Roadmap

[tjtanaa]

1. Port vllm/main feature to ROCm

• Support Llama/Llama-2 models for v0.2.x
• Support SqueezeLLM
• Support YARN
• Merge into upstream vllm ([Continuation] Merge EmbeddedLLM/vllm-rocm into vLLM main vllm-project/vllm#1836)
• Look into supporting multi LORA on ROCm (Add multi-LoRA support vllm-project/vllm#1804)
• Support AQLM Kernel (https://github.com/EmbeddedLLM/vllm/tree/aqlm-rocm)
• Support GGML Kernel (https://github.com/EmbeddedLLM/vllm/tree/ggml-rocm)
• Upstream Cross-Attention kernel to support Llama 3.2 Vision Model
• Prompt https://github.com/LMCache/LMCache

2. Benchmark

• Real-world Distribution benchmark https://blog.vllm.ai/2024/10/23/vllm-serving-amd.html
• Long-Input-Long-Output benchmarking.

[HAN-oQo]

Hi, @tjtanaa
I wonder how the roadmap is going on.
I quite excited to use AWQ quantized format, when can it be supported?

[tjtanaa]

Hi, @tjtanaa I wonder how the roadmap is going on. I quite excited to use AWQ quantized format, when can it be supported?

@HAN-oQo Hi, vLLM authors said they are working on more efficient AWQ implementation on triton. So, we will address the AWQ on ROCm after they have released their new kernel.

[HAN-oQo]

Thank you for answer! @tjtanaa
I also wonder why safetensor format is not supported, and do you have a plan to support it!

Thank you for offering the nice project.

[tjtanaa]

Thank you for answer! @tjtanaa I also wonder why safetensor format is not supported, and do you have a plan to support it!

Thank you for offering the nice project.

@HAN-oQo The loading of safetensors is buggy on ROCm platform. The memory management during loading of safetensors might be causing the issue on ROCm platform. It often encounters this issue when tensor-parallelism is larger than 1; however, loading from pt is totally fine.