| # SGL-new |
|
|
| This repository is a cleaned, submission-oriented copy of the SGL codebase for TextVQA large-only experiments: |
|
|
| 1. `InternVL2-2B` large-only |
| 2. `InternVL2-8B` large-only |
| 3. `InternVL2-26B` large-only |
| 4. `2B vision + 1B mlp1 + 1B language model` hybrid checkpoint large-only |
| 5. `2B vision + 8B mlp1 + 8B language model` hybrid checkpoint large-only |
| 6. `2B vision + 26B mlp1 + 26B language model` hybrid checkpoint large-only |
|
|
| The repository does **not** include checkpoints or datasets. The intended workflow is: |
|
|
| 1. create an environment |
| 2. place checkpoints under `checkpoints/` |
| 3. prepare TextVQA data under `data/` |
| 4. optionally build the hybrid checkpoint |
| 5. run one of the experiment launch scripts |
|
|
|
|
| ## 1. Repository Structure |
|
|
| Main experiment scripts: |
|
|
| - `textvqa2B-largeonly.sh` |
| - `textvqa8B-largeonly.sh` |
| - `textvqa26B-largeonly.sh` |
| - `textvqaHybrid-2Bvision-1Bllm-largeonly.sh` |
| - `textvqaHybrid-2Bvision-8Bllm-largeonly.sh` |
| - `textvqaHybrid-2Bvision-26Bllm-largeonly.sh` |
| - `run_textvqa_three_largeonly.sh` |
| - `run_textvqa_five_largeonly.sh` |
| - `train_textvqaHybrid-2Bvision-26Bllm-mlp.sh` |
|
|
| Core evaluation code: |
|
|
| - `eval/vqa/run_single_model_native.py` |
|
|
| Native single-model helpers: |
|
|
| - `eval/vqa/run_single_model_native.py` |
| - `eval/vqa/run_full_textvqa_native.sh` |
|
|
| Utility scripts: |
|
|
| - `tools/prepare_textvqa_for_sgl.py` |
| - `tools/build_hybrid_checkpoint.py` |
| - `build_hybrid_checkpoint_2bvision_1bllm.sh` |
| - `tools/hybrid_single_infer.py` |
| - `tools/train_hybrid_textvqa_mlp.py` |
| - `build_hybrid_checkpoint_2bvision_26bllm.sh` |
|
|
| Environment helper: |
|
|
| - `setup_sgl_2b_env.sh` |
|
|
|
|
| ## 2. Environment Setup |
|
|
| This repo expects Python 3.10 and a CUDA-enabled PyTorch installation. |
|
|
| ### Option A: manual setup |
|
|
| ```bash |
| conda create -y -n sgl-new python=3.10 |
| conda activate sgl-new |
| |
| pip install --upgrade pip |
| |
| # Install torch/torchvision matching your CUDA version. |
| # Example for CUDA 12.1: |
| pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121 |
| |
| pip install -r requirements.txt |
| ``` |
|
|
| ### Option B: helper script |
|
|
| ```bash |
| bash setup_sgl_2b_env.sh sgl-new |
| conda activate sgl-new |
| |
| # Then install torch/torchvision matching your CUDA version. |
| pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121 |
| ``` |
|
|
| ### Notes |
|
|
| - `flash-attn` is optional. The code can run without it, but may be slower. |
| - The large-only launchers now call Python directly and optionally shard a model with `device_map`. |
| - If `transformers` or `torch` versions are changed substantially, verify that `InternVL` remote-code loading still works. |
|
|
|
|
| ## 3. Checkpoint Layout |
|
|
| Create a directory: |
|
|
| ```bash |
| mkdir -p checkpoints |
| ``` |
|
|
| Place checkpoints under `checkpoints/` with these names: |
|
|
| - `checkpoints/models--OpenGVLab--InternVL2-1B` |
| - `checkpoints/models--OpenGVLab--InternVL2-2B` |
| - `checkpoints/models--OpenGVLab--InternVL2-8B` |
| - `checkpoints/models--OpenGVLab--InternVL2-26B` |
|
|
| The hybrid checkpoint will be created at: |
|
|
| - `checkpoints/InternVL2-1B_2Bvision_hybrid` |
| - `checkpoints/InternVL2-8B_2Bvision_hybrid` |
| - `checkpoints/InternVL2-26B_2Bvision_hybrid` |
|
|
| If you want to use a different checkpoint layout, override `CHECKPOINT_ROOT` or `CHECKPOINT` when launching. |
|
|
|
|
| ## 4. TextVQA Data Preparation |
|
|
| This repo expects SGL-style TextVQA files under: |
|
|
| - `data/textvqa/textvqa_train.jsonl` |
| - `data/textvqa/textvqa_val.jsonl` |
| - `data/textvqa/textvqa_val_questions.json` |
| - `data/textvqa/textvqa_val_annotations.json` |
|
|
| The repo does **not** ship the dataset. |
|
|
| ### 4.1 Download the official TextVQA data |
|
|
| Prepare: |
|
|
| - `TextVQA_0.5.1_train.json` |
| - `TextVQA_0.5.1_val.json` |
| - `TextVQA_0.5.1_test.json` |
| - training/validation images |
| - test images |
|
|
| Place them under: |
|
|
| ```text |
| data/textvqa_official/ |
| ├── TextVQA_0.5.1_train.json |
| ├── TextVQA_0.5.1_val.json |
| ├── TextVQA_0.5.1_test.json |
| ├── train_images/ |
| └── test_images/ |
| ``` |
|
|
| ### 4.2 Convert official data to SGL format |
|
|
| From the repo root: |
|
|
| ```bash |
| python tools/prepare_textvqa_for_sgl.py \ |
| --official-root data/textvqa_official \ |
| --output-root data/textvqa |
| ``` |
|
|
| This script: |
|
|
| - creates `data/textvqa/*.jsonl` |
| - creates `textvqa_val_questions.json` |
| - creates `textvqa_val_annotations.json` |
| - symlinks `train_images` and `test_images` into `data/textvqa/` |
|
|
|
|
| ## 5. Building Hybrid Checkpoints |
|
|
| ### 5.1 2B vision + 1B LLM hybrid |
|
|
| The hybrid experiment means: |
|
|
| - `vision_model` from `InternVL2-2B` |
| - `mlp1` from `InternVL2-1B` |
| - `language_model` from `InternVL2-1B` |
|
|
| Use the convenience wrapper: |
|
|
| ```bash |
| bash build_hybrid_checkpoint_2bvision_1bllm.sh |
| ``` |
|
|
| Equivalent manual command: |
|
|
| ```bash |
| python tools/build_hybrid_checkpoint.py \ |
| --base-checkpoint checkpoints/models--OpenGVLab--InternVL2-1B \ |
| --vision-checkpoint checkpoints/models--OpenGVLab--InternVL2-2B \ |
| --output-dir checkpoints/InternVL2-1B_2Bvision_hybrid |
| ``` |
|
|
| ### 5.2 2B vision + 8B LLM hybrid |
|
|
| The hybrid experiment means: |
|
|
| - `vision_model` from `InternVL2-2B` |
| - `mlp1` from `InternVL2-8B` |
| - `language_model` from `InternVL2-8B` |
|
|
| In this repo, the reproducible builder is: |
|
|
| - `tools/build_hybrid_checkpoint.py` |
|
|
| Run: |
|
|
| ```bash |
| python tools/build_hybrid_checkpoint.py \ |
| --base-checkpoint checkpoints/models--OpenGVLab--InternVL2-8B \ |
| --vision-checkpoint checkpoints/models--OpenGVLab--InternVL2-2B \ |
| --output-dir checkpoints/InternVL2-8B_2Bvision_hybrid |
| ``` |
|
|
| This script starts from the 8B checkpoint, replaces its `vision_model` weights with the 2B `vision_model`, and saves a new merged checkpoint. |
|
|
| ### 5.3 2B vision + 26B LLM hybrid |
|
|
| Use the convenience wrapper: |
|
|
| ```bash |
| bash build_hybrid_checkpoint_2bvision_26bllm.sh |
| ``` |
|
|
| Equivalent manual command: |
|
|
| ```bash |
| python tools/build_hybrid_checkpoint.py \ |
| --base-checkpoint checkpoints/models--OpenGVLab--InternVL2-26B \ |
| --vision-checkpoint checkpoints/models--OpenGVLab--InternVL2-2B \ |
| --output-dir checkpoints/InternVL2-26B_2Bvision_hybrid |
| ``` |
|
|
|
|
| ## 6. How the Experiments Map to Code |
|
|
| ### 6.1 InternVL2-2B large-only |
|
|
| Launcher: |
|
|
| - `textvqa2B-largeonly.sh` |
|
|
| Core code path: |
|
|
| - `eval/vqa/run_single_model_native.py --mode textvqa_eval` |
|
|
| Default checkpoint: |
|
|
| - `checkpoints/models--OpenGVLab--InternVL2-2B` |
|
|
| Run: |
|
|
| ```bash |
| bash textvqa2B-largeonly.sh |
| ``` |
|
|
| Optional overrides: |
|
|
| ```bash |
| CHECKPOINT_ROOT=/path/to/checkpoints \ |
| OUT_DIR=/path/to/output \ |
| GPUS_PER_MODEL=1 \ |
| bash textvqa2B-largeonly.sh |
| ``` |
|
|
|
|
| ### 6.2 InternVL2-8B large-only |
|
|
| Launcher: |
|
|
| - `textvqa8B-largeonly.sh` |
|
|
| Core code path: |
|
|
| - `eval/vqa/run_single_model_native.py --mode textvqa_eval` |
|
|
| Default checkpoint: |
|
|
| - `checkpoints/models--OpenGVLab--InternVL2-8B` |
|
|
| Run: |
|
|
| ```bash |
| bash textvqa8B-largeonly.sh |
| ``` |
|
|
| Optional overrides: |
|
|
| ```bash |
| CHECKPOINT_ROOT=/path/to/checkpoints \ |
| OUT_DIR=/path/to/output \ |
| GPUS_PER_MODEL=1 \ |
| bash textvqa8B-largeonly.sh |
| ``` |
|
|
| ### 6.3 InternVL2-26B large-only |
|
|
| Launcher: |
|
|
| - `textvqa26B-largeonly.sh` |
|
|
| Core code path: |
|
|
| - `eval/vqa/run_single_model_native.py --mode textvqa_eval` |
|
|
| Default checkpoint: |
|
|
| - `checkpoints/models--OpenGVLab--InternVL2-26B` |
|
|
| Run: |
|
|
| ```bash |
| bash textvqa26B-largeonly.sh |
| ``` |
|
|
| Optional overrides: |
|
|
| ```bash |
| CUDA_VISIBLE_DEVICES=0,1 \ |
| CHECKPOINT_ROOT=/path/to/checkpoints \ |
| OUT_DIR=/path/to/output \ |
| GPUS_PER_MODEL=2 \ |
| bash textvqa26B-largeonly.sh |
| ``` |
|
|
| ### 6.4 2B vision + 1B mlp1 + 1B language model large-only |
|
|
| Launcher: |
|
|
| - `textvqaHybrid-2Bvision-1Bllm-largeonly.sh` |
|
|
| Core code path: |
|
|
| - `eval/vqa/run_single_model_native.py --mode textvqa_eval` |
|
|
| Hybrid builder: |
|
|
| - `build_hybrid_checkpoint_2bvision_1bllm.sh` |
| - `tools/build_hybrid_checkpoint.py` |
|
|
| Default checkpoint: |
|
|
| - `checkpoints/InternVL2-1B_2Bvision_hybrid` |
|
|
| Run: |
|
|
| ```bash |
| bash textvqaHybrid-2Bvision-1Bllm-largeonly.sh |
| ``` |
|
|
| Optional overrides: |
|
|
| ```bash |
| CHECKPOINT_ROOT=/path/to/checkpoints \ |
| OUT_DIR=/path/to/output \ |
| GPUS_PER_MODEL=1 \ |
| bash textvqaHybrid-2Bvision-1Bllm-largeonly.sh |
| ``` |
|
|
| ### 6.5 2B vision + 8B mlp1 + 8B language model large-only |
|
|
| Launcher: |
|
|
| - `textvqaHybrid-2Bvision-8Bllm-largeonly.sh` |
|
|
| Core code path: |
|
|
| - `eval/vqa/run_single_model_native.py --mode textvqa_eval` |
|
|
| Hybrid builder: |
|
|
| - `tools/build_hybrid_checkpoint.py` |
|
|
| Default checkpoint: |
|
|
| - `checkpoints/InternVL2-8B_2Bvision_hybrid` |
|
|
| Run: |
|
|
| ```bash |
| bash textvqaHybrid-2Bvision-8Bllm-largeonly.sh |
| ``` |
|
|
| Optional overrides: |
|
|
| ```bash |
| CHECKPOINT_ROOT=/path/to/checkpoints \ |
| OUT_DIR=/path/to/output \ |
| GPUS_PER_MODEL=1 \ |
| bash textvqaHybrid-2Bvision-8Bllm-largeonly.sh |
| ``` |
|
|
| ### 6.6 2B vision + 26B mlp1 + 26B language model large-only |
|
|
| Launcher: |
|
|
| - `textvqaHybrid-2Bvision-26Bllm-largeonly.sh` |
|
|
| Core code path: |
|
|
| - `eval/vqa/run_single_model_native.py --mode textvqa_eval` |
|
|
| Hybrid builder: |
|
|
| - `build_hybrid_checkpoint_2bvision_26bllm.sh` |
| - `tools/build_hybrid_checkpoint.py` |
|
|
| Default checkpoint: |
|
|
| - `checkpoints/InternVL2-26B_2Bvision_hybrid` |
|
|
| Run: |
|
|
| ```bash |
| bash textvqaHybrid-2Bvision-26Bllm-largeonly.sh |
| ``` |
|
|
| Optional overrides: |
|
|
| ```bash |
| CUDA_VISIBLE_DEVICES=0,1 \ |
| CHECKPOINT_ROOT=/path/to/checkpoints \ |
| OUT_DIR=/path/to/output \ |
| GPUS_PER_MODEL=2 \ |
| bash textvqaHybrid-2Bvision-26Bllm-largeonly.sh |
| ``` |
|
|
| ### 6.7 Optional CoT-style reasoning |
|
|
| The native and hybrid inference entry points now support optional reasoning modes: |
|
|
| - `--reasoning-mode none`: default single-pass decoding |
| - `--reasoning-mode prompt`: adds an internal "think step by step" instruction in one pass |
| - `--reasoning-mode two_pass`: first generates explicit reasoning, then compresses it into the final short answer |
|
|
| If you do not set `REASONING_MODE` or `--reasoning-mode`, the code stays on the original normal inference path. |
|
|
| For the hybrid TextVQA launchers, use environment variables: |
|
|
| ```bash |
| REASONING_MODE=two_pass \ |
| REASONING_MAX_NEW_TOKENS=64 \ |
| SAVE_REASONING=1 \ |
| bash textvqaHybrid-2Bvision-8Bllm-largeonly.sh |
| ``` |
|
|
| For the shared-vision launcher: |
|
|
| ```bash |
| REASONING_MODE=two_pass \ |
| REASONING_MAX_NEW_TOKENS=64 \ |
| SAVE_REASONING=1 \ |
| bash textvqaSharedVision-2Bguide-8Btext.sh |
| ``` |
|
|
| To let the small guide model produce a short text hint for the large decoder: |
|
|
| ```bash |
| GUIDE_TEXT_MODE=short_rationale \ |
| GUIDE_TEXT_MAX_NEW_TOKENS=12 \ |
| bash textvqaSharedVision-2Bguide-8Btext.sh |
| ``` |
|
|
| To force a short CoT on the guide branch so its generation changes the visual-token attention scores: |
|
|
| ```bash |
| GUIDE_REASONING_MODE=short_cot \ |
| GUIDE_REASONING_MAX_NEW_TOKENS=1024 \ |
| bash textvqaSharedVision-2Bguide-8Btext.sh |
| ``` |
|
|
| Both options can be enabled together. |
|
|
| For single-image hybrid debugging: |
|
|
| ```bash |
| python tools/hybrid_single_infer.py \ |
| --vision-checkpoint checkpoints/models--OpenGVLab--InternVL2-2B \ |
| --language-checkpoint checkpoints/models--OpenGVLab--InternVL2-8B \ |
| --image-path /path/to/image.jpg \ |
| --prompt "What is the brand name on the sign?" \ |
| --reasoning-mode two_pass \ |
| --reasoning-max-new-tokens 64 \ |
| --answer-format-prompt "Answer the question using a single word or phrase." |
| ``` |
|
|
|
|
| ## 7. Running Sequential Launchers |
|
|
| Use: |
|
|
| ```bash |
| bash run_textvqa_three_largeonly.sh |
| ``` |
|
|
| Default output root: |
|
|
| - `outputs/textvqa_three_largeonly` |
|
|
| This script runs: |
|
|
| 1. 2B |
| 2. 8B |
| 3. hybrid 2B-vision + 8B-LLM |
|
|
| each with its own output subdirectory and launcher log. |
|
|
| To run all five experiments, use: |
|
|
| ```bash |
| bash run_textvqa_five_largeonly.sh |
| ``` |
|
|
| This script adds: |
|
|
| 1. 26B |
| 2. hybrid 2B-vision + 26B-LLM |
|
|
|
|
| ## 8. Minimal Hybrid Fine-Tuning On TextVQA |
|
|
| For a lightweight experiment, this repo also includes a minimal script that: |
|
|
| 1. builds `2B vision + 26B mlp1 + 26B language_model` |
| 2. freezes everything except `mlp1` |
| 3. trains on TextVQA jsonl |
| 4. runs validation inference immediately after training |
|
|
| Launcher: |
|
|
| - `train_textvqaHybrid-2Bvision-26Bllm-mlp.sh` |
|
|
| Core code: |
|
|
| - `tools/train_hybrid_textvqa_mlp.py` |
|
|
| Default demo dataset: |
|
|
| - `/home/yf/snap/SGL_yf/data/textvqa_demo_backup/textvqa_train.jsonl` |
| - `/home/yf/snap/SGL_yf/data/textvqa_demo_backup/textvqa_val.jsonl` |
|
|
| Run: |
|
|
| ```bash |
| bash train_textvqaHybrid-2Bvision-26Bllm-mlp.sh |
| ``` |
|
|
| Important assumptions: |
|
|
| - `UPSTREAM_SGL_ROOT` defaults to `/home/yf/snap/SGL` because this script reuses the upstream `internvl` package. |
| - The default launcher expects local checkpoints at: |
| - `/root/model_ckpts/models--OpenGVLab--InternVL2-2B` |
| - `/root/model_ckpts/models--OpenGVLab--InternVL2-26B` |
| - The minimal implementation currently supports `batch_size=1`. |
|
|
|
|
| ## 9. Native Single-Model Inference Utilities |
|
|
| These are not required for the main large-only experiments, but they are included because they are useful for debugging and single-sample inspection. |
|
|
| ### Single sample or single question |
|
|
| Code: |
|
|
| - `eval/vqa/run_single_model_native.py` |
|
|
| Example: |
|
|
| ```bash |
| python eval/vqa/run_single_model_native.py \ |
| --checkpoint checkpoints/models--OpenGVLab--InternVL2-2B \ |
| --mode single \ |
| --image-path /path/to/image.jpg \ |
| --prompt "What is written on the sign?" \ |
| --max-new-tokens 32 \ |
| --dynamic |
| ``` |
|
|
| ### Full TextVQA native evaluation for 2B and 8B |
|
|
| Code: |
|
|
| - `eval/vqa/run_full_textvqa_native.sh` |
|
|
| Example: |
|
|
| ```bash |
| bash eval/vqa/run_full_textvqa_native.sh outputs/native_eval |
| ``` |
|
|
|
|
| ## 10. Hybrid Single-Sample Debugging Utility |
|
|
| Code: |
|
|
| - `tools/hybrid_single_infer.py` |
|
|
| Example: |
|
|
| ```bash |
| python tools/hybrid_single_infer.py \ |
| --vision-checkpoint checkpoints/models--OpenGVLab--InternVL2-2B \ |
| --language-checkpoint checkpoints/models--OpenGVLab--InternVL2-8B \ |
| --image-path /path/to/image.jpg \ |
| --prompt "What is written on the sign?" \ |
| --dynamic |
| ``` |
|
|
| This script does **not** require a saved hybrid checkpoint. It builds the hybrid model in memory for single-sample inspection. |
|
|
|
|
| ## 11. Output Files |
|
|
| The large-only evaluation script writes outputs under the launcher-provided output directory. |
|
|
| Typical files include one JSON results file per run inside the launcher-provided output directory. |
|
|
|
|
| ## 12. Minimal Reproduction Checklist |
|
|
| For someone receiving this repository, the minimal steps are: |
|
|
| 1. create a Python environment |
| 2. install `torch`, `torchvision`, and `requirements.txt` |
| 3. download `InternVL2-2B`, `InternVL2-8B`, and optionally `InternVL2-26B` into `checkpoints/` |
| 4. download official TextVQA into `data/textvqa_official/` |
| 5. run `python tools/prepare_textvqa_for_sgl.py` |
| 6. run `python tools/build_hybrid_checkpoint.py` |
| 7. run one of: |
| - `bash textvqa2B-largeonly.sh` |
| - `bash textvqa8B-largeonly.sh` |
| - `bash textvqa26B-largeonly.sh` |
| - `bash textvqaHybrid-2Bvision-8Bllm-largeonly.sh` |
| - `bash textvqaHybrid-2Bvision-26Bllm-largeonly.sh` |
|
|
|
|
| ## 13. Important Assumptions |
|
|
| - The code assumes CUDA is available for model inference. |
| - The code assumes TextVQA data is prepared under `data/textvqa/`. |
| - The code assumes checkpoints are available under `checkpoints/` unless overridden. |
| - All large-only experiments use the same evaluation implementation: |
| `eval/vqa/run_single_model_native.py --mode textvqa_eval` |
| - `InternVL2-26B` and the `2B vision + 26B LLM` hybrid usually require multiple visible GPUs. |
|
|
