vlm_npu

Training VLMs with GRPO on NPU:

In this instruction, we will introduce how to train VLMs with GRPO on Ascend NPU.

Examples

This directory contains examples for training vision-language models on NPU with GRPO:

1. Qwen2.5-VL-3B - qwen2_5_vl_3b_geometry3k_grpo.* - GRPO training on Geometry3K with Qwen2.5-VL-2B model dataset
2. Qwen3-VL-2B - qwen3_vl_2b_geometry3k_grpo.* - GRPO training on Geometry3K dataset with Qwen3-VL-2B model

Running the Examples

Qwen2.5-VL-3B

bash examples/vlm_npu/qwen2_5_vl_3b_geometry3k_grpo.sh

Qwen3-VL-2B

bash examples/vlm_npu/qwen3_vl_2b_geometry3k_grpo.sh

Both examples use the same Geometry3K dataset and GRPO training configuration, but with different model architectures. The Qwen3-VL-2B model is smaller and may be more suitable for environments with limited resources.

Testing Qwen2.5-VL-3B

Hardware

The following hardware configuration has been extensively tested:

• NPU: 16x NPU per node
• CPU: 64 cores per node
• Memory: 1TB per node
• Network: RoCE 3.2 Tbps
• Storage:
  • 1TB local storage for single-node experiments
  • 10TB shared storage (NAS) for distributed experiments

Key Contributions

• Trained Qwen2.5VL-3B-instruct model upto 70 epochs with (4 cards+ 4 cards) train-infer configuration. Took around 19hr to finish full training.
• Trained model is tested with more than one benchmark using VLMEvalKit.

Results:

We trained Qwen2.5-VL-3B for 70 epochs on Geometry3K and evaluated the checkpoints using VLMEvalKit on out of distribution tasks such as MathVision, MathVista, and LogicVista. The training was performed on both NPU and GPU and results are as follows:

Method	LogicVista	MathVision_mini	MathVista_mini	Avg.
Base Model	31.0	18.3	52.3	33.8
GRPO-GPU	35.4	20.9	55.9	37.4
GRPO-NPU	35.3	20.5	54.7	36.8