VLLM LoRA Issue: AssertionError With Qwen3-VL-8B-Instruct

Oct 29, 2025 by Admin 58 views

Hey everyone! Today, we're diving into a tricky issue encountered while trying to serve the Qwen3-VL-8B-Instruct model with a LoRA adapter on vLLM v0.11.0. Specifically, we're tackling an AssertionError that pops up in lora_shrink_op.py during the profile run. Let's break down the problem, understand why it's happening, and explore potential solutions.

The Problem: AssertionError in `lora_shrink_op.py`

The core issue is an AssertionError that arises when vLLM attempts a profile run while serving the Qwen3-VL-8B-Instruct model with a LoRA adapter. This error occurs within the lora_shrink_op.py file, indicating a problem specific to the LoRA (Low-Rank Adaptation) implementation within vLLM. For those unfamiliar, LoRA is a technique used to fine-tune large language models (LLMs) more efficiently by training a smaller set of parameters. It's super handy for adapting models to specific tasks without retraining the entire behemoth.

When you try to start the vLLM server with the Qwen3-VL-8B-Instruct model and a LoRA adapter – and this is assuming you've got your command including --enable-lora and your LoRA adapter is primed and ready to roll, either via API or those startup arguments – you might see this error rearing its ugly head. You'll see the engine's core bravely start loading those model weights, but then, BAM!, it falters during the KV cache initialization/profiling phase.

Diving Deeper into the Traceback

Let's dissect a sample traceback to get a clearer picture. Imagine seeing something like this in your logs:

(EngineCore_DP0 pid=9785) WARNING 10-27 22:45:32 [v1/worker/lora_model_runner_mixin.py:42] Regarding multimodal models, vLLM currently only supports adding LoRA to language model.
(EngineCore_DP0 pid=9785) INFO 10-27 22:45:32 [lora/punica_wrapper/punica_selector.py:19] Using PunicaWrapperGPU.
(EngineCore_DP0 pid=9785) INFO 10-27 22:45:32 [v1/worker/gpu_model_runner.py:3344] Encoder cache will be initialized with a budget of 151250 tokens, and profiled with 1 video items of the maximum feature size.
(EngineCore_DP0 pid=9785) ERROR 10-27 22:45:38 [v1/engine/core.py:708] EngineCore failed to start.
(EngineCore_DP0 pid=9785) ERROR 10-27 22:45:38 [v1/engine/core.py:708] Traceback (most recent call last):
...
(EngineCore_DP0 pid=9785) ERROR 10-27 22:45:38 [v1/engine/core.py:708]   File "/home/work/env/miniconda_qwen3vl/lib/python3.10/site-packages/vllm/v1/worker/gpu_worker.py", line 263, in determine_available_memory
(EngineCore_DP0 pid=9785) ERROR 10-27 22:45:38 [v1/engine/core.py:708]     self.model_runner.profile_run()
(EngineCore_DP0 pid=9785) ERROR 10-27 22:45:38 [v1/engine/core.py:708]   File "/home/work/env/miniconda_qwen3vl/lib/python3.10/site-packages/vllm/v1/worker/gpu_model_runner.py", line 3361, in profile_run
(EngineCore_DP0 pid=9785) ERROR 10-27 22:45:38 [v1/engine/core.py:708]     self.model.get_multimodal_embeddings(
(EngineCore_DP0 pid=9785) ERROR 10-27 22:45:38 [v1/engine/core.py:708]   File "/home/work/env/miniconda_qwen3vl/lib/python3.10/site-packages/vllm/model_executor/models/qwen3_vl.py", line 1335, in _process_video_input
(EngineCore_DP0 pid=9785) ERROR 10-27 22:45:38 [v1/engine/core.py:708]     video_embeds = self.visual(pixel_values_videos,
...
(EngineCore_DP0 pid=9785) ERROR 10-27 22:45:38 [v1/engine/core.py:708]   File "/home/work/env/miniconda_qwen3vl/lib/python3.10/site-packages/vllm/lora/punica_wrapper/punica_gpu.py", line 215, in add_lora_linear
(EngineCore_DP0 pid=9785) ERROR 10-27 22:45:38 [v1/engine/core.py:708]     self.add_shrink(
(EngineCore_DP0 pid=9785) ERROR 10-27 22:45:38 [v1/engine/core.py:708]   File "/home/work/env/miniconda_qwen3vl/lib/python3.10/site-packages/vllm/lora/punica_wrapper/punica_gpu.py", line 77, in add_shrink
(EngineCore_DP0 pid=9785) ERROR 10-27 22:45:38 [v1/engine/core.py:708]     lora_shrink(
(EngineCore_DP0 pid=9785) ERROR 10-27 22:45:38 [v1/engine/core.py:708]   File "/home/work/env/miniconda_qwen3vl/lib/python3.10/site-packages/vllm/lora/ops/triton_ops/lora_shrink_op.py", line 149, in _lora_shrink
(EngineCore_DP0 pid=9785) ERROR 10-27 22:45:38 [v1/engine/core.py:708]     assert token_lora_mapping.size(0) == M
(EngineCore_DP0 pid=9785) ERROR 10-27 22:45:38 [v1/engine/core.py:708] AssertionError

From this, we can trace the error back to the _lora_shrink function within lora_shrink_op.py. The assertion token_lora_mapping.size(0) == M fails, implying a mismatch in the expected and actual dimensions of the token_lora_mapping tensor. This usually means there's a snag in how LoRA is being applied, particularly concerning the mapping between tokens and LoRA modifications.

What's the Root Cause?

The most likely culprit here is that vLLM's LoRA implementation is hitting an unexpected code path when dealing with the multimodal components of Qwen3-VL-8B-Instruct. Qwen3-VL is a multimodal model, meaning it can process both text and visual inputs (images/videos). The AssertionError suggests that during the engine's memory profile run, specifically for the multimodal part, the LoRA acceleration code (lora_shrink_op) gets triggered. This code expects certain dimensions in the mapping that aren't there for the visual layers. Essentially, it's trying to apply a text-based LoRA operation to a visual component, which doesn't jive.

The core problem lies in the fact that the lora_shrink_op expects specific mapping dimensions that simply aren't present when processing the visual components of the Qwen3-VL model. It's like trying to fit a square peg into a round hole – the dimensions just don't align.

Expected Behavior

Ideally, vLLM should gracefully handle this scenario. When you load Qwen3-VL-8B-Instruct with a LoRA adapter, the engine should initialize without a hitch. If the LoRA is designed to tweak only the language model backbone, the multimodal (visual/video) parts of the process should either skip the LoRA-specific operations entirely or bypass the problematic lora_shrink_op during the profile run. This would prevent the AssertionError from ever occurring, giving us a smooth startup experience.

How to Fix It? Potential Solutions

Alright, let's get down to brass tacks. How do we actually fix this pesky AssertionError? Here are a few avenues we can explore:

1. Check vLLM Version and Updates

First things first, make sure you're running the latest stable version of vLLM. The vLLM team is constantly squashing bugs and improving compatibility, so an update might just solve the issue. If you're not on the newest version, upgrade and see if the problem magically disappears.

pip install -U vllm

2. Validate LoRA Configuration

Double-check your LoRA adapter configuration. Ensure that it's correctly set up for the Qwen3-VL-8B-Instruct model and that it's primarily targeting the language model components. Mismatched configurations can lead to dimension mismatches and assertion errors.

3. Targeted LoRA Application

If your LoRA adapter is intended for the language model part only, make sure that the vLLM server is configured to apply LoRA solely to those layers. This might involve tweaking the server's settings or the LoRA adapter's metadata to explicitly exclude visual processing layers from LoRA modifications. By being specific about which layers LoRA should affect, we can avoid the problematic lora_shrink_op from being invoked in the wrong context.

4. Debugging `lora_shrink_op.py`

For the brave and technically inclined, diving into the lora_shrink_op.py code might reveal the exact conditions under which the assertion fails. Adding some print statements or using a debugger to inspect the dimensions of token_lora_mapping and M could shed light on the mismatch. This approach can help pinpoint the precise location of the error and inform a more targeted fix.

5. Engage the vLLM Community

If you've tried the above steps and are still banging your head against the wall, it's time to call in the cavalry! The vLLM community is active and helpful. Post your issue on the vLLM GitHub repository, providing a clear description of the problem, your environment details, and any steps you've taken to troubleshoot. The vLLM maintainers or other community members might have encountered the same issue or have insights into potential solutions. Who knows, maybe someone's already cracked this nut!

### Example of Starting the vLLM Server with Qwen3-VL-8B-Instruct and LoRA:
```bash
vllm serve --model ./Qwen3-VL-8B-Instruct/ --lora-modules lora_name=./path/to/your/lora/adapter --enable-lora --max-model-len 2048

Make sure to replace ./Qwen3-VL-8B-Instruct/ with the actual path to your model and ./path/to/your/lora/adapter with the path to your LoRA adapter.

Additional Tips for Troubleshooting

Check CUDA and PyTorch Versions: Ensure that your CUDA and PyTorch versions are compatible with vLLM. Incompatible versions can lead to unexpected errors.
Monitor GPU Memory Usage: Keep an eye on your GPU memory usage. Running out of memory can sometimes manifest as cryptic assertion errors.
Simplify the Setup: Try running vLLM with a minimal configuration to isolate the issue. For example, start with a smaller model or disable LoRA to see if the base setup works.

Wrapping Up

The AssertionError in lora_shrink_op.py when serving Qwen3-VL-8B-Instruct with LoRA is a tough cookie, but it's definitely solvable. By understanding the root cause, exploring potential solutions, and leveraging the vLLM community, we can get this multimodal model up and running with LoRA. So, let's roll up our sleeves, dive into the code, and conquer this challenge! And remember, don't hesitate to share your own experiences and solutions – we're all in this together. Happy coding, folks! 🔥

Remember, this is just the beginning. As vLLM continues to evolve and support more complex models and scenarios, troubleshooting will remain an essential skill. Keep experimenting, keep learning, and keep pushing the boundaries of what's possible! Cheers, and happy model serving!