mirror of
https://github.com/nod-ai/SHARK-Studio.git
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333 lines
13 KiB
Markdown
333 lines
13 KiB
Markdown
# SHARK
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High Performance Machine Learning and Data Analytics for CPUs, GPUs, Accelerators and Heterogeneous Clusters
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[](https://github.com/nod-ai/SHARK/actions/workflows/nightly.yml)
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[](https://github.com/nod-ai/SHARK/actions/workflows/test-models.yml)
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## Installation (Windows, Linux and macOS)
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## Check out the code
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```shell
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git clone https://github.com/nod-ai/SHARK.git
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cd SHARK
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```
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## Setup your Python VirtualEnvironment and Dependencies
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### Windows 10/11 Users
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* Install the latest Python 3.10.x version from [here](https://www.python.org/downloads/windows/)
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* Install Git for Windows from [here](https://git-scm.com/download/win)
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#### Allow the install script to run in Powershell
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```powershell
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set-executionpolicy remotesigned
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```
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#### Setup venv and install necessary packages (torch-mlir, nodLabs/Shark, ...)
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```powershell
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./setup_venv.ps1 #You can re-run this script to get the latest version
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```
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### Linux / macOS Users
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```shell
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./setup_venv.sh
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source shark.venv/bin/activate
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```
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### Run Stable Diffusion on your device - WebUI
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#### Windows 10/11 Users
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```powershell
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(shark.venv) PS C:\Users\nod\SHARK> cd web
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(shark.venv) PS C:\Users\nod\SHARK\web> python index.py
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```
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#### Linux Users
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```shell
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(shark.venv) > cd web
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(shark.venv) > python index.py
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```
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#### Access Stable Diffusion on http://localhost:8080/?__theme=dark
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<img width="1607" alt="webui" src="https://user-images.githubusercontent.com/74956/204939260-b8308bc2-8dc4-47f6-9ac0-f60b66edab99.png">
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### Run Stable Diffusion on your device - Commandline
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#### Install your hardware drivers
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* [AMD RDNA Users] Download the latest driver [here](https://www.amd.com/en/support/kb/release-notes/rn-rad-win-22-11-1-mril-iree)
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* [macOS Users] Download and install the latest Vulkan SDK from [here](https://vulkan.lunarg.com/sdk/home)
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* [Nvidia Users] Download and install the latest CUDA / Vulkan drivers from [here](https://developer.nvidia.com/cuda-downloads)
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Other users please ensure you have your latest vendor drivers and Vulkan SDK from [here](https://vulkan.lunarg.com/sdk/home) and if you are using vulkan check `vulkaninfo` works in a terminal window
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#### Windows 10/11 Users
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```powershell
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(shark.venv) PS C:\g\shark> python .\shark\examples\shark_inference\stable_diffusion\main.py --precision="fp16" --prompt="tajmahal, snow, sunflowers, oil on canvas" --device="vulkan"
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```
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#### Linux / macOS Users
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```shell
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python3.10 shark/examples/shark_inference/stable_diffusion/main.py --precision=fp16 --device=vulkan --prompt="tajmahal, oil on canvas, sunflowers, 4k, uhd"
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```
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You can replace `vulkan` with `cpu` to run on your CPU or with `cuda` to run on CUDA devices. If you have multiple vulkan devices you can address them with `--device=vulkan://1` etc
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The output on a 7900XTX would like:
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```shell
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Stats for run 0:
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Average step time: 47.19188690185547ms/it
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Clip Inference time (ms) = 109.531
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VAE Inference time (ms): 78.590
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Total image generation time: 2.5788655281066895sec
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```
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Here are some samples generated:
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For more options to the Stable Diffusion model read [this](https://github.com/nod-ai/SHARK/blob/main/shark/examples/shark_inference/stable_diffusion/README.md)
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Find us on [SHARK Discord server](https://discord.gg/RUqY2h2s9u) if you have any trouble with running it on your hardware.
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<details>
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<summary>Binary Installation</summary>
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### Setup a new pip Virtual Environment
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This step sets up a new VirtualEnv for Python
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```shell
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python --version #Check you have 3.10 on Linux, macOS or Windows Powershell
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python -m venv shark_venv
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source shark_venv/bin/activate # Use shark_venv/Scripts/activate on Windows
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# If you are using conda create and activate a new conda env
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# Some older pip installs may not be able to handle the recent PyTorch deps
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python -m pip install --upgrade pip
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```
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*macOS Metal* users please install https://sdk.lunarg.com/sdk/download/latest/mac/vulkan-sdk.dmg and enable "System wide install"
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### Install SHARK
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This step pip installs SHARK and related packages on Linux Python 3.7, 3.8, 3.9, 3.10 and macOS Python 3.10
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```shell
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pip install nodai-shark -f https://nod-ai.github.io/SHARK/package-index/ -f https://llvm.github.io/torch-mlir/package-index/ -f https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html --extra-index-url https://download.pytorch.org/whl/nightly/cpu
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```
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### Run shark tank model tests.
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```shell
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pytest tank/test_models.py
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```
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See tank/README.md for a more detailed walkthrough of our pytest suite and CLI.
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### Download and run Resnet50 sample
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```shell
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curl -O https://raw.githubusercontent.com/nod-ai/SHARK/main/shark/examples/shark_inference/resnet50_script.py
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#Install deps for test script
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pip install --pre torch torchvision torchaudio tqdm pillow gsutil --extra-index-url https://download.pytorch.org/whl/nightly/cpu
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python ./resnet50_script.py --device="cpu" #use cuda or vulkan or metal
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```
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### Download and run BERT (MiniLM) sample
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```shell
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curl -O https://raw.githubusercontent.com/nod-ai/SHARK/main/shark/examples/shark_inference/minilm_jit.py
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#Install deps for test script
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pip install transformers torch --extra-index-url https://download.pytorch.org/whl/nightly/cpu
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python ./minilm_jit.py --device="cpu" #use cuda or vulkan or metal
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```
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</details>
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<details>
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<summary>Development, Testing and Benchmarks</summary>
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If you want to use Python3.10 and with TF Import tools you can use the environment variables like:
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Set `USE_IREE=1` to use upstream IREE
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```
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# PYTHON=python3.10 VENV_DIR=0617_venv IMPORTER=1 ./setup_venv.sh
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```
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### Run any of the hundreds of SHARK tank models via the test framework
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```shell
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python -m shark.examples.shark_inference.resnet50_script --device="cpu" # Use gpu | vulkan
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# Or a pytest
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pytest tank/test_models.py -k "MiniLM"
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```
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If you are a *Torch-mlir developer or an IREE developer* and want to test local changes you can uninstall
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the provided packages with `pip uninstall torch-mlir` and / or `pip uninstall iree-compiler iree-runtime` and build locally
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with Python bindings and set your PYTHONPATH as mentioned [here](https://github.com/iree-org/iree/tree/main/docs/api_docs/python#install-iree-binaries)
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for IREE and [here](https://github.com/llvm/torch-mlir/blob/main/development.md#setup-python-environment-to-export-the-built-python-packages)
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for Torch-MLIR.
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### How to use your locally built Torch-MLIR with SHARK
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```shell
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1.) Run `./setup_venv.sh in SHARK` and activate `shark.venv` virtual env.
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2.) Run `pip uninstall torch-mlir`.
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3.) Go to your local Torch-MLIR directory.
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4.) Activate mlir_venv virtual envirnoment.
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5.) Run `pip uninstall -r requirements.txt`.
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6.) Run `pip install -r requirements.txt`.
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7.) Build Torch-MLIR.
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8.) Activate shark.venv virtual environment from the Torch-MLIR directory.
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8.) Run `export PYTHONPATH=`pwd`/build/tools/torch-mlir/python_packages/torch_mlir:`pwd`/examples` in the Torch-MLIR directory.
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9.) Go to the SHARK directory.
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```
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Now the SHARK will use your locally build Torch-MLIR repo.
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## Benchmarking Dispatches
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To produce benchmarks of individual dispatches, you can add `--dispatch_benchmarks=All --dispatch_benchmarks_dir=<output_dir>` to your command line argument.
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If you only want to compile specific dispatches, you can specify them with a space seperated string instead of `"All"`. E.G. `--dispatch_benchmarks="0 1 2 10"`
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if you want to instead incorporate this into a python script, you can pass the `dispatch_benchmarks` and `dispatch_benchmarks_dir` commands when initializing `SharkInference`, and the benchmarks will be generated when compiled. E.G:
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```
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shark_module = SharkInference(
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mlir_model,
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func_name,
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device=args.device,
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mlir_dialect="tm_tensor",
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dispatch_benchmarks="all",
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dispatch_benchmarks_dir="results"
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)
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```
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Output will include:
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- An ordered list ordered-dispatches.txt of all the dispatches with their runtime
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- Inside the specified directory, there will be a directory for each dispatch (there will be mlir files for all dispatches, but only compiled binaries and benchmark data for the specified dispatches)
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- An .mlir file containing the dispatch benchmark
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- A compiled .vmfb file containing the dispatch benchmark
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- An .mlir file containing just the hal executable
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- A compiled .vmfb file of the hal executable
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- A .txt file containing benchmark output
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See tank/README.md for instructions on how to run model tests and benchmarks from the SHARK tank.
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</details>
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<details>
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<summary>API Reference</summary>
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### Shark Inference API
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```
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from shark.shark_importer import SharkImporter
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# SharkImporter imports mlir file from the torch, tensorflow or tf-lite module.
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mlir_importer = SharkImporter(
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torch_module,
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(input),
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frontend="torch", #tf, #tf-lite
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)
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torch_mlir, func_name = mlir_importer.import_mlir(tracing_required=True)
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# SharkInference accepts mlir in linalg, mhlo, and tosa dialect.
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from shark.shark_inference import SharkInference
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shark_module = SharkInference(torch_mlir, func_name, device="cpu", mlir_dialect="linalg")
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shark_module.compile()
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result = shark_module.forward((input))
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```
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### Example demonstrating running MHLO IR.
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```
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from shark.shark_inference import SharkInference
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import numpy as np
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mhlo_ir = r"""builtin.module {
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func.func @forward(%arg0: tensor<1x4xf32>, %arg1: tensor<4x1xf32>) -> tensor<4x4xf32> {
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%0 = chlo.broadcast_add %arg0, %arg1 : (tensor<1x4xf32>, tensor<4x1xf32>) -> tensor<4x4xf32>
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%1 = "mhlo.abs"(%0) : (tensor<4x4xf32>) -> tensor<4x4xf32>
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return %1 : tensor<4x4xf32>
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}
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}"""
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arg0 = np.ones((1, 4)).astype(np.float32)
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arg1 = np.ones((4, 1)).astype(np.float32)
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shark_module = SharkInference(mhlo_ir, func_name="forward", device="cpu", mlir_dialect="mhlo")
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shark_module.compile()
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result = shark_module.forward((arg0, arg1))
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```
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</details>
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## Supported and Validated Models
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SHARK is maintained to support the latest innovations in ML Models:
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| TF HuggingFace Models | SHARK-CPU | SHARK-CUDA | SHARK-METAL |
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|---------------------|----------|----------|-------------|
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| BERT | :green_heart: | :green_heart: | :green_heart: |
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| DistilBERT | :green_heart: | :green_heart: | :green_heart: |
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| GPT2 | :green_heart: | :green_heart: | :green_heart: |
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| BLOOM | :green_heart: | :green_heart: | :green_heart: |
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| Stable Diffusion | :green_heart: | :green_heart: | :green_heart: |
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| Vision Transformer | :green_heart: | :green_heart: | :green_heart: |
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| ResNet50 | :green_heart: | :green_heart: | :green_heart: |
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For a complete list of the models supported in SHARK, please refer to [tank/README.md](https://github.com/nod-ai/SHARK/blob/main/tank/README.md).
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## Communication Channels
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* [SHARK Discord server](https://discord.gg/RUqY2h2s9u): Real time discussions with the SHARK team and other users
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* [GitHub issues](https://github.com/nod-ai/SHARK/issues): Feature requests, bugs etc
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## Related Projects
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<details>
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<summary>IREE Project Channels</summary>
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* [Upstream IREE issues](https://github.com/google/iree/issues): Feature requests,
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bugs, and other work tracking
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* [Upstream IREE Discord server](https://discord.gg/26P4xW4): Daily development
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discussions with the core team and collaborators
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* [iree-discuss email list](https://groups.google.com/forum/#!forum/iree-discuss):
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Announcements, general and low-priority discussion
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</details>
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<details>
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<summary>MLIR and Torch-MLIR Project Channels</summary>
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* `#torch-mlir` channel on the LLVM [Discord](https://discord.gg/xS7Z362) - this is the most active communication channel
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* Torch-MLIR Github issues [here](https://github.com/llvm/torch-mlir/issues)
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* [`torch-mlir` section](https://llvm.discourse.group/c/projects-that-want-to-become-official-llvm-projects/torch-mlir/41) of LLVM Discourse
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* Weekly meetings on Mondays 9AM PST. See [here](https://discourse.llvm.org/t/community-meeting-developer-hour-refactoring-recurring-meetings/62575) for more information.
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* [MLIR topic within LLVM Discourse](https://llvm.discourse.group/c/llvm-project/mlir/31) SHARK and IREE is enabled by and heavily relies on [MLIR](https://mlir.llvm.org).
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</details>
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## License
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nod.ai SHARK is licensed under the terms of the Apache 2.0 License with LLVM Exceptions.
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See [LICENSE](LICENSE) for more information.
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