Qwen3-VL ACL Graph Compilation Failed: Debugging Guide

Hey everyone! Running into errors while trying to compile your Qwen3-VL ACL graph can be frustrating, but don't worry, we're here to help you understand what might be going wrong and how to fix it. This article dives into the common causes of this issue, provides practical solutions, and offers tips for optimizing your setup. So, let's get started and get your Qwen3-VL model up and running!

Understanding the Issue: ACL Graph Compilation Failure

Before diving into the solutions, let's first understand the problem. The ACL (Ascend Computing Language) graph compilation failure typically occurs when the system is unable to translate the computational graph of your model into a format that can be efficiently executed on the Ascend hardware. This process involves several steps, and a failure at any point can lead to the error. The error messages you're seeing, such as "ACLgraph has insufficient available streams," are clues to the underlying issue. Specifically, this error indicates that the system does not have enough resources (streams) to handle the graph compilation process for the configured sizes. Let's break down the key components and potential bottlenecks involved.

Key Components and Potential Bottlenecks:

• Ascend Hardware and Drivers: The Ascend hardware requires specific drivers and libraries to function correctly. Outdated or incompatible drivers can cause compilation failures. Make sure you have the latest compatible drivers installed.
• vLLM and vLLM Ascend: These are the libraries used for serving large language models on Ascend. If the versions are not compatible or if there are bugs in the libraries, it can lead to compilation issues. Ensure you are using stable and compatible versions of vLLM and vLLM Ascend.
• ACL Graph Capture: This is the process of capturing the computational graph for optimization. The system needs sufficient streams (resources) to capture the graph for different input sizes. If the number of available streams is less than required, the capture will fail. The error log highlights this with messages like "ACLgraph has insufficient available streams."
• HcclAllreduce Operator: This operator is used for inter-device communication during distributed training or inference. Errors related to HcclAllreduce often indicate issues with the communication setup between the devices, such as network connectivity or incorrect configurations.
• Memory Allocation: Insufficient memory can also cause compilation failures. The system needs enough memory to load the model, capture the graph, and perform the compilation. Check your memory utilization and ensure you have enough available memory.

Diagnosing the Qwen3-VL ACL Graph Compilation Error

Before attempting any fixes, it's important to accurately diagnose the issue. Here’s a step-by-step guide to help you pinpoint the cause of the compilation failure:

1. Review the Error Logs: The error logs provide valuable information about the failure. Look for specific error messages, tracebacks, and any warnings that might indicate the root cause. The original error log mentions several issues, including:
   • ACLgraph has insufficient available streams: This suggests a resource limitation during graph capture.
   • RuntimeError: The Inner error is reported as above: This indicates a deeper issue within the ACL compilation process.
   • Transport init error: This points to problems with inter-device communication.
2. Check the Environment Variables: Environment variables play a crucial role in configuring the runtime environment. Ensure that the necessary variables are set correctly. Here are some key variables to check:
   • ASCEND_VISIBLE_DEVICES: This variable specifies the IDs of the Ascend devices that vLLM can use. Make sure it is set correctly to match the available devices.
   • LD_LIBRARY_PATH: This variable should include the paths to the Ascend libraries. Verify that the paths are correct and that the libraries are accessible.
   • ASCEND_TOOLKIT_HOME and ASCEND_HOME_PATH: These variables should point to the Ascend toolkit installation directory. Confirm that the paths are accurate.
   • PYTORCH_NPU_ALLOC_CONF: This variable configures memory allocation for the NPU. Check the settings to ensure they are appropriate for your model and hardware.
3. Verify Hardware and Software Compatibility: Ensure that your hardware and software components are compatible. This includes:
   • Ascend Drivers and Toolkit: Check that the installed Ascend drivers and toolkit versions are compatible with your hardware.
   • PyTorch and Torch-NPU: Verify that the PyTorch version and the Torch-NPU plugin are compatible with each other and with the Ascend toolkit.
   • vLLM and vLLM Ascend: Ensure that you are using compatible versions of vLLM and vLLM Ascend.
4. Inspect Network Configuration: If the error logs indicate transport initialization issues, check your network configuration. This is particularly important in multi-device setups. Ensure that:
   • The network interfaces are correctly configured.
   • There are no firewall rules blocking communication between the devices.
   • The devices can communicate with each other using the specified IP addresses.
5. Review the vLLM Configuration: The command-line arguments used to launch vLLM can significantly impact performance and stability. Double-check the following parameters:
   • --tensor-parallel-size: This should match the number of devices you are using for tensor parallelism.
   • --gpu-memory-utilization: This parameter controls how much GPU memory vLLM can use. If it’s set too high, it can lead to out-of-memory errors.
   • --max-model-len: This sets the maximum length of the input sequence. Ensure it is appropriate for your model and hardware.

By following these diagnostic steps, you can narrow down the potential causes of the compilation failure and identify the most appropriate solutions.

Solutions for Qwen3-VL ACL Graph Compilation Errors

Once you've identified the root cause of the issue, you can apply the following solutions. These are tailored to address the common problems encountered during Qwen3-VL ACL graph compilation.

1. Manually Configure Compilation Sizes

One of the most common recommendations in the error logs is to manually configure the compilation sizes. This approach limits the number of graph capture sizes, reducing the resource requirements and potentially resolving the "insufficient streams" error. To do this, you can use the compilation_config parameter. Here's how:

• Modify the vLLM Serve Command: When launching vLLM, add the --compilation-config argument followed by a JSON string that specifies the cudagraph_capture_sizes. For example:

  vllm serve /mnt/hw910test-jfs/models/qwen/Qwen3-VL-235B-A22B-Instruct \
      --served-model-name qwen3vl --port 8080 --max-model-len 40960 \
      --max-num-seqs 256 --tensor-parallel-size 16 --gpu-memory-utilization 0.85 \
      --reasoning-parser qwen3 \
      --compilation-config '{