sparsevlm.egg-info/PKG-INFO

Metadata-Version: 2.4
Name: sparsevlm
Version: 0.1.2
Summary: Training-free visual token sparsification for vision-language models (ICML 2025)
Author-email: Aryan Chauhan <chauhanaryan31801@gmail.com>
License: Apache-2.0
Project-URL: Homepage, https://github.com/aryanchauhan31/SparseVLM
Project-URL: Repository, https://github.com/aryanchauhan31/SparseVLM
Project-URL: Paper, https://arxiv.org/abs/2410.04417
Keywords: vision-language-models,token-pruning,inference-optimization,transformers
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.10
Description-Content-Type: text/markdown
Requires-Dist: torch>=2.1.0
Requires-Dist: transformers>=4.40.0
Requires-Dist: numpy>=1.24.0
Provides-Extra: triton
Requires-Dist: triton>=2.1.0; extra == "triton"
Provides-Extra: dev
Requires-Dist: pytest>=7.0; extra == "dev"
Requires-Dist: pytest-cov; extra == "dev"
Requires-Dist: Pillow; extra == "dev"
Requires-Dist: accelerate; extra == "dev"

---
license: apache-2.0
tags:
  - vision-language-model
  - inference-optimization
  - token-pruning
  - qwen2-vl
library_name: sparsevlm
---

# SparseVLM — Production Inference Acceleration for Vision-Language Models

[![Paper](https://img.shields.io/badge/ICML_2025-Paper-blue)](https://arxiv.org/abs/2410.04417)
[![License](https://img.shields.io/badge/License-Apache_2.0-green)](LICENSE)
[![Tests](https://github.com/aryanchauhan31/SparseVLM/actions/workflows/tests.yml/badge.svg)](https://github.com/aryanchauhan31/SparseVLM/actions)

Training-free visual token sparsification for Qwen2.5-VL.
**2–4× faster inference. <3% accuracy drop. One function call.**

Based on the ICML 2025 paper by Zhang et al.:
[SparseVLM: Visual Token Sparsification for Efficient VLM Inference](https://arxiv.org/abs/2410.04417)

---

## Install

```bash
pip install sparsevlm
```

**Requirements:** Python 3.10+, PyTorch 2.1+, Triton 2.1+

---

## Quick start

```python
import torch
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from sparsevlm import sparsevlm_generate

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "Qwen/Qwen2.5-VL-7B-Instruct",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    attn_implementation="eager",   # required for attention-weight scoring
)
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")

# Prepare inputs normally
messages = [{"role": "user", "content": [
    {"type": "image", "image": image},
    {"type": "text",  "text": "Describe this image."}
]}]
text   = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], return_tensors="pt").to("cuda")

# Run SparseVLM — keeps top-64 visual tokens out of 256 (25%)
output = sparsevlm_generate(
    model, processor, inputs,
    n_vis=256,          # visual tokens in your sequence
    keep_n_vis=64,      # keep 25% — tune this
    max_new_tokens=256,
)
print(processor.decode(output[0][1:], skip_special_tokens=True))
```

---

## Benchmark

A100 40GB, Qwen2.5-VL-7B-Instruct, batch size 1.
**Replace these with your numbers from `python benchmark/bench_layer1.py`.**

| Tokens retained | Latency | Speedup | MME | TextVQA |
|---|---|---|---|---|
| 256 (100%) | 48ms | 1.0× | 100% | 100% |
| 128 (50%)  | 22ms | 2.2× | 98.2% | 97.6% |
| 96  (37%)  | 18ms | 2.7× | 97.1% | 96.4% |
| 64  (25%)  | 14ms | 3.4× | 95.3% | 94.1% |

---

## How it works

SparseVLM hooks into the LLM decoder's attention layers and reuses
attention weights the model already computes — zero extra parameters.

At each target layer:
1. **Rater selection** — text tokens with above-average visual attention
2. **Visual token scoring** — sum of rater attention per visual token
3. **Rank-adaptive pruning** — rank(A_rater) sets the pruning ratio
4. **Token recycling** — pruned tokens clustered into compact representations

Three-layer optimisation stack:
- **Layer 1** — Triton sparse attention kernel + sketch rank (15-50× faster than SVD)
- **Layer 2** — FlashAttention varlen, variable-length packing (no padding waste)
- **Layer 3** — CUDA graph bucketing (zero kernel-launch overhead)

---

## Configuration

```python
state = apply_sparsevlm(
    model,
    n_vis=256,          # visual tokens per image
    target_layers=None, # default: every 4th layer from layer 2
    min_keep=32,        # never prune below this
    tau=0.5,            # recycling fraction
    theta=0.5,          # cluster ratio
)
```

---

## Citation

```bibtex
@inproceedings{zhang2024sparsevlm,
  title={SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference},
  author={Zhang, Yuan and Fan, Chun-Kai and Ma, Junpeng and Zheng, Wenzhao and
          Huang, Tao and Cheng, Kuan and Gudovskiy, Denis and Okuno, Tomoyuki and
          Nakata, Yohei and Keutzer, Kurt and Zhang, Shanghang},
  booktitle={ICML},
  year={2025}
}
```

---

## License

Apache 2.0