Comments (1)
The triton kernel is tailored to AMD at the moment. I would recommend setting VLLM_ATTENTION_BACKEND=xformers instead
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Related Issues (20)
- [RFC]: Encoder/decoder models & feature compatibility HOT 3
- [Usage]: how to use LLM class with AsyncLLMEngine HOT 2
- [Installation]: git clone cutlass fails HOT 7
- [Misc]: Improving VLLM KVCACHE Transfer Efficiency with NCCL P2P Communication HOT 1
- [Feature]: Support block manager v2 for chunked prefill HOT 3
- [Bug]: Phi-3-vision: ERROR 08-09 11:41:40 async_llm_engine.py:56] RuntimeError: stack expects each tensor to be equal size, but got [1933, 4096] at entry 0 and [2509, 4096] at entry 1 HOT 14
- [Bug]: Tensor Parallel > 1 causes desc_act=True GPTQ models to give bad output on ROCm
- [Usage]: Getting empty text using llm.generate of mixtral-8X7b-Instruct AWQ model HOT 1
- [Bug]: prefill/prefix FP8 triton kernel for opt-125m - an illegal memory access was encountered
- [Performance]: vllm inference in CPU instance has generation < 10 tokens / second HOT 2
- [Bug]: LLaMa 3.1 8B/70B/405B all behave poorly and differently using completions API as compared to good chat API HOT 16
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- [Bug]: `facebook/chameleon-30b` triggers assertion error while loading weights HOT 2
- [Bug]: Successfully deployed embedding model 'gte-Qwen2-7B-instruct', but got "TypeError: 'async for' requires an object with __aiter__ method, got coroutine" when calling it HOT 3
- [Bug]: guided regex (using outlines and lm format enforcer) return bad error description on invalid regex
- [Bug]: use `openai_vision_api_client.py` get error HOT 1
- [Misc]: Cross-attention QKV computation is inefficient HOT 1
- [Bug]: Bug in vllm/csrc/quantization/awq /gemm_kernels.cu
- [Bug]: Bug in quantization/awq /gemm_kernels.cu gemm_forward_4bit_cuda_m16nXk32 More result have been write HOT 3
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