STTM Quadtree & Qwen2VL: Memory Issues With Many Frames

Introduction

This document addresses an issue encountered while using the STTM Quadtree implementation with Qwen2VL, specifically regarding out-of-memory (OOM) errors when processing a larger number of frames. The problem arises because the current implementation appears to bypass FlashAttention2, leading to increased memory consumption and limiting the number of frames that can be processed. This article delves into the details of the problem, provides context, and raises questions about how the original experiments managed to handle a larger number of frames.

Problem Description

When utilizing the STTM Quadtree implementation for Qwen2VL, a significant challenge arises due to its apparent bypassing of FlashAttention2. This bypass occurs because the implementation replaces the entire forward pass through monkey patching, as evidenced in the token_merging_qwen2vl_monkey_patch/quadtree_attn_monkey_patch.py file. Specifically, the line Qwen2VLModel.forward = Qwen2VLModel_forward_with_quadtree indicates a complete replacement of the original forward pass function.

The consequence of this bypass is a severe limitation on the number of frames that can be processed before running into out-of-memory (OOM) errors. Tests conducted on a 24 GB GPU using Qwen2VL-2B-Instruct revealed that while processing eight frames works without issues, increasing the frame count to 16 or 32 results in OOM errors. This contrasts sharply with other methods like FrameFusion-Merge and FastV, which retain FlashAttention2 and can handle 32 or more frames without exhausting memory. The evaluation configurations, such as scripts/eval/run_vidqa.sh, include settings designed for up to 256 frames, suggesting a discrepancy between the intended capability and the observed performance. Understanding why the current implementation bypasses FlashAttention2 and how to mitigate this issue is crucial for effectively leveraging STTM Quadtree with Qwen2VL in memory-constrained environments.

Observed Memory Issues

The primary issue at hand is the out-of-memory (OOM) errors encountered when processing video data with a moderate number of frames using the STTM Quadtree implementation on Qwen2VL. The tests were conducted on a system equipped with a 24 GB GPU, using the Qwen2VL-2B-Instruct model. The following observations were made:

• When processing T = 8 frames, the system operates without any memory issues.
• Increasing the frame count to T = 16 frames results in an OOM error, indicating that the memory capacity is exceeded.
• Further increasing the frame count to T ≥ 32 frames consistently leads to OOM errors, reinforcing the memory limitations of the current implementation.

These observations highlight a significant limitation, especially when compared to other methods like FrameFusion-Merge and FastV, which can handle a much larger number of frames (32+) without running into memory issues. The key difference is that FrameFusion-Merge and FastV retain FlashAttention2, which is designed to optimize memory usage during attention mechanisms. The fact that the evaluation configurations include settings for up to 256 frames further underscores the discrepancy between the expected performance and the actual memory limitations observed with the STTM Quadtree implementation. This discrepancy warrants further investigation to understand and address the underlying causes of the OOM errors.

Comparison with Other Methods

When comparing the STTM Quadtree implementation with other methods such as FrameFusion-Merge and FastV, a notable difference emerges in their ability to handle a large number of frames without encountering out-of-memory (OOM) errors. Specifically, FrameFusion-Merge and FastV can comfortably process 32 or more frames, while the STTM Quadtree implementation starts to fail with OOM errors at just 16 frames on a 24 GB GPU using Qwen2VL-2B-Instruct. This discrepancy is primarily attributed to the fact that FrameFusion-Merge and FastV retain FlashAttention2, which is designed to optimize memory usage during attention mechanisms.

FlashAttention2 significantly reduces memory consumption by performing attention computations more efficiently. By bypassing FlashAttention2, the STTM Quadtree implementation loses these memory-saving optimizations, leading to a much higher memory footprint for each frame processed. This is particularly problematic when dealing with video data, where the number of frames can quickly escalate, leading to OOM errors. The evaluation configurations, which include settings for up to 256 frames, suggest that the original intent was to handle a large number of frames. However, the current implementation falls short of this expectation due to the memory limitations imposed by bypassing FlashAttention2.

The ability to process a larger number of frames is crucial for many video analysis tasks, such as video question answering and video summarization. Therefore, understanding why the STTM Quadtree implementation bypasses FlashAttention2 and finding a way to incorporate it is essential for improving the performance and scalability of this method.

Questions Regarding Implementation

Given the observed memory limitations and the apparent bypassing of FlashAttention2 in the STTM Quadtree implementation, several questions arise regarding the original experimental setup and the availability of alternative implementations:

1. How were 128+ frame experiments with STTM Quadtree + Qwen2VL handled?
   • The evaluation configurations provided in the scripts suggest that the system should be able to handle up to 256 frames. However, the observed OOM errors at just 16 frames raise questions about how the original experiments managed to process a much larger number of frames. Were there specific hardware configurations, optimization techniques, or other factors that allowed for processing 128+ frames without encountering memory issues? Understanding the details of the original experimental setup is crucial for replicating those results and identifying potential solutions to the current memory limitations.
2. Is there a FlashAttention2-compatible version of STTM Quadtree available? Or I just missed it?
   • The current implementation appears to bypass FlashAttention2, which is a key optimization for reducing memory consumption during attention mechanisms. Is there an alternative version of the STTM Quadtree implementation that incorporates FlashAttention2 or another memory optimization technique? If such a version exists, it could significantly improve the scalability of the method and allow for processing a larger number of frames without OOM errors. If not, developing a FlashAttention2-compatible version of STTM Quadtree would be a valuable contribution to the field.

Answering these questions is essential for understanding the capabilities and limitations of the STTM Quadtree implementation and for developing strategies to overcome the current memory challenges.

Setup Details

The experiments were conducted using the following setup:

• GPU: A100 80GB

Conclusion

In conclusion, the STTM Quadtree implementation on Qwen2VL faces significant memory limitations due to the apparent bypassing of FlashAttention2. This results in out-of-memory errors when processing more than 8-16 frames on a 24 GB GPU with Qwen2VL-2B-Instruct. The discrepancy between the observed performance and the evaluation configurations, which include settings for up to 256 frames, raises questions about the original experimental setup and the availability of FlashAttention2-compatible versions. Addressing these issues is crucial for improving the scalability and usability of STTM Quadtree in video analysis tasks. Exploring alternative implementations or optimization techniques that incorporate FlashAttention2 could significantly enhance the method's ability to handle a larger number of frames without encountering memory errors.

For more information about FlashAttention and its benefits, you can visit the NVIDIA Developer Blog.