn_implementation eager \ 66 | --save_total_limit 8 \ 67 | --num_train_epochs 1 \ 68 | --run_name $WANDB_RUN_NAME 69 | -------------------------------------------------------------------------------- /src/r1-v/local_scripts/train_qwen2_vl.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | export NCCL_BLOCKING_WAIT=0 4 | export TOKENIZERS_PARALLELISM=false 5 | export OMP_NUM_THREADS=8 6 | export NCCL_IB_DISABLE=0 7 | export NCCL_IB_GID_INDEX=3 8 | export NCCL_SOCKET_IFNAME=eth0 9 | export NCCL_DEBUG=INFO 10 | 11 | GPUS="0,1,2,3,4,5,6,7" 12 | 13 | # 取 worker0 第一个 port 14 | ports=($(echo $METIS_WORKER_0_PORT | tr ',' ' ')) 15 | port=${ports[0]} 16 | port_in_cmd="$(echo "${METIS_WORKER_0_PORT:-2000}" | awk -F',' '{print $1}')" 17 | 18 | echo "total workers: ${ARNOLD_WORKER_NUM}" 19 | echo "cur worker id: ${ARNOLD_ID}" 20 | echo "gpus per worker: ${ARNOLD_WORKER_GPU}" 21 | echo "master ip: ${METIS_WORKER_0_HOST}" 22 | echo "master port: ${port}" 23 | echo "master port in cmd: ${port_in_cmd}" 24 | 25 | # export WANDB_BASE_URL=https://api.wandb.ai 26 | # export WANDB_API_KEY="" 27 | # wandb login $WANDB_API_KEY 28 | 29 | export WANDB_BASE_URL=https://api.wandb.ai 30 | export WANDB_PROJECT=vision-reasoning 31 | export WANDB_API_KEY="" 32 | export WANDB_RUN_NAME=Qwen-VL-2B-GRPO-$(date +%Y-%m-%d-%H-%M-%S) 33 | wandb login $WANDB_API_KEY 34 | 35 | cd /home/tiger/multimodal-open-r1 36 | # pip3 install vllm==0.6.6.post1 37 | pip3 install -e ".[dev]" 38 | pip3 install wandb==0.18.3 39 | 40 | torchrun --nproc_per_node="${ARNOLD_WORKER_GPU}" \ 41 | --nnodes="${ARNOLD_WORKER_NUM}" \ 42 | --node_rank="${ARNOLD_ID}" \ 43 | --master_addr="${METIS_WORKER_0_HOST}" \ 44 | --master_port="${port_in_cmd}" \ 45 | src/open_r1/grpo.py \ 46 | --deepspeed scripts/zero3.json \ 47 | --output_dir checkpoints/${WANDB_RUN_NAME} \ 48 | --model_name_or_path Qwen/Qwen2-VL-2B-Instruct \ 49 | --dataset_name luodian/${DATASET_NAME} \ 50 | --max_prompt_length 8192 \ 51 | --per_device_train_batch_size 1 \ 52 | --gradient_accumulation_steps 1 \ 53 | --logging_steps 1 \ 54 | --bf16 \ 55 | --report_to wandb \ 56 | --gradient_checkpointing true \ 57 | --attn_implementation flash_attention_2 \ 58 | --max_pixels 2359296 \ 59 | --save_total_limit 8 \ 60 | --num_train_epochs 1 \ 61 | --run_name $WANDB_RUN_NAME 62 | -------------------------------------------------------------------------------- /src/r1-v/local_scripts/zero2.json: -------------------------------------------------------------------------------- 1 | { 2 | "fp16": { 3 | "enabled": "auto", 4 | "loss_scale": 0, 5 | "loss_scale_window": 1000, 6 | "initial_scale_power": 16, 7 | "hysteresis": 2, 8 | "min_loss_scale": 1 9 | }, 10 | "bf16": { 11 | "enabled": "auto" 12 | }, 13 | "optimizer": { 14 | "type": "AdamW", 15 | "params": { 16 | "lr": "auto", 17 | "betas": "auto", 18 | "eps": "auto", 19 | "weight_decay": "auto" 20 | } 21 | }, 22 | "zero_optimization": { 23 | "stage": 2, 24 | "offload_optimizer": { 25 | "device": "none", 26 | "pin_memory": true 27 | }, 28 | "allgather_partitions": true, 29 | "allgather_bucket_size": 2e8, 30 | "overlap_comm": false, 31 | "reduce_scatter": true, 32 | "reduce_bucket_size": 2e8, 33 | "contiguous_gradients": true 34 | }, 35 | "gradient_accumulation_steps": "auto", 36 | "gradient_clipping": "auto", 37 | "steps_per_print": 100, 38 | "train_batch_size": "auto", 39 | "train_micro_batch_size_per_gpu": "auto", 40 | "wall_clock_breakdown": false 41 | } -------------------------------------------------------------------------------- /src/r1-v/local_scripts/zero3.json: -------------------------------------------------------------------------------- 1 | { 2 | "fp16": { 3 | "enabled": "auto", 4 | "loss_scale": 0, 5 | "loss_scale_window": 1000, 6 | "initial_scale_power": 16, 7 | "hysteresis": 2, 8 | "min_loss_scale": 1 9 | }, 10 | "bf16": { 11 | "enabled": "auto" 12 | }, 13 | 14 | "zero_optimization": { 15 | "stage": 3, 16 | "offload_optimizer": { 17 | "device": "none", 18 | "pin_memory": true 19 | }, 20 | "offload_param": { 21 | "device": "none", 22 | "pin_memory": true 23 | }, 24 | "overlap_comm": true, 25 | "contiguous_gradients": true, 26 | "sub_group_size": 1e9, 27 | "reduce_bucket_size": "auto", 28 | "stage3_prefetch_bucket_size": "auto", 29 | "stage3_param_persistence_threshold": "auto", 30 | "stage3_max_live_parameters": 1e9, 31 | "stage3_max_reuse_distance": 1e9, 32 | "stage3_gather_16bit_weights_on_model_save": true 33 | }, 34 | 35 | "gradient_accumulation_steps": "auto", 36 | "gradient_clipping": "auto", 37 | "steps_per_print": 100, 38 | "train_batch_size": "auto", 39 | "train_micro_batch_size_per_gpu": "auto", 40 | "wall_clock_breakdown": false 41 | } -------------------------------------------------------------------------------- /src/r1-v/local_scripts/zero3.yaml: -------------------------------------------------------------------------------- 1 | compute_environment: LOCAL_MACHINE 2 | debug: false 3 | deepspeed_config: 4 | deepspeed_multinode_launcher: standard 5 | offload_optimizer_device: none 6 | offload_param_device: none 7 | zero3_init_flag: true 8 | zero3_save_16bit_model: true 9 | zero_stage: 3 10 | distributed_type: DEEPSPEED 11 | downcast_bf16: 'no' 12 | machine_rank: 0 13 | main_training_function: main 14 | mixed_precision: bf16 15 | num_machines: 1 16 | num_processes: 8 17 | rdzv_backend: static 18 | same_network: true 19 | tpu_env: [] 20 | tpu_use_cluster: false 21 | tpu_use_sudo: false 22 | use_cpu: false 23 | -------------------------------------------------------------------------------- /src/r1-v/local_scripts/zero3_offload.json: -------------------------------------------------------------------------------- 1 | { 2 | "fp16": { 3 | "enabled": "auto", 4 | "loss_scale": 0, 5 | "loss_scale_window": 1000, 6 | "initial_scale_power": 16, 7 | "hysteresis": 2, 8 | "min_loss_scale": 1 9 | }, 10 | "bf16": { 11 | "enabled": "auto" 12 | }, 13 | "optimizer": { 14 | "type": "AdamW", 15 | "params": { 16 | "lr": "auto", 17 | "betas": "auto", 18 | "eps": "auto", 19 | "weight_decay": "auto" 20 | } 21 | }, 22 | "zero_optimization": { 23 | "stage": 3, 24 | "offload_optimizer": { 25 | "device": "cpu", 26 | "pin_memory": true 27 | }, 28 | "offload_param": { 29 | "device": "cpu", 30 | "pin_memory": true 31 | }, 32 | "overlap_comm": true, 33 | "contiguous_gradients": true, 34 | "sub_group_size": 1e9, 35 | "reduce_bucket_size": "auto", 36 | "stage3_prefetch_bucket_size": "auto", 37 | "stage3_param_persistence_threshold": "auto", 38 | "stage3_max_live_parameters": 1e9, 39 | "stage3_max_reuse_distance": 1e9, 40 | "gather_16bit_weights_on_model_save": true 41 | }, 42 | "gradient_accumulation_steps": "auto", 43 | "gradient_clipping": "auto", 44 | "train_batch_size": "auto", 45 | "train_micro_batch_size_per_gpu": "auto", 46 | "steps_per_print": 1e5, 47 | "wall_clock_breakdown": false 48 | } -------------------------------------------------------------------------------- /src/r1-v/run_grpo.sh: -------------------------------------------------------------------------------- 1 | cd src/r1-v 2 | 3 | export DEBUG_MODE="true" 4 | export LOG_PATH="./debug_log_2b.txt" 5 | 6 | 7 | 8 | torchrun --nproc_per_node="8" \ 9 | --nnodes="1" \ 10 | --node_rank="0" \ 11 | --master_addr="127.0.0.1" \ 12 | --master_port="12345" \ 13 | src/open_r1/grpo.py \ 14 | --output_dir \ 15 | --model_name_or_path \ 16 | --dataset_name \ 17 | --max_prompt_length 1024 \ 18 | --per_device_train_batch_size 1 \ 19 | --gradient_accumulation_steps 2 \ 20 | --logging_steps 1 \ 21 | --bf16 \ 22 | --report_to wandb \ 23 | --gradient_checkpointing false \ 24 | --attn_implementation flash_attention_2 \ 25 | --max_pixels 401408 \ 26 | --num_train_epochs 2 \ 27 | --run_name Qwen2-VL-2B-GRPO-CLEVR-70k \ 28 | --save_steps 100 \ 29 | --save_only_model true -------------------------------------------------------------------------------- /src/r1-v/setup.cfg: -------------------------------------------------------------------------------- 1 | [isort] 2 | default_section = FIRSTPARTY 3 | ensure_newline_before_comments = True 4 | force_grid_wrap = 0 5 | include_trailing_comma = True 6 | known_first_party = open_r1 7 | known_third_party = 8 | transformers 9 | datasets 10 | fugashi 11 | git 12 | h5py 13 | matplotlib 14 | nltk 15 | numpy 16 | packaging 17 | pandas 18 | psutil 19 | pytest 20 | rouge_score 21 | sacrebleu 22 | seqeval 23 | sklearn 24 | streamlit 25 | torch 26 | tqdm 27 | 28 | line_length = 119 29 | lines_after_imports = 2 30 | multi_line_output = 3 31 | use_parentheses = True 32 | 33 | [flake8] 34 | ignore = E203, E501, E741, W503, W605 35 | max-line-length = 119 36 | per-file-ignores = 37 | # imported but unused 38 | __init__.py: F401 39 | 40 | [tool:pytest] 41 | doctest_optionflags=NUMBER NORMALIZE_WHITESPACE ELLIPSIS -------------------------------------------------------------------------------- /src/r1-v/setup.py: -------------------------------------------------------------------------------- 1 | # Copyright 2025 The HuggingFace Team. All rights reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # 15 | # Adapted from huggingface/transformers: https://github.com/huggingface/transformers/blob/21a2d900eceeded7be9edc445b56877b95eda4ca/setup.py 16 | 17 | 18 | import re 19 | import shutil 20 | from pathlib import Path 21 | 22 | from setuptools import find_packages, setup 23 | 24 | 25 | # Remove stale open_r1.egg-info directory to avoid https://github.com/pypa/pip/issues/5466 26 | stale_egg_info = Path(__file__).parent / "open_r1.egg-info" 27 | if stale_egg_info.exists(): 28 | print( 29 | ( 30 | "Warning: {} exists.\n\n" 31 | "If you recently updated open_r1, this is expected,\n" 32 | "but it may prevent open_r1 from installing in editable mode.\n\n" 33 | "This directory is automatically generated by Python's packaging tools.\n" 34 | "I will remove it now.\n\n" 35 | "See https://github.com/pypa/pip/issues/5466 for details.\n" 36 | ).format(stale_egg_info) 37 | ) 38 | shutil.rmtree(stale_egg_info) 39 | 40 | 41 | # IMPORTANT: all dependencies should be listed here with their version requirements, if any. 42 | # * If a dependency is fast-moving (e.g. transformers), pin to the exact version 43 | _deps = [ 44 | "accelerate>=1.2.1", 45 | "bitsandbytes>=0.43.0", 46 | "black>=24.4.2", 47 | "datasets>=3.2.0", 48 | "deepspeed==0.15.4", 49 | "distilabel[vllm,ray,openai]>=1.5.2", 50 | "einops>=0.8.0", 51 | "flake8>=6.0.0", 52 | "hf_transfer>=0.1.4", 53 | "huggingface-hub[cli]>=0.19.2,<1.0", 54 | "isort>=5.12.0", 55 | "liger_kernel==0.5.2", 56 | "lighteval @ git+https://github.com/huggingface/lighteval.git@4f381b352c0e467b5870a97d41cb66b487a2c503#egg=lighteval[math]", 57 | "math-verify", # Used for math verification in grpo 58 | "packaging>=23.0", 59 | "parameterized>=0.9.0", 60 | "pytest", 61 | "safetensors>=0.3.3", 62 | "sentencepiece>=0.1.99", 63 | "torch>=2.5.1", 64 | "transformers @ git+https://github.com/huggingface/transformers.git@336dc69d63d56f232a183a3e7f52790429b871ef", 65 | "trl==0.14.0", 66 | "vllm==0.6.6.post1", 67 | "wandb>=0.19.1", 68 | "pillow", 69 | ] 70 | 71 | # this is a lookup table with items like: 72 | # 73 | # tokenizers: "tokenizers==0.9.4" 74 | # packaging: "packaging" 75 | # 76 | # some of the values are versioned whereas others aren't. 77 | deps = {b: a for a, b in (re.findall(r"^(([^!=<>~ \[\]]+)(?:\[[^\]]+\])?(?:[!=<>~ ].*)?$)", x)[0] for x in _deps)} 78 | 79 | 80 | def deps_list(*pkgs): 81 | return [deps[pkg] for pkg in pkgs] 82 | 83 | 84 | extras = {} 85 | extras["tests"] = deps_list("pytest", "parameterized") 86 | extras["torch"] = deps_list("torch") 87 | extras["quality"] = deps_list("black", "isort", "flake8") 88 | extras["eval"] = deps_list("lighteval", "math-verify") 89 | extras["dev"] = extras["quality"] + extras["tests"] + extras["eval"] 90 | 91 | # core dependencies shared across the whole project - keep this to a bare minimum :) 92 | install_requires = [ 93 | deps["accelerate"], 94 | deps["bitsandbytes"], 95 | deps["einops"], 96 | deps["datasets"], 97 | deps["deepspeed"], 98 | deps["hf_transfer"], 99 | deps["huggingface-hub"], 100 | deps["liger_kernel"], 101 | deps["packaging"], # utilities from PyPA to e.g., compare versions 102 | deps["safetensors"], 103 | deps["sentencepiece"], 104 | deps["transformers"], 105 | deps["trl"], 106 | ] 107 | 108 | setup( 109 | name="r1-v", 110 | version="0.1.0", # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots) 111 | author="The r1-v team and the Hugging Face team (past and future)", 112 | description="R1-V", 113 | license="Apache", 114 | url="https://github.com/Deep-Agent/R1-V", 115 | package_dir={"": "src"}, 116 | packages=find_packages("src"), 117 | zip_safe=False, 118 | extras_require=extras, 119 | python_requires=">=3.10.9", 120 | install_requires=install_requires, 121 | classifiers=[ 122 | "Development Status :: 3 - Alpha", 123 | "Intended Audience :: Developers", 124 | "Intended Audience :: Education", 125 | "Intended Audience :: Science/Research", 126 | "License :: OSI Approved :: Apache Software License", 127 | "Operating System :: OS Independent", 128 | "Programming Language :: Python :: 3", 129 | "Programming Language :: Python :: 3.10", 130 | "Topic :: Scientific/Engineering :: Artificial Intelligence", 131 | ], 132 | ) 133 | -------------------------------------------------------------------------------- /src/r1-v/src/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hustvl/AlphaDrive/bb4104c680a3e4d70e7e998e08de90f6e0acf8c5/src/r1-v/src/__init__.py -------------------------------------------------------------------------------- /src/r1-v/src/open_r1/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hustvl/AlphaDrive/bb4104c680a3e4d70e7e998e08de90f6e0acf8c5/src/r1-v/src/open_r1/__init__.py -------------------------------------------------------------------------------- /src/r1-v/src/open_r1/evaluate.py: -------------------------------------------------------------------------------- 1 | # Copyright 2025 The HuggingFace Team. All rights reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | """Custom evaluation tasks for LightEval.""" 16 | 17 | from lighteval.metrics.dynamic_metrics import ( 18 | ExprExtractionConfig, 19 | LatexExtractionConfig, 20 | multilingual_extractive_match_metric, 21 | ) 22 | from lighteval.tasks.lighteval_task import LightevalTaskConfig 23 | from lighteval.tasks.requests import Doc 24 | from lighteval.utils.language import Language 25 | 26 | 27 | metric = multilingual_extractive_match_metric( 28 | language=Language.ENGLISH, 29 | fallback_mode="first_match", 30 | precision=5, 31 | gold_extraction_target=(LatexExtractionConfig(),), 32 | pred_extraction_target=(ExprExtractionConfig(), LatexExtractionConfig()), 33 | aggregation_function=max, 34 | ) 35 | 36 | 37 | def prompt_fn(line, task_name: str = None): 38 | """Assumes the model is either prompted to emit \\boxed{answer} or does so automatically""" 39 | return Doc( 40 | task_name=task_name, 41 | query=line["problem"], 42 | choices=[line["solution"]], 43 | gold_index=0, 44 | ) 45 | 46 | 47 | # Define tasks 48 | aime24 = LightevalTaskConfig( 49 | name="aime24", 50 | suite=["custom"], 51 | prompt_function=prompt_fn, 52 | hf_repo="HuggingFaceH4/aime_2024", 53 | hf_subset="default", 54 | hf_avail_splits=["train"], 55 | evaluation_splits=["train"], 56 | few_shots_split=None, 57 | few_shots_select=None, 58 | generation_size=32768, 59 | metric=[metric], 60 | version=1, 61 | ) 62 | math_500 = LightevalTaskConfig( 63 | name="math_500", 64 | suite=["custom"], 65 | prompt_function=prompt_fn, 66 | hf_repo="HuggingFaceH4/MATH-500", 67 | hf_subset="default", 68 | hf_avail_splits=["test"], 69 | evaluation_splits=["test"], 70 | few_shots_split=None, 71 | few_shots_select=None, 72 | generation_size=32768, 73 | metric=[metric], 74 | version=1, 75 | ) 76 | 77 | # Add tasks to the table 78 | TASKS_TABLE = [] 79 | TASKS_TABLE.append(aime24) 80 | TASKS_TABLE.append(math_500) 81 | 82 | # MODULE LOGIC 83 | if __name__ == "__main__": 84 | print([t["name"] for t in TASKS_TABLE]) 85 | print(len(TASKS_TABLE)) 86 | -------------------------------------------------------------------------------- /src/r1-v/src/open_r1/generate.py: -------------------------------------------------------------------------------- 1 | # Copyright 2025 The HuggingFace Team. All rights reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | from typing import Optional 16 | 17 | from distilabel.llms import OpenAILLM 18 | from distilabel.pipeline import Pipeline 19 | from distilabel.steps.tasks import TextGeneration 20 | 21 | 22 | def build_distilabel_pipeline( 23 | model: str, 24 | base_url: str = "http://localhost:8000/v1", 25 | prompt_column: Optional[str] = None, 26 | temperature: Optional[float] = None, 27 | top_p: Optional[float] = None, 28 | max_new_tokens: int = 8192, 29 | num_generations: int = 1, 30 | ) -> Pipeline: 31 | generation_kwargs = {"max_new_tokens": max_new_tokens} 32 | 33 | if temperature is not None: 34 | generation_kwargs["temperature"] = temperature 35 | 36 | if top_p is not None: 37 | generation_kwargs["top_p"] = top_p 38 | 39 | with Pipeline().ray() as pipeline: 40 | TextGeneration( 41 | llm=OpenAILLM( 42 | base_url=base_url, 43 | api_key="something", 44 | model=model, 45 | # thinking can take some time... 46 | timeout=10 * 60, 47 | generation_kwargs=generation_kwargs, 48 | ), 49 | input_mappings={"instruction": prompt_column} if prompt_column is not None else {}, 50 | input_batch_size=64, # on 4 nodes bs ~60+ leads to preemption due to KV cache exhaustion 51 | num_generations=num_generations, 52 | ) 53 | 54 | return pipeline 55 | 56 | 57 | if __name__ == "__main__": 58 | import argparse 59 | 60 | from datasets import load_dataset 61 | 62 | parser = argparse.ArgumentParser(description="Run distilabel pipeline for generating responses with DeepSeek R1") 63 | parser.add_argument( 64 | "--hf-dataset", 65 | type=str, 66 | required=True, 67 | help="HuggingFace dataset to load", 68 | ) 69 | parser.add_argument( 70 | "--hf-dataset-config", 71 | type=str, 72 | required=False, 73 | help="Dataset config to use", 74 | ) 75 | parser.add_argument( 76 | "--hf-dataset-split", 77 | type=str, 78 | default="train", 79 | help="Dataset split to use", 80 | ) 81 | parser.add_argument("--prompt-column", type=str, default="prompt") 82 | parser.add_argument( 83 | "--model", 84 | type=str, 85 | required=True, 86 | help="Model name to use for generation", 87 | ) 88 | parser.add_argument( 89 | "--vllm-server-url", 90 | type=str, 91 | default="http://localhost:8000/v1", 92 | help="URL of the vLLM server", 93 | ) 94 | parser.add_argument( 95 | "--temperature", 96 | type=float, 97 | help="Temperature for generation", 98 | ) 99 | parser.add_argument( 100 | "--top-p", 101 | type=float, 102 | help="Top-p value for generation", 103 | ) 104 | parser.add_argument( 105 | "--max-new-tokens", 106 | type=int, 107 | default=8192, 108 | help="Maximum number of new tokens to generate", 109 | ) 110 | parser.add_argument( 111 | "--num-generations", 112 | type=int, 113 | default=1, 114 | help="Number of generations per problem", 115 | ) 116 | parser.add_argument( 117 | "--hf-output-dataset", 118 | type=str, 119 | required=False, 120 | help="HuggingFace repo to push results to", 121 | ) 122 | parser.add_argument( 123 | "--private", 124 | action="store_true", 125 | help="Whether to make the output dataset private when pushing to HF Hub", 126 | ) 127 | 128 | args = parser.parse_args() 129 | 130 | print("\nRunning with arguments:") 131 | for arg, value in vars(args).items(): 132 | print(f" {arg}: {value}") 133 | print() 134 | 135 | print(f"Loading '{args.hf_dataset}' (config: {args.hf_dataset_config}, split: {args.hf_dataset_split}) dataset...") 136 | dataset = load_dataset(args.hf_dataset, split=args.hf_dataset_split) 137 | print("Dataset loaded!") 138 | 139 | pipeline = build_distilabel_pipeline( 140 | model=args.model, 141 | base_url=args.vllm_server_url, 142 | prompt_column=args.prompt_column, 143 | temperature=args.temperature, 144 | top_p=args.top_p, 145 | max_new_tokens=args.max_new_tokens, 146 | num_generations=args.num_generations, 147 | ) 148 | 149 | print("Running generation pipeline...") 150 | distiset = pipeline.run(dataset=dataset, use_cache=False) 151 | print("Generation pipeline finished!") 152 | 153 | if args.hf_output_dataset: 154 | print(f"Pushing resulting dataset to '{args.hf_output_dataset}'...") 155 | distiset.push_to_hub(args.hf_output_dataset, private=args.private) 156 | print("Dataset pushed!") 157 | -------------------------------------------------------------------------------- /src/r1-v/src/open_r1/grpo.py: -------------------------------------------------------------------------------- 1 | # Copyright 2025 The HuggingFace Team. All rights reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | import os 16 | import re 17 | from collections import Counter 18 | from datetime import datetime 19 | from dataclasses import dataclass, field 20 | from typing import Optional 21 | 22 | from datasets import load_dataset, load_from_disk 23 | from transformers import Qwen2VLForConditionalGeneration 24 | 25 | from math_verify import parse, verify 26 | from open_r1.trainer import Qwen2VLGRPOTrainer, Qwen2VLGRPOVLLMTrainer 27 | from trl import GRPOConfig, GRPOTrainer, ModelConfig, ScriptArguments, TrlParser, get_peft_config 28 | 29 | 30 | @dataclass 31 | class GRPOScriptArguments(ScriptArguments): 32 | """ 33 | Script arguments for the GRPO training script. 34 | 35 | Args: 36 | reward_funcs (`list[str]`): 37 | List of reward functions. Possible values: 'plan_speed_reward', 'plan_path_reward', 'plan_format_reward'. 38 | """ 39 | 40 | reward_funcs: list[str] = field( 41 | default_factory=lambda: ["plan_speed_reward", "plan_path_reward", "plan_format_reward"], 42 | metadata={"help": "List of reward functions. Possible values: 'plan_speed_reward', 'plan_path_reward', 'plan_format_reward'"}, 43 | ) 44 | max_pixels: Optional[int] = field( 45 | default=12845056, 46 | metadata={"help": "Maximum number of pixels for the image"}, 47 | ) 48 | min_pixels: Optional[int] = field( 49 | default=3136, 50 | metadata={"help": "Minimum number of pixels for the image"}, 51 | ) 52 | 53 | 54 | def plan_speed_reward(completions, 55 | solution, 56 | diversity_weight=0.4, 57 | complexity_weights=None, 58 | **kwargs): 59 | """ 60 | planning speed reward function. 61 | """ 62 | if complexity_weights is None: 63 | complexity_weights = { 64 | "ACCELERATE": 0.9, "DECELERATE": 1.0, "STOP": 1.0, "KEEP": 0.8, 65 | } 66 | 67 | rewards = [] 68 | global_decision_count = Counter() 69 | 70 | for completion, sol in zip(completions, solution): 71 | sol_match = re.search(r'(.*?)', sol) 72 | if not sol_match: 73 | rewards.append(0) 74 | continue 75 | ground_truth_words = set(sol_match.group(1).strip().split(', ')) 76 | ground_truth_words = {word for word in ground_truth_words if word in complexity_weights} 77 | 78 | match = re.search(r"(.*?)", completion[0]["content"]) 79 | if match: 80 | content = match.group(1).strip() 81 | else: 82 | content = completion[0]["content"].replace('', '').replace('', '') 83 | 84 | content_word_list = [re.sub(r'[^\w]', '', word) for word in content.split(', ') if word in complexity_weights] 85 | content_words = set(content_word_list) 86 | global_decision_count.update(content_words) 87 | 88 | true_positives = len(content_words & ground_truth_words) 89 | false_positives = len(content_words - ground_truth_words) 90 | false_negatives = len(ground_truth_words - content_words) 91 | 92 | precision = true_positives / (true_positives + false_positives + 1e-6) 93 | recall = true_positives / (true_positives + false_negatives + 1e-6) 94 | if true_positives == 0 and false_positives == 0 and false_negatives == 0: 95 | f1_score = 0 # no match 96 | else: 97 | f1_score = 2 * (precision * recall) / (precision + recall + 1e-6) 98 | 99 | complexity_factor = sum(complexity_weights[word] for word in content_words) / (len(content_words) + 1e-6) 100 | 101 | diversity_factor = [True if global_decision_count[word] == 1 else False for word in content_words] 102 | diversity_factor = True if all(diversity_factor) else False 103 | diversity_factor = diversity_weight if diversity_factor else -diversity_weight 104 | 105 | reward = f1_score * complexity_factor + diversity_factor 106 | 107 | rewards.append(reward) 108 | 109 | return rewards 110 | 111 | 112 | def plan_path_reward(completions, 113 | solution, 114 | diversity_weight=0.4, 115 | complexity_weights=None, 116 | **kwargs): 117 | """ 118 | planning path reward function. 119 | """ 120 | if complexity_weights is None: 121 | complexity_weights = { 122 | "LEFT_TURN": 1.0, "RIGHT_TURN": 1.0, 123 | "LEFT_CHANGE": 1.0, "RIGHT_CHANGE": 1.0, "STRAIGHT": 0.8 124 | } 125 | 126 | rewards = [] 127 | global_decision_count = Counter() 128 | 129 | for completion, sol in zip(completions, solution): 130 | sol_match = re.search(r'(.*?)', sol) 131 | if not sol_match: 132 | rewards.append(0) 133 | continue 134 | ground_truth_words = set(sol_match.group(1).strip().split(', ')) 135 | ground_truth_words = {word for word in ground_truth_words if word in complexity_weights} 136 | 137 | match = re.search(r"(.*?)", completion[0]["content"]) 138 | if match: 139 | content = match.group(1).strip() 140 | else: 141 | content = completion[0]["content"].replace('', '').replace('', '') 142 | 143 | content_word_list = [re.sub(r'[^\w]', '', word) for word in content.split(', ') if word in complexity_weights] 144 | content_words = set(content_word_list) 145 | global_decision_count.update(content_words) 146 | 147 | true_positives = len(content_words & ground_truth_words) 148 | false_positives = len(content_words - ground_truth_words) 149 | false_negatives = len(ground_truth_words - content_words) 150 | 151 | precision = true_positives / (true_positives + false_positives + 1e-6) 152 | recall = true_positives / (true_positives + false_negatives + 1e-6) 153 | if true_positives == 0 and false_positives == 0 and false_negatives == 0: 154 | f1_score = 0 # no match 155 | else: 156 | f1_score = 2 * (precision * recall) / (precision + recall + 1e-6) 157 | 158 | complexity_factor = sum(complexity_weights[word] for word in content_words) / (len(content_words) + 1e-6) 159 | 160 | diversity_factor = [True if global_decision_count[word] == 1 else False for word in content_words] 161 | diversity_factor = True if all(diversity_factor) else False 162 | diversity_factor = diversity_weight if diversity_factor else -diversity_weight 163 | 164 | reward = f1_score * complexity_factor + diversity_factor 165 | 166 | rewards.append(reward) 167 | 168 | return rewards 169 | 170 | 171 | def plan_format_reward(completions, **kwargs): 172 | """Reward function that checks if the completion has a specific format.""" 173 | # check if answer format is xxx\nxxx 174 | pattern = r".*?\s*.*?" 175 | completion_contents = [completion[0]["content"] for completion in completions] 176 | matches = [re.fullmatch(pattern, content, re.DOTALL) for content in completion_contents] 177 | 178 | return [1.0 if match else 0.0 for match in matches] 179 | 180 | 181 | 182 | reward_funcs_registry = { 183 | "plan_format_reward": plan_format_reward, 184 | "plan_speed_reward": plan_speed_reward, 185 | "plan_path_reward": plan_path_reward, 186 | } 187 | 188 | SYSTEM_PROMPT = ( 189 | "A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant " 190 | "first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning " 191 | "process and answer are enclosed within and tags, respectively, i.e., " 192 | " reasoning process here answer here " 193 | ) 194 | 195 | 196 | def main(script_args, training_args, model_args): 197 | # Get reward functions 198 | reward_funcs = [reward_funcs_registry[func] for func in script_args.reward_funcs] 199 | 200 | # Load the dataset 201 | dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config) 202 | 203 | 204 | # Format into conversation 205 | def make_conversation(example): 206 | return { 207 | "prompt": [ 208 | {"role": "system", "content": SYSTEM_PROMPT}, 209 | {"role": "user", "content": example["problem"]}, 210 | ], 211 | } 212 | 213 | # def make_conversation_image(example): 214 | # return { 215 | # "prompt": [ 216 | # {"role": "system", "content": [{"type": "text", "text": SYSTEM_PROMPT}]}, 217 | # { 218 | # "role": "user", 219 | # "content": [ 220 | # {"type": "image"}, 221 | # {"type": "text", "text": example["problem"]}, 222 | # ], 223 | # }, 224 | # ], 225 | # } 226 | 227 | # QUESTION_TEMPLATE = "{Question} Output the thinking process in and final answer (number) in tags." 228 | # QUESTION_TEMPLATE = "{Question} Output the final answer in tags." 229 | 230 | def make_conversation_image(example): 231 | return { 232 | "prompt": [ 233 | { 234 | "role": "user", 235 | "content": [ 236 | {"type": "image"}, 237 | # {"type": "text", "text": QUESTION_TEMPLATE.format(Question=example["problem"])}, 238 | {"type": "text", "text": example["problem"]}, 239 | ], 240 | }, 241 | ], 242 | } 243 | 244 | 245 | if "image" in dataset[script_args.dataset_train_split].features: 246 | print("has image in dataset") 247 | dataset = dataset.map(make_conversation_image) # Utilize multiprocessing for faster mapping 248 | # dataset = dataset.remove_columns(["original_question", "original_answer"]) 249 | 250 | else: 251 | print("no image in dataset") 252 | dataset = dataset.map(make_conversation) 253 | dataset = dataset.remove_columns("messages") 254 | 255 | 256 | trainer_cls = Qwen2VLGRPOTrainer if not training_args.use_vllm else Qwen2VLGRPOVLLMTrainer 257 | print("using: ", trainer_cls) 258 | 259 | # Initialize the GRPO trainer 260 | trainer = trainer_cls( 261 | model=model_args.model_name_or_path, 262 | reward_funcs=reward_funcs, 263 | args=training_args, 264 | train_dataset=dataset[script_args.dataset_train_split], 265 | eval_dataset=dataset[script_args.dataset_test_split] if training_args.eval_strategy != "no" else None, 266 | peft_config=get_peft_config(model_args), 267 | attn_implementation=model_args.attn_implementation, 268 | max_pixels=script_args.max_pixels, 269 | min_pixels=script_args.min_pixels, 270 | ) 271 | 272 | # Train and push the model to the Hub 273 | trainer.train() 274 | 275 | # Save and push to hub 276 | trainer.save_model(training_args.output_dir) 277 | if training_args.push_to_hub: 278 | trainer.push_to_hub(dataset_name=script_args.dataset_name) 279 | 280 | 281 | if __name__ == "__main__": 282 | parser = TrlParser((GRPOScriptArguments, GRPOConfig, ModelConfig)) 283 | script_args, training_args, model_args = parser.parse_args_and_config() 284 | main(script_args, training_args, model_args) 285 | -------------------------------------------------------------------------------- /src/r1-v/src/open_r1/sft.py: -------------------------------------------------------------------------------- 1 | # Copyright 2025 The HuggingFace Team. All rights reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | """ 16 | Supervised fine-tuning script for decoder language models. 17 | 18 | Usage: 19 | 20 | # One 1 node of 8 x H100s 21 | accelerate launch --config_file=configs/zero3.yaml src/open_r1/sft.py \ 22 | --model_name_or_path Qwen/Qwen2.5-1.5B-Instruct \ 23 | --dataset_name HuggingFaceH4/Bespoke-Stratos-17k \ 24 | --learning_rate 2.0e-5 \ 25 | --num_train_epochs 1 \ 26 | --packing \ 27 | --max_seq_length 4096 \ 28 | --per_device_train_batch_size 4 \ 29 | --gradient_accumulation_steps 4 \ 30 | --gradient_checkpointing \ 31 | --bf16 \ 32 | --logging_steps 5 \ 33 | --eval_strategy steps \ 34 | --eval_steps 100 \ 35 | --output_dir data/Qwen2.5-1.5B-Open-R1-Distill 36 | """ 37 | 38 | import logging 39 | import os 40 | import sys 41 | 42 | import datasets 43 | from dataclasses import dataclass, field 44 | from typing import Optional 45 | import torch 46 | import transformers 47 | from datasets import load_dataset 48 | from transformers import AutoTokenizer, set_seed, AutoProcessor 49 | from transformers.trainer_utils import get_last_checkpoint 50 | import trl 51 | from trl import ( 52 | ModelConfig, 53 | ScriptArguments, 54 | SFTTrainer, 55 | TrlParser, 56 | get_kbit_device_map, 57 | get_peft_config, 58 | get_quantization_config, 59 | ) 60 | 61 | from qwen_vl_utils import process_vision_info 62 | logger = logging.getLogger(__name__) 63 | 64 | 65 | @dataclass 66 | class SFTConfig(trl.SFTConfig): 67 | """ 68 | args for callbacks, benchmarks etc 69 | """ 70 | 71 | benchmarks: list[str] = field( 72 | default_factory=lambda: [], metadata={"help": "The benchmarks to run after training."} 73 | ) 74 | callbacks: list[str] = field( 75 | default_factory=lambda: [], metadata={"help": "The callbacks to run during training."} 76 | ) 77 | system_prompt: Optional[str] = field( 78 | default=None, 79 | metadata={"help": "The optional system prompt to use for benchmarking."}, 80 | ) 81 | hub_model_revision: Optional[str] = field( 82 | default="main", 83 | metadata={"help": "The Hub model branch to push the model to."}, 84 | ) 85 | overwrite_hub_revision: bool = field(default=False, metadata={"help": "Whether to overwrite the Hub revision."}) 86 | push_to_hub_revision: bool = field(default=False, metadata={"help": "Whether to push to a Hub revision/branch."}) 87 | 88 | 89 | 90 | processor = None 91 | 92 | 93 | def convert_example(example): 94 | """ 95 | correct example into "messages" 96 | eg: 97 | { 98 | "system": "You are a helpful assistant.", 99 | "conversations": [ 100 | {"from": "user", "value": "How many objects are included in this image?", 101 | "image_path": "/path/to/image.png"}, 102 | {"from": "assistant", "value": "\nI can see 10 objects\n\n\n10\n"} 103 | ] 104 | } 105 | """ 106 | messages = [] 107 | # if "system" in example: 108 | # messages.append({ 109 | # "role": "system", 110 | # "content": [{"type": "text", "text": example["system"]}], 111 | # }) 112 | # else: 113 | # SYSTEM_PROMPT = ( 114 | # "A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant " 115 | # "first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning " 116 | # "process and answer are enclosed within and tags, respectively, i.e., " 117 | # " reasoning process here answer here " 118 | # ) 119 | 120 | # SYSTEM_PROMPT = ( 121 | # "A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The answer is enclosed within tags, i.e., " 122 | # " answer here " 123 | # ) 124 | 125 | # messages.append({ 126 | # "role": "system", 127 | # "content": [{"type": "text", "text": SYSTEM_PROMPT}], 128 | # }) 129 | 130 | thinking = example.get("thinking") 131 | problem = example.get("problem") 132 | solution = example.get("solution") 133 | image = example.get("image") 134 | messages.append({ 135 | "role": "user", 136 | "content": [ 137 | {"type": "text", "text": problem}, 138 | {"type": "image", "image": image}, 139 | ] 140 | }) 141 | # messages.append({ 142 | # "role": "assistant", 143 | # "content": f"{thinking}\n{solution}", 144 | # }) 145 | messages.append({ 146 | "role": "assistant", 147 | "content": f"{solution}", 148 | }) 149 | 150 | example["messages"] = messages 151 | return example 152 | 153 | 154 | def collate_fn(examples): 155 | texts = [ 156 | processor.apply_chat_template( convert_example(example)["messages"], tokenize=False, add_generation_prompt=True) 157 | for example in examples 158 | ] 159 | image_inputs = [] 160 | for example in examples: 161 | imgs, vids = process_vision_info(example["messages"]) 162 | image_inputs.append(imgs) 163 | batch = processor( 164 | text=texts, 165 | images=image_inputs, 166 | return_tensors="pt", 167 | padding=True, 168 | ) 169 | labels = batch["input_ids"].clone() 170 | labels[labels == processor.tokenizer.pad_token_id] = -100 171 | image_token_id = processor.tokenizer.convert_tokens_to_ids(processor.image_token) 172 | labels[labels == image_token_id] = -100 173 | batch["labels"] = labels 174 | 175 | return batch 176 | 177 | 178 | def main(script_args, training_args, model_args): 179 | # Set seed for reproducibility 180 | set_seed(training_args.seed) 181 | 182 | ############### 183 | # Setup logging 184 | ############### 185 | logging.basicConfig( 186 | format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", 187 | datefmt="%Y-%m-%d %H:%M:%S", 188 | handlers=[logging.StreamHandler(sys.stdout)], 189 | ) 190 | log_level = training_args.get_process_log_level() 191 | logger.setLevel(log_level) 192 | datasets.utils.logging.set_verbosity(log_level) 193 | transformers.utils.logging.set_verbosity(log_level) 194 | transformers.utils.logging.enable_default_handler() 195 | transformers.utils.logging.enable_explicit_format() 196 | 197 | # Log on each process a small summary 198 | logger.warning( 199 | f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" 200 | + f" distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" 201 | ) 202 | logger.info(f"Model parameters {model_args}") 203 | logger.info(f"Script parameters {script_args}") 204 | logger.info(f"Data parameters {training_args}") 205 | 206 | # Check for last checkpoint 207 | last_checkpoint = None 208 | if os.path.isdir(training_args.output_dir): 209 | last_checkpoint = get_last_checkpoint(training_args.output_dir) 210 | if last_checkpoint is not None and training_args.resume_from_checkpoint is None: 211 | logger.info(f"Checkpoint detected, resuming training at {last_checkpoint=}.") 212 | 213 | ################ 214 | # Load datasets 215 | ################ 216 | 217 | dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config) 218 | 219 | ################ 220 | # Load tokenizer 221 | ################ 222 | global processor 223 | if "vl" in model_args.model_name_or_path.lower(): 224 | processor = AutoProcessor.from_pretrained( 225 | model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code 226 | ) 227 | logger.info("Using AutoProcessor for vision-language model.") 228 | else: 229 | processor = AutoTokenizer.from_pretrained( 230 | model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code, use_fast=True 231 | ) 232 | logger.info("Using AutoTokenizer for text-only model.") 233 | if hasattr(processor, "pad_token") and processor.pad_token is None: 234 | processor.pad_token = processor.eos_token 235 | elif hasattr(processor.tokenizer, "pad_token") and processor.tokenizer.pad_token is None: 236 | processor.tokenizer.pad_token = processor.tokenizer.eos_token 237 | 238 | ################### 239 | # Model init kwargs 240 | ################### 241 | logger.info("*** Initializing model kwargs ***") 242 | torch_dtype = ( 243 | model_args.torch_dtype if model_args.torch_dtype in ["auto", None] else getattr(torch, model_args.torch_dtype) 244 | ) 245 | quantization_config = get_quantization_config(model_args) 246 | model_kwargs = dict( 247 | revision=model_args.model_revision, 248 | trust_remote_code=model_args.trust_remote_code, 249 | attn_implementation=model_args.attn_implementation, 250 | torch_dtype=torch_dtype, 251 | use_cache=False if training_args.gradient_checkpointing else True, 252 | device_map=get_kbit_device_map() if quantization_config is not None else None, 253 | quantization_config=quantization_config, 254 | ) 255 | # training_args.model_init_kwargs = model_kwargs 256 | from transformers import Qwen2VLForConditionalGeneration 257 | model = Qwen2VLForConditionalGeneration.from_pretrained( 258 | model_args.model_name_or_path, **model_kwargs 259 | ) 260 | ############################ 261 | # Initialize the SFT Trainer 262 | ############################ 263 | training_args.dataset_kwargs = { 264 | "skip_prepare_dataset": True, 265 | } 266 | training_args.remove_unused_columns = False 267 | trainer = SFTTrainer( 268 | model=model, 269 | args=training_args, 270 | train_dataset=dataset[script_args.dataset_train_split], 271 | eval_dataset=dataset[script_args.dataset_test_split] if training_args.eval_strategy != "no" else None, 272 | processing_class=processor.tokenizer, 273 | data_collator=collate_fn, 274 | peft_config=get_peft_config(model_args) 275 | ) 276 | 277 | ############### 278 | # Training loop 279 | ############### 280 | logger.info("*** Train ***") 281 | checkpoint = None 282 | if training_args.resume_from_checkpoint is not None: 283 | checkpoint = training_args.resume_from_checkpoint 284 | elif last_checkpoint is not None: 285 | checkpoint = last_checkpoint 286 | train_result = trainer.train(resume_from_checkpoint=checkpoint) 287 | metrics = train_result.metrics 288 | metrics["train_samples"] = len(dataset[script_args.dataset_train_split]) 289 | trainer.log_metrics("train", metrics) 290 | trainer.save_metrics("train", metrics) 291 | trainer.save_state() 292 | 293 | ################################## 294 | # Save model and create model card 295 | ################################## 296 | logger.info("*** Save model ***") 297 | trainer.save_model(training_args.output_dir) 298 | processor.save_pretrained(training_args.output_dir) 299 | logger.info(f"Model saved to {training_args.output_dir}") 300 | 301 | # Save everything else on main process 302 | kwargs = { 303 | "dataset_name": script_args.dataset_name, 304 | "tags": ["R1-V"], 305 | } 306 | if trainer.accelerator.is_main_process: 307 | trainer.create_model_card(**kwargs) 308 | # Restore k,v cache for fast inference 309 | trainer.model.config.use_cache = True 310 | trainer.model.config.save_pretrained(training_args.output_dir) 311 | ############# 312 | # push to hub 313 | ############# 314 | 315 | if training_args.push_to_hub: 316 | logger.info("Pushing to hub...") 317 | trainer.push_to_hub(**kwargs) 318 | processor.push_to_hub(training_args.hub_model_id) 319 | 320 | 321 | 322 | 323 | if __name__ == "__main__": 324 | parser = TrlParser((ScriptArguments, SFTConfig, ModelConfig)) 325 | script_args, training_args, model_args = parser.parse_args_and_config() 326 | main(script_args, training_args, model_args) 327 | -------------------------------------------------------------------------------- /src/r1-v/src/open_r1/trainer/__init__.py: -------------------------------------------------------------------------------- 1 | from .grpo_trainer import Qwen2VLGRPOTrainer 2 | from .vllm_grpo_trainer import Qwen2VLGRPOVLLMTrainer 3 | 4 | __all__ = ["Qwen2VLGRPOTrainer", "Qwen2VLGRPOVLLMTrainer"] 5 | -------------------------------------------------------------------------------- /src/r1-v/temp_image.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hustvl/AlphaDrive/bb4104c680a3e4d70e7e998e08de90f6e0acf8c5/src/r1-v/temp_image.png -------------------------------------------------------------------------------- /src/scripts/run_grpo_clevr.sh: -------------------------------------------------------------------------------- 1 | export DEBUG_MODE="true" # Enable Debug if you want to see the rollout of model during RL 2 | export LOG_PATH="./debug_log_2b.txt" 3 | 4 | torchrun --nproc_per_node="8" \ 5 | --nnodes="1" \ 6 | --node_rank="0" \ 7 | --master_addr="127.0.0.1" \ 8 | --master_port="12345" \ 9 | src/open_r1/grpo.py \ 10 | --output_dir \ 11 | --model_name_or_path \ 12 | --dataset_name \ #https://huggingface.co/datasets/leonardPKU/clevr_cogen_a_train 13 | --max_prompt_length 1024 \ 14 | --per_device_train_batch_size 1 \ 15 | --gradient_accumulation_steps 2 \ 16 | --logging_steps 1 \ 17 | --bf16 \ 18 | --report_to wandb \ 19 | --gradient_checkpointing false \ 20 | --attn_implementation flash_attention_2 \ 21 | --max_pixels 401408 \ 22 | --num_train_epochs 2 \ 23 | --run_name Qwen2-VL-2B-GRPO-CLEVR-70k \ 24 | --save_steps 100 \ 25 | --save_only_model true -------------------------------------------------------------------------------- /src/scripts/run_grpo_vllm.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | # The latest vllm==0.7.2 is required for this script: pip3 install vllm==0.7.2 4 | 5 | 6 | export DEBUG_MODE="true" 7 | export LOG_PATH="./vllm_run.txt" 8 | 9 | QWEN_PATH="PATH_TO_QWEN_2B_CKPT" 10 | HF_DATASET="MMInstruction/Clevr_CoGenT_TrainA_70K_Complex" 11 | OUTPUT_DIR="OUTPUT_DIR" 12 | RUN_NAME="RUN_NAME_FOR_WANDB" 13 | 14 | # NOTE: you are expected to use X + 1 cards for X training proc and 1 vLLM proc 15 | # e.g., the visible devices should be 0,1,2,3,4 for 5 cards, and --nproc_per_node="4" 16 | 17 | CUDA_VISIBLE_DEVICES="0,1,2,3,4" torchrun --nproc_per_node="4" \ 18 | --nnodes="1" \ 19 | --node_rank="0" \ 20 | --master_addr="127.0.0.1" \ 21 | --master_port="12345" \ 22 | src/open_r1/grpo.py --use_vllm True \ 23 | --output_dir $OUTPUT_DIR \ 24 | --model_name_or_path $QWEN_PATH \ 25 | --dataset_name $HF_DATASET \ 26 | --max_prompt_length 512 \ 27 | --max_completion_length 1024 \ 28 | --temperature 1.0 \ 29 | --num_generations 4 \ 30 | --per_device_train_batch_size 1 \ 31 | --gradient_accumulation_steps 4 \ 32 | --logging_steps 1 \ 33 | --bf16 \ 34 | --report_to wandb \ 35 | --gradient_checkpointing true \ 36 | --attn_implementation flash_attention_2 \ 37 | --max_pixels 400000 \ 38 | --max_steps 13125 \ 39 | --run_name $RUN_NAME \ 40 | --save_steps 1000 \ 41 | --save_only_model true 42 | -------------------------------------------------------------------------------- /src/scripts/run_sft_clevr.sh: -------------------------------------------------------------------------------- 1 | ACCELERATE_LOG_LEVEL=info accelerate launch --config_file src/open-r1-multimodal/configs/zero2.yaml src/open-r1-multimodal/src/open_r1/sft.py --config src/open-r1-multimodal/configs/qwen2vl_sft_config.yaml -------------------------------------------------------------------------------- /src/scripts/test_grpo_geoqa_multigpu.sh: -------------------------------------------------------------------------------- 1 | r1_v_path=/workspace/xxx/github/R1-V 2 | cd ${r1_v_path} 3 | 4 | model_path=${r1_v_path}/output/train@geo170k/checkpoint-30 5 | batch_size=4 6 | output_path=${r1_v_path}/output/train@geo170k/eval/res@checkpoint-30.json 7 | prompt_path=${r1_v_path}/src/eval/prompts/geoqa_test_prompts.jsonl 8 | gpu_ids=0,1,2,3,4,5,6,7 9 | 10 | python src/eval/test_qwen2vl_geoqa_multigpu.py \ 11 | --model_path ${model_path} \ 12 | --batch_size ${batch_size} \ 13 | --output_path ${output_path} \ 14 | --prompt_path ${prompt_path} \ 15 | --gpu_ids ${gpu_ids} 16 | -------------------------------------------------------------------------------- /train_tools/run_train_grpo.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | # dynamic settings 4 | export ENV_NAME="/path/to/your/python/env" 5 | export MODEL_PATH="/path/to/your/Qwen2-VL-2B-Instruct" 6 | export OUT_NAME="Qwen2-VL-2B-EXP" 7 | export TRAIN_DATA="/path/to/your/train/data" 8 | export EVAL_DATA="/path/to/your/val/data" 9 | export EVAL_SAVE_NAME="eval_result.json" 10 | export OUTDIR="/path/to/your/out_dir" 11 | export WORKING_PATH="path/to/your/AlphaDrive" 12 | 13 | cd ${WORKING_PATH} 14 | 15 | 16 | # setup environments 17 | echo "Setup environments..." 18 | # export NCCL_P2P_DISABLE="1" 19 | # export NCCL_IB_DISABLE="1" 20 | 21 | mkdir -p ${OUTDIR} 22 | 23 | 24 | echo "Training Process..." 25 | cd src/r1-v 26 | 27 | 28 | $ENV_NAME/bin/torchrun --nproc_per_node="8" \ 29 | --nnodes="2" \ 30 | --node_rank="0" \ 31 | --master_addr="127.0.0.1" \ 32 | --master_port="12345" \ 33 | src/open_r1/grpo.py \ 34 | --output_dir $OUTDIR/$OUT_NAME \ 35 | --model_name_or_path $MODEL_PATH \ 36 | --dataset_name $TRAIN_DATA \ 37 | --max_prompt_length 1024 \ 38 | --per_device_train_batch_size 1 \ 39 | --gradient_accumulation_steps 2 \ 40 | --logging_steps 1 \ 41 | --bf16 \ 42 | --report_to tensorboard \ 43 | --gradient_checkpointing false \ 44 | --attn_implementation flash_attention_2 \ 45 | --max_pixels 401408 \ 46 | --reward_funcs "plan_speed_reward" "plan_path_reward" "plan_format_reward" \ 47 | --num_train_epochs 1 \ 48 | --run_name $OUT_NAME \ 49 | --save_steps 1000 \ 50 | --save_only_model true \ 51 | --num_generations 2 # number of outputs G in grpo, reduce it would lead to faster training and smaller memory cost but higher variance 52 | 53 | 54 | echo "Validation Process..." 55 | cd ${WORKING_PATH} 56 | $ENV_NAME/bin/python eval_tools/qwen2vl_plan_cmd_eval_grpo.py \ 57 | --eval_data_path $EVAL_DATA \ 58 | --model_path $OUTDIR/$OUT_NAME \ 59 | --save_path $OUTDIR/$OUT_NAME/$EVAL_SAVE_NAME 60 | -------------------------------------------------------------------------------- /train_tools/run_train_sft.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | # dynamic settings 4 | export ENV_NAME="/path/to/your/python/env" 5 | export MODEL_PATH="/path/to/your/Qwen2-VL-2B-Instruct" 6 | export OUT_NAME="Qwen2-VL-2B-EXP" 7 | export EVAL_DATA="/path/to/your/val/data" 8 | export EVAL_SAVE_NAME="eval_result.json" 9 | export OUTDIR="/path/to/your/out_dir" 10 | export WORKING_PATH="path/to/your/AlphaDrive" 11 | 12 | cd ${WORKING_PATH} 13 | 14 | 15 | # setup environments 16 | echo "Setup environments..." 17 | # export NCCL_P2P_DISABLE="1" 18 | # export NCCL_IB_DISABLE="1" 19 | 20 | mkdir -p ${OUTDIR} 21 | 22 | 23 | echo "Training Process..." 24 | $ENV_NAME/bin/accelerate launch --config_file src/r1-v/configs/zero2.yaml src/r1-v/src/open_r1/sft.py --config src/r1-v/configs/qwen2vl_sft_config.yaml 25 | 26 | 27 | echo "Validation Process..." 28 | $ENV_NAME/bin/python eval_tools/qwen2vl_plan_cmd_eval_sft.py \ 29 | --eval_data_path $EVAL_DATA \ 30 | --model_path $OUTDIR/$OUT_NAME \ 31 | --save_path $OUTDIR/$OUT_NAME/$EVAL_SAVE_NAME 32 | --------------------------------------------------------------------------------