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[Misc] Split up pooling tasks (vllm-project#10820)
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Signed-off-by: DarkLight1337 <[email protected]>
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DarkLight1337 authored and llsj14 committed Dec 11, 2024
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2 changes: 2 additions & 0 deletions docs/source/index.rst
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:caption: Models

models/supported_models
models/generative_models
models/pooling_models
models/adding_model
models/enabling_multimodal_inputs

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146 changes: 146 additions & 0 deletions docs/source/models/generative_models.rst
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.. _generative_models:

Generative Models
=================

vLLM provides first-class support for generative models, which covers most of LLMs.

In vLLM, generative models implement the :class:`~vllm.model_executor.models.VllmModelForTextGeneration` interface.
Based on the final hidden states of the input, these models output log probabilities of the tokens to generate,
which are then passed through :class:`~vllm.model_executor.layers.Sampler` to obtain the final text.

Offline Inference
-----------------

The :class:`~vllm.LLM` class provides various methods for offline inference.
See :ref:`Engine Arguments <engine_args>` for a list of options when initializing the model.

For generative models, the only supported :code:`task` option is :code:`"generate"`.
Usually, this is automatically inferred so you don't have to specify it.

``LLM.generate``
^^^^^^^^^^^^^^^^

The :class:`~vllm.LLM.generate` method is available to all generative models in vLLM.
It is similar to `its counterpart in HF Transformers <https://huggingface.co/docs/transformers/main/en/main_classes/text_generation#transformers.GenerationMixin.generate>`__,
except that tokenization and detokenization are also performed automatically.

.. code-block:: python
llm = LLM(model="facebook/opt-125m")
outputs = llm.generate("Hello, my name is")
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
You can optionally control the language generation by passing :class:`~vllm.SamplingParams`.
For example, you can use greedy sampling by setting :code:`temperature=0`:

.. code-block:: python
llm = LLM(model="facebook/opt-125m")
params = SamplingParams(temperature=0)
outputs = llm.generate("Hello, my name is", params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
A code example can be found in `examples/offline_inference.py <https://github.com/vllm-project/vllm/blob/main/examples/offline_inference.py>`_.

``LLM.beam_search``
^^^^^^^^^^^^^^^^^^^

The :class:`~vllm.LLM.beam_search` method implements `beam search <https://huggingface.co/docs/transformers/en/generation_strategies#beam-search-decoding>`__ on top of :class:`~vllm.LLM.generate`.
For example, to search using 5 beams and output at most 50 tokens:

.. code-block:: python
llm = LLM(model="facebook/opt-125m")
params = BeamSearchParams(beam_width=5, max_tokens=50)
outputs = llm.generate("Hello, my name is", params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
``LLM.chat``
^^^^^^^^^^^^

The :class:`~vllm.LLM.chat` method implements chat functionality on top of :class:`~vllm.LLM.generate`.
In particular, it accepts input similar to `OpenAI Chat Completions API <https://platform.openai.com/docs/api-reference/chat>`__
and automatically applies the model's `chat template <https://huggingface.co/docs/transformers/en/chat_templating>`__ to format the prompt.

.. important::

In general, only instruction-tuned models have a chat template.
Base models may perform poorly as they are not trained to respond to the chat conversation.

.. code-block:: python
llm = LLM(model="meta-llama/Meta-Llama-3-8B-Instruct")
conversation = [
{
"role": "system",
"content": "You are a helpful assistant"
},
{
"role": "user",
"content": "Hello"
},
{
"role": "assistant",
"content": "Hello! How can I assist you today?"
},
{
"role": "user",
"content": "Write an essay about the importance of higher education.",
},
]
outputs = llm.chat(conversation)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
A code example can be found in `examples/offline_inference_chat.py <https://github.com/vllm-project/vllm/blob/main/examples/offline_inference_chat.py>`_.

If the model doesn't have a chat template or you want to specify another one,
you can explicitly pass a chat template:

.. code-block:: python
from vllm.entrypoints.chat_utils import load_chat_template
# You can find a list of existing chat templates under `examples/`
custom_template = load_chat_template(chat_template="<path_to_template>")
print("Loaded chat template:", custom_template)
outputs = llm.chat(conversation, chat_template=custom_template)
Online Inference
----------------

Our `OpenAI Compatible Server <../serving/openai_compatible_server>`__ can be used for online inference.
Please click on the above link for more details on how to launch the server.

Completions API
^^^^^^^^^^^^^^^

Our Completions API is similar to ``LLM.generate`` but only accepts text.
It is compatible with `OpenAI Completions API <https://platform.openai.com/docs/api-reference/completions>`__
so that you can use OpenAI client to interact with it.
A code example can be found in `examples/openai_completion_client.py <https://github.com/vllm-project/vllm/blob/main/examples/openai_completion_client.py>`_.

Chat API
^^^^^^^^

Our Chat API is similar to ``LLM.chat``, accepting both text and :ref:`multi-modal inputs <multimodal_inputs>`.
It is compatible with `OpenAI Chat Completions API <https://platform.openai.com/docs/api-reference/chat>`__
so that you can use OpenAI client to interact with it.
A code example can be found in `examples/openai_chat_completion_client.py <https://github.com/vllm-project/vllm/blob/main/examples/openai_chat_completion_client.py>`_.
99 changes: 99 additions & 0 deletions docs/source/models/pooling_models.rst
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.. _pooling_models:

Pooling Models
==============

vLLM also supports pooling models, including embedding, reranking and reward models.

In vLLM, pooling models implement the :class:`~vllm.model_executor.models.VllmModelForPooling` interface.
These models use a :class:`~vllm.model_executor.layers.Pooler` to aggregate the final hidden states of the input
before returning them.

.. note::

We currently support pooling models primarily as a matter of convenience.
As shown in the :ref:`Compatibility Matrix <compatibility_matrix>`, most vLLM features are not applicable to
pooling models as they only work on the generation or decode stage, so performance may not improve as much.

Offline Inference
-----------------

The :class:`~vllm.LLM` class provides various methods for offline inference.
See :ref:`Engine Arguments <engine_args>` for a list of options when initializing the model.

For pooling models, we support the following :code:`task` options:

- Embedding (:code:`"embed"` / :code:`"embedding"`)
- Classification (:code:`"classify"`)
- Sentence Pair Scoring (:code:`"score"`)
- Reward Modeling (:code:`"reward"`)

The selected task determines the default :class:`~vllm.model_executor.layers.Pooler` that is used:

- Embedding: Extract only the hidden states corresponding to the last token, and apply normalization.
- Classification: Extract only the hidden states corresponding to the last token, and apply softmax.
- Sentence Pair Scoring: Extract only the hidden states corresponding to the last token, and apply softmax.
- Reward Modeling: Extract all of the hidden states and return them directly.

When loading `Sentence Transformers <https://huggingface.co/sentence-transformers>`__ models,
we attempt to override the default pooler based on its Sentence Transformers configuration file (:code:`modules.json`).

You can customize the model's pooling method via the :code:`override_pooler_config` option,
which takes priority over both the model's and Sentence Transformers's defaults.

``LLM.encode``
^^^^^^^^^^^^^^

The :class:`~vllm.LLM.encode` method is available to all pooling models in vLLM.
It returns the aggregated hidden states directly.

.. code-block:: python
llm = LLM(model="intfloat/e5-mistral-7b-instruct", task="embed")
outputs = llm.encode("Hello, my name is")
outputs = model.encode(prompts)
for output in outputs:
embeddings = output.outputs.embedding
print(f"Prompt: {prompt!r}, Embeddings (size={len(embeddings)}: {embeddings!r}")
A code example can be found in `examples/offline_inference_embedding.py <https://github.com/vllm-project/vllm/blob/main/examples/offline_inference_embedding.py>`_.

``LLM.score``
^^^^^^^^^^^^^

The :class:`~vllm.LLM.score` method outputs similarity scores between sentence pairs.
It is primarily designed for `cross-encoder models <https://www.sbert.net/examples/applications/cross-encoder/README.html>`__.
These types of models serve as rerankers between candidate query-document pairs in RAG systems.

.. note::

vLLM can only perform the model inference component (e.g. embedding, reranking) of RAG.
To handle RAG at a higher level, you should use integration frameworks such as `LangChain <https://github.com/langchain-ai/langchain>`_.

You can use `these tests <https://github.com/vllm-project/vllm/blob/main/tests/models/embedding/language/test_scoring.py>`_ as reference.

Online Inference
----------------

Our `OpenAI Compatible Server <../serving/openai_compatible_server>`__ can be used for online inference.
Please click on the above link for more details on how to launch the server.

Embeddings API
^^^^^^^^^^^^^^

Our Embeddings API is similar to ``LLM.encode``, accepting both text and :ref:`multi-modal inputs <multimodal_inputs>`.

The text-only API is compatible with `OpenAI Embeddings API <https://platform.openai.com/docs/api-reference/embeddings>`__
so that you can use OpenAI client to interact with it.
A code example can be found in `examples/openai_embedding_client.py <https://github.com/vllm-project/vllm/blob/main/examples/openai_embedding_client.py>`_.

The multi-modal API is an extension of the `OpenAI Embeddings API <https://platform.openai.com/docs/api-reference/embeddings>`__
that incorporates `OpenAI Chat Completions API <https://platform.openai.com/docs/api-reference/chat>`__,
so it is not part of the OpenAI standard. Please see :ref:`this page <multimodal_inputs>` for more details on how to use it.

Score API
^^^^^^^^^

Our Score API is similar to ``LLM.score``.
Please see `this page <../serving/openai_compatible_server.html#score-api-for-cross-encoder-models>`__ for more details on how to use it.
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