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‎.gitignore

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# Visual Studio files
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.env
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.vs/
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.vscode/
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*.sdf
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*.opensdf
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*.VC.opendb
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*.suo
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*.user
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# macOS files
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.DS_Store
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# CMake artifacts
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build/
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build-*/
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# Python
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__pycache__
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_python_build/
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dist/
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wheelhouse
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*.egg-info
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*.whl
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*.venv
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#Model artifacts
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*.pt
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*.safetensors
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*.gguf
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*.vmfb

‎LICENSE

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‎README.md

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# SHARK Modeling and Serving Libraries
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**WARNING: This is an early preview that is in progress. It is not ready for
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general use.**
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## Development Getting Started
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Use this as a guide to get started developing the project using pinned,
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pre-release dependencies. You are welcome to deviate as you see fit, but
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these canonical directions mirror what the CI does.
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### Setup a venv
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We recommend setting up a virtual environment (venv). The project is configured
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to ignore `.venv` directories, and editors like VSCode pick them up by default.
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```
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python -m venv --prompt sharktank .venv
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source .venv/bin/activate
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```
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### Install PyTorch for Your System
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If no explicit action is taken, the default PyTorch version will be installed.
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This will give you a current CUDA-based version. Install a different variant
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by doing so explicitly first:
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*CPU:*
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```
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pip install -r pytorch-cpu-requirements.txt
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```
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*ROCM:*
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```
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pip install -r pytorch-rocm-requirements.txt
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```
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### Install Development Packages
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This assumes you have `SHARK-Turbine` checked out adjacent (note that for the
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moment we rely on pre-release versions, so installation is a bit harder).
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```
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pip install -f https://iree.dev/pip-release-links.html -e ../SHARK-Turbine/core/
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pip install -e sharktank
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pip install -e shortfin
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```
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### Running Tests
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```
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pytest sharktank
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pytest shortfin
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```

‎llm/pyproject.toml

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‎pytorch-cpu-requirements.txt

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--pre
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--index-url https://download.pytorch.org/whl/test/cpu
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torch==2.3.0

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--pre
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--index-url https://download.pytorch.org/whl/nightly/rocm6.0
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# TODO: PyTorch ROCM doesn't seem to have a 2.3 RC published, so we just
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# get a nightly.
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torch>=2.3.0.dev1,<2.4

‎requirements.txt

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numpy>=1.26.3
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onnx>=1.15.0
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pytest>=8.0.0
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pytest-xdist>=3.5.0
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mypy==1.8.0
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types-requests==2.31.0.20240125
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# It is expected that you have installed a PyTorch version/variant specific
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# to your needs, so we only include a minimum version spec.
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torch>=2.3.0

‎serving/pyproject.toml

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‎serving/requirements.txt

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‎llm/README.md ‎sharktank/README.md

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# Turbine-LLM
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# SHARK Tank
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Light weight inference optimized layers and models for popular LLMs.
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**WARNING: This is an early preview that is in progress. It is not ready for
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general use.**
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Light weight inference optimized layers and models for popular genai
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applications.
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This sub-project is a work in progress. It is intended to be a repository of
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layers, model recipes, and conversion tools from popular LLM quantization
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### Perform batched inference in PyTorch on a paged llama derived LLM:
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```shell
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python -m turbine_llm.examples.paged_llm_v1 \
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python -m sharktank.examples.paged_llm_v1 \
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--hf-dataset=open_llama_3b_v2_f16_gguf \
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"Prompt 1" \
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"Prompt 2" ...
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### Export an IREE compilable batched LLM for serving:
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```shell
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python -m turbine_llm.examples.export_paged_llm_v1 --hf-dataset=open_llama_3b_v2_f16_gguf
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python -m sharktank.examples.export_paged_llm_v1 --hf-dataset=open_llama_3b_v2_f16_gguf
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```
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### Dump parsed information about a model from a gguf file:
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```shell
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python -m turbine_llm.tools.dump_gguf --hf-dataset=open_llama_3b_v2_f16_gguf
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python -m sharktank.tools.dump_gguf --hf-dataset=open_llama_3b_v2_f16_gguf
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```

‎llm/mypy.ini ‎sharktank/mypy.ini

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explicit_package_bases = True
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mypy_path = $MYPY_CONFIG_FILE_DIR
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packages = turbine_llm
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packages = sharktank

‎sharktank/pyproject.toml

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[build-system]
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requires = ["setuptools", "wheel"]
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build-backend = "setuptools.build_meta"
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[tool.pytest.ini_options]
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addopts = "-ra"
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testpaths = [
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"tests",
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]
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pythonpath = [
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".",
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]

‎llm/requirements.txt ‎sharktank/requirements.txt

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‎llm/setup.cfg ‎sharktank/setup.cfg

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