Nvidia T4 --cpu-offload-gb error

General
1

I am running vllm 0.8.4 with two Nvidia T4 in a Ubuntu VM environment, it tells me because my cuda on T4 is below 8.0, it will run the V0 engine. when I use --cpu-offload-gb, it will give me an error message saying variable and tensor have incompatible type.

does anyone have experience with this?

2

It seems likely that the issue you’re encountering with the --cpu-offload-gb option is related to the compatibility of data types between the variable and tensor. This could be due to the fact that the T4 GPU has a compute capability of 7.5, which may not fully support certain features or optimizations that require a higher compute capability, such as bfloat16 operations. According to the vLLM documentation, the T4 GPU is supported, but certain features may be limited due to its compute capability.

My understanding is that the error message about incompatible types might be related to the way vLLM handles data types when offloading to the CPU. The --cpu-offload-gb option is intended to offload some of the model’s memory requirements to the CPU, effectively increasing the available memory for the model. However, this requires a fast CPU-GPU interconnect and proper handling of data types between the CPU and GPU. If the data types are not compatible, it could lead to errors during execution.

Would you like more detail on potential solutions or workarounds for this issue?

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3

yes if you have other ways to achieve it would be great.

4

To address the issue with --cpu-offload-gb and achieve better memory management on your Nvidia T4 GPUs, you might consider the following approaches:

  1. Quantization: Use model quantization to reduce the memory footprint. Quantization can significantly decrease the model size by using lower precision data types, such as int8 or float16, instead of float32. This can help fit larger models into the available GPU memory. You can specify the quantization method using the quantization parameter when initializing the model in vLLM.

  2. Tensor Parallelism: If your model is too large to fit into a single GPU, you can use tensor parallelism to split the model across multiple GPUs. This can be done by setting the tensor_parallel_size parameter to the number of GPUs you have. This approach allows you to leverage the combined memory of both GPUs.

  3. Reduce Context Length: Lower the max_model_len parameter to reduce the context length of the model. This can help decrease the memory usage during inference.

  4. Adjust GPU Memory Utilization: Use the --gpu-memory-utilization parameter to control how much of the GPU memory is used by vLLM. Setting this to a lower value can help prevent out-of-memory errors by leaving some buffer space.

  5. Use a Different Backend: If the issue persists, consider using a different backend that might be more compatible with your hardware setup. For example, using a different attention backend or disabling certain optimizations that are not supported on your hardware.

Here’s an example of how you might configure vLLM with some of these options:

from vllm import LLM

llm = LLM(
    model="your-model-name",
    quantization="int8",  # or another supported quantization method
    tensor_parallel_size=2,  # Use both GPUs
    max_model_len=2048,  # Adjust as needed
    gpu_memory_utilization=0.8  # Adjust to leave some buffer
)

If you need further assistance or specific code examples, feel free to ask!

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5

this is strange. I saw other people talked about using T4 on vLLM with cpu-offload-gb option; BTW, I get the same error message if I use the swap-space option

6

It seems likely that the issue you’re experiencing with both --cpu-offload-gb and --swap-space options on a T4 GPU is related to the handling of memory and data types between the CPU and GPU. The T4 GPU, with its compute capability of 7.5, may have limitations when it comes to certain operations or optimizations that require higher compute capabilities, such as bfloat16 operations.

Here are a few steps you can take to potentially resolve the issue:

  1. Check Data Types: Ensure that the data types used in your model are compatible with the T4 GPU. You might need to explicitly set the data type to float16 or int8 if bfloat16 is causing issues.

  2. Update Drivers and Libraries: Make sure that your Nvidia drivers and CUDA libraries are up to date. Sometimes, compatibility issues can be resolved with the latest updates.

  3. Use Tensor Parallelism: If the model is too large, consider using tensor parallelism to distribute the model across multiple GPUs. This can help manage memory usage more effectively.

  4. Reduce Model Size: If possible, try using a smaller version of the model or reducing the context length (max_model_len) to decrease memory usage.

  5. Experiment with Different Configurations: Try different combinations of --cpu-offload-gb, --swap-space, and --gpu-memory-utilization to find a configuration that works for your setup.

If these steps do not resolve the issue, it might be helpful to check the vLLM GitHub issues page for any updates or similar issues reported by other users. Additionally, you can consider reaching out to the vLLM community for further assistance.

Would you like more detailed guidance on any of these steps?

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