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[Doc] Create a new "Usage" section (#10827)

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docs/source/serving/compatibility_matrix.rst
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.. _compatibility_matrix:
Compatibility Matrix
====================
The tables below show mutually exclusive features and the support on some hardware.
.. note::
Check the '✗' with links to see tracking issue for unsupported feature/hardware combination.
Feature x Feature
-----------------
.. raw:: html
<style>
/* Make smaller to try to improve readability */
td {
font-size: 0.8rem;
text-align: center;
}
th {
text-align: center;
font-size: 0.8rem;
}
</style>
.. list-table::
:header-rows: 1
:widths: auto
* - Feature
- :ref:`CP <chunked-prefill>`
- :ref:`APC <apc>`
- :ref:`LoRA <lora>`
- :abbr:`prmpt adptr (Prompt Adapter)`
- :ref:`SD <spec_decode>`
- CUDA graph
- :abbr:`emd (Embedding Models)`
- :abbr:`enc-dec (Encoder-Decoder Models)`
- :abbr:`logP (Logprobs)`
- :abbr:`prmpt logP (Prompt Logprobs)`
- :abbr:`async output (Async Output Processing)`
- multi-step
- :abbr:`mm (Multimodal)`
- best-of
- beam-search
- :abbr:`guided dec (Guided Decoding)`
* - :ref:`CP <chunked-prefill>`
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
* - :ref:`APC <apc>`
- ✅
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
* - :ref:`LoRA <lora>`
- `✗ <https://github.com/vllm-project/vllm/pull/9057>`__
- ✅
-
-
-
-
-
-
-
-
-
-
-
-
-
-
* - :abbr:`prmpt adptr (Prompt Adapter)`
- ✅
- ✅
- ✅
-
-
-
-
-
-
-
-
-
-
-
-
-
* - :ref:`SD <spec_decode>`
- ✅
- ✅
- ✗
- ✅
-
-
-
-
-
-
-
-
-
-
-
-
* - CUDA graph
- ✅
- ✅
- ✅
- ✅
- ✅
-
-
-
-
-
-
-
-
-
-
-
* - :abbr:`emd (Embedding Models)`
- ✗
- ✗
- ✗
- ✗
- ✗
- ✗
-
-
-
-
-
-
-
-
-
-
* - :abbr:`enc-dec (Encoder-Decoder Models)`
- ✗
- `✗ <https://github.com/vllm-project/vllm/issues/7366>`__
- ✗
- ✗
- `✗ <https://github.com/vllm-project/vllm/issues/7366>`__
- ✅
- ✅
-
-
-
-
-
-
-
-
-
* - :abbr:`logP (Logprobs)`
- ✅
- ✅
- ✅
- ✅
- ✅
- ✅
- ✗
- ✅
-
-
-
-
-
-
-
-
* - :abbr:`prmpt logP (Prompt Logprobs)`
- ✅
- ✅
- ✅
- ✅
- `✗ <https://github.com/vllm-project/vllm/pull/8199>`__
- ✅
- ✗
- ✅
- ✅
-
-
-
-
-
-
-
* - :abbr:`async output (Async Output Processing)`
- ✅
- ✅
- ✅
- ✅
- ✗
- ✅
- ✗
- ✗
- ✅
- ✅
-
-
-
-
-
-
* - multi-step
- ✗
- ✅
- ✗
- ✅
- ✗
- ✅
- ✗
- ✗
- ✅
- `✗ <https://github.com/vllm-project/vllm/issues/8198>`__
- ✅
-
-
-
-
-
* - :abbr:`mm (Multimodal)`
- ✅
- `✗ <https://github.com/vllm-project/vllm/pull/8348>`__
- `✗ <https://github.com/vllm-project/vllm/pull/7199>`__
- ?
- ?
- ✅
- ✅
- ✅
- ✅
- ✅
- ✅
- ?
-
-
-
-
* - best-of
- ✅
- ✅
- ✅
- ✅
- `✗ <https://github.com/vllm-project/vllm/issues/6137>`__
- ✅
- ✗
- ✅
- ✅
- ✅
- ?
- `✗ <https://github.com/vllm-project/vllm/issues/7968>`__
- ✅
-
-
-
* - beam-search
- ✅
- ✅
- ✅
- ✅
- `✗ <https://github.com/vllm-project/vllm/issues/6137>`__
- ✅
- ✗
- ✅
- ✅
- ✅
- ?
- `✗ <https://github.com/vllm-project/vllm/issues/7968>`__
- ?
- ✅
-
-
* - :abbr:`guided dec (Guided Decoding)`
- ✅
- ✅
- ?
- ?
- ✅
- ✅
- ✗
- ?
- ✅
- ✅
- ✅
- `✗ <https://github.com/vllm-project/vllm/issues/9893>`__
- ?
- ✅
- ✅
-
Feature x Hardware
^^^^^^^^^^^^^^^^^^
.. list-table::
:header-rows: 1
:widths: auto
* - Feature
- Volta
- Turing
- Ampere
- Ada
- Hopper
- CPU
- AMD
* - :ref:`CP <chunked-prefill>`
- `✗ <https://github.com/vllm-project/vllm/issues/2729>`__
- ✅
- ✅
- ✅
- ✅
- ✅
- ✅
* - :ref:`APC <apc>`
- `✗ <https://github.com/vllm-project/vllm/issues/3687>`__
- ✅
- ✅
- ✅
- ✅
- ✅
- ✅
* - :ref:`LoRA <lora>`
- ✅
- ✅
- ✅
- ✅
- ✅
- `✗ <https://github.com/vllm-project/vllm/pull/4830>`__
- ✅
* - :abbr:`prmpt adptr (Prompt Adapter)`
- ✅
- ✅
- ✅
- ✅
- ✅
- `✗ <https://github.com/vllm-project/vllm/issues/8475>`__
- ✅
* - :ref:`SD <spec_decode>`
- ✅
- ✅
- ✅
- ✅
- ✅
- ✅
- ✅
* - CUDA graph
- ✅
- ✅
- ✅
- ✅
- ✅
- ✗
- ✅
* - :abbr:`emd (Embedding Models)`
- ✅
- ✅
- ✅
- ✅
- ✅
- ✅
- ?
* - :abbr:`enc-dec (Encoder-Decoder Models)`
- ✅
- ✅
- ✅
- ✅
- ✅
- ✅
- ✗
* - :abbr:`mm (Multimodal)`
- ✅
- ✅
- ✅
- ✅
- ✅
- ✅
- ✅
* - :abbr:`logP (Logprobs)`
- ✅
- ✅
- ✅
- ✅
- ✅
- ✅
- ✅
* - :abbr:`prmpt logP (Prompt Logprobs)`
- ✅
- ✅
- ✅
- ✅
- ✅
- ✅
- ✅
* - :abbr:`async output (Async Output Processing)`
- ✅
- ✅
- ✅
- ✅
- ✅
- ✗
- ✗
* - multi-step
- ✅
- ✅
- ✅
- ✅
- ✅
- `✗ <https://github.com/vllm-project/vllm/issues/8477>`__
- ✅
* - best-of
- ✅
- ✅
- ✅
- ✅
- ✅
- ✅
- ✅
* - beam-search
- ✅
- ✅
- ✅
- ✅
- ✅
- ✅
- ✅
* - :abbr:`guided dec (Guided Decoding)`
- ✅
- ✅
- ✅
- ✅
- ✅
- ✅
- ✅

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Environment Variables
========================
vLLM uses the following environment variables to configure the system:
.. warning::
Please note that ``VLLM_PORT`` and ``VLLM_HOST_IP`` set the port and ip for vLLM's **internal usage**. It is not the port and ip for the API server. If you use ``--host $VLLM_HOST_IP`` and ``--port $VLLM_PORT`` to start the API server, it will not work.
All environment variables used by vLLM are prefixed with ``VLLM_``. **Special care should be taken for Kubernetes users**: please do not name the service as ``vllm``, otherwise environment variables set by Kubernetes might conflict with vLLM's environment variables, because `Kubernetes sets environment variables for each service with the capitalized service name as the prefix <https://kubernetes.io/docs/concepts/services-networking/service/#environment-variables>`_.
.. literalinclude:: ../../../vllm/envs.py
:language: python
:start-after: begin-env-vars-definition
:end-before: end-env-vars-definition

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Frequently Asked Questions
===========================
Q: How can I serve multiple models on a single port using the OpenAI API?
A: Assuming that you're referring to using OpenAI compatible server to serve multiple models at once, that is not currently supported, you can run multiple instances of the server (each serving a different model) at the same time, and have another layer to route the incoming request to the correct server accordingly.
----------------------------------------
Q: Which model to use for offline inference embedding?
A: If you want to use an embedding model, try: https://huggingface.co/intfloat/e5-mistral-7b-instruct. Instead models, such as Llama-3-8b, Mistral-7B-Instruct-v0.3, are generation models rather than an embedding model
----------------------------------------
Q: Can the output of a prompt vary across runs in vLLM?
A: Yes, it can. vLLM does not guarantee stable log probabilities (logprobs) for the output tokens. Variations in logprobs may occur due to
numerical instability in Torch operations or non-deterministic behavior in batched Torch operations when batching changes. For more details,
see the `Numerical Accuracy section <https://pytorch.org/docs/stable/notes/numerical_accuracy.html#batched-computations-or-slice-computations>`_.
In vLLM, the same requests might be batched differently due to factors such as other concurrent requests,
changes in batch size, or batch expansion in speculative decoding. These batching variations, combined with numerical instability of Torch operations,
can lead to slightly different logit/logprob values at each step. Such differences can accumulate, potentially resulting in
different tokens being sampled. Once a different token is sampled, further divergence is likely.
**Mitigation Strategies**
- For improved stability and reduced variance, use `float32`. Note that this will require more memory.
- If using `bfloat16`, switching to `float16` can also help.
- Using request seeds can aid in achieving more stable generation for temperature > 0, but discrepancies due to precision differences may still occur.

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@ -32,7 +32,7 @@ We currently support the following OpenAI APIs:
- [Completions API](https://platform.openai.com/docs/api-reference/completions)
- *Note: `suffix` parameter is not supported.*
- [Chat Completions API](https://platform.openai.com/docs/api-reference/chat)
- [Vision](https://platform.openai.com/docs/guides/vision)-related parameters are supported; see [Using VLMs](../models/vlm.rst).
- [Vision](https://platform.openai.com/docs/guides/vision)-related parameters are supported; see [Multimodal Inputs](../usage/multimodal_inputs.rst).
- *Note: `image_url.detail` parameter is not supported.*
- We also support `audio_url` content type for audio files.
- Refer to [vllm.entrypoints.chat_utils](https://github.com/vllm-project/vllm/tree/main/vllm/entrypoints/chat_utils.py) for the exact schema.
@ -41,7 +41,7 @@ We currently support the following OpenAI APIs:
- [Embeddings API](https://platform.openai.com/docs/api-reference/embeddings)
- Instead of `inputs`, you can pass in a list of `messages` (same schema as Chat Completions API),
which will be treated as a single prompt to the model according to its chat template.
- This enables multi-modal inputs to be passed to embedding models, see [Using VLMs](../models/vlm.rst).
- This enables multi-modal inputs to be passed to embedding models, see [this page](../usage/multimodal_inputs.rst) for details.
- *Note: You should run `vllm serve` with `--task embedding` to ensure that the model is being run in embedding mode.*
## Score API for Cross Encoder Models

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# Usage Stats Collection
vLLM collects anonymous usage data by default to help the engineering team better understand which hardware and model configurations are widely used. This data allows them to prioritize their efforts on the most common workloads. The collected data is transparent, does not contain any sensitive information, and will be publicly released for the community's benefit.
## What data is collected?
You can see the up to date list of data collected by vLLM in the [usage_lib.py](https://github.com/vllm-project/vllm/blob/main/vllm/usage/usage_lib.py).
Here is an example as of v0.4.0:
```json
{
"uuid": "fbe880e9-084d-4cab-a395-8984c50f1109",
"provider": "GCP",
"num_cpu": 24,
"cpu_type": "Intel(R) Xeon(R) CPU @ 2.20GHz",
"cpu_family_model_stepping": "6,85,7",
"total_memory": 101261135872,
"architecture": "x86_64",
"platform": "Linux-5.10.0-28-cloud-amd64-x86_64-with-glibc2.31",
"gpu_count": 2,
"gpu_type": "NVIDIA L4",
"gpu_memory_per_device": 23580639232,
"model_architecture": "OPTForCausalLM",
"vllm_version": "0.3.2+cu123",
"context": "LLM_CLASS",
"log_time": 1711663373492490000,
"source": "production",
"dtype": "torch.float16",
"tensor_parallel_size": 1,
"block_size": 16,
"gpu_memory_utilization": 0.9,
"quantization": null,
"kv_cache_dtype": "auto",
"enable_lora": false,
"enable_prefix_caching": false,
"enforce_eager": false,
"disable_custom_all_reduce": true
}
```
You can preview the collected data by running the following command:
```bash
tail ~/.config/vllm/usage_stats.json
```
## Opt-out of Usage Stats Collection
You can opt-out of usage stats collection by setting the VLLM_NO_USAGE_STATS or DO_NOT_TRACK environment variable, or by creating a ~/.config/vllm/do_not_track file:
```bash
# Any of the following methods can disable usage stats collection
export VLLM_NO_USAGE_STATS=1
export DO_NOT_TRACK=1
mkdir -p ~/.config/vllm && touch ~/.config/vllm/do_not_track
```