$ pip install torch==2.2.0.dev20231206+rocm5.7 --index-url https://download.pytorch.org/whl/nightly/rocm5.7 # tested version
1. Install `flash attention for ROCm <https://github.com/ROCmSoftwarePlatform/flash-attention/tree/flash_attention_for_rocm>`_
Install ROCm's flash attention (v2.0.4) following the instructions from `ROCmSoftwarePlatform/flash-attention <https://github.com/ROCmSoftwarePlatform/flash-attention/tree/flash_attention_for_rocm#amd-gpurocm-support>`_
.. note::
- If you are using rocm5.7 with pytorch 2.1.0 onwards, you don't need to apply the `hipify_python.patch`. You can build the ROCm flash attention directly.
- If you fail to install `ROCmSoftwarePlatform/flash-attention`, try cloning from the commit `6fd2f8e572805681cd67ef8596c7e2ce521ed3c6`.
- ROCm's Flash-attention-2 (v2.0.4) does not support sliding windows attention.
- You might need to downgrade the "ninja" version to 1.10 it is not used when compiling flash-attention-2 (e.g. `pip install ninja==1.10.2.4`)
2. Setup `xformers==0.0.23` without dependencies, and apply patches to adapt for ROCm flash attention
.. _build_from_source_docker_rocm:
.. code-block:: console
Option 1: Build from source with docker (recommended)
First, build a docker image from `Dockerfile.rocm <https://github.com/vllm-project/vllm/blob/main/Dockerfile.rocm>`_ and launch a docker container from the image.
.. code-block:: console
`Dockerfile.rocm <https://github.com/vllm-project/vllm/blob/main/Dockerfile.rocm>`_ uses ROCm 6.0 by default, but also supports ROCm 5.7.
It provides flexibility to customize the build of docker image using the following arguments:
$ cd vllm
$ pip install -U -r requirements-rocm.txt
$ python setup.py install # This may take 5-10 minutes. Currently, `pip install .`` does not work for ROCm installation
* `BASE_IMAGE`: specifies the base image used when running ``docker build``, specifically the PyTorch on ROCm base image. We have tested ROCm 5.7 and ROCm 6.0. The default is `rocm/pytorch:rocm6.0_ubuntu20.04_py3.9_pytorch_2.1.1`
* `BUILD_FA`: specifies whether to build CK flash-attention. The default is 1. For `Radeon RX 7900 series (gfx1100) <https://rocm.docs.amd.com/projects/radeon/en/latest/index.html>`_, this should be set to 0 before flash-attention supports this target.
* `FX_GFX_ARCHS`: specifies the GFX architecture that is used to build CK flash-attention, for example, `gfx90a;gfx942` for MI200 and MI300. The default is `gfx90a;gfx942`
* `FA_BRANCH`: specifies the branch used to build the CK flash-attention in `ROCm's flash-attention repo <https://github.com/ROCmSoftwarePlatform/flash-attention>`_. The default is `ae7928c`
* `BUILD_TRITON`: specifies whether to build triton flash-attention. The default value is 1.
Their values can be passed in when running ``docker build`` with ``--build-arg`` options.
To build vllm on ROCm 6.0 for MI200 and MI300 series, you can use the default:
You can build and install vLLM from source:
.. code-block:: console
Build a docker image from `Dockerfile.rocm`, and launch a docker container.
$ docker build -f Dockerfile.rocm -t vllm-rocm .
The `Dockerfile.rocm` is designed to support both ROCm 5.7 and ROCm 6.0 and later versions. It provides flexibility to customize the build of docker image using the following arguments:
To build vllm on ROCm 6.0 for Radeon RX7900 series (gfx1100), you should specify ``BUILD_FA`` as below:
* `BASE_IMAGE`: specifies the base image used when running ``docker build``, specifically the PyTorch on ROCm base image. We have tested ROCm 5.7 and ROCm 6.0. The default is `rocm/pytorch:rocm6.0_ubuntu20.04_py3.9_pytorch_2.1.1`
* `FX_GFX_ARCHS`: specifies the GFX architecture that is used to build flash-attention, for example, `gfx90a;gfx942` for MI200 and MI300. The default is `gfx90a;gfx942`
* `FA_BRANCH`: specifies the branch used to build the flash-attention in `ROCmSoftwarePlatform's flash-attention repo <https://github.com/ROCmSoftwarePlatform/flash-attention>`_. The default is `3d2b6f5`
* `BUILD_FA`: specifies whether to build flash-attention. For `Radeon RX 7900 series (gfx1100) <https://rocm.docs.amd.com/projects/radeon/en/latest/index.html>`_, this should be set to 0 before flash-attention supports this target.
.. code-block:: console
Their values can be passed in when running ``docker build`` with ``--build-arg`` options.
To build vllm on ROCm 6.0, you can use the default:
To run the above docker image ``vllm-rocm``, use the below command:
.. code-block:: console
$ docker build -f Dockerfile.rocm -t vllm-rocm .
$ docker run -it \
--network=host \
--group-add=video \
...
...
@@ -133,7 +74,13 @@ To build vllm on ROCm 6.0, you can use the default:
vllm-rocm \
bash
Alternatively, if you plan to install vLLM-ROCm on a local machine or start from a fresh docker image (e.g. rocm/pytorch), you can follow the steps below:
Where the `<path/to/model>` is the location where the model is stored, for example, the weights for llama2 or llama3 models.
.. _build_from_source_rocm:
Option 2: Build from source
---------------------------
0. Install prerequisites (skip if you are already in an environment/docker with the following installed):
...
...
@@ -141,32 +88,50 @@ Alternatively, if you plan to install vLLM-ROCm on a local machine or start from
1. Install `flash attention for ROCm <https://github.com/ROCmSoftwarePlatform/flash-attention/tree/flash_attention_for_rocm>`_
For installing PyTorch, you can start from a fresh docker image, e.g, `rocm6.0.2_ubuntu22.04_py3.10_pytorch_2.1.2`, `rocm/pytorch:rocm6.0_ubuntu20.04_py3.9_pytorch_2.1.1`, `rocm/pytorch-nightly`.
Install ROCm's flash attention (v2.0.4) following the instructions from `ROCmSoftwarePlatform/flash-attention <https://github.com/ROCmSoftwarePlatform/flash-attention/tree/flash_attention_for_rocm#amd-gpurocm-support>`_
Alternatively, you can install pytorch using pytorch wheels. You can check Pytorch installation guild in Pytorch `Getting Started <https://pytorch.org/get-started/locally/>`_
1. Install `Triton flash attention for ROCm <https://github.com/ROCm/triton>`_
Install ROCm's Triton flash attention (the default triton-mlir branch) following the instructions from `ROCm/triton <https://github.com/ROCm/triton/blob/triton-mlir/README.md>`_
2. Optionally, if you choose to use CK flash attention, you can install `flash attention for ROCm <https://github.com/ROCm/flash-attention/tree/flash_attention_for_rocm>`_
Install ROCm's flash attention (v2.0.4) following the instructions from `ROCm/flash-attention <https://github.com/ROCm/flash-attention/tree/flash_attention_for_rocm#amd-gpurocm-support>`_
.. note::
- If you are using rocm5.7 with pytorch 2.1.0 onwards, you don't need to apply the `hipify_python.patch`. You can build the ROCm flash attention directly.
- If you fail to install `ROCmSoftwarePlatform/flash-attention`, try cloning from the commit `6fd2f8e572805681cd67ef8596c7e2ce521ed3c6`.
- If you fail to install `ROCm/flash-attention`, try cloning from the commit `6fd2f8e572805681cd67ef8596c7e2ce521ed3c6`.
- ROCm's Flash-attention-2 (v2.0.4) does not support sliding windows attention.
- You might need to downgrade the "ninja" version to 1.10 it is not used when compiling flash-attention-2 (e.g. `pip install ninja==1.10.2.4`)
2. Setup `xformers==0.0.23` without dependencies, and apply patches to adapt for ROCm flash attention
.. code-block:: console
$ pip install xformers==0.0.23 --no-deps
$ bash patch_xformers.rocm.sh
3. Build vLLM.
.. code-block:: console
.. code-block:: console
$ cd vllm
$ pip install -U -r requirements-rocm.txt
$ python setup.py install # This may take 5-10 minutes.
$ cd vllm
$ pip install -U -r requirements-rocm.txt
$ python setup.py install # This may take 5-10 minutes. Currently, `pip install .`` does not work for ROCm installation
.. note::
- You may need to turn on the ``--enforce-eager`` flag if you experience process hang when running the `benchmark_thoughput.py` script to test your installation.
.. tip::
- You may need to turn on the ``--enforce-eager`` flag if you experience process hang when running the `benchmark_thoughput.py` script to test your installation.
- Triton flash attention is used by default. For benchmarking purposes, it is recommended to run a warm up step before collecting perf numbers.
- To use CK flash-attention, please use this flag ``export VLLM_USE_FLASH_ATTN_TRITON=0`` to turn off triton flash attention.
- The ROCm version of pytorch, ideally, should match the ROCm driver version.
vLLM supports an experimental feature chunked prefill. Chunked prefill allows to chunk large prefills into smaller chunks and batch them together with decode requests.
By default, vLLM scheduler prioritizes prefills and doesn't batch prefill and decode to the same batch. This policy optimizes the TTFT (time to thefirst token), but incurs slower ITL (inter token latency) and inefficient GPU utilization.
Once chunked prefill is enabled, the policy is changed to
- prioritize decode requests. It batches all pending decode requests to the batch before scheduling any prefill.
- When there are available token_budget (`max_num_batched_tokens`), it schedules pending prefills. If a last pending prefill request cannot fit into `max_num_batched_tokens`, it chunks it.
This policy has two benefits.
- It improves ITL (inter token latency) and generation decode because decode requests are prioritized.
- It helps achieve better GPU utilization by locating compute-bound (prefill) and memory-bound (decode) requests to the same batch.
You can tune the performance by changing `max_num_batched_tokens`.
By default, it is set to 512, which has the best ITL on A100 in the initial benchmark.
Smaller batch size achieves better ITL because there are fewer prefills interrupting decodes.
Higher batch size achieves better TTFT as you can put more prefill to the batch.
If `max_num_batched_tokens` is the same as `max_model_len`, that's almost the equivalent to the default scheduling policy (except that it still prioritizes decodes).
Note that the default batch size (512) is optimized for ITL, and it may have lower throughput than the default scheduler. We recommend you set `max_num_batched_tokens > 2048` for throughput.
See related papers for more details (https://arxiv.org/pdf/2401.08671 or https://arxiv.org/pdf/2308.16369).
vLLM docker image is currently designed to be run under the root user (contribution welcomed for changing this!). It will try to load library at runtime under the root user's home directory, e.g. `/root/.config/vllm/nccl/cu12/libnccl.so.2.18.1` . If you are running the container under a different user, you may need to change the permissions of the library (and all the parent directories) to allow the user to access it. Then run vLLM with environment variable `VLLM_NCCL_SO_PATH=/root/.config/vllm/nccl/cu12/libnccl.so.2.18.1` .
@@ -4,7 +4,7 @@ vLLM provides an HTTP server that implements OpenAI's [Completions](https://plat
You can start the server using Python, or using [Docker](deploying_with_docker.rst):
```bash
python -m vllm.entrypoints.openai.api_server --modelmistralai/Mistral-7B-Instruct-v0.2--dtype auto --api-key token-abc123
python -m vllm.entrypoints.openai.api_server --modelNousResearch/Meta-Llama-3-8B-Instruct --dtype auto --api-key token-abc123
```
To call the server, you can use the official OpenAI Python client library, or any other HTTP client.
...
...
@@ -16,7 +16,7 @@ client = OpenAI(
)
completion=client.chat.completions.create(
model="mistralai/Mistral-7B-Instruct-v0.2",
model="NousResearch/Meta-Llama-3-8B-Instruct",
messages=[
{"role":"user","content":"Hello!"}
]
...
...
@@ -37,7 +37,7 @@ Or directly merge them into the JSON payload if you are using HTTP call directly
```python
completion=client.chat.completions.create(
model="mistralai/Mistral-7B-Instruct-v0.2",
model="NousResearch/Meta-Llama-3-8B-Instruct",
messages=[
{"role":"user","content":"Classify this sentiment: vLLM is wonderful!"}
],
...
...
@@ -87,7 +87,7 @@ In order for the language model to support chat protocol, vLLM requires the mode
a chat template in its tokenizer configuration. The chat template is a Jinja2 template that
specifies how are roles, messages, and other chat-specific tokens are encoded in the input.
An example chat template for `mistralai/Mistral-7B-Instruct-v0.2` can be found [here](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2#instruction-format)
An example chat template for `NousResearch/Meta-Llama-3-8B-Instruct` can be found [here](https://github.com/meta-llama/llama3?tab=readme-ov-file#instruction-tuned-models)
Some models do not provide a chat template even though they are instruction/chat fine-tuned. For those model,
you can manually specify their chat template in the `--chat-template` parameter with the file path to the chat
