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

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 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:

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:

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:

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 build_hybrid_checkpoint_2bvision_1bllm.sh

Equivalent manual command:

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:

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 build_hybrid_checkpoint_2bvision_26bllm.sh

Equivalent manual command:

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 textvqa2B-largeonly.sh

Optional overrides:

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 textvqa8B-largeonly.sh

Optional overrides:

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 textvqa26B-largeonly.sh

Optional overrides:

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 textvqaHybrid-2Bvision-1Bllm-largeonly.sh

Optional overrides:

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 textvqaHybrid-2Bvision-8Bllm-largeonly.sh

Optional overrides:

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 textvqaHybrid-2Bvision-26Bllm-largeonly.sh

Optional overrides:

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:

REASONING_MODE=two_pass \
REASONING_MAX_NEW_TOKENS=64 \
SAVE_REASONING=1 \
bash textvqaHybrid-2Bvision-8Bllm-largeonly.sh

For the shared-vision launcher:

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:

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:

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:

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 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 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 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:

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 eval/vqa/run_full_textvqa_native.sh outputs/native_eval

10. Hybrid Single-Sample Debugging Utility

Code:

• tools/hybrid_single_infer.py

Example:

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.