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Unverified Commit 32aa2059 authored by Rafael Vasquez's avatar Rafael Vasquez Committed by GitHub
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[Docs] Convert rST to MyST (Markdown) (#11145) 


Signed-off-by: default avatarRafael Vasquez <rafvasq21@gmail.com>
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.gitignore
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......@@ -81,6 +81,8 @@ instance/
docs/_build/
docs/source/getting_started/examples/*.rst
!**/*.template.rst
docs/source/getting_started/examples/*.md
!**/*.template.md
# PyBuilder
.pybuilder/
......
Dockerfile
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......@@ -2,7 +2,7 @@
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/dev/dockerfile/dockerfile.md and
# docs/source/assets/dev/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
......
docs/requirements-docs.txt
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sphinx==6.2.1
sphinx-book-theme==1.0.1
sphinx-copybutton==0.5.2
myst-parser==2.0.0
myst-parser==3.0.1
sphinx-argparse==0.4.0
msgspec
cloudpickle
......
docs/source/automatic_prefix_caching/apc.md 0 → 100644
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(apc)=
# Introduction
## What is Automatic Prefix Caching
Automatic Prefix Caching (APC in short) caches the KV cache of existing queries, so that a new query can directly reuse the KV cache if it shares the same prefix with one of the existing queries, allowing the new query to skip the computation of the shared part.
```{note}
Technical details on how vLLM implements APC are in the next page.
```
## Enabling APC in vLLM
Set `enable_prefix_caching=True` in vLLM engine to enable APC. Here is an example:
```python
import time
from vllm import LLM, SamplingParams
# A prompt containing a large markdown table. The table is randomly generated by GPT-4.
LONG_PROMPT = "You are a helpful assistant in recognizes the content of tables in markdown format. Here is a table as follows.\n# Table\n" + """
| ID | Name | Age | Occupation | Country | Email | Phone Number | Address |
|-----|---------------|-----|---------------|---------------|------------------------|----------------|------------------------------|
| 1 | John Doe | 29 | Engineer | USA | john.doe@example.com | 555-1234 | 123 Elm St, Springfield, IL |
| 2 | Jane Smith | 34 | Doctor | Canada | jane.smith@example.com | 555-5678 | 456 Oak St, Toronto, ON |
| 3 | Alice Johnson | 27 | Teacher | UK | alice.j@example.com | 555-8765 | 789 Pine St, London, UK |
| 4 | Bob Brown | 45 | Artist | Australia | bob.b@example.com | 555-4321 | 321 Maple St, Sydney, NSW |
| 5 | Carol White | 31 | Scientist | New Zealand | carol.w@example.com | 555-6789 | 654 Birch St, Wellington, NZ |
| 6 | Dave Green | 28 | Lawyer | Ireland | dave.g@example.com | 555-3456 | 987 Cedar St, Dublin, IE |
| 7 | Emma Black | 40 | Musician | USA | emma.b@example.com | 555-1111 | 246 Ash St, New York, NY |
| 8 | Frank Blue | 37 | Chef | Canada | frank.b@example.com | 555-2222 | 135 Spruce St, Vancouver, BC |
| 9 | Grace Yellow | 50 | Engineer | UK | grace.y@example.com | 555-3333 | 864 Fir St, Manchester, UK |
| 10 | Henry Violet | 32 | Artist | Australia | henry.v@example.com | 555-4444 | 753 Willow St, Melbourne, VIC|
| 11 | Irene Orange | 26 | Scientist | New Zealand | irene.o@example.com | 555-5555 | 912 Poplar St, Auckland, NZ |
| 12 | Jack Indigo | 38 | Teacher | Ireland | jack.i@example.com | 555-6666 | 159 Elm St, Cork, IE |
| 13 | Karen Red | 41 | Lawyer | USA | karen.r@example.com | 555-7777 | 357 Cedar St, Boston, MA |
| 14 | Leo Brown | 30 | Chef | Canada | leo.b@example.com | 555-8888 | 246 Oak St, Calgary, AB |
| 15 | Mia Green | 33 | Musician | UK | mia.g@example.com | 555-9999 | 975 Pine St, Edinburgh, UK |
| 16 | Noah Yellow | 29 | Doctor | Australia | noah.y@example.com | 555-0000 | 864 Birch St, Brisbane, QLD |
| 17 | Olivia Blue | 35 | Engineer | New Zealand | olivia.b@example.com | 555-1212 | 753 Maple St, Hamilton, NZ |
| 18 | Peter Black | 42 | Artist | Ireland | peter.b@example.com | 555-3434 | 912 Fir St, Limerick, IE |
| 19 | Quinn White | 28 | Scientist | USA | quinn.w@example.com | 555-5656 | 159 Willow St, Seattle, WA |
| 20 | Rachel Red | 31 | Teacher | Canada | rachel.r@example.com | 555-7878 | 357 Poplar St, Ottawa, ON |
| 21 | Steve Green | 44 | Lawyer | UK | steve.g@example.com | 555-9090 | 753 Elm St, Birmingham, UK |
| 22 | Tina Blue | 36 | Musician | Australia | tina.b@example.com | 555-1213 | 864 Cedar St, Perth, WA |
| 23 | Umar Black | 39 | Chef | New Zealand | umar.b@example.com | 555-3435 | 975 Spruce St, Christchurch, NZ|
| 24 | Victor Yellow | 43 | Engineer | Ireland | victor.y@example.com | 555-5657 | 246 Willow St, Galway, IE |
| 25 | Wendy Orange | 27 | Artist | USA | wendy.o@example.com | 555-7879 | 135 Elm St, Denver, CO |
| 26 | Xavier Green | 34 | Scientist | Canada | xavier.g@example.com | 555-9091 | 357 Oak St, Montreal, QC |
| 27 | Yara Red | 41 | Teacher | UK | yara.r@example.com | 555-1214 | 975 Pine St, Leeds, UK |
| 28 | Zack Blue | 30 | Lawyer | Australia | zack.b@example.com | 555-3436 | 135 Birch St, Adelaide, SA |
| 29 | Amy White | 33 | Musician | New Zealand | amy.w@example.com | 555-5658 | 159 Maple St, Wellington, NZ |
| 30 | Ben Black | 38 | Chef | Ireland | ben.b@example.com | 555-7870 | 246 Fir St, Waterford, IE |
"""
def get_generation_time(llm, sampling_params, prompts):
# time the generation
start_time = time.time()
output = llm.generate(prompts, sampling_params=sampling_params)
end_time = time.time()
# print the output and generation time
print(f"Output: {output[0].outputs[0].text}")
print(f"Generation time: {end_time - start_time} seconds.")
# set enable_prefix_caching=True to enable APC
llm = LLM(
model='lmsys/longchat-13b-16k',
enable_prefix_caching=True
)
sampling_params = SamplingParams(temperature=0, max_tokens=100)
# Querying the age of John Doe
get_generation_time(
llm,
sampling_params,
LONG_PROMPT + "Question: what is the age of John Doe? Your answer: The age of John Doe is ",
)
# Querying the age of Zack Blue
# This query will be faster since vllm avoids computing the KV cache of LONG_PROMPT again.
get_generation_time(
llm,
sampling_params,
LONG_PROMPT + "Question: what is the age of Zack Blue? Your answer: The age of Zack Blue is ",
)
```
## Example workloads
We describe two example workloads, where APC can provide huge performance benefit:
- Long document query, where the user repeatedly queries the same long document (e.g. software manual or annual report) with different queries. In this case, instead of processing the long document again and again, APC allows vLLM to process this long document *only once*, and all future requests can avoid recomputing this long document by reusing its KV cache. This allows vLLM to serve future requests with much higher throughput and much lower latency.
- Multi-round conversation, where the user may chat with the application multiple times in the same chatting session. In this case, instead of processing the whole chatting history again and again, APC allows vLLM to reuse the processing results of the chat history across all future rounds of conversation, allowing vLLM to serve future requests with much higher throughput and much lower latency.
## Limits
APC in general does not reduce the performance of vLLM. With that being said, APC only reduces the time of processing the queries (the prefilling phase) and does not reduce the time of generating new tokens (the decoding phase). So APC does not bring performance gain when vLLM spends most of the time generating answers to the queries (e.g. when the length of the answer is long), or new queries do not share the same prefix with any of existing queries (so that the computation cannot be reused).
docs/source/automatic_prefix_caching/apc.rst deleted 100644 → 0
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.. _apc:
Introduction
============
What is Automatic Prefix Caching
--------------------------------
Automatic Prefix Caching (APC in short) caches the KV cache of existing queries, so that a new query can directly reuse the KV cache if it shares the same prefix with one of the existing queries, allowing the new query to skip the computation of the shared part.
.. note::
Technical details on how vLLM implements APC are in the next page.
Enabling APC in vLLM
--------------------
Set ``enable_prefix_caching=True`` in vLLM engine to enable APC. Here is an example:
.. code-block:: python
import time
from vllm import LLM, SamplingParams
# A prompt containing a large markdown table. The table is randomly generated by GPT-4.
LONG_PROMPT = "You are a helpful assistant in recognizes the content of tables in markdown format. Here is a table as follows.\n# Table\n" + """
| ID | Name | Age | Occupation | Country | Email | Phone Number | Address |
|-----|---------------|-----|---------------|---------------|------------------------|----------------|------------------------------|
| 1 | John Doe | 29 | Engineer | USA | john.doe@example.com | 555-1234 | 123 Elm St, Springfield, IL |
| 2 | Jane Smith | 34 | Doctor | Canada | jane.smith@example.com | 555-5678 | 456 Oak St, Toronto, ON |
| 3 | Alice Johnson | 27 | Teacher | UK | alice.j@example.com | 555-8765 | 789 Pine St, London, UK |
| 4 | Bob Brown | 45 | Artist | Australia | bob.b@example.com | 555-4321 | 321 Maple St, Sydney, NSW |
| 5 | Carol White | 31 | Scientist | New Zealand | carol.w@example.com | 555-6789 | 654 Birch St, Wellington, NZ |
| 6 | Dave Green | 28 | Lawyer | Ireland | dave.g@example.com | 555-3456 | 987 Cedar St, Dublin, IE |
| 7 | Emma Black | 40 | Musician | USA | emma.b@example.com | 555-1111 | 246 Ash St, New York, NY |
| 8 | Frank Blue | 37 | Chef | Canada | frank.b@example.com | 555-2222 | 135 Spruce St, Vancouver, BC |
| 9 | Grace Yellow | 50 | Engineer | UK | grace.y@example.com | 555-3333 | 864 Fir St, Manchester, UK |
| 10 | Henry Violet | 32 | Artist | Australia | henry.v@example.com | 555-4444 | 753 Willow St, Melbourne, VIC|
| 11 | Irene Orange | 26 | Scientist | New Zealand | irene.o@example.com | 555-5555 | 912 Poplar St, Auckland, NZ |
| 12 | Jack Indigo | 38 | Teacher | Ireland | jack.i@example.com | 555-6666 | 159 Elm St, Cork, IE |
| 13 | Karen Red | 41 | Lawyer | USA | karen.r@example.com | 555-7777 | 357 Cedar St, Boston, MA |
| 14 | Leo Brown | 30 | Chef | Canada | leo.b@example.com | 555-8888 | 246 Oak St, Calgary, AB |
| 15 | Mia Green | 33 | Musician | UK | mia.g@example.com | 555-9999 | 975 Pine St, Edinburgh, UK |
| 16 | Noah Yellow | 29 | Doctor | Australia | noah.y@example.com | 555-0000 | 864 Birch St, Brisbane, QLD |
| 17 | Olivia Blue | 35 | Engineer | New Zealand | olivia.b@example.com | 555-1212 | 753 Maple St, Hamilton, NZ |
| 18 | Peter Black | 42 | Artist | Ireland | peter.b@example.com | 555-3434 | 912 Fir St, Limerick, IE |
| 19 | Quinn White | 28 | Scientist | USA | quinn.w@example.com | 555-5656 | 159 Willow St, Seattle, WA |
| 20 | Rachel Red | 31 | Teacher | Canada | rachel.r@example.com | 555-7878 | 357 Poplar St, Ottawa, ON |
| 21 | Steve Green | 44 | Lawyer | UK | steve.g@example.com | 555-9090 | 753 Elm St, Birmingham, UK |
| 22 | Tina Blue | 36 | Musician | Australia | tina.b@example.com | 555-1213 | 864 Cedar St, Perth, WA |
| 23 | Umar Black | 39 | Chef | New Zealand | umar.b@example.com | 555-3435 | 975 Spruce St, Christchurch, NZ|
| 24 | Victor Yellow | 43 | Engineer | Ireland | victor.y@example.com | 555-5657 | 246 Willow St, Galway, IE |
| 25 | Wendy Orange | 27 | Artist | USA | wendy.o@example.com | 555-7879 | 135 Elm St, Denver, CO |
| 26 | Xavier Green | 34 | Scientist | Canada | xavier.g@example.com | 555-9091 | 357 Oak St, Montreal, QC |
| 27 | Yara Red | 41 | Teacher | UK | yara.r@example.com | 555-1214 | 975 Pine St, Leeds, UK |
| 28 | Zack Blue | 30 | Lawyer | Australia | zack.b@example.com | 555-3436 | 135 Birch St, Adelaide, SA |
| 29 | Amy White | 33 | Musician | New Zealand | amy.w@example.com | 555-5658 | 159 Maple St, Wellington, NZ |
| 30 | Ben Black | 38 | Chef | Ireland | ben.b@example.com | 555-7870 | 246 Fir St, Waterford, IE |
"""
def get_generation_time(llm, sampling_params, prompts):
# time the generation
start_time = time.time()
output = llm.generate(prompts, sampling_params=sampling_params)
end_time = time.time()
# print the output and generation time
print(f"Output: {output[0].outputs[0].text}")
print(f"Generation time: {end_time - start_time} seconds.")
# set enable_prefix_caching=True to enable APC
llm = LLM(
model='lmsys/longchat-13b-16k',
enable_prefix_caching=True
)
sampling_params = SamplingParams(temperature=0, max_tokens=100)
# Querying the age of John Doe
get_generation_time(
llm,
sampling_params,
LONG_PROMPT + "Question: what is the age of John Doe? Your answer: The age of John Doe is ",
)
# Querying the age of Zack Blue
# This query will be faster since vllm avoids computing the KV cache of LONG_PROMPT again.
get_generation_time(
llm,
sampling_params,
LONG_PROMPT + "Question: what is the age of Zack Blue? Your answer: The age of Zack Blue is ",
)
Example workloads
-----------------
We describe two example workloads, where APC can provide huge performance benefit:
- Long document query, where the user repeatedly queries the same long document (e.g. software manual or annual report) with different queries. In this case, instead of processing the long document again and again, APC allows vLLM to process this long document *only once*, and all future requests can avoid recomputing this long document by reusing its KV cache. This allows vLLM to serve future requests with much higher throughput and much lower latency.
- Multi-round conversation, where the user may chat with the application multiple times in the same chatting session. In this case, instead of processing the whole chatting history again and again, APC allows vLLM to reuse the processing results of the chat history across all future rounds of conversation, allowing vLLM to serve future requests with much higher throughput and much lower latency.
Limits
------
APC in general does not reduce the performance of vLLM. With that being said, APC only reduces the time of processing the queries (the prefilling phase) and does not reduce the time of generating new tokens (the decoding phase). So APC does not bring performance gain when vLLM spends most of the time generating answers to the queries (e.g. when the length of the answer is long), or new queries do not share the same prefix with any of existing queries (so that the computation cannot be reused).
docs/source/community/meetups.md 0 → 100644
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(meetups)=
# vLLM Meetups
We host regular meetups in San Francisco Bay Area every 2 months. We will share the project updates from the vLLM team and have guest speakers from the industry to share their experience and insights. Please find the materials of our previous meetups below:
- [The seventh vLLM meetup](https://lu.ma/h0qvrajz), with Snowflake, November 14th 2024. [[Slides]](https://docs.google.com/presentation/d/1e3CxQBV3JsfGp30SwyvS3eM_tW-ghOhJ9PAJGK6KR54/edit?usp=sharing)
- [The sixth vLLM meetup](https://lu.ma/87q3nvnh), with NVIDIA, September 9th 2024. [[Slides]](https://docs.google.com/presentation/d/1wrLGwytQfaOTd5wCGSPNhoaW3nq0E-9wqyP7ny93xRs/edit?usp=sharing)
- [The fifth vLLM meetup](https://lu.ma/lp0gyjqr), with AWS, July 24th 2024. [[Slides]](https://docs.google.com/presentation/d/1RgUD8aCfcHocghoP3zmXzck9vX3RCI9yfUAB2Bbcl4Y/edit?usp=sharing)
- [The fourth vLLM meetup](https://lu.ma/agivllm), with Cloudflare and BentoML, June 11th 2024. [[Slides]](https://docs.google.com/presentation/d/1iJ8o7V2bQEi0BFEljLTwc5G1S10_Rhv3beed5oB0NJ4/edit?usp=sharing)
- [The third vLLM meetup](https://robloxandvllmmeetup2024.splashthat.com/), with Roblox, April 2nd 2024. [[Slides]](https://docs.google.com/presentation/d/1A--47JAK4BJ39t954HyTkvtfwn0fkqtsL8NGFuslReM/edit?usp=sharing)
- [The second vLLM meetup](https://lu.ma/ygxbpzhl), with IBM Research, January 31st 2024. [[Slides]](https://docs.google.com/presentation/d/12mI2sKABnUw5RBWXDYY-HtHth4iMSNcEoQ10jDQbxgA/edit?usp=sharing) [[Video (vLLM Update)]](https://youtu.be/Y0C-DUvEnZQ) [[Video (IBM Research & torch.compile)]](https://youtu.be/m0dMtFLI-dg)
- [The first vLLM meetup](https://lu.ma/first-vllm-meetup), with a16z, October 5th 2023. [[Slides]](https://docs.google.com/presentation/d/1QL-XPFXiFpDBh86DbEegFXBXFXjix4v032GhShbKf3s/edit?usp=sharing)
We are always looking for speakers and sponsors at San Francisco Bay Area and potentially other locations. If you are interested in speaking or sponsoring, please contact us at [vllm-questions@lists.berkeley.edu](mailto:vllm-questions@lists.berkeley.edu).
docs/source/community/meetups.rst deleted 100644 → 0
View file @ 94d545a1
.. _meetups:
vLLM Meetups
============
We host regular meetups in San Francisco Bay Area every 2 months. We will share the project updates from the vLLM team and have guest speakers from the industry to share their experience and insights. Please find the materials of our previous meetups below:
- `The seventh vLLM meetup <https://lu.ma/h0qvrajz>`__, with Snowflake, November 14th 2024. `[Slides] <https://docs.google.com/presentation/d/1e3CxQBV3JsfGp30SwyvS3eM_tW-ghOhJ9PAJGK6KR54/edit?usp=sharing>`__
- `The sixth vLLM meetup <https://lu.ma/87q3nvnh>`__, with NVIDIA, September 9th 2024. `[Slides] <https://docs.google.com/presentation/d/1wrLGwytQfaOTd5wCGSPNhoaW3nq0E-9wqyP7ny93xRs/edit?usp=sharing>`__
- `The fifth vLLM meetup <https://lu.ma/lp0gyjqr>`__, with AWS, July 24th 2024. `[Slides] <https://docs.google.com/presentation/d/1RgUD8aCfcHocghoP3zmXzck9vX3RCI9yfUAB2Bbcl4Y/edit?usp=sharing>`__
- `The fourth vLLM meetup <https://lu.ma/agivllm>`__, with Cloudflare and BentoML, June 11th 2024. `[Slides] <https://docs.google.com/presentation/d/1iJ8o7V2bQEi0BFEljLTwc5G1S10_Rhv3beed5oB0NJ4/edit?usp=sharing>`__
- `The third vLLM meetup <https://robloxandvllmmeetup2024.splashthat.com/>`__, with Roblox, April 2nd 2024. `[Slides] <https://docs.google.com/presentation/d/1A--47JAK4BJ39t954HyTkvtfwn0fkqtsL8NGFuslReM/edit?usp=sharing>`__
- `The second vLLM meetup <https://lu.ma/ygxbpzhl>`__, with IBM Research, January 31st 2024. `[Slides] <https://docs.google.com/presentation/d/12mI2sKABnUw5RBWXDYY-HtHth4iMSNcEoQ10jDQbxgA/edit?usp=sharing>`__ `[Video (vLLM Update)] <https://youtu.be/Y0C-DUvEnZQ>`__ `[Video (IBM Research & torch.compile)] <https://youtu.be/m0dMtFLI-dg>`__
- `The first vLLM meetup <https://lu.ma/first-vllm-meetup>`__, with a16z, October 5th 2023. `[Slides] <https://docs.google.com/presentation/d/1QL-XPFXiFpDBh86DbEegFXBXFXjix4v032GhShbKf3s/edit?usp=sharing>`__
We are always looking for speakers and sponsors at San Francisco Bay Area and potentially other locations. If you are interested in speaking or sponsoring, please contact us at `vllm-questions@lists.berkeley.edu <mailto:vllm-questions@lists.berkeley.edu>`__.
docs/source/conf.py
View file @ 32aa2059
......@@ -51,7 +51,7 @@ templates_path = ['_templates']
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path.
exclude_patterns: List[str] = ["**/*.template.rst"]
exclude_patterns: List[str] = ["**/*.template.md"]
# Exclude the prompt "$" when copying code
copybutton_prompt_text = r"\$ "
......
docs/source/contributing/dockerfile/dockerfile.md 0 → 100644
View file @ 32aa2059
# Dockerfile
See [here](https://github.com/vllm-project/vllm/blob/main/Dockerfile) for the main Dockerfile to construct
the image for running an OpenAI compatible server with vLLM. More information about deploying with Docker can be found [here](https://docs.vllm.ai/en/stable/serving/deploying_with_docker.html).
Below is a visual representation of the multi-stage Dockerfile. The build graph contains the following nodes:
- All build stages
- The default build target (highlighted in grey)
- External images (with dashed borders)
The edges of the build graph represent:
- FROM ... dependencies (with a solid line and a full arrow head)
- COPY --from=... dependencies (with a dashed line and an empty arrow head)
- RUN --mount=(.\*)from=... dependencies (with a dotted line and an empty diamond arrow head)
> ```{figure} ../../assets/dev/dockerfile-stages-dependency.png
> :align: center
> :alt: query
> :width: 100%
> ```
>
> Made using: <https://github.com/patrickhoefler/dockerfilegraph>
>
> Commands to regenerate the build graph (make sure to run it **from the \`root\` directory of the vLLM repository** where the dockerfile is present):
>
> ```bash
> dockerfilegraph -o png --legend --dpi 200 --max-label-length 50 --filename Dockerfile
> ```
>
> or in case you want to run it directly with the docker image:
>
> ```bash
> docker run \
> --rm \
> --user "$(id -u):$(id -g)" \
> --workdir /workspace \
> --volume "$(pwd)":/workspace \
> ghcr.io/patrickhoefler/dockerfilegraph:alpine \
> --output png \
> --dpi 200 \
> --max-label-length 50 \
> --filename Dockerfile \
> --legend
> ```
>
> (To run it for a different file, you can pass in a different argument to the flag `--filename`.)
docs/source/contributing/dockerfile/dockerfile.rst deleted 100644 → 0
View file @ 94d545a1
Dockerfile
====================
See `here <https://github.com/vllm-project/vllm/blob/main/Dockerfile>`__ for the main Dockerfile to construct
the image for running an OpenAI compatible server with vLLM. More information about deploying with Docker can be found `here <https://docs.vllm.ai/en/stable/serving/deploying_with_docker.html>`__.
Below is a visual representation of the multi-stage Dockerfile. The build graph contains the following nodes:
- All build stages
- The default build target (highlighted in grey)
- External images (with dashed borders)
The edges of the build graph represent:
- FROM ... dependencies (with a solid line and a full arrow head)
- COPY --from=... dependencies (with a dashed line and an empty arrow head)
- RUN --mount=(.*)from=... dependencies (with a dotted line and an empty diamond arrow head)
.. figure:: ../../assets/dev/dockerfile-stages-dependency.png
:alt: query
:width: 100%
:align: center
Made using: https://github.com/patrickhoefler/dockerfilegraph
Commands to regenerate the build graph (make sure to run it **from the `root` directory of the vLLM repository** where the dockerfile is present):
.. code:: bash
dockerfilegraph -o png --legend --dpi 200 --max-label-length 50 --filename Dockerfile
or in case you want to run it directly with the docker image:
.. code:: bash
docker run \
--rm \
--user "$(id -u):$(id -g)" \
--workdir /workspace \
--volume "$(pwd)":/workspace \
ghcr.io/patrickhoefler/dockerfilegraph:alpine \
--output png \
--dpi 200 \
--max-label-length 50 \
--filename Dockerfile \
--legend
(To run it for a different file, you can pass in a different argument to the flag `--filename`.)
\ No newline at end of file
docs/source/contributing/overview.rst → docs/source/contributing/overview.md
View file @ 32aa2059
Contributing to vLLM
=====================
# Contributing to vLLM
Thank you for your interest in contributing to vLLM! Our community is open to everyone and welcomes all kinds of contributions, no matter how small or large. There are several ways you can contribute to the project:
......@@ -12,132 +11,121 @@ We also believe in the power of community support; thus, answering queries, offe
Finally, one of the most impactful ways to support us is by raising awareness about vLLM. Talk about it in your blog posts and highlight how it's driving your incredible projects. Express your support on social media if you're using vLLM, or simply offer your appreciation by starring our repository!
License
-------
## License
See `LICENSE <https://github.com/vllm-project/vllm/tree/main/LICENSE>`_.
See [LICENSE](https://github.com/vllm-project/vllm/tree/main/LICENSE).
Developing
----------
## Developing
Depending on the kind of development you'd like to do (e.g. Python, CUDA), you can choose to build vLLM with or without compilation. Check out the `building from source <https://docs.vllm.ai/en/latest/getting_started/installation.html#build-from-source>`_ documentation for details.
Depending on the kind of development you'd like to do (e.g. Python, CUDA), you can choose to build vLLM with or without compilation. Check out the [building from source](https://docs.vllm.ai/en/latest/getting_started/installation.html#build-from-source) documentation for details.
Testing
-------
## Testing
.. code-block:: bash
```bash
pip install -r requirements-dev.txt
pip install -r requirements-dev.txt
# linting and formatting
bash format.sh
# Static type checking
mypy
# Unit tests
pytest tests/
```
# linting and formatting
bash format.sh
# Static type checking
mypy
# Unit tests
pytest tests/
```{note}
Currently, the repository does not pass the `mypy` tests.
```
.. note:: Currently, the repository does not pass the ``mypy`` tests.
# Contribution Guidelines
Contribution Guidelines
=======================
## Issues
Issues
------
If you encounter a bug or have a feature request, please [search existing issues](https://github.com/vllm-project/vllm/issues?q=is%3Aissue) first to see if it has already been reported. If not, please [file a new issue](https://github.com/vllm-project/vllm/issues/new/choose), providing as much relevant information as possible.
If you encounter a bug or have a feature request, please `search existing issues <https://github.com/vllm-project/vllm/issues?q=is%3Aissue>`_ first to see if it has already been reported. If not, please `file a new issue <https://github.com/vllm-project/vllm/issues/new/choose>`_, providing as much relevant information as possible.
```{important}
If you discover a security vulnerability, please follow the instructions [here](https://github.com/vllm-project/vllm/tree/main/SECURITY.md#reporting-a-vulnerability).
```
.. important::
If you discover a security vulnerability, please follow the instructions `here <https://github.com/vllm-project/vllm/tree/main/SECURITY.md#reporting-a-vulnerability>`_.
Pull Requests & Code Reviews
----------------------------
## Pull Requests & Code Reviews
Thank you for your contribution to vLLM! Before submitting the pull request,
please ensure the PR meets the following criteria. This helps vLLM maintain the
code quality and improve the efficiency of the review process.
DCO and Signed-off-by
^^^^^^^^^^^^^^^^^^^^^
### DCO and Signed-off-by
When contributing changes to this project, you must agree to the `DCO <https://github.com/vllm-project/vllm/tree/main/DCO>`_.
Commits must include a ``Signed-off-by:`` header which certifies agreement with
the terms of the `DCO <https://github.com/vllm-project/vllm/tree/main/DCO>`_.
When contributing changes to this project, you must agree to the [DCO](https://github.com/vllm-project/vllm/tree/main/DCO).
Commits must include a `Signed-off-by:` header which certifies agreement with
the terms of the [DCO](https://github.com/vllm-project/vllm/tree/main/DCO).
Using ``-s`` with ``git commit`` will automatically add this header.
Using `-s` with `git commit` will automatically add this header.
PR Title and Classification
^^^^^^^^^^^^^^^^^^^^^^^^^^^
### PR Title and Classification
Only specific types of PRs will be reviewed. The PR title is prefixed
appropriately to indicate the type of change. Please use one of the following:
- ``[Bugfix]`` for bug fixes.
- ``[CI/Build]`` for build or continuous integration improvements.
- ``[Doc]`` for documentation fixes and improvements.
- ``[Model]`` for adding a new model or improving an existing model. Model name
- `[Bugfix]` for bug fixes.
- `[CI/Build]` for build or continuous integration improvements.
- `[Doc]` for documentation fixes and improvements.
- `[Model]` for adding a new model or improving an existing model. Model name
should appear in the title.
- ``[Frontend]`` For changes on the vLLM frontend (e.g., OpenAI API server,
``LLM`` class, etc.)
- ``[Kernel]`` for changes affecting CUDA kernels or other compute kernels.
- ``[Core]`` for changes in the core vLLM logic (e.g., ``LLMEngine``,
``AsyncLLMEngine``, ``Scheduler``, etc.)
- ``[Hardware][Vendor]`` for hardware-specific changes. Vendor name should
appear in the prefix (e.g., ``[Hardware][AMD]``).
- ``[Misc]`` for PRs that do not fit the above categories. Please use this
- `[Frontend]` For changes on the vLLM frontend (e.g., OpenAI API server,
`LLM` class, etc.)
- `[Kernel]` for changes affecting CUDA kernels or other compute kernels.
- `[Core]` for changes in the core vLLM logic (e.g., `LLMEngine`,
`AsyncLLMEngine`, `Scheduler`, etc.)
- `[Hardware][Vendor]` for hardware-specific changes. Vendor name should
appear in the prefix (e.g., `[Hardware][AMD]`).
- `[Misc]` for PRs that do not fit the above categories. Please use this
sparingly.
.. note::
If the PR spans more than one category, please include all relevant prefixes.
```{note}
If the PR spans more than one category, please include all relevant prefixes.
```
Code Quality
^^^^^^^^^^^^
### Code Quality
The PR needs to meet the following code quality standards:
- We adhere to `Google Python style guide
<https://google.github.io/styleguide/pyguide.html>`_ and `Google C++ style guide
<https://google.github.io/styleguide/cppguide.html>`_.
- Pass all linter checks. Please use `format.sh
<https://github.com/vllm-project/vllm/blob/main/format.sh>`_ to format your
- We adhere to [Google Python style guide](https://google.github.io/styleguide/pyguide.html) and [Google C++ style guide](https://google.github.io/styleguide/cppguide.html).
- Pass all linter checks. Please use [format.sh](https://github.com/vllm-project/vllm/blob/main/format.sh) to format your
code.
- The code needs to be well-documented to ensure future contributors can easily
understand the code.
- Include sufficient tests to ensure the project stays correct and robust. This
includes both unit tests and integration tests.
- Please add documentation to ``docs/source/`` if the PR modifies the
- Please add documentation to `docs/source/` if the PR modifies the
user-facing behaviors of vLLM. It helps vLLM users understand and utilize the
new features or changes.
Adding or Changing Kernels
^^^^^^^^^^^^^^^^^^^^^^^^^^
### Adding or Changing Kernels
Each custom kernel needs a schema and one or more implementations to be registered with PyTorch.
- Make sure custom ops are registered following PyTorch guidelines:
`Custom C++ and CUDA Operators <https://pytorch.org/tutorials/advanced/cpp_custom_ops.html#cpp-custom-ops-tutorial>`_
and `The Custom Operators Manual <https://docs.google.com/document/d/1_W62p8WJOQQUzPsJYa7s701JXt0qf2OfLub2sbkHOaU>`_.
- Custom operations that return ``Tensors`` require meta-functions.
[Custom C++ and CUDA Operators](https://pytorch.org/tutorials/advanced/cpp_custom_ops.html#cpp-custom-ops-tutorial)
and [The Custom Operators Manual](https://docs.google.com/document/d/1_W62p8WJOQQUzPsJYa7s701JXt0qf2OfLub2sbkHOaU).
- Custom operations that return `Tensors` require meta-functions.
Meta-functions should be implemented and registered in Python so that dynamic
dims can be handled automatically. See above documents for a description of
meta-functions.
- Use `torch.library.opcheck() <https://pytorch.org/docs/stable/library.html#torch.library.opcheck>`_
- Use [torch.library.opcheck()](https://pytorch.org/docs/stable/library.html#torch.library.opcheck)
to test the function registration and meta-function for any registered ops.
See ``tests/kernels`` for examples.
See `tests/kernels` for examples.
- When changing the C++ signature of an existing op, the schema must be updated
to reflect the changes.
- If a new custom type is needed, see the following document:
`Custom Class Support in PT2 <https://docs.google.com/document/d/18fBMPuOJ0fY5ZQ6YyrHUppw9FA332CpNtgB6SOIgyuA>`_.
[Custom Class Support in PT2](https://docs.google.com/document/d/18fBMPuOJ0fY5ZQ6YyrHUppw9FA332CpNtgB6SOIgyuA).
Notes for Large Changes
^^^^^^^^^^^^^^^^^^^^^^^
### Notes for Large Changes
Please keep the changes as concise as possible. For major architectural changes
(>500 LOC excluding kernel/data/config/test), we would expect a GitHub issue
(RFC) discussing the technical design and justification. Otherwise, we will tag
it with ``rfc-required`` and might not go through the PR.
it with `rfc-required` and might not go through the PR.
What to Expect for the Reviews
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
### What to Expect for the Reviews
The goal of the vLLM team is to be a *transparent reviewing machine*. We would
like to make the review process transparent and efficient and make sure no
......@@ -150,15 +138,14 @@ review process:
- After the PR is assigned, the reviewer will provide status updates every 2-3
days. If the PR is not reviewed within 7 days, please feel free to ping the
reviewer or the vLLM team.
- After the review, the reviewer will put an ``action-required`` label on the PR
- After the review, the reviewer will put an `action-required` label on the PR
if there are changes required. The contributor should address the comments and
ping the reviewer to re-review the PR.
- Please respond to all comments within a reasonable time frame. If a comment
isn't clear or you disagree with a suggestion, feel free to ask for
clarification or discuss the suggestion.
Thank You
---------
## Thank You
Finally, thank you for taking the time to read these guidelines and for your interest in contributing to vLLM.
All of your contributions help make vLLM a great tool and community for everyone!
docs/source/contributing/profiling/profiling_index.md 0 → 100644
View file @ 32aa2059
# Profiling vLLM
We support tracing vLLM workers using the `torch.profiler` module. You can enable tracing by setting the `VLLM_TORCH_PROFILER_DIR` environment variable to the directory where you want to save the traces: `VLLM_TORCH_PROFILER_DIR=/mnt/traces/`
The OpenAI server also needs to be started with the `VLLM_TORCH_PROFILER_DIR` environment variable set.
When using `benchmarks/benchmark_serving.py`, you can enable profiling by passing the `--profile` flag.
```{warning}
Only enable profiling in a development environment.
```
Traces can be visualized using <https://ui.perfetto.dev/>.
```{tip}
Only send a few requests through vLLM when profiling, as the traces can get quite large. Also, no need to untar the traces, they can be viewed directly.
```
```{tip}
To stop the profiler - it flushes out all the profile trace files to the directory. This takes time, for example for about 100 requests worth of data for a llama 70b, it takes about 10 minutes to flush out on a H100.
Set the env variable VLLM_RPC_TIMEOUT to a big number before you start the server. Say something like 30 minutes.
`export VLLM_RPC_TIMEOUT=1800000`
```
## Example commands and usage:
### Offline Inference:
Refer to [examples/offline_inference_with_profiler.py](https://github.com/vllm-project/vllm/blob/main/examples/offline_inference_with_profiler.py) for an example.
### OpenAI Server:
```bash
VLLM_TORCH_PROFILER_DIR=./vllm_profile python -m vllm.entrypoints.openai.api_server --model meta-llama/Meta-Llama-3-70B
```
benchmark_serving.py:
```bash
python benchmarks/benchmark_serving.py --backend vllm --model meta-llama/Meta-Llama-3-70B --dataset-name sharegpt --dataset-path sharegpt.json --profile --num-prompts 2
```
docs/source/contributing/profiling/profiling_index.rst deleted 100644 → 0
View file @ 94d545a1
==============
Profiling vLLM
==============
We support tracing vLLM workers using the ``torch.profiler`` module. You can enable tracing by setting the ``VLLM_TORCH_PROFILER_DIR`` environment variable to the directory where you want to save the traces: ``VLLM_TORCH_PROFILER_DIR=/mnt/traces/``
The OpenAI server also needs to be started with the ``VLLM_TORCH_PROFILER_DIR`` environment variable set.
When using ``benchmarks/benchmark_serving.py``, you can enable profiling by passing the ``--profile`` flag.
.. warning::
Only enable profiling in a development environment.
Traces can be visualized using https://ui.perfetto.dev/.
.. tip::
Only send a few requests through vLLM when profiling, as the traces can get quite large. Also, no need to untar the traces, they can be viewed directly.
.. tip::
To stop the profiler - it flushes out all the profile trace files to the directory. This takes time, for example for about 100 requests worth of data for a llama 70b, it takes about 10 minutes to flush out on a H100.
Set the env variable VLLM_RPC_TIMEOUT to a big number before you start the server. Say something like 30 minutes.
``export VLLM_RPC_TIMEOUT=1800000``
Example commands and usage:
===========================
Offline Inference:
------------------
Refer to `examples/offline_inference_with_profiler.py <https://github.com/vllm-project/vllm/blob/main/examples/offline_inference_with_profiler.py>`_ for an example.
OpenAI Server:
--------------
.. code-block:: bash
VLLM_TORCH_PROFILER_DIR=./vllm_profile python -m vllm.entrypoints.openai.api_server --model meta-llama/Meta-Llama-3-70B
benchmark_serving.py:
.. code-block:: bash
python benchmarks/benchmark_serving.py --backend vllm --model meta-llama/Meta-Llama-3-70B --dataset-name sharegpt --dataset-path sharegpt.json --profile --num-prompts 2
\ No newline at end of file
docs/source/design/arch_overview.rst → docs/source/design/arch_overview.md
View file @ 32aa2059
.. _arch_overview:
(arch-overview)=
Architecture Overview
======================
# Architecture Overview
This document provides an overview of the vLLM architecture.
.. contents:: Table of Contents
:local:
:depth: 2
```{contents} Table of Contents
:depth: 2
:local: true
```
Entrypoints
-----------
## Entrypoints
vLLM provides a number of entrypoints for interacting with the system. The
following diagram shows the relationship between them.
.. image:: /assets/design/arch_overview/entrypoints.excalidraw.png
:alt: Entrypoints Diagram
```{image} /assets/design/arch_overview/entrypoints.excalidraw.png
:alt: Entrypoints Diagram
```
LLM Class
^^^^^^^^^
### LLM Class
The LLM class provides the primary Python interface for doing offline inference,
which is interacting with a model without using a separate model inference
......@@ -27,75 +26,70 @@ server.
Here is a sample of `LLM` class usage:
.. code-block:: python
```python
from vllm import LLM, SamplingParams
from vllm import LLM, SamplingParams
# Define a list of input prompts
prompts = [
# Define a list of input prompts
prompts = [
"Hello, my name is",
"The capital of France is",
"The largest ocean is",
]
]
# Define sampling parameters
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
# Define sampling parameters
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
# Initialize the LLM engine with the OPT-125M model
llm = LLM(model="facebook/opt-125m")
# Initialize the LLM engine with the OPT-125M model
llm = LLM(model="facebook/opt-125m")
# Generate outputs for the input prompts
outputs = llm.generate(prompts, sampling_params)
# Generate outputs for the input prompts
outputs = llm.generate(prompts, sampling_params)
# Print the generated outputs
for output in outputs:
# Print the generated outputs
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
More API details can be found in the :doc:`Offline Inference
More API details can be found in the {doc}`Offline Inference
</dev/offline_inference/offline_index>` section of the API docs.
The code for the `LLM` class can be found in `vllm/entrypoints/llm.py
<https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/llm.py>`_.
The code for the `LLM` class can be found in [vllm/entrypoints/llm.py](https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/llm.py).
OpenAI-compatible API server
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
### OpenAI-compatible API server
The second primary interface to vLLM is via its OpenAI-compatible API server.
This server can be started using the `vllm serve` command.
.. code-block:: bash
vllm serve <model>
```bash
vllm serve <model>
```
The code for the `vllm` CLI can be found in `vllm/scripts.py
<https://github.com/vllm-project/vllm/blob/main/vllm/scripts.py>`_.
The code for the `vllm` CLI can be found in [vllm/scripts.py](https://github.com/vllm-project/vllm/blob/main/vllm/scripts.py).
Sometimes you may see the API server entrypoint used directly instead of via the
`vllm` CLI command. For example:
.. code-block:: bash
```bash
python -m vllm.entrypoints.openai.api_server --model <model>
```
python -m vllm.entrypoints.openai.api_server --model <model>
That code can be found in [vllm/entrypoints/openai/api_server.py](https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/openai/api_server.py).
That code can be found in `vllm/entrypoints/openai/api_server.py
<https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/openai/api_server.py>`_.
More details on the API server can be found in the :doc:`OpenAI Compatible
More details on the API server can be found in the {doc}`OpenAI Compatible
Server </serving/openai_compatible_server>` document.
LLM Engine
----------
## LLM Engine
The `LLMEngine` and `AsyncLLMEngine` classes are central to the functioning of
the vLLM system, handling model inference and asynchronous request processing.
.. image:: /assets/design/arch_overview/llm_engine.excalidraw.png
:alt: LLMEngine Diagram
```{image} /assets/design/arch_overview/llm_engine.excalidraw.png
:alt: LLMEngine Diagram
```
LLMEngine
^^^^^^^^^
### LLMEngine
The `LLMEngine` class is the core component of the vLLM engine. It is
responsible for receiving requests from clients and generating outputs from the
......@@ -105,21 +99,15 @@ processing.
- **Input Processing**: Handles tokenization of input text using the specified
tokenizer.
- **Scheduling**: Chooses which requests are processed in each step.
- **Model Execution**: Manages the execution of the language model, including
distributed execution across multiple GPUs.
- **Output Processing**: Processes the outputs generated by the model, decoding the
token IDs from a language model into human-readable text.
The code for `LLMEngine` can be found in `vllm/engine/llm_engine.py`_.
.. _vllm/engine/llm_engine.py: https://github.com/vllm-project/vllm/tree/main/vllm/engine/llm_engine.py
The code for `LLMEngine` can be found in [vllm/engine/llm_engine.py].
AsyncLLMEngine
^^^^^^^^^^^^^^
### AsyncLLMEngine
The `AsyncLLMEngine` class is an asynchronous wrapper for the `LLMEngine` class.
It uses `asyncio` to create a background loop that continuously processes
......@@ -128,54 +116,46 @@ can handle multiple concurrent requests and stream outputs to clients.
The OpenAI-compatible API server uses the `AsyncLLMEngine`. There is also a demo
API server that serves as a simpler example in
`vllm/entrypoints/api_server.py`_.
[vllm/entrypoints/api_server.py].
.. _vllm/entrypoints/api_server.py: https://github.com/vllm-project/vllm/tree/main/vllm/entrypoints/api_server.py
The code for `AsyncLLMEngine` can be found in [vllm/engine/async_llm_engine.py].
The code for `AsyncLLMEngine` can be found in `vllm/engine/async_llm_engine.py`_.
.. _vllm/engine/async_llm_engine.py: https://github.com/vllm-project/vllm/tree/main/vllm/engine/async_llm_engine.py
Worker
------
## Worker
A worker is a process that runs the model inference. vLLM follows the common
practice of using one process to control one accelerator device, such as GPUs.
For example, if we use tensor parallelism of size 2 and pipeline parallelism of
size 2, we will have 4 workers in total. Workers are identified by their
``rank`` and ``local_rank``. ``rank`` is used for global orchestration, while
``local_rank`` is mainly used for assigning the accelerator device and accessing
`rank` and `local_rank`. `rank` is used for global orchestration, while
`local_rank` is mainly used for assigning the accelerator device and accessing
local resources such as the file system and shared memory.
Model Runner
------------
## Model Runner
Every worker has one model runner object, responsible for loading and running
the model. Much of the model execution logic resides here, such as preparing
input tensors and capturing cudagraphs.
Model
-----
## Model
Every model runner object has one model object, which is the actual
``torch.nn.Module`` instance. See :ref:`huggingface_integration` for how various
`torch.nn.Module` instance. See [huggingface_integration](#huggingface-integration) for how various
configurations affect the class we ultimately get.
Class Hierarchy
---------------
## Class Hierarchy
The following figure shows the class hierarchy of vLLM:
.. figure:: /assets/design/hierarchy.png
:alt: query
:width: 100%
:align: center
> ```{figure} /assets/design/hierarchy.png
> :align: center
> :alt: query
> :width: 100%
> ```
There are several important design choices behind this class hierarchy:
1. **Extensibility**: All classes in the hierarchy accept a configuration object
containing all the necessary information. The `VllmConfig
<https://github.com/vllm-project/vllm/blob/d1c6799b8870e513bf4f2305cbf6cda9fc3d773b/vllm/config.py#L2036>`__
1\. **Extensibility**: All classes in the hierarchy accept a configuration object
containing all the necessary information. The [VllmConfig](https://github.com/vllm-project/vllm/blob/d1c6799b8870e513bf4f2305cbf6cda9fc3d773b/vllm/config.py#L2036)
class is the main configuration object that is passed around. The class
hierarchy is quite deep, and every class needs to read the configuration it is
interested in. By encapsulating all configurations in one object, we can easily
......@@ -188,7 +168,7 @@ the `VllmConfig` class, and the model runner can access it directly. We don't
need to change the constructor of the engine, worker, or model class to pass the
new configuration option.
2. **Uniformity**: The model runner needs a unified interface to create and
2\. **Uniformity**: The model runner needs a unified interface to create and
initialize the model. vLLM supports more than 50 types of popular open-source
models. Each model has its own initialization logic. If the constructor
signature varies with models, the model runner does not know how to call the
......@@ -200,19 +180,17 @@ of a vision model and a language model. By making the constructor uniform, we
can easily create a vision model and a language model and compose them into a
vision-language model.
.. note::
````{note}
To support this change, all vLLM models' signatures have been updated to:
To support this change, all vLLM models' signatures have been updated to:
```python
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
```
.. code-block:: python
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
To avoid accidentally passing incorrect arguments, the constructor is now keyword-only. This ensures that the constructor will raise an error if old configurations are passed. vLLM developers have already made this change for all models within vLLM. For out-of-tree registered models, developers need to update their models, for example by adding shim code to adapt the old constructor signature to the new one:
To avoid accidentally passing incorrect arguments, the constructor is now keyword-only. This ensures that the constructor will raise an error if old configurations are passed. vLLM developers have already made this change for all models within vLLM. For out-of-tree registered models, developers need to update their models, for example by adding shim code to adapt the old constructor signature to the new one:
.. code-block:: python
class MyOldModel(nn.Module):
```python
class MyOldModel(nn.Module):
def __init__(
self,
config,
......@@ -223,8 +201,8 @@ vision-language model.
) -> None:
...
from vllm.config import VllmConfig
class MyNewModel(MyOldModel):
from vllm.config import VllmConfig
class MyNewModel(MyOldModel):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
......@@ -232,14 +210,16 @@ vision-language model.
lora_config = vllm_config.lora_config
super().__init__(config, cache_config, quant_config, lora_config, prefix)
if __version__ >= "0.6.4":
if __version__ >= "0.6.4":
MyModel = MyNewModel
else:
else:
MyModel = MyOldModel
```
This way, the model can work with both old and new versions of vLLM.
This way, the model can work with both old and new versions of vLLM.
````
3. **Sharding and Quantization at Initialization**: Certain features require
3\. **Sharding and Quantization at Initialization**: Certain features require
changing the model weights. For example, tensor parallelism needs to shard the
model weights, and quantization needs to quantize the model weights. There are
two possible ways to implement this feature. One way is to change the model
......@@ -252,23 +232,27 @@ initialized, we need to load the full 810GB weights to every GPU and then shard
the weights, leading to a huge memory overhead. Instead, if we shard the weights
during the model initialization, every layer will only create a shard of the
weights it needs, leading to a much smaller memory overhead. The same idea
applies to quantization. Note that we also add an additional argument ``prefix``
applies to quantization. Note that we also add an additional argument `prefix`
to the model's constructor so that the model can initialize itself differently
based on the prefix. This is useful for non-uniform quantization, where
different parts of the model are quantized differently. The ``prefix`` is
usually an empty string for the top-level model and a string like ``"vision"``
or ``"language"`` for the sub-models. In general, it matches the name of the
different parts of the model are quantized differently. The `prefix` is
usually an empty string for the top-level model and a string like `"vision"`
or `"language"` for the sub-models. In general, it matches the name of the
module's state dict in the checkpoint file.
One disadvantage of this design is that it is hard to write unit tests for
individual components in vLLM because every component needs to be initialized by
a complete config object. We solve this problem by providing a default
initialization function that creates a default config object with all fields set
to ``None``. If the component we want to test only cares about a few fields in
to `None`. If the component we want to test only cares about a few fields in
the config object, we can create a default config object and set the fields we
care about. This way, we can test the component in isolation. Note that many
tests in vLLM are end-to-end tests that test the whole system, so this is not a
big problem.
In summary, the complete config object ``VllmConfig`` can be treated as an
In summary, the complete config object `VllmConfig` can be treated as an
engine-level global state that is shared among all vLLM classes.
[vllm/engine/async_llm_engine.py]: https://github.com/vllm-project/vllm/tree/main/vllm/engine/async_llm_engine.py
[vllm/engine/llm_engine.py]: https://github.com/vllm-project/vllm/tree/main/vllm/engine/llm_engine.py
[vllm/entrypoints/api_server.py]: https://github.com/vllm-project/vllm/tree/main/vllm/entrypoints/api_server.py
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(huggingface-integration)=
# Integration with HuggingFace
This document describes how vLLM integrates with HuggingFace libraries. We will explain step by step what happens under the hood when we run `vllm serve`.
Let's say we want to serve the popular QWen model by running `vllm serve Qwen/Qwen2-7B`.
1. The `model` argument is `Qwen/Qwen2-7B`. vLLM determines whether this model exists by checking for the corresponding config file `config.json`. See this [code snippet](https://github.com/vllm-project/vllm/blob/10b67d865d92e376956345becafc249d4c3c0ab7/vllm/transformers_utils/config.py#L162-L182) for the implementation. Within this process:
- If the `model` argument corresponds to an existing local path, vLLM will load the config file directly from this path.
- If the `model` argument is a HuggingFace model ID consisting of a username and model name, vLLM will first try to use the config file from the HuggingFace local cache, using the `model` argument as the model name and the `--revision` argument as the revision. See [their website](https://huggingface.co/docs/huggingface_hub/en/package_reference/environment_variables#hfhome) for more information on how the HuggingFace cache works.
- If the `model` argument is a HuggingFace model ID but it is not found in the cache, vLLM will download the config file from the HuggingFace model hub. Refer to [this function](https://github.com/vllm-project/vllm/blob/10b67d865d92e376956345becafc249d4c3c0ab7/vllm/transformers_utils/config.py#L91) for the implementation. The input arguments include the `model` argument as the model name, the `--revision` argument as the revision, and the environment variable `HF_TOKEN` as the token to access the model hub. In our case, vLLM will download the [config.json](https://huggingface.co/Qwen/Qwen2-7B/blob/main/config.json) file.
2. After confirming the existence of the model, vLLM loads its config file and converts it into a dictionary. See this [code snippet](https://github.com/vllm-project/vllm/blob/10b67d865d92e376956345becafc249d4c3c0ab7/vllm/transformers_utils/config.py#L185-L186) for the implementation.
3. Next, vLLM [inspects](https://github.com/vllm-project/vllm/blob/10b67d865d92e376956345becafc249d4c3c0ab7/vllm/transformers_utils/config.py#L189) the `model_type` field in the config dictionary to [generate](https://github.com/vllm-project/vllm/blob/10b67d865d92e376956345becafc249d4c3c0ab7/vllm/transformers_utils/config.py#190-L216) the config object to use. There are some `model_type` values that vLLM directly supports; see [here](https://github.com/vllm-project/vllm/blob/10b67d865d92e376956345becafc249d4c3c0ab7/vllm/transformers_utils/config.py#L48) for the list. If the `model_type` is not in the list, vLLM will use [AutoConfig.from_pretrained](https://huggingface.co/docs/transformers/en/model_doc/auto#transformers.AutoConfig.from_pretrained) to load the config class, with `model`, `--revision`, and `--trust_remote_code` as the arguments. Please note that:
- HuggingFace also has its own logic to determine the config class to use. It will again use the `model_type` field to search for the class name in the transformers library; see [here](https://github.com/huggingface/transformers/tree/main/src/transformers/models) for the list of supported models. If the `model_type` is not found, HuggingFace will use the `auto_map` field from the config JSON file to determine the class name. Specifically, it is the `AutoConfig` field under `auto_map`. See [DeepSeek](https://huggingface.co/deepseek-ai/DeepSeek-V2.5/blob/main/config.json) for an example.
- The `AutoConfig` field under `auto_map` points to a module path in the model's repository. To create the config class, HuggingFace will import the module and use the `from_pretrained` method to load the config class. This can generally cause arbitrary code execution, so it is only executed when `--trust_remote_code` is enabled.
4. Subsequently, vLLM applies some historical patches to the config object. These are mostly related to RoPE configuration; see [here](https://github.com/vllm-project/vllm/blob/127c07480ecea15e4c2990820c457807ff78a057/vllm/transformers_utils/config.py#L244) for the implementation.
5. Finally, vLLM can reach the model class we want to initialize. vLLM uses the `architectures` field in the config object to determine the model class to initialize, as it maintains the mapping from architecture name to model class in [its registry](https://github.com/vllm-project/vllm/blob/127c07480ecea15e4c2990820c457807ff78a057/vllm/model_executor/models/registry.py#L80). If the architecture name is not found in the registry, it means this model architecture is not supported by vLLM. For `Qwen/Qwen2-7B`, the `architectures` field is `["Qwen2ForCausalLM"]`, which corresponds to the `Qwen2ForCausalLM` class in [vLLM's code](https://github.com/vllm-project/vllm/blob/127c07480ecea15e4c2990820c457807ff78a057/vllm/model_executor/models/qwen2.py#L364). This class will initialize itself depending on various configs.
Beyond that, there are two more things vLLM depends on HuggingFace for.
1. **Tokenizer**: vLLM uses the tokenizer from HuggingFace to tokenize the input text. The tokenizer is loaded using [AutoTokenizer.from_pretrained](https://huggingface.co/docs/transformers/en/model_doc/auto#transformers.AutoTokenizer.from_pretrained) with the `model` argument as the model name and the `--revision` argument as the revision. It is also possible to use a tokenizer from another model by specifying the `--tokenizer` argument in the `vllm serve` command. Other relevant arguments are `--tokenizer-revision` and `--tokenizer-mode`. Please check HuggingFace's documentation for the meaning of these arguments. This part of the logic can be found in the [get_tokenizer](https://github.com/vllm-project/vllm/blob/127c07480ecea15e4c2990820c457807ff78a057/vllm/transformers_utils/tokenizer.py#L87) function. After obtaining the tokenizer, notably, vLLM will cache some expensive attributes of the tokenizer in [get_cached_tokenizer](https://github.com/vllm-project/vllm/blob/127c07480ecea15e4c2990820c457807ff78a057/vllm/transformers_utils/tokenizer.py#L24).
2. **Model weight**: vLLM downloads the model weight from the HuggingFace model hub using the `model` argument as the model name and the `--revision` argument as the revision. vLLM provides the argument `--load-format` to control what files to download from the model hub. By default, it will try to load the weights in the safetensors format and fall back to the PyTorch bin format if the safetensors format is not available. We can also pass `--load-format dummy` to skip downloading the weights.
- It is recommended to use the safetensors format, as it is efficient for loading in distributed inference and also safe from arbitrary code execution. See the [documentation](https://huggingface.co/docs/safetensors/en/index) for more information on the safetensors format. This part of the logic can be found [here](https://github.com/vllm-project/vllm/blob/10b67d865d92e376956345becafc249d4c3c0ab7/vllm/model_executor/model_loader/loader.py#L385). Please note that:
This completes the integration between vLLM and HuggingFace.
In summary, vLLM reads the config file `config.json`, tokenizer, and model weight from the HuggingFace model hub or a local directory. It uses the config class from either vLLM, HuggingFace transformers, or loads the config class from the model's repository.
docs/source/design/huggingface_integration.rst deleted 100644 → 0
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.. _huggingface_integration:
Integration with HuggingFace
===================================
This document describes how vLLM integrates with HuggingFace libraries. We will explain step by step what happens under the hood when we run ``vllm serve``.
Let's say we want to serve the popular QWen model by running ``vllm serve Qwen/Qwen2-7B``.
1. The ``model`` argument is ``Qwen/Qwen2-7B``. vLLM determines whether this model exists by checking for the corresponding config file ``config.json``. See this `code snippet <https://github.com/vllm-project/vllm/blob/10b67d865d92e376956345becafc249d4c3c0ab7/vllm/transformers_utils/config.py#L162-L182>`__ for the implementation. Within this process:
- If the ``model`` argument corresponds to an existing local path, vLLM will load the config file directly from this path.
- If the ``model`` argument is a HuggingFace model ID consisting of a username and model name, vLLM will first try to use the config file from the HuggingFace local cache, using the ``model`` argument as the model name and the ``--revision`` argument as the revision. See `their website <https://huggingface.co/docs/huggingface_hub/en/package_reference/environment_variables#hfhome>`__ for more information on how the HuggingFace cache works.
- If the ``model`` argument is a HuggingFace model ID but it is not found in the cache, vLLM will download the config file from the HuggingFace model hub. Refer to `this function <https://github.com/vllm-project/vllm/blob/10b67d865d92e376956345becafc249d4c3c0ab7/vllm/transformers_utils/config.py#L91>`__ for the implementation. The input arguments include the ``model`` argument as the model name, the ``--revision`` argument as the revision, and the environment variable ``HF_TOKEN`` as the token to access the model hub. In our case, vLLM will download the `config.json <https://huggingface.co/Qwen/Qwen2-7B/blob/main/config.json>`__ file.
2. After confirming the existence of the model, vLLM loads its config file and converts it into a dictionary. See this `code snippet <https://github.com/vllm-project/vllm/blob/10b67d865d92e376956345becafc249d4c3c0ab7/vllm/transformers_utils/config.py#L185-L186>`__ for the implementation.
3. Next, vLLM `inspects <https://github.com/vllm-project/vllm/blob/10b67d865d92e376956345becafc249d4c3c0ab7/vllm/transformers_utils/config.py#L189>`__ the ``model_type`` field in the config dictionary to `generate <https://github.com/vllm-project/vllm/blob/10b67d865d92e376956345becafc249d4c3c0ab7/vllm/transformers_utils/config.py#190-L216>`__ the config object to use. There are some ``model_type`` values that vLLM directly supports; see `here <https://github.com/vllm-project/vllm/blob/10b67d865d92e376956345becafc249d4c3c0ab7/vllm/transformers_utils/config.py#L48>`__ for the list. If the ``model_type`` is not in the list, vLLM will use `AutoConfig.from_pretrained <https://huggingface.co/docs/transformers/en/model_doc/auto#transformers.AutoConfig.from_pretrained>`__ to load the config class, with ``model``, ``--revision``, and ``--trust_remote_code`` as the arguments. Please note that:
- HuggingFace also has its own logic to determine the config class to use. It will again use the ``model_type`` field to search for the class name in the transformers library; see `here <https://github.com/huggingface/transformers/tree/main/src/transformers/models>`__ for the list of supported models. If the ``model_type`` is not found, HuggingFace will use the ``auto_map`` field from the config JSON file to determine the class name. Specifically, it is the ``AutoConfig`` field under ``auto_map``. See `DeepSeek <https://huggingface.co/deepseek-ai/DeepSeek-V2.5/blob/main/config.json>`__ for an example.
- The ``AutoConfig`` field under ``auto_map`` points to a module path in the model's repository. To create the config class, HuggingFace will import the module and use the ``from_pretrained`` method to load the config class. This can generally cause arbitrary code execution, so it is only executed when ``--trust_remote_code`` is enabled.
4. Subsequently, vLLM applies some historical patches to the config object. These are mostly related to RoPE configuration; see `here <https://github.com/vllm-project/vllm/blob/127c07480ecea15e4c2990820c457807ff78a057/vllm/transformers_utils/config.py#L244>`__ for the implementation.
5. Finally, vLLM can reach the model class we want to initialize. vLLM uses the ``architectures`` field in the config object to determine the model class to initialize, as it maintains the mapping from architecture name to model class in `its registry <https://github.com/vllm-project/vllm/blob/127c07480ecea15e4c2990820c457807ff78a057/vllm/model_executor/models/registry.py#L80>`__. If the architecture name is not found in the registry, it means this model architecture is not supported by vLLM. For ``Qwen/Qwen2-7B``, the ``architectures`` field is ``["Qwen2ForCausalLM"]``, which corresponds to the ``Qwen2ForCausalLM`` class in `vLLM's code <https://github.com/vllm-project/vllm/blob/127c07480ecea15e4c2990820c457807ff78a057/vllm/model_executor/models/qwen2.py#L364>`__. This class will initialize itself depending on various configs.
Beyond that, there are two more things vLLM depends on HuggingFace for.
1. **Tokenizer**: vLLM uses the tokenizer from HuggingFace to tokenize the input text. The tokenizer is loaded using `AutoTokenizer.from_pretrained <https://huggingface.co/docs/transformers/en/model_doc/auto#transformers.AutoTokenizer.from_pretrained>`__ with the ``model`` argument as the model name and the ``--revision`` argument as the revision. It is also possible to use a tokenizer from another model by specifying the ``--tokenizer`` argument in the ``vllm serve`` command. Other relevant arguments are ``--tokenizer-revision`` and ``--tokenizer-mode``. Please check HuggingFace's documentation for the meaning of these arguments. This part of the logic can be found in the `get_tokenizer <https://github.com/vllm-project/vllm/blob/127c07480ecea15e4c2990820c457807ff78a057/vllm/transformers_utils/tokenizer.py#L87>`__ function. After obtaining the tokenizer, notably, vLLM will cache some expensive attributes of the tokenizer in `get_cached_tokenizer <https://github.com/vllm-project/vllm/blob/127c07480ecea15e4c2990820c457807ff78a057/vllm/transformers_utils/tokenizer.py#L24>`__.
2. **Model weight**: vLLM downloads the model weight from the HuggingFace model hub using the ``model`` argument as the model name and the ``--revision`` argument as the revision. vLLM provides the argument ``--load-format`` to control what files to download from the model hub. By default, it will try to load the weights in the safetensors format and fall back to the PyTorch bin format if the safetensors format is not available. We can also pass ``--load-format dummy`` to skip downloading the weights.
- It is recommended to use the safetensors format, as it is efficient for loading in distributed inference and also safe from arbitrary code execution. See the `documentation <https://huggingface.co/docs/safetensors/en/index>`__ for more information on the safetensors format. This part of the logic can be found `here <https://github.com/vllm-project/vllm/blob/10b67d865d92e376956345becafc249d4c3c0ab7/vllm/model_executor/model_loader/loader.py#L385>`__. Please note that:
This completes the integration between vLLM and HuggingFace.
In summary, vLLM reads the config file ``config.json``, tokenizer, and model weight from the HuggingFace model hub or a local directory. It uses the config class from either vLLM, HuggingFace transformers, or loads the config class from the model's repository.
docs/source/design/input_processing/input_processing_pipeline.rst → docs/source/design/input_processing/input_processing_pipeline.md
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.. _input_processing_pipeline:
(input-processing-pipeline)=
Input Processing Pipeline
=========================
# Input Processing Pipeline
1. Input data is passed to :class:`~vllm.LLMEngine` (or :class:`~vllm.AsyncLLMEngine`).
1. Input data is passed to {class}`~vllm.LLMEngine` (or {class}`~vllm.AsyncLLMEngine`).
2. Tokenize the data if necessary.
3. Process the inputs using :meth:`INPUT_REGISTRY.process_input <vllm.inputs.registry.InputRegistry.process_input>`.
3. Process the inputs using {meth}`INPUT_REGISTRY.process_input <vllm.inputs.registry.InputRegistry.process_input>`.
- For example, add placeholder tokens to reserve KV cache for multi-modal embeddings.
4. Send the processed inputs to :class:`~vllm.executor.executor_base.ExecutorBase`.
4. Send the processed inputs to {class}`~vllm.executor.executor_base.ExecutorBase`.
5. Distribute the inputs via :class:`~vllm.worker.worker_base.WorkerBase` to :class:`~vllm.worker.model_runner_base.ModelRunnerBase`.
5. Distribute the inputs via {class}`~vllm.worker.worker_base.WorkerBase` to {class}`~vllm.worker.model_runner_base.ModelRunnerBase`.
6. If the data contains multi-modal data, convert it into keyword arguments using :meth:`MULTIMODAL_REGISTRY.map_input <vllm.multimodal.MultiModalRegistry.map_input>`.
6. If the data contains multi-modal data, convert it into keyword arguments using {meth}`MULTIMODAL_REGISTRY.map_input <vllm.multimodal.MultiModalRegistry.map_input>`.
- For example, convert a :class:`PIL.Image.Image` input to its pixel values for a vision model.
- For example, convert a {class}`PIL.Image.Image` input to its pixel values for a vision model.
docs/source/design/input_processing/model_inputs_index.rst → docs/source/design/input_processing/model_inputs_index.md
View file @ 32aa2059
.. _input_processing:
(input-processing)=
Input Processing
================
# Input Processing
```{eval-rst}
.. currentmodule:: vllm.inputs
```
Each model can override parts of vLLM's :ref:`input processing pipeline <input_processing_pipeline>` via
:data:`~vllm.inputs.INPUT_REGISTRY` and :data:`~vllm.multimodal.MULTIMODAL_REGISTRY`.
Each model can override parts of vLLM's [input processing pipeline](#input-processing-pipeline) via
{data}`~vllm.inputs.INPUT_REGISTRY` and {data}`~vllm.multimodal.MULTIMODAL_REGISTRY`.
Currently, this mechanism is only utilized in :ref:`multi-modal <multi_modality>` models for preprocessing multi-modal input
Currently, this mechanism is only utilized in [multi-modal](#multi-modality) models for preprocessing multi-modal input
data in addition to input prompt, but it can be extended to text-only language models when needed.
Guides
++++++
## Guides
.. toctree::
:maxdepth: 1
```{toctree}
:maxdepth: 1
input_processing_pipeline
input_processing_pipeline
```
Module Contents
+++++++++++++++
## Module Contents
LLM Engine Inputs
-----------------
### LLM Engine Inputs
```{eval-rst}
.. autoclass:: vllm.inputs.DecoderOnlyInputs
:members:
:show-inheritance:
```
Registry
--------
### Registry
```{eval-rst}
.. autodata:: vllm.inputs.INPUT_REGISTRY
```
```{eval-rst}
.. automodule:: vllm.inputs.registry
:members:
:show-inheritance:
```
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