add qwen3

vllm-v0.6.2/docs/source/contributing/dockerfile/dockerfile.rst

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+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`.)
+
+   

vllm-v0.6.2/docs/source/contributing/overview.rst

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+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:
+
+- Identify and report any issues or bugs.
+- Request or add support for a new model.
+- Suggest or implement new features.
+- Improve documentation or contribute a how-to guide.
+
+We also believe in the power of community support; thus, answering queries, offering PR reviews, and assisting others are also highly regarded and beneficial contributions.
+
+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
+-------
+
+See `LICENSE <https://github.com/vllm-project/vllm/tree/main/LICENSE>`_.
+
+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.
+
+Testing
+-------
+
+.. code-block:: bash
+
+    pip install -r requirements-dev.txt
+
+    # linting and formatting
+    bash format.sh
+    # Static type checking
+    mypy
+    # Unit tests
+    pytest tests/
+
+.. note:: Currently, the repository does not pass the ``mypy`` tests.
+
+Contribution Guidelines
+=======================
+
+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>`_.
+
+Using ``-s`` with ``git commit`` will automatically add this header.
+
+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.
+
+.. 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
+----------------------------
+
+Please check the PR checklist in the `PR template <https://github.com/vllm-project/vllm/tree/main/.github/PULL_REQUEST_TEMPLATE.md>`_ for a detailed guide for contribution.
+
+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!

vllm-v0.6.2/docs/source/contributing/profiling/profiling_index.rst

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+==============
+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