Build wheels (#613)

* Build wheels

* Update nightly.yml

* Update nightly.yml

* Update nightly.yml

.github/workflows/gh-pages-releases.yml

@@ -23,7 +23,7 @@ jobs:
       - run: git fetch --all
       - run: git switch github-pages
       - run: git config --global user.email "none@none.com"
-      - run: git config --global user.name "nod-team"
+      - run: git config --global user.name "nod-ai"
       - run: mv /tmp/index.html package-index/index.html
       - run: git add package-index/index.html
 

.github/workflows/nightly.yml

@@ -9,7 +9,80 @@ on:
   workflow_dispatch:
 
 jobs:
-  build:
+  windows-build:
+    runs-on: windows-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.10"]
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Compute version
+      shell: powershell
+      run: |
+        $package_version = $(Get-Date -UFormat "%Y%m%d")+"."+${{ github.run_number }}
+        $package_version_ = $(Get-Date -UFormat "%Y%m%d")+"_"+${{ github.run_number }}
+        $tag_name=$package_version
+        echo "package_version=$package_version" | Out-File -FilePath $Env:GITHUB_ENV -Encoding utf8 -Append
+        echo "package_version_=$package_version_" | Out-File -FilePath $Env:GITHUB_ENV -Encoding utf8 -Append
+        echo "tag_name=$tag_name" | Out-File -FilePath $Env:GITHUB_ENV -Encoding utf8 -Append
+
+    - name: Create Release
+      id: create_release
+      uses: actions/create-release@v1
+      env:
+        GITHUB_TOKEN: ${{ secrets.NODAI_INVOCATION_TOKEN }}
+      with:
+        tag_name: ${{ env.tag_name }}
+        release_name: nod.ai SHARK ${{ env.tag_name }}
+        body: |
+          Automatic snapshot release of nod.ai SHARK.
+        draft: true
+        prerelease: false
+
+    - name: Build Package 
+      shell: powershell
+      run: |
+        ./setup_venv.ps1
+        pyinstaller web/shark_sd.spec
+        mv ./dist/shark_sd.exe ./dist/shark_sd_${{ env.package_version_ }}.exe
+
+        
+    # GHA windows VM OOMs so disable for now
+    #- name: Build and validate the SHARK Runtime package
+    #  shell: powershell
+    #  run: |
+    #    $env:SHARK_PACKAGE_VERSION=${{ env.package_version }}
+    #    pip wheel -v -w dist . --pre -f https://download.pytorch.org/whl/nightly/torch -f https://llvm.github.io/torch-mlir/package-index/ -f https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html
+
+    - uses: actions/upload-artifact@v2
+      with:
+        path: dist/*
+    
+    - name: Upload Release Assets
+      id: upload-release-assets
+      uses: dwenegar/upload-release-assets@v1
+      env:
+        GITHUB_TOKEN: ${{ secrets.NODAI_INVOCATION_TOKEN }}
+      with:
+        release_id: ${{ steps.create_release.outputs.id }}
+        assets_path: ./dist/*
+
+    - name: Publish Release
+      id: publish_release
+      uses: eregon/publish-release@v1
+      env:
+        GITHUB_TOKEN: ${{ secrets.NODAI_INVOCATION_TOKEN }}
+      with:
+        release_id: ${{ steps.create_release.outputs.id }}
+
+  linux-build:
 
     runs-on: a100
     strategy:
@@ -32,40 +105,12 @@ jobs:
         key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}
         restore-keys: |
           ${{ runner.os }}-pip-
-    
-    - name: Compute version
-      run: |
-        package_version="$(printf '%(%Y%m%d)T.${{ github.run_number }}')"
-        tag_name="${package_version}"
-        echo "package_version=${package_version}" >> $GITHUB_ENV
-        echo "tag_name=${tag_name}" >> $GITHUB_ENV    
-    - name: Set Environment Variables
-      run: |
-        echo "SHORT_SHA=`git rev-parse --short=4 HEAD`" >> $GITHUB_ENV
-        echo "DATE=$(date +'%Y-%m-%d')" >> $GITHUB_ENV
-    - name: Create Release
-      id: create_release
-      uses: actions/create-release@v1
-      env:
-        GITHUB_TOKEN: ${{ secrets.NODAI_INVOCATION_TOKEN }}
-      with:
-        tag_name: ${{ env.tag_name }}
-        release_name: nod.ai SHARK ${{ env.tag_name }}
-        body: |
-          Automatic snapshot release of nod.ai SHARK.
-        draft: true
-        prerelease: false
-    - name: Find Torch-MLIR Release
-      run: |
-        TM_HTML_URL="$(python3 -c "import urllib.request, json, sys; u=json.loads(urllib.request.urlopen('https://api.github.com/repos/llvm/torch-mlir/releases/latest').read().decode()).get('html_url', False); print(u) if u else sys.exit(1);")"
-        TM_RELEASE_DIR=${TM_HTML_URL/"tag"/"expanded_assets"}
-        echo "TM_RELEASE_DIR=${TM_RELEASE_DIR}" >> $GITHUB_ENV
+
     - name: Install dependencies
       run: |
-        echo "Torch-MLIR Release DIR is ${{ env.TM_RELEASE_DIR }}"
         python -m pip install --upgrade pip
         python -m pip install flake8 pytest toml
-        if [ -f requirements.txt ]; then pip install -r requirements.txt -f ${{ env.TM_RELEASE_DIR }} -f https://github.com/nod-ai/SHARK-Runtime/releases; fi
+        if [ -f requirements.txt ]; then pip install -r requirements.txt -f https://llvm.github.io/torch-mlir/package-index/ -f https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html; fi
     - name: Lint with flake8
       run: |
         # stop the build if there are Python syntax errors or undefined names
@@ -74,13 +119,14 @@ jobs:
         flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics --exclude shark.venv,lit.cfg.py 
     - name: Build and validate the IREE package
       if: ${{ matrix.backend == 'IREE' }}
+      continue-on-error: true
       run: |
         cd $GITHUB_WORKSPACE
         USE_IREE=1 VENV_DIR=iree.venv ./setup_venv.sh
         source iree.venv/bin/activate
         package_version="$(printf '%(%Y%m%d)T.${{ github.run_number }}')"
         SHARK_PACKAGE_VERSION=${package_version} \
-        pip wheel -v -w wheelhouse . --pre -f https://download.pytorch.org/whl/nightly/torch -f ${{ env.TM_RELEASE_DIR }} -f https://github.com/iree-org/iree/releases
+        pip wheel -v -w wheelhouse . --pre -f https://download.pytorch.org/whl/nightly/torch -f https://llvm.github.io/torch-mlir/package-index/ -f https://iree-org.github.io/iree/pip-release-links.html
         # Install the built wheel
         pip install ./wheelhouse/nodai*
         # Validate the Models
@@ -91,8 +137,8 @@ jobs:
         if !(grep -Fxq " failed" pytest_results.txt) 
           then 
             export SHA=$(git log -1 --format='%h')
-            gsutil -m cp -r $GITHUB_WORKSPACE/gen_shark_tank/* gs://shark_tank/$SHA
-            gsutil -m cp -r gs://shark_tank/$SHA/* gs://shark_tank/latest/
+            gsutil -m cp -r $GITHUB_WORKSPACE/gen_shark_tank/* gs://shark_tank/${DATE}_$SHA
+            gsutil -m cp -r gs://shark_tank/${DATE}_$SHA/* gs://shark_tank/latest/
         fi
         rm -rf ./wheelhouse/nodai*
 
@@ -104,29 +150,10 @@ jobs:
         source shark.venv/bin/activate
         package_version="$(printf '%(%Y%m%d)T.${{ github.run_number }}')"
         SHARK_PACKAGE_VERSION=${package_version} \
-        pip wheel -v -w wheelhouse . --pre -f https://download.pytorch.org/whl/nightly/torch -f ${{ env.TM_RELEASE_DIR }} -f https://github.com/nod-ai/SHARK-Runtime/releases
+        pip wheel -v -w wheelhouse . --pre -f https://download.pytorch.org/whl/nightly/torch -f https://llvm.github.io/torch-mlir/package-index/ -f https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html
         # Install the built wheel
         pip install ./wheelhouse/nodai*
         # Validate the Models
-        pytest --ci --ci_sha=${SHORT_SHA} --local_tank_cache="./gen_shark_tank/" tank/test_models.py |
+        pytest --ci --ci_sha=${SHORT_SHA} tank/test_models.py |
           tail -n 1 |
           tee -a pytest_results.txt
-    
-    - name: Upload Release Assets
-      if: ${{ matrix.backend == 'SHARK' }}
-      id: upload-release-assets
-      uses: dwenegar/upload-release-assets@v1
-      env:
-        GITHUB_TOKEN: ${{ secrets.NODAI_INVOCATION_TOKEN }}
-      with:
-        release_id: ${{ steps.create_release.outputs.id }}
-        assets_path: ${GITHUB_WORKSPACE}/wheelhouse/nodai_*.whl
-
-    - name: Publish Release
-      if: ${{ matrix.backend == 'SHARK' }}
-      id: publish_release
-      uses: eregon/publish-release@v1
-      env:
-        GITHUB_TOKEN: ${{ secrets.NODAI_INVOCATION_TOKEN }}
-      with:
-        release_id: ${{ steps.create_release.outputs.id }}

.github/workflows/test-models.yml

@@ -10,6 +10,14 @@ on:
     branches: [ main ]
   workflow_dispatch:
 
+# Ensure that only a single job or workflow using the same
+# concurrency group will run at a time. This would cancel
+# any in-progress jobs in the same github workflow and github
+# ref (e.g. refs/heads/main or refs/pull/<pr_number>/merge).
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
   build-validate:
     strategy:
@@ -32,8 +40,6 @@ jobs:
             suite: cuda
           - os: MacStudio
             suite: cpu
-          - os: MacStudio
-            suite: vulkan
           - os: icelake
             suite: vulkan
           - os: icelake
@@ -90,7 +96,7 @@ jobs:
         cd $GITHUB_WORKSPACE
         PYTHON=python${{ matrix.python-version }} BENCHMARK=1 IMPORTER=1 ./setup_venv.sh
         source shark.venv/bin/activate
-        pytest --benchmark --ci --ci_sha=${SHORT_SHA} --local_tank_cache="/data/anush" tank/test_models.py -k cpu
+        pytest --benchmark --ci --ci_sha=${SHORT_SHA} -s --local_tank_cache="/data/anush/shark_cache" tank/test_models.py -k cpu --update_tank
         gsutil cp ./bench_results.csv gs://shark-public/builder/bench_results/${DATE}/bench_results_cpu_${SHORT_SHA}.csv
         gsutil cp gs://shark-public/builder/bench_results/${DATE}/bench_results_cpu_${SHORT_SHA}.csv gs://shark-public/builder/bench_results/latest/bench_results_cpu_latest.csv
 
@@ -100,14 +106,29 @@ jobs:
         cd $GITHUB_WORKSPACE
         PYTHON=python${{ matrix.python-version }} BENCHMARK=1 IMPORTER=1 ./setup_venv.sh
         source shark.venv/bin/activate
-        pytest --benchmark --ci --ci_sha=${SHORT_SHA} --local_tank_cache="/data/anush" tank/test_models.py -k cuda
+        pytest --benchmark --ci --ci_sha=${SHORT_SHA} -s --local_tank_cache="/data/anush/shark_cache" tank/test_models.py -k cuda --update_tank
         gsutil cp ./bench_results.csv gs://shark-public/builder/bench_results/${DATE}/bench_results_cuda_${SHORT_SHA}.csv
         gsutil cp gs://shark-public/builder/bench_results/${DATE}/bench_results_cuda_${SHORT_SHA}.csv gs://shark-public/builder/bench_results/latest/bench_results_cuda_latest.csv
 
-    - name: Validate Vulkan Models
-      if: matrix.suite == 'vulkan'
+    - name: Validate Vulkan Models (MacOS)
+      if: matrix.suite == 'vulkan' && matrix.os == 'MacStudio'
       run: |
         cd $GITHUB_WORKSPACE
-        PYTHON=python${{ matrix.python-version }} BENCHMARK=1 IMPORTER=1 ./setup_venv.sh
+        PYTHON=python${{ matrix.python-version }} IMPORTER=1 ./setup_venv.sh
         source shark.venv/bin/activate
-        pytest --ci --ci_sha=${SHORT_SHA} --local_tank_cache="/data/anush" tank/test_models.py -k vulkan
+        echo "VULKAN SDK PATH wo setup: $VULKAN_SDK"
+        cd /Users/anush/VulkanSDK/1.3.224.1/
+        source setup-env.sh
+        cd $GITHUB_WORKSPACE
+        echo "VULKAN SDK PATH with setup: $VULKAN_SDK"
+        echo $PATH
+        pip list | grep -E "torch|iree"
+        pytest --ci --ci_sha=${SHORT_SHA} --local_tank_cache="/Volumes/builder/anush/shark_cache" tank/test_models.py -k vulkan --update_tank
+
+    - name: Validate Vulkan Models (a100)
+      if: matrix.suite == 'vulkan' && matrix.os != 'MacStudio'
+      run: |
+        cd $GITHUB_WORKSPACE
+        PYTHON=python${{ matrix.python-version }} ./setup_venv.sh
+        source shark.venv/bin/activate
+        pytest --benchmark --ci --ci_sha=${SHORT_SHA} -s --local_tank_cache="/data/anush/shark_cache" tank/test_models.py -k vulkan --update_tank

.gitignore

@@ -31,7 +31,6 @@ MANIFEST
 #  Usually these files are written by a python script from a template
 #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 *.manifest
-*.spec
 
 # Installer logs
 pip-log.txt
@@ -163,7 +162,13 @@ cython_debug/
 # Shark related artefacts
 *venv/
 shark_tmp/
+*.vmfb
+.use-iree
 
 # ORT related artefacts
 cache_models/
 onnx_models/
+
+#web logging
+web/logs/
+web/stored_results/stable_diffusion/

README.md

@@ -5,25 +5,123 @@ High Performance Machine Learning and Data Analytics for CPUs, GPUs, Accelerator
 [![Nightly Release](https://github.com/nod-ai/SHARK/actions/workflows/nightly.yml/badge.svg)](https://github.com/nod-ai/SHARK/actions/workflows/nightly.yml)
 [![Validate torch-models on Shark Runtime](https://github.com/nod-ai/SHARK/actions/workflows/test-models.yml/badge.svg)](https://github.com/nod-ai/SHARK/actions/workflows/test-models.yml)
 
-## Communication Channels
 
-*   [SHARK Discord server](https://discord.gg/RUqY2h2s9u): Real time discussions with the SHARK team and other users
-*   [GitHub issues](https://github.com/nod-ai/SHARK/issues): Feature requests, bugs etc
+## Installation (Windows, Linux and macOS)
+
+## Check out the code
+
+```shell
+git clone https://github.com/nod-ai/SHARK.git
+cd SHARK
+```
+
+## Setup your Python VirtualEnvironment and Dependencies
+
+### Windows 10/11 Users
+
+* Install the latest Python 3.10.x version from [here](https://www.python.org/downloads/windows/)
+
+* Install Git for Windows from [here](https://git-scm.com/download/win)
+
+#### Allow the install script to run in Powershell
+```powershell
+set-executionpolicy remotesigned
+```
+
+#### Setup venv and install necessary packages (torch-mlir, nodLabs/Shark, ...)
+```powershell
+./setup_venv.ps1 #You can re-run this script to get the latest version
+```
+
+### Linux / macOS Users
+
+```shell
+./setup_venv.sh
+source shark.venv/bin/activate
+```
 
 
-## Installation
+### Run Stable Diffusion on your device - WebUI
+
+#### Windows 10/11 Users
+```powershell
+(shark.venv) PS C:\Users\nod\SHARK> cd web
+(shark.venv) PS C:\Users\nod\SHARK\web> python index.py
+```
+#### Linux Users
+```shell
+(shark.venv) > cd web
+(shark.venv) > python index.py
+```
+
+#### Access Stable Diffusion on http://localhost:8080/?__theme=dark
+
+
+<img width="1607" alt="webui" src="https://user-images.githubusercontent.com/74956/204939260-b8308bc2-8dc4-47f6-9ac0-f60b66edab99.png">
+
+
+
+### Run Stable Diffusion on your device - Commandline
+
+#### Install your hardware drivers
+* [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)
+* [macOS Users] Download and install the latest Vulkan SDK from [here](https://vulkan.lunarg.com/sdk/home)
+* [Nvidia Users] Download and install the latest CUDA / Vulkan drivers from [here](https://developer.nvidia.com/cuda-downloads)
+
+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
+
+
+#### Windows 10/11 Users
+```powershell
+(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"
+```
+
+#### Linux / macOS Users
+```shell
+python3.10 shark/examples/shark_inference/stable_diffusion/main.py --precision=fp16 --device=vulkan --prompt="tajmahal, oil on canvas, sunflowers, 4k, uhd"
+```
+
+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
+
+The output on a 6900XT would like:
+
+```shell 
+44it [00:08,  5.14it/s]i = 44 t = 120 (191ms)
+45it [00:08,  5.15it/s]i = 45 t = 100 (191ms)
+46it [00:08,  5.16it/s]i = 46 t = 80 (191ms)
+47it [00:09,  5.16it/s]i = 47 t = 60 (193ms)
+48it [00:09,  5.15it/s]i = 48 t = 40 (195ms)
+49it [00:09,  5.12it/s]i = 49 t = 20 (196ms)
+50it [00:09,  5.14it/s]
+Average step time: 192.8154182434082ms/it
+Total image generation runtime (s): 10.390909433364868
+(shark.venv) PS C:\g\shark>
+```
+
+Here are some samples generated:
+
+![tajmahal, snow, sunflowers, oil on canvas_0](https://user-images.githubusercontent.com/74956/204934186-141f7e43-6eb2-4e89-a99c-4704d20444b3.jpg)
+
+![a photo of a crab playing a trumpet](https://user-images.githubusercontent.com/74956/204933258-252e7240-8548-45f7-8253-97647d38313d.jpg)
+
+
+
+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)
+
+Find us on [SHARK Discord server](https://discord.gg/RUqY2h2s9u) if you have any trouble with running it on your hardware. 
+
 
 <details>
-  <summary>Installation (Linux and macOS)</summary>
+  <summary>Binary Installation</summary>
 
 ### Setup a new pip Virtual Environment
 
 This step sets up a new VirtualEnv for Python
 
 ```shell
-python --version #Check you have 3.7->3.10 on Linux or 3.10 on macOS
+python --version #Check you have 3.10 on Linux, macOS or Windows Powershell
 python -m venv shark_venv
-source shark_venv/bin/activate
+source shark_venv/bin/activate   # Use shark_venv/Scripts/activate on Windows
 
 # If you are using conda create and activate a new conda env
 
@@ -38,9 +136,14 @@ python -m pip install --upgrade pip
 This step pip installs SHARK and related packages on Linux Python 3.7, 3.8, 3.9, 3.10 and macOS Python 3.10
 
 ```shell
-pip install nodai-shark -f https://nod-ai.github.io/SHARK/package-index/ -f https://llvm.github.io/torch-mlir/package-index/ -f https://github.com/nod-ai/shark-runtime/releases --extra-index-url https://download.pytorch.org/whl/nightly/cpu
+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
 ```
-If you are on an Intel macOS machine you need this [workaround](https://github.com/nod-ai/SHARK/issues/102) for an upstream issue.
+
+### Run shark tank model tests.
+```shell
+pytest tank/test_models.py
+```
+See tank/README.md for a more detailed walkthrough of our pytest suite and CLI.
 
 ### Download and run Resnet50 sample
 
@@ -61,29 +164,27 @@ python ./minilm_jit.py --device="cpu"  #use cuda or vulkan or metal
 </details>
 
 
+
 <details>
-  <summary>Source Installation</summary>
+  <summary>Development, Testing and Benchmarks</summary>
 
-## Check out the code
+If you want to use Python3.10 and with TF Import tools you can use the environment variables like:
+Set `USE_IREE=1` to use upstream IREE
+```
+# PYTHON=python3.10 VENV_DIR=0617_venv IMPORTER=1 ./setup_venv.sh 
+```
 
+### Run any of the hundreds of SHARK tank models via the test framework
 ```shell
-git clone https://github.com/nod-ai/SHARK.git
-```
-
-## Setup your Python VirtualEnvironment and Dependencies
-```shell
-# Setup venv and install necessary packages (torch-mlir, nodLabs/Shark, ...).
-./setup_venv.sh
-source shark.venv/bin/activate
-```
-For example if you want to use Python3.10 and upstream IREE with TF Import tools you can use the environment variables like:
-```
-# PYTHON=python3.10 VENV_DIR=0617_venv IMPORTER=1 USE_IREE=1 ./setup_venv.sh 
+python -m  shark.examples.shark_inference.resnet50_script --device="cpu" # Use gpu | vulkan
+# Or a pytest
+pytest tank/test_models.py -k "MiniLM"
 ```
+  
 
 If you are a *Torch-mlir developer or an IREE developer* and want to test local changes you can uninstall
 the provided packages with `pip uninstall torch-mlir` and / or `pip uninstall iree-compiler iree-runtime` and build locally
-with Python bindings and set your PYTHONPATH as mentioned [here](https://google.github.io/iree/bindings/python/)
+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)
 for IREE and [here](https://github.com/llvm/torch-mlir/blob/main/development.md#setup-python-environment-to-export-the-built-python-packages)
 for Torch-MLIR.
 
@@ -102,82 +203,39 @@ for Torch-MLIR.
 ```
 Now the SHARK will use your locally build Torch-MLIR repo.
 
-### Run a demo script
-```shell
-python -m  shark.examples.shark_inference.resnet50_script --device="cpu" # Use gpu | vulkan
-# Or a pytest
-pytest tank/test_models.py -k "MiniLM"
+
+## Benchmarking Dispatches
+
+To produce benchmarks of individual dispatches, you can add `--dispatch_benchmarks=All --dispatch_benchmarks_dir=<output_dir>` to your command line argument.  
+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"`
+
+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:
+
 ```
+shark_module = SharkInference(
+        mlir_model,
+        func_name,
+        device=args.device,
+        mlir_dialect="tm_tensor",
+        dispatch_benchmarks="all",
+        dispatch_benchmarks_dir="results"
+    )
+```
+
+Output will include:
+- An ordered list ordered-dispatches.txt of all the dispatches with their runtime
+- 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)
+- An .mlir file containing the dispatch benchmark 
+- A compiled .vmfb file containing the dispatch benchmark
+- An .mlir file containing just the hal executable
+- A compiled .vmfb file of the hal executable
+- A .txt file containing benchmark output
+
+
+See tank/README.md for instructions on how to run model tests and benchmarks from the SHARK tank.
 
 </details>
 
-<details>
-  <summary>Testing and Benchmarks</summary>
-
-### Run all model tests on CPU/GPU/VULKAN/Metal
-```shell
-pytest tank/test_models.py
-
-# If on Linux for multithreading on CPU (faster results):
-pytest tank/test_models.py -n auto
-```
-
-### Running specific tests
-```shell
-
-# Search for test cases by including a keyword that matches all or part of the test case's name;
-pytest tank/test_models.py -k "keyword" 
-
-# Test cases are named uniformly by format test_module_<model_name_underscores_only>_<torch/tf>_<static/dynamic>_<device>.
-
-# Example: Test all models on nvidia gpu:
-pytest tank/test_models.py -k "cuda"
-
-# Example: Test all tensorflow resnet models on Vulkan backend:
-pytest tank/test_models.py -k "resnet and tf and vulkan"
-
-# Exclude a test case:
-pytest tank/test_models.py -k "not ..."
-
-### Run benchmarks on SHARK tank pytests and generate bench_results.csv with results.
-
-(the following requires source installation with `IMPORTER=1 ./setup_venv.sh`)
-
-```shell
-pytest --benchmark tank/test_models.py
-  
-# Just do static GPU benchmarks for PyTorch tests:
-pytest --benchmark tank/test_models.py -k "pytorch and static and cuda"
-
-```
-  
-### Benchmark Resnet50, MiniLM on CPU
-
-(requires source installation with `IMPORTER=1 ./setup_venv.sh`)  
-  
-```shell
-# We suggest running the following commands as root before running benchmarks on CPU:
-  
-cat /sys/devices/system/cpu/cpu*/topology/thread_siblings_list | awk -F, '{print $2}' | sort -n | uniq | ( while read X ; do echo $X ; echo 0 > /sys/devices/system/cpu/cpu$X/online ; done )
-echo 1 > /sys/devices/system/cpu/intel_pstate/no_turbo
-
-# Benchmark canonical Resnet50 on CPU via pytest
-pytest --benchmark tank/test_models -k "resnet50 and tf_static_cpu"
-
-# Benchmark canonical MiniLM on CPU via pytest
-pytest --benchmark tank/test_models -k "MiniLM and cpu"
-
-# Benchmark MiniLM on CPU via transformer-benchmarks:
-git clone --recursive https://github.com/nod-ai/transformer-benchmarks.git
-cd transformer-benchmarks
-./perf-ci.sh -n
-# Check detail.csv for MLIR/IREE results.
-
-```
-
-</details>
-
-
 <details>
   <summary>API Reference</summary>
 
@@ -228,160 +286,26 @@ result = shark_module.forward((arg0, arg1))
 ```
 </details>
 
-
 ## Supported and Validated Models
 
-<details>
-  <summary>PyTorch Models</summary>
+SHARK is maintained to support the latest innovations in ML Models: 
 
-### Huggingface PyTorch Models
+| TF HuggingFace Models | SHARK-CPU | SHARK-CUDA | SHARK-METAL |
+|---------------------|----------|----------|-------------|
+| BERT                | :green_heart:         | :green_heart:         | :green_heart:            |
+| DistilBERT         | :green_heart:         | :green_heart:         | :green_heart:            |
+| GPT2         | :green_heart:         | :green_heart:         | :green_heart:            |
+| BLOOM         | :green_heart:         | :green_heart:         | :green_heart:            |
+| Stable Diffusion         | :green_heart:         | :green_heart:         | :green_heart:            |
+| Vision Transformer       | :green_heart:         | :green_heart:         | :green_heart:            |
+| ResNet50         | :green_heart:         | :green_heart:         | :green_heart:            |
 
-| Hugging Face Models | Torch-MLIR lowerable | SHARK-CPU | SHARK-CUDA | SHARK-METAL |
-|---------------------|----------------------|----------|----------|-------------|
-| BERT                | :green_heart: (JIT)          | :green_heart:         | :green_heart:         | :green_heart:            |
-| Albert              | :green_heart: (JIT)            | :green_heart:         | :green_heart:         | :green_heart:            |
-| BigBird             | :green_heart: (AOT)            |          |          |             |
-| DistilBERT          | :green_heart: (JIT)            | :green_heart:         | :green_heart:         | :green_heart:            |
-| GPT2                | :broken_heart: (AOT)            |          |          |             |
-| MobileBert          | :green_heart: (JIT)            | :green_heart:         | :green_heart:         | :green_heart:            |
+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).
 
-### Torchvision  Models
+## Communication Channels
 
-| TORCHVISION Models | Torch-MLIR lowerable | SHARK-CPU | SHARK-CUDA | SHARK-METAL |
-|--------------------|----------------------|----------|----------|-------------|
-| AlexNet            | :green_heart: (Script)         | :green_heart:         | :green_heart:         | :green_heart:            |
-| DenseNet121        | :green_heart: (Script)         |          |          |             |
-| MNasNet1_0         | :green_heart: (Script)         | :green_heart:         | :green_heart:         | :green_heart:            |
-| MobileNetV2        | :green_heart: (Script)         | :green_heart:         | :green_heart:         | :green_heart:            |
-| MobileNetV3        | :green_heart: (Script)         | :green_heart:         | :green_heart:         | :green_heart:            |
-| Unet               | :broken_heart: (Script)         |          |          |             |
-| Resnet18           | :green_heart: (Script)         | :green_heart:         |  :green_heart:        | :green_heart:            |
-| Resnet50           | :green_heart: (Script)         | :green_heart:         |   :green_heart:       | :green_heart:            |
-| Resnet101           | :green_heart: (Script)         | :green_heart:         |   :green_heart:       | :green_heart:            |
-| Resnext50_32x4d    | :green_heart: (Script)         | :green_heart:         | :green_heart:         | :green_heart:            |
-| ShuffleNet_v2      | :broken_heart: (Script)         |          |          |             |
-| SqueezeNet         | :green_heart: (Script)         | :green_heart:         |   :green_heart:       | :green_heart:            |
-| EfficientNet       | :green_heart: (Script)         |          |          |             |
-| Regnet             | :green_heart: (Script)         | :green_heart:         | :green_heart:         | :green_heart:            |
-| Resnest            | :broken_heart: (Script)         |          |          |             |
-| Vision Transformer | :green_heart: (Script)         |          |          |             |
-| VGG 16             | :green_heart: (Script)         | :green_heart:         |   :green_heart:       |             |
-| Wide Resnet        | :green_heart: (Script)         | :green_heart:         | :green_heart:         | :green_heart:            |
-| RAFT               | :broken_heart: (JIT)            |          |          |             |
-
-For more information refer to [MODEL TRACKING SHEET](https://docs.google.com/spreadsheets/d/15PcjKeHZIrB5LfDyuw7DGEEE8XnQEX2aX8lm8qbxV8A/edit#gid=0)
-
-### PyTorch Training Models
-
-| Models | Torch-MLIR lowerable | SHARK-CPU | SHARK-CUDA | SHARK-METAL |
-|---------------------|----------------------|----------|----------|-------------|
-| BERT                | :broken_heart:           | :broken_heart:         |          |             |
-| FullyConnected                | :green_heart:           | :green_heart:         |          |             |
-
-</details>
-
-<details>
-  <summary>JAX Models</summary>
-
-
-### JAX  Models
-
-| Models | JAX-MHLO lowerable | SHARK-CPU | SHARK-CUDA | SHARK-METAL |
-|---------------------|----------------------|----------|----------|-------------|
-| DALL-E                | :broken_heart:           | :broken_heart:         |          |             |
-| FullyConnected                | :green_heart:           | :green_heart:         |          |             |
-
-</details>
-
-<details>
-  <summary>TFLite Models</summary>
-
-### TFLite Models
-
-| Models | TOSA/LinAlg  | SHARK-CPU | SHARK-CUDA | SHARK-METAL |
-|---------------------|----------------------|----------|----------|-------------|
-| BERT                | :broken_heart:           | :broken_heart:         |          |             |
-| FullyConnected      | :green_heart:           | :green_heart:         |          |             |
-| albert | :green_heart:           | :green_heart:         |          |             |
-| asr_conformer | :green_heart:           | :green_heart:         |          |             |
-| bird_classifier | :green_heart:           | :green_heart:         |          |             |
-| cartoon_gan | :green_heart:           | :green_heart:         |          |             |
-| craft_text | :green_heart:           | :green_heart:         |          |             |
-| deeplab_v3 | :green_heart:           | :green_heart:         |          |             |
-| densenet | :green_heart:           | :green_heart:         |          |             |
-| east_text_detector | :green_heart:           | :green_heart:         |          |             |
-| efficientnet_lite0_int8 | :green_heart:           | :green_heart:         |          |             |
-| efficientnet | :green_heart:           | :green_heart:         |          |             |
-| gpt2 | :green_heart:           | :green_heart:         |          |             |
-| image_stylization | :green_heart:           | :green_heart:         |          |             |
-| inception_v4 | :green_heart:           | :green_heart:         |          |             |
-| inception_v4_uint8 | :green_heart:           | :green_heart:         |          |             |
-| lightning_fp16 | :green_heart:           | :green_heart:         |          |             |
-| lightning_i8 | :green_heart:           | :green_heart:         |          |             |
-| lightning | :green_heart:           | :green_heart:         |          |             |
-| magenta | :green_heart:           | :green_heart:         |          |             |
-| midas | :green_heart:           | :green_heart:         |          |             |
-| mirnet | :green_heart:           | :green_heart:         |          |             |
-| mnasnet | :green_heart:           | :green_heart:         |          |             |
-| mobilebert_edgetpu_s_float | :green_heart:           | :green_heart:         |          |             |
-| mobilebert_edgetpu_s_quant | :green_heart:           | :green_heart:         |          |             |
-| mobilebert | :green_heart:           | :green_heart:         |          |             |
-| mobilebert_tf2_float | :green_heart:           | :green_heart:         |          |             |
-| mobilebert_tf2_quant | :green_heart:           | :green_heart:         |          |             |
-| mobilenet_ssd_quant | :green_heart:           | :green_heart:         |          |             |
-| mobilenet_v1 | :green_heart:           | :green_heart:         |          |             |
-| mobilenet_v1_uint8 | :green_heart:           | :green_heart:         |          |             |
-| mobilenet_v2_int8 | :green_heart:           | :green_heart:         |          |             |
-| mobilenet_v2 | :green_heart:           | :green_heart:         |          |             |
-| mobilenet_v2_uint8 | :green_heart:           | :green_heart:         |          |             |
-| mobilenet_v3-large | :green_heart:           | :green_heart:         |          |             |
-| mobilenet_v3-large_uint8 | :green_heart:           | :green_heart:         |          |             |
-| mobilenet_v35-int8 | :green_heart:           | :green_heart:         |          |             |
-| nasnet | :green_heart:           | :green_heart:         |          |             |
-| person_detect | :green_heart:           | :green_heart:         |          |             |
-| posenet | :green_heart:           | :green_heart:         |          |             |
-| resnet_50_int8 | :green_heart:           | :green_heart:         |          |             |
-| rosetta | :green_heart:           | :green_heart:         |          |             |
-| spice | :green_heart:           | :green_heart:         |          |             |
-| squeezenet | :green_heart:           | :green_heart:         |          |             |
-| ssd_mobilenet_v1 | :green_heart:           | :green_heart:         |          |             |
-| ssd_mobilenet_v1_uint8 | :green_heart:           | :green_heart:         |          |             |
-| ssd_mobilenet_v2_fpnlite | :green_heart:           | :green_heart:         |          |             |
-| ssd_mobilenet_v2_fpnlite_uint8 | :green_heart:           | :green_heart:         |          |             |
-| ssd_mobilenet_v2_int8 | :green_heart:           | :green_heart:         |          |             |
-| ssd_mobilenet_v2 | :green_heart:           | :green_heart:         |          |             |
-| ssd_spaghettinet_large | :green_heart:           | :green_heart:         |          |             |
-| ssd_spaghettinet_large_uint8 | :green_heart:           | :green_heart:         |          |             |
-| visual_wake_words_i8 | :green_heart:           | :green_heart:         |          |             |
-
-</details>
-
-<details>
-  <summary>TF Models</summary>
-
-### Tensorflow Models (Inference)
-
-| Hugging Face Models | tf-mhlo lowerable | SHARK-CPU | SHARK-CUDA | SHARK-METAL |
-|---------------------|----------------------|----------|----------|-------------|
-| BERT                | :green_heart:          | :green_heart:         | :green_heart:         | :green_heart:            |
-| albert-base-v2              | :green_heart:            | :green_heart:         | :green_heart:         | :green_heart:            |
-| DistilBERT          | :green_heart:            | :green_heart:         | :green_heart:         | :green_heart:            |
-| CamemBert                | :green_heart:          | :green_heart:         | :green_heart:         | :green_heart:            |
-| ConvBert              | :green_heart:            | :green_heart:         | :green_heart:         | :green_heart:            |
-| Deberta              |            |         |          |             |
-| electra          | :green_heart:            | :green_heart:         | :green_heart:         | :green_heart:            |
-| funnel              |            |         |          |             |
-| layoutlm              | :green_heart:            | :green_heart:         | :green_heart:         | :green_heart:            |
-| longformer              |            |         |          |             |
-| mobile-bert                | :green_heart:          | :green_heart:         | :green_heart:         | :green_heart:            |
-| remembert              |            |         |          |             |
-| tapas              |            |         |          |             |
-| flaubert                | :green_heart:          | :green_heart:         | :green_heart:         | :green_heart:            |
-| roberta                | :green_heart:          | :green_heart:         | :green_heart:         | :green_heart:            |
-| xlm-roberta              | :green_heart:            | :green_heart:         | :green_heart:         | :green_heart:            |
-| mpnet              | :green_heart:            | :green_heart:         | :green_heart:         | :green_heart:            |
-
-</details>
+*   [SHARK Discord server](https://discord.gg/RUqY2h2s9u): Real time discussions with the SHARK team and other users
+*   [GitHub issues](https://github.com/nod-ai/SHARK/issues): Feature requests, bugs etc
 
 ## Related Projects
 

benchmarks/tests/test_benchmark.py

@@ -42,7 +42,7 @@ class TFHuggingFaceLanguage(tf.Module):
             input_ids=x, attention_mask=y, token_type_ids=z, training=False
         )
 
-    @tf.function(input_signature=tf_bert_input)
+    @tf.function(input_signature=tf_bert_input, jit_compile=True)
     def forward(self, input_ids, attention_mask, token_type_ids):
         return self.m.predict(input_ids, attention_mask, token_type_ids)
 

conftest.py

@@ -36,6 +36,12 @@ def pytest_addoption(parser):
         default="False",
         help="Enables uploading of reproduction artifacts upon test case failure during iree-compile or validation. Must be passed with --ci_sha option ",
     )
+    parser.addoption(
+        "--update_tank",
+        action="store_true",
+        default="False",
+        help="Update local shark tank with latest artifacts.",
+    )
     parser.addoption(
         "--ci_sha",
         action="store",

cpp/.gitignore

@@ -0,0 +1,3 @@
+*.mlir
+*.vmfb
+*.ini

cpp/README.md

@@ -54,5 +54,29 @@ python -m pip install tensorflow
 
 *Run the vulkan_gui*
 ```bash
-./build/vulkan_gui/iree-samples-vulkan-gui
+./build/vulkan_gui/iree-samples-resnet-vulkan-gui
+```
+
+## Other models
+A tool for benchmarking other models is built and can be invoked with a command like the following
+```bash
+./build/vulkan_gui/iree-vulkan-gui --module-file=path/to/.vmfb --function_input=...
+```
+see `./build/vulkan_gui/iree-vulkan-gui --help` for an explanation on the function input. For example, stable diffusion unet can be tested with the following commands:
+```bash
+wget https://storage.googleapis.com/shark_tank/quinn/stable_diff_tf/stable_diff_tf.mlir
+iree-compile --iree-input-type=mhlo --iree-vm-bytecode-module-output-format=flatbuffer-binary --iree-hal-target-backends=vulkan --mlir-print-debuginfo --mlir-print-op-on-diagnostic=false --iree-llvm-target-cpu-features=host -iree-vulkan-target-triple=rdna2-unknown-linux --iree-stream-resource-index-bits=64 --iree-vm-target-index-bits=64 stable_diff_tf.mlir -o stable_diff_tf.vmfb
+./build/vulkan_gui/iree-vulkan-gui --module-file=stable_diff_tf.vmfb --function_input=2x4x64x64xf32 --function_input=1xf32 --function_input=2x77x768xf32
+```
+VAE and Autoencoder are also available
+```bash
+# VAE
+wget https://storage.googleapis.com/shark_tank/quinn/stable_diff_tf/vae_tf/vae.mlir
+iree-compile --iree-input-type=mhlo --iree-vm-bytecode-module-output-format=flatbuffer-binary --iree-hal-target-backends=vulkan --mlir-print-debuginfo --mlir-print-op-on-diagnostic=false --iree-llvm-target-cpu-features=host -iree-vulkan-target-triple=rdna2-unknown-linux --iree-stream-resource-index-bits=64 --iree-vm-target-index-bits=64 vae.mlir -o vae.vmfb
+./build/vulkan_gui/iree-vulkan-gui --module-file=stable_diff_tf.vmfb --function_input=1x4x64x64xf32
+
+# CLIP Autoencoder
+wget https://storage.googleapis.com/shark_tank/quinn/stable_diff_tf/clip_tf/clip_autoencoder.mlir
+iree-compile --iree-input-type=mhlo --iree-vm-bytecode-module-output-format=flatbuffer-binary --iree-hal-target-backends=vulkan --mlir-print-debuginfo --mlir-print-op-on-diagnostic=false --iree-llvm-target-cpu-features=host -iree-vulkan-target-triple=rdna2-unknown-linux --iree-stream-resource-index-bits=64 --iree-vm-target-index-bits=64 clip_autoencoder.mlir -o clip_autoencoder.vmfb
+./build/vulkan_gui/iree-vulkan-gui --module-file=stable_diff_tf.vmfb --function_input=1x77xi32 --function_input=1x77xi32
 ```

cpp/save_img.py

@@ -1,7 +1,6 @@
 import numpy as np
 import tensorflow as tf
 from shark.shark_inference import SharkInference
-from shark.shark_downloader import download_tf_model
 
 
 def load_and_preprocess_image(fname: str):

cpp/vulkan_gui/CMakeLists.txt

@@ -40,45 +40,77 @@ set(IMGUI_DIR ${CMAKE_BINARY_DIR}/_deps/imgui-src)
 message("Looking for Imgui in ${IMGUI_DIR}")
 include_directories(${IMGUI_DIR} ${IMGUI_DIR}/backends ..)
 
-# Define the sample executable.
-set(_NAME "iree-samples-vulkan-gui")
-add_executable(${_NAME} "")
-target_sources(${_NAME}
-  PRIVATE
-    vulkan_inference_gui.cc
-    "${IMGUI_DIR}/backends/imgui_impl_sdl.cpp"
-    "${IMGUI_DIR}/backends/imgui_impl_vulkan.cpp"
-    "${IMGUI_DIR}/imgui.cpp"
-    "${IMGUI_DIR}/imgui_draw.cpp"
-    "${IMGUI_DIR}/imgui_demo.cpp"
-    "${IMGUI_DIR}/imgui_tables.cpp"
-    "${IMGUI_DIR}/imgui_widgets.cpp"
-)
-set_target_properties(${_NAME} PROPERTIES OUTPUT_NAME "iree-samples-vulkan-gui")
-target_include_directories(${_NAME} PUBLIC
-    $<BUILD_INTERFACE:${CMAKE_CURRENT_BINARY_DIR}>
-)
-target_link_libraries(${_NAME}
-  SDL2::SDL2
-  Vulkan::Vulkan
-  iree_runtime_runtime
-  iree_base_internal_main
-  iree_hal_drivers_vulkan_registration_registration
-  iree_modules_hal_hal
-  iree_vm_vm
-  iree_vm_bytecode_module
-  iree_vm_cc
+
+function(iree_vulkan_sample)
+
+  cmake_parse_arguments(
+    _RULE
+    ""
+    "NAME"
+    "SRCS"
+    ${ARGN}
+  )
+
+
+  # Define the sample executable.
+  set(_NAME "${_RULE_NAME}")
+  set(SRCS "${_RULE_SRCS}")
+  add_executable(${_NAME} "")
+  target_sources(${_NAME}
+    PRIVATE
+      ${SRCS}
+      "${IMGUI_DIR}/backends/imgui_impl_sdl.cpp"
+      "${IMGUI_DIR}/backends/imgui_impl_vulkan.cpp"
+      "${IMGUI_DIR}/imgui.cpp"
+      "${IMGUI_DIR}/imgui_draw.cpp"
+      "${IMGUI_DIR}/imgui_demo.cpp"
+      "${IMGUI_DIR}/imgui_tables.cpp"
+      "${IMGUI_DIR}/imgui_widgets.cpp"
+  )
+  set_target_properties(${_NAME} PROPERTIES OUTPUT_NAME "${_NAME}")
+  target_include_directories(${_NAME} PUBLIC
+      $<BUILD_INTERFACE:${CMAKE_CURRENT_BINARY_DIR}>
+  )
+  target_link_libraries(${_NAME}
+    SDL2::SDL2
+    Vulkan::Vulkan
+    iree_runtime_runtime
+    iree_base_internal_main
+    iree_hal_drivers_vulkan_registration_registration
+    iree_modules_hal_hal
+    iree_vm_vm
+    iree_vm_bytecode_module
+    iree_vm_cc
+    iree_tooling_vm_util_cc
+    iree_tooling_context_util
+  )
+
+  if(${CMAKE_SYSTEM_NAME} STREQUAL "Windows")
+    set(_GUI_LINKOPTS "-SUBSYSTEM:CONSOLE")
+  else()
+    set(_GUI_LINKOPTS "")
+  endif()
+
+  target_link_options(${_NAME}
+    PRIVATE
+      ${_GUI_LINKOPTS}
+  )
+endfunction()
+
+iree_vulkan_sample(
+    NAME
+      iree-samples-resnet-vulkan-gui
+
+    SRCS
+      vulkan_resnet_inference_gui.cc
 )
 
-if(${CMAKE_SYSTEM_NAME} STREQUAL "Windows")
-  set(_GUI_LINKOPTS "-SUBSYSTEM:CONSOLE")
-else()
-  set(_GUI_LINKOPTS "")
-endif()
+iree_vulkan_sample(
+    NAME
+      iree-vulkan-gui
 
-target_link_options(${_NAME}
-  PRIVATE
-    ${_GUI_LINKOPTS}
+    SRCS
+      vulkan_inference_gui.cc
 )
 
 message(STATUS "Configured vulkan_gui sample successfully")

cpp/vulkan_gui/vulkan_inference_gui.cc

@@ -18,6 +18,12 @@
 #include <set>
 #include <vector>
 #include <fstream>
+#include <array>
+#include <cstdio>
+#include <cstdlib>
+#include <iterator>
+#include <string>
+#include <utility>
 
 #include "iree/hal/drivers/vulkan/api.h"
 
@@ -30,6 +36,15 @@
 #include "iree/vm/bytecode_module.h"
 #include "iree/vm/ref_cc.h"
 
+// iree-run-module
+#include "iree/base/internal/flags.h"
+#include "iree/base/status_cc.h"
+#include "iree/base/tracing.h"
+#include "iree/modules/hal/types.h"
+#include "iree/tooling/comparison.h"
+#include "iree/tooling/context_util.h"
+#include "iree/tooling/vm_util_cc.h"
+
 // Other dependencies (helpers, etc.)
 #include "iree/base/internal/main.h"
 
@@ -38,6 +53,49 @@
 #define STB_IMAGE_IMPLEMENTATION
 #include "stb_image.h"
 
+IREE_FLAG(string, entry_function, "",
+          "Name of a function contained in the module specified by module_file "
+          "to run.");
+
+// TODO(benvanik): move --function_input= flag into a util.
+static iree_status_t parse_function_io(iree_string_view_t flag_name,
+                                       void* storage,
+                                       iree_string_view_t value) {
+  auto* list = (std::vector<std::string>*)storage;
+  list->push_back(std::string(value.data, value.size));
+  return iree_ok_status();
+}
+static void print_function_io(iree_string_view_t flag_name, void* storage,
+                              FILE* file) {
+  auto* list = (std::vector<std::string>*)storage;
+  if (list->empty()) {
+    fprintf(file, "# --%.*s=\n", (int)flag_name.size, flag_name.data);
+  } else {
+    for (size_t i = 0; i < list->size(); ++i) {
+      fprintf(file, "--%.*s=\"%s\"\n", (int)flag_name.size, flag_name.data,
+              list->at(i).c_str());
+    }
+  }
+}
+static std::vector<std::string> FLAG_function_inputs;
+IREE_FLAG_CALLBACK(
+    parse_function_io, print_function_io, &FLAG_function_inputs, function_input,
+    "An input (a) value or (b) buffer of the format:\n"
+    "  (a) scalar value\n"
+    "     value\n"
+    "     e.g.: --function_input=\"3.14\"\n"
+    "  (b) buffer:\n"
+    "     [shape]xtype=[value]\n"
+    "     e.g.: --function_input=\"2x2xi32=1 2 3 4\"\n"
+    "Optionally, brackets may be used to separate the element values:\n"
+    "  2x2xi32=[[1 2][3 4]]\n"
+    "Raw binary files can be read to provide buffer contents:\n"
+    "  2x2xi32=@some/file.bin\n"
+    "numpy npy files (from numpy.save) can be read to provide 1+ values:\n"
+    "  @some.npy\n"
+    "Each occurrence of the flag indicates an input in the order they were\n"
+    "specified on the command line.");
+
 typedef struct iree_file_toc_t {
   const char* name;             // the file's original name
   char* data;             // beginning of the file
@@ -87,225 +145,6 @@ static void check_vk_result(VkResult err) {
   abort();
 }
 
-// Helper function to find Vulkan memory type bits. See ImGui_ImplVulkan_MemoryType() in imgui_impl_vulkan.cpp
-uint32_t findMemoryType(uint32_t type_filter, VkMemoryPropertyFlags properties)
-{
-  VkPhysicalDeviceMemoryProperties mem_properties;
-  vkGetPhysicalDeviceMemoryProperties(g_PhysicalDevice, &mem_properties);
-
-  for (uint32_t i = 0; i < mem_properties.memoryTypeCount; i++)
-  {
-    if ((type_filter & (1 << i)) && (mem_properties.memoryTypes[i].propertyFlags & properties) == properties)
-    {
-      return i;
-    }
-  }
-
-  return 0xFFFFFFFF; // Unable to find memoryType
-}
-
-// Helper function to load an image with common settings and return a VkDescriptorSet as a sort of Vulkan pointer
-bool LoadTextureFromFile(const char* filename, VkDescriptorSet* img_ds, int* image_width, int* image_height)
-{
-  // Specifying 4 channels forces stb to load the image in RGBA which is an easy format for Vulkan
-  int image_channels = 4;
-  unsigned char* image_data = stbi_load(filename, image_width, image_height, 0, image_channels);
-
-  if (image_data == NULL)
-  {
-    return false;
-  }
-
-  // Calculate allocation size (in number of bytes)
-  size_t image_size = (*image_width)*(*image_height)*image_channels;
-
-  VkResult err;
-
-  // Create the Vulkan image.
-  VkImage texture_image;
-  VkDeviceMemory texture_image_memory;
-  {
-    VkImageCreateInfo info = {};
-    info.sType = VK_STRUCTURE_TYPE_IMAGE_CREATE_INFO;
-    info.imageType = VK_IMAGE_TYPE_2D;
-    info.format = VK_FORMAT_R8G8B8A8_UNORM;
-    info.extent.width = *image_width;
-    info.extent.height = *image_height;
-    info.extent.depth = 1;
-    info.mipLevels = 1;
-    info.arrayLayers = 1;
-    info.samples = VK_SAMPLE_COUNT_1_BIT;
-    info.tiling = VK_IMAGE_TILING_OPTIMAL;
-    info.usage = VK_IMAGE_USAGE_SAMPLED_BIT | VK_IMAGE_USAGE_TRANSFER_DST_BIT;
-    info.sharingMode = VK_SHARING_MODE_EXCLUSIVE;
-    info.initialLayout = VK_IMAGE_LAYOUT_UNDEFINED;
-    err = vkCreateImage(g_Device, &info, g_Allocator, &texture_image);
-    check_vk_result(err);
-    VkMemoryRequirements req;
-    vkGetImageMemoryRequirements(g_Device, texture_image, &req);
-    VkMemoryAllocateInfo alloc_info = {};
-    alloc_info.sType = VK_STRUCTURE_TYPE_MEMORY_ALLOCATE_INFO;
-    alloc_info.allocationSize = req.size;
-    alloc_info.memoryTypeIndex = findMemoryType(req.memoryTypeBits, VK_MEMORY_PROPERTY_DEVICE_LOCAL_BIT);
-    err = vkAllocateMemory(g_Device, &alloc_info, g_Allocator, &texture_image_memory);
-    check_vk_result(err);
-    err = vkBindImageMemory(g_Device, texture_image, texture_image_memory, 0);
-    check_vk_result(err);
-  }
-
-  // Create the Image View
-  VkImageView image_view;
-  {
-    VkImageViewCreateInfo info = {};
-    info.sType = VK_STRUCTURE_TYPE_IMAGE_VIEW_CREATE_INFO;
-    info.image = texture_image;
-    info.viewType = VK_IMAGE_VIEW_TYPE_2D;
-    info.format = VK_FORMAT_R8G8B8A8_UNORM;
-    info.subresourceRange.aspectMask = VK_IMAGE_ASPECT_COLOR_BIT;
-    info.subresourceRange.levelCount = 1;
-    info.subresourceRange.layerCount = 1;
-    err = vkCreateImageView(g_Device, &info, g_Allocator, &image_view);
-    check_vk_result(err);
-  }
-
-  // Create Sampler
-  VkSampler sampler;
-  {
-    VkSamplerCreateInfo sampler_info{};
-    sampler_info.sType = VK_STRUCTURE_TYPE_SAMPLER_CREATE_INFO;
-    sampler_info.magFilter = VK_FILTER_LINEAR;
-    sampler_info.minFilter = VK_FILTER_LINEAR;
-    sampler_info.mipmapMode  = VK_SAMPLER_MIPMAP_MODE_LINEAR;
-    sampler_info.addressModeU = VK_SAMPLER_ADDRESS_MODE_REPEAT; // outside image bounds just use border color
-    sampler_info.addressModeV = VK_SAMPLER_ADDRESS_MODE_REPEAT;
-    sampler_info.addressModeW = VK_SAMPLER_ADDRESS_MODE_REPEAT;
-    sampler_info.minLod = -1000;
-    sampler_info.maxLod = 1000;
-    sampler_info.maxAnisotropy = 1.0f;
-    err = vkCreateSampler(g_Device, &sampler_info, g_Allocator, &sampler);
-    check_vk_result(err);
-  }
-
-  // Create Descriptor Set using ImGUI's implementation
-  *img_ds = ImGui_ImplVulkan_AddTexture(sampler, image_view, VK_IMAGE_LAYOUT_SHADER_READ_ONLY_OPTIMAL);
-
-  // Create Upload Buffer
-  VkBuffer upload_buffer;
-  VkDeviceMemory upload_buffer_memory;
-  {
-    VkBufferCreateInfo buffer_info = {};
-    buffer_info.sType = VK_STRUCTURE_TYPE_BUFFER_CREATE_INFO;
-    buffer_info.size = image_size;
-    buffer_info.usage = VK_BUFFER_USAGE_TRANSFER_SRC_BIT;
-    buffer_info.sharingMode = VK_SHARING_MODE_EXCLUSIVE;
-    err = vkCreateBuffer(g_Device, &buffer_info, g_Allocator, &upload_buffer);
-    check_vk_result(err);
-    VkMemoryRequirements req;
-    vkGetBufferMemoryRequirements(g_Device, upload_buffer, &req);
-    VkMemoryAllocateInfo alloc_info = {};
-    alloc_info.sType = VK_STRUCTURE_TYPE_MEMORY_ALLOCATE_INFO;
-    alloc_info.allocationSize = req.size;
-    alloc_info.memoryTypeIndex = findMemoryType(req.memoryTypeBits, VK_MEMORY_PROPERTY_HOST_VISIBLE_BIT);
-    err = vkAllocateMemory(g_Device, &alloc_info, g_Allocator, &upload_buffer_memory);
-    check_vk_result(err);
-    err = vkBindBufferMemory(g_Device, upload_buffer, upload_buffer_memory, 0);
-    check_vk_result(err);
-  }
-
-  // Upload to Buffer:
-  {
-    void* map = NULL;
-    err = vkMapMemory(g_Device, upload_buffer_memory, 0, image_size, 0, &map);
-    check_vk_result(err);
-    memcpy(map, image_data, image_size);
-    VkMappedMemoryRange range[1] = {};
-    range[0].sType = VK_STRUCTURE_TYPE_MAPPED_MEMORY_RANGE;
-    range[0].memory = upload_buffer_memory;
-    range[0].size = image_size;
-    err = vkFlushMappedMemoryRanges(g_Device, 1, range);
-    check_vk_result(err);
-    vkUnmapMemory(g_Device, upload_buffer_memory);
-  }
-
-  // Release image memory using stb
-  stbi_image_free(image_data);
-
-  // Create a command buffer that will perform following steps when hit in the command queue.
-  // TODO: this works in the example, but may need input if this is an acceptable way to access the pool/create the command buffer.
-  VkCommandPool command_pool = g_MainWindowData.Frames[g_MainWindowData.FrameIndex].CommandPool;
-  VkCommandBuffer command_buffer;
-  {
-    VkCommandBufferAllocateInfo alloc_info{};
-    alloc_info.sType = VK_STRUCTURE_TYPE_COMMAND_BUFFER_ALLOCATE_INFO;
-    alloc_info.level = VK_COMMAND_BUFFER_LEVEL_PRIMARY;
-    alloc_info.commandPool = command_pool;
-    alloc_info.commandBufferCount = 1;
-
-    err = vkAllocateCommandBuffers(g_Device, &alloc_info, &command_buffer);
-    check_vk_result(err);
-
-    VkCommandBufferBeginInfo begin_info = {};
-    begin_info.sType = VK_STRUCTURE_TYPE_COMMAND_BUFFER_BEGIN_INFO;
-    begin_info.flags |= VK_COMMAND_BUFFER_USAGE_ONE_TIME_SUBMIT_BIT;
-    err = vkBeginCommandBuffer(command_buffer, &begin_info);
-    check_vk_result(err);
-  }
-
-  // Copy to Image
-  {
-    VkImageMemoryBarrier copy_barrier[1] = {};
-    copy_barrier[0].sType = VK_STRUCTURE_TYPE_IMAGE_MEMORY_BARRIER;
-    copy_barrier[0].dstAccessMask = VK_ACCESS_TRANSFER_WRITE_BIT;
-    copy_barrier[0].oldLayout = VK_IMAGE_LAYOUT_UNDEFINED;
-    copy_barrier[0].newLayout = VK_IMAGE_LAYOUT_TRANSFER_DST_OPTIMAL;
-    copy_barrier[0].srcQueueFamilyIndex = VK_QUEUE_FAMILY_IGNORED;
-    copy_barrier[0].dstQueueFamilyIndex = VK_QUEUE_FAMILY_IGNORED;
-    copy_barrier[0].image = texture_image;
-    copy_barrier[0].subresourceRange.aspectMask = VK_IMAGE_ASPECT_COLOR_BIT;
-    copy_barrier[0].subresourceRange.levelCount = 1;
-    copy_barrier[0].subresourceRange.layerCount = 1;
-    vkCmdPipelineBarrier(command_buffer, VK_PIPELINE_STAGE_HOST_BIT, VK_PIPELINE_STAGE_TRANSFER_BIT, 0, 0, NULL, 0, NULL, 1, copy_barrier);
-
-    VkBufferImageCopy region = {};
-    region.imageSubresource.aspectMask = VK_IMAGE_ASPECT_COLOR_BIT;
-    region.imageSubresource.layerCount = 1;
-    region.imageExtent.width = *image_width;
-    region.imageExtent.height = *image_height;
-    region.imageExtent.depth = 1;
-    vkCmdCopyBufferToImage(command_buffer, upload_buffer, texture_image, VK_IMAGE_LAYOUT_TRANSFER_DST_OPTIMAL, 1, &region);
-
-    VkImageMemoryBarrier use_barrier[1] = {};
-    use_barrier[0].sType = VK_STRUCTURE_TYPE_IMAGE_MEMORY_BARRIER;
-    use_barrier[0].srcAccessMask = VK_ACCESS_TRANSFER_WRITE_BIT;
-    use_barrier[0].dstAccessMask = VK_ACCESS_SHADER_READ_BIT;
-    use_barrier[0].oldLayout = VK_IMAGE_LAYOUT_TRANSFER_DST_OPTIMAL;
-    use_barrier[0].newLayout = VK_IMAGE_LAYOUT_SHADER_READ_ONLY_OPTIMAL;
-    use_barrier[0].srcQueueFamilyIndex = VK_QUEUE_FAMILY_IGNORED;
-    use_barrier[0].dstQueueFamilyIndex = VK_QUEUE_FAMILY_IGNORED;
-    use_barrier[0].image = texture_image;
-    use_barrier[0].subresourceRange.aspectMask = VK_IMAGE_ASPECT_COLOR_BIT;
-    use_barrier[0].subresourceRange.levelCount = 1;
-    use_barrier[0].subresourceRange.layerCount = 1;
-    vkCmdPipelineBarrier(command_buffer, VK_PIPELINE_STAGE_TRANSFER_BIT, VK_PIPELINE_STAGE_FRAGMENT_SHADER_BIT, 0, 0, NULL, 0, NULL, 1, use_barrier);
-  }
-
-  // End command buffer
-  {
-    VkSubmitInfo end_info = {};
-    end_info.sType = VK_STRUCTURE_TYPE_SUBMIT_INFO;
-    end_info.commandBufferCount = 1;
-    end_info.pCommandBuffers = &command_buffer;
-    err = vkEndCommandBuffer(command_buffer);
-    check_vk_result(err);
-    err = vkQueueSubmit(g_Queue, 1, &end_info, VK_NULL_HANDLE);
-    check_vk_result(err);
-    err = vkDeviceWaitIdle(g_Device);
-    check_vk_result(err);
-  }
-
-  return true;
-}
-
 // Returns the names of the Vulkan layers used for the given IREE
 // |extensibility_set| and |features|.
 std::vector<const char*> GetIreeLayers(
@@ -723,7 +562,16 @@ namespace iree {
 
 extern "C" int iree_main(int argc, char** argv) {
 
-  fprintf(stdout, "starting yo\n");
+  iree_flags_parse_checked(IREE_FLAGS_PARSE_MODE_DEFAULT, &argc, &argv);
+  if (argc > 1) {
+    // Avoid iree-run-module spinning endlessly on stdin if the user uses single
+    // dashes for flags.
+    printf(
+        "[ERROR] unexpected positional argument (expected none)."
+        " Did you use pass a flag with a single dash ('-')?"
+        " Use '--' instead.\n");
+    return 1;
+  }
 
   // --------------------------------------------------------------------------
   // Create a window.
@@ -835,8 +683,6 @@ extern "C" int iree_main(int argc, char** argv) {
 
   // Demo state.
   bool show_iree_window = true;
-  // --------------------------------------------------------------------------
-
   // --------------------------------------------------------------------------
   // Setup IREE.
 
@@ -900,69 +746,44 @@ extern "C" int iree_main(int argc, char** argv) {
 
 
   // Load bytecode module
-  iree_file_toc_t module_file_toc;
-  const char network_model[] = "resnet50_tf.vmfb";
-  fprintf(stdout, "Loading: %s\n", network_model);
-  if (load_file(network_model, &module_file_toc.data, &module_file_toc.size) == false)
-  {
-      abort();
-      return 1;
-  }
-  fprintf(stdout, "module size: %zu\n", module_file_toc.size);
-
-  static float input_res50[224*224*3];
-  static float output_res50[1000];
-
-  char filename[] = "dog_imagenet.jpg";
-  fprintf(stdout, "loading: %s\n", filename);
-  int x,y,n;
-  //unsigned char *image_raw = stbi_load(filename, &x, &y, &n, 3);
-  stbi_load(filename, &x, &y, &n, 3);
-  fprintf(stdout, "res: %i x %i x %i\n", x, y, n);
-
-  /* Preprocessing needs to go here. For now use a buffer preprocessed in python.
-
-  //convert image into floating point format
-  for(int i=0;i<224*224*3;i++)
-  {
-    input_res50[i]= ((float)image_raw[i])/255.0f;
-  }*/
-
-  std::ifstream fin("dog.bin", std::ifstream::in | std::ifstream::binary);
-  fin.read((char*)input_res50, 224*224*3*sizeof(float));
-
-  // load image again so imgui can display it
-  int my_image_width = 0;
-  int my_image_height = 0;
-  VkDescriptorSet my_image_texture = 0;
-  bool ret = LoadTextureFromFile(filename, &my_image_texture, &my_image_width, &my_image_height);
-  fprintf(stdout, "creating vulkan image: %s\n", ret ?"OK":"FAIL");
-  IM_ASSERT(ret);
+  //iree_file_toc_t module_file_toc;
+  //const char network_model[] = "resnet50_tf.vmfb";
+  //fprintf(stdout, "Loading: %s\n", network_model);
+  //if (load_file(network_model, &module_file_toc.data, &module_file_toc.size) == false)
+  //{
+  //    abort();
+  //    return 1;
+  //}
+  //fprintf(stdout, "module size: %zu\n", module_file_toc.size);
 
   iree_vm_module_t* bytecode_module = nullptr;
-  IREE_CHECK_OK(iree_vm_bytecode_module_create(
-      iree_instance,
-      iree_const_byte_span_t{
-          reinterpret_cast<const uint8_t*>(module_file_toc.data),
-          module_file_toc.size},
-      iree_allocator_null(), iree_allocator_system(), &bytecode_module));
-  // Query for details about what is in the loaded module.
-  iree_vm_module_signature_t bytecode_module_signature =
-      iree_vm_module_signature(bytecode_module);
-  fprintf(stdout, "Module loaded, have <%" PRIhsz "> exported functions:\n",
-          bytecode_module_signature.export_function_count);
-  for (int i = 0; i < bytecode_module_signature.export_function_count; ++i) {
-    iree_vm_function_t function;
-    IREE_CHECK_OK(iree_vm_module_lookup_function_by_ordinal(
-        bytecode_module, IREE_VM_FUNCTION_LINKAGE_EXPORT, i, &function));
-    auto function_name = iree_vm_function_name(&function);
-    auto function_signature = iree_vm_function_signature(&function);
+  iree_status_t module_status = iree_tooling_load_module_from_flags(
+      iree_instance, iree_allocator_system(), &bytecode_module);
+  if (!iree_status_is_ok(module_status))
+    return -1;
+  //IREE_CHECK_OK(iree_vm_bytecode_module_create(
+  //    iree_instance,
+  //    iree_const_byte_span_t{
+  //        reinterpret_cast<const uint8_t*>(module_file_toc.data),
+  //        module_file_toc.size},
+  //    iree_allocator_null(), iree_allocator_system(), &bytecode_module));
+  //// Query for details about what is in the loaded module.
+  //iree_vm_module_signature_t bytecode_module_signature =
+  //    iree_vm_module_signature(bytecode_module);
+  //fprintf(stdout, "Module loaded, have <%" PRIhsz "> exported functions:\n",
+  //        bytecode_module_signature.export_function_count);
+  //for (int i = 0; i < bytecode_module_signature.export_function_count; ++i) {
+  //  iree_vm_function_t function;
+  //  IREE_CHECK_OK(iree_vm_module_lookup_function_by_ordinal(
+  //      bytecode_module, IREE_VM_FUNCTION_LINKAGE_EXPORT, i, &function));
+  //  auto function_name = iree_vm_function_name(&function);
+  //  auto function_signature = iree_vm_function_signature(&function);
 
-    fprintf(stdout, "  %d: '%.*s' with calling convention '%.*s'\n", i,
-            (int)function_name.size, function_name.data,
-            (int)function_signature.calling_convention.size,
-            function_signature.calling_convention.data);
-  }
+  //  fprintf(stdout, "  %d: '%.*s' with calling convention '%.*s'\n", i,
+  //          (int)function_name.size, function_name.data,
+  //          (int)function_signature.calling_convention.size,
+  //          function_signature.calling_convention.data);
+  //}
 
   // Allocate a context that will hold the module state across invocations.
   iree_vm_context_t* iree_context = nullptr;
@@ -988,33 +809,42 @@ extern "C" int iree_main(int argc, char** argv) {
         // Write inputs into mappable buffers.
         iree_hal_allocator_t* allocator =
             iree_hal_device_allocator(iree_vk_device);
-        iree_hal_memory_type_t input_memory_type =
-            static_cast<iree_hal_memory_type_t>(
-                IREE_HAL_MEMORY_TYPE_HOST_LOCAL |
-                IREE_HAL_MEMORY_TYPE_DEVICE_VISIBLE);
-        iree_hal_buffer_usage_t input_buffer_usage =
-            static_cast<iree_hal_buffer_usage_t>(IREE_HAL_BUFFER_USAGE_DEFAULT);
-        iree_hal_buffer_params_t buffer_params;
-        buffer_params.type = input_memory_type;
-        buffer_params.usage = input_buffer_usage;
-        buffer_params.access = IREE_HAL_MEMORY_ACCESS_READ | IREE_HAL_MEMORY_ACCESS_WRITE;
+        //iree_hal_memory_type_t input_memory_type =
+        //    static_cast<iree_hal_memory_type_t>(
+        //        IREE_HAL_MEMORY_TYPE_HOST_LOCAL |
+        //        IREE_HAL_MEMORY_TYPE_DEVICE_VISIBLE);
+        //iree_hal_buffer_usage_t input_buffer_usage =
+        //    static_cast<iree_hal_buffer_usage_t>(IREE_HAL_BUFFER_USAGE_DEFAULT);
+        //iree_hal_buffer_params_t buffer_params;
+        //buffer_params.type = input_memory_type;
+        //buffer_params.usage = input_buffer_usage;
+        //buffer_params.access = IREE_HAL_MEMORY_ACCESS_READ | IREE_HAL_MEMORY_ACCESS_WRITE;
 
        // Wrap input buffers in buffer views.
 
-        iree_hal_buffer_view_t* input0_buffer_view = nullptr;
-        constexpr iree_hal_dim_t input_buffer_shape[] = {1, 224, 224, 3};
-        IREE_CHECK_OK(iree_hal_buffer_view_allocate_buffer(
-            allocator,
-            /*shape_rank=*/4, /*shape=*/input_buffer_shape,
-            IREE_HAL_ELEMENT_TYPE_FLOAT_32,
-            IREE_HAL_ENCODING_TYPE_DENSE_ROW_MAJOR, buffer_params,
-            iree_make_const_byte_span(&input_res50, sizeof(input_res50)),
-            &input0_buffer_view));
-
         vm::ref<iree_vm_list_t> inputs;
-        IREE_CHECK_OK(iree_vm_list_create(/*element_type=*/nullptr, 6, iree_allocator_system(), &inputs));
-        auto input0_buffer_view_ref = iree_hal_buffer_view_move_ref(input0_buffer_view);
-        IREE_CHECK_OK(iree_vm_list_push_ref_move(inputs.get(), &input0_buffer_view_ref));
+        iree_status_t input_status = ParseToVariantList(
+            allocator,
+            iree::span<const std::string>{FLAG_function_inputs.data(),
+                                          FLAG_function_inputs.size()},
+            iree_allocator_system(), &inputs);
+        if (!iree_status_is_ok(input_status))
+            return -1;
+        //vm::ref<iree_vm_list_t> inputs;
+        //IREE_CHECK_OK(iree_vm_list_create(/*element_type=*/nullptr, 6, iree_allocator_system(), &inputs));
+
+        //iree_hal_buffer_view_t* input0_buffer_view = nullptr;
+        //constexpr iree_hal_dim_t input_buffer_shape[] = {1, 224, 224, 3};
+        //IREE_CHECK_OK(iree_hal_buffer_view_allocate_buffer(
+        //    allocator,
+        //    /*shape_rank=*/4, /*shape=*/input_buffer_shape,
+        //    IREE_HAL_ELEMENT_TYPE_FLOAT_32,
+        //    IREE_HAL_ENCODING_TYPE_DENSE_ROW_MAJOR, buffer_params,
+        //    iree_make_const_byte_span(&input_res50, sizeof(input_res50)),
+        //    &input0_buffer_view));
+
+        //auto input0_buffer_view_ref = iree_hal_buffer_view_move_ref(input0_buffer_view);
+        //IREE_CHECK_OK(iree_vm_list_push_ref_move(inputs.get(), &input0_buffer_view_ref));
 
         // Prepare outputs list to accept results from the invocation.
 
@@ -1023,6 +853,7 @@ extern "C" int iree_main(int argc, char** argv) {
         IREE_CHECK_OK(iree_vm_list_create(/*element_type=*/nullptr, kOutputCount * sizeof(float), iree_allocator_system(), &outputs));
 
   // --------------------------------------------------------------------------
+
   // Main loop.
   bool done = false;
   while (!done) {
@@ -1076,46 +907,11 @@ extern "C" int iree_main(int argc, char** argv) {
                                      /*policy=*/nullptr, inputs.get(),
                                      outputs.get(), iree_allocator_system()));
 
-        // Read back the results.
-        auto* output_buffer_view = reinterpret_cast<iree_hal_buffer_view_t*>(
-            iree_vm_list_get_ref_deref(outputs.get(),
-            0,
-            iree_hal_buffer_view_get_descriptor()));
-        IREE_CHECK_OK(iree_hal_device_transfer_d2h(
-            iree_vk_device,
-            iree_hal_buffer_view_buffer(output_buffer_view),
-            0,
-            output_res50, sizeof(output_res50),
-            IREE_HAL_TRANSFER_BUFFER_FLAG_DEFAULT, iree_infinite_timeout()));
 
         // we want to run continuously so we can use tools like RenderDoc, RGP, etc...
         dirty = true;
       }
 
-      // find maxarg from results
-      float max = 0.0f;
-      int max_idx = -1;
-      for(int i=0;i<1000;i++)
-      {
-        if (output_res50[i] > max)
-        {
-          max = output_res50[i];
-          max_idx = i;
-        }
-      }
-
-      ImGui::Text("pointer = %p", my_image_texture);
-      ImGui::Text("size = %d x %d", my_image_width, my_image_height);
-      ImGui::Image((ImTextureID)my_image_texture, ImVec2(my_image_width, my_image_height));
-
-      // Display the latest computation output.
-      ImGui::Text("Max   idx = [%i]", max_idx);
-      ImGui::Text("Max value = [%f]", max);
-
-      ImGui::Text("Resnet50 categories:");
-      ImGui::PlotHistogram("Histogram", output_res50, IM_ARRAYSIZE(output_res50), 0, NULL, 0.0f, 1.0f, ImVec2(0,80));
-      ImGui::Separator();
-
       // Framerate counter.
       ImGui::Text("Application average %.3f ms/frame (%.1f FPS)",
                   1000.0f / ImGui::GetIO().Framerate, ImGui::GetIO().Framerate);
@@ -1137,6 +933,7 @@ extern "C" int iree_main(int argc, char** argv) {
   iree_vm_module_release(bytecode_module);
   iree_vm_context_release(iree_context);
   iree_hal_device_release(iree_vk_device);
+  iree_hal_allocator_release(allocator);
   iree_hal_driver_release(iree_vk_driver);
   iree_hal_vulkan_syms_release(iree_vk_syms);
   iree_vm_instance_release(iree_instance);

generate_sharktank.py

@@ -205,14 +205,14 @@ if __name__ == "__main__":
     parser.add_argument(
         "--torch_model_csv",
         type=lambda x: is_valid_file(x),
-        default="./tank/pytorch/torch_model_list.csv",
+        default="./tank/torch_model_list.csv",
         help="""Contains the file with torch_model name and args.
-             Please see: https://github.com/nod-ai/SHARK/blob/main/tank/pytorch/torch_model_list.csv""",
+             Please see: https://github.com/nod-ai/SHARK/blob/main/tank/torch_model_list.csv""",
     )
     parser.add_argument(
         "--tf_model_csv",
         type=lambda x: is_valid_file(x),
-        default="./tank/tf/tf_model_list.csv",
+        default="./tank/tf_model_list.csv",
         help="Contains the file with tf model name and args.",
     )
     parser.add_argument(

pyproject.toml

@@ -4,9 +4,9 @@ requires = [
     "wheel",
     "packaging",
 
-    "numpy==1.22.4",
-    "torch-mlir>=20220428.420",
-    "iree-compiler>=20220427.13",
-    "iree-runtime>=20220427.13",
+    "numpy>=1.22.4",
+    "torch-mlir>=20221021.633",
+    "iree-compiler>=20221022.190",
+    "iree-runtime>=20221022.190",
 ]
 build-backend = "setuptools.build_meta"

requirements-importer-macos.txt

@@ -1,4 +1,4 @@
--f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
+-f https://download.pytorch.org/whl/nightly/cpu/
 --pre
 
 numpy

requirements-importer.txt

@@ -2,7 +2,6 @@
 --pre
 
 numpy==1.22.4
-torch
 torchvision
 
 tqdm
@@ -14,7 +13,8 @@ iree-tools-tf
 
 # TensorFlow and JAX.
 gin-config
-tensorflow
+tensorflow==2.10
+keras==2.10
 #tf-models-nightly
 #tensorflow-text-nightly
 transformers

requirements.txt

@@ -1,14 +1,22 @@
 setuptools
 wheel
+pyinstaller
 
 # SHARK Runner
 tqdm
 
 # SHARK Downloader
-gsutil
+google-cloud-storage
 
 # Testing
 pytest
 pytest-xdist
 Pillow
 parameterized
+
+# Add transformers, diffusers and scipy since it most commonly used
+transformers
+diffusers
+scipy
+ftfy
+gradio

setup.py

@@ -10,8 +10,8 @@ PACKAGE_VERSION = os.environ.get("SHARK_PACKAGE_VERSION") or "0.0.4"
 backend_deps = []
 if "NO_BACKEND" in os.environ.keys():
     backend_deps = [
-        "iree-compiler>=20220427.13",
-        "iree-runtime>=20220427.13",
+        "iree-compiler>=20221022.190",
+        "iree-runtime>=20221022.190",
     ]
 
 setup(
@@ -33,11 +33,11 @@ setup(
         "Operating System :: OS Independent",
     ],
     packages=find_packages(exclude=("examples")),
-    python_requires=">=3.7",
+    python_requires=">=3.9",
     install_requires=[
         "numpy",
         "PyYAML",
-        "torch-mlir>=20220428.420",
+        "torch-mlir>=20221021.633",
     ]
     + backend_deps,
 )

setup_venv.ps1

@@ -0,0 +1,40 @@
+#Write-Host "Installing python"
+
+#Start-Process winget install Python.Python.3.10 '/quiet InstallAllUsers=1 PrependPath=1' -wait -NoNewWindow
+
+#Write-Host "python installation completed successfully"
+
+#Write-Host "Reload environment variables"
+#$env:Path = [System.Environment]::GetEnvironmentVariable("Path","Machine") + ";" + [System.Environment]::GetEnvironmentVariable("Path","User")
+#Write-Host "Reloaded environment variables"
+
+
+# redirect stderr into stdout
+$p = &{python -V} 2>&1
+# check if an ErrorRecord was returned
+$version = if($p -is [System.Management.Automation.ErrorRecord])
+{
+    # grab the version string from the error message
+    $p.Exception.Message
+}
+else
+{
+    # otherwise return as is
+    $p
+}
+
+Write-Host "Python version found is"
+Write-Host $p
+
+
+Write-Host "Installing Build Dependencies"
+python -m venv .\shark.venv\
+.\shark.venv\Scripts\activate
+pip install -r requirements.txt
+pip install --pre torch-mlir torch torchvision --extra-index-url https://download.pytorch.org/whl/nightly/cpu -f https://llvm.github.io/torch-mlir/package-index/
+pip install --upgrade -f https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html iree-compiler iree-runtime
+Write-Host "Building SHARK..."
+pip install -e . -f https://llvm.github.io/torch-mlir/package-index/ -f https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html
+pip install diffusers transformers scipy pillow gradio
+Write-Host "Build and installation completed successfully"
+Write-Host "Source your venv with ./shark.venv/Scripts/activate"

setup_venv.sh

@@ -76,11 +76,16 @@ fi
 $PYTHON -m pip install --upgrade pip || die "Could not upgrade pip"
 $PYTHON -m pip install --upgrade -r "$TD/requirements.txt"
 if [ "$torch_mlir_bin" = true ]; then
-  $PYTHON -m pip install --pre torch-mlir -f https://llvm.github.io/torch-mlir/package-index/
-  if [ $? -eq 0 ];then
-    echo "Successfully Installed torch-mlir"
+  if [[ $(uname -s) = 'Darwin' ]]; then
+    echo "MacOS detected. Installing torch-mlir from .whl, to avoid dependency problems with torch."
+    $PYTHON -m pip install --pre --no-cache-dir  torch-mlir -f https://llvm.github.io/torch-mlir/package-index/ -f https://download.pytorch.org/whl/nightly/torch/
   else
-    echo "Could not install torch-mlir" >&2
+    $PYTHON -m pip install --pre torch-mlir -f https://llvm.github.io/torch-mlir/package-index/
+    if [ $? -eq 0 ];then
+      echo "Successfully Installed torch-mlir"
+    else
+      echo "Could not install torch-mlir" >&2
+    fi
   fi
 else
   echo "${Red}No binaries found for Python $PYTHON_VERSION_X_Y on $(uname -s)"
@@ -89,37 +94,41 @@ else
   exit 1
 fi
 if [[ -z "${USE_IREE}" ]]; then
-  RUNTIME="nod-ai/SHARK-Runtime"
+  rm .use-iree
+  RUNTIME="https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html"
 else
-  RUNTIME="google/iree"
+  touch ./.use-iree
+  RUNTIME="https://iree-org.github.io/iree/pip-release-links.html"
 fi
 if [[ -z "${NO_BACKEND}" ]]; then
   echo "Installing ${RUNTIME}..."
-  $PYTHON -m pip install --find-links https://github.com/${RUNTIME}/releases iree-compiler iree-runtime
+  $PYTHON -m pip install --upgrade --find-links ${RUNTIME} iree-compiler iree-runtime
 else
   echo "Not installing a backend, please make sure to add your backend to PYTHONPATH"
 fi
+
 if [[ ! -z "${IMPORTER}" ]]; then
   echo "${Yellow}Installing importer tools.."
   if [[ $(uname -s) = 'Linux' ]]; then
     echo "${Yellow}Linux detected.. installing Linux importer tools"
-    $PYTHON -m pip install --upgrade -r "$TD/requirements-importer.txt" -f https://github.com/${RUNTIME}/releases --extra-index-url https://download.pytorch.org/whl/nightly/cpu
+    #Always get the importer tools from upstream IREE
+    $PYTHON -m pip install --no-warn-conflicts --upgrade -r "$TD/requirements-importer.txt" -f https://iree-org.github.io/iree/pip-release-links.html --extra-index-url https://download.pytorch.org/whl/nightly/cpu
   elif [[ $(uname -s) = 'Darwin' ]]; then
     echo "${Yellow}macOS detected.. installing macOS importer tools"
     #Conda seems to have some problems installing these packages and hope they get resolved upstream.
-    $PYTHON -m pip install --upgrade -r "$TD/requirements-importer-macos.txt" -f https://github.com/${RUNTIME}/releases --extra-index-url https://download.pytorch.org/whl/nightly/cpu
+    $PYTHON -m pip install --no-warn-conflicts --upgrade -r "$TD/requirements-importer-macos.txt" -f ${RUNTIME} --extra-index-url https://download.pytorch.org/whl/nightly/cpu
   fi
 fi
 
-$PYTHON -m pip install -e . -f https://llvm.github.io/torch-mlir/package-index/ -f https://github.com/${RUNTIME}/releases
+$PYTHON -m pip install --no-warn-conflicts -e . -f https://llvm.github.io/torch-mlir/package-index/ -f ${RUNTIME} -f https://download.pytorch.org/whl/nightly/torch/
 
 if [[ $(uname -s) = 'Linux' && ! -z "${BENCHMARK}" ]]; then
   $PYTHON -m pip uninstall -y torch torchvision
-  $PYTHON -m pip install --pre torch torchvision --extra-index-url https://download.pytorch.org/whl/nightly/cu116
+  $PYTHON -m pip install --pre torch torchvision --extra-index-url https://download.pytorch.org/whl/nightly/cu117
   if [ $? -eq 0 ];then
-    echo "Successfully Installed torch + cu116."
+    echo "Successfully Installed torch + cu117."
   else
-    echo "Could not install torch + cu116." >&2
+    echo "Could not install torch + cu117." >&2
   fi
 fi
 

shark/examples/shark_inference/CLIPModel_tf.py

@@ -22,7 +22,7 @@ class CLIPModule(tf.Module):
             input_ids=x, attention_mask=y, pixel_values=z
         )
 
-    @tf.function(input_signature=clip_vit_inputs)
+    @tf.function(input_signature=clip_vit_inputs, jit_compile=True)
     def forward(self, input_ids, attention_mask, pixel_values):
         return self.m.predict(
             input_ids, attention_mask, pixel_values

shark/examples/shark_inference/ESRGAN/README.md

@@ -0,0 +1,15 @@
+## Running ESRGAN
+
+```
+1. pip install numpy opencv-python
+2. mkdir InputImages
+   (this is where all the input images will reside in)
+3. mkdir OutputImages
+   (this is where the model will generate all the images)
+4. mkdir models
+   (save the .pth checkpoint file here)
+5. python esrgan.py
+```
+
+- Download [RRDB_ESRGAN_x4.pth](https://drive.google.com/drive/u/0/folders/17VYV_SoZZesU6mbxz2dMAIccSSlqLecY) and place it in the `models` directory as mentioned above in step 4.
+- Credits : [ESRGAN](https://github.com/xinntao/ESRGAN)

shark/examples/shark_inference/ESRGAN/esrgan.py

@@ -0,0 +1,240 @@
+from ast import arg
+import os.path as osp
+import glob
+import cv2
+import numpy as np
+import torch
+
+from torch.fx.experimental.proxy_tensor import make_fx
+from torch._decomp import get_decompositions
+from shark.shark_inference import SharkInference
+import torch_mlir
+import tempfile
+import functools
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def make_layer(block, n_layers):
+    layers = []
+    for _ in range(n_layers):
+        layers.append(block())
+    return nn.Sequential(*layers)
+
+
+class ResidualDenseBlock_5C(nn.Module):
+    def __init__(self, nf=64, gc=32, bias=True):
+        super(ResidualDenseBlock_5C, self).__init__()
+        # gc: growth channel, i.e. intermediate channels
+        self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
+        self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
+        self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)
+        self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)
+        self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)
+        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+        # initialization
+        # mutil.initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
+
+    def forward(self, x):
+        x1 = self.lrelu(self.conv1(x))
+        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
+        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
+        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
+        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
+        return x5 * 0.2 + x
+
+
+class RRDB(nn.Module):
+    """Residual in Residual Dense Block"""
+
+    def __init__(self, nf, gc=32):
+        super(RRDB, self).__init__()
+        self.RDB1 = ResidualDenseBlock_5C(nf, gc)
+        self.RDB2 = ResidualDenseBlock_5C(nf, gc)
+        self.RDB3 = ResidualDenseBlock_5C(nf, gc)
+
+    def forward(self, x):
+        out = self.RDB1(x)
+        out = self.RDB2(out)
+        out = self.RDB3(out)
+        return out * 0.2 + x
+
+
+class RRDBNet(nn.Module):
+    def __init__(self, in_nc, out_nc, nf, nb, gc=32):
+        super(RRDBNet, self).__init__()
+        RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
+
+        self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
+        self.RRDB_trunk = make_layer(RRDB_block_f, nb)
+        self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
+        #### upsampling
+        self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
+        self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
+        self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
+        self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
+
+        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+    def forward(self, x):
+        fea = self.conv_first(x)
+        trunk = self.trunk_conv(self.RRDB_trunk(fea))
+        fea = fea + trunk
+
+        fea = self.lrelu(
+            self.upconv1(F.interpolate(fea, scale_factor=2, mode="nearest"))
+        )
+        fea = self.lrelu(
+            self.upconv2(F.interpolate(fea, scale_factor=2, mode="nearest"))
+        )
+        out = self.conv_last(self.lrelu(self.HRconv(fea)))
+
+        return out
+
+
+############### Parsing args #####################
+import argparse
+
+p = argparse.ArgumentParser(
+    description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter
+)
+
+p.add_argument("--device", type=str, default="cpu", help="the device to use")
+p.add_argument(
+    "--mlir_loc",
+    type=str,
+    default=None,
+    help="location of the model's mlir file",
+)
+args = p.parse_args()
+###################################################
+
+
+def inference(input_m):
+    return model(input_m)
+
+
+def load_mlir(mlir_loc):
+    import os
+
+    if mlir_loc == None:
+        return None
+    print(f"Trying to load the model from {mlir_loc}.")
+    with open(os.path.join(mlir_loc)) as f:
+        mlir_module = f.read()
+    return mlir_module
+
+
+def compile_through_fx(model, inputs, mlir_loc=None):
+
+    module = load_mlir(mlir_loc)
+    if module == None:
+        fx_g = make_fx(
+            model,
+            decomposition_table=get_decompositions(
+                [
+                    torch.ops.aten.embedding_dense_backward,
+                    torch.ops.aten.native_layer_norm_backward,
+                    torch.ops.aten.slice_backward,
+                    torch.ops.aten.select_backward,
+                    torch.ops.aten.norm.ScalarOpt_dim,
+                    torch.ops.aten.native_group_norm,
+                    torch.ops.aten.upsample_bilinear2d.vec,
+                    torch.ops.aten.split.Tensor,
+                    torch.ops.aten.split_with_sizes,
+                ]
+            ),
+        )(inputs)
+
+        fx_g.graph.set_codegen(torch.fx.graph.CodeGen())
+        fx_g.recompile()
+
+        def strip_overloads(gm):
+            """
+            Modifies the target of graph nodes in :attr:`gm` to strip overloads.
+            Args:
+                gm(fx.GraphModule): The input Fx graph module to be modified
+            """
+            for node in gm.graph.nodes:
+                if isinstance(node.target, torch._ops.OpOverload):
+                    node.target = node.target.overloadpacket
+            gm.recompile()
+
+        strip_overloads(fx_g)
+
+        ts_g = torch.jit.script(fx_g)
+
+        print("Torchscript graph generated successfully")
+        module = torch_mlir.compile(
+            ts_g,
+            inputs,
+            torch_mlir.OutputType.LINALG_ON_TENSORS,
+            use_tracing=False,
+            verbose=False,
+        )
+
+    mlir_model = str(module)
+    func_name = "forward"
+    shark_module = SharkInference(
+        mlir_model, func_name, device=args.device, mlir_dialect="linalg"
+    )
+    shark_module.compile()
+
+    return shark_module
+
+
+model_path = "models/RRDB_ESRGAN_x4.pth"  # models/RRDB_ESRGAN_x4.pth OR models/RRDB_PSNR_x4.pth
+# device = torch.device('cuda')  # if you want to run on CPU, change 'cuda' -> cpu
+device = torch.device("cpu")
+
+test_img_folder = "InputImages/*"
+
+model = RRDBNet(3, 3, 64, 23, gc=32)
+model.load_state_dict(torch.load(model_path), strict=True)
+model.eval()
+model = model.to(device)
+
+print("Model path {:s}. \nTesting...".format(model_path))
+
+if __name__ == "__main__":
+    idx = 0
+    for path in glob.glob(test_img_folder):
+        idx += 1
+        base = osp.splitext(osp.basename(path))[0]
+        print(idx, base)
+        # read images
+        img = cv2.imread(path, cv2.IMREAD_COLOR)
+        img = img * 1.0 / 255
+        img = torch.from_numpy(
+            np.transpose(img[:, :, [2, 1, 0]], (2, 0, 1))
+        ).float()
+        img_LR = img.unsqueeze(0)
+        img_LR = img_LR.to(device)
+
+        with torch.no_grad():
+            shark_module = compile_through_fx(inference, img_LR)
+            shark_output = shark_module.forward((img_LR,))
+            shark_output = torch.from_numpy(shark_output)
+            shark_output = (
+                shark_output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
+            )
+            esrgan_output = (
+                model(img_LR).data.squeeze().float().cpu().clamp_(0, 1).numpy()
+            )
+        # SHARK OUTPUT
+        shark_output = np.transpose(shark_output[[2, 1, 0], :, :], (1, 2, 0))
+        shark_output = (shark_output * 255.0).round()
+        cv2.imwrite(
+            "OutputImages/{:s}_rlt_shark_output.png".format(base), shark_output
+        )
+        print("Generated SHARK's output")
+        # ESRGAN OUTPUT
+        esrgan_output = np.transpose(esrgan_output[[2, 1, 0], :, :], (1, 2, 0))
+        esrgan_output = (esrgan_output * 255.0).round()
+        cv2.imwrite(
+            "OutputImages/{:s}_rlt_esrgan_output.png".format(base),
+            esrgan_output,
+        )
+        print("Generated ESRGAN's output")

shark/examples/shark_inference/albert_maskfill_tf.py

@@ -28,7 +28,7 @@ class AlbertModule(tf.Module):
         self.m = TFAutoModelForMaskedLM.from_pretrained("albert-base-v2")
         self.m.predict = lambda x, y: self.m(input_ids=x, attention_mask=y)
 
-    @tf.function(input_signature=t5_inputs)
+    @tf.function(input_signature=t5_inputs, jit_compile=True)
     def forward(self, input_ids, attention_mask):
         return self.m.predict(input_ids, attention_mask)
 

shark/examples/shark_inference/bloom_tank.py

@@ -1,7 +1,9 @@
 from shark.shark_inference import SharkInference
-from shark.shark_downloader import download_torch_model
+from shark.shark_downloader import download_model
 
-mlir_model, func_name, inputs, golden_out = download_torch_model("bloom")
+mlir_model, func_name, inputs, golden_out = download_model(
+    "bloom", frontend="torch"
+)
 
 shark_module = SharkInference(
     mlir_model, func_name, device="cpu", mlir_dialect="tm_tensor"

shark/examples/shark_inference/gpt2_tf.py

@@ -19,7 +19,7 @@ class GPT2Module(tf.Module):
 
         self.m.predict = lambda x, y: self.m(input_ids=x, attention_mask=y)
 
-    @tf.function(input_signature=gpt2_inputs)
+    @tf.function(input_signature=gpt2_inputs, jit_compile=True)
     def forward(self, input_ids, attention_mask):
         return self.m.predict(input_ids, attention_mask)
 

shark/examples/shark_inference/minilm_benchmark_tf.py

@@ -26,7 +26,7 @@ class BertModule(tf.Module):
             input_ids=x, attention_mask=y, token_type_ids=z, training=False
         )
 
-    @tf.function(input_signature=bert_input)
+    @tf.function(input_signature=bert_input, jit_compile=True)
     def forward(self, input_ids, attention_mask, token_type_ids):
         return self.m.predict(input_ids, attention_mask, token_type_ids)
 

shark/examples/shark_inference/minilm_jit.py

@@ -1,9 +1,10 @@
 from shark.shark_inference import SharkInference
-from shark.shark_downloader import download_torch_model
+from shark.shark_downloader import download_model
 
 
-mlir_model, func_name, inputs, golden_out = download_torch_model(
-    "microsoft/MiniLM-L12-H384-uncased"
+mlir_model, func_name, inputs, golden_out = download_model(
+    "microsoft/MiniLM-L12-H384-uncased",
+    frontend="torch",
 )
 
 

shark/examples/shark_inference/minilm_tf.py

@@ -26,7 +26,7 @@ class BertModule(tf.Module):
             input_ids=x, attention_mask=y, token_type_ids=z, training=False
         )
 
-    @tf.function(input_signature=bert_input)
+    @tf.function(input_signature=bert_input, jit_compile=True)
     def forward(self, input_ids, attention_mask, token_type_ids):
         return self.m.predict(input_ids, attention_mask, token_type_ids)
 

shark/examples/shark_inference/resnet50_script.py

@@ -5,7 +5,7 @@ import torchvision.models as models
 from torchvision import transforms
 import sys
 from shark.shark_inference import SharkInference
-from shark.shark_downloader import download_torch_model
+from shark.shark_downloader import download_model
 
 
 ################################## Preprocessing inputs and model ############
@@ -66,10 +66,12 @@ labels = load_labels()
 
 
 ## Can pass any img or input to the forward module.
-mlir_model, func_name, inputs, golden_out = download_torch_model("resnet50")
+mlir_model, func_name, inputs, golden_out = download_model(
+    "resnet50", frontend="torch"
+)
 
 shark_module = SharkInference(mlir_model, func_name, mlir_dialect="linalg")
-# shark_module.compile()
+shark_module.compile()
 path = shark_module.save_module()
 shark_module.load_module(path)
 result = shark_module.forward((img.detach().numpy(),))

shark/examples/shark_inference/stable_diff.py

@@ -47,7 +47,7 @@ def load_mlir(mlir_loc):
     return mlir_module
 
 
-def compile_through_fx(model, inputs, mlir_loc=None):
+def compile_through_fx(model, inputs, mlir_loc=None, extra_args=[]):
 
     module = load_mlir(mlir_loc)
     if mlir_loc == None:
@@ -98,9 +98,12 @@ def compile_through_fx(model, inputs, mlir_loc=None):
     func_name = "forward"
 
     shark_module = SharkInference(
-        mlir_model, func_name, device=args.device, mlir_dialect="tm_tensor"
+        mlir_model,
+        func_name,
+        device=args.device,
+        mlir_dialect="tm_tensor",
     )
-    shark_module.compile()
+    shark_module.compile(extra_args)
 
     return shark_module
 
@@ -161,6 +164,7 @@ if __name__ == "__main__":
         unet,
         (latent_model_input, torch.tensor([1.0]), text_embeddings),
         args.mlir_loc,
+        ["--iree-flow-enable-conv-nchw-to-nhwc-transform"],
     )
 
     # torch.jit.script(unet)

shark/examples/shark_inference/stable_diff_f16.py

@@ -10,20 +10,59 @@ from torch._decomp import get_decompositions
 import torch_mlir
 import tempfile
 import numpy as np
-import os
 
-##############################################################################
+# pip install diffusers
+# pip install scipy
+
+############### Parsing args #####################
+import argparse
+
+p = argparse.ArgumentParser(
+    description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter
+)
+
+p.add_argument(
+    "--prompt",
+    type=str,
+    default="a photograph of an astronaut riding a horse",
+    help="the text prompt to use",
+)
+p.add_argument("--device", type=str, default="cpu", help="the device to use")
+p.add_argument("--steps", type=int, default=50, help="the device to use")
+p.add_argument("--mlir_loc", type=str, default=None, help="the device to use")
+p.add_argument("--vae_loc", type=str, default=None, help="the device to use")
+args = p.parse_args()
+
+#####################################################
+
+
+def fp16_unet():
+    from shark.shark_downloader import download_model
+
+    mlir_model, func_name, inputs, golden_out = download_model(
+        "stable_diff_f16_18_OCT",
+        tank_url="gs://shark_tank/prashant_nod",
+        frontend="torch",
+    )
+    shark_module = SharkInference(
+        mlir_model, func_name, device=args.device, mlir_dialect="linalg"
+    )
+    shark_module.compile()
+    return shark_module
 
 
 def load_mlir(mlir_loc):
+    import os
+
     if mlir_loc == None:
         return None
+    print(f"Trying to load the model from {mlir_loc}.")
     with open(os.path.join(mlir_loc)) as f:
         mlir_module = f.read()
     return mlir_module
 
 
-def compile_through_fx(model, inputs, device, mlir_loc=None):
+def compile_through_fx(model, inputs, mlir_loc=None):
 
     module = load_mlir(mlir_loc)
     if mlir_loc == None:
@@ -74,106 +113,79 @@ def compile_through_fx(model, inputs, device, mlir_loc=None):
     func_name = "forward"
 
     shark_module = SharkInference(
-        mlir_model, func_name, device=device, mlir_dialect="tm_tensor"
+        mlir_model, func_name, device=args.device, mlir_dialect="linalg"
     )
     shark_module.compile()
 
     return shark_module
 
 
-##############################################################################
+if __name__ == "__main__":
 
-DEBUG = False
-compiled_module = {}
+    YOUR_TOKEN = "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"
 
-
-def stable_diff_inf(prompt: str, steps, device: str):
-
-    args = {}
-    args["prompt"] = [prompt]
-    args["steps"] = steps
-    args["device"] = device
-    args["mlir_loc"] = "./stable_diffusion.mlir"
-    output_loc = (
-        f"stored_results/stable_diffusion/{prompt}_{int(steps)}_{device}.jpg"
+    # 1. Load the autoencoder model which will be used to decode the latents into image space.
+    vae = AutoencoderKL.from_pretrained(
+        "CompVis/stable-diffusion-v1-4",
+        subfolder="vae",
+        use_auth_token=YOUR_TOKEN,
     )
 
-    global DEBUG
-    global compiled_module
+    # 2. Load the tokenizer and text encoder to tokenize and encode the text.
+    tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
+    text_encoder = CLIPTextModel.from_pretrained(
+        "openai/clip-vit-large-patch14"
+    )
 
-    DEBUG = False
-    log_write = open(r"logs/stable_diffusion_log.txt", "w")
-    if log_write:
-        DEBUG = True
+    class VaeModel(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.vae = AutoencoderKL.from_pretrained(
+                "CompVis/stable-diffusion-v1-4",
+                subfolder="vae",
+                use_auth_token=YOUR_TOKEN,
+            )
 
-    if args["device"] not in compiled_module.keys():
-        YOUR_TOKEN = "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"
+        def forward(self, input):
+            return self.vae.decode(input, return_dict=False)[0]
 
-        # 1. Load the autoencoder model which will be used to decode the latents into image space.
-        compiled_module["vae"] = AutoencoderKL.from_pretrained(
-            "CompVis/stable-diffusion-v1-4",
-            subfolder="vae",
-            use_auth_token=YOUR_TOKEN,
-        )
+    vae = VaeModel()
+    vae_input = torch.rand(1, 4, 64, 64)
+    shark_vae = compile_through_fx(vae, (vae_input,), args.vae_loc)
 
-        # 2. Load the tokenizer and text encoder to tokenize and encode the text.
-        compiled_module["tokenizer"] = CLIPTokenizer.from_pretrained(
-            "openai/clip-vit-large-patch14"
-        )
-        compiled_module["text_encoder"] = CLIPTextModel.from_pretrained(
-            "openai/clip-vit-large-patch14"
-        )
-        if DEBUG:
-            log_write.write("Compiling the Unet module.\n")
+    # Wrap the unet model to return tuples.
+    class UnetModel(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.unet = UNet2DConditionModel.from_pretrained(
+                "CompVis/stable-diffusion-v1-4",
+                subfolder="unet",
+                use_auth_token=YOUR_TOKEN,
+            )
+            self.in_channels = self.unet.in_channels
+            self.train(False)
 
-        # Wrap the unet model to return tuples.
-        class UnetModel(torch.nn.Module):
-            def __init__(self):
-                super().__init__()
-                self.unet = UNet2DConditionModel.from_pretrained(
-                    "CompVis/stable-diffusion-v1-4",
-                    subfolder="unet",
-                    use_auth_token=YOUR_TOKEN,
-                )
-                self.in_channels = self.unet.in_channels
-                self.train(False)
+    def forward(self, x, y, z):
+        return self.unet.forward(x, y, z, return_dict=False)[0]
 
-            def forward(self, x, y, z):
-                return self.unet.forward(x, y, z, return_dict=False)[0]
+    # # 3. The UNet model for generating the latents.
+    unet = UnetModel()
 
-        # 3. The UNet model for generating the latents.
-        unet = UnetModel()
-        latent_model_input = torch.rand([2, 4, 64, 64])
-        text_embeddings = torch.rand([2, 77, 768])
-        shark_unet = compile_through_fx(
-            unet,
-            (latent_model_input, torch.tensor([1.0]), text_embeddings),
-            args["device"],
-            args["mlir_loc"],
-        )
-        compiled_module[args["device"]] = shark_unet
-        if DEBUG:
-            log_write.write("Compilation successful.\n")
+    shark_unet = fp16_unet()
 
-        compiled_module["unet"] = unet
-        compiled_module["scheduler"] = LMSDiscreteScheduler(
-            beta_start=0.00085,
-            beta_end=0.012,
-            beta_schedule="scaled_linear",
-            num_train_timesteps=1000,
-        )
+    scheduler = LMSDiscreteScheduler(
+        beta_start=0.00085,
+        beta_end=0.012,
+        beta_schedule="scaled_linear",
+        num_train_timesteps=1000,
+    )
 
-    shark_unet = compiled_module[args["device"]]
-    vae = compiled_module["vae"]
-    unet = compiled_module["unet"]
-    tokenizer = compiled_module["tokenizer"]
-    text_encoder = compiled_module["text_encoder"]
-    scheduler = compiled_module["scheduler"]
+    prompt = [args.prompt]
 
     height = 512  # default height of Stable Diffusion
     width = 512  # default width of Stable Diffusion
 
-    num_inference_steps = int(args["steps"])  # Number of denoising steps
+    num_inference_steps = args.steps  # Number of denoising steps
 
     guidance_scale = 7.5  # Scale for classifier-free guidance
 
@@ -181,10 +193,10 @@ def stable_diff_inf(prompt: str, steps, device: str):
         42
     )  # Seed generator to create the inital latent noise
 
-    batch_size = len(args["prompt"])
+    batch_size = len(prompt)
 
     text_input = tokenizer(
-        args["prompt"],
+        prompt,
         padding="max_length",
         max_length=tokenizer.model_max_length,
         truncation=True,
@@ -208,30 +220,41 @@ def stable_diff_inf(prompt: str, steps, device: str):
         (batch_size, unet.in_channels, height // 8, width // 8),
         generator=generator,
     )
+    # latents = latents.to(torch_device)
+
     scheduler.set_timesteps(num_inference_steps)
+
     latents = latents * scheduler.sigmas[0]
+    # print(latents, latents.shape)
 
     for i, t in tqdm(enumerate(scheduler.timesteps)):
 
-        if DEBUG:
-            log_write.write(f"i = {i} t = {t}\n")
+        print(f"i = {i} t = {t}")
         # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
         latent_model_input = torch.cat([latents] * 2)
         sigma = scheduler.sigmas[i]
         latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)
 
         # predict the noise residual
-        latent_model_input_numpy = latent_model_input.detach().numpy()
-        text_embeddings_numpy = text_embeddings.detach().numpy()
+
+        # with torch.no_grad():
+        # noise_pred = unet(latent_model_input, t, encoder_hidden_states=text_embeddings)
+
+        latent_model_input_numpy = (
+            latent_model_input.detach().numpy().astype(np.half)
+        )
+        text_embeddings_numpy = (
+            text_embeddings.detach().numpy().astype(np.half)
+        )
 
         noise_pred = shark_unet.forward(
             (
                 latent_model_input_numpy,
-                np.array([t]).astype(np.float32),
+                np.array([t]).astype(np.half),
                 text_embeddings_numpy,
             )
         )
-        noise_pred = torch.from_numpy(noise_pred)
+        noise_pred = torch.from_numpy(noise_pred).to(torch.float32)
 
         # perform guidance
         noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
@@ -242,21 +265,16 @@ def stable_diff_inf(prompt: str, steps, device: str):
         # compute the previous noisy sample x_t -> x_t-1
         latents = scheduler.step(noise_pred, i, latents)["prev_sample"]
 
+    # print("Latents shape : ", latents.shape)
+
     # scale and decode the image latents with vae
     latents = 1 / 0.18215 * latents
-    image = vae.decode(latents).sample
+    latents_numpy = latents.detach().numpy()
+    image = shark_vae.forward((latents_numpy,))
+    image = torch.from_numpy(image)
 
     image = (image / 2 + 0.5).clamp(0, 1)
     image = image.detach().cpu().permute(0, 2, 3, 1).numpy()
     images = (image * 255).round().astype("uint8")
     pil_images = [Image.fromarray(image) for image in images]
-    output = pil_images[0]
-    # save the output image with the prompt name.
-    output.save(os.path.join(output_loc))
-    log_write.close()
-
-    std_output = ""
-    with open(r"logs/stable_diffusion_log.txt", "r") as log_read:
-        std_output = log_read.read()
-
-    return output, std_output
+    pil_images[0].save("astro.jpg")

shark/examples/shark_inference/stable_diff_tf.py

@@ -17,7 +17,7 @@ from keras_cv.models.generative.stable_diffusion.text_encoder import (
 )
 
 from shark.shark_inference import SharkInference
-from shark.shark_downloader import download_tf_model
+from shark.shark_downloader import download_model
 from PIL import Image
 
 # pip install "git+https://github.com/keras-team/keras-cv.git"
@@ -75,8 +75,8 @@ class SharkStableDiffusion:
         # Create models
         self.text_encoder = TextEncoder(MAX_PROMPT_LENGTH)
 
-        mlir_model, func_name, inputs, golden_out = download_tf_model(
-            "stable_diff", tank_url="gs://shark_tank/quinn"
+        mlir_model, func_name, inputs, golden_out = download_model(
+            "stable_diff", tank_url="gs://shark_tank/quinn", frontend="tf"
         )
         shark_module = SharkInference(
             mlir_model, func_name, device=device, mlir_dialect="mhlo"

shark/examples/shark_inference/stable_diffusion/.gitignore

@@ -0,0 +1,2 @@
+*.vmfb
+*.jpg

shark/examples/shark_inference/stable_diffusion/README.md

@@ -0,0 +1,44 @@
+# STABLE DIFFUSION
+
+## Installation
+
+Follow setup instructions in the main [README.md](https://github.com/nod-ai/SHARK#readme) for regular usage. 
+
+## Debug commands and other advanced usage follows.
+
+```shell
+python main.py --precision="fp32"|"fp16" --device="cpu"|"cuda"|"vulkan" --import_mlir|--no-import_mlir --prompt "enter the text" 
+
+```
+
+## dump all dispatch .spv and isa using amdllpc
+
+```shell
+python main.py --precision="fp16" --device="vulkan" --iree-vulkan-target-triple=rdna3-unknown-linux --no-load_vmfb --dispatch_benchmarks="all" --dispatch_benchmarks_dir="SD_dispatches" --dump_isa
+```
+
+## Compile and save the .vmfb (using vulkan fp16 as an example):
+
+```shell
+python shark/examples/shark_inference/stable_diffusion/main.py --precision=fp16 --device=vulkan --steps=50 --save_vmfb
+```
+
+## Capture an RGP trace
+
+```shell
+python shark/examples/shark_inference/stable_diffusion/main.py --precision=fp16 --device=vulkan --steps=50 --save_vmfb --enable_rgp
+```
+
+## Run the vae module with iree-benchmark-module (NCHW, fp16, vulkan, for example):
+
+```shell
+iree-benchmark-module --module_file=/path/to/output/vmfb --entry_function=forward --device=vulkan --function_input=1x4x64x64xf16  
+```
+
+## Run the unet module with iree-benchmark-module (same config as above):
+```shell
+##if you want to use .npz inputs:
+unzip ~/.local/shark_tank/<your unet>/inputs.npz
+
+iree-benchmark-module --module_file=/path/to/output/vmfb --entry_function=forward --function_input=@arr_0.npy --function_input=1xf16 --function_input=@arr_2.npy --function_input=@arr_3.npy --function_input=@arr_4.npy  
+```

shark/examples/shark_inference/stable_diffusion/download_hf_models.py

@@ -0,0 +1,25 @@
+from PIL import Image
+import requests
+
+from transformers import CLIPProcessor, CLIPModel
+
+model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
+processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+inputs = processor(
+    text=["a photo of a cat", "a photo of a dog"],
+    images=image,
+    return_tensors="pt",
+    padding=True,
+)
+
+outputs = model(**inputs)
+logits_per_image = (
+    outputs.logits_per_image
+)  # this is the image-text similarity score
+probs = logits_per_image.softmax(
+    dim=1
+)  # we can take the softmax to get the label probabilities

shark/examples/shark_inference/stable_diffusion/main.py

@@ -0,0 +1,188 @@
+from transformers import CLIPTextModel, CLIPTokenizer
+import torch
+from PIL import Image
+from diffusers import (
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    DDIMScheduler,
+    DPMSolverMultistepScheduler,
+    EulerDiscreteScheduler,
+)
+from tqdm.auto import tqdm
+import numpy as np
+from stable_args import args
+from utils import get_shark_model, set_iree_runtime_flags
+from opt_params import get_unet, get_vae, get_clip
+import time
+from model_wrappers import get_vae_mlir
+from shark.iree_utils.compile_utils import dump_isas
+
+# Helper function to profile the vulkan device.
+def start_profiling(file_path="foo.rdc", profiling_mode="queue"):
+    if args.vulkan_debug_utils and "vulkan" in args.device:
+        import iree
+
+        print(f"Profiling and saving to {file_path}.")
+        vulkan_device = iree.runtime.get_device(args.device)
+        vulkan_device.begin_profiling(mode=profiling_mode, file_path=file_path)
+        return vulkan_device
+    return None
+
+
+def end_profiling(device):
+    if device:
+        return device.end_profiling()
+
+
+if __name__ == "__main__":
+
+    dtype = torch.float32 if args.precision == "fp32" else torch.half
+
+    prompt = args.prompts
+    height = 512  # default height of Stable Diffusion
+    width = 512  # default width of Stable Diffusion
+    if args.version == "v2":
+        height = 768
+        width = 768
+
+    num_inference_steps = args.steps  # Number of denoising steps
+
+    # Scale for classifier-free guidance
+    guidance_scale = torch.tensor(args.guidance_scale).to(torch.float32)
+
+    generator = torch.manual_seed(
+        args.seed
+    )  # Seed generator to create the inital latent noise
+
+    batch_size = len(prompt)
+
+    set_iree_runtime_flags()
+    unet = get_unet()
+    vae = get_vae()
+    clip = get_clip()
+    if args.dump_isa:
+        dump_isas(args.dispatch_benchmarks_dir)
+
+    tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
+    scheduler = DPMSolverMultistepScheduler.from_pretrained(
+        "CompVis/stable-diffusion-v1-4",
+        subfolder="scheduler",
+    )
+    if args.version == "v2":
+        tokenizer = CLIPTokenizer.from_pretrained(
+            "stabilityai/stable-diffusion-2", subfolder="tokenizer"
+        )
+
+        scheduler = DPMSolverMultistepScheduler.from_pretrained(
+            "stabilityai/stable-diffusion-2",
+            subfolder="scheduler",
+        )
+
+    if args.version == "v2.1base":
+        tokenizer = CLIPTokenizer.from_pretrained(
+            "stabilityai/stable-diffusion-2-1-base", subfolder="tokenizer"
+        )
+
+        scheduler = EulerDiscreteScheduler.from_pretrained(
+            "stabilityai/stable-diffusion-2-1-base",
+            subfolder="scheduler",
+        )
+    start = time.time()
+
+    text_input = tokenizer(
+        prompt,
+        padding="max_length",
+        max_length=args.max_length,
+        truncation=True,
+        return_tensors="pt",
+    )
+
+    clip_inf_start = time.time()
+    text_embeddings = clip.forward((text_input.input_ids,))
+    clip_inf_end = time.time()
+    text_embeddings = torch.from_numpy(text_embeddings).to(dtype)
+    max_length = text_input.input_ids.shape[-1]
+    uncond_input = tokenizer(
+        [""] * batch_size,
+        padding="max_length",
+        max_length=max_length,
+        return_tensors="pt",
+    )
+    uncond_clip_inf_start = time.time()
+    uncond_embeddings = clip.forward((uncond_input.input_ids,))
+    uncond_clip_inf_end = time.time()
+    uncond_embeddings = torch.from_numpy(uncond_embeddings).to(dtype)
+
+    text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+    latents = torch.randn(
+        (batch_size, 4, height // 8, width // 8),
+        generator=generator,
+        dtype=torch.float32,
+    ).to(dtype)
+
+    scheduler.set_timesteps(num_inference_steps)
+    scheduler.is_scale_input_called = True
+
+    latents = latents * scheduler.init_noise_sigma
+    text_embeddings_numpy = text_embeddings.detach().numpy()
+    avg_ms = 0
+
+    for i, t in tqdm(enumerate(scheduler.timesteps)):
+        step_start = time.time()
+        print(f"i = {i} t = {t}", end="")
+        timestep = torch.tensor([t]).to(dtype).detach().numpy()
+        latent_model_input = scheduler.scale_model_input(latents, t)
+        latents_numpy = latent_model_input.detach().numpy()
+
+        profile_device = start_profiling(file_path="unet.rdc")
+
+        noise_pred = unet.forward(
+            (
+                latents_numpy,
+                timestep,
+                text_embeddings_numpy,
+                guidance_scale,
+            )
+        )
+
+        end_profiling(profile_device)
+
+        noise_pred = torch.from_numpy(noise_pred)
+        step_time = time.time() - step_start
+        avg_ms += step_time
+        step_ms = int((step_time) * 1000)
+        print(f" ({step_ms}ms)")
+
+        latents = scheduler.step(noise_pred, t, latents).prev_sample
+
+    avg_ms = 1000 * avg_ms / args.steps
+    print(f"Average step time: {avg_ms}ms/it")
+
+    # scale and decode the image latents with vae
+    latents = 1 / 0.18215 * latents
+    # latents = latents.
+    latents_numpy = latents.detach().numpy()
+    profile_device = start_profiling(file_path="vae.rdc")
+    vae_start = time.time()
+    image = vae.forward((latents_numpy,))
+    vae_end = time.time()
+    end_profiling(profile_device)
+    image = torch.from_numpy(image)
+    image = image.detach().cpu().permute(0, 2, 3, 1) * 255.0
+    images = image.numpy().round().astype("uint8")
+    total_end = time.time()
+
+    clip_inf_time = (clip_inf_end - clip_inf_start) * 1000
+    uncond_clip_inf_time = (uncond_clip_inf_end - uncond_clip_inf_start) * 1000
+    avg_clip_inf = (clip_inf_time + uncond_clip_inf_time) / 2
+    vae_inf_time = (vae_end - vae_start) * 1000
+    print(
+        f"Clip Inference Avg time (ms) = ({clip_inf_time:.3f} + {uncond_clip_inf_time:.3f}) / 2 = {avg_clip_inf:.3f}"
+    )
+    print(f"VAE Inference time (ms): {vae_inf_time:.3f}")
+    print(f"Total image generation runtime (s): {total_end - start:.4f}")
+
+    pil_images = [Image.fromarray(image) for image in images]
+    for i in range(batch_size):
+        pil_images[i].save(f"{args.prompts[i]}_{i}.jpg")

shark/examples/shark_inference/stable_diffusion/model_wrappers.py

@@ -0,0 +1,173 @@
+from diffusers import AutoencoderKL, UNet2DConditionModel, PNDMScheduler
+from transformers import CLIPTextModel
+from utils import compile_through_fx
+from stable_args import args
+import torch
+
+BATCH_SIZE = len(args.prompts)
+
+model_config = {
+    "v2": "stabilityai/stable-diffusion-2",
+    "v1.4": "CompVis/stable-diffusion-v1-4",
+}
+
+model_input = {
+    "v2": {
+        "clip": (torch.randint(1, 2, (1, 77)),),
+        "vae": (torch.randn(1, 4, 96, 96),),
+        "unet": (
+            torch.randn(1, 4, 96, 96),  # latents
+            torch.tensor([1]).to(torch.float32),  # timestep
+            torch.randn(2, 77, 1024),  # embedding
+            torch.tensor(1).to(torch.float32),  # guidance_scale
+        ),
+    },
+    "v1.4": {
+        "clip": (torch.randint(1, 2, (1, 77)),),
+        "vae": (torch.randn(1, 4, 64, 64),),
+        "unet": (
+            torch.randn(1, 4, 64, 64),
+            torch.tensor([1]).to(torch.float32),  # timestep
+            torch.randn(2, 77, 768),
+            torch.tensor(1).to(torch.float32),
+        ),
+    },
+}
+
+# revision param for from_pretrained defaults to "main" => fp32
+model_revision = "fp16" if args.precision == "fp16" else "main"
+
+
+def get_clip_mlir(model_name="clip_text", extra_args=[]):
+
+    text_encoder = CLIPTextModel.from_pretrained(
+        "openai/clip-vit-large-patch14"
+    )
+    if args.version == "v2":
+        text_encoder = CLIPTextModel.from_pretrained(
+            model_config[args.version], subfolder="text_encoder"
+        )
+
+    class CLIPText(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.text_encoder = text_encoder
+
+        def forward(self, input):
+            return self.text_encoder(input)[0]
+
+    clip_model = CLIPText()
+    shark_clip = compile_through_fx(
+        clip_model,
+        model_input[args.version]["clip"],
+        model_name=model_name,
+        extra_args=extra_args,
+    )
+    return shark_clip
+
+
+def get_vae_mlir(model_name="vae", extra_args=[]):
+    class VaeModel(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.vae = AutoencoderKL.from_pretrained(
+                model_config[args.version],
+                subfolder="vae",
+                revision=model_revision,
+            )
+
+        def forward(self, input):
+            x = self.vae.decode(input, return_dict=False)[0]
+            return (x / 2 + 0.5).clamp(0, 1)
+
+    vae = VaeModel()
+    if args.precision == "fp16":
+        vae = vae.half().cuda()
+        inputs = tuple(
+            [
+                inputs.half().cuda()
+                for inputs in model_input[args.version]["vae"]
+            ]
+        )
+    else:
+        inputs = model_input[args.version]["vae"]
+
+    shark_vae = compile_through_fx(
+        vae,
+        inputs,
+        model_name=model_name,
+        extra_args=extra_args,
+    )
+    return shark_vae
+
+
+def get_vae_encode_mlir(model_name="vae_encode", extra_args=[]):
+    class VaeEncodeModel(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.vae = AutoencoderKL.from_pretrained(
+                model_config[args.version],
+                subfolder="vae",
+                revision="fp16",
+            )
+
+        def forward(self, x):
+            input = 2 * (x - 0.5)
+            return self.vae.encode(input, return_dict=False)[0]
+
+    vae = VaeEncodeModel()
+    vae = vae.half().cuda()
+    inputs = tuple(
+        [inputs.half().cuda() for inputs in model_input[args.version]["vae"]]
+    )
+    shark_vae = compile_through_fx(
+        vae,
+        inputs,
+        model_name=model_name,
+        extra_args=extra_args,
+    )
+    return shark_vae
+
+
+def get_unet_mlir(model_name="unet", extra_args=[]):
+    class UnetModel(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.unet = UNet2DConditionModel.from_pretrained(
+                model_config[args.version],
+                subfolder="unet",
+                revision=model_revision,
+            )
+            self.in_channels = self.unet.in_channels
+            self.train(False)
+
+        def forward(self, latent, timestep, text_embedding, guidance_scale):
+            # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
+            latents = torch.cat([latent] * 2)
+            unet_out = self.unet.forward(
+                latents, timestep, text_embedding, return_dict=False
+            )[0]
+            noise_pred_uncond, noise_pred_text = unet_out.chunk(2)
+            noise_pred = noise_pred_uncond + guidance_scale * (
+                noise_pred_text - noise_pred_uncond
+            )
+            return noise_pred
+
+    unet = UnetModel()
+    if args.precision == "fp16":
+        unet = unet.half().cuda()
+        inputs = tuple(
+            [
+                inputs.half().cuda() if len(inputs.shape) != 0 else inputs
+                for inputs in model_input[args.version]["unet"]
+            ]
+        )
+    else:
+        inputs = model_input[args.version]["unet"]
+    shark_unet = compile_through_fx(
+        unet,
+        inputs,
+        model_name=model_name,
+        extra_args=extra_args,
+    )
+    return shark_unet

shark/examples/shark_inference/stable_diffusion/opt_params.py

@@ -0,0 +1,153 @@
+import sys
+from model_wrappers import (
+    get_vae_mlir,
+    get_vae_encode_mlir,
+    get_unet_mlir,
+    get_clip_mlir,
+)
+from stable_args import args
+from utils import get_shark_model
+
+BATCH_SIZE = len(args.prompts)
+if BATCH_SIZE != 1:
+    sys.exit("Only batch size 1 is supported.")
+
+
+def get_unet():
+    iree_flags = []
+    if len(args.iree_vulkan_target_triple) > 0:
+        iree_flags.append(
+            f"-iree-vulkan-target-triple={args.iree_vulkan_target_triple}"
+        )
+    # Tuned model is present for `fp16` precision.
+    if args.precision == "fp16":
+        if args.use_tuned:
+            bucket = "gs://shark_tank/vivian"
+            model_name = "unet_1dec_fp16_tuned"
+            return get_shark_model(bucket, model_name, iree_flags)
+        else:
+            bucket = "gs://shark_tank/stable_diffusion"
+            model_name = "unet_1dec_fp16"
+            if args.version == "v2.1base":
+                model_name = "unet2base_8dec_fp16"
+            iree_flags += [
+                "--iree-flow-enable-padding-linalg-ops",
+                "--iree-flow-linalg-ops-padding-size=32",
+                "--iree-flow-enable-conv-img2col-transform",
+            ]
+            if args.import_mlir:
+                return get_unet_mlir(model_name, iree_flags)
+            return get_shark_model(bucket, model_name, iree_flags)
+
+    # Tuned model is not present for `fp32` case.
+    if args.precision == "fp32":
+        bucket = "gs://shark_tank/stable_diffusion"
+        model_name = "unet_1dec_fp32"
+        iree_flags += [
+            "--iree-flow-enable-conv-nchw-to-nhwc-transform",
+            "--iree-flow-enable-padding-linalg-ops",
+            "--iree-flow-linalg-ops-padding-size=16",
+        ]
+        if args.import_mlir:
+            return get_unet_mlir(model_name, iree_flags)
+        return get_shark_model(bucket, model_name, iree_flags)
+
+    if args.precision == "int8":
+        bucket = "gs://shark_tank/prashant_nod"
+        model_name = "unet_int8"
+        iree_flags += [
+            "--iree-flow-enable-padding-linalg-ops",
+            "--iree-flow-linalg-ops-padding-size=32",
+        ]
+        sys.exit("int8 model is currently in maintenance.")
+        # # TODO: Pass iree_flags to the exported model.
+        # if args.import_mlir:
+        # sys.exit(
+        # "--import_mlir is not supported for the int8 model, try --no-import_mlir flag."
+        # )
+        # return get_shark_model(bucket, model_name, iree_flags)
+
+
+def get_vae():
+    iree_flags = []
+    if len(args.iree_vulkan_target_triple) > 0:
+        iree_flags.append(
+            f"-iree-vulkan-target-triple={args.iree_vulkan_target_triple}"
+        )
+    if args.precision in ["fp16", "int8"]:
+        bucket = "gs://shark_tank/stable_diffusion"
+        model_name = "vae_1dec_fp16"
+        if args.version == "v2.1base":
+            model_name = "vae2base_8dec_fp16"
+        iree_flags += [
+            "--iree-flow-enable-padding-linalg-ops",
+            "--iree-flow-linalg-ops-padding-size=32",
+            "--iree-flow-enable-conv-img2col-transform",
+        ]
+        if args.import_mlir:
+            return get_vae_mlir(model_name, iree_flags)
+        return get_shark_model(bucket, model_name, iree_flags)
+
+    if args.precision == "fp32":
+        bucket = "gs://shark_tank/stable_diffusion"
+        model_name = "vae_1dec_fp32"
+        iree_flags += [
+            "--iree-flow-enable-conv-nchw-to-nhwc-transform",
+            "--iree-flow-enable-padding-linalg-ops",
+            "--iree-flow-linalg-ops-padding-size=16",
+        ]
+        if args.import_mlir:
+            return get_vae_mlir(model_name, iree_flags)
+        return get_shark_model(bucket, model_name, iree_flags)
+
+
+def get_vae_encode():
+    iree_flags = []
+    if len(args.iree_vulkan_target_triple) > 0:
+        iree_flags.append(
+            f"-iree-vulkan-target-triple={args.iree_vulkan_target_triple}"
+        )
+    if args.precision in ["fp16", "int8"]:
+        bucket = "gs://shark_tank/stable_diffusion"
+        model_name = "vae_encode_1dec_fp16"
+        if args.version == "v2":
+            model_name = "vae2_encode_29nov_fp16"
+        iree_flags += [
+            "--iree-flow-enable-conv-nchw-to-nhwc-transform",
+            "--iree-flow-enable-padding-linalg-ops",
+            "--iree-flow-linalg-ops-padding-size=32",
+        ]
+        if args.import_mlir:
+            return get_vae_encode_mlir(model_name, iree_flags)
+        return get_shark_model(bucket, model_name, iree_flags)
+
+    if args.precision == "fp32":
+        bucket = "gs://shark_tank/stable_diffusion"
+        model_name = "vae_encode_1dec_fp32"
+        iree_flags += [
+            "--iree-flow-enable-conv-nchw-to-nhwc-transform",
+            "--iree-flow-enable-padding-linalg-ops",
+            "--iree-flow-linalg-ops-padding-size=16",
+        ]
+        if args.import_mlir:
+            return get_vae_mlir(model_name, iree_flags)
+        return get_shark_model(bucket, model_name, iree_flags)
+
+
+def get_clip():
+    iree_flags = []
+    if len(args.iree_vulkan_target_triple) > 0:
+        iree_flags.append(
+            f"-iree-vulkan-target-triple={args.iree_vulkan_target_triple}"
+        )
+    bucket = "gs://shark_tank/stable_diffusion"
+    model_name = "clip_1dec_fp32"
+    if args.version == "v2.1base":
+        model_name = "clip2base_8dec_fp32"
+    iree_flags += [
+        "--iree-flow-linalg-ops-padding-size=16",
+        "--iree-flow-enable-padding-linalg-ops",
+    ]
+    if args.import_mlir:
+        return get_clip_mlir(model_name, iree_flags)
+    return get_shark_model(bucket, model_name, iree_flags)

shark/examples/shark_inference/stable_diffusion/profiling_with_iree.md

@@ -0,0 +1,44 @@
+Compile / Run Instructions:
+
+To compile .vmfb for SD (vae, unet, CLIP), run the following commands with the .mlir in your local shark_tank cache (default location for Linux users is `~/.local/shark_tank`). These will be available once the script from [this README](https://github.com/nod-ai/SHARK/blob/main/shark/examples/shark_inference/stable_diffusion/README.md) is run once.
+Running the script mentioned above with the `--save_vmfb` flag will also save the .vmfb in your SHARK base directory if you want to skip straight to benchmarks.
+
+Compile Commands FP32/FP16: 
+
+```shell
+Vulkan AMD: 
+iree-compile --iree-input-type=none --iree-hal-target-backends=vulkan --iree-vulkan-target-triple=rdna2-unknown-linux --iree-stream-resource-index-bits=64 --iree-vm-target-index-bits=64 /path/to/input/mlir -o /path/to/output/vmfb
+
+#  add --mlir-print-debuginfo --mlir-print-op-on-diagnostic=true for debug
+#  use –iree-input-type=mhlo for tf models
+
+CUDA NVIDIA:
+iree-compile --iree-input-type=none --iree-hal-target-backends=cuda --iree-stream-resource-index-bits=64 --iree-vm-target-index-bits=64 /path/to/input/mlir -o /path/to/output/vmfb
+
+CPU:
+iree-compile --iree-input-type=none --iree-hal-target-backends=llvm-cpu  --iree-stream-resource-index-bits=64 --iree-vm-target-index-bits=64 /path/to/input/mlir -o /path/to/output/vmfb
+```
+
+
+
+Run / Benchmark Command (FP32 - NCHW):
+(NEED to use BS=2 since we do two forward passes to unet as a result of classifier free guidance.)
+
+```shell
+## Vulkan AMD:
+iree-benchmark-module --module_file=/path/to/output/vmfb --entry_function=forward --device=vulkan --function_input=1x4x64x64xf32 --function_input=1xf32 --function_input=2x77x768xf32 --function_input=f32=1.0 --function_input=f32=1.0
+
+## CUDA:
+iree-benchmark-module --module_file=/path/to/vmfb --entry_function=forward --device=cuda  --function_input=1x4x64x64xf32 --function_input=1xf32 --function_input=2x77x768xf32 --function_input=f32=1.0 --function_input=f32=1.0
+
+## CPU:
+iree-benchmark-module --module_file=/path/to/vmfb --entry_function=forward --device=local-task  --function_input=1x4x64x64xf32 --function_input=1xf32 --function_input=2x77x768xf32 --function_input=f32=1.0 --function_input=f32=1.0
+
+```
+
+Run via vulkan_gui for RGP Profiling:
+
+To build the vulkan app for profiling UNet follow the instructions [here](https://github.com/nod-ai/SHARK/tree/main/cpp) and then run the following command from the cpp directory with your compiled stable_diff.vmfb
+```shell
+./build/vulkan_gui/iree-vulkan-gui --module_file=/path/to/unet.vmfb --function_input=1x4x64x64xf32 --function_input=1xf32 --function_input=2x77x768xf32 --function_input=f32=1.0 --function_input=f32=1.0
+```

shark/examples/shark_inference/stable_diffusion/stable_args.py

@@ -0,0 +1,128 @@
+import argparse
+
+p = argparse.ArgumentParser(
+    description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter
+)
+
+p.add_argument(
+    "--prompts",
+    nargs="+",
+    default=["a photograph of an astronaut riding a horse"],
+    help="text of which images to be generated.",
+)
+p.add_argument(
+    "--device", type=str, default="cpu", help="device to run the model."
+)
+p.add_argument(
+    "--steps",
+    type=int,
+    default=50,
+    help="the no. of steps to do the sampling.",
+)
+
+p.add_argument(
+    "--version",
+    type=str,
+    default="v1.4",
+    help="Specify version of stable diffusion model",
+)
+
+p.add_argument(
+    "--seed",
+    type=int,
+    default=42,
+    help="the seed to use.",
+)
+p.add_argument(
+    "--guidance_scale",
+    type=float,
+    default=7.5,
+    help="the value to be used for guidance scaling.",
+)
+
+p.add_argument(
+    "--import_mlir",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="imports the model from torch module to shark_module otherwise downloads the model from shark_tank.",
+)
+
+p.add_argument(
+    "--precision", type=str, default="fp32", help="precision to run the model."
+)
+
+p.add_argument(
+    "--max_length",
+    type=int,
+    default=77,
+    help="max length of the tokenizer output.",
+)
+
+p.add_argument(
+    "--load_vmfb",
+    default=True,
+    action=argparse.BooleanOptionalAction,
+    help="attempts to load the model from a precompiled flatbuffer and compiles + saves it if not found.",
+)
+
+p.add_argument(
+    "--save_vmfb",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="saves the compiled flatbuffer to the local directory",
+)
+
+p.add_argument(
+    "--iree-vulkan-target-triple",
+    type=str,
+    default="",
+    help="Specify target triple for vulkan",
+)
+
+p.add_argument(
+    "--vulkan_debug_utils",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="Profiles vulkan device and collects the .rdc info",
+)
+
+p.add_argument(
+    "--use_tuned",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="Download and use the tuned version of the model if available",
+)
+
+p.add_argument(
+    "--dump_isa",
+    default=False,
+    action="store_true",
+    help="When enabled call amdllpc to get ISA dumps. use with dispatch benchmarks.",
+)
+
+p.add_argument(
+    "--dispatch_benchmarks",
+    default=None,
+    help='dispatches to return benchamrk data on.  use "All" for all, and None for none.',
+)
+
+p.add_argument(
+    "--dispatch_benchmarks_dir",
+    default="temp_dispatch_benchmarks",
+    help='directory where you want to store dispatch data generated with "--dispatch_benchmarks"',
+)
+
+p.add_argument(
+    "--vulkan_large_heap_block_size",
+    default="4294967296",
+    help="flag for setting VMA preferredLargeHeapBlockSize for vulkan device, default is 4G",
+)
+
+p.add_argument(
+    "--enable_rgp",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="flag for inserting debug frames between iterations for use with rgp.",
+)
+
+args = p.parse_args()

shark/examples/shark_inference/stable_diffusion/stable_diffusion_amd.md

@@ -0,0 +1,111 @@
+# Stable Diffusion optimized for AMD RDNA2/RDNA3 GPUs
+
+## Install the latest AMD Drivers
+
+### RDNA2 Drivers:
+*AMD Software: Adrenalin Edition 22.11.1 for MLIR/IREE Driver Version 22.20.29.09 for Windows® 10 and Windows® 11 (Windows Driver Store Version 31.0.12029.9003)*
+
+https://www.amd.com/en/support/kb/release-notes/rn-rad-win-22-11-1-mlir-iree
+
+Note that if you previously tried Stable Diffusion with a different driver, it may be necessary to clear vulkan cache after changing drivers.
+
+For Windows users this can be done by clearing the contents of `C:\Users\<username>\AppData\Local\AMD\VkCache\`. On Linux the same cache is typically located at `~/.cache/AMD/VkCache/`.
+
+## Installation
+
+Download the latest Windows `shark_sd.exe` [here](https://storage.googleapis.com/shark-public/anush/windows/shark_sd_05122022v3.exe). Accept if Windows warns of an unsigned .exe.
+
+#### Access Stable Diffusion on http://localhost:8080/?__theme=dark
+
+
+<img width="1607" alt="webui" src="https://user-images.githubusercontent.com/74956/204939260-b8308bc2-8dc4-47f6-9ac0-f60b66edab99.png">
+
+
+Here are some samples generated:
+
+![tajmahal, snow, sunflowers, oil on canvas_0](https://user-images.githubusercontent.com/74956/204934186-141f7e43-6eb2-4e89-a99c-4704d20444b3.jpg)
+
+![a photo of a crab playing a trumpet](https://user-images.githubusercontent.com/74956/204933258-252e7240-8548-45f7-8253-97647d38313d.jpg)
+
+
+<details>
+  <summary>Advanced Installation </summary>
+
+## Setup your Python VirtualEnvironment and Dependencies
+
+### Windows 10/11 Users
+
+* Install the latest Python 3.10.x version from [here](https://www.python.org/downloads/windows/)
+
+* Install Git for Windows from [here](https://git-scm.com/download/win)
+
+#### Allow the install script to run in Powershell
+```powershell
+set-executionpolicy remotesigned 
+```
+
+#### Setup venv and install necessary packages (torch-mlir, nodLabs/Shark, ...)
+```powershell
+git clone https://github.com/nod-ai/SHARK.git
+cd SHARK
+./setup_venv.ps1 #You can re-run this script to get the latest version
+```
+
+### Linux
+
+```shell
+git clone https://github.com/nod-ai/SHARK.git
+cd SHARK
+./setup_venv.sh
+source shark.venv/bin/activate
+```
+
+### Run Stable Diffusion on your device - WebUI
+
+#### Windows 10/11 Users
+```powershell
+(shark.venv) PS C:\Users\nod\SHARK> cd web
+(shark.venv) PS C:\Users\nod\SHARK\web> python index.py
+```
+#### Linux Users
+```shell
+(shark.venv) > cd web
+(shark.venv) > python index.py
+```
+
+
+
+### Run Stable Diffusion on your device - Commandline
+
+#### Windows 10/11 Users
+```powershell
+(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"
+```
+
+#### Linux
+```shell
+python3.10 shark/examples/shark_inference/stable_diffusion/main.py --precision=fp16 --device=vulkan --prompt="tajmahal, oil on canvas, sunflowers, 4k, uhd"
+```
+
+The output on a 6900XT would like:
+
+```shell 
+44it [00:08,  5.14it/s]i = 44 t = 120 (191ms)
+45it [00:08,  5.15it/s]i = 45 t = 100 (191ms)
+46it [00:08,  5.16it/s]i = 46 t = 80 (191ms)
+47it [00:09,  5.16it/s]i = 47 t = 60 (193ms)
+48it [00:09,  5.15it/s]i = 48 t = 40 (195ms)
+49it [00:09,  5.12it/s]i = 49 t = 20 (196ms)
+50it [00:09,  5.14it/s]
+Average step time: 192.8154182434082ms/it
+Total image generation runtime (s): 10.390909433364868
+(shark.venv) PS C:\g\shark>
+```
+
+
+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)
+</details>
+<details>
+  <summary>Discord link</summary>
+Find us on [SHARK Discord server](https://discord.gg/RUqY2h2s9u) if you have any trouble with running it on your hardware. 
+</details>

shark/examples/shark_inference/stable_diffusion/utils.py

@@ -0,0 +1,83 @@
+import os
+
+import torch
+from shark.shark_inference import SharkInference
+from stable_args import args
+from shark.shark_importer import import_with_fx
+from shark.iree_utils.vulkan_utils import set_iree_vulkan_runtime_flags
+
+
+def _compile_module(shark_module, model_name, extra_args=[]):
+    if args.load_vmfb or args.save_vmfb:
+        device = (
+            args.device
+            if "://" not in args.device
+            else "-".join(args.device.split("://"))
+        )
+        extended_name = "{}_{}".format(model_name, device)
+        vmfb_path = os.path.join(os.getcwd(), extended_name + ".vmfb")
+        if args.load_vmfb and os.path.isfile(vmfb_path) and not args.save_vmfb:
+            print(f"loading existing vmfb from: {vmfb_path}")
+            shark_module.load_module(vmfb_path, extra_args=extra_args)
+        else:
+            if args.save_vmfb:
+                print("Saving to {}".format(vmfb_path))
+            else:
+                print(
+                    "No vmfb found. Compiling and saving to {}".format(
+                        vmfb_path
+                    )
+                )
+            path = shark_module.save_module(
+                os.getcwd(), extended_name, extra_args
+            )
+            shark_module.load_module(path, extra_args=extra_args)
+    else:
+        shark_module.compile(extra_args)
+    return shark_module
+
+
+# Downloads the model from shark_tank and returns the shark_module.
+def get_shark_model(tank_url, model_name, extra_args=[]):
+    from shark.shark_downloader import download_model
+
+    mlir_model, func_name, inputs, golden_out = download_model(
+        model_name,
+        tank_url=tank_url,
+        frontend="torch",
+    )
+    shark_module = SharkInference(
+        mlir_model, func_name, device=args.device, mlir_dialect="linalg"
+    )
+    return _compile_module(shark_module, model_name, extra_args)
+
+
+# Converts the torch-module into a shark_module.
+def compile_through_fx(model, inputs, model_name, extra_args=[]):
+
+    mlir_module, func_name = import_with_fx(model, inputs)
+
+    shark_module = SharkInference(
+        mlir_module,
+        func_name,
+        device=args.device,
+        mlir_dialect="linalg",
+    )
+
+    return _compile_module(shark_module, model_name, extra_args)
+
+
+def set_iree_runtime_flags():
+
+    vulkan_runtime_flags = [
+        f"--vulkan_large_heap_block_size={args.vulkan_large_heap_block_size}",
+    ]
+    if args.enable_rgp:
+        vulkan_runtime_flags += [
+            f"--enable_rgp=true",
+            f"--vulkan_debug_utils=true",
+        ]
+    if "vulkan" in args.device:
+        set_iree_vulkan_runtime_flags(flags=vulkan_runtime_flags)
+
+    return

shark/examples/shark_inference/t5_tf.py

@@ -18,7 +18,7 @@ class T5Module(tf.Module):
         self.m = TFT5Model.from_pretrained("t5-small")
         self.m.predict = lambda x, y: self.m(input_ids=x, decoder_input_ids=y)
 
-    @tf.function(input_signature=t5_inputs)
+    @tf.function(input_signature=t5_inputs, jit_compile=True)
     def forward(self, input_ids, decoder_input_ids):
         return self.m.predict(input_ids, decoder_input_ids)
 

shark/examples/shark_inference/v_diffusion.py

@@ -1,8 +1,10 @@
 from shark.shark_inference import SharkInference
-from shark.shark_downloader import download_torch_model
+from shark.shark_downloader import download_model
 
 
-mlir_model, func_name, inputs, golden_out = download_torch_model("v_diffusion")
+mlir_model, func_name, inputs, golden_out = download_model(
+    "v_diffusion", frontend="torch"
+)
 
 shark_module = SharkInference(
     mlir_model, func_name, device="vulkan", mlir_dialect="linalg"

shark/examples/shark_training/bert_training_tf.py

@@ -52,7 +52,8 @@ class BertModule(tf.Module):
         input_signature=[
             bert_input,  # inputs
             tf.TensorSpec(shape=[BATCH_SIZE], dtype=tf.int32),  # labels
-        ]
+        ],
+        jit_compile=True,
     )
     def forward(self, inputs, labels):
         with tf.GradientTape() as tape:

shark/examples/shark_training/stable-diffusion-img2img/README.md

@@ -0,0 +1,41 @@
+# Stable Diffusion Img2Img model
+
+## Installation
+
+<details>
+  <summary>Installation (Linux)</summary>
+
+### Activate shark.venv Virtual Environment
+
+```shell
+source shark.venv/bin/activate
+
+# Some older pip installs may not be able to handle the recent PyTorch deps
+python -m pip install --upgrade pip
+```
+
+### Install dependencies
+
+# Run the setup.sh script
+
+```shell
+./setup.sh
+```
+
+### Run the Stable diffusion Img2Img model
+
+To run the model with the default set of images and params, run:
+```shell
+python stable_diffusion_img2img.py
+```
+To run the model with your set of images, and parameters you need to specify the following params:
+1.) Input images directory with the arg `--input_dir` containing 3-5 images.
+2.) What to teach the model? Using the arg `--what_to_teach`, allowed values are `object` or `style`.
+3.) Placeholder token using the arg `--placeholder_token`, that represents your new concept. It should be passed with the opening and closing angle brackets. For ex: token is `cat-toy`, it should be passed as `<cat-toy>`.
+4.) Initializer token using the arg `--initializer_token`, which summarise what is your new concept.
+
+For the result, you need to pass the text prompt with the arg: `--prompt`. The prompt string should contain a "*s" in it, which will be replaced by the placeholder token during the inference.
+
+By default the result images will go into the `sd_result` dir. To specify your output dir use the arg: `--output_dir`.
+
+The default value of max_training_steps is `3000`, which takes some hours to complete. You can pass the smaller value with the arg `--training_steps`. Specify the number of images to be sampled for the result with the `--num_inference_samples` arg.

shark/examples/shark_training/stable-diffusion-img2img/setup.sh

@@ -0,0 +1,25 @@
+#!/bin/bash
+
+TD="$(cd $(dirname $0) && pwd)"
+if [ -z "$PYTHON" ]; then
+  PYTHON="$(which python3)"
+fi
+
+function die() {
+  echo "Error executing command: $*"
+  exit 1
+}
+
+PYTHON_VERSION_X_Y=`${PYTHON} -c 'import sys; version=sys.version_info[:2]; print("{0}.{1}".format(*version))'`
+
+echo "Python: $PYTHON"
+echo "Python version: $PYTHON_VERSION_X_Y"
+
+mkdir input_images
+
+wget https://huggingface.co/datasets/valhalla/images/resolve/main/2.jpeg -P input_images/
+wget https://huggingface.co/datasets/valhalla/images/resolve/main/3.jpeg -P input_images/
+wget https://huggingface.co/datasets/valhalla/images/resolve/main/5.jpeg -P input_images/
+wget https://huggingface.co/datasets/valhalla/images/resolve/main/6.jpeg -P input_images/
+
+pip install diffusers["training"]==0.4.1 transformers ftfy opencv-python

shark/examples/shark_training/stable-diffusion-img2img/stable_diffusion_img2img.py

@@ -0,0 +1,597 @@
+# Textual-inversion fine-tuning for Stable Diffusion using diffusers
+# This script shows how to "teach" Stable Diffusion a new concept via
+# textual-inversion using 🤗 Hugging Face [🧨 Diffusers library](https://github.com/huggingface/diffusers).
+# By using just 3-5 images you can teach new concepts to Stable Diffusion
+# and personalize the model on your own images.
+
+import argparse
+import itertools
+import math
+import os
+import random
+import cv2
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch.utils.data import Dataset
+
+import PIL
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from accelerate.utils import set_seed
+from diffusers import (
+    AutoencoderKL,
+    DDPMScheduler,
+    PNDMScheduler,
+    StableDiffusionPipeline,
+    UNet2DConditionModel,
+)
+from diffusers.hub_utils import init_git_repo, push_to_hub
+from diffusers.optimization import get_scheduler
+from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
+from PIL import Image
+from torchvision import transforms
+from tqdm.auto import tqdm
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+
+YOUR_TOKEN = "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"
+
+p = argparse.ArgumentParser(
+    description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter
+)
+p.add_argument(
+    "--input_dir",
+    type=str,
+    default="input_images/",
+    help="the directory contains the images used for fine tuning",
+)
+p.add_argument(
+    "--output_dir",
+    type=str,
+    default="sd_result",
+    help="the directory contains the images used for fine tuning",
+)
+p.add_argument(
+    "--training_steps",
+    type=int,
+    default=3000,
+    help="the maximum number of training steps",
+)
+p.add_argument("--seed", type=int, default=42, help="the random seed")
+p.add_argument(
+    "--what_to_teach",
+    type=str,
+    choices=["object", "style"],
+    default="object",
+    help="what is it that you are teaching?",
+)
+p.add_argument(
+    "--placeholder_token",
+    type=str,
+    default="<cat-toy>",
+    help="It is the token you are going to use to represent your new concept",
+)
+p.add_argument(
+    "--initializer_token",
+    type=str,
+    default="toy",
+    help="It is a word that can summarise what is your new concept",
+)
+p.add_argument(
+    "--inference_steps",
+    type=int,
+    default=50,
+    help="the number of steps for inference",
+)
+p.add_argument(
+    "--num_inference_samples",
+    type=int,
+    default=4,
+    help="the number of samples for inference",
+)
+p.add_argument(
+    "--prompt",
+    type=str,
+    default="a grafitti in a wall with a *s on it",
+    help="the text prompt to use",
+)
+args = p.parse_args()
+
+if "*s" not in args.prompt:
+    raise ValueError(
+        f'The prompt should have a "*s" which will be replaced by a placeholder token.'
+    )
+
+prompt1, prompt2 = args.prompt.split("*s")
+args.prompt = prompt1 + args.placeholder_token + prompt2
+
+pretrained_model_name_or_path = "CompVis/stable-diffusion-v1-4"
+
+# Load input images.
+images = []
+for filename in os.listdir(args.input_dir):
+    img = cv2.imread(os.path.join(args.input_dir, filename))
+    if img is not None:
+        images.append(img)
+
+# Setup the prompt templates for training
+imagenet_templates_small = [
+    "a photo of a {}",
+    "a rendering of a {}",
+    "a cropped photo of the {}",
+    "the photo of a {}",
+    "a photo of a clean {}",
+    "a photo of a dirty {}",
+    "a dark photo of the {}",
+    "a photo of my {}",
+    "a photo of the cool {}",
+    "a close-up photo of a {}",
+    "a bright photo of the {}",
+    "a cropped photo of a {}",
+    "a photo of the {}",
+    "a good photo of the {}",
+    "a photo of one {}",
+    "a close-up photo of the {}",
+    "a rendition of the {}",
+    "a photo of the clean {}",
+    "a rendition of a {}",
+    "a photo of a nice {}",
+    "a good photo of a {}",
+    "a photo of the nice {}",
+    "a photo of the small {}",
+    "a photo of the weird {}",
+    "a photo of the large {}",
+    "a photo of a cool {}",
+    "a photo of a small {}",
+]
+
+imagenet_style_templates_small = [
+    "a painting in the style of {}",
+    "a rendering in the style of {}",
+    "a cropped painting in the style of {}",
+    "the painting in the style of {}",
+    "a clean painting in the style of {}",
+    "a dirty painting in the style of {}",
+    "a dark painting in the style of {}",
+    "a picture in the style of {}",
+    "a cool painting in the style of {}",
+    "a close-up painting in the style of {}",
+    "a bright painting in the style of {}",
+    "a cropped painting in the style of {}",
+    "a good painting in the style of {}",
+    "a close-up painting in the style of {}",
+    "a rendition in the style of {}",
+    "a nice painting in the style of {}",
+    "a small painting in the style of {}",
+    "a weird painting in the style of {}",
+    "a large painting in the style of {}",
+]
+
+# Setup the dataset
+class TextualInversionDataset(Dataset):
+    def __init__(
+        self,
+        data_root,
+        tokenizer,
+        learnable_property="object",  # [object, style]
+        size=512,
+        repeats=100,
+        interpolation="bicubic",
+        flip_p=0.5,
+        set="train",
+        placeholder_token="*",
+        center_crop=False,
+    ):
+
+        self.data_root = data_root
+        self.tokenizer = tokenizer
+        self.learnable_property = learnable_property
+        self.size = size
+        self.placeholder_token = placeholder_token
+        self.center_crop = center_crop
+        self.flip_p = flip_p
+
+        self.image_paths = [
+            os.path.join(self.data_root, file_path)
+            for file_path in os.listdir(self.data_root)
+        ]
+
+        self.num_images = len(self.image_paths)
+        self._length = self.num_images
+
+        if set == "train":
+            self._length = self.num_images * repeats
+
+        self.interpolation = {
+            "linear": PIL.Image.LINEAR,
+            "bilinear": PIL.Image.BILINEAR,
+            "bicubic": PIL.Image.BICUBIC,
+            "lanczos": PIL.Image.LANCZOS,
+        }[interpolation]
+
+        self.templates = (
+            imagenet_style_templates_small
+            if learnable_property == "style"
+            else imagenet_templates_small
+        )
+        self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)
+
+    def __len__(self):
+        return self._length
+
+    def __getitem__(self, i):
+        example = {}
+        image = Image.open(self.image_paths[i % self.num_images])
+
+        if not image.mode == "RGB":
+            image = image.convert("RGB")
+
+        placeholder_string = self.placeholder_token
+        text = random.choice(self.templates).format(placeholder_string)
+
+        example["input_ids"] = self.tokenizer(
+            text,
+            padding="max_length",
+            truncation=True,
+            max_length=self.tokenizer.model_max_length,
+            return_tensors="pt",
+        ).input_ids[0]
+
+        # default to score-sde preprocessing
+        img = np.array(image).astype(np.uint8)
+
+        if self.center_crop:
+            crop = min(img.shape[0], img.shape[1])
+            h, w, = (
+                img.shape[0],
+                img.shape[1],
+            )
+            img = img[
+                (h - crop) // 2 : (h + crop) // 2,
+                (w - crop) // 2 : (w + crop) // 2,
+            ]
+
+        image = Image.fromarray(img)
+        image = image.resize(
+            (self.size, self.size), resample=self.interpolation
+        )
+
+        image = self.flip_transform(image)
+        image = np.array(image).astype(np.uint8)
+        image = (image / 127.5 - 1.0).astype(np.float32)
+
+        example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1)
+        return example
+
+
+# Setting up the model
+# Load the tokenizer and add the placeholder token as a additional special token.
+# Please read and if you agree accept the LICENSE
+# [here](https://huggingface.co/CompVis/stable-diffusion-v1-4) if you see an error
+tokenizer = CLIPTokenizer.from_pretrained(
+    pretrained_model_name_or_path,
+    subfolder="tokenizer",
+    use_auth_token=YOUR_TOKEN,
+)
+
+# Add the placeholder token in tokenizer
+num_added_tokens = tokenizer.add_tokens(args.placeholder_token)
+if num_added_tokens == 0:
+    raise ValueError(
+        f"The tokenizer already contains the token {args.placeholder_token}. Please pass a different"
+        " `placeholder_token` that is not already in the tokenizer."
+    )
+
+# Get token ids for our placeholder and initializer token.
+# This code block will complain if initializer string is not a single token
+# Convert the initializer_token, placeholder_token to ids
+token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False)
+# Check if initializer_token is a single token or a sequence of tokens
+if len(token_ids) > 1:
+    raise ValueError("The initializer token must be a single token.")
+
+initializer_token_id = token_ids[0]
+placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token)
+
+# Load the Stable Diffusion model
+# Load models and create wrapper for stable diffusion
+text_encoder = CLIPTextModel.from_pretrained(
+    pretrained_model_name_or_path,
+    subfolder="text_encoder",
+    use_auth_token=YOUR_TOKEN,
+)
+vae = AutoencoderKL.from_pretrained(
+    pretrained_model_name_or_path,
+    subfolder="vae",
+    use_auth_token=YOUR_TOKEN,
+)
+unet = UNet2DConditionModel.from_pretrained(
+    pretrained_model_name_or_path,
+    subfolder="unet",
+    use_auth_token=YOUR_TOKEN,
+)
+
+# We have added the `placeholder_token` in the `tokenizer` so we resize the token embeddings here,
+#  this will a new embedding vector in the token embeddings for our `placeholder_token`
+text_encoder.resize_token_embeddings(len(tokenizer))
+
+# Initialise the newly added placeholder token with the embeddings of the initializer token
+token_embeds = text_encoder.get_input_embeddings().weight.data
+token_embeds[placeholder_token_id] = token_embeds[initializer_token_id]
+
+# In Textual-Inversion we only train the newly added embedding vector,
+# so lets freeze rest of the model parameters here.
+
+
+def freeze_params(params):
+    for param in params:
+        param.requires_grad = False
+
+
+# Freeze vae and unet
+freeze_params(vae.parameters())
+freeze_params(unet.parameters())
+# Freeze all parameters except for the token embeddings in text encoder
+params_to_freeze = itertools.chain(
+    text_encoder.text_model.encoder.parameters(),
+    text_encoder.text_model.final_layer_norm.parameters(),
+    text_encoder.text_model.embeddings.position_embedding.parameters(),
+)
+freeze_params(params_to_freeze)
+
+# Creating our training data
+
+train_dataset = TextualInversionDataset(
+    data_root=args.input_dir,
+    tokenizer=tokenizer,
+    size=512,
+    placeholder_token=args.placeholder_token,
+    repeats=100,
+    learnable_property=args.what_to_teach,  # Option selected above between object and style
+    center_crop=False,
+    set="train",
+)
+
+
+def create_dataloader(train_batch_size=1):
+    return torch.utils.data.DataLoader(
+        train_dataset, batch_size=train_batch_size, shuffle=True
+    )
+
+
+# Create noise_scheduler for training.
+noise_scheduler = DDPMScheduler(
+    beta_start=0.00085,
+    beta_end=0.012,
+    beta_schedule="scaled_linear",
+    num_train_timesteps=1000,
+    tensor_format="pt",
+)
+
+# Define hyperparameters for our training
+hyperparameters = {
+    "learning_rate": 5e-04,
+    "scale_lr": True,
+    "max_train_steps": args.training_steps,
+    "train_batch_size": 1,
+    "gradient_accumulation_steps": 4,
+    "seed": args.seed,
+    "output_dir": "sd-concept-output",
+}
+
+
+def training_function(text_encoder, vae, unet):
+    logger = get_logger(__name__)
+
+    train_batch_size = hyperparameters["train_batch_size"]
+    gradient_accumulation_steps = hyperparameters[
+        "gradient_accumulation_steps"
+    ]
+    learning_rate = hyperparameters["learning_rate"]
+    max_train_steps = hyperparameters["max_train_steps"]
+    output_dir = hyperparameters["output_dir"]
+
+    accelerator = Accelerator(
+        gradient_accumulation_steps=gradient_accumulation_steps,
+    )
+
+    train_dataloader = create_dataloader(train_batch_size)
+
+    if hyperparameters["scale_lr"]:
+        learning_rate = (
+            learning_rate
+            * gradient_accumulation_steps
+            * train_batch_size
+            * accelerator.num_processes
+        )
+
+    # Initialize the optimizer
+    optimizer = torch.optim.AdamW(
+        text_encoder.get_input_embeddings().parameters(),  # only optimize the embeddings
+        lr=learning_rate,
+    )
+
+    text_encoder, optimizer, train_dataloader = accelerator.prepare(
+        text_encoder, optimizer, train_dataloader
+    )
+
+    # Move vae and unet to device
+    vae.to(accelerator.device)
+    unet.to(accelerator.device)
+
+    # Keep vae and unet in eval model as we don't train these
+    vae.eval()
+    unet.eval()
+
+    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
+    num_update_steps_per_epoch = math.ceil(
+        len(train_dataloader) / gradient_accumulation_steps
+    )
+    num_train_epochs = math.ceil(max_train_steps / num_update_steps_per_epoch)
+
+    # Train!
+    total_batch_size = (
+        train_batch_size
+        * accelerator.num_processes
+        * gradient_accumulation_steps
+    )
+
+    logger.info("***** Running training *****")
+    logger.info(f"  Num examples = {len(train_dataset)}")
+    logger.info(f"  Instantaneous batch size per device = {train_batch_size}")
+    logger.info(
+        f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}"
+    )
+    logger.info(
+        f"  Gradient Accumulation steps = {gradient_accumulation_steps}"
+    )
+    logger.info(f"  Total optimization steps = {max_train_steps}")
+    # Only show the progress bar once on each machine.
+    progress_bar = tqdm(
+        range(max_train_steps), disable=not accelerator.is_local_main_process
+    )
+    progress_bar.set_description("Steps")
+    global_step = 0
+
+    for epoch in range(num_train_epochs):
+        text_encoder.train()
+        for step, batch in enumerate(train_dataloader):
+            with accelerator.accumulate(text_encoder):
+                # Convert images to latent space
+                latents = (
+                    vae.encode(batch["pixel_values"])
+                    .latent_dist.sample()
+                    .detach()
+                )
+                latents = latents * 0.18215
+
+                # Sample noise that we'll add to the latents
+                noise = torch.randn(latents.shape).to(latents.device)
+                bsz = latents.shape[0]
+                # Sample a random timestep for each image
+                timesteps = torch.randint(
+                    0,
+                    noise_scheduler.num_train_timesteps,
+                    (bsz,),
+                    device=latents.device,
+                ).long()
+
+                # Add noise to the latents according to the noise magnitude at each timestep
+                # (this is the forward diffusion process)
+                noisy_latents = noise_scheduler.add_noise(
+                    latents, noise, timesteps
+                )
+
+                # Get the text embedding for conditioning
+                encoder_hidden_states = text_encoder(batch["input_ids"])[0]
+
+                # Predict the noise residual
+                noise_pred = unet(
+                    noisy_latents, timesteps, encoder_hidden_states
+                ).sample
+
+                loss = (
+                    F.mse_loss(noise_pred, noise, reduction="none")
+                    .mean([1, 2, 3])
+                    .mean()
+                )
+                accelerator.backward(loss)
+
+                # Zero out the gradients for all token embeddings except the newly added
+                # embeddings for the concept, as we only want to optimize the concept embeddings
+                if accelerator.num_processes > 1:
+                    grads = (
+                        text_encoder.module.get_input_embeddings().weight.grad
+                    )
+                else:
+                    grads = text_encoder.get_input_embeddings().weight.grad
+                # Get the index for tokens that we want to zero the grads for
+                index_grads_to_zero = (
+                    torch.arange(len(tokenizer)) != placeholder_token_id
+                )
+                grads.data[index_grads_to_zero, :] = grads.data[
+                    index_grads_to_zero, :
+                ].fill_(0)
+
+                optimizer.step()
+                optimizer.zero_grad()
+
+            # Checks if the accelerator has performed an optimization step behind the scenes
+            if accelerator.sync_gradients:
+                progress_bar.update(1)
+                global_step += 1
+
+            logs = {"loss": loss.detach().item()}
+            progress_bar.set_postfix(**logs)
+
+            if global_step >= max_train_steps:
+                break
+
+        accelerator.wait_for_everyone()
+
+    # Create the pipeline using using the trained modules and save it.
+    if accelerator.is_main_process:
+        pipeline = StableDiffusionPipeline(
+            text_encoder=accelerator.unwrap_model(text_encoder),
+            vae=vae,
+            unet=unet,
+            tokenizer=tokenizer,
+            scheduler=PNDMScheduler(
+                beta_start=0.00085,
+                beta_end=0.012,
+                beta_schedule="scaled_linear",
+                skip_prk_steps=True,
+            ),
+            safety_checker=StableDiffusionSafetyChecker.from_pretrained(
+                "CompVis/stable-diffusion-safety-checker"
+            ),
+            feature_extractor=CLIPFeatureExtractor.from_pretrained(
+                "openai/clip-vit-base-patch32"
+            ),
+        )
+        pipeline.save_pretrained(output_dir)
+        # Also save the newly trained embeddings
+        learned_embeds = (
+            accelerator.unwrap_model(text_encoder)
+            .get_input_embeddings()
+            .weight[placeholder_token_id]
+        )
+        learned_embeds_dict = {
+            args.placeholder_token: learned_embeds.detach().cpu()
+        }
+        torch.save(
+            learned_embeds_dict, os.path.join(output_dir, "learned_embeds.bin")
+        )
+
+
+import accelerate
+
+accelerate.notebook_launcher(
+    training_function, args=(text_encoder, vae, unet), num_processes=1
+)
+
+# Set up the pipeline
+pipe = StableDiffusionPipeline.from_pretrained(
+    hyperparameters["output_dir"],
+    # torch_dtype=torch.float16,
+)
+
+all_images = []
+for _ in range(args.num_inference_samples):
+    images = pipe(
+        [args.prompt],
+        num_inference_steps=args.inference_steps,
+        guidance_scale=7.5,
+    ).images
+    all_images.extend(images)
+
+# output_path = os.path.abspath(os.path.join(os.getcwd(), args.output_dir))
+if not os.path.isdir(args.output_dir):
+    os.mkdir(args.output_dir)
+
+[
+    image.save(f"{args.output_dir}/{i}.jpeg")
+    for i, image in enumerate(all_images)
+]

shark/iree_utils/_common.py

@@ -37,7 +37,64 @@ def run_cmd(cmd):
         sys.exit("Exiting program due to error running:", cmd)
 
 
-IREE_DEVICE_MAP = {
+def iree_device_map(device):
+
+    from iree.runtime import get_driver, get_device
+
+    def get_all_devices(driver_name):
+        driver = get_driver(driver_name)
+        device_list_src = driver.query_available_devices()
+        device_list = []
+        for device_dict in device_list_src:
+            device_list.append(f"{driver_name}://{device_dict['path']}")
+        device_list.sort()
+        return device_list
+
+    # only supported for vulkan as of now
+    if "vulkan://" in device:
+        device_list = get_all_devices("vulkan")
+        _, d_index = device.split("://")
+        matched_index = None
+        match_with_index = False
+        if 0 <= len(d_index) <= 2:
+            try:
+                d_index = int(d_index)
+            except:
+                print(
+                    f"{d_index} is not valid index or uri. Will choose device 0"
+                )
+                d_index = 0
+            match_with_index = True
+
+        if len(device_list) > 1:
+            print("List of available vulkan devices:")
+            for i, d in enumerate(device_list):
+                print(f"vulkan://{i} => {d}")
+                if (match_with_index and d_index == i) or (
+                    not match_with_index and d == device
+                ):
+                    matched_index = i
+            print(
+                f"Choosing device vulkan://{matched_index}\nTo choose another device please specify device index or uri accordingly."
+            )
+            return get_device(device_list[matched_index])
+        elif len(device_list) == 1:
+            print(f"Found one vulkan device: {device_list[0]}. Using this.")
+            return get_device(device_list[0])
+        else:
+            print(
+                f"No device found! returning device corresponding to driver name: vulkan"
+            )
+            return _IREE_DEVICE_MAP["vulkan"]
+    else:
+        return _IREE_DEVICE_MAP[device]
+
+
+def get_supported_device_list():
+    return list(_IREE_DEVICE_MAP.keys())
+
+
+_IREE_DEVICE_MAP = {
     "cpu": "local-task",
     "cuda": "cuda",
     "vulkan": "vulkan",
@@ -46,7 +103,14 @@ IREE_DEVICE_MAP = {
     "intel-gpu": "level_zero",
 }
 
-IREE_TARGET_MAP = {
+
+def iree_target_map(device):
+    if "://" in device:
+        device = device.split("://")[0]
+    return _IREE_TARGET_MAP[device]
+
+
+_IREE_TARGET_MAP = {
     "cpu": "llvm-cpu",
     "cuda": "cuda",
     "vulkan": "vulkan",
@@ -58,6 +122,9 @@ IREE_TARGET_MAP = {
 # Finds whether the required drivers are installed for the given device.
 def check_device_drivers(device):
     """Checks necessary drivers present for gpu and vulkan devices"""
+    if "://" in device:
+        device = device.split("://")[0]
+
     if device == "cuda":
         try:
             subprocess.check_output("nvidia-smi")

shark/iree_utils/benchmark_utils.py

@@ -13,12 +13,13 @@
 # limitations under the License.
 
 import iree.runtime.scripts.iree_benchmark_module as benchmark_module
-from shark.iree_utils._common import run_cmd, IREE_DEVICE_MAP
+from shark.iree_utils._common import run_cmd, iree_device_map
+from shark.iree_utils.cpu_utils import get_cpu_count
 import numpy as np
 import os
 import re
 
-UNIT_TO_SECOND_MAP = {"ms": 0.001, "s": 1}
+UNIT_TO_SECOND_MAP = {"us": 1e-6, "ms": 0.001, "s": 1}
 
 
 def tensor_to_type_str(input_tensors: tuple, mlir_dialect: str):
@@ -69,10 +70,40 @@ def build_benchmark_args(
         # TODO: Replace name of train with actual train fn name.
         fn_name = "train"
     benchmark_cl.append(f"--entry_function={fn_name}")
-    benchmark_cl.append(f"--device={IREE_DEVICE_MAP[device]}")
+    benchmark_cl.append(f"--device={iree_device_map(device)}")
     mlir_input_types = tensor_to_type_str(input_tensors, mlir_dialect)
     for mlir_input in mlir_input_types:
         benchmark_cl.append(f"--function_input={mlir_input}")
+    if device == "cpu":
+        num_cpus = get_cpu_count()
+        if num_cpus is not None:
+            benchmark_cl.append(f"--task_topology_max_group_count={num_cpus}")
+    time_extractor = "| awk 'END{{print $2 $3}}'"
+    benchmark_cl.append(time_extractor)
+    return benchmark_cl
+
+
+def build_benchmark_args_non_tensor_input(
+    input_file: str,
+    device: str,
+    inputs: tuple,
+    mlir_dialect: str,
+    function_name: str,
+):
+    """
+    Inputs: input_file leading to vmfb, input_tensor to function, target device,
+    and whether it is training or not.
+    Outputs: string that execute benchmark-module on target model.
+    """
+    path = benchmark_module.__path__[0]
+    benchmarker_path = os.path.join(path, "..", "..", "iree-benchmark-module")
+    benchmark_cl = [benchmarker_path, f"--module_file={input_file}"]
+    # TODO: The function named can be passed as one of the args.
+    if function_name:
+        benchmark_cl.append(f"--entry_function={function_name}")
+    benchmark_cl.append(f"--device={iree_device_map(device)}")
+    for input in inputs:
+        benchmark_cl.append(f"--function_input={input}")
     time_extractor = "| awk 'END{{print $2 $3}}'"
     benchmark_cl.append(time_extractor)
     return benchmark_cl

shark/iree_utils/compile_utils.py

@@ -13,12 +13,17 @@
 # limitations under the License.
 import iree.runtime as ireert
 import iree.compiler as ireec
-from shark.iree_utils._common import IREE_DEVICE_MAP, IREE_TARGET_MAP
+from shark.iree_utils._common import iree_device_map, iree_target_map
+from shark.iree_utils.benchmark_utils import *
+from shark.parser import shark_args
 import numpy as np
 import os
+import re
 
 # Get the iree-compile arguments given device.
-def get_iree_device_args(device):
+def get_iree_device_args(device, extra_args=[]):
+    if "://" in device:
+        device = device.split("://")[0]
     if device == "cpu":
         from shark.iree_utils.cpu_utils import get_iree_cpu_args
 
@@ -30,7 +35,7 @@ def get_iree_device_args(device):
     if device in ["metal", "vulkan"]:
         from shark.iree_utils.vulkan_utils import get_iree_vulkan_args
 
-        return get_iree_vulkan_args()
+        return get_iree_vulkan_args(extra_args=extra_args)
     if device == "rocm":
         from shark.iree_utils.gpu_utils import get_iree_rocm_args
 
@@ -62,14 +67,178 @@ def get_iree_common_args():
     ]
 
 
+# Args that are suitable only for certain models or groups of models.
+# shark_args are passed down from pytests to control which models compile with these flags,
+# but they can also be set in shark/parser.py
+def get_model_specific_args():
+    ms_args = []
+    if shark_args.enable_conv_transform == True:
+        ms_args += ["--iree-flow-enable-conv-nchw-to-nhwc-transform"]
+    return ms_args
+
+
+def create_dispatch_dirs(bench_dir, device):
+    protected_files = ["ordered-dispatches.txt"]
+    bench_dir_path = bench_dir.split("/")
+    bench_dir_path[-1] = "temp_" + bench_dir_path[-1]
+    tmp_bench_dir = "/".join(bench_dir_path)
+    for f_ in os.listdir(bench_dir):
+        if os.path.isfile(f"{bench_dir}/{f_}") and f_ not in protected_files:
+            dir_name = re.sub("\.\S*$", "", f_)
+            if os.path.exists(f"{bench_dir}/{dir_name}"):
+                os.system(f"rm -rf {bench_dir}/{dir_name}")
+            os.system(f"mkdir {bench_dir}/{dir_name}")
+            os.system(f"mv {bench_dir}/{f_} {bench_dir}/{dir_name}/{f_}")
+    for f_ in os.listdir(tmp_bench_dir):
+        if os.path.isfile(f"{tmp_bench_dir}/{f_}"):
+            dir_name = ""
+            for d_ in os.listdir(bench_dir):
+                if re.search(f"{d_}(?=\D)", f_):
+                    dir_name = d_
+            if dir_name != "":
+                os.system(
+                    f"mv {tmp_bench_dir}/{f_} {bench_dir}/{dir_name}/{dir_name}_benchmark.mlir"
+                )
+
+
+def dump_isas(bench_dir):
+    for d_ in os.listdir(bench_dir):
+        if os.path.isdir(f"{bench_dir}/{d_}"):
+            for f_ in os.listdir(f"{bench_dir}/{d_}"):
+                if f_.endswith(".spv"):
+                    os.system(
+                        f"amdllpc -gfxip 11.0 {bench_dir}/{d_}/{f_} -v > \
+                         {bench_dir}/{d_}/isa.txt"
+                    )
+
+
+def compile_benchmark_dirs(bench_dir, device, dispatch_benchmarks):
+    benchmark_runtimes = {}
+    dispatch_list = []
+    all_dispatches = False
+
+    if dispatch_benchmarks.lower().strip() == "all":
+        all_dispatches = True
+    else:
+        try:
+            dispatch_list = [
+                int(dispatch_index)
+                for dispatch_index in dispatch_benchmarks.split(" ")
+            ]
+        except:
+            print("ERROR: Invalid dispatch benchmarks")
+            return None
+    for d_ in os.listdir(bench_dir):
+        if os.path.isdir(f"{bench_dir}/{d_}"):
+            in_dispatches = False
+            for dispatch in dispatch_list:
+                if str(dispatch) in d_:
+                    in_dispatches = True
+            if all_dispatches or in_dispatches:
+                for f_ in os.listdir(f"{bench_dir}/{d_}"):
+
+                    if "benchmark.mlir" in f_:
+                        dispatch_file = open(f"{bench_dir}/{d_}/{f_}", "r")
+                        module = dispatch_file.read()
+                        dispatch_file.close()
+
+                        flatbuffer_blob = ireec.compile_str(
+                            module, target_backends=[iree_target_map(device)]
+                        )
+
+                        vmfb_file = open(
+                            f"{bench_dir}/{d_}/{d_}_benchmark.vmfb", "wb"
+                        )
+                        vmfb_file.write(flatbuffer_blob)
+                        vmfb_file.close()
+
+                        config = get_iree_runtime_config(device)
+                        vm_module = ireert.VmModule.from_flatbuffer(
+                            config.vm_instance, flatbuffer_blob
+                        )
+
+                        benchmark_cl = build_benchmark_args_non_tensor_input(
+                            input_file=f"{bench_dir}/{d_}/{d_}_benchmark.vmfb",
+                            device=device,
+                            inputs=(0,),
+                            mlir_dialect="linalg",
+                            function_name="",
+                        )
+
+                        benchmark_bash = open(
+                            f"{bench_dir}/{d_}/{d_}_benchmark.sh", "w+"
+                        )
+                        benchmark_bash.write("#!/bin/bash\n")
+                        benchmark_bash.write(" ".join(benchmark_cl))
+                        benchmark_bash.close()
+
+                        benchmark_data = run_benchmark_module(benchmark_cl)
+
+                        benchmark_file = open(
+                            f"{bench_dir}/{d_}/{d_}_data.txt", "w+"
+                        )
+                        benchmark_file.write(f"DISPATCH: {d_}\n")
+                        benchmark_file.write(str(benchmark_data) + "\n")
+                        benchmark_file.write(
+                            "SHARK BENCHMARK RESULT: "
+                            + str(1 / (benchmark_data * 0.001))
+                            + "\n"
+                        )
+                        benchmark_file.close()
+
+                        benchmark_runtimes[d_] = 1 / (benchmark_data * 0.001)
+
+                    elif ".mlir" in f_ and "benchmark" not in f_:
+                        dispatch_file = open(f"{bench_dir}/{d_}/{f_}", "r")
+                        module = dispatch_file.read()
+                        dispatch_file.close()
+
+                        module = re.sub(
+                            "hal.executable private",
+                            "hal.executable public",
+                            module,
+                        )
+
+                        flatbuffer_blob = ireec.compile_str(
+                            module,
+                            target_backends=[iree_target_map(device)],
+                            extra_args=["--compile-mode=hal-executable"],
+                        )
+
+                        spirv_file = open(
+                            f"{bench_dir}/{d_}/{d_}_spirv.vmfb", "wb"
+                        )
+                        spirv_file.write(flatbuffer_blob)
+                        spirv_file.close()
+
+    ordered_dispatches = [
+        (k, v)
+        for k, v in sorted(
+            benchmark_runtimes.items(), key=lambda item: item[1]
+        )
+    ][::-1]
+    f_ = open(f"{bench_dir}/ordered-dispatches.txt", "w+")
+    for dispatch in ordered_dispatches:
+        f_.write(f"{dispatch[0]}: {dispatch[1]}ms\n")
+    f_.close()
+
+
 def compile_module_to_flatbuffer(
-    module, device, frontend, func_name, model_config_path
+    module,
+    device,
+    frontend,
+    func_name,
+    model_config_path,
+    extra_args,
+    model_name="None",
 ):
     # Setup Compile arguments wrt to frontends.
     input_type = ""
     args = get_iree_frontend_args(frontend)
-    args += get_iree_device_args(device)
+    args += get_iree_device_args(device, extra_args)
     args += get_iree_common_args()
+    args += get_model_specific_args()
+    args += extra_args
 
     if frontend in ["tensorflow", "tf"]:
         input_type = "mhlo"
@@ -78,25 +247,23 @@ def compile_module_to_flatbuffer(
     elif frontend in ["tflite", "tflite-tosa"]:
         input_type = "tosa"
     elif frontend in ["tm_tensor"]:
-        input_type = frontend
+        input_type = ireec.InputType.TM_TENSOR
 
     # TODO: make it simpler.
     # Compile according to the input type, else just try compiling.
-    if input_type not in ["mhlo", "tosa"]:
-        module = str(module)
     if input_type != "":
         # Currently for MHLO/TOSA.
         flatbuffer_blob = ireec.compile_str(
             module,
-            target_backends=[IREE_TARGET_MAP[device]],
+            target_backends=[iree_target_map(device)],
             extra_args=args,
             input_type=input_type,
         )
     else:
         # Currently for Torch.
         flatbuffer_blob = ireec.compile_str(
-            str(module),
-            target_backends=[IREE_TARGET_MAP[device]],
+            module,
+            target_backends=[iree_target_map(device)],
             extra_args=args,
         )
 
@@ -105,7 +272,7 @@ def compile_module_to_flatbuffer(
 
 def get_iree_module(flatbuffer_blob, device, func_name):
     # Returns the compiled module and the configs.
-    config = ireert.Config(IREE_DEVICE_MAP[device])
+    config = get_iree_runtime_config(device)
     vm_module = ireert.VmModule.from_flatbuffer(
         config.vm_instance, flatbuffer_blob
     )
@@ -121,10 +288,11 @@ def get_iree_compiled_module(
     frontend: str = "torch",
     func_name: str = "forward",
     model_config_path: str = None,
+    extra_args: list = [],
 ):
     """Given a module returns the compiled .vmfb and configs"""
     flatbuffer_blob = compile_module_to_flatbuffer(
-        module, device, frontend, func_name, model_config_path
+        module, device, frontend, func_name, model_config_path, extra_args
     )
     return get_iree_module(flatbuffer_blob, device, func_name)
 
@@ -146,12 +314,18 @@ def export_iree_module_to_vmfb(
     mlir_dialect: str = "linalg",
     func_name: str = "forward",
     model_config_path: str = None,
+    module_name: str = None,
+    extra_args: list = [],
 ):
     # Compiles the module given specs and saves it as .vmfb file.
     flatbuffer_blob = compile_module_to_flatbuffer(
-        module, device, mlir_dialect, func_name, model_config_path
+        module, device, mlir_dialect, func_name, model_config_path, extra_args
     )
-    module_name = f"{mlir_dialect}_{func_name}_{device}"
+    if module_name is None:
+        device_name = (
+            device if "://" not in device else "-".join(device.split("://"))
+        )
+        module_name = f"{mlir_dialect}_{func_name}_{device_name}"
     filename = os.path.join(directory, module_name + ".vmfb")
     print(f"Saved vmfb in {filename}.")
     with open(filename, "wb") as f:
@@ -187,4 +361,14 @@ def get_results(compiled_vm, input, config, frontend="torch"):
         res = np.array(data, dtype=object)
         return np.copy(res)
     else:
-        return np.copy(np.asarray(result, dtype=result.dtype))
+        return result.to_host()
+
+
+def get_iree_runtime_config(device):
+    device = iree_device_map(device)
+    if type(device) == ireert.HalDevice:
+        config = ireert.Config(device=device)
+    else:
+        driver_name = device.split("://")[0] if "://" in device else device
+        config = ireert.Config(driver_name=driver_name)
+    return config

shark/iree_utils/cpu_utils.py

@@ -16,6 +16,17 @@
 
 import subprocess
 
+
+def get_cpu_count():
+    import multiprocessing
+
+    try:
+        cpu_count = multiprocessing.cpu_count()
+        return cpu_count
+    except NotImplementedError:
+        return None
+
+
 # Get the default cpu args.
 def get_iree_cpu_args():
     find_triple_cmd = "uname -s -m"

shark/iree_utils/vulkan_utils.py

@@ -14,12 +14,42 @@
 
 # All the iree_vulkan related functionalities go here.
 
+from os import linesep
 from shark.iree_utils._common import run_cmd
+import iree.runtime as ireert
+from sys import platform
 
 
-def get_vulkan_triple_flag():
-    vulkan_device_cmd = "vulkaninfo | grep deviceName"
-    vulkan_device = run_cmd(vulkan_device_cmd).strip()
+def get_vulkan_device_name():
+    vulkaninfo_dump = run_cmd("vulkaninfo").split(linesep)
+    vulkaninfo_list = [s.strip() for s in vulkaninfo_dump if "deviceName" in s]
+    if len(vulkaninfo_list) == 0:
+        raise ValueError("No device name found in VulkanInfo!")
+    if len(vulkaninfo_list) > 1:
+        print(
+            f"Found {len(vulkaninfo_list)} device names. choosing first one: {vulkaninfo_list[0]}"
+        )
+    return vulkaninfo_list[0]
+
+
+def get_os_name():
+    if platform.startswith("linux"):
+        return "linux"
+    elif platform == "darwin":
+        return "macos"
+    elif platform == "win32":
+        return "windows"
+    else:
+        print("Cannot detect OS type, defaulting to linux.")
+        return "linux"
+
+
+def get_vulkan_triple_flag(extra_args=[]):
+    if "-iree-vulkan-target-triple=" in " ".join(extra_args):
+        print(f"Using target triple from command line args")
+        return None
+    system_os = get_os_name()
+    vulkan_device = get_vulkan_device_name()
     if all(x in vulkan_device for x in ("Apple", "M1")):
         print(f"Found {vulkan_device} Device. Using m1-moltenvk-macos")
         return "-iree-vulkan-target-triple=m1-moltenvk-macos"
@@ -27,21 +57,26 @@ def get_vulkan_triple_flag():
         print("Found Apple M2 Device. Using m1-moltenvk-macos")
         return "-iree-vulkan-target-triple=m1-moltenvk-macos"
     elif all(x in vulkan_device for x in ("A100", "SXM4")):
-        print(f"Found {vulkan_device} Device. Using ampere-rtx3080-linux")
-        return "-iree-vulkan-target-triple=ampere-rtx3080-linux"
+        print(
+            f"Found {vulkan_device} Device. Using ampere-rtx3080-{system_os}"
+        )
+        return f"-iree-vulkan-target-triple=ampere-rtx3080-{system_os}"
     elif all(x in vulkan_device for x in ("RTX", "3090")):
-        print(f"Found {vulkan_device} Device. Using ampere-rtx3090-linux")
-        return "-iree-vulkan-target-triple=ampere-rtx3090-linux"
-    elif any(x in vulkan_device for x in ("Radeon", "RX 5")):
         print(
-            "Found AMD Radeon RX 5000 series device. Using rdna1-5700xt-linux"
+            f"Found {vulkan_device} Device. Using ampere-rtx3090-{system_os}"
         )
-        return "-iree-vulkan-target-triple=rdna1-5700xt-linux"
-    elif all(x in vulkan_device for x in ("Radeon", "RX 6")):
+        return f"-iree-vulkan-target-triple=ampere-rtx3090-{system_os}"
+    elif all(x in vulkan_device for x in ("RTX", "4090")):
         print(
-            "Found AMD Radeon RX 6000 series device. Using rdna2-unknown-linux"
+            f"Found {vulkan_device} Device. Using ampere-rtx3090-{system_os}"
         )
-        return "-iree-vulkan-target-triple=rdna2-unknown-linux"
+        return f"-iree-vulkan-target-triple=ampere-rtx3090-{system_os}"
+    elif all(x in vulkan_device for x in ("AMD", "7900")):
+        print(f"Found {vulkan_device} Device. Using rdna3-7900-{system_os}")
+        return f"-iree-vulkan-target-triple=rdna3-7900-{system_os}"
+    elif any(x in vulkan_device for x in ("AMD", "Radeon")):
+        print(f"Found AMD device. Using rdna2-unknown-{system_os}")
+        return f"-iree-vulkan-target-triple=rdna2-unknown-{system_os}"
     else:
         print(
             """Optimized kernel for your target device is not added yet.
@@ -52,10 +87,16 @@ def get_vulkan_triple_flag():
         return None
 
 
-def get_iree_vulkan_args():
+def get_iree_vulkan_args(extra_args=[]):
     # vulkan_flag = ["--iree-flow-demote-i64-to-i32"]
     vulkan_flag = []
-    vulkan_triple_flag = get_vulkan_triple_flag()
+    vulkan_triple_flag = get_vulkan_triple_flag(extra_args)
     if vulkan_triple_flag is not None:
         vulkan_flag.append(vulkan_triple_flag)
     return vulkan_flag
+
+
+def set_iree_vulkan_runtime_flags(flags):
+    for flag in flags:
+        ireert.flags.parse_flags(flag)
+    return

shark/model_annotation.py

@@ -12,6 +12,19 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+"""
+Usage:
+This function takes the model mlir file and the tuned config file as input,
+and output a new mlir file with lowering configs annotated on certain ops.
+There are two ways to utilize the function:
+1. Call model_annotation function within another python script
+from shark.model_annotation import model_annotation
+with create_context() as ctx:
+   module = model_annotation(ctx, input_contents=..., config_path=..., search_op=...)
+2. Run model_annotation.py directly
+python model_annotation.py path_to_original_mlir path_to_config_file
+"""
+
 import json
 import os
 import sys
@@ -105,7 +118,9 @@ def add_attributes(op: ir.Operation, config: Dict):
 
 
 def parse_config(config: Dict):
-    if config["pipeline"] == "GPU" or config["pipeline"] == "GPU_TENSORCORE":
+    split_k = None
+    pipeline_depth = None
+    if "GPU" in config["pipeline"]:
         pipeline = (
             "LLVMGPUMatmulSimt"
             if config["pipeline"] == "GPU"
@@ -113,24 +128,31 @@ def parse_config(config: Dict):
         )
         tile_sizes = [config["work_group_tile_sizes"]]
         workgroup_size = config["work_group_sizes"]
-        try:
+        if "pipeline_depth" in config.keys():
             pipeline_depth = config["pipeline_depth"]
-        except:
-            pipeline_depth = None
-        try:
+        if "split_k" in config.keys():
             split_k = config["split_k"]
-        except:
-            split_k = None
-    else:
+    elif "SPIRV" in config["pipeline"]:
         pipeline = config["pipeline"]
         tile_sizes = [
             config["work_group_tile_sizes"],
-            config["l1_tile_sizes"],
-            config["vector_tile_sizes"],
+            config["parallel_tile_sizes"],
+            config["reduction_tile_sizes"],
+        ]
+        if "vector_tile_sizes" in config.keys():
+            tile_sizes += [config["vector_tile_sizes"]]
+        if "window_tile_sizes" in config.keys():
+            tile_sizes += [config["window_tile_sizes"]]
+        workgroup_size = config["work_group_sizes"]
+    else:
+        # For IREE CPU pipelines
+        pipeline = config["pipeline"]
+        tile_sizes = [
+            config["work_group_tile_sizes"],
+            config["parallel_tile_sizes"],
+            config["reduction_tile_sizes"],
         ]
         workgroup_size = []
-        split_k = None
-        pipeline_depth = None
     return tile_sizes, pipeline, workgroup_size, split_k, pipeline_depth
 
 

shark/parser.py

@@ -93,4 +93,23 @@ parser.add_argument(
     help="Specify where to save downloaded shark_tank artifacts. If this is not set, the default is ~/.local/shark_tank/.",
 )
 
+parser.add_argument(
+    "--dispatch_benchmarks",
+    default=None,
+    help='dispatches to return benchamrk data on.  use "All" for all, and None for none.',
+)
+
+parser.add_argument(
+    "--dispatch_benchmarks_dir",
+    default="temp_dispatch_benchmarks",
+    help='directory where you want to store dispatch data generated with "--dispatch_benchmarks"',
+)
+
+parser.add_argument(
+    "--enable_conv_transform",
+    default=False,
+    action="store",
+    help="Enables the --iree-flow-enable-conv-nchw-to-nhwc-transform flag.",
+)
+
 shark_args, unknown = parser.parse_known_args()

shark/shark_benchmark_runner.py

@@ -39,29 +39,54 @@ class OnnxFusionOptions(object):
         self.no_attention_mask = False
 
 
+def check_requirements(frontend):
+    import importlib
+
+    has_pkgs = False
+    if frontend == "torch":
+        tv_spec = importlib.util.find_spec("torchvision")
+        has_pkgs = tv_spec is not None
+
+    elif frontend in ["tensorflow", "tf"]:
+        keras_spec = importlib.util.find_spec("keras")
+        tf_spec = importlib.util.find_spec("tensorflow")
+        has_pkgs = keras_spec is not None and tf_spec is not None
+
+    return has_pkgs
+
+
 class SharkBenchmarkRunner(SharkRunner):
     # SharkRunner derived class with Benchmarking capabilities.
     def __init__(
         self,
-        mlir_module: str,
+        mlir_module: bytes,
         function_name: str = "forward",
         device: str = "none",
         mlir_dialect: str = "linalg",
+        extra_args: list = [],
     ):
         self.device = shark_args.device if device == "none" else device
         self.frontend_model = None
         self.vmfb_file = None
         self.mlir_dialect = mlir_dialect
+        self.extra_args = extra_args
         SharkRunner.__init__(
             self,
             mlir_module,
             function_name,
             device,
             self.mlir_dialect,
+            self.extra_args,
+            compile_vmfb=True,
         )
         if self.vmfb_file == None:
             self.vmfb_file = export_iree_module_to_vmfb(
-                mlir_module, device, shark_args.repro_dir, self.mlir_dialect
+                mlir_module,
+                device,
+                shark_args.repro_dir,
+                self.mlir_dialect,
+                function_name,
+                extra_args=self.extra_args,
             )
 
     def setup_cl(self, input_tensors):
@@ -71,7 +96,6 @@ class SharkBenchmarkRunner(SharkRunner):
             input_tensors,
             mlir_dialect=self.mlir_dialect,
         )
-        print(self.benchmark_cl)
 
     def benchmark_frontend(self, modelname):
         if self.mlir_dialect in ["linalg", "torch"]:
@@ -116,30 +140,31 @@ class SharkBenchmarkRunner(SharkRunner):
         import tensorflow as tf
         from tank.model_utils_tf import get_tf_model
 
-        model, input, = get_tf_model(
-            modelname
-        )[:2]
-        frontend_model = model
+        tf_device = "/GPU:0" if self.device == "cuda" else "/CPU:0"
+        with tf.device(tf_device):
+            model, input, = get_tf_model(
+                modelname
+            )[:2]
+            frontend_model = model
 
-        for i in range(shark_args.num_warmup_iterations):
-            frontend_model.forward(*input)
+            for i in range(shark_args.num_warmup_iterations):
+                frontend_model.forward(*input)
 
-        begin = time.time()
-        for i in range(shark_args.num_iterations):
-            out = frontend_model.forward(*input)
-            if i == shark_args.num_iterations - 1:
-                end = time.time()
-                break
-        print(
-            f"TF benchmark:{shark_args.num_iterations/(end-begin)} iter/second, Total Iterations:{shark_args.num_iterations}"
-        )
-        return [
-            f"{shark_args.num_iterations/(end-begin)}",
-            f"{((end-begin)/shark_args.num_iterations)*1000}",
-        ]
+            begin = time.time()
+            for i in range(shark_args.num_iterations):
+                out = frontend_model.forward(*input)
+                if i == shark_args.num_iterations - 1:
+                    end = time.time()
+                    break
+            print(
+                f"TF benchmark:{shark_args.num_iterations/(end-begin)} iter/second, Total Iterations:{shark_args.num_iterations}"
+            )
+            return [
+                f"{shark_args.num_iterations/(end-begin)}",
+                f"{((end-begin)/shark_args.num_iterations)*1000}",
+            ]
 
     def benchmark_c(self):
-        print(self.benchmark_cl)
         result = run_benchmark_module(self.benchmark_cl)
         print(f"Shark-IREE-C benchmark:{result} iter/second")
         return [f"{result}", f"{1000/result}"]
@@ -249,19 +274,15 @@ for currently supported models. Exiting benchmark ONNX."
                     return [param_count, model_tags, model_notes]
 
     def compare_bench_results(self, baseline: str, result: str):
-        # Takes two numbers represented as strings and returns "<n>x slower/faster", as in "result is <n>x slower than baseline".
-        a = float(baseline)
-        b = float(result)
-        if a < b:
-            # result slower than baseline
-            comparison = (b - a) / a
-            comp_str = f"{round(comparison, 2)}x slower"
-        elif a > b:
-            # result faster than baseline
+        if baseline is not None:
+            # Takes a baseline and a result string and calculates a comparison, e.g. "1.04x baseline".
+            a = float(baseline)
+            b = float(result)
             comparison = a / b
-            comp_str = f"{round(comparison, 2)}x faster"
+            comp_str = f"{round(comparison, 2)}x baseline"
         else:
-            comp_str = "equal"
+            comp_str = "N/A"
+
         return comp_str
 
     def benchmark_all_csv(
@@ -311,17 +332,21 @@ for currently supported models. Exiting benchmark ONNX."
                 ) = ["", "", ""]
                 if e == "frontend":
                     bench_result["engine"] = frontend
-                    (
-                        bench_result["iter/sec"],
-                        bench_result["ms/iter"],
-                    ) = self.benchmark_frontend(modelname)
-                    self.frontend_result = bench_result["ms/iter"]
-                    bench_result["vs. PyTorch/TF"] = "="
-                    (
-                        bench_result["param_count"],
-                        bench_result["tags"],
-                        bench_result["notes"],
-                    ) = self.get_metadata(modelname)
+                    if check_requirements(frontend):
+                        (
+                            bench_result["iter/sec"],
+                            bench_result["ms/iter"],
+                        ) = self.benchmark_frontend(modelname)
+                        self.frontend_result = bench_result["ms/iter"]
+                        bench_result["vs. PyTorch/TF"] = "baseline"
+                        (
+                            bench_result["param_count"],
+                            bench_result["tags"],
+                            bench_result["notes"],
+                        ) = self.get_metadata(modelname)
+                    else:
+                        self.frontend_result = None
+                        continue
 
                 elif e == "shark_python":
                     bench_result["engine"] = "shark_python"

shark/shark_downloader.py

@@ -14,11 +14,51 @@
 
 import numpy as np
 import os
-import urllib.request
-import json
-import hashlib
+import sys
 from pathlib import Path
 from shark.parser import shark_args
+from google.cloud import storage
+
+
+def download_public_file(
+    full_gs_url, destination_folder_name, single_file=False
+):
+    """Downloads a public blob from the bucket."""
+    # bucket_name = "gs://your-bucket-name/path/to/file"
+    # destination_file_name = "local/path/to/file"
+
+    storage_client = storage.Client.create_anonymous_client()
+    bucket_name = full_gs_url.split("/")[2]
+    source_blob_name = None
+    dest_filename = None
+    desired_file = None
+    if single_file:
+
+        desired_file = full_gs_url.split("/")[-1]
+        source_blob_name = "/".join(full_gs_url.split("/")[3:-1])
+        destination_folder_name, dest_filename = os.path.split(
+            destination_folder_name
+        )
+    else:
+        source_blob_name = "/".join(full_gs_url.split("/")[3:])
+    bucket = storage_client.bucket(bucket_name)
+    blobs = bucket.list_blobs(prefix=source_blob_name)
+    if not os.path.exists(destination_folder_name):
+        os.mkdir(destination_folder_name)
+    for blob in blobs:
+        blob_name = blob.name.split("/")[-1]
+        if single_file:
+            if blob_name == desired_file:
+                destination_filename = os.path.join(
+                    destination_folder_name, dest_filename
+                )
+                blob.download_to_filename(destination_filename)
+            else:
+                continue
+
+        destination_filename = os.path.join(destination_folder_name, blob_name)
+        blob.download_to_filename(destination_filename)
+
 
 input_type_to_np_dtype = {
     "float32": np.float32,
@@ -50,8 +90,7 @@ if custom_path:
 else:
     WORKDIR = os.path.join(home, ".local/shark_tank/")
     print(
-        f"shark_tank local cache is located at {WORKDIR} . You may change this by setting the --local_tank_cache="
-        " pytest flag"
+        f"shark_tank local cache is located at {WORKDIR} . You may change this by setting the --local_tank_cache= flag"
     )
 
 # Checks whether the directory and files exists.
@@ -88,185 +127,65 @@ def check_dir_exists(model_name, frontend="torch", dynamic=""):
 
 
 # Downloads the torch model from gs://shark_tank dir.
-def download_torch_model(
-    model_name, dynamic=False, tank_url="gs://shark_tank/latest"
+def download_model(
+    model_name,
+    dynamic=False,
+    tank_url="gs://shark_tank/latest",
+    frontend=None,
+    tuned=None,
 ):
     model_name = model_name.replace("/", "_")
     dyn_str = "_dynamic" if dynamic else ""
     os.makedirs(WORKDIR, exist_ok=True)
-    model_dir_name = model_name + "_torch"
-
-    def gs_download_model():
-        gs_command = (
-            'gsutil -o "GSUtil:parallel_process_count=1" cp -r '
-            + tank_url
-            + "/"
-            + model_dir_name
-            + " "
-            + WORKDIR
-        )
-        if os.system(gs_command) != 0:
-            raise Exception("model not present in the tank. Contact Nod Admin")
-
-    if not check_dir_exists(model_dir_name, frontend="torch", dynamic=dyn_str):
-        gs_download_model()
-    else:
-        model_dir = os.path.join(WORKDIR, model_dir_name)
-        local_hash = str(np.load(os.path.join(model_dir, "hash.npy")))
-        gs_hash = (
-            'gsutil -o "GSUtil:parallel_process_count=1" cp '
-            + tank_url
-            + "/"
-            + model_dir_name
-            + "/hash.npy"
-            + " "
-            + os.path.join(model_dir, "upstream_hash.npy")
-        )
-        if os.system(gs_hash) != 0:
-            raise Exception("hash of the model not present in the tank.")
-        upstream_hash = str(
-            np.load(os.path.join(model_dir, "upstream_hash.npy"))
-        )
-        if local_hash != upstream_hash:
-            if shark_args.update_tank == True:
-                gs_download_model()
-            else:
-                print(
-                    "Hash does not match upstream in gs://shark_tank/. If you are using SHARK Downloader with locally generated artifacts, this is working as intended."
-                )
-
+    model_dir_name = model_name + "_" + frontend
     model_dir = os.path.join(WORKDIR, model_dir_name)
-    with open(
-        os.path.join(model_dir, model_name + dyn_str + "_torch.mlir")
-    ) as f:
-        mlir_file = f.read()
-
-    function_name = str(np.load(os.path.join(model_dir, "function_name.npy")))
-    inputs = np.load(os.path.join(model_dir, "inputs.npz"))
-    golden_out = np.load(os.path.join(model_dir, "golden_out.npz"))
-
-    inputs_tuple = tuple([inputs[key] for key in inputs])
-    golden_out_tuple = tuple([golden_out[key] for key in golden_out])
-    return mlir_file, function_name, inputs_tuple, golden_out_tuple
-
-
-# Downloads the tflite model from gs://shark_tank dir.
-def download_tflite_model(
-    model_name, dynamic=False, tank_url="gs://shark_tank/latest"
-):
-    dyn_str = "_dynamic" if dynamic else ""
-    os.makedirs(WORKDIR, exist_ok=True)
-    model_dir_name = model_name + "_tflite"
-
-    def gs_download_model():
-        gs_command = (
-            'gsutil -o "GSUtil:parallel_process_count=1" cp -r '
-            + tank_url
-            + "/"
-            + model_dir_name
-            + " "
-            + WORKDIR
-        )
-        if os.system(gs_command) != 0:
-            raise Exception("model not present in the tank. Contact Nod Admin")
+    full_gs_url = tank_url.rstrip("/") + "/" + model_dir_name
 
     if not check_dir_exists(
-        model_dir_name, frontend="tflite", dynamic=dyn_str
+        model_dir_name, frontend=frontend, dynamic=dyn_str
     ):
-        gs_download_model()
+        print(f"Downloading artifacts for model {model_name}...")
+        download_public_file(full_gs_url, model_dir)
     else:
-        model_dir = os.path.join(WORKDIR, model_dir_name)
-        local_hash = str(np.load(os.path.join(model_dir, "hash.npy")))
-        gs_hash = (
-            'gsutil -o "GSUtil:parallel_process_count=1" cp '
-            + tank_url
-            + "/"
-            + model_dir_name
-            + "/hash.npy"
-            + " "
-            + os.path.join(model_dir, "upstream_hash.npy")
-        )
-        if os.system(gs_hash) != 0:
-            raise Exception("hash of the model not present in the tank.")
-        upstream_hash = str(
-            np.load(os.path.join(model_dir, "upstream_hash.npy"))
-        )
-        if local_hash != upstream_hash:
-            if shark_args.update_tank == True:
-                gs_download_model()
-            else:
-                print(
-                    "Hash does not match upstream in gs://shark_tank/. If you are using SHARK Downloader with locally generated artifacts, this is working as intended."
-                )
+        if not _internet_connected():
+            print(
+                "No internet connection. Using the model already present in the tank."
+            )
+        else:
+            local_hash = str(np.load(os.path.join(model_dir, "hash.npy")))
+            gs_hash_url = (
+                tank_url.rstrip("/") + "/" + model_dir_name + "/hash.npy"
+            )
+            download_public_file(
+                gs_hash_url,
+                os.path.join(model_dir, "upstream_hash.npy"),
+                single_file=True,
+            )
+            upstream_hash = str(
+                np.load(os.path.join(model_dir, "upstream_hash.npy"))
+            )
+            if local_hash != upstream_hash:
+                if shark_args.update_tank == True:
+                    print(f"Updating artifacts for model {model_name}...")
+                    download_public_file(full_gs_url, WORKDIR)
+                else:
+                    print(
+                        "Hash does not match upstream in gs://shark_tank/. If you are using SHARK Downloader with locally generated artifacts, this is working as intended."
+                    )
 
     model_dir = os.path.join(WORKDIR, model_dir_name)
-    with open(
-        os.path.join(model_dir, model_name + dyn_str + "_tflite.mlir")
-    ) as f:
-        mlir_file = f.read()
-
-    function_name = str(np.load(os.path.join(model_dir, "function_name.npy")))
-    inputs = np.load(os.path.join(model_dir, "inputs.npz"))
-    golden_out = np.load(os.path.join(model_dir, "golden_out.npz"))
-
-    inputs_tuple = tuple([inputs[key] for key in inputs])
-    golden_out_tuple = tuple([golden_out[key] for key in golden_out])
-    return mlir_file, function_name, inputs_tuple, golden_out_tuple
-
-
-def download_tf_model(
-    model_name, tuned=None, tank_url="gs://shark_tank/latest"
-):
-    model_name = model_name.replace("/", "_")
-    os.makedirs(WORKDIR, exist_ok=True)
-    model_dir_name = model_name + "_tf"
-
-    def gs_download_model():
-        gs_command = (
-            'gsutil -o "GSUtil:parallel_process_count=1" cp -r '
-            + tank_url
-            + "/"
-            + model_dir_name
-            + " "
-            + WORKDIR
-        )
-        if os.system(gs_command) != 0:
-            raise Exception("model not present in the tank. Contact Nod Admin")
-
-    if not check_dir_exists(model_dir_name, frontend="tf"):
-        gs_download_model()
-    else:
-        model_dir = os.path.join(WORKDIR, model_dir_name)
-        local_hash = str(np.load(os.path.join(model_dir, "hash.npy")))
-        gs_hash = (
-            'gsutil -o "GSUtil:parallel_process_count=1" cp '
-            + tank_url
-            + "/"
-            + model_dir_name
-            + "/hash.npy"
-            + " "
-            + os.path.join(model_dir, "upstream_hash.npy")
-        )
-        if os.system(gs_hash) != 0:
-            raise Exception("hash of the model not present in the tank.")
-        upstream_hash = str(
-            np.load(os.path.join(model_dir, "upstream_hash.npy"))
-        )
-        if local_hash != upstream_hash:
-            if shark_args.update_tank == True:
-                gs_download_model()
-            else:
-                print(
-                    "Hash does not match upstream in gs://shark_tank/. If you are using SHARK Downloader with locally generated artifacts, this is working as intended."
-                )
-
-    model_dir = os.path.join(WORKDIR, model_dir_name)
-    suffix = "_tf.mlir" if tuned is None else "_tf_" + tuned + ".mlir"
+    suffix = (
+        "_" + frontend + ".mlir"
+        if tuned is None
+        else "_" + frontend + "_" + tuned + ".mlir"
+    )
     filename = os.path.join(model_dir, model_name + suffix)
     if not os.path.isfile(filename):
-        filename = os.path.join(model_dir, model_name + "_tf.mlir")
+        filename = os.path.join(
+            model_dir, model_name + "_" + frontend + ".mlir"
+        )
 
-    with open(filename) as f:
+    with open(filename, mode="rb") as f:
         mlir_file = f.read()
 
     function_name = str(np.load(os.path.join(model_dir, "function_name.npy")))
@@ -276,3 +195,13 @@ def download_tf_model(
     inputs_tuple = tuple([inputs[key] for key in inputs])
     golden_out_tuple = tuple([golden_out[key] for key in golden_out])
     return mlir_file, function_name, inputs_tuple, golden_out_tuple
+
+
+def _internet_connected():
+    import requests as req
+
+    try:
+        req.get("http://1.1.1.1")
+        return True
+    except:
+        return False

shark/shark_importer.py

@@ -75,21 +75,24 @@ class SharkImporter:
             self.module, self.inputs, is_dynamic, tracing_required
         )
 
-    def _tf_mlir(self, func_name):
+    def _tf_mlir(self, func_name, save_dir="./shark_tmp/"):
         from iree.compiler import tf as tfc
 
         return tfc.compile_module(
-            self.module, exported_names=[func_name], import_only=True
+            self.module,
+            exported_names=[func_name],
+            import_only=True,
+            output_file=save_dir,
         )
 
-    def _tflite_mlir(self, func_name):
+    def _tflite_mlir(self, func_name, save_dir="./shark_tmp/"):
         from iree.compiler import tflite as tflitec
-        from shark.iree_utils._common import IREE_TARGET_MAP
 
         self.mlir_model = tflitec.compile_file(
             self.raw_model_file,  # in tflite, it is a path to .tflite file, not a tflite interpreter
             input_type="tosa",
             import_only=True,
+            output_file=save_dir,
         )
         return self.mlir_model
 
@@ -99,6 +102,7 @@ class SharkImporter:
         is_dynamic=False,
         tracing_required=False,
         func_name="forward",
+        save_dir="./shark_tmp/",
     ):
         if self.frontend in ["torch", "pytorch"]:
             if self.inputs == None:
@@ -108,15 +112,15 @@ class SharkImporter:
                 sys.exit(1)
             return self._torch_mlir(is_dynamic, tracing_required), func_name
         if self.frontend in ["tf", "tensorflow"]:
-            return self._tf_mlir(func_name), func_name
+            return self._tf_mlir(func_name, save_dir), func_name
         if self.frontend in ["tflite", "tf-lite"]:
             func_name = "main"
-            return self._tflite_mlir(func_name), func_name
+            return self._tflite_mlir(func_name, save_dir), func_name
 
     # Converts the frontend specific tensors into np array.
     def convert_to_numpy(self, array_tuple: tuple):
         if self.frontend in ["torch", "pytorch"]:
-            return [x.detach().numpy() for x in array_tuple]
+            return [x.detach().cpu().numpy() for x in array_tuple]
         if self.frontend in ["tf", "tensorflow"]:
             return [x.numpy() for x in array_tuple]
 
@@ -130,19 +134,20 @@ class SharkImporter:
         outputs_name = "golden_out.npz"
         func_file_name = "function_name"
         model_name_mlir = model_name + "_" + self.frontend + ".mlir"
+        try:
+            inputs = [x.cpu().detach() for x in inputs]
+        except AttributeError:
+            try:
+                inputs = [x.numpy() for x in inputs]
+            except AttributeError:
+                inputs = [x for x in inputs]
         np.savez(os.path.join(dir, inputs_name), *inputs)
         np.savez(os.path.join(dir, outputs_name), *outputs)
         np.save(os.path.join(dir, func_file_name), np.array(func_name))
 
-        mlir_str = mlir_data
         if self.frontend == "torch":
-            mlir_str = mlir_data.operation.get_asm()
-        elif self.frontend == "tf":
-            mlir_str = mlir_data.decode("utf-8")
-        elif self.frontend == "tflite":
-            mlir_str = mlir_data.decode("utf-8")
-        with open(os.path.join(dir, model_name_mlir), "w") as mlir_file:
-            mlir_file.write(mlir_str)
+            with open(os.path.join(dir, model_name_mlir), "wb") as mlir_file:
+                mlir_file.write(mlir_data)
 
         return
 
@@ -159,9 +164,13 @@ class SharkImporter:
                 f"There is no input provided: {self.inputs}, please provide inputs or simply run import_mlir."
             )
             sys.exit(1)
-
+        model_name_mlir = model_name + "_" + self.frontend + ".mlir"
+        artifact_path = os.path.join(dir, model_name_mlir)
         imported_mlir = self.import_mlir(
-            is_dynamic, tracing_required, func_name
+            is_dynamic,
+            tracing_required,
+            func_name,
+            save_dir=artifact_path,
         )
         # TODO: Make sure that any generic function name is accepted. Currently takes in the default function names.
         # TODO: Check for multiple outputs.
@@ -171,7 +180,7 @@ class SharkImporter:
             golden_out = self.module(*self.inputs)
             if torch.is_tensor(golden_out):
                 golden_out = tuple(
-                    golden_out.detach().numpy(),
+                    golden_out.detach().cpu().numpy(),
                 )
             else:
                 golden_out = self.convert_to_numpy(golden_out)
@@ -234,3 +243,58 @@ class SharkImporter:
                 self.inputs,
                 golden_out,
             )
+
+
+# Applies fx conversion to the model and imports the mlir.
+def import_with_fx(model, inputs, debug=False):
+    import torch
+    from torch.fx.experimental.proxy_tensor import make_fx
+    from torch._decomp import get_decompositions
+
+    # TODO: Control the decompositions.
+    fx_g = make_fx(
+        model,
+        decomposition_table=get_decompositions(
+            [
+                torch.ops.aten.embedding_dense_backward,
+                torch.ops.aten.native_layer_norm_backward,
+                torch.ops.aten.slice_backward,
+                torch.ops.aten.select_backward,
+                torch.ops.aten.norm.ScalarOpt_dim,
+                torch.ops.aten.native_group_norm,
+                torch.ops.aten.upsample_bilinear2d.vec,
+                torch.ops.aten.split.Tensor,
+                torch.ops.aten.split_with_sizes,
+            ]
+        ),
+    )(*inputs)
+
+    fx_g.graph.set_codegen(torch.fx.graph.CodeGen())
+    fx_g.recompile()
+
+    def strip_overloads(gm):
+        """
+        Modifies the target of graph nodes in :attr:`gm` to strip overloads.
+        Args:
+            gm(fx.GraphModule): The input Fx graph module to be modified
+        """
+        for node in gm.graph.nodes:
+            if isinstance(node.target, torch._ops.OpOverload):
+                node.target = node.target.overloadpacket
+        gm.recompile()
+
+    strip_overloads(fx_g)
+
+    mlir_importer = SharkImporter(
+        fx_g,
+        inputs,
+        frontend="torch",
+    )
+
+    if debug:
+        (mlir_module, func_name), _, _ = mlir_importer.import_debug()
+        return mlir_module, func_name
+
+    mlir_module, func_name = mlir_importer.import_mlir()
+
+    return mlir_module, func_name

shark/shark_inference.py

@@ -12,6 +12,8 @@
 from shark.iree_utils.compile_utils import (
     export_iree_module_to_vmfb,
     load_flatbuffer,
+    create_dispatch_dirs,
+    compile_benchmark_dirs,
 )
 import os
 from shark.shark_runner import SharkRunner
@@ -37,7 +39,7 @@ class SharkInference:
     Attributes
     ----------
     mlir_module : str
-        mlir_module represented in string.
+        mlir_module represented in string; modules from torch-mlir are serialized in bytecode format.
     function_name : str
         function to execute in the given mlir_module.
     device : str
@@ -63,21 +65,48 @@ class SharkInference:
 
     def __init__(
         self,
-        mlir_module: str,
+        mlir_module: bytes,
         function_name: str = "forward",
         device: str = "none",
         mlir_dialect: str = "linalg",
         is_benchmark: bool = False,
+        dispatch_benchmark: str = None,
+        dispatch_benchmark_dir: str = "temp_dispatch_benchmarks",
     ):
         self.mlir_module = mlir_module
         self.function_name = function_name
         self.device = shark_args.device if device == "none" else device
         self.mlir_dialect = mlir_dialect
         self.is_benchmark = is_benchmark
+        self.dispatch_benchmarks = (
+            shark_args.dispatch_benchmarks
+            if dispatch_benchmark is None
+            else dispatch_benchmark
+        )
+        self.dispatch_benchmarks_dir = (
+            shark_args.dispatch_benchmarks_dir
+            if dispatch_benchmark_dir == "temp_dispatch_benchmarks"
+            else dispatch_benchmark_dir
+        )
 
         self.shark_runner = None
 
-    def compile(self):
+    def compile(self, extra_args=[]):
+
+        if self.dispatch_benchmarks is not None:
+            extra_args.append(
+                f"--iree-hal-dump-executable-sources-to={self.dispatch_benchmarks_dir}"
+            )
+            extra_args.append(
+                f"--iree-hal-dump-executable-binaries-to={self.dispatch_benchmarks_dir}"
+            )
+            temp_dir = self.dispatch_benchmarks_dir.split("/")
+            temp_dir[-1] = "temp_" + temp_dir[-1]
+            temp_dir = "/".join(temp_dir)
+            self.temp_dispatch_benchmarks_dir = temp_dir
+            extra_args.append(
+                f"--iree-hal-dump-executable-benchmarks-to={self.temp_dispatch_benchmarks_dir}"
+            )
 
         if self.is_benchmark == True:
             from shark.shark_benchmark_runner import SharkBenchmarkRunner
@@ -87,6 +116,7 @@ class SharkInference:
                 self.function_name,
                 self.device,
                 self.mlir_dialect,
+                extra_args=extra_args,
             )
 
         else:
@@ -95,8 +125,18 @@ class SharkInference:
                 self.function_name,
                 self.device,
                 self.mlir_dialect,
+                extra_args=extra_args,
             )
 
+        if self.dispatch_benchmarks is not None:
+            create_dispatch_dirs(self.dispatch_benchmarks_dir, self.device)
+            compile_benchmark_dirs(
+                self.dispatch_benchmarks_dir,
+                self.device,
+                self.dispatch_benchmarks,
+            )
+            os.system(f"rm -rf {self.temp_dispatch_benchmarks_dir}")
+
     # inputs are considered to be tuple of np.array.
     def forward(self, inputs: tuple):
         return self.shark_runner.run(inputs)
@@ -144,21 +184,24 @@ class SharkInference:
 
     # TODO: Instead of passing directory and having names decided by the module
     # , user may want to save the module with manual names.
-    def save_module(self, dir=os.getcwd()):
+    def save_module(self, dir=os.getcwd(), module_name=None, extra_args=[]):
         return export_iree_module_to_vmfb(
             self.mlir_module,
             self.device,
             dir,
             self.mlir_dialect,
             self.function_name,
+            module_name=module_name,
+            extra_args=extra_args,
         )
 
     # load and return the module.
-    def load_module(self, path):
+    def load_module(self, path, extra_args=[]):
         self.shark_runner = SharkRunner(
             function_name=self.function_name,
             device=self.device,
             compile_vmfb=False,
+            extra_args=extra_args,
         )
         (
             self.shark_runner.iree_compilation_module,

shark/shark_runner.py

@@ -61,19 +61,21 @@ class SharkRunner:
 
     def __init__(
         self,
-        mlir_module: str = "none",
+        mlir_module: bytes = None,
         function_name: str = "forward",
         device: str = "none",
         mlir_dialect: str = "linalg",
+        extra_args: list = [],
         compile_vmfb: bool = True,
     ):
         self.mlir_module = mlir_module
         self.function_name = function_name
         self.device = shark_args.device if device == "none" else device
         self.mlir_dialect = mlir_dialect
+        self.extra_args = extra_args
 
         if check_device_drivers(self.device):
-            device_driver_info(self.device)
+            print(device_driver_info(self.device))
             sys.exit(1)
 
         if compile_vmfb == True:
@@ -86,6 +88,7 @@ class SharkRunner:
                 self.device,
                 self.mlir_dialect,
                 func_name=self.function_name,
+                extra_args=self.extra_args,
             )
 
     def run(self, inputs: tuple):

shark/torch_mlir_utils.py

@@ -17,6 +17,7 @@ import torch_mlir
 from torch_mlir_e2e_test.linalg_on_tensors_backends import refbackend
 import tempfile
 from shark.parser import shark_args
+import io
 
 
 def get_module_name_for_asm_dump(module):
@@ -55,9 +56,8 @@ def get_torch_mlir_module(
     input: tuple,
     dynamic: bool,
     jit_trace: bool,
-    from_torchscript: bool = False,
 ):
-    """Get the MLIR's linalg-on-tensors module from torchscipt module."""
+    """Get the MLIR's linalg-on-tensors module from the torchscipt module."""
     ignore_traced_shapes = False
     if dynamic:
         input = create_dynamic_placeholders(input)
@@ -66,11 +66,14 @@ def get_torch_mlir_module(
 
     tempfile.tempdir = shark_args.repro_dir
 
-    module = torch_mlir.compile(
+    mlir_module = torch_mlir.compile(
         module,
         input,
         output_type=torch_mlir.OutputType.LINALG_ON_TENSORS,
         use_tracing=jit_trace,
         ignore_traced_shapes=ignore_traced_shapes,
     )
-    return module
+    bytecode_stream = io.BytesIO()
+    mlir_module.operation.write_bytecode(bytecode_stream)
+    bytecode = bytecode_stream.getvalue()
+    return bytecode

tank/README.md

@@ -1,3 +1,211 @@
+## Supported and Validated Models
+
+### PyTorch HuggingFace Models
+
+| PyTorch Language Models | Torch-MLIR lowerable | SHARK-CPU | SHARK-CUDA | SHARK-METAL |
+|---------------------|----------------------|----------|----------|-------------|
+| BERT                | :green_heart: (JIT)          | :green_heart:         | :green_heart:         | :green_heart:            |
+| Albert              | :green_heart: (JIT)            | :green_heart:         | :green_heart:         | :green_heart:            |
+| BigBird             | :green_heart: (AOT)            |          |          |             |
+| dbmdz/ConvBERT      | :green_heart:          | :green_heart:         | :green_heart:         | :green_heart:            |
+| DistilBERT          | :broken_heart: (JIT)            |          |          |             |
+| GPT2                | :green_heart:            | :green_heart:         |  :green_heart:        | :green_heart:            |
+| MobileBert          | :green_heart: (JIT)            | :green_heart:         | :green_heart:         | :green_heart:            |
+| microsoft/beit      | :green_heart:                  | :green_heart:         | :broken_heart:         | :broken_heart:            |
+| facebook/deit       | :green_heart:          | :green_heart:         | :broken_heart:         | :broken_heart:            |
+| facebook/convnext   | :green_heart:          | :green_heart:         | :green_heart:         | :green_heart:            |
+
+### Torchvision  Models
+
+| TORCHVISION Models | Torch-MLIR lowerable | SHARK-CPU | SHARK-CUDA | SHARK-METAL |
+|--------------------|----------------------|----------|----------|-------------|
+| AlexNet            | :green_heart: (Script)         | :green_heart:         | :green_heart:         | :green_heart:            |
+| MobileNetV2        | :green_heart: (Script)         | :green_heart:         | :green_heart:         | :green_heart:            |
+| MobileNetV3        | :green_heart: (Script)         | :green_heart:         | :green_heart:         | :green_heart:            |
+| Unet               | :green_heart: (Script)         | :green_heart:         | :green_heart:         | :green_heart:            |
+| Resnet18           | :green_heart: (Script)         | :green_heart:         |  :green_heart:        | :green_heart:            |
+| Resnet50           | :green_heart: (Script)         | :green_heart:         |   :green_heart:       | :green_heart:            |
+| Resnet101           | :green_heart: (Script)         | :green_heart:         |   :green_heart:       | :green_heart:            |
+| Resnext50_32x4d    | :green_heart: (Script)         |          |          |             |
+| SqueezeNet         | :green_heart: (Script)         | :green_heart:         |   :broken_heart:       | :broken_heart:            |
+| EfficientNet       | :green_heart: (Script)         |          |          |             |
+| Regnet             | :green_heart: (Script)         |          |          |             |
+| Resnest            | :broken_heart: (Script)         |          |          |             |
+| Vision Transformer | :green_heart: (Script)         | :green_heart:         | :green_heart:         | :green_heart:            |
+| VGG 16             | :green_heart: (Script)         | :green_heart:         |   :green_heart:       |             |
+| Wide Resnet        | :green_heart: (Script)         | :green_heart:         | :green_heart:         | :green_heart:            |
+| RAFT               | :broken_heart: (JIT)            |          |          |             |
+
+For more information refer to [MODEL TRACKING SHEET](https://docs.google.com/spreadsheets/d/15PcjKeHZIrB5LfDyuw7DGEEE8XnQEX2aX8lm8qbxV8A/edit#gid=0)
+
+### Tensorflow Models (Inference)
+
+| Hugging Face Models | tf-mhlo lowerable | SHARK-CPU | SHARK-CUDA | SHARK-METAL |
+|---------------------|----------------------|----------|----------|-------------|
+| BERT                | :green_heart:          | :green_heart:         | :green_heart:         | :green_heart:            |
+| MiniLM                | :green_heart:          | :green_heart:         | :green_heart:         | :green_heart:            |
+| albert-base-v2              | :green_heart:            | :green_heart:         | :green_heart:         | :green_heart:            |
+| DistilBERT          | :green_heart:            | :green_heart:         | :green_heart:         | :green_heart:            |
+| CamemBert                | :green_heart:          | :green_heart:         | :green_heart:         | :green_heart:            |
+| ConvBert              | :green_heart:            | :green_heart:         | :green_heart:         | :green_heart:            |
+| Deberta              |            |         |          |             |
+| electra          | :green_heart:            | :green_heart:         | :green_heart:         | :green_heart:            |
+| funnel              |            |         |          |             |
+| layoutlm              | :green_heart:            | :green_heart:         | :green_heart:         | :green_heart:            |
+| longformer              |            |         |          |             |
+| mobile-bert                | :green_heart:          | :green_heart:         | :green_heart:         | :green_heart:            |
+| rembert              |            |         |          |             |
+| tapas              |            |         |          |             |
+| flaubert                | :broken_heart:          | :green_heart:         | :green_heart:         | :green_heart:            |
+| roberta                | :green_heart:          | :green_heart:         | :green_heart:         | :green_heart:            |
+| xlm-roberta              | :green_heart:            | :green_heart:         | :green_heart:         | :green_heart:            |
+| mpnet              | :green_heart:            | :green_heart:         | :green_heart:         | :green_heart:            |
+
+### PyTorch Training Models
+
+| Models | Torch-MLIR lowerable | SHARK-CPU | SHARK-CUDA | SHARK-METAL |
+|---------------------|----------------------|----------|----------|-------------|
+| BERT                | :green_heart:           | :green_heart:         |          |             |
+| FullyConnected                | :green_heart:           | :green_heart:         |          |             |
+
+### JAX  Models
+
+| Models | JAX-MHLO lowerable | SHARK-CPU | SHARK-CUDA | SHARK-METAL |
+|---------------------|----------------------|----------|----------|-------------|
+| DALL-E                | :broken_heart:           | :broken_heart:         |          |             |
+| FullyConnected                | :green_heart:           | :green_heart:         |          |             |
+
+<details>
+  <summary>TFLite Models</summary>
+
+### TFLite Models
+
+| Models | TOSA/LinAlg  | SHARK-CPU | SHARK-CUDA | SHARK-METAL |
+|---------------------|----------------------|----------|----------|-------------|
+| BERT                | :broken_heart:           | :broken_heart:         |          |             |
+| FullyConnected      | :green_heart:           | :green_heart:         |          |             |
+| albert | :green_heart:           | :green_heart:         |          |             |
+| asr_conformer | :green_heart:           | :green_heart:         |          |             |
+| bird_classifier | :green_heart:           | :green_heart:         |          |             |
+| cartoon_gan | :green_heart:           | :green_heart:         |          |             |
+| craft_text | :green_heart:           | :green_heart:         |          |             |
+| deeplab_v3 | :green_heart:           | :green_heart:         |          |             |
+| densenet | :green_heart:           | :green_heart:         |          |             |
+| east_text_detector | :green_heart:           | :green_heart:         |          |             |
+| efficientnet_lite0_int8 | :green_heart:           | :green_heart:         |          |             |
+| efficientnet | :green_heart:           | :green_heart:         |          |             |
+| gpt2 | :green_heart:           | :green_heart:         |          |             |
+| image_stylization | :green_heart:           | :green_heart:         |          |             |
+| inception_v4 | :green_heart:           | :green_heart:         |          |             |
+| inception_v4_uint8 | :green_heart:           | :green_heart:         |          |             |
+| lightning_fp16 | :green_heart:           | :green_heart:         |          |             |
+| lightning_i8 | :green_heart:           | :green_heart:         |          |             |
+| lightning | :green_heart:           | :green_heart:         |          |             |
+| magenta | :green_heart:           | :green_heart:         |          |             |
+| midas | :green_heart:           | :green_heart:         |          |             |
+| mirnet | :green_heart:           | :green_heart:         |          |             |
+| mnasnet | :green_heart:           | :green_heart:         |          |             |
+| mobilebert_edgetpu_s_float | :green_heart:           | :green_heart:         |          |             |
+| mobilebert_edgetpu_s_quant | :green_heart:           | :green_heart:         |          |             |
+| mobilebert | :green_heart:           | :green_heart:         |          |             |
+| mobilebert_tf2_float | :green_heart:           | :green_heart:         |          |             |
+| mobilebert_tf2_quant | :green_heart:           | :green_heart:         |          |             |
+| mobilenet_ssd_quant | :green_heart:           | :green_heart:         |          |             |
+| mobilenet_v1 | :green_heart:           | :green_heart:         |          |             |
+| mobilenet_v1_uint8 | :green_heart:           | :green_heart:         |          |             |
+| mobilenet_v2_int8 | :green_heart:           | :green_heart:         |          |             |
+| mobilenet_v2 | :green_heart:           | :green_heart:         |          |             |
+| mobilenet_v2_uint8 | :green_heart:           | :green_heart:         |          |             |
+| mobilenet_v3-large | :green_heart:           | :green_heart:         |          |             |
+| mobilenet_v3-large_uint8 | :green_heart:           | :green_heart:         |          |             |
+| mobilenet_v35-int8 | :green_heart:           | :green_heart:         |          |             |
+| nasnet | :green_heart:           | :green_heart:         |          |             |
+| person_detect | :green_heart:           | :green_heart:         |          |             |
+| posenet | :green_heart:           | :green_heart:         |          |             |
+| resnet_50_int8 | :green_heart:           | :green_heart:         |          |             |
+| rosetta | :green_heart:           | :green_heart:         |          |             |
+| spice | :green_heart:           | :green_heart:         |          |             |
+| squeezenet | :green_heart:           | :green_heart:         |          |             |
+| ssd_mobilenet_v1 | :green_heart:           | :green_heart:         |          |             |
+| ssd_mobilenet_v1_uint8 | :green_heart:           | :green_heart:         |          |             |
+| ssd_mobilenet_v2_fpnlite | :green_heart:           | :green_heart:         |          |             |
+| ssd_mobilenet_v2_fpnlite_uint8 | :green_heart:           | :green_heart:         |          |             |
+| ssd_mobilenet_v2_int8 | :green_heart:           | :green_heart:         |          |             |
+| ssd_mobilenet_v2 | :green_heart:           | :green_heart:         |          |             |
+| ssd_spaghettinet_large | :green_heart:           | :green_heart:         |          |             |
+| ssd_spaghettinet_large_uint8 | :green_heart:           | :green_heart:         |          |             |
+| visual_wake_words_i8 | :green_heart:           | :green_heart:         |          |             |
+
+</details>
+
+## Testing and Benchmarks
+
+### Run all model tests on CPU/GPU/VULKAN/Metal
+
+For a list of models included in our pytest model suite, see https://github.com/nod-ai/SHARK/blob/main/tank/all_models.csv
+
+```shell
+pytest tank/test_models.py
+
+# Models included in the pytest suite can be found listed in all_models.csv.
+
+# If on Linux for multithreading on CPU (faster results):
+pytest tank/test_models.py -n auto
+```
+
+### Running specific tests
+```shell
+
+# Search for test cases by including a keyword that matches all or part of the test case's name;
+pytest tank/test_models.py -k "keyword" 
+
+# Test cases are named uniformly by format test_module_<model_name_underscores_only>_<torch/tf>_<static/dynamic>_<device>.
+
+# Example: Test all models on nvidia gpu:
+pytest tank/test_models.py -k "cuda"
+
+# Example: Test all tensorflow resnet models on Vulkan backend:
+pytest tank/test_models.py -k "resnet and tf and vulkan"
+
+# Exclude a test case:
+pytest tank/test_models.py -k "not ..."
+
+### Run benchmarks on SHARK tank pytests and generate bench_results.csv with results.
+
+(the following requires source installation with `IMPORTER=1 ./setup_venv.sh`)
+
+```shell
+pytest --benchmark tank/test_models.py
+  
+# Just do static GPU benchmarks for PyTorch tests:
+pytest --benchmark tank/test_models.py -k "pytorch and static and cuda"
+
+```
+  
+### Benchmark Resnet50, MiniLM on CPU
+
+(requires source installation with `IMPORTER=1 ./setup_venv.sh`)  
+  
+```shell
+# We suggest running the following commands as root before running benchmarks on CPU:
+  
+cat /sys/devices/system/cpu/cpu*/topology/thread_siblings_list | awk -F, '{print $2}' | sort -n | uniq | ( while read X ; do echo $X ; echo 0 > /sys/devices/system/cpu/cpu$X/online ; done )
+echo 1 > /sys/devices/system/cpu/intel_pstate/no_turbo
+
+# Benchmark canonical Resnet50 on CPU via pytest
+pytest --benchmark tank/test_models.py -k "resnet50 and tf_static_cpu"
+
+# Benchmark canonical MiniLM on CPU via pytest
+pytest --benchmark tank/test_models.py -k "MiniLM and cpu"
+
+# Benchmark MiniLM on CPU via transformer-benchmarks:
+git clone --recursive https://github.com/nod-ai/transformer-benchmarks.git
+cd transformer-benchmarks
+./perf-ci.sh -n
+# Check detail.csv for MLIR/IREE results.
+
+```
+
 To run the fine tuning example, from the root SHARK directory, run:
 
 ```shell
@@ -11,3 +219,5 @@ if running from a google vm, you can view jupyter notebooks on your local system
 gcloud compute ssh <YOUR_INSTANCE_DETAILS> --ssh-flag="-N -L localhost:8888:localhost:8888"
 ```
 
+
+

tank/all_models.csv

@@ -1,34 +1,35 @@
-resnet50,mhlo,tf,1e-02,1e-3,default
-albert-base-v2,mhlo,tf,1e-02,1e-3,default
-roberta-base,mhlo,tf,1e-02,1e-3,default
-bert-base-uncased,mhlo,tf,1e-2,1e-3,default
-camembert-base,mhlo,tf,1e-2,1e-3,default
-dbmdz/convbert-base-turkish-cased,mhlo,tf,1e-2,1e-3,default
-distilbert-base-uncased,mhlo,tf,1e-2,1e-3,default
-facebook/convnext-tiny-224,mhlo,tf,1e-2,1e-3,tf_vit
-funnel-transformer/small,mhlo,tf,1e-2,1e-3,default
-google/electra-small-discriminator,mhlo,tf,1e-2,1e-3,default
-google/mobilebert-uncased,mhlo,tf,1e-2,1e-3,default
-google/vit-base-patch16-224,mhlo,tf,1e-2,1e-3,tf_vit
-hf-internal-testing/tiny-random-flaubert,mhlo,tf,1e-2,1e-3,default
-microsoft/MiniLM-L12-H384-uncased,mhlo,tf,1e-2,1e-3,tf_hf
-microsoft/layoutlm-base-uncased,mhlo,tf,1e-2,1e-3,default
-microsoft/mpnet-base,mhlo,tf,1e-2,1e-3,default
-albert-base-v2,linalg,torch,1e-2,1e-3,default
-alexnet,linalg,torch,1e-2,1e-3,default
-bert-base-cased,linalg,torch,1e-2,1e-3,default
-bert-base-uncased,linalg,torch,1e-2,1e-3,default
-distilbert-base-uncased,linalg,torch,1e-2,1e-3,default
-facebook/deit-small-distilled-patch16-224,linalg,torch,1e-2,1e-3,default
-google/vit-base-patch16-224,linalg,torch,1e-2,1e-3,default
-microsoft/beit-base-patch16-224-pt22k-ft22k,linalg,torch,1e-2,1e-3,default
-microsoft/MiniLM-L12-H384-uncased,linalg,torch,1e-2,1e-3,default
-microsoft/resnet-50,linalg,torch,1e-2,1e-3,default
-google/mobilebert-uncased,linalg,torch,1e-2,1e-3,default
-mobilenet_v3_small,linalg,torch,1e-2,1e-3,default
-nvidia/mit-b0,linalg,torch,1e-2,1e-3,default
-resnet101,linalg,torch,1e-2,1e-3,default
-resnet18,linalg,torch,1e-2,1e-3,default
-resnet50,linalg,torch,1e-2,1e-3,default
-squeezenet1_0,linalg,torch,1e-2,1e-3,default
-wide_resnet50_2,linalg,torch,1e-2,1e-3,default
+resnet50,mhlo,tf,1e-2,1e-3,default,nhcw-nhwc
+albert-base-v2,mhlo,tf,1e-2,1e-2,default,None
+roberta-base,mhlo,tf,1e-02,1e-3,default,nhcw-nhwc
+bert-base-uncased,mhlo,tf,1e-2,1e-3,default,None
+camembert-base,mhlo,tf,1e-2,1e-3,default,None
+dbmdz/convbert-base-turkish-cased,mhlo,tf,1e-2,1e-3,default,nhcw-nhwc
+distilbert-base-uncased,mhlo,tf,1e-2,1e-3,default,None
+facebook/convnext-tiny-224,mhlo,tf,1e-2,1e-3,tf_vit,nhcw-nhwc,
+funnel-transformer/small,mhlo,tf,1e-2,1e-3,default,None
+google/electra-small-discriminator,mhlo,tf,1e-2,1e-3,default,None
+google/mobilebert-uncased,mhlo,tf,1e-2,1e-3,default,None
+google/vit-base-patch16-224,mhlo,tf,1e-2,1e-3,tf_vit,nhcw-nhwc
+hf-internal-testing/tiny-random-flaubert,mhlo,tf,1e-2,1e-3,default,None
+microsoft/MiniLM-L12-H384-uncased,mhlo,tf,1e-2,1e-3,tf_hf,None
+microsoft/layoutlm-base-uncased,mhlo,tf,1e-2,1e-3,default,None
+microsoft/mpnet-base,mhlo,tf,1e-2,1e-2,default,None
+albert-base-v2,linalg,torch,1e-2,1e-3,default,None
+alexnet,linalg,torch,1e-2,1e-3,default,None
+bert-base-cased,linalg,torch,1e-2,1e-3,default,None
+bert-base-uncased,linalg,torch,1e-2,1e-3,default,None
+facebook/deit-small-distilled-patch16-224,linalg,torch,1e-2,1e-3,default,nhcw-nhwc
+google/vit-base-patch16-224,linalg,torch,1e-2,1e-3,default,nhcw-nhwc
+microsoft/beit-base-patch16-224-pt22k-ft22k,linalg,torch,1e-2,1e-3,default,nhcw-nhwc
+microsoft/MiniLM-L12-H384-uncased,linalg,torch,1e-2,1e-3,default,None
+microsoft/resnet-50,linalg,torch,1e-2,1e-3,default,nhcw-nhwc
+google/mobilebert-uncased,linalg,torch,1e-2,1e-3,default,None
+mobilenet_v3_small,linalg,torch,1e-1,1e-2,default,nhcw-nhwc
+nvidia/mit-b0,linalg,torch,1e-2,1e-3,default,None
+resnet101,linalg,torch,1e-2,1e-3,default,nhcw-nhwc
+resnet18,linalg,torch,1e-2,1e-3,default,None
+resnet50,linalg,torch,1e-2,1e-3,default,nhcw-nhwc
+squeezenet1_0,linalg,torch,1e-2,1e-3,default,nhcw-nhwc
+wide_resnet50_2,linalg,torch,1e-2,1e-3,default,nhcw-nhwc
+efficientnet-v2-s,mhlo,tf,1e-02,1e-3,default,nhcw-nhwc
+mnasnet1_0,linalg,torch,1e-2,1e-3,default,nhcw-nhwc

tank/examples/MiniLM_tf/huggingface_MiniLM_gen.py

@@ -32,7 +32,7 @@ class BertModule(tf.Module):
             input_ids=x, attention_mask=y, token_type_ids=z, training=False
         )
 
-    @tf.function(input_signature=bert_input)
+    @tf.function(input_signature=bert_input, jit_compile=True)
     def predict(self, input_word_ids, input_mask, segment_ids):
         return self.m.predict(input_word_ids, input_mask, segment_ids)
 

tank/examples/MiniLM_tf/huggingface_MiniLM_run.py

@@ -33,7 +33,7 @@ class BertModule(tf.Module):
             input_ids=x, attention_mask=y, token_type_ids=z, training=False
         )
 
-    @tf.function(input_signature=bert_input)
+    @tf.function(input_signature=bert_input, jit_compile=True)
     def predict(self, input_ids, attention_mask, token_type_ids):
         return self.m.predict(input_ids, attention_mask, token_type_ids)
 

tank/examples/MiniLM_tf/seq_classification.py

@@ -52,7 +52,7 @@ class SeqClassification(tf.Module):
         )
         self.m.predict = lambda x, y: self.m(input_ids=x, attention_mask=y)[0]
 
-    @tf.function(input_signature=inputs_signature)
+    @tf.function(input_signature=inputs_signature, jit_compile=True)
     def forward(self, input_ids, attention_mask):
         return tf.math.softmax(
             self.m.predict(input_ids, attention_mask), axis=-1

tank/examples/bert-base-uncased_tosa_torch/bert_base_uncased_tosa.py

@@ -1,8 +1,9 @@
 from shark.shark_inference import SharkInference
-from shark.shark_downloader import download_torch_model
+from shark.shark_downloader import download_model
 
-mlir_model, func_name, inputs, golden_out = download_torch_model(
-    "bert-base-uncased_tosa"
+mlir_model, func_name, inputs, golden_out = download_model(
+    "bert-base-uncased_tosa",
+    frontend="torch",
 )
 
 shark_module = SharkInference(

tank/examples/bert_fine_tuning/bert_fine_tune_tf.py

@@ -72,7 +72,8 @@ class BertModule(tf.Module):
         input_signature=[
             bert_input,  # inputs
             tf.TensorSpec(shape=[BATCH_SIZE], dtype=tf.int32),  # labels
-        ]
+        ],
+        jit_compile=True,
     )
     def learn(self, inputs, labels):
         with tf.GradientTape() as tape:

tank/examples/bert_tf/bert_large_gen.py

@@ -60,7 +60,8 @@ class BertModule(tf.Module):
                 shape=[BATCH_SIZE, SEQUENCE_LENGTH], dtype=tf.int32
             ),  # input2: segment_ids
             tf.TensorSpec([BATCH_SIZE], tf.int32),  # input3: labels
-        ]
+        ],
+        jit_compile=True,
     )
     def learn(self, input_word_ids, input_mask, segment_ids, labels):
         with tf.GradientTape() as tape:
@@ -75,7 +76,7 @@ class BertModule(tf.Module):
         self.optimizer.apply_gradients(zip(gradients, variables))
         return loss
 
-    @tf.function(input_signature=bert_input)
+    @tf.function(input_signature=bert_input, jit_compile=True)
     def predict(self, input_word_ids, input_mask, segment_ids):
         inputs = [input_word_ids, input_mask, segment_ids]
         return self.m.predict(inputs)

tank/examples/bert_tf/bert_large_run.py

@@ -57,7 +57,8 @@ class BertModule(tf.Module):
         input_signature=[
             bert_input,  # inputs
             tf.TensorSpec(shape=[BATCH_SIZE], dtype=tf.int32),  # labels
-        ]
+        ],
+        jit_compile=True,
     )
     def learn(self, inputs, labels):
         with tf.GradientTape() as tape:

tank/examples/bert_tf/bert_large_tf.py

@@ -50,7 +50,8 @@ class BertModule(tf.Module):
         input_signature=[
             bert_input,  # inputs
             tf.TensorSpec(shape=[BATCH_SIZE], dtype=tf.int32),  # labels
-        ]
+        ],
+        jit_compile=True,
     )
     def learn(self, inputs, labels):
         with tf.GradientTape() as tape:

tank/examples/bert_tf/bert_small_gen.py

@@ -57,7 +57,8 @@ class BertModule(tf.Module):
                 shape=[BATCH_SIZE, SEQUENCE_LENGTH], dtype=tf.int32
             ),  # input2: segment_ids
             tf.TensorSpec([BATCH_SIZE], tf.int32),  # input3: labels
-        ]
+        ],
+        jit_compile=True,
     )
     def learn(self, input_word_ids, input_mask, segment_ids, labels):
         with tf.GradientTape() as tape:
@@ -72,7 +73,7 @@ class BertModule(tf.Module):
         self.optimizer.apply_gradients(zip(gradients, variables))
         return loss
 
-    @tf.function(input_signature=bert_input)
+    @tf.function(input_signature=bert_input, jit_compile=True)
     def predict(self, input_word_ids, input_mask, segment_ids):
         inputs = [input_word_ids, input_mask, segment_ids]
         return self.m.predict(inputs)

tank/examples/bert_tf/bert_small_run.py

@@ -53,7 +53,8 @@ class BertModule(tf.Module):
         input_signature=[
             bert_input,  # inputs
             tf.TensorSpec(shape=[BATCH_SIZE], dtype=tf.int32),  # labels
-        ]
+        ],
+        jit_compile=True,
     )
     def learn(self, inputs, labels):
         with tf.GradientTape() as tape:

tank/examples/bert_tf/bert_small_tf_run.py

@@ -46,7 +46,8 @@ class BertModule(tf.Module):
         input_signature=[
             bert_input,  # inputs
             tf.TensorSpec(shape=[BATCH_SIZE], dtype=tf.int32),  # labels
-        ]
+        ],
+        jit_compile=True,
     )
     def learn(self, inputs, labels):
         with tf.GradientTape() as tape:

tank/examples/bert_tf/seq_classification.py

@@ -0,0 +1,83 @@
+#!/usr/bin/env python
+from transformers import TFAutoModelForSequenceClassification, AutoTokenizer
+import tensorflow as tf
+from shark.shark_inference import SharkInference
+from shark.parser import shark_args
+import argparse
+
+
+seq_parser = argparse.ArgumentParser(
+    description="Shark Sequence Classification."
+)
+seq_parser.add_argument(
+    "--hf_model_name",
+    type=str,
+    default="bert-base-uncased",
+    help="Hugging face model to run sequence classification.",
+)
+
+seq_args, unknown = seq_parser.parse_known_args()
+
+
+BATCH_SIZE = 1
+MAX_SEQUENCE_LENGTH = 16
+
+# Create a set of input signature.
+inputs_signature = [
+    tf.TensorSpec(shape=[BATCH_SIZE, MAX_SEQUENCE_LENGTH], dtype=tf.int32),
+    tf.TensorSpec(shape=[BATCH_SIZE, MAX_SEQUENCE_LENGTH], dtype=tf.int32),
+]
+
+# For supported models please see here:
+# https://huggingface.co/docs/transformers/model_doc/auto#transformers.TFAutoModelForSequenceClassification
+
+
+def preprocess_input(text="This is just used to compile the model"):
+    tokenizer = AutoTokenizer.from_pretrained(seq_args.hf_model_name)
+    inputs = tokenizer(
+        text,
+        padding="max_length",
+        return_tensors="tf",
+        truncation=True,
+        max_length=MAX_SEQUENCE_LENGTH,
+    )
+    return inputs
+
+
+class SeqClassification(tf.Module):
+    def __init__(self, model_name):
+        super(SeqClassification, self).__init__()
+        self.m = TFAutoModelForSequenceClassification.from_pretrained(
+            model_name, output_attentions=False, num_labels=2
+        )
+        self.m.predict = lambda x, y: self.m(input_ids=x, attention_mask=y)[0]
+
+    @tf.function(input_signature=inputs_signature, jit_compile=True)
+    def forward(self, input_ids, attention_mask):
+        return tf.math.softmax(
+            self.m.predict(input_ids, attention_mask), axis=-1
+        )
+
+
+if __name__ == "__main__":
+    inputs = preprocess_input()
+    shark_module = SharkInference(
+        SeqClassification(seq_args.hf_model_name),
+        (inputs["input_ids"], inputs["attention_mask"]),
+    )
+    shark_module.set_frontend("tensorflow")
+    shark_module.compile()
+    print(f"Model has been successfully compiled on {shark_args.device}")
+
+    while True:
+        input_text = input(
+            "Enter the text to classify (press q or nothing to exit): "
+        )
+        if not input_text or input_text == "q":
+            break
+        inputs = preprocess_input(input_text)
+        print(
+            shark_module.forward(
+                (inputs["input_ids"], inputs["attention_mask"])
+            )
+        )

tank/examples/deberta-base_tf/deberta-base_tf_test.py

@@ -1,6 +1,6 @@
 from shark.iree_utils._common import check_device_drivers, device_driver_info
 from shark.shark_inference import SharkInference
-from shark.shark_downloader import download_tf_model
+from shark.shark_downloader import download_model
 from shark.parser import shark_args
 from tank.test_utils import get_valid_test_params, shark_test_name_func
 from parameterized import parameterized
@@ -21,8 +21,8 @@ class DebertaBaseModuleTester:
         self.benchmark = benchmark
 
     def create_and_check_module(self, dynamic, device):
-        model, func_name, inputs, golden_out = download_tf_model(
-            "microsoft/deberta-base"
+        model, func_name, inputs, golden_out = download_model(
+            "microsoft/deberta-base", frontend="tf"
         )
 
         shark_module = SharkInference(

tank/examples/gpt2-64/gpt2-64_tflite_test.py

@@ -1,5 +1,5 @@
 import numpy as np
-from shark.shark_downloader import download_tflite_model
+from shark.shark_downloader import download_model
 from shark.shark_inference import SharkInference
 import pytest
 import unittest
@@ -58,8 +58,8 @@ class GptTfliteModuleTester:
         shark_args.save_vmfb = self.save_vmfb
 
         # Preprocess to get SharkImporter input args
-        mlir_model, func_name, inputs, tflite_results = download_tflite_model(
-            model_name="gpt2-64"
+        mlir_model, func_name, inputs, tflite_results = download_model(
+            model_name="gpt2-64", backend="tflite"
         )
         shark_module = SharkInference(
             mlir_module=mlir_model,

tank/examples/opt/opt_torch_test.py

@@ -20,10 +20,6 @@ class OPTModuleTester:
         self.benchmark = benchmark
 
     def create_and_check_module(self, dynamic, device, model_name):
-        # model_mlir, func_name, input, act_out = download_torch_model(
-        #     "opt", dynamic
-        # )
-
         tokenizer = GPT2Tokenizer.from_pretrained(model_name)
         # config = OPTConfig()
         # opt_model = OPTModel(config)

tank/examples/rembert_tf/rembert_tf_test.py

@@ -1,6 +1,6 @@
 from shark.iree_utils._common import check_device_drivers, device_driver_info
 from shark.shark_inference import SharkInference
-from shark.shark_downloader import download_tf_model
+from shark.shark_downloader import download_model
 from tank.test_utils import get_valid_test_params, shark_test_name_func
 from parameterized import parameterized
 
@@ -18,8 +18,8 @@ class RemBertModuleTester:
         self.benchmark = benchmark
 
     def create_and_check_module(self, dynamic, device):
-        model, func_name, inputs, golden_out = download_tf_model(
-            "google/rembert"
+        model, func_name, inputs, golden_out = download_model(
+            "google/rembert", frontend="tf"
         )
 
         shark_module = SharkInference(

tank/examples/tapas-base_tf/tapas-base_tf_test.py

@@ -1,6 +1,6 @@
 from shark.iree_utils._common import check_device_drivers, device_driver_info
 from shark.shark_inference import SharkInference
-from shark.shark_downloader import download_tf_model
+from shark.shark_downloader import download_model
 
 import iree.compiler as ireec
 import unittest
@@ -16,8 +16,9 @@ class TapasBaseModuleTester:
         self.benchmark = benchmark
 
     def create_and_check_module(self, dynamic, device):
-        model, func_name, inputs, golden_out = download_tf_model(
-            "google/tapas-base"
+        model, func_name, inputs, golden_out = download_model(
+            "google/tapas-base",
+            frontend="tf",
         )
 
         shark_module = SharkInference(

tank/examples/v_diffusion_pytorch/cfg_sample_from_mlir.py

@@ -15,7 +15,7 @@ from torchvision.transforms import functional as TF
 from tqdm import trange
 
 from shark.shark_inference import SharkInference
-from shark.shark_downloader import download_torch_model
+from shark.shark_downloader import download_model
 import numpy as np
 
 import sys
@@ -191,7 +191,9 @@ x_in = x[0:min_batch_size, :, :, :]
 ts = x_in.new_ones([x_in.shape[0]])
 t_in = t[0] * ts
 
-mlir_model, func_name, inputs, golden_out = download_torch_model("v_diffusion")
+mlir_model, func_name, inputs, golden_out = download_model(
+    "v_diffusion", frontend="torch"
+)
 
 shark_module = SharkInference(
     mlir_model, func_name, device=args.runtime_device, mlir_dialect="linalg"

tank/model_metadata.csv

@@ -27,3 +27,5 @@ microsoft/mpnet-base,False,False,-,-,-
 roberta-base,False,False,-,-,-
 xlm-roberta-base,False,False,-,-,-
 facebook/convnext-tiny-224,False,False,-,-,-
+efficientnet-v2-s,False,False,22M,"image-classification,cnn","Includes MBConv and Fused-MBConv"
+mnasnet1_0,False,True,-,"cnn, torchvision, mobile, architecture-search","Outperforms other mobile CNNs on Accuracy vs. Latency"

tank/model_utils.py

@@ -15,6 +15,7 @@ vision_models = [
     "squeezenet1_0",
     "wide_resnet50_2",
     "mobilenet_v3_small",
+    "mnasnet1_0",
 ]
 hf_img_cls_models = [
     "google/vit-base-patch16-224",
@@ -142,13 +143,14 @@ def get_vision_model(torch_model):
     import torchvision.models as models
 
     vision_models_dict = {
-        "alexnet": models.alexnet(pretrained=True),
-        "resnet18": models.resnet18(pretrained=True),
-        "resnet50": models.resnet50(pretrained=True),
-        "resnet101": models.resnet101(pretrained=True),
-        "squeezenet1_0": models.squeezenet1_0(pretrained=True),
-        "wide_resnet50_2": models.wide_resnet50_2(pretrained=True),
-        "mobilenet_v3_small": models.mobilenet_v3_small(pretrained=True),
+        "alexnet": models.alexnet(weights="DEFAULT"),
+        "resnet18": models.resnet18(weights="DEFAULT"),
+        "resnet50": models.resnet50(weights="DEFAULT"),
+        "resnet101": models.resnet101(weights="DEFAULT"),
+        "squeezenet1_0": models.squeezenet1_0(weights="DEFAULT"),
+        "wide_resnet50_2": models.wide_resnet50_2(weights="DEFAULT"),
+        "mobilenet_v3_small": models.mobilenet_v3_small(weights="DEFAULT"),
+        "mnasnet1_0": models.mnasnet1_0(weights="DEFAULT"),
     }
     if isinstance(torch_model, str):
         torch_model = vision_models_dict[torch_model]
@@ -160,6 +162,8 @@ def get_vision_model(torch_model):
 
 ################################################################################
 
+####################### Other PyTorch HF Models ###############################
+
 # Utility function for comparing two tensors (torch).
 def compare_tensors(torch_tensor, numpy_tensor, rtol=1e-02, atol=1e-03):
     # torch_to_numpy = torch_tensor.detach().numpy()

tank/model_utils_tf.py

@@ -6,24 +6,12 @@ from transformers import (
     TFBertModel,
 )
 
-visible_default = tf.config.list_physical_devices("GPU")
-try:
-    tf.config.set_visible_devices([], "GPU")
-    visible_devices = tf.config.get_visible_devices()
-    for device in visible_devices:
-        assert device.device_type != "GPU"
-except:
-    # Invalid device or cannot modify virtual devices once initialized.
-    pass
-
 BATCH_SIZE = 1
 MAX_SEQUENCE_LENGTH = 128
 
 ################################## MHLO/TF models #########################################
 # TODO : Generate these lists or fetch model source from tank/tf/tf_model_list.csv
-keras_models = [
-    "resnet50",
-]
+keras_models = ["resnet50", "efficientnet-v2-s"]
 maskedlm_models = [
     "albert-base-v2",
     "bert-base-uncased",
@@ -87,7 +75,7 @@ class TFHuggingFaceLanguage(tf.Module):
             input_ids=x, attention_mask=y, token_type_ids=z, training=False
         )
 
-    @tf.function(input_signature=tf_bert_input)
+    @tf.function(input_signature=tf_bert_input, jit_compile=True)
     def forward(self, input_ids, attention_mask, token_type_ids):
         return self.m.predict(input_ids, attention_mask, token_type_ids)
 
@@ -162,7 +150,7 @@ class MaskedLM(tf.Module):
         )
         self.m.predict = lambda x, y: self.m(input_ids=x, attention_mask=y)[0]
 
-    @tf.function(input_signature=input_signature_maskedlm)
+    @tf.function(input_signature=input_signature_maskedlm, jit_compile=True)
     def forward(self, input_ids, attention_mask):
         return self.m.predict(input_ids, attention_mask)
 
@@ -178,42 +166,81 @@ def get_causal_lm_model(hf_name, text="Hello, this is the default text."):
 ##################### TensorFlow Keras Resnet Models #########################################################
 # Static shape, including batch size (1).
 # Can be dynamic once dynamic shape support is ready.
-INPUT_SHAPE = [1, 224, 224, 3]
-
-tf_model = tf.keras.applications.resnet50.ResNet50(
-    weights="imagenet", include_top=True, input_shape=tuple(INPUT_SHAPE[1:])
-)
+RESNET_INPUT_SHAPE = [1, 224, 224, 3]
+EFFICIENTNET_INPUT_SHAPE = [1, 384, 384, 3]
 
 
 class ResNetModule(tf.Module):
     def __init__(self):
         super(ResNetModule, self).__init__()
-        self.m = tf_model
+        self.m = tf.keras.applications.resnet50.ResNet50(
+            weights="imagenet",
+            include_top=True,
+            input_shape=tuple(RESNET_INPUT_SHAPE[1:]),
+        )
         self.m.predict = lambda x: self.m.call(x, training=False)
 
-    @tf.function(input_signature=[tf.TensorSpec(INPUT_SHAPE, tf.float32)])
+    @tf.function(
+        input_signature=[tf.TensorSpec(RESNET_INPUT_SHAPE, tf.float32)],
+        jit_compile=True,
+    )
     def forward(self, inputs):
         return self.m.predict(inputs)
 
+    def input_shape(self):
+        return RESNET_INPUT_SHAPE
 
-def load_image(path_to_image):
+    def preprocess_input(self, image):
+        return tf.keras.applications.resnet50.preprocess_input(image)
+
+
+class EfficientNetModule(tf.Module):
+    def __init__(self):
+        super(EfficientNetModule, self).__init__()
+        self.m = tf.keras.applications.efficientnet_v2.EfficientNetV2S(
+            weights="imagenet",
+            include_top=True,
+            input_shape=tuple(EFFICIENTNET_INPUT_SHAPE[1:]),
+        )
+        self.m.predict = lambda x: self.m.call(x, training=False)
+
+    @tf.function(
+        input_signature=[tf.TensorSpec(EFFICIENTNET_INPUT_SHAPE, tf.float32)],
+        jit_compile=True,
+    )
+    def forward(self, inputs):
+        return self.m.predict(inputs)
+
+    def input_shape(self):
+        return EFFICIENTNET_INPUT_SHAPE
+
+    def preprocess_input(self, image):
+        return tf.keras.applications.efficientnet_v2.preprocess_input(image)
+
+
+def load_image(path_to_image, width, height, channels):
     image = tf.io.read_file(path_to_image)
-    image = tf.image.decode_image(image, channels=3)
-    image = tf.image.resize(image, (224, 224))
+    image = tf.image.decode_image(image, channels=channels)
+    image = tf.image.resize(image, (width, height))
     image = image[tf.newaxis, :]
     return image
 
 
 def get_keras_model(modelname):
-    model = ResNetModule()
+    if modelname == "efficientnet-v2-s":
+        model = EfficientNetModule()
+    else:
+        model = ResNetModule()
+
     content_path = tf.keras.utils.get_file(
         "YellowLabradorLooking_new.jpg",
         "https://storage.googleapis.com/download.tensorflow.org/example_images/YellowLabradorLooking_new.jpg",
     )
-    content_image = load_image(content_path)
-    input_tensor = tf.keras.applications.resnet50.preprocess_input(
-        content_image
+    input_shape = model.input_shape()
+    content_image = load_image(
+        content_path, input_shape[1], input_shape[2], input_shape[3]
     )
+    input_tensor = model.preprocess_input(content_image)
     input_data = tf.expand_dims(input_tensor, 0)
     actual_out = model.forward(*input_data)
     return model, input_data, actual_out
@@ -240,7 +267,7 @@ class AutoModelImageClassfication(tf.Module):
         )
         self.m.predict = lambda x: self.m(x)
 
-    @tf.function(input_signature=input_signature_img_cls)
+    @tf.function(input_signature=input_signature_img_cls, jit_compile=True)
     def forward(self, inputs):
         return self.m.predict(inputs)
 

tank/test_models.py

@@ -1,15 +1,11 @@
 from shark.iree_utils._common import (
     check_device_drivers,
     device_driver_info,
-    IREE_DEVICE_MAP,
+    get_supported_device_list,
 )
 from shark.iree_utils.vulkan_utils import get_vulkan_triple_flag
 from parameterized import parameterized
-from shark.shark_downloader import (
-    download_tf_model,
-    download_torch_model,
-    download_tflite_model,
-)
+from shark.shark_downloader import download_model
 from shark.shark_inference import SharkInference
 from shark.parser import shark_args
 import iree.compiler as ireec
@@ -20,6 +16,7 @@ import csv
 import tempfile
 import os
 import shutil
+import multiprocessing
 
 
 def load_csv_and_convert(filename, gen=False):
@@ -41,6 +38,7 @@ def load_csv_and_convert(filename, gen=False):
                     "rtol": float(row[3]),
                     "atol": float(row[4]),
                     "out_type": row[5],
+                    "flags": row[6],
                 }
             )
     # This is a pytest workaround
@@ -59,7 +57,7 @@ def get_valid_test_params():
     """
     device_list = [
         device
-        for device in IREE_DEVICE_MAP.keys()
+        for device in get_supported_device_list()
         if not check_device_drivers(device)
     ]
     dynamic_list = (True, False)
@@ -130,24 +128,19 @@ class SharkModuleTester:
         self.config = config
 
     def create_and_check_module(self, dynamic, device):
+
         shark_args.local_tank_cache = self.local_tank_cache
-        if self.config["framework"] == "tf":
-            model, func_name, inputs, golden_out = download_tf_model(
-                self.config["model_name"],
-                tank_url=self.tank_url,
-            )
-        elif self.config["framework"] == "torch":
-            model, func_name, inputs, golden_out = download_torch_model(
-                self.config["model_name"],
-                tank_url=self.tank_url,
-            )
-        elif self.config["framework"] == "tflite":
-            model, func_name, inputs, golden_out = download_tflite_model(
-                model_name=self.config["model_name"],
-                tank_url=self.tank_url,
-            )
-        else:
-            model, func_name, inputs, golden_out = None, None, None, None
+        shark_args.update_tank = self.update_tank
+        if "nhcw-nhwc" in self.config["flags"] and not os.path.isfile(
+            ".use-iree"
+        ):
+            shark_args.enable_conv_transform = True
+
+        model, func_name, inputs, golden_out = download_model(
+            self.config["model_name"],
+            tank_url=self.tank_url,
+            frontend=self.config["framework"],
+        )
 
         shark_module = SharkInference(
             model,
@@ -240,6 +233,16 @@ class SharkModuleTester:
         return expected, logits
 
 
+def run_test(module_tester, dynamic, device):
+    tempdir = tempfile.TemporaryDirectory(
+        prefix=module_tester.tmp_prefix, dir="./shark_tmp/"
+    )
+    module_tester.temp_dir = tempdir.name
+
+    with ireec.tools.TempFileSaver(tempdir.name):
+        module_tester.create_and_check_module(dynamic, device)
+
+
 class SharkModuleTest(unittest.TestCase):
     @pytest.fixture(autouse=True)
     def configure(self, pytestconfig):
@@ -266,21 +269,18 @@ class SharkModuleTest(unittest.TestCase):
         self.module_tester.local_tank_cache = self.pytestconfig.getoption(
             "local_tank_cache"
         )
+        self.module_tester.update_tank = self.pytestconfig.getoption(
+            "update_tank"
+        )
         self.module_tester.tank_url = self.pytestconfig.getoption("tank_url")
-        if (
-            config["model_name"] == "distilbert-base-uncased"
-            and config["framework"] == "torch"
-        ):
-            pytest.xfail(reason="https://github.com/nod-ai/SHARK/issues/354")
-        if (
-            config["model_name"] == "facebook/convnext-tiny-224"
-            and device == "cuda"
-        ):
-            pytest.xfail(reason="https://github.com/nod-ai/SHARK/issues/311")
-        if (
-            config["model_name"] == "google/vit-base-patch16-224"
-            and device == "cuda"
-        ):
+        if config["model_name"] == "efficientnet-v2-s" and device in [
+            "metal",
+            "vulkan",
+        ]:
+            pytest.xfail(reason="https://github.com/nod-ai/SHARK/issues/575")
+        if config[
+            "model_name"
+        ] == "google/vit-base-patch16-224" and device in ["cuda"]:
             pytest.xfail(reason="https://github.com/nod-ai/SHARK/issues/311")
         if config["model_name"] == "resnet50" and device in [
             "metal",
@@ -291,8 +291,6 @@ class SharkModuleTest(unittest.TestCase):
                     pytest.xfail(
                         reason="M2: Assert Error & M1: CompilerToolError"
                     )
-        if config["model_name"] == "google/rembert":
-            pytest.skip(reason="Model too large to convert.")
         if config[
             "model_name"
         ] == "dbmdz/convbert-base-turkish-cased" and device in [
@@ -311,7 +309,6 @@ class SharkModuleTest(unittest.TestCase):
                 reason="https://github.com/nod-ai/SHARK/issues/311, https://github.com/nod-ai/SHARK/issues/342"
             )
         if config["model_name"] == "funnel-transformer/small" and device in [
-            "cpu",
             "cuda",
             "metal",
             "vulkan",
@@ -319,13 +316,6 @@ class SharkModuleTest(unittest.TestCase):
             pytest.xfail(
                 reason="failing in the iree-compiler passes, see https://github.com/nod-ai/SHARK/issues/201"
             )
-        if (
-            config["model_name"] == "google/vit-base-patch16-224"
-            and device == "cuda"
-        ):
-            pytest.xfail(reason="https://github.com/nod-ai/SHARK/issues/311")
-        if config["model_name"] == "microsoft/mpnet-base":
-            pytest.xfail(reason="https://github.com/nod-ai/SHARK/issues/203")
         if config["model_name"] == "nvidia/mit-b0":
             pytest.xfail(reason="https://github.com/nod-ai/SHARK/issues/343")
         if (
@@ -356,13 +346,74 @@ class SharkModuleTest(unittest.TestCase):
             pytest.xfail(
                 reason="Numerics Issues: https://github.com/nod-ai/SHARK/issues/388"
             )
-        if config["model_name"] == "mobilenet_v3_small" and device in [
-            "metal",
-            "vulkan",
+        if config["model_name"] == "mobilenet_v3_small" and device not in [
+            "cpu"
         ]:
             pytest.xfail(
                 reason="Numerics Issues: https://github.com/nod-ai/SHARK/issues/388"
             )
+        if config["model_name"] == "mnasnet1_0" and device not in [
+            "cpu",
+            "cuda",
+        ]:
+            pytest.xfail(
+                reason="Numerics Issues: https://github.com/nod-ai/SHARK/issues/388"
+            )
+        if config["model_name"] == "hf-internal-testing/tiny-random-flaubert":
+            pytest.xfail(reason="Transformers API mismatch")
+        if config["model_name"] == "alexnet" and device in ["metal", "vulkan"]:
+            pytest.xfail(reason="Assertion Error: Zeros Output")
+        if (
+            config["model_name"] == "camembert-base"
+            and dynamic == False
+            and device in ["metal", "vulkan"]
+        ):
+            pytest.xfail(
+                reason="chlo.broadcast_compare failed to satify constraint"
+            )
+        if (
+            config["model_name"] == "roberta-base"
+            and dynamic == False
+            and device in ["metal", "vulkan"]
+        ):
+            pytest.xfail(
+                reason="chlo.broadcast_compare failed to satify constraint"
+            )
+        if config["model_name"] in [
+            "microsoft/MiniLM-L12-H384-uncased",
+            "wide_resnet50_2",
+            "resnet50",
+            "resnet18",
+            "resnet101",
+            "microsoft/resnet-50",
+        ] and device in ["metal", "vulkan"]:
+            pytest.xfail(reason="Vulkan Numerical Error (mostly conv)")
+        if config[
+            "model_name"
+        ] == "dbmdz/convbert-base-turkish-cased" and device in ["cuda", "cpu"]:
+            pytest.xfail(reason="https://github.com/nod-ai/SHARK/issues/463")
+        if (
+            config["model_name"]
+            in [
+                "facebook/convnext-tiny-224",
+                "squeezenet1_0",
+            ]
+            and device == "rocm"
+        ):
+            pytest.xfail(
+                reason="iree-compile buffer limit issue: https://github.com/nod-ai/SHARK/issues/475"
+            )
+        if (
+            config["model_name"]
+            in [
+                "funnel-transformer/small",
+                "mobilenet_v3_small",
+            ]
+            and device == "rocm"
+        ):
+            pytest.xfail(
+                reason="Numerics issues: https://github.com/nod-ai/SHARK/issues/476"
+            )
         if config["framework"] == "tf" and dynamic == True:
             pytest.skip(
                 reason="Dynamic shapes not supported for this framework."
@@ -376,10 +427,11 @@ class SharkModuleTest(unittest.TestCase):
         if not os.path.isdir("./shark_tmp/"):
             os.mkdir("./shark_tmp/")
 
-        tempdir = tempfile.TemporaryDirectory(
-            prefix=self.module_tester.tmp_prefix, dir="./shark_tmp/"
+        # We must create a new process each time we benchmark a model to allow
+        # for Tensorflow to release GPU resources. Using the same process to
+        # benchmark multiple models leads to OOM.
+        p = multiprocessing.Process(
+            target=run_test, args=(self.module_tester, dynamic, device)
         )
-        self.module_tester.temp_dir = tempdir.name
-
-        with ireec.tools.TempFileSaver(tempdir.name):
-            self.module_tester.create_and_check_module(dynamic, device)
+        p.start()
+        p.join()

tank/tf/README.md

@@ -1,15 +0,0 @@
-## Running SharkInference on CPUs, GPUs and MAC.
-
-
-### Run the binary sequence_classification.
-#### The models supported are: [hugging face sequence classification](https://huggingface.co/docs/transformers/model_doc/auto#transformers.TFAutoModelForSequenceClassification)
-```shell
-./seq_classification.py --hf_model_name="hf_model" --device="cpu" # Use gpu | vulkan
-```
-
-Once the model is compiled to run on the device mentioned, we can pass in text and 
-get the logits.
-
-
-
-