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README.md
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运行于【海光 DCU】系列算力卡的【文本生成】引擎，基于 vLLM 引擎进行架构特别适配优化，支持 Qwen、DeepSeek、Llama 等最新开源模型。
因具体模型之间的启动方式和具体镜像会有略微差别，请详细查看 `/enginex` 目录下各个支持模型的启动测试方式。
源镜像：harbor.sourcefind.cn:5443/dcu/admin/base/vllm:0.9.2-ubuntu22.04-dtk25.04.2-1226-das1.7-py3.10-20251226
## 可支持模型列表
可在项目文件夹 `/enginex` 下查看具体可支持模型文件的运行方式。
支持模型列表：
- jinaai/jina-embeddings-v3
- deepseek-ai/DeepSeek-R1
- Qwen/QwQ-32B
- deepseek-ai/DeepSeek-V3
- deepseek-ai/DeepSeek-V3.1
- LLaMA_Fastchat_pytorch
- Qwen/Qwen3-30B-A3B
- Qwen-7B_fastllm
- ChatGLM-6B_fastllm
- ZhipuAI/ChatGLM-6B
- Shanghai_AI_Laboratory/internlm-chat-7b
- ZhipuAI/glm-4v-9b
- ZhipuAI/GLM-4-9B-0414
- deepseek-ai/DeepSeek-Coder-V2-Base
- openai-community/gpt2
- ZhipuAI/chatglm2-6b
- Qwen/Qwen-7B-Chat
- baichuan-inc/Baichuan2-13B-Chat
- ZhipuAI/chatglm3-6b
- deepseek-ai/DeepSeek-V2
- Qwen/Qwen2.5-Omni-7B
- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
- deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
- deepseek-ai/DeepSeek-R1-Distill-Llama-8B
- deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
- deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
- deepseek-ai/DeepSeek-R1-Distill-Llama-70B
- LLM-Research/Meta-Llama-3-8B-Instruct
- Qwen/Qwen1.5-14B-Chat
- Qwen/Qwen2-7B
- Qwen/Qwen3-Embedding-0.6B
- baichuan-inc/baichuan-7B
- openai-community/gpt2
- gaodema/GME-Qwen2-VL
- OpenBMB/MiniCPM3-4B
- ZhipuAI/glm-10b-chinese
- 01ai/Yi-6B-Chat
- 01ai/Yi-34B-Chat
- ZhipuAI/glm-4-9b-chat
- deepseek-ai/DeepSeek-OCR
- Qwen/Qwen2.5-Coder-0.5B-Instruct
- Qwen/Qwen2.5-Coder-1.5B-Instruct
- Qwen/Qwen2.5-Coder-3B-Instruct
- Qwen/Qwen2.5-Coder-7B-Instruct
- Qwen/Qwen2.5-Coder-14B-Instruct
- Qwen/Qwen2.5-Coder-0.5B
- Qwen/Qwen2.5-Coder-1.5B
- Qwen/Qwen2.5-Coder-3B
- Qwen/Qwen2.5-Coder-7B
- Qwen/Qwen2.5-Coder-14B
- Qwen/Qwen2.5-Coder-32B
- deepseek-ai/DeepSeek-V3.2-Exp
- ZhipuAI/GLM-4.1V-9B-Thinking
- ZhipuAI/GLM-4.1V-9B-Base
- Shanghai_AI_Laboratory/internlm2_5-7b
- Shanghai_AI_Laboratory/internlm2-chat-20b
- Shanghai_AI_Laboratory/internlm2-7b
- Shanghai_AI_Laboratory/internlm2_5-20b
- TeleAI/telechat-7B
- TeleAI/TeleChat-12B-v2
- OpenBMB/MiniCPM-2B-dpo-bf16
- LLM-Research/Phi-4-multimodal-instruct
- LLM-Research/Mistral-7B-Instruct-v0.3
- Shanghai_AI_Laboratory/internlm2_5-7b-chat
- shakechen/Llama-2-7b-hf
- Qwen/Qwen2-Audio-7B-Instruct
- AI-ModelScope/gemma-2-2b
- AI-ModelScope/falcon-7b-instruct
- Duxiaoman-DI/XuanYuan-13B-Chat
- ZhipuAI/GLM-4.6
- LLM-Research/Codestral-22B-v0.1
- facebook/llm-compiler-7b
- 01ai/Yi-1.5-6B-Chat
- FreedomIntelligence/HuatuoGPT-o1-8B
- ZhipuAI/GLM-Z1-32B-0414
- Salesforce/Llama-xLAM-2-8b-fc-r
- Qwen/Qwen3-235B-A22B
- Qwen/Qwen3-Coder-480B-A35B-Instruct
版本：0.9.2

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enginex/Baichuan2-13B-Chat.md
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# 运行方式
```python
# 推荐使用docker方式运行，提供拉取的docker镜像：
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.1.0-ubuntu20.04-dtk24.04.1-py3.10
docker run -dit --shm-size 80g --network=host --name=baichuan2 --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /opt/hyhal/:/opt/hyhal/:ro image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-ubuntu20.04-dtk24.04.1-py3.10 /bin/bash
docker exec -it baichuan2 /bin/bash
# 安装docker中没有的依赖:
pip install -r requirements.txt -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
```

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enginex/ChatGLM-6B.md
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# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.1.0-centos7.6-dtk24.04-py310
docker run -dit --network=host --name=chatglm --privileged --device=/dev/kfd --device=/dev/dri --ipc=host --shm-size=16G --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root --ulimit stack=-1:-1 --ulimit memlock=-1:-1 -v /opt/hyhal/:/opt/hyhal/:ro git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.1.0-centos7.6-dtk24.04-py310 /usr/sbin/init
docker exec -it chatglm /bin/bash
pip install transformers==4.28.0 -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
pip install accelerate sentencepiece mdtex2html gradio rouge_chinese nltk jieba datasets protobuf peft pydantic==1.10.9 -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
```

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# 运行方式
推荐使用docker方式运行，提供拉取的docker镜像：
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/custom:glm-ft-v1.0
# 自定义容器名
# 当前工程所在路径
docker run -it --name= -v :/work -w /work --device=/dev/kfd --device=/dev/dri --security-opt seccomp=unconfined --cap-add=SYS_PTRACE --shm-size=16G --group-add 39 git.modelhub.org.cn:9443/enginex-hygon/custom:glm-ft-v1.0 /bin/bash
```

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# 运行方式
推荐使用docker方式运行，提供拉取的docker镜像：
```python
# 推荐使用docker方式运行，提供拉取的docker镜像：
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.1.0-centos7.6-dtk24.04-py310
# 进入docker，安装docker中没有的依赖:
docker run -dit --network=host --name=chatglm --privileged --device=/dev/kfd --device=/dev/dri --ipc=host --shm-size=16G --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root --ulimit stack=-1:-1 --ulimit memlock=-1:-1 -v /opt/hyhal/:/opt/hyhal/:ro image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-centos7.6-dtk24.04-py310 /usr/sbin/init
docker exec -it chatglm /bin/bash
pip install transformers==4.28.0 -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
pip install accelerate sentencepiece mdtex2html gradio rouge_chinese nltk jieba datasets protobuf peft pydantic==1.10.9 -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
```

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# 运行方式
推荐使用docker方式运行，提供拉取的docker镜像：
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.1.0-ubuntu20.04-dtk24.04.1-py3.10
```
进入docker，安装docker中没有的依赖:
```python
docker run -dit --network=host --name=chatglm3 --privileged --device=/dev/kfd --device=/dev/dri --ipc=host --shm-size=16G -v /opt/hyhal/:/opt/hyhal/:ro --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root --ulimit stack=-1:-1 --ulimit memlock=-1:-1 image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-ubuntu20.04-dtk24.04.1-py3.10
docker exec -it chatglm3 /bin/bash
pip install -r requirements.txt -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
cd finetune_demo
pip install -r requirements.txt -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
```

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enginex/Codestral-22B-v0.1.md
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# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.1.0-centos7.6-dtk24.04-py310
docker run -it -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro --shm-size=80G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
cd /your_code_path/codestral_pytorch
pip install -r requirements.txt
pip install -U huggingface_hub hf_transfer
export HF_ENDPOINT=https://hf-mirror.com
```

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enginex/DeepSeek-Coder-V2-Base.md
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# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.1.0-centos7.6-dtk24.04-py310
docker run -it -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro --shm-size=80G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
cd /your_code_path/deepseek-coder-v2_pytorch
pip install -r requirements.txt
pip install -U huggingface_hub hf_transfer
export HF_ENDPOINT=https://hf-mirror.com
```

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enginex/DeepSeek-OCR.md
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# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/vllm:0.8.5-ubuntu22.04-dtk25.04.1-rc5-das1.6-py3.10-20250724
docker run -it --shm-size 200g --network=host --name {docker_name} --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro {imageID} bash
cd /your_code_path/deepseek-ocr_pytorch
```

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enginex/DeepSeek-R1-Distill.md
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# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.3.0-ubuntu22.04-dtk24.04.3-py3.10
docker run --shm-size 500g --network=host --name=dpskv3 --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home/ -v /opt/hyhal:/opt/hyhal:ro -it <your IMAGE ID> bash
pip install https://download.sourcefind.cn:65024/directlink/4/lmslim/DAS1.3/lmslim-0.1.2+das.dtk24043-cp310-cp310-manylinux_2_28_x86_64.whl
pip install https://download.sourcefind.cn:65024/directlink/4/vllm/DAS1.3/vllm-0.6.2+das.opt1.dtk24043-cp310-cp310-manylinux_2_28_x86_64.whl
```

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enginex/DeepSeek-R1.md
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# 运行方式
```python
docker git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.3.0-ubuntu22.04-dtk24.04.3-py3.10
docker run --shm-size 500g --network=host --name=dpskr1 --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home/ -v /opt/hyhal:/opt/hyhal:ro -it <your IMAGE ID> bash
cd inference
pip install -r requirements.txt
```

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enginex/DeepSeek-R1_ollama.md
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# 运行方式
```python
docker git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.3.0-py3.10-dtk24.04.3-ubuntu20.04
docker run --shm-size 500g --network=host --name=dpskr1 --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home/ -v /opt/hyhal:/opt/hyhal:ro -it <your IMAGE ID> bash
cd inference
pip install -r requirements.txt
```

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# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.1.0-centos7.6-dtk24.04-py310
docker run -it -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro --shm-size=80G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
cd /your_code_path/deepseek-v2_pytorch
pip install -r requirements.txt
```

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# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/vllm:0.9.2-ubuntu22.04-dtk25.04.1-rc5-rocblas101839-0811-das1.6-py3.10-20250812-beta
docker run -it --name {docker_name} --device=/dev/kfd --privileged --network=host --device=/dev/dri --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v /your_code_path:/your_code_path -v /opt/hyhal:/opt/hyhal:ro -v /module/DeepSeek-V3.1:/your_model_path/DeepSeek-V3.1 --group-add video --shm-size 64G {imageID} bash
cd /your_code_path/deepseek-v3.1_vllm
```

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enginex/DeepSeek-V3.2-Exp.md
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# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/vllm:0.9.2-ubuntu22.04-dtk25.04.1-rc5-rocblas104381-0915-das1.6-py3.10-20250916-rc2-ds3.2
docker run -it --shm-size 200g --network=host --name {docker_name} --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro {imageID} bash
cd /your_code_path/deepseek-v3.2-exp_vllm
```

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# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.3.0-ubuntu22.04-dtk24.04.3-py3.10
docker run --shm-size 500g --network=host --name=dpskv3 --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home/ -v /opt/hyhal:/opt/hyhal:ro -it <your IMAGE ID> bash
cd inference
pip install -r requirements.txt
```

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enginex/GLM-4-9B-0414.md
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# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.1.0-ubuntu20.04-dtk24.04.2-py3.10
docker run -it -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro --shm-size=64G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name glm-4v bash
cd /path/your_code_data/
pip install -r requirements.txt -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
#开发者社区下载bitsandbytes
pip install bitsandbytes-0.42.0+das1.1.gitce85679.abi1.dtk2404.torch2.1.0-py3-none-any.whl
```

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# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/vllm:0.8.5-ubuntu22.04-dtk25.04.1-rc5-das1.6-py3.10-20250711
docker run -it --shm-size 200g --network=host --name {docker_name} --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro {imageID} bash
cd /your_code_path/glm-4.1v_pytorch
pip install transformers==4.53.2
```

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# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/vllm:0.9.2-ubuntu22.04-dtk25.04.1-rc5-rocblas104381-0915-das1.6-py3.10-20250916-rc2
docker run -it --shm-size 200g --network=host --name {docker_name} --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro {imageID} bash
cd /your_code_path/glm-4.6_vllm
```

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enginex/GLM-Z1-32B-0414.md
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# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.4.1-ubuntu22.04-dtk25.04-py3.10
docker run -it --shm-size 200g --network=host --name {docker_name} --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro {imageID} bash
cd /your_code_path/glm-z1_pytorch
pip install transformers&gt;=4.51.3
```

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# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.4.1-ubuntu22.04-dtk25.04-py3.10
docker run --shm-size 100g --network=host --name=gme --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home/ -v /opt/hyhal:/opt/hyhal:ro -it bash
pip install -r requirements.txt
```

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enginex/HuatuoGPT-o1-8B.md
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# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.3.0-ubuntu22.04-dtk24.04.3-py3.10
docker run --shm-size 50g --network=host --name=huatuo --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home/ -v /opt/hyhal:/opt/hyhal:ro -it bash
pip install -r requirements.txt
pip uninstall vllm
pip install https://download.sourcefind.cn:65024/directlink/4/lmslim/DAS1.3/lmslim-0.1.2+das.dtk24043-cp310-cp310-manylinux_2_28_x86_64.whl
pip install https://download.sourcefind.cn:65024/directlink/4/vllm/DAS1.3/vllm-0.6.2+das.opt1.dtk24043-cp310-cp310-manylinux_2_28_x86_64.whl
```

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enginex/Internlm.md
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# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.3.0-ubuntu22.04-dtk24.04.3-py3.10
# <img id="">用上面拉取docker镜像的ID替换
# 主机端路径
# 容器映射路径
# 若要在主机端和容器端映射端口需要删除--network host参数
docker run -it --name internlm_vllm --privileged --shm-size=64G --device=/dev/kfd --device=/dev/dri/ --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --ulimit memlock=-1:-1 --ipc=host --network host --group-add video -v /opt/hyhal:/opt/hyhal -v : <img id=""> /bin/bash
```

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# 运行方式
```python
拉取镜像：
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.1.0-ubuntu20.04-dtk24.04.1-py3.10
创建并启动容器：
docker run --shm-size 64g --network=host --name=llama_fastchat --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v /opt/hyhal:/opt/hyhal:ro -v : -it bash
cp -r mpirun/* ./
cd FastChat-main
pip3 install -e .
cd ../transformers-main
pip3 install -e .
pip3 uninstall wandb
pip3 install mpi4py
cd ..
```

7
enginex/Llama-2-7b-hf.md
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@@ -1,7 +0,0 @@
# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.1.0-ubuntu20.04-dtk24.04.2-py3.10
docker run -it -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal:/opt/hyhal:ro --shm-size=32G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
```

11
enginex/Llama-xLAM-2-8b-fc-r.md
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@@ -1,11 +0,0 @@
# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.4.1-ubuntu22.04-dtk25.04-py3.10
docker run --shm-size 100g --network=host --name=wan --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home/ -v /opt/hyhal:/opt/hyhal:ro -it bash
pip install -r requirements.txt
pip install -e .
```

10
enginex/Meta-Llama-3-8B-Instruct.md
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@@ -1,10 +0,0 @@
# 运行方式
```python
# 推荐使用docker方式运行，提供拉取的docker镜像：
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.1.0-centos7.6-dtk24.04-py310
docker run -it -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro --shm-size=80G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
cd /your_code_path/llama3_pytorch
pip install -e .
```

9
enginex/MiniCPM-2B-dpo-bf16.md
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@@ -1,9 +0,0 @@
# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.1.0-ubuntu20.04-dtk24.04.1-py3.10
# 为以上拉取的docker的镜像ID替换
docker run -it --shm-size=32G -v $PWD/MiniCPM:/home/MiniCPM -v /opt/hyhal:/opt/hyhal --network=host --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name minicpm bash
cd /home/MiniCPM
pip install -r finetune/requirements.txt # finetune/requirements.txt
```

9
enginex/MiniCPM3-4B.md
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@@ -1,9 +0,0 @@
# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.1.0-ubuntu20.04-dtk24.04.1-py3.10
# 为以上拉取的docker的镜像ID替换
docker run -it --shm-size=32G -v $PWD/MiniCPM:/home/MiniCPM -v /opt/hyhal:/opt/hyhal --network=host --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name minicpm bash
cd /home/MiniCPM
pip install -r finetune/requirements.txt # finetune/requirements.txt
```

8
enginex/Mistral-7B-Instruct-v0.3.md
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@@ -1,8 +0,0 @@
# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/custom:vllm0.8.5-ubuntu22.04-dtk25.04-rc7-das1.5-py3.10-20250521-fixpy-rocblas0521-beta2
docker run -it --shm-size 200g --network=host --name {docker_name} --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro {imageID} bash
cd /your_code_path/mistral_pytorch
```

12
enginex/Phi-4-multimodal-instruct.md
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@@ -1,12 +0,0 @@
# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.3.0-py3.10-dtk24.04.3-ubuntu20.04
docker run -it --shm-size=1024G -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal:/opt/hyhal --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name phi-4 bash # 为以上拉取的docker的镜像ID替换
git clone http://developer.sourcefind.cn/codes/modelzoo/phi-4-multimodal-instruct_pytorch.git
cd /path/your_code_data/
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
```

9
enginex/QwQ-32B.md
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@@ -1,9 +0,0 @@
# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.4.1-ubuntu22.04-dtk25.04-py3.10
# <your IMAGE ID>为以上拉取的docker的镜像ID替换，本镜像为：dee41741fb40
docker run -it --shm-size=64G --network host -v $PWD/QwQ-32B:/home/QwQ-32B -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name qwq <your IMAGE ID> bash
cd /home/QwQ-32B
pip install -r requirements.txt
```

9
enginex/Qwen-7B-Chat.md
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@@ -1,9 +0,0 @@
# 运行方式
```python
# 推荐使用docker方式运行，提供拉取的docker镜像：
docker pull git.modelhub.org.cn:9443/enginex-hygon/git.modelhub.org.cn:9443/enginex-hygon/pytorch:1.13.1-centos7.6-dtk-23.04-py38-latest
# 自定义容器名
# 当前工程所在路径
docker run -it --name= -v :/work --device=/dev/kfd --device=/dev/dri --security-opt seccomp=unconfined --cap-add=SYS_PTRACE --shm-size=16G --group-add 39 image.sourcefind.cn:5000/dcu/admin/base/pytorch:1.13.1-centos7.6-dtk-23.04-py38-latest /bin/bash
```

8
enginex/Qwen-7B_fastllm.md
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@@ -1,8 +0,0 @@
# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:1.13.1-centos7.6-dtk-23.04-py38-latest
# 自定义容器名
# 当前工程所在路径
docker run -it --name= -v :/work --device=/dev/kfd --device=/dev/dri --security-opt seccomp=unconfined --cap-add=SYS_PTRACE --shm-size=16G --group-add 39 git.modelhub.org.cn:9443/enginex-hygon/pytorch:1.13.1-centos7.6-dtk-23.04-py38-latest /bin/bash
```

9
enginex/Qwen1.5-14B-Chat.md
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@@ -1,9 +0,0 @@
# 运行方式
```python
# 推荐使用docker方式运行，提供拉取的docker镜像：
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.1.0-ubuntu20.04-dtk24.04.1-py3.10
docker run -it --shm-size=1024G -v $PWD/qwen1.5-pytorch:/home/Qwen1.5-pytorch -v /opt/hyhal:/opt/hyhal --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name Qwen1.5-pytorch bash # 为以上拉取的docker的镜像ID替换，本镜像为：ffa1f63239fc
cd /home/Qwen1.5-pytorch
pip install -r requirement.txt -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
```

11
enginex/Qwen2-7B.md
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@@ -1,11 +0,0 @@
# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.1.0-ubuntu20.04-dtk24.04.1-py3.10
docker run -it --shm-size=1024G -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal:/opt/hyhal --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name qwen2_72B_pytorch bash # 为以上拉取的docker的镜像ID替换，本镜像为：a4dd5be0ca23
pip install https://cancon.hpccube.com:65024/directlink/4/vllm/DAS1.1.1/vllm-0.5.0+das.opt1.3e2c63a.dtk2404.torch2.1.0-cp310-cp310-linux_x86_64.whl
cd /path/your_code_data/
cd LLaMA-Factory
pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
pip install e . -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
```

15
enginex/Qwen2-Audio-7B-Instruct.md
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@@ -1,15 +0,0 @@
# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.3.0-ubuntu22.04-dtk24.04.3-py3.10
docker run -it -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro --shm-size=64G --privileged=true --network=host --device=/dev/kfd --device=/dev/dri/ --group-add video --name qwen2-audio bash
cd /path/your_code_data/Qwen2-Audio/demo
pip install -r requirements_web_demo.txt -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
pip install git+https://github.com/modelscope/swift.git#egg=ms-swift[llm]
pip install git+https://github.com/huggingface/transformers.git
```

13
enginex/Qwen2.5-Omni-7B.md
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@@ -1,13 +0,0 @@
# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.4.1-ubuntu22.04-dtk25.04-py3.10-fixpy
# 为以上拉取的docker的镜像ID替换，本镜像为：e77c15729879
docker run -it --shm-size=64G -v $PWD/Qwen2.5-Omni:/home/Qwen2.5-Omni -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name qomni bash
cd /home/Qwen2.5-Omni
pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple
unzip f742a644ca32e65758c3adb36225aef1731bd2a8.zip
cd transformers-f742a644ca32e65758c3adb36225aef1731bd2a8
pip install -e . # 作者限定只能使用transformers==4.50.0.dev0
```

9
enginex/Qwen3-30B-A3B.md
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@@ -1,9 +0,0 @@
# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/vllm:0.8.5-ubuntu22.04-dtk25.04.1-rc5-das1.6-py3.10-20250724
docker run -it --name {docker_name} --device=/dev/kfd --privileged --network=host --device=/dev/dri --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v /public/LLM-Models:/home/LLM-Models:ro -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal:/opt/hyhal:ro --group-add video --shm-size 64G {imageID} bash
cd /your_code_path/qwen3-30b-a3b_vllm
```

10
enginex/Qwen3-Embedding-0.6B.md
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@@ -1,10 +0,0 @@
# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/custom:vllm0.8.5-ubuntu22.04-dtk25.04-rc7-das1.5-py3.10-20250521-fixpy-rocblas0521-beta2
docker run -it --shm-size 200g --network=host --name {docker_name} --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro {imageID} bash
cd /your_code_path/qwen3-embedding_pytorch
pip install transformers&gt;=4.51.0
pip install sentence-transformers&gt;=2.7.0
```

10
enginex/Qwen3.md
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@@ -1,10 +0,0 @@
# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/custom:vllm0.8.4-ubuntu22.04-dtk25.04-rc7-das1.5-py3.10-20250429-dev-qwen3-only
# docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.4.1-ubuntu22.04-dtk25.04-py3.10-fixpy
# 为以上拉取的docker的镜像ID替换，本镜像为：6e12a1c4ae4d
docker run -it --shm-size=64G -v $PWD/Qwen3:/home/Qwen3 -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name qwen3 bash
cd /home/Qwen3
pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple
```

9
enginex/Qwen:Qwen3-8B.md
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@@ -1,9 +0,0 @@
# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/custom:vllm0.8.4-ubuntu22.04-dtk25.04-rc7-das1.5-py3.10-20250429-dev-qwen3-only
# <your IMAGE ID>为以上拉取的docker的镜像ID替换
docker run -it --shm-size=64G -v $PWD/Qwen3:/home/Qwen3 -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name qwen3 <your IMAGE ID> bash
cd /home/Qwen3
pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple
```

15
enginex/TeleChat-12B-v2.md
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@@ -1,15 +0,0 @@
# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.1.0-ubuntu20.04-dtk24.04.1-py3.10
创建并启动容器：
docker run --shm-size 80g --network=host --name=telechat --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v /opt/hyhal:/opt/hyhal:ro -v : -it bash
安装依赖：
cd TeleChat
pip install -r requirements.txt -i https://pypi.mirrors.ustc.edu.cn/simple/
pip install 'ms-swift[llm]' -U -i https://pypi.mirrors.ustc.edu.cn/simple/
pip install optimum -i https://pypi.mirrors.ustc.edu.cn/simple/
pip install auto-gptq -i https://pypi.mirrors.ustc.edu.cn/simple/
```

17
enginex/XuanYuan-13B-Chat.md
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@@ -1,17 +0,0 @@
# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.1.0-ubuntu20.04-dtk24.04.1-py3.10
docker run -it -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro --shm-size=64G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name xuanyuan bash
cd /path/your_code_data/
pip install -r requirements.txt -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
cd LLaMA-Factory
pip install -e ".[torch,metrics]"
```

10
enginex/Yi-1.5-6B-Chat.md
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@@ -1,10 +0,0 @@
# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.1.0-py3.10-dtk24.04.3-ubuntu20.04
docker run -it --shm-size=1024G -v : -v /opt/hyhal:/opt/hyhal --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name Yi-1.5 bash # 为以上拉取的docker的镜像ID替换
cd /home/Yi-1.5-pytorch
pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
pip uninstall vllm
```

9
enginex/Yi-34B-Chat.md
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@@ -1,9 +0,0 @@
# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.1.0-ubuntu20.04-dtk24.04.1-py3.10
# <img id="">用上面拉取docker镜像的ID替换
# 主机端路径
# 容器映射路径
docker run -it --name yi --shm-size=64G --device=/dev/kfd --device=/dev/dri/ --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v /opt/hyhal:/opt/hyhal:ro --ulimit memlock=-1:-1 --ipc=host --network=host --group-add video -v : <img id=""> /bin/bash
```

9
enginex/Yi-6B-Chat.md
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@@ -1,9 +0,0 @@
# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.1.0-ubuntu20.04-dtk24.04.1-py3.10
# <img id="">用上面拉取docker镜像的ID替换
# 主机端路径
# 容器映射路径
docker run -it --name yi --shm-size=64G --device=/dev/kfd --device=/dev/dri/ --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v /opt/hyhal:/opt/hyhal:ro --ulimit memlock=-1:-1 --ipc=host --network=host --group-add video -v : <img id=""> /bin/bash
```

7
enginex/baichuan-7B.md
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@@ -1,7 +0,0 @@
# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.1.0-ubuntu20.04-dtk24.04.1-py3.10
docker run -dit --network=host --name=baichuan -v /opt/hyhal:/opt/hyhal:ro --privileged --device=/dev/kfd --device=/dev/dri --ipc=host --shm-size=16G --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root --ulimit stack=-1:-1 --ulimit memlock=-1:-1 image.sourcefind.cn:5000/dcu/admin/base/pytorch:1.13.1-centos7.6-dtk-23.04-py38-latest /bin/bash
docker exec -it baichuan /bin/bash
```

8
enginex/chatglm2-6b.md
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@@ -1,8 +0,0 @@
# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.1.0-ubuntu20.04-dtk24.04.1-py3.10
docker exec -it chatglm /bin/bash
pip install transformers==4.28.0 -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
pip install accelerate sentencepiece mdtex2html gradio rouge_chinese nltk jieba datasets==2.20.0 protobuf peft==0.5.0 pydantic==1.10.9 -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
```

9
enginex/falcon-7b-instruct.md
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@@ -1,9 +0,0 @@
# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.1.0-ubuntu20.04-dtk24.04.2-py3.10
docker run -it -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro --shm-size=80G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
cd /your_code_path/falcon_pytorch
pip install -r requirements.txt
```

9
enginex/gemma-2-2b.md
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@@ -1,9 +0,0 @@
# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.1.0-ubuntu20.04-dtk24.04.2-py3.10
docker run -it -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro --shm-size=80G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
cd /your_code_path/gemma2_pytorch
pip install -r requirements.txt
```

9
enginex/glm-10b-chinese.md
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@@ -1,9 +0,0 @@
# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.1.0-ubuntu20.04-dtk24.04.1-py3.10
# 为以上拉取的docker的镜像ID替换
docker run -it --shm-size=32G -v $PWD/MiniCPM:/home/MiniCPM -v /opt/hyhal:/opt/hyhal --network=host --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name minicpm bash
cd /home/MiniCPM
pip install -r finetune/requirements.txt # finetune/requirements.txt
```

10
enginex/glm-4-9b-chat.md
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@@ -1,10 +0,0 @@
# 运行方式
```python
dcoker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.4.1-ubuntu22.04-dtk25.04-py3.10
docker run -it --shm-size 200g --network=host --name {docker_name} --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro {imageID} bash
cd /your_code_path/glm-4_pytorch
pip install -r inference/requirements.txt
pip install -r finetune/requirements.txt
```

13
enginex/glm-4v-9b.md
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@@ -1,13 +0,0 @@
# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.1.0-ubuntu20.04-dtk24.04.1-py3.10
docker run -it -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro --shm-size=64G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name glm-4v bash
cd /path/your_code_data/
pip install -r requirements.txt -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
#开发者社区下载bitsandbytes
pip install bitsandbytes-0.42.0+das1.1.gitce85679.abi1.dtk2404.torch2.1.0-py3-none-any.whl
```

8
enginex/gpt2.md
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# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:1.10.0-centos7.6-dtk-23.04-py37-latest
docker run -dit --network=host --name=gpt2_pytorch --privileged --device=/dev/kfd --device=/dev/dri --ipc=host --shm-size=16G --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root --ulimit stack=-1:-1 --ulimit memlock=-1:-1 image.sourcefind.cn:5000/dcu/admin/base/pytorch:1.10.0-centos7.6-dtk-23.04-py37-latest
docker exec -it gpt2_pytorch /bin/bash
pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
```

9
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# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.1.0-ubuntu20.04-dtk24.04.1-py3.10(推荐)
# <img id="">用上面拉取docker镜像的ID替换
# 主机端路径
# 容器映射路径
docker run -it --name baichuan --shm-size=1024G --device=/dev/kfd --device=/dev/dri/ --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v /opt/hyhal:/opt/hyhal:ro --ulimit memlock=-1:-1 --ipc=host --network host --group-add video -v : <img id=""> /bin/bash
```

8
enginex/jina-embeddings-v3.md
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# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/custom:vllm0.8.5-ubuntu22.04-dtk25.04-rc7-das1.5-py3.10-20250612-fixpy-rocblas0611-rc2
docker run -it --shm-size 200g --network=host --name {docker_name} --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro {imageID} bash
cd /your_code_path/jina-embeddings-v3_vllm
```

11
enginex/llm-compiler-7b.md
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# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.1.0-centos7.6-dtk24.04-py310
docker run -it -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro --shm-size=80G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name docker_name imageID bash
cd /your_code_path/llm-compiler_pytorch
pip install -r requirements.txt
pip install -U huggingface_hub hf_transfer
export HF_ENDPOINT=https://hf-mirror.com
```

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# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.4.1-ubuntu22.04-dtk25.04.1-py3.10
docker run -it --name {name} --shm-size=1024G --device=/dev/kfd --device=/dev/dri/ --privileged --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --ulimit memlock=-1:-1 --ipc=host --network host --group-add video -v /opt/hyhal:/opt/hyhal:ro -v {}:{} {docker_image} /bin/bash
# 修改1 {name} 需要改为自定义名称
# 修改2 {docker_image} 需要需要创建容器的对应镜像名称
# 修改3 -v 挂载路径到容器指定路径
pip install -r requirements.txt
cd LLaMA-Factory
pip install -e ".[torch,metrics]"
pip install deepspeed-0.14.2+das.opt1.dtk25041-cp310-cp310-manylinux_2_28_x86_64.whl
```

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enginex/telechat-7B.md
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# 运行方式
```python
docker pull git.modelhub.org.cn:9443/enginex-hygon/pytorch:2.1.0-ubuntu20.04-dtk24.04.1-py3.10
创建并启动容器：
docker run --shm-size 80g --network=host --name=telechat --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v /opt/hyhal:/opt/hyhal:ro -v : -it bash
安装依赖：
cd TeleChat
pip install -r requirements.txt -i https://pypi.mirrors.ustc.edu.cn/simple/
pip install 'ms-swift[llm]' -U -i https://pypi.mirrors.ustc.edu.cn/simple/
pip install optimum -i https://pypi.mirrors.ustc.edu.cn/simple/
pip install auto-gptq -i https://pypi.mirrors.ustc.edu.cn/simple/
```

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""vLLM: a high-throughput and memory-efficient inference engine for LLMs"""
# The version.py should be independent library, and we always import the
# version library first. Such assumption is critical for some customization.
from .version import __version__, __version_tuple__ # isort:skip
import typing
# The environment variables override should be imported before any other
# modules to ensure that the environment variables are set before any
# other modules are imported.
import vllm.env_override # noqa: F401
MODULE_ATTRS = {
"AsyncEngineArgs": ".engine.arg_utils:AsyncEngineArgs",
"EngineArgs": ".engine.arg_utils:EngineArgs",
"AsyncLLMEngine": ".engine.async_llm_engine:AsyncLLMEngine",
"LLMEngine": ".engine.llm_engine:LLMEngine",
"LLM": ".entrypoints.llm:LLM",
"initialize_ray_cluster": ".executor.ray_utils:initialize_ray_cluster",
"PromptType": ".inputs:PromptType",
"TextPrompt": ".inputs:TextPrompt",
"TokensPrompt": ".inputs:TokensPrompt",
"ModelRegistry": ".model_executor.models:ModelRegistry",
"SamplingParams": ".sampling_params:SamplingParams",
"PoolingParams": ".pooling_params:PoolingParams",
"ClassificationOutput": ".outputs:ClassificationOutput",
"ClassificationRequestOutput": ".outputs:ClassificationRequestOutput",
"CompletionOutput": ".outputs:CompletionOutput",
"EmbeddingOutput": ".outputs:EmbeddingOutput",
"EmbeddingRequestOutput": ".outputs:EmbeddingRequestOutput",
"PoolingOutput": ".outputs:PoolingOutput",
"PoolingRequestOutput": ".outputs:PoolingRequestOutput",
"RequestOutput": ".outputs:RequestOutput",
"ScoringOutput": ".outputs:ScoringOutput",
"ScoringRequestOutput": ".outputs:ScoringRequestOutput",
}
if typing.TYPE_CHECKING:
from vllm.engine.arg_utils import AsyncEngineArgs, EngineArgs
from vllm.engine.async_llm_engine import AsyncLLMEngine
from vllm.engine.llm_engine import LLMEngine
from vllm.entrypoints.llm import LLM
from vllm.executor.ray_utils import initialize_ray_cluster
from vllm.inputs import PromptType, TextPrompt, TokensPrompt
from vllm.model_executor.models import ModelRegistry
from vllm.outputs import (ClassificationOutput,
ClassificationRequestOutput, CompletionOutput,
EmbeddingOutput, EmbeddingRequestOutput,
PoolingOutput, PoolingRequestOutput,
RequestOutput, ScoringOutput,
ScoringRequestOutput)
from vllm.pooling_params import PoolingParams
from vllm.sampling_params import SamplingParams
else:
def __getattr__(name: str) -> typing.Any:
from importlib import import_module
if name in MODULE_ATTRS:
module_name, attr_name = MODULE_ATTRS[name].split(":")
module = import_module(module_name, __package__)
return getattr(module, attr_name)
else:
raise AttributeError(
f'module {__package__} has no attribute {name}')
__all__ = [
"__version__",
"__version_tuple__",
"LLM",
"ModelRegistry",
"PromptType",
"TextPrompt",
"TokensPrompt",
"SamplingParams",
"RequestOutput",
"CompletionOutput",
"PoolingOutput",
"PoolingRequestOutput",
"EmbeddingOutput",
"EmbeddingRequestOutput",
"ClassificationOutput",
"ClassificationRequestOutput",
"ScoringOutput",
"ScoringRequestOutput",
"LLMEngine",
"EngineArgs",
"AsyncLLMEngine",
"AsyncEngineArgs",
"initialize_ray_cluster",
"PoolingParams",
]

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Optional
import torch
from vllm.logger import init_logger
logger = init_logger(__name__)
try:
import intel_extension_for_pytorch as ipex
except ImportError as e:
logger.warning("Import error msg: %s", e.msg)
class ipex_ops:
@staticmethod
def _reshape_activation_tensor(
x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
num = x.size(0)
d = x.size(1) // 2
x = x.reshape(num, 2, d)
x1, x2 = torch.chunk(x, chunks=2, dim=1)
x1 = x1.reshape(num, d)
x2 = x2.reshape(num, d)
return x1, x2
@staticmethod
def silu_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
ipex.llm.functional.silu_and_mul(x, out)
@staticmethod
def gelu_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
ipex.llm.functional.gelu_and_mul(x, out)
@staticmethod
def gelu_tanh_and_mul(out: torch.Tensor, x: torch.Tensor) -> None:
ipex.llm.functional.gelu_and_mul(x, out)
@staticmethod
def gelu_fast(x: torch.Tensor) -> torch.Tensor:
return torch.nn.functional.gelu(x)
@staticmethod
def gelu_new(x: torch.Tensor) -> torch.Tensor:
return torch.nn.functional.gelu(x)
@staticmethod
def gelu_quick(out: torch.Tensor, x: torch.Tensor) -> None:
ipex.llm.functional.gelu_quick(x, out)
@staticmethod
def paged_attention_v1(
out: torch.Tensor,
query: torch.Tensor,
key_cache: torch.Tensor,
value_cache: torch.Tensor,
num_kv_heads: int,
scale: float,
block_tables: torch.Tensor,
context_lens: torch.Tensor,
block_size: int,
max_context_len: int,
alibi_slopes: Optional[torch.Tensor],
kv_cache_dtype: str,
k_scale: float,
v_scale: float,
tp_rank: int = 0,
blocksparse_local_blocks: int = 0,
blocksparse_vert_stride: int = 0,
blocksparse_block_size: int = 64,
blocksparse_head_sliding_step: int = 0,
) -> None:
assert kv_cache_dtype == "auto"
num_heads = out.size(1)
num_queries_per_tokens = num_heads // num_kv_heads
ipex.llm.modules.PagedAttention.single_query_kv_attention(
out,
query.contiguous(),
key_cache.view_as(value_cache),
value_cache,
num_queries_per_tokens,
scale,
block_tables,
context_lens,
block_size,
max_context_len,
alibi_slopes,
)
@staticmethod
def paged_attention_v2(
out: torch.Tensor,
exp_sum: torch.Tensor,
max_logits: torch.Tensor,
tmp_out: torch.Tensor,
query: torch.Tensor,
key_cache: torch.Tensor,
value_cache: torch.Tensor,
num_kv_heads: int,
scale: float,
block_tables: torch.Tensor,
context_lens: torch.Tensor,
block_size: int,
max_context_len: int,
alibi_slopes: Optional[torch.Tensor],
kv_cache_dtype: str,
k_scale: float,
v_scale: float,
tp_rank: int = 0,
blocksparse_local_blocks: int = 0,
blocksparse_vert_stride: int = 0,
blocksparse_block_size: int = 64,
blocksparse_head_sliding_step: int = 0,
) -> None:
assert kv_cache_dtype == "auto"
num_heads = out.size(1)
num_queries_per_tokens = num_heads // num_kv_heads
ipex.llm.modules.PagedAttention.single_query_kv_attention(
out,
query.contiguous(),
key_cache.view_as(value_cache),
value_cache,
num_queries_per_tokens,
scale,
block_tables,
context_lens,
block_size,
max_context_len,
alibi_slopes,
)
@staticmethod
def rotary_embedding(
positions: torch.Tensor, # [batch_size, seq_len]
query: torch.Tensor, # [batch_size, seq_len, num_heads*head_size]
key: torch.Tensor, # [batch_size, seq_len, num_kv_heads*head_size]
head_size: int,
cos_sin_cache: torch.Tensor, # [cos_sin_dim, rot_dim]
is_neox: bool,
) -> None:
rot_dim = cos_sin_cache.size(1)
ipex.llm.functional.rotary_embedding_batched(positions, query, key,
head_size, cos_sin_cache,
is_neox, rot_dim)
@staticmethod
def batched_rotary_embedding(positions: torch.Tensor, query: torch.Tensor,
key: torch.Tensor, head_size: int,
cos_sin_cache: torch.Tensor, is_neox: bool,
rot_dim: int,
cos_sin_cache_offsets: torch.Tensor) -> None:
ipex.llm.functional.rotary_embedding_batched(positions, query, key,
head_size, cos_sin_cache,
is_neox, rot_dim,
cos_sin_cache_offsets)
@staticmethod
def rms_norm(input: torch.Tensor, weight: torch.Tensor,
epsilon: float) -> torch.Tensor:
return ipex.llm.functional.rms_norm(input, weight, epsilon)
@staticmethod
def fused_add_rms_norm(input: torch.Tensor, residual: torch.Tensor,
weight: torch.Tensor, epsilon: float) -> None:
tmp = ipex.llm.functional.add_rms_norm(residual, input, weight, None,
epsilon, True)
input.copy_(tmp)
@staticmethod
def varlen_attention(
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
out: torch.Tensor,
seqlen_q: torch.Tensor,
seqlen_k: torch.Tensor,
alibi_slopes: Optional[torch.Tensor],
max_seqlen_q: int,
max_seqlen_k: int,
pdropout: float,
softmax_scale: float,
zero_tensors: bool,
is_causal: bool,
return_softmax: bool,
gen_: torch.Generator,
window_size_left: float,
window_size_right: float,
logits_soft_cap: float,
) -> None:
if ipex.__version__.endswith("cpu"):
if logits_soft_cap != 0.0:
raise ValueError("IPEX CPU does not support logits_soft_cap")
assert alibi_slopes is None
assert window_size_left < 0 and window_size_right < 0
ipex.llm.functional.varlen_attention(query.contiguous(),
key.contiguous(),
value.contiguous(), out,
seqlen_q.int(),
seqlen_k.int(), max_seqlen_q,
max_seqlen_k, pdropout,
softmax_scale, zero_tensors,
is_causal, return_softmax,
gen_)
else: # XPU build
ipex.llm.functional.varlen_attention(
query.contiguous(), key.contiguous(), value.contiguous(), out,
seqlen_q.int(), seqlen_k.int(), alibi_slopes, max_seqlen_q,
max_seqlen_k, pdropout, softmax_scale, zero_tensors, is_causal,
return_softmax, gen_, window_size_left, window_size_right,
logits_soft_cap)
@staticmethod
def reshape_and_cache(
key: torch.Tensor,
value: torch.Tensor,
key_cache: torch.Tensor,
value_cache: torch.Tensor,
slot_mapping: torch.Tensor,
kv_cache_dtype: str,
k_scale: float,
v_scale: float,
) -> None:
assert kv_cache_dtype == "auto"
ipex.llm.modules.PagedAttention.reshape_and_cache(
key, value, key_cache, value_cache, slot_mapping)
@staticmethod
def reshape_and_cache_flash(
key: torch.Tensor,
value: torch.Tensor,
key_cache: torch.Tensor,
value_cache: torch.Tensor,
slot_mapping: torch.Tensor,
kv_cache_dtype: str,
k_scale: Optional[torch.Tensor] = None,
v_scale: Optional[torch.Tensor] = None,
k_scale_float: float = 1.0,
v_scale_float: float = 1.0,
) -> None:
assert kv_cache_dtype == "auto"
# TODO: support FP8 kv cache.
ipex.llm.modules.PagedAttention.reshape_and_cache_flash(
key, value, key_cache, value_cache, slot_mapping)
@staticmethod
def flash_attn_varlen_func(
out: torch.Tensor,
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
cu_seqlens_q: torch.Tensor,
seqused_k: torch.Tensor, # we don't support this in ipex kernel
max_seqlen_q: int,
max_seqlen_k: int,
softmax_scale: float,
causal: bool,
block_table: torch.Tensor,
alibi_slopes: Optional[torch.Tensor],
window_size: Optional[list[int]] = None,
softcap: Optional[float] = 0.0,
cu_seqlens_k: Optional[torch.Tensor] = None,
# The following parameters are not used in ipex kernel currently,
# we keep API compatible to CUDA's.
scheduler_metadata=None,
fa_version: int = 2,
q_descale=None,
k_descale=None,
v_descale=None,
num_splits=0,
):
if cu_seqlens_k is None:
# cu_seqlens_k is not used in ipex kernel.
cu_seqlens_k = torch.cumsum(seqused_k, dim=0)
cu_seqlens_k = torch.cat([
torch.tensor([0], device=seqused_k.device, dtype=torch.int32),
cu_seqlens_k
]).to(torch.int32)
real_window_size: tuple[int, int]
if window_size is None:
real_window_size = (-1, -1)
else:
assert len(window_size) == 2
real_window_size = (window_size[0], window_size[1])
return ipex.llm.modules.PagedAttention.flash_attn_varlen_func(
out,
q.contiguous(),
k,
v,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
softmax_scale,
causal,
block_table,
alibi_slopes,
softcap=softcap,
window_size_left=real_window_size[0],
window_size_right=real_window_size[1],
k_scale=1.0,
v_scale=1.0,
)
@staticmethod
def get_scheduler_metadata(
batch_size,
max_seqlen_q,
max_seqlen_k,
num_heads_q,
num_heads_kv,
headdim,
cache_seqlens: torch.Tensor,
qkv_dtype=torch.bfloat16,
headdim_v=None,
cu_seqlens_q: Optional[torch.Tensor] = None,
cu_seqlens_k_new: Optional[torch.Tensor] = None,
cache_leftpad: Optional[torch.Tensor] = None,
page_size: Optional[int] = None,
max_seqlen_k_new=0,
causal=False,
window_size=(-1, -1), # -1 means infinite context window
has_softcap=False,
num_splits=0, # Can be tuned for speed
pack_gqa=None, # Can be tuned for speed
sm_margin=0, # Can be tuned if some SMs are used for communication
) -> None:
logger.warning_once(
"get_scheduler_metadata is not implemented for ipex_ops, "
"returning None.")
return None
@staticmethod
def copy_blocks(key_caches: list[torch.Tensor],
value_caches: list[torch.Tensor],
block_mapping: torch.Tensor) -> None:
torch.xpu.copy_blocks( # type: ignore
key_caches,
value_caches,
block_mapping,
)
@staticmethod
def swap_blocks(src: torch.Tensor, dst: torch.Tensor,
block_mapping: torch.Tensor) -> None:
torch.xpu.swap_blocks(src, dst, block_mapping) # type: ignore

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from dataclasses import dataclass
@dataclass
class AdapterMapping:
# Per every token in input_ids:
index_mapping: tuple[int, ...]
# Per sampled token:
prompt_mapping: tuple[int, ...]
def __post_init__(self):
self.index_mapping = tuple(self.index_mapping)
self.prompt_mapping = tuple(self.prompt_mapping)

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from abc import ABC, abstractmethod
from typing import Any, Callable, Optional, TypeVar
from torch import nn
from vllm.logger import init_logger
from vllm.utils import LRUCache
logger = init_logger(__name__)
class AdapterModel(ABC):
def __init__(self, model_id=None):
self.id = model_id
@abstractmethod
def from_local_checkpoint(cls, model_dir, model_id=None, **kwargs):
# Common initialization code
# Load weights or embeddings from local checkpoint
raise NotImplementedError("Subclasses must implement this method.")
T = TypeVar('T')
class AdapterLRUCache(LRUCache[int, T]):
def __init__(self, capacity: int, deactivate_fn: Callable[[int], object]):
super().__init__(capacity)
self.deactivate_fn = deactivate_fn
def _on_remove(self, key: int, value: Optional[T]):
logger.debug("Removing adapter int id: %d", key)
self.deactivate_fn(key)
return super()._on_remove(key, value)
class AdapterModelManager(ABC):
def __init__(
self,
model: nn.Module,
):
"""Create a AdapterModelManager and adapter for a given model.
Args:
model: the model to be adapted.
"""
self.model: nn.Module = model
self._registered_adapters: dict[int, Any] = {}
# Dict instead of a Set for compatibility with LRUCache.
self._active_adapters: dict[int, None] = {}
self.adapter_type = 'Adapter'
self._last_mapping = None
def __len__(self) -> int:
return len(self._registered_adapters)
@property
@abstractmethod
def adapter_slots(self) -> int:
raise NotImplementedError
@property
@abstractmethod
def capacity(self) -> int:
raise NotImplementedError
@abstractmethod
def activate_adapter(self, adapter_id: int) -> bool:
raise NotImplementedError
@abstractmethod
def deactivate_adapter(self, adapter_id: int) -> bool:
raise NotImplementedError
@abstractmethod
def add_adapter(self, adapter: Any) -> bool:
raise NotImplementedError
@abstractmethod
def set_adapter_mapping(self, mapping: Any) -> None:
raise NotImplementedError
@abstractmethod
def remove_adapter(self, adapter_id: int) -> bool:
raise NotImplementedError
@abstractmethod
def remove_all_adapters(self) -> None:
raise NotImplementedError
@abstractmethod
def get_adapter(self, adapter_id: int) -> Optional[Any]:
raise NotImplementedError
@abstractmethod
def list_adapters(self) -> dict[int, Any]:
raise NotImplementedError
@abstractmethod
def pin_adapter(self, adapter_id: int) -> bool:
raise NotImplementedError

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from abc import ABC, abstractmethod
class AdapterRequest(ABC):
"""
Base class for adapter requests.
"""
@property
@abstractmethod
def adapter_id(self) -> int:
raise NotImplementedError
def __post_init__(self) -> None:
if self.adapter_id < 1:
raise ValueError(f"id must be > 0, got {self.adapter_id}")
def __eq__(self, value: object) -> bool:
return isinstance(
value, self.__class__) and self.adapter_id == value.adapter_id
def __hash__(self) -> int:
return hash(self.adapter_id)

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Any, Callable, Optional
## model functions
def deactivate_adapter(adapter_id: int, active_adapters: dict[int, None],
deactivate_func: Callable) -> bool:
if adapter_id in active_adapters:
deactivate_func(adapter_id)
active_adapters.pop(adapter_id)
return True
return False
def add_adapter(adapter: Any, registered_adapters: dict[int, Any],
capacity: int, add_func: Callable) -> bool:
if adapter.id not in registered_adapters:
if len(registered_adapters) >= capacity:
raise RuntimeError('No free adapter slots.')
add_func(adapter)
registered_adapters[adapter.id] = adapter
return True
return False
def set_adapter_mapping(mapping: Any, last_mapping: Any,
set_mapping_func: Callable) -> Any:
if last_mapping != mapping:
set_mapping_func(mapping)
return mapping
return last_mapping
def remove_adapter(adapter_id: int, registered_adapters: dict[int, Any],
deactivate_func: Callable) -> bool:
deactivate_func(adapter_id)
return bool(registered_adapters.pop(adapter_id, None))
def list_adapters(registered_adapters: dict[int, Any]) -> dict[int, Any]:
return dict(registered_adapters)
def get_adapter(adapter_id: int,
registered_adapters: dict[int, Any]) -> Optional[Any]:
return registered_adapters.get(adapter_id)
## worker functions
def set_active_adapters_worker(requests: set[Any], mapping: Optional[Any],
apply_adapters_func,
set_adapter_mapping_func) -> None:
apply_adapters_func(requests)
set_adapter_mapping_func(mapping)
def add_adapter_worker(adapter_request: Any, list_adapters_func,
load_adapter_func, add_adapter_func,
activate_adapter_func) -> bool:
if adapter_request.adapter_id in list_adapters_func():
return False
loaded_adapter = load_adapter_func(adapter_request)
loaded = add_adapter_func(loaded_adapter)
activate_adapter_func(loaded_adapter.id)
return loaded
def apply_adapters_worker(adapter_requests: set[Any], list_adapters_func,
adapter_slots: int, remove_adapter_func,
add_adapter_func) -> None:
models_that_exist = list_adapters_func()
models_map = {
adapter_request.adapter_id: adapter_request
for adapter_request in adapter_requests if adapter_request
}
if len(models_map) > adapter_slots:
raise RuntimeError(
f"Number of requested models ({len(models_map)}) is greater "
f"than the number of GPU model slots "
f"({adapter_slots}).")
new_models = set(models_map)
models_to_add = new_models - models_that_exist
models_to_remove = models_that_exist - new_models
for adapter_id in models_to_remove:
remove_adapter_func(adapter_id)
for adapter_id in models_to_add:
add_adapter_func(models_map[adapter_id])
def list_adapters_worker(adapter_manager_list_adapters_func) -> set[int]:
return set(adapter_manager_list_adapters_func())

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from abc import ABC, abstractmethod
from typing import Any, Optional
import torch
class AbstractWorkerManager(ABC):
def __init__(self, device: torch.device):
self.device = device
@property
@abstractmethod
def is_enabled(self) -> bool:
raise NotImplementedError
@abstractmethod
def set_active_adapters(self, requests: set[Any],
mapping: Optional[Any]) -> None:
raise NotImplementedError
@abstractmethod
def add_adapter(self, adapter_request: Any) -> bool:
raise NotImplementedError
@abstractmethod
def remove_adapter(self, adapter_id: int) -> bool:
raise NotImplementedError
@abstractmethod
def remove_all_adapters(self) -> None:
raise NotImplementedError
@abstractmethod
def list_adapters(self) -> set[int]:
raise NotImplementedError

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from dataclasses import dataclass
from pathlib import Path
from typing import Literal
from urllib.parse import urljoin
import numpy.typing as npt
from vllm.utils import PlaceholderModule
from .base import VLLM_S3_BUCKET_URL, get_vllm_public_assets
try:
import librosa
except ImportError:
librosa = PlaceholderModule("librosa") # type: ignore[assignment]
ASSET_DIR = "multimodal_asset"
AudioAssetName = Literal["winning_call", "mary_had_lamb"]
@dataclass(frozen=True)
class AudioAsset:
name: AudioAssetName
@property
def filename(self) -> str:
return f"{self.name}.ogg"
@property
def audio_and_sample_rate(self) -> tuple[npt.NDArray, float]:
audio_path = get_vllm_public_assets(filename=self.filename,
s3_prefix=ASSET_DIR)
return librosa.load(audio_path, sr=None)
def get_local_path(self) -> Path:
return get_vllm_public_assets(filename=self.filename,
s3_prefix=ASSET_DIR)
@property
def url(self) -> str:
return urljoin(VLLM_S3_BUCKET_URL, f"{ASSET_DIR}/{self.name}.ogg")

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from functools import lru_cache
from pathlib import Path
from typing import Optional
import vllm.envs as envs
from vllm.connections import global_http_connection
VLLM_S3_BUCKET_URL = "https://vllm-public-assets.s3.us-west-2.amazonaws.com"
def get_cache_dir() -> Path:
"""Get the path to the cache for storing downloaded assets."""
path = Path(envs.VLLM_ASSETS_CACHE)
path.mkdir(parents=True, exist_ok=True)
return path
@lru_cache
def get_vllm_public_assets(filename: str,
s3_prefix: Optional[str] = None) -> Path:
"""
Download an asset file from ``s3://vllm-public-assets``
and return the path to the downloaded file.
"""
asset_directory = get_cache_dir() / "vllm_public_assets"
asset_directory.mkdir(parents=True, exist_ok=True)
asset_path = asset_directory / filename
if not asset_path.exists():
if s3_prefix is not None:
filename = s3_prefix + "/" + filename
global_http_connection.download_file(
f"{VLLM_S3_BUCKET_URL}/{filename}",
asset_path,
timeout=envs.VLLM_IMAGE_FETCH_TIMEOUT)
return asset_path

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from dataclasses import dataclass
from typing import Literal
import torch
from PIL import Image
from .base import get_vllm_public_assets
VLM_IMAGES_DIR = "vision_model_images"
ImageAssetName = Literal["stop_sign", "cherry_blossom"]
@dataclass(frozen=True)
class ImageAsset:
name: ImageAssetName
@property
def pil_image(self) -> Image.Image:
image_path = get_vllm_public_assets(filename=f"{self.name}.jpg",
s3_prefix=VLM_IMAGES_DIR)
return Image.open(image_path)
@property
def image_embeds(self) -> torch.Tensor:
"""
Image embeddings, only used for testing purposes with llava 1.5.
"""
image_path = get_vllm_public_assets(filename=f"{self.name}.pt",
s3_prefix=VLM_IMAGES_DIR)
return torch.load(image_path, map_location="cpu", weights_only=True)

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from dataclasses import dataclass
from functools import lru_cache
from typing import Any, ClassVar, Literal, Optional
import cv2
import numpy as np
import numpy.typing as npt
from huggingface_hub import hf_hub_download
from PIL import Image
from vllm.utils import PlaceholderModule
from .base import get_cache_dir
try:
import librosa
except ImportError:
librosa = PlaceholderModule("librosa") # type: ignore[assignment]
@lru_cache
def download_video_asset(filename: str) -> str:
"""
Download and open an image from huggingface
repo: raushan-testing-hf/videos-test
"""
video_directory = get_cache_dir() / "video-example-data"
video_directory.mkdir(parents=True, exist_ok=True)
video_path = video_directory / filename
video_path_str = str(video_path)
if not video_path.exists():
video_path_str = hf_hub_download(
repo_id="raushan-testing-hf/videos-test",
filename=filename,
repo_type="dataset",
cache_dir=video_directory,
)
return video_path_str
def video_to_ndarrays(path: str, num_frames: int = -1) -> npt.NDArray:
cap = cv2.VideoCapture(path)
if not cap.isOpened():
raise ValueError(f"Could not open video file {path}")
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
frames = []
num_frames = num_frames if num_frames > 0 else total_frames
frame_indices = np.linspace(0, total_frames - 1, num_frames, dtype=int)
for idx in range(total_frames):
ok = cap.grab() # next img
if not ok:
break
if idx in frame_indices: # only decompress needed
ret, frame = cap.retrieve()
if ret:
frames.append(frame)
frames = np.stack(frames)
if len(frames) < num_frames:
raise ValueError(f"Could not read enough frames from video file {path}"
f" (expected {num_frames} frames, got {len(frames)})")
return frames
def video_to_pil_images_list(path: str,
num_frames: int = -1) -> list[Image.Image]:
frames = video_to_ndarrays(path, num_frames)
return [
Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
for frame in frames
]
def video_get_metadata(path: str) -> dict[str, Any]:
cap = cv2.VideoCapture(path)
if not cap.isOpened():
raise ValueError(f"Could not open video file {path}")
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
fps = cap.get(cv2.CAP_PROP_FPS)
duration = total_frames / fps if fps > 0 else 0
metadata = {
"total_num_frames": total_frames,
"fps": fps,
"duration": duration,
"video_backend": "opencv"
}
return metadata
VideoAssetName = Literal["baby_reading"]
@dataclass(frozen=True)
class VideoAsset:
name: VideoAssetName
num_frames: int = -1
_NAME_TO_FILE: ClassVar[dict[VideoAssetName, str]] = {
"baby_reading": "sample_demo_1.mp4",
}
@property
def filename(self) -> str:
return self._NAME_TO_FILE[self.name]
@property
def pil_images(self) -> list[Image.Image]:
video_path = download_video_asset(self.filename)
ret = video_to_pil_images_list(video_path, self.num_frames)
return ret
@property
def np_ndarrays(self) -> npt.NDArray:
video_path = download_video_asset(self.filename)
ret = video_to_ndarrays(video_path, self.num_frames)
return ret
@property
def metadata(self) -> dict[str, Any]:
video_path = download_video_asset(self.filename)
ret = video_get_metadata(video_path)
return ret
def get_audio(self, sampling_rate: Optional[float] = None) -> npt.NDArray:
"""
Read audio data from the video asset, used in Qwen2.5-Omni examples.
See also: examples/offline_inference/qwen2_5_omni/only_thinker.py
"""
video_path = download_video_asset(self.filename)
return librosa.load(video_path, sr=sampling_rate)[0]

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from vllm.attention.backends.abstract import (AttentionBackend,
AttentionMetadata,
AttentionMetadataBuilder,
AttentionState, AttentionType)
from vllm.attention.layer import Attention
from vllm.attention.selector import get_attn_backend
__all__ = [
"Attention",
"AttentionBackend",
"AttentionMetadata",
"AttentionType",
"AttentionMetadataBuilder",
"Attention",
"AttentionState",
"get_attn_backend",
]

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from abc import ABC, abstractmethod
from contextlib import contextmanager
from dataclasses import dataclass, fields
from typing import (TYPE_CHECKING, Any, Dict, Generic, List, Optional,
Protocol, Set, Tuple, Type, TypeVar)
import torch
from vllm.multimodal import MultiModalPlaceholderMap
if TYPE_CHECKING:
from vllm.worker.model_runner_base import (ModelRunnerBase,
ModelRunnerInputBase,
ModelRunnerInputBuilderBase)
class AttentionType:
"""
Attention type.
Use string to be compatible with `torch.compile`.
"""
# Decoder attention between previous layer Q/K/V
DECODER = "decoder"
# Encoder attention between previous layer Q/K/V for encoder-decoder
ENCODER = "encoder"
# Encoder attention between previous layer Q/K/V
ENCODER_ONLY = "encoder_only"
# Attention between dec. Q and enc. K/V for encoder-decoder
ENCODER_DECODER = "encoder_decoder"
class AttentionBackend(ABC):
"""Abstract class for attention backends."""
# For some attention backends, we allocate an output tensor before
# calling the custom op. When piecewise cudagraph is enabled, this
# makes sure the output tensor is allocated inside the cudagraph.
accept_output_buffer: bool = False
@staticmethod
@abstractmethod
def get_name() -> str:
raise NotImplementedError
@staticmethod
@abstractmethod
def get_impl_cls() -> Type["AttentionImpl"]:
raise NotImplementedError
@staticmethod
@abstractmethod
def get_metadata_cls() -> Type["AttentionMetadata"]:
raise NotImplementedError
@staticmethod
@abstractmethod
def get_state_cls() -> Type["AttentionState"]:
raise NotImplementedError
@classmethod
def make_metadata(cls, *args, **kwargs) -> "AttentionMetadata":
return cls.get_metadata_cls()(*args, **kwargs)
@staticmethod
@abstractmethod
def get_builder_cls() -> Type["AttentionMetadataBuilder"]:
raise NotImplementedError
@staticmethod
@abstractmethod
def get_kv_cache_shape(
num_blocks: int,
block_size: int,
num_kv_heads: int,
head_size: int,
) -> Tuple[int, ...]:
raise NotImplementedError
@staticmethod
def get_kv_cache_stride_order() -> Tuple[int, ...]:
raise NotImplementedError
@staticmethod
@abstractmethod
def swap_blocks(
src_kv_cache: torch.Tensor,
dst_kv_cache: torch.Tensor,
src_to_dst: torch.Tensor,
) -> None:
raise NotImplementedError
@staticmethod
@abstractmethod
def copy_blocks(
kv_caches: List[torch.Tensor],
src_to_dists: torch.Tensor,
) -> None:
raise NotImplementedError
def advance_step(self, model_input: "ModelRunnerInputBase",
sampled_token_ids: Optional[torch.Tensor],
block_size: int, num_seqs: int, num_queries: int) -> None:
raise NotImplementedError
@dataclass
class AttentionMetadata:
"""Attention metadata for prefill and decode batched together."""
# Total number of prefill requests.
num_prefills: int
# Number of prefill tokens.
num_prefill_tokens: int
# Number of decode tokens. Note that it is equivalent to the number of
# decode requests.
num_decode_tokens: int
# (num_tokens,). The indices of the token slots that input tokens will be
# stored into. E.g., if `slot_mapping` is [35, 2, 17] and the block size
# is 16, the three tokens are stored in the 3rd slot in block 2, 2nd slot
# in block 0, and 1st slot in block 1, respectively.
slot_mapping: torch.Tensor
# The index maps that relate multi-modal embeddings to the corresponding
# placeholders.
#
# N.B. These aren't really related to attention and don't belong on this
# type -- this is just a temporary solution to make them available to
# `model_executable`.
multi_modal_placeholder_index_maps: Optional[Dict[
str, MultiModalPlaceholderMap.IndexMap]]
# Enable/disable KV scales calculation. This is so that we can disable the
# calculation until after prefill and cuda graph capture.
enable_kv_scales_calculation: bool
@property
@abstractmethod
def prefill_metadata(self) -> Optional["AttentionMetadata"]:
"""Return the attention metadata that's required to run prefill
attention."""
pass
@property
@abstractmethod
def decode_metadata(self) -> Optional["AttentionMetadata"]:
"""Return the attention metadata that's required to run decode
attention."""
pass
def asdict_zerocopy(self,
skip_fields: Optional[Set[str]] = None
) -> Dict[str, Any]:
"""Similar to dataclasses.asdict, but avoids deepcopying."""
if skip_fields is None:
skip_fields = set()
# Note that if we add dataclasses as fields, they will need
# similar handling.
return {
field.name: getattr(self, field.name)
for field in fields(self) if field.name not in skip_fields
}
T = TypeVar("T", bound=AttentionMetadata)
class AttentionState(ABC, Generic[T]):
"""Holds attention backend-specific objects reused during the
lifetime of the model runner."""
@abstractmethod
def __init__(self, runner: "ModelRunnerBase"):
...
@abstractmethod
@contextmanager
def graph_capture(self, max_batch_size: int):
"""Context manager used when capturing CUDA graphs."""
yield
@abstractmethod
def graph_clone(self, batch_size: int) -> "AttentionState[T]":
"""Clone attention state to save in CUDA graph metadata."""
...
@abstractmethod
def graph_capture_get_metadata_for_batch(
self,
batch_size: int,
is_encoder_decoder_model: bool = False) -> T:
"""Get attention metadata for CUDA graph capture of batch_size."""
...
@abstractmethod
def get_graph_input_buffers(
self,
attn_metadata: T,
is_encoder_decoder_model: bool = False) -> Dict[str, Any]:
"""Get attention-specific input buffers for CUDA graph capture."""
...
@abstractmethod
def prepare_graph_input_buffers(
self,
input_buffers: Dict[str, Any],
attn_metadata: T,
is_encoder_decoder_model: bool = False) -> None:
"""In-place modify input buffers dict for CUDA graph replay."""
...
@abstractmethod
def begin_forward(self, model_input: "ModelRunnerInputBase") -> None:
"""Prepare state for forward pass."""
...
class AttentionMetadataBuilder(ABC, Generic[T]):
"""Abstract class for attention metadata builders."""
@abstractmethod
def __init__(self, input_builder: "ModelRunnerInputBuilderBase") -> None:
"""Create the builder, remember some configuration and parameters."""
raise NotImplementedError
@abstractmethod
def prepare(self) -> None:
"""Prepare for one batch."""
raise NotImplementedError
@abstractmethod
def build(self, seq_lens: List[int], query_lens: List[int],
cuda_graph_pad_size: int, batch_size: int) -> T:
"""Build attention metadata with on-device tensors."""
raise NotImplementedError
class AttentionLayer(Protocol):
_q_scale: torch.Tensor
_k_scale: torch.Tensor
_v_scale: torch.Tensor
_k_scale_float: float
_v_scale_float: float
_prob_scale: torch.Tensor
def forward(
self,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
kv_cache: torch.Tensor,
attn_metadata: AttentionMetadata,
) -> torch.Tensor:
...
class AttentionImpl(ABC, Generic[T]):
@abstractmethod
def __init__(
self,
num_heads: int,
head_size: int,
scale: float,
num_kv_heads: Optional[int] = None,
alibi_slopes: Optional[List[float]] = None,
sliding_window: Optional[int] = None,
kv_cache_dtype: str = "auto",
blocksparse_params: Optional[Dict[str, Any]] = None,
logits_soft_cap: Optional[float] = None,
attn_type: str = AttentionType.DECODER,
kv_sharing_target_layer_name: Optional[str] = None,
) -> None:
raise NotImplementedError
@abstractmethod
def forward(
self,
layer: AttentionLayer,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
kv_cache: torch.Tensor,
attn_metadata: T,
output: Optional[torch.Tensor] = None,
output_scale: Optional[torch.Tensor] = None,
) -> torch.Tensor:
raise NotImplementedError
def fused_output_quant_supported(self, dtype: torch.dtype, static: bool,
group_shape: tuple[int, int]):
"""
Does this attention implementation support fused output quantization.
This is used by the AttnFusionPass to only fuse output quantization
onto implementations that support it.
TODO(luka) merge parameters into QuantDescriptor
:param dtype: quantized dtype
:param static: static or dynamic quantization
:param group_shape: quant group shape. (-1, -1) for per-tensor.
:return: is fusion supported for this type of quantization
"""
return False
class MLAAttentionImpl(AttentionImpl[T], Generic[T]):
@abstractmethod
def forward(
self,
layer: AttentionLayer,
hidden_states_or_cq: torch.Tensor,
kv_c_normed: torch.Tensor,
k_pe: torch.Tensor,
kv_cache: torch.Tensor,
attn_metadata: T,
output: Optional[torch.Tensor] = None,
output_scale: Optional[torch.Tensor] = None,
) -> torch.Tensor:
raise NotImplementedError
def is_quantized_kv_cache(kv_cache_dtype: str) -> bool:
return kv_cache_dtype != "auto"

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Tuple, Type
import torch
from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
AttentionLayer,
AttentionMetadata, AttentionType)
from vllm.attention.backends.utils import (CommonAttentionState,
CommonMetadataBuilder)
from vllm.attention.ops.blocksparse_attention.interface import (
LocalStridedBlockSparseAttn, get_head_sliding_step)
from vllm.attention.ops.paged_attn import PagedAttention
from vllm.distributed import (get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size)
@dataclass
class BlocksparseParams:
max_seqlen: int
# Num q heads per tensor-parallel rank/partition
num_heads: int # per TP partition
# Num kv heads per tensor-parallel rank/partition
num_kv_heads: int
# block size used for blocksparse attention.
# This is the block_size used in `local_blocks`, `vert_stride`.
block_size: int
# Number of blocks for local attention, i.e., number of
# local attended tokens / `sparse_block_size`
local_blocks: int
# Attend to one block per every `vert_stride` blocks.
# Controlling the sparsity
vert_stride: int
"""
If to use the same vertical stride offset for all heads,
i.e., attend to the same block of tokens on all heads.
By default, it is False, i.e., attention on the non-local
blocks depends on the `head_idx`, that is on
blocks satisfying
`(block_idx + head_idx * head_sliding_step + 1) % vert_stride == 0`
where `head_sliding_step=max(1, int(vert_stride / num_total_heads))`,
`block_idx = position_id // sparse_block_size`.
See `..ops.blocksparse_attention.utils:get_sparse_attn_mask`
for more detail.
"""
homo_head: bool = False
# If within a group, the kv offsets that each q attends is the same or no.
homo_head_group: bool = False
# Decided by homo_head and homo_head group
head_sliding_step: int = field(init=False)
# range of q heads to for a TP rank
active_head_range: Tuple = field(init=False)
def __post_init__(self):
assert self.block_size > 0
assert self.local_blocks >= 0
assert self.vert_stride >= 1
tp_size = get_tensor_model_parallel_world_size()
tp_rank = get_tensor_model_parallel_rank()
total_heads = tp_size * self.num_heads
total_kv_heads = tp_size * self.num_kv_heads
if self.homo_head:
self.head_sliding_step = 0
elif self.homo_head_group:
head_sliding_step = get_head_sliding_step(total_kv_heads,
self.vert_stride)
# negative indicates sliding along kv heads, i.e., homo q group
self.head_sliding_step = -head_sliding_step
else:
self.head_sliding_step = get_head_sliding_step(
total_heads, self.vert_stride)
self.active_head_range = (
tp_rank * self.num_heads,
(tp_rank + 1) * self.num_heads,
)
class BlocksparseFlashAttentionBackend(AttentionBackend):
@staticmethod
def get_name() -> str:
return "BLOCK_SPARSE_FLASH_ATTN"
@staticmethod
def get_impl_cls() -> Type["BlocksparseFlashAttentionImpl"]:
return BlocksparseFlashAttentionImpl
@staticmethod
def get_metadata_cls() -> Type["AttentionMetadata"]:
return BlocksparseFlashAttentionMetadata
@staticmethod
def get_builder_cls() -> Type["BlocksparseFlashAttentionMetadataBuilder"]:
return BlocksparseFlashAttentionMetadataBuilder
@staticmethod
def get_state_cls() -> Type["CommonAttentionState"]:
return CommonAttentionState
@staticmethod
def get_kv_cache_shape(
num_blocks: int,
block_size: int,
num_kv_heads: int,
head_size: int,
) -> Tuple[int, ...]:
return PagedAttention.get_kv_cache_shape(num_blocks, block_size,
num_kv_heads, head_size)
@staticmethod
def swap_blocks(
src_kv_cache: torch.Tensor,
dst_kv_cache: torch.Tensor,
src_to_dst: Dict[int, int],
) -> None:
PagedAttention.swap_blocks(src_kv_cache, dst_kv_cache, src_to_dst)
@staticmethod
def copy_blocks(
kv_caches: List[torch.Tensor],
src_to_dists: Dict[int, List[int]],
) -> None:
PagedAttention.copy_blocks(kv_caches, src_to_dists)
@dataclass
class BlocksparseFlashAttentionMetadata(AttentionMetadata):
"""A copy of Metadata for FlashAttentionBackend,
to avoid having to install flash_attn.
NOTE: Any python object stored here is not updated when it is
cuda-graph replayed. If you have values that need to be changed
dynamically, it should be stored in tensor. The tensor has to be
updated from `CUDAGraphRunner.forward` API.
"""
# (batch_size,). The sequence length per sequence. Sequence length means
# the computed tokens + new tokens None if it is a decoding.
seq_lens: Optional[List[int]]
# seq_lens stored as a tensor.
seq_lens_tensor: Optional[torch.Tensor]
# NOTE(sang): Definition of context_len, query_len, and seq_len.
# |---------- N-1 iteration --------|
# |---------------- N iteration ---------------------|
# |- tokenA -|......................|-- newTokens ---|
# |---------- context_len ----------|
# |-------------------- seq_len ----------------------|
# |-- query_len ---|
# Maximum query length in the batch. None for decoding.
max_query_len: Optional[int]
# Maximum sequence length among prefill batch. 0 if there are decoding
# requests only.
max_prefill_seq_len: int
# Maximum sequence length among decode batch. 0 if there are prefill
# requests only.
max_decode_seq_len: int
# (batch_size + 1,). The cumulative subquery lengths of the sequences in
# the batch, used to index into subquery. E.g., if the subquery length
# is [4, 6], it is [0, 4, 10].
query_start_loc: Optional[torch.Tensor]
# (batch_size + 1,). The cumulative sequence lengths of the sequences in
# the batch, used to index into sequence. E.g., if the sequence length is
# [4, 6], it is [0, 4, 10].
seq_start_loc: Optional[torch.Tensor]
# (batch_size,) A tensor of context lengths (tokens that are computed
# so far).
context_lens_tensor: Optional[torch.Tensor]
# (batch_size, max_blocks_per_seq).
# Block addresses per sequence. (Seq id -> list of physical block)
# E.g., [0, 1, 2] means tokens are stored in 0th, 1st, and 2nd blocks
# in the kv cache. Each block can contain up to block_size tokens.
# 2nd dimensions are padded up to max_blocks_per_seq if it is cuda-graph
# captured.
block_tables: Optional[torch.Tensor]
# Whether or not if cuda graph is enabled.
# Cuda-graph is currently enabled for decoding only.
# TODO(woosuk): Move `use_cuda_graph` out since it's unrelated to attention.
use_cuda_graph: bool
# Max number of query tokens for among request in the batch.
max_decode_query_len: Optional[int] = None
_cached_prefill_metadata: Optional[
"BlocksparseFlashAttentionMetadata"] = None
_cached_decode_metadata: Optional[
"BlocksparseFlashAttentionMetadata"] = None
block_tables_list: Optional[List[int]] = None
@property
def prefill_metadata(
self) -> Optional["BlocksparseFlashAttentionMetadata"]:
if self.num_prefills == 0:
return None
if self._cached_prefill_metadata is not None:
return self._cached_prefill_metadata
assert self.seq_lens is not None
assert self.seq_lens_tensor is not None
assert self.query_start_loc is not None
assert self.context_lens_tensor is not None
assert self.block_tables is not None
assert self.seq_start_loc is not None
self._cached_prefill_metadata = BlocksparseFlashAttentionMetadata(
num_prefills=self.num_prefills,
num_prefill_tokens=self.num_prefill_tokens,
num_decode_tokens=0,
slot_mapping=self.slot_mapping[:self.num_prefill_tokens],
multi_modal_placeholder_index_maps=self.
multi_modal_placeholder_index_maps,
enable_kv_scales_calculation=self.enable_kv_scales_calculation,
seq_lens=self.seq_lens[:self.num_prefills],
seq_lens_tensor=self.seq_lens_tensor[:self.num_prefills],
max_query_len=self.max_query_len,
max_prefill_seq_len=self.max_prefill_seq_len,
max_decode_seq_len=0,
query_start_loc=self.query_start_loc[:self.num_prefills + 1],
seq_start_loc=self.seq_start_loc[:self.num_prefills + 1],
context_lens_tensor=self.context_lens_tensor[:self.num_prefills],
block_tables=self.block_tables[:self.num_prefills],
use_cuda_graph=False,
block_tables_list=self.block_tables_list
)
return self._cached_prefill_metadata
@property
def decode_metadata(self) -> Optional["BlocksparseFlashAttentionMetadata"]:
if self.num_decode_tokens == 0:
return None
if self._cached_decode_metadata is not None:
return self._cached_decode_metadata
assert self.block_tables is not None
assert self.seq_lens_tensor is not None
self._cached_decode_metadata = BlocksparseFlashAttentionMetadata(
num_prefills=0,
num_prefill_tokens=0,
num_decode_tokens=self.num_decode_tokens,
slot_mapping=self.slot_mapping[self.num_prefill_tokens:],
multi_modal_placeholder_index_maps=None,
enable_kv_scales_calculation=False,
seq_lens=None,
seq_lens_tensor=self.seq_lens_tensor[self.num_prefills:],
max_query_len=None,
max_prefill_seq_len=0,
max_decode_seq_len=self.max_decode_seq_len,
query_start_loc=None,
seq_start_loc=None,
context_lens_tensor=None,
block_tables=self.block_tables[self.num_prefills:],
use_cuda_graph=self.use_cuda_graph,
block_tables_list=self.block_tables_list
)
return self._cached_decode_metadata
class BlocksparseFlashAttentionMetadataBuilder(
CommonMetadataBuilder[BlocksparseFlashAttentionMetadata]):
_metadata_cls = BlocksparseFlashAttentionMetadata
class BlocksparseFlashAttentionImpl(AttentionImpl):
"""
If the input tensors contain prompt tokens, the layout is as follows:
|<--------------- num_prompt_tokens -------------->|
|<--prompt_0-->|<--prompt_1-->|...|<--prompt_N-1-->|
Otherwise, the layout is as follows:
|<------------------ num_generation_tokens (M) ----------------->|
|<--generation_0-->|..........|<--generation_M-1-->|<--padding-->|
Generation tokens can contain padding when cuda-graph is used.
Currently, prompt tokens don't contain any padding.
The prompts might have different lengths, while the generation tokens
always have length 1.
"""
def __init__(
self,
num_heads: int,
head_size: int,
scale: float,
num_kv_heads: int,
alibi_slopes: Optional[List[float]],
sliding_window: Optional[int],
kv_cache_dtype: str,
blocksparse_params: Optional[Dict[str, Any]] = None,
logits_soft_cap: Optional[float] = None,
attn_type: str = AttentionType.DECODER,
kv_sharing_target_layer_name: Optional[str] = None,
) -> None:
if kv_sharing_target_layer_name is not None:
raise NotImplementedError("KV sharing is not supported in V0.")
assert blocksparse_params is not None
assert alibi_slopes is None, ValueError(
"Alibi not support for blocksparse flash attention.")
assert sliding_window is None, ValueError(
"sliding_window is invalid for blocksparse attention.")
assert logits_soft_cap is None, ValueError(
"logits_soft_cap is invalid for blocksparse attention.")
if "num_heads" not in blocksparse_params:
blocksparse_params["num_heads"] = num_heads
if "num_kv_heads" not in blocksparse_params:
blocksparse_params["num_kv_heads"] = num_kv_heads or num_heads
self.blocksparse_params = BlocksparseParams(**blocksparse_params)
self.kv_cache_dtype = kv_cache_dtype
self.num_heads = num_heads
self.head_size = head_size
self.scale = float(scale)
self.alibi_slopes = alibi_slopes
self.num_kv_heads = num_kv_heads
self.num_queries_per_kv = self.num_heads // self.num_kv_heads
self.local_blocks = self.blocksparse_params.local_blocks
self.vert_stride = self.blocksparse_params.vert_stride
self.sparse_block_size = self.blocksparse_params.block_size
self.head_sliding_step = self.blocksparse_params.head_sliding_step
supported_head_sizes = PagedAttention.get_supported_head_sizes()
if head_size not in supported_head_sizes:
raise ValueError(
f"Head size {head_size} is not supported by PagedAttention. "
f"Supported head sizes are: {supported_head_sizes}.")
self.tp_size = get_tensor_model_parallel_world_size()
self.tp_rank = get_tensor_model_parallel_rank()
total_num_heads = num_heads * self.tp_size
self.bs_attn = LocalStridedBlockSparseAttn(
total_num_heads,
self.blocksparse_params.max_seqlen,
self.blocksparse_params.local_blocks,
self.blocksparse_params.vert_stride,
self.blocksparse_params.block_size,
homo_head=self.blocksparse_params.homo_head,
active_head_range=self.blocksparse_params.active_head_range,
)
if attn_type != AttentionType.DECODER:
raise NotImplementedError("Encoder self-attention and "
"encoder/decoder cross-attention "
"are not implemented for "
"BlocksparseFlashAttentionImpl")
def forward(
self,
layer: AttentionLayer,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
kv_cache: torch.Tensor,
attn_metadata: BlocksparseFlashAttentionMetadata,
output: Optional[torch.Tensor] = None,
output_scale: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""Forward pass with FlashAttention and PagedAttention.
Args:
query: shape = [num_tokens, num_heads * head_size]
key: shape = [num_tokens, num_kv_heads * head_size]
value: shape = [num_tokens, num_kv_heads * head_size]
kv_cache = [2, num_blocks, block_size * num_kv_heads * head_size]
NOTE: kv_cache will be an empty tensor with shape [0]
for profiling run.
attn_metadata: Metadata for attention.
Returns:
shape = [num_tokens, num_heads * head_size]
"""
if output_scale is not None:
raise NotImplementedError(
"fused output quantization is not yet supported"
" for BlocksparseFlashAttentionImpl")
num_tokens, hidden_size = query.shape
# Reshape the query, key, and value tensors.
query = query.view(-1, self.num_heads, self.head_size)
key = key.view(-1, self.num_kv_heads, self.head_size)
value = value.view(-1, self.num_kv_heads, self.head_size)
if kv_cache.numel() > 0:
key_cache, value_cache = PagedAttention.split_kv_cache(
kv_cache, self.num_kv_heads, self.head_size)
# Reshape the input keys and values and store them in the cache.
# If kv_cache is not provided, the new key and value tensors are
# not cached. This happens during the initial memory profiling run.
PagedAttention.write_to_paged_cache(
key,
value,
key_cache,
value_cache,
attn_metadata.slot_mapping,
self.kv_cache_dtype,
layer._k_scale,
layer._v_scale,
)
if prefill_meta := attn_metadata.prefill_metadata:
# Prompt run.
# normal attention
# When block_tables are not filled, it means q and k are the
# prompt, and they have the same length.
assert kv_cache.numel() == 0 \
or prefill_meta.block_tables is None \
or prefill_meta.block_tables.numel() == 0, \
"Does not support prefix-enabled attention."
output = self.bs_attn(
q=query,
k=key,
v=value,
cu_seqlens_q=prefill_meta.seq_start_loc,
cu_seqlens_k=prefill_meta.seq_start_loc,
sm_scale=self.scale,
)
if decode_meta := attn_metadata.decode_metadata:
# Decoding run.
output = PagedAttention.forward_decode(
query,
key_cache,
value_cache,
decode_meta.block_tables,
decode_meta.seq_lens_tensor,
self.blocksparse_params.max_seqlen,
self.kv_cache_dtype,
self.num_kv_heads,
self.scale,
self.alibi_slopes,
layer._k_scale,
layer._v_scale,
tp_rank=self.tp_rank,
blocksparse_local_blocks=self.local_blocks,
blocksparse_vert_stride=self.vert_stride,
blocksparse_block_size=self.sparse_block_size,
blocksparse_head_sliding_step=self.head_sliding_step,
)
assert output is not None
# Reshape the output tensor.
return output.view(num_tokens, hidden_size)

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Type
import torch
import vllm._custom_ops as ops
from vllm._ipex_ops import ipex_ops
from vllm.attention.backends.abstract import (AttentionBackend,
AttentionMetadataBuilder,
AttentionType,
is_quantized_kv_cache)
from vllm.attention.backends.mla.common import MLACommonImpl, MLACommonState
from vllm.attention.backends.torch_sdpa import TorchSDPAMetadata
from vllm.utils import make_tensor_with_pad
from vllm.worker.cpu_model_runner import ModelInputForCPUBuilder
class CPUMLABackend(AttentionBackend):
@staticmethod
def get_name() -> str:
return "CPU_MLA"
@staticmethod
def get_metadata_cls() -> Type["CPUMLAMetadata"]:
return CPUMLAMetadata
@staticmethod
def get_builder_cls() -> Type["CPUMLAMetadataBuilder"]:
return CPUMLAMetadataBuilder
@staticmethod
def get_state_cls() -> Type["MLACommonState"]:
return MLACommonState
@staticmethod
def get_impl_cls() -> Type["CPUMLAImpl"]:
return CPUMLAImpl
@staticmethod
def get_kv_cache_shape(
num_blocks: int,
block_size: int,
num_kv_heads: int, # assumed to be 1 for MLA
head_size: int,
) -> Tuple[int, ...]:
return (num_blocks, block_size, head_size)
@staticmethod
def swap_blocks(
src_kv_cache: torch.Tensor,
dst_kv_cache: torch.Tensor,
src_to_dst: torch.Tensor,
) -> None:
ops.swap_blocks(src_kv_cache, dst_kv_cache, src_to_dst)
@staticmethod
def copy_blocks(
kv_caches: List[torch.Tensor],
src_to_dists: torch.Tensor,
) -> None:
ops.copy_blocks_mla(kv_caches, src_to_dists)
@staticmethod
def get_supported_head_sizes() -> List[int]:
return [576]
@dataclass
class CPUMLAMetadata(TorchSDPAMetadata):
# New for MLA
# Input positions for rotrary embeddings since for MLA the rotary
# position embeddings are applied inside the attention backend
input_positions: torch.Tensor = None
# required by MLACommonImpl
is_profile_run: bool = False
class CPUMLAMetadataBuilder(AttentionMetadataBuilder[CPUMLAMetadata]):
def __init__(self, input_builder: ModelInputForCPUBuilder) -> None:
self.chunked_prefill = input_builder.chunked_prefill
self.input_builder = input_builder
assert not self.chunked_prefill, \
"chunked prefill is currently not supported"
def prepare(self):
self.input_data = self.input_builder.input_data
def build(self, seq_lens, query_lens, cuda_graph_pad_size, batch_size):
input_data = self.input_data
prefill_seq_lens = seq_lens[0:input_data.num_prefills]
prefill_query_lens = query_lens[0:input_data.num_prefills]
slot_mapping = torch.tensor(input_data.slot_mapping,
dtype=torch.long,
device="cpu")
# metadata for prefill
if input_data.num_prefills > 0:
query_lens_tensor = torch.tensor(prefill_query_lens,
dtype=torch.int32,
device="cpu")
kv_lens_tensor = torch.tensor(prefill_seq_lens,
dtype=torch.int32,
device="cpu")
query_start_loc = torch.zeros(input_data.num_prefills + 1,
dtype=torch.int32,
device="cpu")
kv_start_loc = torch.zeros(input_data.num_prefills + 1,
dtype=torch.int32,
device="cpu")
torch.cumsum(query_lens_tensor,
dim=0,
dtype=torch.int32,
out=query_start_loc[1:])
torch.cumsum(kv_lens_tensor,
dim=0,
dtype=torch.int32,
out=kv_start_loc[1:])
max_query_len = max(prefill_query_lens)
max_kv_len = max(prefill_seq_lens)
# for chunked-prefill
if self.chunked_prefill:
prefill_block_tables = make_tensor_with_pad(
self.input_data.prefill_block_tables,
pad=0,
dtype=torch.int32,
device="cpu",
)
else:
prefill_block_tables = None
else:
query_start_loc = None
kv_start_loc = None
max_query_len = None
max_kv_len = None
prefill_block_tables = None
# metadata for decode
if input_data.num_decode_tokens != 0:
seq_lens_tensor = torch.tensor(
input_data.seq_lens[input_data.num_prefills:],
dtype=torch.int32,
device="cpu",
)
block_tables = make_tensor_with_pad(
self.input_data.decode_block_tables,
pad=0,
dtype=torch.int32,
device="cpu",
)
else:
block_tables = torch.tensor([])
seq_lens_tensor = torch.tensor(
input_data.seq_lens[:input_data.num_prefills],
dtype=torch.int32,
device="cpu",
)
# For multi-modal models
placeholder_index_maps = None
if len(input_data.multi_modal_inputs_list) != 0:
placeholder_index_maps = {
modality: placeholder_map.index_map()
for modality, placeholder_map in
input_data.multi_modal_placeholder_maps.items()
}
return CPUMLAMetadata(
chunked_prefill=self.chunked_prefill,
seq_lens=prefill_seq_lens,
seq_lens_tensor=seq_lens_tensor,
max_query_len=max_query_len,
max_kv_len=max_kv_len,
prefill_query_start_loc=query_start_loc,
kv_start_loc=kv_start_loc,
max_decode_seq_len=input_data.max_decode_seq_len,
num_prefills=input_data.num_prefills,
num_prefill_tokens=input_data.num_prefill_tokens,
num_decode_tokens=input_data.num_decode_tokens,
block_tables=block_tables,
prefill_block_tables=prefill_block_tables,
slot_mapping=slot_mapping,
multi_modal_placeholder_index_maps=placeholder_index_maps,
enable_kv_scales_calculation=False,
input_positions=torch.tensor([self.input_data.input_positions]))
class CPUMLAImpl(MLACommonImpl[CPUMLAMetadata]):
def __init__(
self,
num_heads: int,
head_size: int,
scale: float,
num_kv_heads: int,
alibi_slopes: Optional[List[float]],
sliding_window: Optional[int],
kv_cache_dtype: str,
blocksparse_params: Optional[Dict[str, Any]],
logits_soft_cap: Optional[float],
attn_type: str,
kv_sharing_target_layer_name: Optional[str],
# MLA Specific Arguments
**mla_args) -> None:
super().__init__(num_heads, head_size, scale, num_kv_heads,
alibi_slopes, sliding_window, kv_cache_dtype,
blocksparse_params, logits_soft_cap, attn_type,
kv_sharing_target_layer_name, **mla_args)
unsupported_features = [
alibi_slopes, sliding_window, blocksparse_params, logits_soft_cap
]
if any(unsupported_features):
raise NotImplementedError(
"CPUMLAImpl does not support one of the following: "
"alibi_slopes, sliding_window, blocksparse_params, "
"logits_soft_cap")
if attn_type != AttentionType.DECODER:
raise NotImplementedError("Encoder self-attention and "
"encoder/decoder cross-attention "
"are not implemented for "
"CPUMLAImpl")
# states is implemented.
if is_quantized_kv_cache(self.kv_cache_dtype):
raise NotImplementedError(
"CPUMLAImpl with FP8 KV cache not yet supported")
def _forward_prefill(
self,
q: torch.Tensor,
kv_c_normed: torch.Tensor,
k_pe: torch.Tensor,
kv_c_and_k_pe_cache: torch.Tensor,
attn_metadata: CPUMLAMetadata, # type: ignore[override]
) -> torch.Tensor:
prefill_metadata = attn_metadata.prefill_metadata
assert prefill_metadata is not None
kv_nope = self.kv_b_proj(kv_c_normed)[0].view(\
-1, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
k_nope, v = kv_nope\
.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
k = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))), dim=-1)
# For MLA the v head dim is smaller than qk head dim so we pad out
# v with 0s to match the qk head dim
v_padded = torch.nn.functional.pad(v, [0, q.shape[-1] - v.shape[-1]],
value=0)
output = torch.empty_like(q)
ipex_ops.varlen_attention(
query=q,
key=k,
value=v_padded,
out=output,
seqlen_q=prefill_metadata.prefill_query_start_loc,
seqlen_k=prefill_metadata.prefill_query_start_loc,
max_seqlen_q=prefill_metadata.max_query_len,
max_seqlen_k=prefill_metadata.max_query_len,
pdropout=0.0,
softmax_scale=self.scale,
zero_tensors=False,
is_causal=True,
return_softmax=False,
gen_=None,
logits_soft_cap=0.0,
window_size_left=-1,
window_size_right=-1,
alibi_slopes=None,
)
# remove padding
output = output.view(-1, self.num_heads,
q.shape[-1])[..., :v.shape[-1]]
return output.reshape(-1, self.num_heads * v.shape[-1])
def _forward_decode(
self,
q_nope: torch.Tensor,
q_pe: torch.Tensor,
kv_c_and_k_pe_cache: torch.Tensor,
attn_metadata: CPUMLAMetadata, # type: ignore[override]
) -> torch.Tensor:
assert kv_c_and_k_pe_cache.numel() > 0
decode_meta = attn_metadata.decode_metadata
assert decode_meta is not None
q = torch.cat([q_nope, q_pe], dim=-1)
o = q.new_empty(q.shape[0], self.num_heads, self.kv_lora_rank)
# Run MQA
ops.mla_decode_kvcache_cpu(o, q, kv_c_and_k_pe_cache, self.scale,
decode_meta.block_tables,
decode_meta.seq_lens_tensor)
return self._v_up_proj(o)

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from contextlib import contextmanager
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Type
import torch
from vllm.attention.backends.abstract import (AttentionType,
is_quantized_kv_cache)
from vllm.attention.backends.mla.common import (MLACommonBackend,
MLACommonImpl,
MLACommonMetadata,
MLACommonMetadataBuilder,
MLACommonState)
from vllm.attention.ops.flashmla import (flash_mla_with_kvcache,
get_mla_metadata,
is_flashmla_supported)
if TYPE_CHECKING:
from vllm.worker.model_runner import ModelInputForGPUWithSamplingMetadata
class FlashMLABackend(MLACommonBackend):
@staticmethod
def get_name() -> str:
return "FLASHMLA"
@staticmethod
def get_impl_cls() -> Type["FlashMLAImpl"]:
return FlashMLAImpl
@staticmethod
def get_metadata_cls() -> Type["FlashMLAMetadata"]:
return FlashMLAMetadata
@staticmethod
def get_builder_cls() -> Type["FlashMLAMetadataBuilder"]:
return FlashMLAMetadataBuilder
@staticmethod
def get_state_cls() -> Type["FlashMLAState"]:
return FlashMLAState
@dataclass
class FlashMLAMetadata(MLACommonMetadata):
decode_tile_scheduler_metadata: Optional[Tuple[torch.Tensor,
torch.Tensor]] = None
decode_num_splits: Optional[torch.Tensor] = None
@property
def decode_metadata(self):
decode_metadata = super().decode_metadata
# TODO: cache assignment?
if decode_metadata is not None:
decode_metadata.decode_tile_scheduler_metadata=\
self.decode_tile_scheduler_metadata
decode_metadata.decode_num_splits=\
self.decode_num_splits
return decode_metadata
def advance_step(self,
model_input: "ModelInputForGPUWithSamplingMetadata",
sampled_token_ids: Optional[torch.Tensor],
block_size: int,
num_seqs: int,
num_queries: int,
turn_prefills_into_decodes: bool = False):
raise NotImplementedError(
"advance_step is not implemented for FlashMLA")
class FlashMLAMetadataBuilder(MLACommonMetadataBuilder[FlashMLAMetadata]):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.num_q_heads = self.runner.model_config.get_num_attention_heads(
self.runner.parallel_config)
def build(self, seq_lens: List[int], query_lens: List[int],
cuda_graph_pad_size: int, batch_size: int):
m = super().build(seq_lens, query_lens, cuda_graph_pad_size,
batch_size)
if m.num_decode_tokens > 0:
m.decode_tile_scheduler_metadata, m.decode_num_splits = \
get_mla_metadata(
m.seq_lens_tensor[m.num_prefills:],
self.num_q_heads,
1, # MQA for the decode path
)
return m
class FlashMLAState(MLACommonState[FlashMLAMetadata]):
def __init__(self, *args, **kwds):
super().__init__(*args, **kwds)
self.num_q_heads = self.runner.model_config.get_num_attention_heads(
self.runner.parallel_config)
@contextmanager
def graph_capture(self, max_batch_size: int):
# Run a dummy `get_mla_metadata` so we can get the right shapes
self._graph_decoder_tile_scheduler_metadata, \
self._graph_decode_num_splits = get_mla_metadata(
torch.ones(
max_batch_size, dtype=torch.int32, device=self.runner.device),
self.num_q_heads,
1, # MQA for the decode path
)
with super().graph_capture(max_batch_size):
yield
del self._graph_decoder_tile_scheduler_metadata
del self._graph_decode_num_splits
def graph_capture_get_metadata_for_batch(
self, batch_size: int, is_encoder_decoder_model: bool = False):
metadata = super().graph_capture_get_metadata_for_batch(
batch_size, is_encoder_decoder_model)
assert metadata.num_decode_tokens > 0
decoder_tile_scheduler_metadata, decode_num_splits = get_mla_metadata(
self._graph_seq_lens[:batch_size],
self.num_q_heads,
1, # MQA for the decode path
)
self._graph_decoder_tile_scheduler_metadata.copy_(
decoder_tile_scheduler_metadata)
self._graph_decode_num_splits[:batch_size + 1].copy_(decode_num_splits)
metadata.decode_tile_scheduler_metadata=\
self._graph_decoder_tile_scheduler_metadata
metadata.decode_num_splits=\
self._graph_decode_num_splits[:batch_size + 1]
return metadata
def get_graph_input_buffers(self,
attn_metadata,
is_encoder_decoder_model: bool = False):
input_buffers = super().get_graph_input_buffers(
attn_metadata, is_encoder_decoder_model)
input_buffers["decode_tile_scheduler_metadata"] = \
attn_metadata.decode_metadata.decode_tile_scheduler_metadata
input_buffers["decode_num_splits"] = \
attn_metadata.decode_metadata.decode_num_splits
return input_buffers
def prepare_graph_input_buffers(self,
input_buffers,
attn_metadata,
is_encoder_decoder_model: bool = False):
super().prepare_graph_input_buffers(input_buffers, attn_metadata,
is_encoder_decoder_model)
input_buffers["decode_tile_scheduler_metadata"].copy_(
attn_metadata.decode_metadata.decode_tile_scheduler_metadata)
input_buffers["decode_num_splits"].copy_(
attn_metadata.decode_metadata.decode_num_splits)
class FlashMLAImpl(MLACommonImpl[FlashMLAMetadata]):
def __init__(
self,
num_heads: int,
head_size: int,
scale: float,
num_kv_heads: int,
alibi_slopes: Optional[List[float]],
sliding_window: Optional[int],
kv_cache_dtype: str,
blocksparse_params: Optional[Dict[str, Any]],
logits_soft_cap: Optional[float],
attn_type: str,
kv_sharing_target_layer_name: Optional[str] = None,
# MLA Specific Arguments
**mla_args) -> None:
super().__init__(num_heads, head_size, scale, num_kv_heads,
alibi_slopes, sliding_window, kv_cache_dtype,
blocksparse_params, logits_soft_cap, attn_type,
kv_sharing_target_layer_name, **mla_args)
assert is_flashmla_supported(), \
"FlashMLA is not supported on this device"
unsupported_features = [
alibi_slopes, sliding_window, blocksparse_params, logits_soft_cap
]
if any(unsupported_features):
raise NotImplementedError(
"FlashMLAImpl does not support one of the following: "
"alibi_slopes, sliding_window, blocksparse_params, "
"logits_soft_cap")
if attn_type != AttentionType.DECODER:
raise NotImplementedError("Encoder self-attention and "
"encoder/decoder cross-attention "
"are not implemented for "
"FlashMLAImpl")
if is_quantized_kv_cache(self.kv_cache_dtype):
if self.kv_cache_dtype != "fp8":
raise NotImplementedError(
"FlashMLA with other KV cache not yet supported")
def _forward_decode(
self,
q_nope: torch.Tensor,
q_pe: torch.Tensor,
kv_c_and_k_pe_cache: torch.Tensor,
attn_metadata: FlashMLAMetadata,
k_scale = None,
kv_cache_dtype = "auto",
) -> torch.Tensor:
assert kv_c_and_k_pe_cache.numel() > 0
decode_meta = attn_metadata.decode_metadata
assert decode_meta is not None
q = torch.cat([q_nope, q_pe], dim=-1)\
.unsqueeze(1) # Add seqlen dim of 1 (decode)
o, _ = flash_mla_with_kvcache(
q=q,
k_cache=kv_c_and_k_pe_cache.unsqueeze(-2), # Add head dim of 1
block_table=decode_meta.block_tables,
cache_seqlens=decode_meta.seq_lens_tensor,
head_dim_v=self.kv_lora_rank,
tile_scheduler_metadata=decode_meta.decode_tile_scheduler_metadata,
num_splits=decode_meta.decode_num_splits,
softmax_scale=self.scale,
causal=True,
k_scale = k_scale,
kv_cache_dtype = kv_cache_dtype,
)
return self._v_up_proj(o)

318
vllm/attention/backends/hpu_attn.py Normal file
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
###############################################################################
# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company
###############################################################################
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Type
import torch
import vllm_hpu_extension.kernels as kernels
import vllm_hpu_extension.ops as ops
from vllm_hpu_extension.flags import enabled_flags
from vllm_hpu_extension.utils import Matmul, Softmax, VLLMKVCache
from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
AttentionLayer,
AttentionMetadata, AttentionType,
is_quantized_kv_cache)
from vllm.attention.backends.utils import CommonAttentionState
from vllm.attention.ops.hpu_paged_attn import (HPUPagedAttention,
HPUPagedAttentionMetadata)
from vllm.logger import init_logger
logger = init_logger(__name__)
class HPUAttentionBackend(AttentionBackend):
@staticmethod
def get_name() -> str:
return "HPU_ATTN"
@staticmethod
def get_impl_cls() -> Type["HPUAttentionImpl"]:
return HPUAttentionImpl
@staticmethod
def get_metadata_cls() -> Type["AttentionMetadata"]:
return HPUAttentionMetadata
@staticmethod
def get_state_cls() -> Type["CommonAttentionState"]:
return CommonAttentionState
@staticmethod
def get_kv_cache_shape(
num_blocks: int,
block_size: int,
num_kv_heads: int,
head_size: int,
) -> Tuple[int, ...]:
return HPUPagedAttention.get_kv_cache_shape(num_blocks, block_size,
num_kv_heads, head_size)
@staticmethod
def swap_blocks(
src_kv_cache: torch.Tensor,
dst_kv_cache: torch.Tensor,
src_to_dsts: torch.Tensor,
) -> None:
HPUPagedAttention.swap_blocks(src_kv_cache, dst_kv_cache, src_to_dsts)
@staticmethod
def copy_blocks(
kv_caches: List[torch.Tensor],
src_to_dsts: torch.Tensor,
) -> None:
HPUPagedAttention.copy_blocks(kv_caches, src_to_dsts)
@dataclass
class HPUAttentionMetadata(HPUPagedAttentionMetadata, AttentionMetadata):
"""Metadata for HPUAttentionbackend."""
# Currently, input sequences can only contain all prompts
# or all decoding. True if all sequences are prompts.
is_prompt: bool
attn_bias: Optional[torch.Tensor]
seq_lens_tensor: Optional[torch.Tensor]
context_lens_tensor: Optional[torch.Tensor]
class HPUAttentionImpl(AttentionImpl, torch.nn.Module):
"""
If the input tensors contain prompt tokens, the layout is as follows:
|<--------------- num_prefill_tokens ----------------->|
|<--prefill_0-->|<--prefill_1-->|...|<--prefill_N-1--->|
Otherwise, the layout is as follows:
|<----------------- num_decode_tokens ------------------>|
|<--decode_0-->|..........|<--decode_M-1-->|<--padding-->|
Generation tokens can contain padding when cuda-graph is used.
Currently, prompt tokens don't contain any padding.
The prompts might have different lengths, while the generation tokens
always have length 1.
"""
def __init__(
self,
num_heads: int,
head_size: int,
scale: float,
num_kv_heads: int,
alibi_slopes: Optional[List[float]],
sliding_window: Optional[int],
kv_cache_dtype: str,
blocksparse_params: Optional[Dict[str, Any]] = None,
max_seq_len: int = 4096,
attn_type: str = AttentionType.DECODER,
kv_sharing_target_layer_name: Optional[str] = None,
use_irope: bool = False,
) -> None:
super(AttentionImpl, self).__init__()
if kv_sharing_target_layer_name is not None:
raise NotImplementedError("KV sharing is not supported in V0.")
if use_irope:
logger.warning_once(
"Using irope in HPU is not supported yet, it will fall back "
"to global attention for long context.")
self.kv_cache_dtype = kv_cache_dtype
self.num_heads = num_heads
self.head_size = head_size
self.scale = float(scale)
self.matmul_qk = Matmul()
self.softmax = Softmax()
self.matmul_av = Matmul()
self.batch2block_matmul = Matmul()
self.block2batch_matmul = Matmul()
self.k_cache = VLLMKVCache()
self.v_cache = VLLMKVCache()
self.fused_scaled_dot_product_attention = kernels.fsdpa()
self.prefill_impl = 'naive'
if "flex_attention" in enabled_flags():
self.prefill_impl = 'flex'
if "fsdpa" in enabled_flags():
assert alibi_slopes is None, \
'Prefill with FusedSDPA not supported with alibi slopes!'
self.prefill_impl = 'fsdpa'
self.num_kv_heads = num_heads if num_kv_heads is None else num_kv_heads
self.sliding_window = sliding_window
self.alibi_slopes = alibi_slopes
if alibi_slopes is not None:
alibi_slopes_tensor = torch.tensor(alibi_slopes,
dtype=torch.bfloat16)
self.alibi_slopes = alibi_slopes_tensor
self.num_queries_per_kv = self.num_heads // self.num_kv_heads
if self.prefill_impl == 'fsdpa':
assert alibi_slopes is None, \
'Prefill with FusedSDPA not supported with alibi slopes!'
supported_head_sizes = HPUPagedAttention.get_supported_head_sizes()
if head_size not in supported_head_sizes:
raise ValueError(
f"Head size {head_size} is not supported by PagedAttention. "
f"Supported head sizes are: {supported_head_sizes}.")
self.attn_type = attn_type
if self.attn_type != AttentionType.DECODER:
raise NotImplementedError("Encoder self-attention and "
"encoder/decoder cross-attention "
"are not implemented for "
"HPUAttentionImpl")
if is_quantized_kv_cache(self.kv_cache_dtype):
raise NotImplementedError(
"HPUAttention with FP8 KV cache not yet supported")
def forward(
self,
layer: AttentionLayer,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
kv_cache: torch.Tensor,
attn_metadata: HPUAttentionMetadata,
output: Optional[torch.Tensor] = None,
output_scale: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""Forward pass with xFormers and PagedAttention.
Args:
query: shape = [num_tokens, num_heads * head_size]
key: shape = [num_tokens, num_kv_heads * head_size]
value: shape = [num_tokens, num_kv_heads * head_size]
kv_cache = [2, num_blocks, block_size * num_kv_heads * head_size]
attn_metadata: Metadata for attention.
Returns:
shape = [num_tokens, num_heads * head_size]
"""
if output_scale is not None:
raise NotImplementedError(
"fused output quantization is not yet supported"
" for HPUAttentionImpl")
batch_size, seq_len, hidden_size = query.shape
_, seq_len_kv, _ = key.shape
key = key.view(-1, self.num_kv_heads, self.head_size)
value = value.view(-1, self.num_kv_heads, self.head_size)
block_indices = attn_metadata.block_indices
block_offsets = attn_metadata.block_offsets
key_cache = None
value_cache = None
if attn_metadata.is_prompt and self.attn_type \
is not AttentionType.ENCODER_ONLY:
key = key.unflatten(0, (block_indices.size(0), -1))
value = value.unflatten(0, (block_indices.size(0), -1))
if kv_cache is not None and isinstance(kv_cache, tuple):
key_cache, value_cache = HPUPagedAttention.split_kv_cache(
kv_cache, self.num_kv_heads, self.head_size)
# Reshape the input keys and values and store them in the cache.
# If kv_cache is not provided, the new key and value tensors are
# not cached. This happens during the initial memory profiling run.
key_cache = self.k_cache(key, key_cache, block_indices,
block_offsets)
value_cache = self.v_cache(value, value_cache, block_indices,
block_offsets)
if attn_metadata.is_prompt:
# Prompt run.
query_shape = (batch_size, seq_len, self.num_heads, self.head_size)
kv_shape = (batch_size, seq_len_kv, self.num_kv_heads,
self.head_size)
attn_bias = attn_metadata.attn_bias
if attn_bias is not None and self.alibi_slopes is not None:
position_bias = _make_alibi_bias(self.alibi_slopes,
self.num_kv_heads,
attn_bias.dtype,
attn_bias.shape[-1])
attn_bias = attn_bias.tile((1, self.num_kv_heads, 1, 1))
attn_bias.add_(position_bias)
block_list = attn_metadata.block_list if attn_metadata \
and attn_metadata.block_list is not None else None
out = ops.prompt_attention(
impl=self.prefill_impl,
query=query.view(query_shape),
key=key.view(kv_shape),
value=value.view(kv_shape),
is_causal=True,
attn_bias=attn_bias,
valid_seq_lengths=attn_metadata.seq_lens_tensor,
**self.common_attention_args(block_list, key_cache,
value_cache))
output = out.reshape(batch_size, seq_len, hidden_size)
else:
# Decoding run.
output = HPUPagedAttention.forward_decode(
query=query,
block_mapping=attn_metadata.block_mapping,
block_bias=attn_metadata.attn_bias,
block_groups=attn_metadata.block_groups,
**self.common_attention_args(attn_metadata.block_list,
key_cache, value_cache))
# Reshape the output tensor.
return output.view(batch_size, seq_len, hidden_size)
def common_attention_args(self,
block_list=None,
key_cache=None,
value_cache=None):
fsdpa_op = self.fused_scaled_dot_product_attention.apply \
if self.fused_scaled_dot_product_attention is not None else None
return {
'scale': self.scale,
'matmul_qk_op': self.matmul_qk,
'matmul_av_op': self.matmul_av,
'batch2block_matmul_op': self.batch2block_matmul,
'block2batch_matmul_op': self.block2batch_matmul,
'fsdpa_op': fsdpa_op,
'keys_fetch_func': self.k_cache.fetch_from_cache,
'values_fetch_func': self.v_cache.fetch_from_cache,
'softmax_op': self.softmax,
'block_list': block_list,
'key_cache': key_cache,
'value_cache': value_cache,
}
def _make_alibi_bias(
alibi_slopes: torch.Tensor,
num_kv_heads: int,
dtype: torch.dtype,
seq_len: int,
) -> torch.Tensor:
bias = torch.arange(seq_len, dtype=dtype)
# NOTE(zhuohan): HF uses
# `bias = bias[None, :].repeat(seq_len, 1)`
# here. We find that both biases give the same results, but
# the bias below more accurately follows the original ALiBi
# paper.
# Calculate a matrix where each element represents ith element- jth
# element.
bias = bias[None, :] - bias[:, None]
padded_len = (seq_len + 7) // 8 * 8
num_heads = alibi_slopes.shape[0]
bias = torch.empty(
1, # batch size
num_heads,
seq_len,
padded_len,
device=alibi_slopes.device,
dtype=dtype,
)[:, :, :, :seq_len].copy_(bias)
bias.mul_(alibi_slopes[:, None, None])
if num_heads != num_kv_heads:
bias = bias.unflatten(1, (num_kv_heads, num_heads // num_kv_heads))
return bias

403
vllm/attention/backends/ipex_attn.py Normal file
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
""" Attention layer with torch scaled_dot_product_attention
and PagedAttention."""
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Type
import torch
from vllm._ipex_ops import ipex_ops
from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
AttentionLayer,
AttentionMetadata, AttentionType,
is_quantized_kv_cache)
from vllm.attention.backends.utils import CommonAttentionState
from vllm.attention.ops.paged_attn import (PagedAttention,
PagedAttentionMetadata)
from vllm.logger import init_logger
logger = init_logger(__name__)
_PARTITION_SIZE = 512
class IpexAttnBackend(AttentionBackend):
@staticmethod
def get_name() -> str:
return "IPEX"
@staticmethod
def get_impl_cls() -> Type["IpexAttnBackendImpl"]:
return IpexAttnBackendImpl
@staticmethod
def get_metadata_cls() -> Type["IpexAttnMetadata"]:
return IpexAttnMetadata
@staticmethod
def get_state_cls() -> Type["CommonAttentionState"]:
return CommonAttentionState
@staticmethod
def get_kv_cache_shape(
num_blocks: int,
block_size: int,
num_kv_heads: int,
head_size: int,
) -> Tuple[int, ...]:
return PagedAttention.get_kv_cache_shape(num_blocks, block_size,
num_kv_heads, head_size)
@staticmethod
def swap_blocks(
src_kv_cache: torch.Tensor,
dst_kv_cache: torch.Tensor,
src_to_dst: torch.Tensor,
) -> None:
from vllm._ipex_ops import ipex_ops as ops
ops.swap_blocks(src_kv_cache, dst_kv_cache, src_to_dst)
@staticmethod
def copy_blocks(
kv_caches: List[torch.Tensor],
src_to_dists: torch.Tensor,
) -> None:
from vllm._ipex_ops import ipex_ops as ops
key_caches = [kv_cache[0] for kv_cache in kv_caches]
value_caches = [kv_cache[1] for kv_cache in kv_caches]
ops.copy_blocks(key_caches, value_caches, src_to_dists)
@dataclass
class IpexAttnMetadata(AttentionMetadata, PagedAttentionMetadata):
"""Metadata for IpexAttnBackend.
"""
# Currently, input sequences can only contain all prompts
# or all decoding. True if all sequences are prompts.
is_prompt: bool
slot_mapping: torch.Tensor
seq_lens: Optional[List[int]]
seqlen_q: Optional[torch.Tensor]
max_seqlen: Optional[int]
def __post_init__(self):
# Set during the execution of the first attention op.
# It is a list because it is needed to set per prompt
# when alibi slopes is used. It is because of the limitation
# from xformer API.
# will not appear in the __repr__ and __init__
self.attn_bias: Optional[List[torch.Tensor]] = None
@property
def prefill_metadata(self) -> Optional["IpexAttnMetadata"]:
# Currently chunked prefill is not supported
if self.num_decode_tokens == 0:
assert self.num_prefills > 0
return self
return None
@property
def decode_metadata(self) -> Optional["IpexAttnMetadata"]:
# Currently chunked prefill is not supported
if self.num_prefills > 0:
assert self.num_decode_tokens == 0
return None
return self
class IpexAttnBackendImpl(AttentionImpl[IpexAttnMetadata]):
def __init__(
self,
num_heads: int,
head_size: int,
scale: float,
num_kv_heads: int,
alibi_slopes: Optional[List[float]],
sliding_window: Optional[int],
kv_cache_dtype: str,
blocksparse_params: Optional[Dict[str, Any]] = None,
logits_soft_cap: Optional[float] = None,
attn_type: str = AttentionType.DECODER,
kv_sharing_target_layer_name: Optional[str] = None,
use_irope: bool = False,
) -> None:
if kv_sharing_target_layer_name is not None:
raise NotImplementedError("KV sharing is not supported in V0.")
if use_irope:
logger.warning_once(
"Using irope in Ipex is not supported yet, it will fall"
" back to global attention for long context.")
if blocksparse_params is not None:
raise ValueError(
"IPEX backend does not support block-sparse attention.")
self.num_heads = num_heads
self.head_size = head_size
self.scale = float(scale)
self.num_kv_heads = num_kv_heads
if alibi_slopes is not None:
alibi_slopes = torch.tensor(alibi_slopes, dtype=torch.float32)
self.alibi_slopes = alibi_slopes
self.sliding_window = sliding_window
self.kv_cache_dtype = kv_cache_dtype
self.num_queries_per_kv = self.num_heads // self.num_kv_heads
self.need_mask = (self.sliding_window is not None)
if logits_soft_cap is None:
logits_soft_cap = -1
self.logits_soft_cap = logits_soft_cap
supported_head_sizes = PagedAttention.get_supported_head_sizes()
if head_size not in supported_head_sizes:
raise ValueError(
f"Head size {head_size} is not supported by PagedAttention. "
f"Supported head sizes are: {supported_head_sizes}.")
if is_quantized_kv_cache(kv_cache_dtype):
raise NotImplementedError(
"IPEX backend does not support FP8 KV cache. "
"Please use xFormers backend instead.")
if attn_type != AttentionType.DECODER:
raise NotImplementedError("Encoder self-attention and "
"encoder/decoder cross-attention "
"are not implemented for "
"IpexAttnBackendImpl")
def split_kv_cache(
self,
kv_cache: torch.Tensor,
num_kv_heads: int,
head_size: int,
) -> Tuple[torch.Tensor, torch.Tensor]:
x = 1
num_blocks = kv_cache.shape[1]
key_cache = kv_cache[0]
key_cache = key_cache.view(num_blocks, num_kv_heads, head_size // x,
-1, x)
value_cache = kv_cache[1]
value_cache = value_cache.view(num_blocks, num_kv_heads, head_size, -1)
return key_cache, value_cache
def forward(
self,
layer: AttentionLayer,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
kv_cache: torch.Tensor,
attn_metadata: IpexAttnMetadata, # type: ignore
output: Optional[torch.Tensor] = None,
output_scale: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""Forward pass with IPEX varlen_attention and PagedAttention.
Args:
query: shape = [num_tokens, num_heads * head_size]
key: shape = [num_tokens, num_kv_heads * head_size]
value: shape = [num_tokens, num_kv_heads * head_size]
kv_cache = [2, num_blocks, block_size * num_kv_heads * head_size]
NOTE: kv_cache will be an empty tensor with shape [0]
for profiling run.
attn_metadata: Metadata for attention.
Returns:
shape = [num_tokens, num_heads * head_size]
"""
if output_scale is not None:
raise NotImplementedError(
"fused output quantization is not yet supported"
" for IpexAttentionImpl")
assert layer._k_scale_float == 1.0 and layer._v_scale_float == 1.0
num_tokens, hidden_size = query.shape
# Reshape the query, key, and value tensors.
query = query.view(-1, self.num_heads, self.head_size)
key = key.view(-1, self.num_kv_heads, self.head_size)
value = value.view(-1, self.num_kv_heads, self.head_size)
if kv_cache.numel() > 0:
key_cache, value_cache = self.split_kv_cache(
kv_cache, self.num_kv_heads, self.head_size)
ipex_ops.reshape_and_cache(
key,
value,
key_cache,
value_cache,
attn_metadata.slot_mapping.flatten(),
self.kv_cache_dtype,
layer._k_scale_float,
layer._v_scale_float,
)
if attn_metadata.is_prompt:
assert attn_metadata.seq_lens is not None
if (kv_cache.numel() == 0
or attn_metadata.block_tables.numel() == 0):
if self.num_kv_heads != self.num_heads:
key = key.repeat_interleave(self.num_queries_per_kv, dim=1)
value = value.repeat_interleave(self.num_queries_per_kv,
dim=1)
if attn_metadata.attn_bias is None:
if self.sliding_window is not None:
att_masks = _make_sliding_window_bias(
attn_metadata.seq_lens, self.sliding_window,
query.dtype) # type: ignore
else:
att_masks = _make_sliding_window_bias(
attn_metadata.seq_lens, None, dtype=query.dtype)
attn_metadata.attn_bias = att_masks
output = torch.empty(
(num_tokens, self.num_heads, self.head_size),
dtype=query.dtype,
device=query.device)
ipex_ops.varlen_attention(
query,
key,
value,
output,
attn_metadata.seqlen_q,
attn_metadata.seqlen_q,
self.alibi_slopes,
attn_metadata.max_seqlen,
attn_metadata.max_seqlen,
pdropout=0.0,
softmax_scale=self.scale,
zero_tensors=False,
is_causal=True,
return_softmax=False,
gen_=None,
window_size_left=-1,
window_size_right=-1,
logits_soft_cap=self.logits_soft_cap,
)
else:
# prefix-enabled attention
raise RuntimeError(
"IPEX backend doesn't support prefix decoding.")
else:
# Decoding run.
max_seq_len = attn_metadata.max_decode_seq_len
output = torch.empty_like(query)
block_size = value_cache.shape[3]
num_seqs, num_heads, head_size = query.shape
max_num_partitions = ((max_seq_len + _PARTITION_SIZE - 1) //
_PARTITION_SIZE)
# NOTE(woosuk): We use a simple heuristic to decide whether to use
# PagedAttention V1 or V2. If the number of partitions is 1, we use
# V1 to avoid the overhead of reduction. Also, if the number of
# sequences or heads is large, we use V1 since there is enough work
# to parallelize.
# TODO(woosuk): Tune this heuristic.
# For context len > 8192, use V2 kernel to avoid shared memory
# shortage.
use_v1 = (max_seq_len <= 8192 and
(max_num_partitions == 1 or num_seqs * num_heads > 512))
if use_v1:
# Run PagedAttention V1.
ipex_ops.paged_attention_v1(
output,
query,
key_cache,
value_cache,
self.num_kv_heads,
self.scale,
attn_metadata.block_tables,
attn_metadata.seq_lens_tensor,
block_size,
max_seq_len,
self.alibi_slopes,
self.kv_cache_dtype,
layer._k_scale_float,
layer._v_scale_float,
)
else:
# Run PagedAttention V2.
assert _PARTITION_SIZE % block_size == 0
tmp_output = torch.empty(
size=(num_seqs, num_heads, max_num_partitions, head_size),
dtype=output.dtype,
device=output.device,
)
exp_sums = torch.empty(
size=(num_seqs, num_heads, max_num_partitions),
dtype=torch.float32,
device=output.device,
)
max_logits = torch.empty_like(exp_sums)
ipex_ops.paged_attention_v2(
output,
exp_sums,
max_logits,
tmp_output,
query,
key_cache,
value_cache,
self.num_kv_heads,
self.scale,
attn_metadata.block_tables,
attn_metadata.seq_lens_tensor,
block_size,
max_seq_len,
self.alibi_slopes,
self.kv_cache_dtype,
layer._k_scale_float,
layer._v_scale_float,
)
# Reshape the output tensor.
return output.view(-1, self.num_heads * self.head_size)
def _make_alibi_bias(
alibi_slopes: torch.Tensor,
dtype: torch.dtype,
seq_lens: List[int],
) -> List[torch.Tensor]:
attn_biases = []
for seq_len in seq_lens:
bias = torch.arange(seq_len, dtype=dtype, device=alibi_slopes.device)
# NOTE(zhuohan): HF uses
# `bias = bias[None, :].repeat(seq_len, 1)`
# here. We find that both biases give the same results, but
# the bias below more accurately follows the original ALiBi
# paper.
bias = bias[None, :] - bias[:, None]
num_heads = alibi_slopes.shape[0]
bias = bias[None, :].repeat((num_heads, 1, 1))
bias.mul_(alibi_slopes[:, None, None])
inf_mask = torch.empty(
(1, seq_len, seq_len),
dtype=bias.dtype,
device=alibi_slopes.device).fill_(-torch.inf).triu_(diagonal=1)
attn_biases.append((bias + inf_mask).to(dtype))
return attn_biases
def _make_sliding_window_bias(
seq_lens: List[int],
window_size: Optional[int],
dtype: torch.dtype,
) -> List[torch.Tensor]:
attn_biases = []
for seq_len in seq_lens:
tensor = torch.full(
(1, seq_len, seq_len),
dtype=dtype,
fill_value=1,
)
shift = 0
mask = torch.tril(tensor, diagonal=shift).to(dtype) # type: ignore
if window_size is not None:
mask = torch.triu(mask, diagonal=shift - window_size + 1)
mask = torch.log(mask)
attn_biases.append(mask.to(dtype))
return attn_biases

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Type
import torch
import torch_xla.experimental.custom_kernel # Required to register custom ops.
from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
AttentionLayer,
AttentionMetadata, AttentionType,
is_quantized_kv_cache)
from vllm.attention.backends.utils import CommonAttentionState
from vllm.logger import init_logger
logger = init_logger(__name__)
class PallasAttentionBackend(AttentionBackend):
@staticmethod
def get_name() -> str:
return "PALLAS"
@staticmethod
def get_impl_cls() -> Type["PallasAttentionBackendImpl"]:
return PallasAttentionBackendImpl
@staticmethod
def get_metadata_cls() -> Type["PallasMetadata"]:
return PallasMetadata
@staticmethod
def get_state_cls() -> Type["CommonAttentionState"]:
return CommonAttentionState
@staticmethod
def get_kv_cache_shape(
num_blocks: int,
block_size: int,
num_kv_heads: int,
head_size: int,
) -> Tuple[int, ...]:
return (num_kv_heads, num_blocks, block_size, head_size)
@staticmethod
def swap_blocks(
src_kv_cache: torch.Tensor,
dst_kv_cache: torch.Tensor,
src_to_dst: torch.Tensor,
) -> None:
raise RuntimeError("swap_blocks is not used for the TPU backend.")
@torch.compile(backend="openxla")
@staticmethod
def copy_blocks(
kv_caches: List[Tuple[torch.Tensor, torch.Tensor]],
src_to_dists: Tuple[torch.Tensor, torch.Tensor],
) -> None:
src_indices, dst_indices = src_to_dists
for k_cache, v_cache in kv_caches:
torch.ops.xla.dynamo_set_buffer_donor_(k_cache, True)
k_cache[:, dst_indices] = k_cache[:, src_indices]
torch.ops.xla.dynamo_set_buffer_donor_(v_cache, True)
v_cache[:, dst_indices] = v_cache[:, src_indices]
@dataclass
class PallasMetadata(AttentionMetadata):
# Currently, input sequences can only contain all prefills
# or all decoding.
block_tables: Optional[torch.Tensor] = None
context_lens: Optional[torch.Tensor] = None
effective_query_lens: Optional[torch.Tensor] = None
@property
def prefill_metadata(self) -> Optional["PallasMetadata"]:
if self.num_prefills == 0:
return None
assert self.num_decode_tokens == 0
return self
@property
def decode_metadata(self) -> Optional["PallasMetadata"]:
if self.num_decode_tokens == 0:
return None
assert self.num_prefills == 0
assert self.num_prefill_tokens == 0
assert self.block_tables is not None
assert self.context_lens is not None
return self
class PallasAttentionBackendImpl(AttentionImpl):
def __init__(
self,
num_heads: int,
head_size: int,
scale: float,
num_kv_heads: int,
alibi_slopes: Optional[List[float]],
sliding_window: Optional[int],
kv_cache_dtype: str,
blocksparse_params: Optional[Dict[str, Any]] = None,
logits_soft_cap: Optional[float] = None,
attn_type: str = AttentionType.DECODER,
kv_sharing_target_layer_name: Optional[str] = None,
use_irope: bool = False,
) -> None:
if kv_sharing_target_layer_name is not None:
raise NotImplementedError("KV sharing is not supported in V0.")
if use_irope:
logger.warning_once(
"Using irope in Pallas is not supported yet, it will fall back "
"to global attention for long context.")
self.num_heads = num_heads
self.head_size = head_size
self.scale = float(scale)
self.num_kv_heads = num_kv_heads
self.num_queries_per_kv = self.num_heads // self.num_kv_heads
self.logits_soft_cap = logits_soft_cap
if head_size % 128 != 0:
raise NotImplementedError(
f"Head size must be a multiple of 128, found {head_size}.")
if alibi_slopes is not None:
raise NotImplementedError("Alibi slopes is not supported.")
if sliding_window is not None:
raise NotImplementedError("Sliding window is not supported.")
if is_quantized_kv_cache(kv_cache_dtype):
raise NotImplementedError("FP8 KV cache dtype is not supported.")
if blocksparse_params is not None:
raise NotImplementedError("Blocksparse is not supported.")
if torch_xla.tpu.version() < 4:
raise NotImplementedError("TPU version must be 4 or higher.")
self.megacore_mode = None
tpu_env = torch_xla.tpu.get_tpu_env()
tpu_type = (tpu_env.get("ACCELERATOR_TYPE", None)
or tpu_env.get("TYPE", None)
or tpu_env.get("TPU_ACCELERATOR_TYPE", None))
assert tpu_type is not None
tpu_type = tpu_type.lower()
if (("lite" not in tpu_type) and ("v6" not in tpu_type)):
if self.num_kv_heads % 2 == 0:
self.megacore_mode = "kv_head"
else:
# NOTE(woosuk): If the batch size is not a multiple of 2, the
# megacore mode will be None.
self.megacore_mode = "batch"
if attn_type != AttentionType.DECODER:
raise NotImplementedError("Encoder self-attention and "
"encoder/decoder cross-attention "
"are not implemented for "
"PallasAttentionBackendImpl")
def forward(
self,
layer: AttentionLayer,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
kv_cache: Tuple[torch.Tensor, torch.Tensor],
attn_metadata: PallasMetadata,
output: Optional[torch.Tensor] = None,
output_scale: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""Forward pass with Pallas attention.
Args:
query: shape = [batch_size, seq_len, num_heads * head_size]
key: shape = [batch_size, seq_len, num_kv_heads * head_size]
value: shape = [batch_size, seq_len, num_kv_heads * head_size]
kv_cache[0] = [num_kv_heads, num_blocks, block_size, head_size]
kv_cache[1] = [num_kv_heads, num_blocks, block_size, head_size]
NOTE: kv_cache[0] and kv_cache[1] will be an empty tensor
with shape [0] for profiling run.
attn_metadata: Metadata for attention.
Returns:
shape = [batch_size, seq_len, num_heads * head_size]
"""
if output_scale is not None:
raise NotImplementedError(
"fused output quantization is not yet supported"
" for PallasAttentionImpl")
assert layer._k_scale_float == 1.0 and layer._v_scale_float == 1.0
batch_size, seq_len, hidden_size = query.shape
query = query.view(batch_size, seq_len, self.num_heads, self.head_size)
key = key.view(batch_size, seq_len, self.num_kv_heads, self.head_size)
value = value.view(batch_size, seq_len, self.num_kv_heads,
self.head_size)
if kv_cache[0].numel() > 0:
slot_mapping = attn_metadata.slot_mapping
key_cache, value_cache = kv_cache
write_to_kv_cache(key, value, key_cache, value_cache, slot_mapping)
query = query * self.scale
if attn_metadata.num_prefills > 0:
if attn_metadata.block_tables is None:
# Prefill without paged KV cache.
assert seq_len % 16 == 0, (
"Pallas FlashAttention kernel requires seq_len to be a "
f"multiple of 16 but got {seq_len}")
# Handle GQA/MQA.
if self.num_kv_heads != self.num_heads:
key = key.repeat_interleave(self.num_queries_per_kv,
dim=-2)
key = key.view(batch_size, seq_len, self.num_heads,
self.head_size)
value = value.repeat_interleave(self.num_queries_per_kv,
dim=-2)
value = value.view(batch_size, seq_len, self.num_heads,
self.head_size)
# FlashAttention kernel requires the input shape to be
# [batch_size, num_heads, seq_len, d_model]
# while the input is [batch_size, seq_len, num_heads, d_model].
# Permute the input to match the required format.
output = torch.ops.xla.flash_attention(
query.permute(0, 2, 1, 3),
key.permute(0, 2, 1, 3),
value.permute(0, 2, 1, 3),
True,
)
output = output.permute(0, 2, 1, 3)
else:
# Prefill with paged KV cache.
# TODO(woosuk): Tune the below knobs.
num_kv_pages_per_compute_block = 16
num_queries_per_compute_block = 16
assert seq_len % num_queries_per_compute_block == 0
output = torch.ops.xla.multi_queries_paged_attention(
query,
key_cache,
value_cache,
attn_metadata.context_lens,
attn_metadata.block_tables,
attn_metadata.effective_query_lens,
num_kv_pages_per_compute_block,
num_queries_per_compute_block,
use_kernel=True,
attn_logits_soft_cap=self.logits_soft_cap,
)
else:
# Decoding run.
assert kv_cache[0].numel() > 0
query = query.squeeze(dim=1)
pages_per_compute_block = 16 # TODO(woosuk): Tune this value.
assert attn_metadata.block_tables is not None
assert attn_metadata.context_lens is not None
# NOTE(woosuk): The PagedAttention Pallas kernel stores the entire
# block table in SMEM. Therefore, if the block table is too large,
# the kernel compilation will fail. To avoid this, we split the
# batch dimension into smaller chunks and run the kernel multiple
# times.
MAX_SMEM_USAGE = 512 * 1024
size_per_seq = 4 * attn_metadata.block_tables.shape[1]
max_num_seq = MAX_SMEM_USAGE // size_per_seq
if batch_size <= max_num_seq:
output = paged_attention(
query,
key_cache,
value_cache,
attn_metadata.context_lens,
attn_metadata.block_tables,
pages_per_compute_block,
self.megacore_mode,
attn_logits_soft_cap=self.logits_soft_cap,
)
else:
chunk_size = max_num_seq
# Make sure the chunk size is a multiple of 2.
chunk_size = chunk_size // 2 * 2
num_chunks = (batch_size + chunk_size - 1) // chunk_size
output = torch.empty_like(query)
for chunk_idx in range(num_chunks):
chunk_start = chunk_idx * chunk_size
chunk_end = chunk_start + chunk_size
# NOTE(woosuk): We skip this line because it causes Dynamo
# compilation error. Instead, we rely on the slice operation
# to handle the out-of-bound case.
# chunk_end = min(chunk_end, batch_size)
chunk_output = paged_attention(
query[chunk_start:chunk_end],
key_cache,
value_cache,
attn_metadata.context_lens[chunk_start:chunk_end],
attn_metadata.block_tables[chunk_start:chunk_end],
pages_per_compute_block,
self.megacore_mode,
attn_logits_soft_cap=self.logits_soft_cap,
)
output[chunk_start:chunk_end] = chunk_output
# Reshape the output tensor.
return output.reshape(batch_size, seq_len, hidden_size)
def write_to_kv_cache(
key: torch.Tensor,
value: torch.Tensor,
key_cache: torch.Tensor,
value_cache: torch.Tensor,
slot_mapping: torch.Tensor,
) -> None:
torch.ops.xla.dynamo_set_buffer_donor_(key_cache, True)
torch.ops.xla.dynamo_set_buffer_donor_(value_cache, True)
key = key.flatten(0, 2)
value = value.flatten(0, 2)
key_cache = key_cache.flatten(0, 2)
value_cache = value_cache.flatten(0, 2)
key_cache.index_copy_(0, slot_mapping, key)
value_cache.index_copy_(0, slot_mapping, value)
def paged_attention(
query: torch.Tensor,
key_cache: torch.Tensor,
value_cache: torch.Tensor,
context_lens: torch.Tensor,
block_tables: torch.Tensor,
pages_per_compute_block: int,
megacore_mode: Optional[str],
*,
attn_logits_soft_cap: Optional[float],
) -> torch.Tensor:
batch_size = query.shape[0]
if megacore_mode == "batch" and batch_size % 2 != 0:
megacore_mode = None
else:
megacore_mode = megacore_mode
return torch.ops.xla.paged_attention(
query,
key_cache,
value_cache,
context_lens,
block_tables,
pages_per_compute_block,
megacore_mode=megacore_mode,
attn_logits_soft_cap=attn_logits_soft_cap,
)

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from collections import defaultdict
from dataclasses import dataclass
from itertools import accumulate
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Type
import torch
from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
AttentionMetadata,
AttentionMetadataBuilder)
from vllm.attention.backends.utils import CommonAttentionState
from vllm.multimodal import MultiModalPlaceholderMap
if TYPE_CHECKING:
from vllm.worker.model_runner import (ModelInputForGPUBuilder,
ModelInputForGPUWithSamplingMetadata)
from vllm.utils import async_tensor_h2d
# Placeholder attention backend for models like Mamba and pooling models that
# lack attention.
class PlaceholderAttentionBackend(AttentionBackend):
"""Placeholder backend for when no attention is needed."""
@staticmethod
def get_name() -> str:
return "NO_ATTENTION"
@staticmethod
def get_impl_cls() -> Type["PlaceholderAttentionImpl"]:
return PlaceholderAttentionImpl
@staticmethod
def get_builder_cls() -> Type["PlaceholderAttentionMetadataBuilder"]:
return PlaceholderAttentionMetadataBuilder
@staticmethod
def get_metadata_cls() -> Type["PlaceholderAttentionMetadata"]:
return PlaceholderAttentionMetadata
@staticmethod
def get_state_cls() -> Type["CommonAttentionState"]:
return CommonAttentionState
@staticmethod
def get_kv_cache_shape(
num_blocks: int,
block_size: int,
num_kv_heads: int,
head_size: int,
) -> Tuple[int, ...]:
return (1, 1, 1, 1, 1)
@staticmethod
def swap_blocks(
src_kv_cache: torch.Tensor,
dst_kv_cache: torch.Tensor,
src_to_dst: torch.Tensor,
) -> None:
return
@staticmethod
def copy_blocks(
kv_caches: List[torch.Tensor],
src_to_dists: torch.Tensor,
) -> None:
return
@dataclass
class PlaceholderAttentionMetadata(AttentionMetadata):
"""Attention metadata for prefill and decode batched together."""
# (batch_size,). The sequence length per sequence. Sequence length means
# the computed tokens + new tokens None if it is a decoding.
seq_lens: Optional[List[int]]
# seq_lens stored as a tensor.
seq_lens_tensor: Optional[torch.Tensor]
# Maximum sequence length among prefill batch. 0 if there are decoding
# requests only.
max_prefill_seq_len: int
# Maximum sequence length among decode batch. 0 if there are prefill
# requests only.
max_decode_seq_len: int
# (batch_size,) A tensor of context lengths (tokens that are computed
# so far).
context_lens_tensor: Optional[torch.Tensor]
# Whether or not if cuda graph is enabled.
# Cuda-graph is currently enabled for decoding only.
# TODO(woosuk): Move `use_cuda_graph` out since it's unrelated to attention.
use_cuda_graph: bool
# Maximum query length in the batch.
max_query_len: Optional[int]
# Max number of query tokens among request in the batch.
max_decode_query_len: Optional[int]
# (batch_size + 1,). The cumulative subquery lengths of the sequences in
# the batch, used to index into subquery. E.g., if the subquery length
# is [4, 6], it is [0, 4, 10].
query_start_loc: Optional[torch.Tensor] = None
# (batch_size + 1,). The cumulative sequence lengths of the sequences in
# the batch, used to index into sequence. E.g., if the sequence length is
# [4, 6], it is [0, 4, 10].
seq_start_loc: Optional[torch.Tensor] = None
# Placeholder.
block_tables: Optional[torch.Tensor] = None
_cached_prefill_metadata: Optional["PlaceholderAttentionMetadata"] = None
_cached_decode_metadata: Optional["PlaceholderAttentionMetadata"] = None
@property
def prefill_metadata(self) -> Optional["PlaceholderAttentionMetadata"]:
if self.num_prefills == 0:
return None
if self._cached_prefill_metadata is not None:
return self._cached_prefill_metadata
# Compute some attn_metadata fields which default to None
query_start_loc = (None if self.query_start_loc is None else
self.query_start_loc[:self.num_prefills + 1])
seq_lens = (None if self.seq_lens is None else
self.seq_lens[:self.num_prefills])
seq_lens_tensor = (None if self.seq_lens_tensor is None else
self.seq_lens_tensor[:self.num_prefills])
seq_start_loc = (None if self.seq_start_loc is None else
self.seq_start_loc[:self.num_prefills + 1])
context_lens_tensor = (None if self.context_lens_tensor is None else
self.context_lens_tensor[:self.num_prefills])
# Placeholders
slot_mapping = torch.empty(0)
block_tables = torch.empty(0)
self._cached_prefill_metadata = PlaceholderAttentionMetadata(
num_prefills=self.num_prefills,
num_prefill_tokens=self.num_prefill_tokens,
num_decode_tokens=0,
slot_mapping=slot_mapping,
multi_modal_placeholder_index_maps=self.
multi_modal_placeholder_index_maps,
enable_kv_scales_calculation=self.enable_kv_scales_calculation,
seq_lens=seq_lens,
seq_lens_tensor=seq_lens_tensor,
max_decode_query_len=0,
max_query_len=self.max_query_len,
max_prefill_seq_len=self.max_prefill_seq_len,
max_decode_seq_len=0,
query_start_loc=query_start_loc,
seq_start_loc=seq_start_loc,
context_lens_tensor=context_lens_tensor,
block_tables=block_tables,
use_cuda_graph=False,
)
return self._cached_prefill_metadata
@property
def decode_metadata(self) -> Optional["PlaceholderAttentionMetadata"]:
if self.num_decode_tokens == 0:
return None
if self._cached_decode_metadata is not None:
return self._cached_decode_metadata
assert self.seq_lens_tensor is not None
# Placeholders
slot_mapping = torch.empty(0)
block_tables = torch.empty(0)
seq_lens_tensor = (None if self.seq_lens_tensor is None else
self.seq_lens_tensor[self.num_prefills:])
self._cached_decode_metadata = PlaceholderAttentionMetadata(
num_prefills=0,
num_prefill_tokens=0,
num_decode_tokens=self.num_decode_tokens,
slot_mapping=slot_mapping,
multi_modal_placeholder_index_maps=None,
enable_kv_scales_calculation=True,
seq_lens=None,
seq_lens_tensor=seq_lens_tensor,
max_decode_query_len=self.max_decode_query_len,
max_query_len=None,
max_prefill_seq_len=0,
max_decode_seq_len=self.max_decode_seq_len,
query_start_loc=(self.query_start_loc[self.num_prefills:] -
self.query_start_loc[self.num_prefills])
if self.query_start_loc is not None else None,
seq_start_loc=self.seq_start_loc[self.num_prefills:]
if self.seq_start_loc is not None else None,
context_lens_tensor=None,
block_tables=block_tables,
use_cuda_graph=self.use_cuda_graph,
)
return self._cached_decode_metadata
def advance_step(self,
model_input: "ModelInputForGPUWithSamplingMetadata",
sampled_token_ids: Optional[torch.Tensor],
block_size: int,
num_seqs: int,
num_queries: int,
turn_prefills_into_decodes: bool = False):
"""
Update metadata in-place to advance one decode step.
"""
# When using cudagraph, the num_seqs is padded to the next captured
# batch sized, but num_queries tracks the actual number of requests in
# the batch. For --enforce-eager mode, num_seqs == num_queries
if num_seqs != num_queries:
assert num_seqs > num_queries
assert self.use_cuda_graph
assert not turn_prefills_into_decodes, \
("Multi-Step + Chunked-Prefill is not supported for attention-free"
"models. turn_prefills_into_decodes is a "
"Multi-Step + Chunked-Prefill specific parameter.")
assert self.seq_lens is not None
assert self.max_decode_seq_len == max(self.seq_lens)
assert self.num_prefills == 0
assert self.num_prefill_tokens == 0
assert self.num_decode_tokens == num_seqs
assert self.seq_lens is not None
assert len(self.seq_lens) == num_seqs
assert self.seq_lens_tensor is not None
assert self.seq_lens_tensor.shape == (num_seqs, )
assert self.max_query_len == 1
assert self.max_prefill_seq_len == 0
assert self.query_start_loc is not None
assert self.query_start_loc.shape == (num_queries + 1, )
assert self.seq_start_loc is not None
assert self.seq_start_loc.shape == (num_seqs + 1, )
assert self.context_lens_tensor is not None
assert self.context_lens_tensor.shape == (num_queries, )
# Update query lengths. Note that we update only queries and not seqs,
# since tensors may be padded due to captured cuda graph batch size
for i in range(num_queries):
self.seq_lens[i] += 1
self.max_decode_seq_len = max(self.seq_lens)
# Update sequences, masking off entries greater than num_queries
device = self.seq_lens_tensor.device
mask = torch.arange(self.seq_lens_tensor.size(0),
device=device) < num_queries
self.seq_lens_tensor += mask.to(self.seq_lens_tensor.dtype)
if sampled_token_ids is not None:
model_input.input_tokens.masked_scatter_(
mask, sampled_token_ids[:num_queries])
class PlaceholderAttentionMetadataBuilder(
AttentionMetadataBuilder[PlaceholderAttentionMetadata]):
def __init__(self, input_builder: "ModelInputForGPUBuilder"):
self.input_builder = input_builder
self.runner = input_builder.runner
def prepare(self):
self.prefill_seq_lens: List[int] = []
self.context_lens: List[int] = []
self.curr_seq_lens: List[int] = []
self.multimodal_placeholder_maps: Dict[
str,
MultiModalPlaceholderMap] = defaultdict(MultiModalPlaceholderMap)
self.num_prefills = 0
self.num_prefill_tokens = 0
self.num_decode_tokens = 0
def _add_seq_group(
self, inter_data: "ModelInputForGPUBuilder.InterDataForSeqGroup",
chunked_prefill_enabled: bool):
"""Add a sequence group to the metadata. Specifically update/append
1. context length.
"""
is_prompt = inter_data.is_prompt
for (seq_id, token_len, seq_len, curr_seq_len, query_len, context_len,
curr_sliding_window_block) in zip(
inter_data.seq_ids, [len(t) for t in inter_data.input_tokens],
inter_data.orig_seq_lens, inter_data.seq_lens,
inter_data.query_lens, inter_data.context_lens,
inter_data.curr_sliding_window_blocks):
self.context_lens.append(context_len)
if is_prompt:
mm_maps = inter_data.multi_modal_placeholder_maps
if mm_maps:
for modality, placeholders in mm_maps.items():
self.multimodal_placeholder_maps[modality].extend(
placeholders)
self.num_prefills += 1
self.num_prefill_tokens += token_len
self.prefill_seq_lens.append(seq_len)
else:
self.num_decode_tokens += query_len
self.curr_seq_lens.append(curr_seq_len)
def build(self, seq_lens: List[int], query_lens: List[int],
cuda_graph_pad_size: int, batch_size: int):
"""Build attention metadata with on-device tensors.
Args:
seq_lens: The maybe padded sequence lengths of the input sequences.
query_lens: The query lengths of the input sequences.
cuda_graph_pad_size: The padding size for cuda graph.
-1 if cuda graph is not used.
batch_size: The maybe padded batch size.
"""
# Some input builders such as ModelInputForCPUBuilder do not have the
# "inter_data_list" attribute.
# Let's check inter_data_list exists before we reference it.
if hasattr(self.input_builder, "inter_data_list"):
for inter_data in self.input_builder.inter_data_list:
self._add_seq_group(inter_data,
self.input_builder.chunked_prefill_enabled)
device = self.runner.device
use_captured_graph = cuda_graph_pad_size != -1
max_query_len = max(query_lens)
decode_query_lens = query_lens[self.num_prefills:]
if len(decode_query_lens) > 0:
max_decode_query_len = max(decode_query_lens)
else:
max_decode_query_len = 1
max_prefill_seq_len = max(self.prefill_seq_lens, default=0)
max_decode_seq_len = max(self.curr_seq_lens, default=0)
num_decode_tokens = self.num_decode_tokens
query_start_loc = list(accumulate(query_lens, initial=0))
seq_start_loc = list(accumulate(seq_lens, initial=0))
if use_captured_graph:
num_decode_tokens = batch_size - self.num_prefill_tokens
assert max_query_len > 0, ("query_lens: {}".format(query_lens))
assert device is not None
context_lens_tensor = async_tensor_h2d(self.context_lens, torch.int,
device, self.runner.pin_memory)
seq_lens_tensor = async_tensor_h2d(seq_lens, torch.int, device,
self.runner.pin_memory)
query_start_loc_tensor = async_tensor_h2d(query_start_loc, torch.int32,
device,
self.runner.pin_memory)
seq_start_loc_tensor = async_tensor_h2d(seq_start_loc, torch.int32,
device, self.runner.pin_memory)
placeholder_index_maps = {
modality: placeholder_map.index_map()
for modality, placeholder_map in
self.multimodal_placeholder_maps.items()
}
# Placeholders
slot_mapping_tensor = torch.empty(0)
block_tables = torch.empty(0)
return PlaceholderAttentionMetadata(
num_prefills=self.num_prefills,
slot_mapping=slot_mapping_tensor,
multi_modal_placeholder_index_maps=placeholder_index_maps,
enable_kv_scales_calculation=True,
num_prefill_tokens=self.num_prefill_tokens,
num_decode_tokens=num_decode_tokens,
seq_lens=seq_lens,
seq_lens_tensor=seq_lens_tensor,
max_query_len=max_query_len,
max_decode_query_len=max_decode_query_len,
max_prefill_seq_len=max_prefill_seq_len,
max_decode_seq_len=max_decode_seq_len,
query_start_loc=query_start_loc_tensor,
seq_start_loc=seq_start_loc_tensor,
context_lens_tensor=context_lens_tensor,
block_tables=block_tables,
use_cuda_graph=use_captured_graph,
)
class PlaceholderAttentionImpl(AttentionImpl):
def __init__(self, *args, **kwargs) -> None:
return
def forward(self, *args, **kwargs) -> torch.Tensor:
raise NotImplementedError

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