vllm
https://github.com/vllm-project/vllm
Python
A high-throughput and memory-efficient inference and serving engine for LLMs
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- Issues
- [Feature]: Precise model device placement
- [Usage]: How to use Multi-instance in Vllm? (Model replication on multiple GPUs)
- [Feature]: Return hidden states (in progress?)
- [Bug]: As V100 does not support FlashAttention, it is not possible to run the gemma model, hopefully it can support the xformers way to run it
- [Feature]: ROPE scaling supported by vLLM gemma2
- [Bug]: Batch expansion doesn't work with lora
- [Bug]: When running gemma2 7b, an error is reported [rank0]: RuntimeError: CUDA error: CUBLAS_STATUS_EXECUTION_FAILED when calling `cublasGemmEx( handle, opa, opb, m, n, k, &falpha, a, CUDA_R_16BF, lda, b, CUDA_R_16BF, ldb, &fbeta, c, CUDA_R_16BF, ldc, compute_type, CUBLAS_GEMM_DEFAULT_TENSOR_OP)` Set up according to the prompts: os.environ['VLLM_ATTENTION_BACKEND'] = 'FLASHINFER' print("Environment variable set for VLLM_ATTENTION_BACKEND:", os.getenv('VLLM_ATTENTION_BACKEND'))
- [Bug]: CUDA error when using multiple GPUs
- [Feature]: Model ChatGLMForCausalLM does not support LoRA, but LoRA is enabled.
- [Performance]: the performance with chunked-prefill-enabled is lower than default
- Docs
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