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]: Return hidden states (in progress?)
- [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'))
- [Model] Implement DualChunkAttention for Qwen2 Models
- [Feature]: deepseek-v2 awq support
- [Feature]: multi-lora support older nvidia gpus.
- [Hardware][Nvidia][Core][Feature] new feature add: vmm(virtual memory manage) kv cache for nvidia gpu
- [Feature]: Add readiness endpoint /ready and return /health earlier (vLLM on Kubernetes)
- [Bug]: call for stack trace for "Watchdog caught collective operation timeout"
- [Kernel] Unify the kernel used in flash attention backend
- [Bug]: benchmark_serving.py cannot calculate Median TTFT correctly
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- Python not yet supported