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[Docs] Remove references to VLLM_ATTENTION_BACKEND (#30564)

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Signed-off-by: Matthew Bonanni <mbonanni@redhat.com>
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docs/getting_started/quickstart.md
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@ -281,17 +281,27 @@ Alternatively, you can use the `openai` Python package:
Currently, vLLM supports multiple backends for efficient Attention computation across different platforms and accelerator architectures. It automatically selects the most performant backend compatible with your system and model specifications. Currently, vLLM supports multiple backends for efficient Attention computation across different platforms and accelerator architectures. It automatically selects the most performant backend compatible with your system and model specifications.
If desired, you can also manually set the backend of your choice by configuring the environment variable `VLLM_ATTENTION_BACKEND` to one of the following options: If desired, you can also manually set the backend of your choice using the `--attention-backend` CLI argument:
```bash
# For online serving
vllm serve Qwen/Qwen2.5-1.5B-Instruct --attention-backend FLASH_ATTN
# For offline inference
python script.py --attention-backend FLASHINFER
```
Some of the available backend options include:
- On NVIDIA CUDA: `FLASH_ATTN` or `FLASHINFER`. - On NVIDIA CUDA: `FLASH_ATTN` or `FLASHINFER`.
- On AMD ROCm: `TRITON_ATTN`, `ROCM_ATTN`, `ROCM_AITER_FA` or `ROCM_AITER_UNIFIED_ATTN`. - On AMD ROCm: `TRITON_ATTN`, `ROCM_ATTN`, `ROCM_AITER_FA` or `ROCM_AITER_UNIFIED_ATTN`.
For AMD ROCm, you can further control the specific Attention implementation using the following variables: For AMD ROCm, you can further control the specific Attention implementation using the following options:
- Triton Unified Attention: `VLLM_ROCM_USE_AITER=0 VLLM_V1_USE_PREFILL_DECODE_ATTENTION=0 VLLM_ROCM_USE_AITER_MHA=0` - Triton Unified Attention: Set the environment variables `VLLM_ROCM_USE_AITER=0 VLLM_ROCM_USE_AITER_MHA=0` and pass `--attention-config.use_prefill_decode_attention=false` as a CLI argument.
- AITER Unified Attention: `VLLM_ROCM_USE_AITER=1 VLLM_USE_AITER_UNIFIED_ATTENTION=1 VLLM_V1_USE_PREFILL_DECODE_ATTENTION=0 VLLM_ROCM_USE_AITER_MHA=0` - AITER Unified Attention: Set the environment variables `VLLM_ROCM_USE_AITER=1 VLLM_USE_AITER_UNIFIED_ATTENTION=1 VLLM_ROCM_USE_AITER_MHA=0` and pass `--attention-config.use_prefill_decode_attention=false` as a CLI argument.
- Triton Prefill-Decode Attention: `VLLM_ROCM_USE_AITER=1 VLLM_V1_USE_PREFILL_DECODE_ATTENTION=1 VLLM_ROCM_USE_AITER_MHA=0` - Triton Prefill-Decode Attention: Set the environment variables `VLLM_ROCM_USE_AITER=1 VLLM_ROCM_USE_AITER_MHA=0` and pass `--attention-config.use_prefill_decode_attention=true` as a CLI argument.
- AITER Multi-head Attention: `VLLM_ROCM_USE_AITER=1 VLLM_V1_USE_PREFILL_DECODE_ATTENTION=0 VLLM_ROCM_USE_AITER_MHA=1` - AITER Multi-head Attention: Set the environment variables `VLLM_ROCM_USE_AITER=1 VLLM_ROCM_USE_AITER_MHA=1` and pass `--attention-config.use_prefill_decode_attention=false` as a CLI argument.
!!! warning !!! warning
There are no pre-built vllm wheels containing Flash Infer, so you must install it in your environment first. Refer to the [Flash Infer official docs](https://docs.flashinfer.ai/) or see [docker/Dockerfile](../../docker/Dockerfile) for instructions on how to install it. There are no pre-built vllm wheels containing Flash Infer, so you must install it in your environment first. Refer to the [Flash Infer official docs](https://docs.flashinfer.ai/) or see [docker/Dockerfile](../../docker/Dockerfile) for instructions on how to install it.