Troubleshooting Memory Allocation Failures With AMD GPUs In Llama.cpp

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Hey guys, let's dive into this intriguing eval bug that's causing some headaches with memory allocation. It seems like when any layer of my large language model is placed on my second GPU, specifically an AMD Radeon RX 7900 XTX using official drivers, I'm running into memory allocation failures. This is a pretty specific issue, but let's break down what's happening and how to potentially troubleshoot it.

Understanding the Problem

So, what's the core of this memory allocation failure? It appears that the llama.cpp library, which is being used here, struggles to manage memory across multiple GPUs when even a single layer is assigned to the AMD GPU. The system in question has a powerful setup: an RTX 4060 Ti paired with the RX 7900 XTX. This combination should be a powerhouse for AI model inference, but the current configuration leads to crashes due to memory issues. The commands that consistently fail point to a problem with how memory is being distributed and accessed across the two GPUs, especially when the AMD GPU is involved.

Let's dig a bit deeper into what memory allocation actually means in this context. When you load a large language model like gpt-oss-120B, the model's parameters and intermediate computations need to be stored in GPU memory. The -ngl flag in llama-server dictates how many layers are offloaded to the GPU. When you split the layers across multiple GPUs using --tensor-split, you're essentially telling the system to divide the model across the available devices. The issue arises when the memory management for the AMD GPU isn't handled correctly, leading to an "ErrorOutOfDeviceMemory" error. This error indicates that the system couldn't allocate the requested amount of memory on the AMD GPU, causing the program to crash.

Analyzing the Successful and Failed Commands

To get a clearer picture, let's compare the successful and failed commands provided. The successful commands, such as:

vulkan\llama-server -m gpt-oss-120b-F16.gguf -c 16384 -ngl 8 --main-gpu 1 --tensor-split 6,0
vulkan\llama-server -m gpt-oss-120b-F16.gguf -c 16384 -ngl 6 --main-gpu 1 --tensor-split 1,0

These commands work because they primarily use the NVIDIA GPU (--main-gpu 1) and limit the AMD GPU's involvement (--tensor-split 6,0 or --tensor-split 1,0). The --tensor-split flag defines how the tensors are split across devices. For instance, 6,0 means 6 parts on the main GPU (NVIDIA) and 0 parts on the second GPU (AMD). This setup avoids the memory allocation issues on the AMD GPU.

Now, consider the failed commands, such as:

vulkan\llama-server -m gpt-oss-120b-F16.gguf -c 16384 -ngl 4 --main-gpu 1
vulkan\llama-server -m gpt-oss-120b-F16.gguf -c 16384 -ngl 6 --main-gpu 0 --tensor-split 1,0
vulkan\llama-server -m gpt-oss-120b-F16.gguf -c 16384 -ngl 7 --main-gpu 0 --tensor-split 1,0

These commands fail because they either attempt to offload layers to the AMD GPU without proper splitting (-ngl 4 --main-gpu 1) or try to use the AMD GPU as the main GPU (--main-gpu 0) while still assigning some tensors to it (--tensor-split 1,0). The consistent failure pattern suggests that the AMD GPU is having trouble handling even a small portion of the model when it comes to memory allocation.

Examining the Log Output

The log output provides crucial clues about the memory allocation problem. Let's focus on a typical failure log:

ggml_vulkan: Device memory allocation of size 2221155856 failed.
ggml_vulkan: Requested buffer size exceeds device memory allocation limit: ErrorOutOfDeviceMemory
ggml_gallocr_reserve_n: failed to allocate Vulkan1 buffer of size 2221155856
graph_reserve: failed to allocate compute buffers
lama_init_from_model: failed to initialize the context: failed to allocate compute pp buffers
common_init_from_params: failed to create context with model 'gpt-oss-120b-F16.gguf'
srv    load_model: failed to load model, 'gpt-oss-120b-F16.gguf'

This output clearly indicates that the Vulkan backend is failing to allocate memory on the AMD GPU (Vulkan1). The error message "Device memory allocation of size 2221155856 failed" is a direct indicator of the memory allocation problem. The system is requesting a buffer of approximately 2.2 GB, but the AMD GPU cannot fulfill this request, leading to the failure. The subsequent errors, such as "failed to allocate compute buffers" and "failed to initialize the context," are cascading effects of this initial memory allocation failure.

Potential Causes and Troubleshooting Steps

Alright, guys, let's brainstorm some potential causes for this memory allocation failure and walk through some troubleshooting steps. This is where we put on our detective hats and start digging!

Driver Issues

One of the most common culprits for GPU-related issues is the driver. Since the problem seems specific to the AMD GPU, it's a good starting point to examine the AMD drivers. Here's what we can do:

  1. Ensure you're using the latest official AMD drivers: Sometimes, older drivers have bugs or compatibility issues that can cause memory allocation problems. Visit the AMD support website and download the latest drivers for your RX 7900 XTX. It's super important to do a clean installation, which means completely removing the old drivers before installing the new ones. AMD provides a tool for this called AMD Cleanup Utility, and using it can help prevent conflicts.
  2. Consider rolling back to a previous driver version: Sometimes, the newest drivers can introduce new issues. If the problem started after a driver update, try rolling back to the previous version. AMD keeps an archive of older drivers on their website, so you can easily download and install a previous version.
  3. Check for driver conflicts: If you've recently installed other hardware or software, there might be conflicts with the AMD drivers. Look for any warning messages in the Device Manager or Event Viewer that might indicate a conflict. You can also try uninstalling recently installed software to see if that resolves the issue.

Insufficient Virtual Memory

Another potential cause is insufficient virtual memory. Virtual memory is a combination of your RAM and a portion of your hard drive that Windows uses as if it were RAM. If the system runs out of virtual memory, it can lead to memory allocation failures.

Here's how to check and adjust your virtual memory settings:

  1. Open System Properties: You can do this by searching for "System" in the Start menu and clicking on "System".
  2. Click on "Advanced system settings": This will open the System Properties window.
  3. Go to the "Advanced" tab: Under the "Performance" section, click on "Settings".
  4. Go to the "Advanced" tab again: Under the "Virtual memory" section, click on "Change".
  5. Adjust the settings: Uncheck the "Automatically manage paging file size for all drives" box. Select the drive where Windows is installed (usually C:), and then select "Custom size". Set the initial size and maximum size to values that are significantly larger than your RAM. A good starting point is to set both values to 1.5 to 2 times your RAM. For example, if you have 32GB of RAM, you could set the initial size to 49152 MB (48 GB) and the maximum size to 65536 MB (64 GB).
  6. Click "Set" and then "OK": Restart your computer for the changes to take effect.

Hardware Limitations or Faults

While less likely, there's always a possibility of hardware limitations or faults. This could be related to the AMD GPU itself or the system's power supply. Here's what to consider:

  1. Check GPU Memory: Ensure that the AMD GPU has sufficient memory to load the model. The RX 7900 XTX has a substantial amount of VRAM, but it's still worth verifying that it's not being exhausted by other applications or processes.
  2. Monitor GPU Temperature and Power: Overheating or insufficient power can cause GPU instability and memory allocation failures. Use monitoring software like GPU-Z or AMD Adrenalin to check the GPU temperature and power consumption during model loading and inference. If the temperature is consistently high or the power draw exceeds the card's specifications, it could indicate a hardware issue.
  3. Test with a Different Power Supply: If you suspect the power supply might be the issue, try testing with a different power supply that meets the system's requirements. A failing or underpowered power supply can cause all sorts of weird issues, including memory allocation failures.
  4. Run GPU Stress Tests: Use tools like FurMark or 3DMark to stress-test the AMD GPU. These tests can help identify hardware instability or faults that might be contributing to the problem. If the GPU fails the stress tests, it could indicate a hardware issue that needs to be addressed.

Software Conflicts and Configuration Issues

Sometimes, the issue might stem from conflicts between different software components or incorrect configurations within the llama.cpp library itself. Here's how we can investigate:

  1. Review llama.cpp Parameters: Double-check the command-line parameters you're using with llama-server. Ensure that the -ngl and --tensor-split values are correctly configured for your setup. Experiment with different values to see if that resolves the issue. For instance, try reducing the number of layers offloaded to the GPU (-ngl) or adjusting the tensor split ratios.
  2. Check Vulkan Configuration: Vulkan is the graphics API used by llama.cpp for GPU acceleration. Ensure that Vulkan is properly configured on your system and that the AMD GPU is being correctly detected. You can use tools like Vulkaninfo to check the Vulkan configuration and verify that both GPUs are listed.
  3. Disable Conflicting Software: Certain software, such as GPU monitoring tools or overclocking utilities, can sometimes interfere with memory allocation. Try disabling these tools temporarily to see if that resolves the issue.
  4. Test with a Minimal Configuration: Try running llama-server with a minimal configuration to isolate the problem. For example, you could start by offloading only a few layers to the GPU or running the server without any additional command-line parameters. If the issue disappears with a minimal configuration, you can gradually add more features to identify the specific setting that's causing the problem.

Code-Level Bugs in llama.cpp

It's also possible that there's a bug within the llama.cpp library itself, particularly in how it handles memory allocation across multiple GPUs with AMD drivers. If you've exhausted all other troubleshooting steps, this might be the most likely explanation.

  1. Check for Known Issues: Search the llama.cpp GitHub repository for similar issues. There might be existing bug reports or discussions related to memory allocation problems with AMD GPUs. This can give you insights into whether the issue is already known and if there are any workarounds available.
  2. Update llama.cpp: Make sure you're using the latest version of llama.cpp. Bug fixes and performance improvements are often included in new releases, so updating might resolve the issue. In this case, the user is already on version 6096 (fd1234cb), but it's always worth checking for even newer commits.
  3. Report the Bug: If you can't find a solution and suspect a bug in llama.cpp, report the issue on the GitHub repository. Provide detailed information about your system configuration, the commands you're using, and the error messages you're seeing. This helps the developers understand the problem and work on a fix.

Wrapping Up

Alright, guys, that's a comprehensive look at this memory allocation failure issue with llama.cpp and AMD GPUs. We've covered a range of potential causes, from driver problems to hardware limitations and even code-level bugs. By systematically working through the troubleshooting steps, you should be able to narrow down the cause and hopefully find a solution.

Remember, dealing with complex issues like this can be frustrating, but breaking down the problem into smaller, manageable steps is key. And don't hesitate to reach out to the llama.cpp community or AMD support for help. They might have additional insights or solutions that can get you back on track. Happy troubleshooting!