Fix: FileNotFoundError With Two GPUs And Disk Offload

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Addressing FileNotFoundError: [Errno 2] No Such File or Directory with vLLM and Two GPUs

Experiencing the dreaded FileNotFoundError: [Errno 2] when trying to offload to disk using two GPUs in vLLM? You're not alone! This issue often crops up when testing the LMCache disk offload function, and it can be a real head-scratcher. Let's dive into the details, figure out what's going on, and how to potentially fix it.

Understanding the Issue: The Heart of the Problem

At the core of the problem is a FileNotFoundError, which, as the name suggests, means the system can't find a specific file it's looking for. In this case, it's the vllm@-workspace*.pt file. This file is related to the cached data when using LMCache with vLLM, especially when offloading the key-value (KV) cache to disk. When you're running vLLM with a single GPU, things might work smoothly. However, when you introduce a second GPU, the system might stumble, leading to this error.

The error message itself, FileNotFoundError: [Errno 2] No such file or directory: '/workspace/repo/llm_startup/kv_cache/vllm@-workspace-models-Qwen-Qwen3-30B-A3B@2@0@-9d3bc4495bffe4.pt', gives us a crucial clue: the system is looking for a .pt file (likely a PyTorch file) in a specific directory within your workspace. The naming convention vllm@-workspace... suggests this is a temporary file generated by vLLM for caching purposes. The fact that it can't be found indicates a potential issue with how these files are being created, accessed, or managed when using multiple GPUs.

The traceback provides further insight, pinpointing the error's origin within the LMCache and vLLM interaction. Specifically, the issue arises during the remove operation in the local_disk_backend.py file of LMCache. This suggests that the system is trying to remove a cached file, but it can't find it, leading to the error. This often happens when using disk offload with two GPUs because the file management and synchronization between the GPUs might not be perfectly coordinated, resulting in one GPU trying to access or delete a file that the other GPU hasn't finished writing or is still using.

Replicating the Bug: Steps to Trigger the Error

To really understand the issue, let's walk through the steps to reproduce it. This will help you confirm if you're facing the same problem and allow you to test any potential fixes.

  1. Start a Docker Container: This ensures a consistent environment for running vLLM and LMCache.

  2. Initiate vLLM Serving: This is where the magic happens. The following command is used to start the vLLM service with specific configurations:

    export LMCACHE_LOCAL_DISK='file:///workspace/repo/llm_startup/kv_cache/' && export LMCACHE_LOCAL_CPU='False' && export LMCACHE_MAX_LOCAL_DISK_SIZE='50' && export LMCACHE_MAX_LOCAL_CPU_SIZE='5' && export LMCACHE_CHUNK_SIZE='256' && vllm serve --max-log-len=200 --model=/workspace/models/Qwen/Qwen3-30B-A3B --served-model-name=atom --gpu-memory-utilization=0.9 --port=8011 --root-path=/openai --trust-remote-code --enable-auto-tool-choice --tool-call-parser=hermes --kv-transfer-config '{"kv_connector": "LMCacheConnectorV1", "kv_role": "kv_both"}' -tp=2 --no-enable-prefix-caching

    Let's break down this command:

    • export LMCACHE_LOCAL_DISK='file:///workspace/repo/llm_startup/kv_cache/': This sets the directory where LMCache will store cached data on disk.
    • export LMCACHE_LOCAL_CPU='False': This disables caching on the CPU.
    • export LMCACHE_MAX_LOCAL_DISK_SIZE='50': This sets the maximum disk space (in GB) that LMCache can use.
    • export LMCACHE_MAX_LOCAL_CPU_SIZE='5': This sets the maximum CPU space (in GB) that LMCache can use.
    • export LMCACHE_CHUNK_SIZE='256': This sets the chunk size for data transfer.
    • vllm serve ...: This is the core command to start the vLLM service.
    • --max-log-len=200: This limits the maximum length of the log.
    • --model=/workspace/models/Qwen/Qwen3-30B-A3B: This specifies the model to be used (Qwen3-30B-A3B in this case).
    • --served-model-name=atom: This sets the name of the served model.
    • --gpu-memory-utilization=0.9: This sets the GPU memory utilization to 90%.
    • --port=8011: This sets the port for the service.
    • --root-path=/openai: This sets the root path for the API.
    • --trust-remote-code: This allows loading code from the model's repository.
    • --enable-auto-tool-choice: This enables automatic tool choice.
    • --tool-call-parser=hermes: This sets the tool call parser.
    • --kv-transfer-config '{"kv_connector": "LMCacheConnectorV1", "kv_role": "kv_both"}': This configures the KV cache transfer using LMCache.
    • -tp=2: This is the key part that triggers the issue – it specifies the use of two GPUs (tensor parallelism).
    • --no-enable-prefix-caching: This disables prefix caching.

  3. Run a Benchmark Script: This simulates workload and triggers the caching mechanism.

  4. Observe the Error: Keep an eye out for the FileNotFoundError in the logs, similar to the one described earlier.

Dissecting the Error Log: What the Traceback Tells Us

The error log is your friend! It contains valuable information about what went wrong. Let's break down the key parts of the traceback provided:

(Worker_TP0 pid=13601) FileNotFoundError: [Errno 2] No such file or directory: '/workspace/repo/llm_startup/kv_cache/vllm@-workspace-models-Qwen-Qwen3-30B-A3B@2@0@-9d3bc4495bffe4.pt'
...
(Worker_TP0 pid=13601) ERROR 11-10 19:31:06 [multiproc_executor.py:671] ...
(Worker_TP0 pid=13601) ERROR 11-10 19:31:06 [core.py:710] RuntimeError: Worker failed with error '[Errno 2] No such file or directory: ...', please check the stack trace above for the root cause
  • FileNotFoundError: [Errno 2] ...: This is the primary error, confirming the missing file.
  • /workspace/repo/llm_startup/kv_cache/vllm@-workspace-models-Qwen-Qwen3-30B-A3B@2@0@-9d3bc4495bffe4.pt: This is the path to the missing file. Notice the @2 in the filename – this likely indicates the use of two GPUs.
  • (Worker_TP0 pid=13601): This tells us that the error occurred in a worker process related to tensor parallelism (TP0).
  • [multiproc_executor.py:671]: This points to the multiproc_executor.py file, which is responsible for managing multiprocessing in vLLM.
  • [core.py:710] RuntimeError: ... Worker failed with error ...: This indicates that a worker process failed, leading to a runtime error. The message suggests checking the stack trace for the root cause.

The traceback further reveals that the error occurs during the wait_for_save operation within lmcache/integration/vllm/vllm_v1_adapter.py. This function is responsible for ensuring that the KV cache is properly saved before proceeding. The error then bubbles down through several layers of LMCache, including the storage_manager.py and local_disk_backend.py, eventually leading to the os.remove(path) call, which fails because the file doesn't exist.

Potential Causes and Solutions: Cracking the Code

So, what's causing this file-not-found mystery? Here are some potential culprits and how to tackle them:

  1. Race Condition: This is a common suspect in multi-GPU scenarios. Imagine this: one GPU starts writing the .pt file to disk, while the other GPU, thinking the file is ready, tries to remove or access it. This can happen if the synchronization between the GPUs isn't perfect.

    • Solution: Implementing proper locking mechanisms or synchronization primitives within LMCache could prevent this race condition. This would ensure that one GPU waits for the other to finish writing before attempting to remove the file. Consider using file locking mechanisms or shared memory synchronization primitives to ensure that file operations are properly synchronized between the two GPU processes.

  2. File Permissions: It's possible that the user running the vLLM service doesn't have the necessary permissions to create, modify, or delete files in the specified kv_cache directory.

    • Solution: Double-check the file permissions of the kv_cache directory. Make sure the user running vLLM has read, write, and execute permissions. You can use the chmod command to modify permissions if needed (e.g., chmod 777 /workspace/repo/llm_startup/kv_cache). Ensure that the user account running the vLLM service has the necessary permissions to read, write, and delete files in the cache directory. Use ls -l to check existing permissions and chown to change file ownership if necessary.

  3. Disk Space Issues: If the disk is running low on space, LMCache might be trying to evict cached files to make room for new data. If this eviction process is not handled correctly in a multi-GPU setup, it could lead to files being removed prematurely.

    • Solution: Monitor disk space usage. If the disk is consistently full, consider increasing the disk space allocated to the container or the host system. Also, review the LMCache configuration (LMCACHE_MAX_LOCAL_DISK_SIZE) to ensure it's appropriately sized. Monitor disk space utilization to ensure sufficient space for cache operations. Adjust the LMCACHE_MAX_LOCAL_DISK_SIZE environment variable to increase the maximum disk space available for caching if needed.

  4. Configuration Mismatch: It's possible that there's a mismatch in the configuration between vLLM and LMCache when using multiple GPUs. For example, the caching directory might not be properly shared or synchronized between the GPU processes.

    • Solution: Review the vLLM and LMCache configurations, especially the environment variables related to caching (LMCACHE_LOCAL_DISK, etc.). Ensure that these settings are consistent across all GPU processes. Verify that the caching directory is accessible and properly configured for all GPU processes. Check environment variables related to caching (e.g., LMCACHE_LOCAL_DISK) and ensure they are correctly set for multi-GPU usage.

  5. LMCache Bug: It's always possible that there's a bug in LMCache itself, specifically related to multi-GPU disk offloading.

    • Solution: Check the LMCache issue tracker on GitHub (or wherever the project is hosted) to see if anyone else has reported a similar issue. If so, there might be a known workaround or a fix in a newer version. If not, consider creating a new issue, providing detailed information about your setup and the error you're seeing. Check the LMCache issue tracker or forums for similar reports and potential workarounds. Consider filing a bug report with detailed information about your setup and the error logs.

  6. vLLM and LMCache Version Incompatibility: In some cases, specific versions of vLLM and LMCache may not be fully compatible, leading to unexpected errors.

    • Solution: Ensure that you are using compatible versions of vLLM and LMCache. Check the documentation or release notes for both projects to identify any known compatibility issues. Try downgrading or upgrading either vLLM or LMCache to a version that is known to work well together. Review the compatibility matrix or release notes for vLLM and LMCache to identify recommended versions. Try using a combination of vLLM and LMCache versions known to be stable and compatible.

Taming the Beast: Practical Steps to Resolve the Error

Now that we've explored the potential causes, let's get practical. Here's a step-by-step approach to tackle this FileNotFoundError:

  1. Double-Check the Basics: Start by verifying the simple things. Is the kv_cache directory actually created? Does it exist at the specified path? Do the user running vLLM have the necessary permissions? A quick check can save you hours of debugging.
  2. Examine the Logs Closely: The error logs are your best friend. Scrutinize the traceback, looking for clues about the exact point of failure. Pay attention to any messages related to file access, caching, or multi-GPU synchronization.
  3. Simplify the Setup: Try to isolate the issue. Can you reproduce the error with a smaller model? With a simpler benchmark script? By reducing the number of GPUs to one (to see if the problem disappears)? This can help you narrow down the cause.
  4. Implement Locking (If Applicable): If you suspect a race condition, explore ways to implement file locking or synchronization mechanisms in LMCache. This might involve modifying the LMCache code itself, which is an advanced step, but it can be effective.
  5. Monitor Disk Space: Keep an eye on disk usage, especially when running long benchmarks. If the disk fills up, that's a strong indicator of a space-related issue.
  6. Consult the Community: Don't be afraid to ask for help! Post your issue on the LMCache or vLLM forums, Stack Overflow, or relevant online communities. Be sure to include detailed information about your setup, the error you're seeing, and the steps you've taken to reproduce it.
  7. Consider a Minimal Reproducible Example (MRE): When seeking help, providing a minimal, self-contained example that demonstrates the issue is invaluable. This allows others to quickly understand the problem and offer solutions. Create a simplified version of your setup that reproduces the error, and share it with the community.

The Expected Outcome: Smooth Multi-GPU Disk Offloading

The goal here is to achieve stable and efficient disk offloading with multiple GPUs in vLLM. When everything is working correctly, you should be able to run your benchmark scripts without encountering the FileNotFoundError. LMCache should seamlessly manage the KV cache, offloading data to disk as needed, and the two GPUs should work in harmony to accelerate inference.

By systematically investigating the potential causes and applying the solutions outlined above, you'll be well-equipped to conquer this error and unlock the full potential of multi-GPU vLLM with disk offloading. Remember, debugging is a journey, and each error you encounter is a learning opportunity. So, stay curious, keep experimenting, and don't give up!

This comprehensive guide should help you tackle the FileNotFoundError and get your vLLM setup running smoothly. Good luck, and happy coding!