[SD] Add support for a compiled version of the discrete Euler scheduler (#657)

* Add Shark version of euler scheduler

* Add Shark version of euler scheduler to web ui

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

@@ -0,0 +1,37 @@
+# See: https://github.com/llvm/torch-mlir/issues/1374
+name: Publish releases page
+
+on:
+  workflow_dispatch:
+
+jobs:
+  scrape_and_publish_releases:
+    name: "Scrape and publish releases"
+    runs-on: ubuntu-latest
+
+    # Don't run this in everyone's forks.
+    if: github.repository == 'nod-ai/SHARK'
+
+    steps:
+      - name: Checking out repository
+        uses: actions/checkout@v2
+        with:
+          token: ${{ secrets.NODAI_INVOCATION_TOKEN }}
+      - name: Run scrape releases script
+        run: python ./build_tools/scrape_releases.py nod-ai SHARK > /tmp/index.html
+        shell: bash
+      - 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-ai"
+      - run: mv /tmp/index.html package-index/index.html
+      - run: git add package-index/index.html
+
+      # Only try to make a commit if the file has changed.
+      - run: git diff --cached --exit-code || git commit -m "Update releases."
+
+      - name: GitHub Push
+        uses: ad-m/github-push-action@v0.6.0
+        with:
+          github_token: ${{ secrets.NODAI_INVOCATION_TOKEN }}
+          branch: github-pages

.github/workflows/nightly.yml

@@ -9,13 +9,87 @@ 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:
       fail-fast: false
       matrix:
         python-version: ["3.10"]
+        backend: [IREE, SHARK]
 
     steps:
     - uses: actions/checkout@v3
@@ -31,63 +105,56 @@ 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: 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 ${{ env.tag_name }}
-        body: |
-          Automatic snapshot release of nod.ai SHARK.
-        draft: true
-        prerelease: false        
+
     - name: Install dependencies
       run: |
+        echo "DATE=$(date +'%Y-%m-%d')" >> $GITHUB_ENV
         python -m pip install --upgrade pip
         python -m pip install flake8 pytest toml
-        if [ -f requirements.txt ]; then pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/nightly/cpu  -f https://github.com/llvm/torch-mlir/releases -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
         flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics --exclude shark.venv,lit.cfg.py 
         # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
         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 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
+        /bin/bash "$GITHUB_WORKSPACE/build_tools/populate_sharktank_ci.sh"
+        pytest --ci --ci_sha=${SHORT_SHA} --local_tank_cache="./gen_shark_tank/" -k "not metal" |
+          tail -n 1 |
+          tee -a pytest_results.txt
+        if !(grep -Fxq " failed" pytest_results.txt) 
+          then 
+            export SHA=$(git log -1 --format='%h')
+            gsutil -m cp -r $GITHUB_WORKSPACE/gen_shark_tank/* gs://shark_tank/${DATE}_$SHA
+            gsutil -m cp -r gs://shark_tank/${DATE}_$SHA/* gs://shark_tank/latest/
+        fi
+        rm -rf ./wheelhouse/nodai*
 
-    - name: Build and validate the package
+    - name: Build and validate the SHARK Runtime package
+      if: ${{ matrix.backend == 'SHARK' }}
       run: |
         cd $GITHUB_WORKSPACE
         ./setup_venv.sh
         source shark.venv/bin/activate
         package_version="$(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 https://github.com/llvm/torch-mlir/releases -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 -k 'not benchmark' --ignore=benchmarks/tests/test_hf_benchmark.py --ignore=benchmarks/tests/test_benchmark.py --ignore=shark/tests/test_shark_importer.py --ignore=tank/tf/
-    
-    - 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: ./wheelhouse/nodai_*.whl
-
-    - name: Publish Release
-      id: publish_release
-      uses: eregon/publish-release@v1
-      env:
-        GITHUB_TOKEN: ${{ secrets.NODAI_INVOCATION_TOKEN }}
-      with:
-        release_id: ${{ steps.create_release.outputs.id }}
+        pytest --ci --ci_sha=${SHORT_SHA} -k "not metal" |
+          tail -n 1 |
+          tee -a pytest_results.txt

.github/workflows/test-models.yml

@@ -6,17 +6,31 @@ name: Validate Models on Shark Runtime
 on:
   push:
     branches: [ main ]
+    paths-ignore:
+      - '**.md'
+      - 'shark/examples/**'
   pull_request:
     branches: [ main ]
+    paths-ignore:
+      - '**.md'
+      - 'shark/examples/**'
   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:
       fail-fast: true
       matrix:
-        os: [a100, MacStudio, ubuntu-latest]
-        suite: [cpu,gpu,vulkan]
+        os: [icelake, a100, MacStudio, ubuntu-latest]
+        suite: [cpu,cuda,vulkan]
         python-version: ["3.10"]
         include:
           - os: ubuntu-latest
@@ -25,27 +39,38 @@ jobs:
           - os: ubuntu-latest
             suite: vulkan
           - os: ubuntu-latest
-            suite: gpu
+            suite: cuda
           - os: ubuntu-latest
             suite: cpu
           - os: MacStudio
-            suite: gpu
+            suite: cuda
           - os: MacStudio
             suite: cpu
+          - os: icelake
+            suite: vulkan
+          - os: icelake
+            suite: cuda
+          - os: a100
+            suite: cpu
 
     runs-on: ${{ matrix.os }}
 
     steps:
     - uses: actions/checkout@v3
     
+    - 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: Set up Python Version File ${{ matrix.python-version }}
-      if: matrix.os == 'a100' ||  matrix.os == 'ubuntu-latest'
+      if: matrix.os == 'a100' ||  matrix.os == 'ubuntu-latest' ||  matrix.os == 'icelake'
       run: |
         # See https://github.com/actions/setup-python/issues/433
         echo ${{ matrix.python-version }} >> $GITHUB_WORKSPACE/.python-version
     
     - name: Set up Python ${{ matrix.python-version }}
-      if: matrix.os == 'a100' ||  matrix.os == 'ubuntu-latest'
+      if: matrix.os == 'a100' ||  matrix.os == 'ubuntu-latest' ||  matrix.os == 'icelake'
       uses: actions/setup-python@v4
       with:
         python-version: '${{ matrix.python-version }}'
@@ -71,26 +96,41 @@ jobs:
         # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
         flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics --exclude lit.cfg.py
 
-    - name: Validate CPU Models
+    - name: Validate Models on CPU
       if: matrix.suite == 'cpu'
       run: |
         cd $GITHUB_WORKSPACE
-        PYTHON=python${{ matrix.python-version }} ./setup_venv.sh
+        PYTHON=python${{ matrix.python-version }} BENCHMARK=1 IMPORTER=1 ./setup_venv.sh
         source shark.venv/bin/activate
-        pytest -k 'cpu' --ignore=shark/tests/test_shark_importer.py --ignore=benchmarks/tests/test_hf_benchmark.py --ignore=benchmarks/tests/test_benchmark.py 
+        pytest --benchmark --ci --ci_sha=${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
 
-    - name: Validate GPU Models
-      if: matrix.suite == 'gpu'
+    - name: Validate Models on NVIDIA GPU
+      if: matrix.suite == 'cuda'
+      run: |
+        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=${SHORT_SHA} -s --local_tank_cache="/data/anush/shark_cache" tank/test_models.py -k cuda --update_tank
+        gsutil cp ./bench_results.csv gs://shark-public/builder/bench_results/${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 (MacOS)
+      if: matrix.suite == 'vulkan' && matrix.os == 'MacStudio'
+      run: |
+        cd $GITHUB_WORKSPACE
+        PYTHON=python${{ matrix.python-version }} IMPORTER=1 ./setup_venv.sh
+        source shark.venv/bin/activate
+        export DYLD_LIBRARY_PATH=/usr/local/lib/
+        echo $PATH
+        pip list | grep -E "torch|iree"
+        pytest -s --ci --ci_sha=${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 -k "gpu" --ignore=shark/tests/test_shark_importer.py --ignore=benchmarks/tests/test_hf_benchmark.py --ignore=benchmarks/tests/test_benchmark.py 
-
-    - name: Validate Vulkan Models
-      if: matrix.suite == 'vulkan'
-      run: |
-        cd $GITHUB_WORKSPACE
-        PYTHON=python${{ matrix.python-version }} ./setup_venv.sh
-        source shark.venv/bin/activate
-        pytest -k 'vulkan' --ignore=shark/tests/test_shark_importer.py --ignore=benchmarks/tests/test_hf_benchmark.py --ignore=benchmarks/tests/test_benchmark.py
+        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 +162,14 @@ cython_debug/
 # Shark related artefacts
 *venv/
 shark_tmp/
+*.vmfb
+.use-iree
+tank/dict_configs.py
 
 # ORT related artefacts
 cache_models/
 onnx_models/
+
+#web logging
+web/logs/
+web/stored_results/stable_diffusion/

LICENSE

@@ -0,0 +1,218 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+    TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+    1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
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+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
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+
+      "Work" shall mean the work of authorship, whether in Source or
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+    Licensed under the Apache License, Version 2.0 (the "License");
+    you may not use this file except in compliance with the License.
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+       http://www.apache.org/licenses/LICENSE-2.0
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+    distributed under the License is distributed on an "AS IS" BASIS,
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+Software.

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
 
@@ -38,16 +136,21 @@ 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://github.com/nod-ai/SHARK/releases -f https://github.com/llvm/torch-mlir/releases -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
 
 ```shell
 curl -O https://raw.githubusercontent.com/nod-ai/SHARK/main/shark/examples/shark_inference/resnet50_script.py
 #Install deps for test script
-pip install --pre torch torchvision torchaudio tqdm pillow --extra-index-url https://download.pytorch.org/whl/nightly/cpu
+pip install --pre torch torchvision torchaudio tqdm pillow gsutil --extra-index-url https://download.pytorch.org/whl/nightly/cpu
 python ./resnet50_script.py --device="cpu"  #use cuda or vulkan or metal
 ```
 
@@ -61,78 +164,78 @@ 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
-
-```shell
-git clone https://github.com/nod-ai/SHARK.git
+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 
 ```
 
-## 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 
-```
-
-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/)
-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.
-
-### Run a demo script
+### Run any of the hundreds of SHARK tank models via the test framework
 ```shell
 python -m  shark.examples.shark_inference.resnet50_script --device="cpu" # Use gpu | vulkan
 # Or a pytest
-pytest tank/tf/hf_masked_lm/albert-base-v2_test.py::AlbertBaseModuleTest::test_module_static_cpu
+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://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.
+
+### How to use your locally built Torch-MLIR with SHARK
+```shell
+1.) Run `./setup_venv.sh in SHARK` and activate `shark.venv` virtual env.
+2.) Run `pip uninstall torch-mlir`.
+3.) Go to your local Torch-MLIR directory.
+4.) Activate mlir_venv virtual envirnoment.
+5.) Run `pip uninstall -r requirements.txt`.
+6.) Run `pip install -r requirements.txt`.
+7.) Build Torch-MLIR.
+8.) Activate shark.venv virtual environment from the Torch-MLIR directory.
+8.) Run `export PYTHONPATH=`pwd`/build/tools/torch-mlir/python_packages/torch_mlir:`pwd`/examples` in the Torch-MLIR directory.
+9.) Go to the SHARK directory.
+```
+Now the SHARK will use your locally build Torch-MLIR repo.
+
+
+## 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</summary>
-
-### Run all model tests on CPU/GPU/VULKAN/Metal
-```shell
-pytest tank
-
-# If on Linux for quicker results:
-pytest tank -n auto
-```
-
-### Running specific tests
-```shell
-# Run tests for a specific model:
-pytest tank/<MODEL_NAME> #i.e., pytest tank/bert-base-uncased
-
-# Run tests for a specific case:
-pytest tank/<MODEL_NAME>/<MODEL_TEST>.py::<MODEL>ModuleTest::<CASE>
-# i.e., pytest tank/bert-base-uncased/bert-base-uncased_test.py::BertModuleTest::test_module_static_cpu
-# For frontends other than pytorch, if available for a model, add frontend to filename: tank/bert-base-uncased/bert-base-uncased_tf_test.py
-
-# Run all tests, including tests for benchmarking and SHARK modules:
-# From base SHARK directory,
-pytest
-```
-
-### Run all model benchmark tests on CPU/GPU/VULKAN/Metal
-```shell
-pytest benchmarks
-```
-</details>
-
-
 <details>
   <summary>API Reference</summary>
 
@@ -183,160 +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)
 

build_tools/populate_sharktank_ci.sh

@@ -0,0 +1,5 @@
+#!/bin/bash
+
+IMPORTER=1 ./setup_venv.sh
+source $GITHUB_WORKSPACE/shark.venv/bin/activate
+python generate_sharktank.py --upload=False --ci_tank_dir=True

build_tools/scrape_releases.py

@@ -0,0 +1,37 @@
+"""Scrapes the github releases API to generate a static pip-install-able releases page.
+
+See https://github.com/llvm/torch-mlir/issues/1374
+"""
+import argparse
+import json
+
+import requests
+
+# Parse arguments
+parser = argparse.ArgumentParser()
+parser.add_argument("owner", type=str)
+parser.add_argument("repo", type=str)
+args = parser.parse_args()
+
+# Get releases
+response = requests.get(
+    f"https://api.github.com/repos/{args.owner}/{args.repo}/releases"
+)
+body = json.loads(response.content)
+
+# Parse releases
+releases = []
+for row in body:
+    for asset in row["assets"]:
+        releases.append((asset["name"], asset["browser_download_url"]))
+
+# Output HTML
+html = """<!DOCTYPE html>
+<html>
+  <body>
+"""
+for name, url in releases:
+    html += f"    <a href='{url}'>{name}</a><br />\n"
+html += """  </body>
+</html>"""
+print(html)

conftest.py

@@ -1,17 +1,5 @@
 def pytest_addoption(parser):
     # Attaches SHARK command-line arguments to the pytest machinery.
-    parser.addoption(
-        "--save_mlir",
-        action="store_true",
-        default="False",
-        help="Pass option to save input MLIR",
-    )
-    parser.addoption(
-        "--save_vmfb",
-        action="store_true",
-        default="False",
-        help="Pass option to save IREE output .vmfb",
-    )
     parser.addoption(
         "--benchmark",
         action="store_true",
@@ -19,8 +7,56 @@ def pytest_addoption(parser):
         help="Pass option to benchmark and write results.csv",
     )
     parser.addoption(
-        "--save_temps",
+        "--onnx_bench",
         action="store_true",
         default="False",
-        help="Saves IREE reproduction artifacts for filing upstream issues.",
+        help="Add ONNX benchmark results to pytest benchmarks.",
+    )
+    parser.addoption(
+        "--tf32",
+        action="store_true",
+        default="False",
+        help="Use TensorFloat-32 calculations.",
+    )
+    parser.addoption(
+        "--save_repro",
+        action="store_true",
+        default="False",
+        help="Pass option to save reproduction artifacts to SHARK/shark_tmp/test_case/",
+    )
+    parser.addoption(
+        "--save_fails",
+        action="store_true",
+        default="False",
+        help="Save reproduction artifacts for a test case only if it fails. Default is False.",
+    )
+    parser.addoption(
+        "--ci",
+        action="store_true",
+        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",
+        default="None",
+        help="Passes the github SHA of the CI workflow to include in google storage directory for reproduction artifacts.",
+    )
+    parser.addoption(
+        "--local_tank_cache",
+        action="store",
+        default="",
+        help="Specify the directory in which all downloaded shark_tank artifacts will be cached.",
+    )
+    parser.addoption(
+        "--tank_url",
+        type=str,
+        default="gs://shark_tank/latest",
+        help="URL to bucket from which to download SHARK tank artifacts. Default is gs://shark_tank/latest",
     )

cpp/.gitignore

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

cpp/CMakeLists.txt

@@ -0,0 +1,52 @@
+# Copyright 2022 The IREE Authors
+#
+# Licensed under the Apache License v2.0 with LLVM Exceptions.
+# See https://llvm.org/LICENSE.txt for license information.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+cmake_minimum_required(VERSION 3.21...3.23)
+
+#-------------------------------------------------------------------------------
+# Project configuration
+#-------------------------------------------------------------------------------
+
+project(iree-samples C CXX)
+set(CMAKE_C_STANDARD 11)
+set(CMAKE_CXX_STANDARD 17)
+set_property(GLOBAL PROPERTY USE_FOLDERS ON)
+
+#-------------------------------------------------------------------------------
+# Core project dependency
+#-------------------------------------------------------------------------------
+
+message(STATUS "Fetching core IREE repo (this may take a few minutes)...")
+# Note: for log output, set -DFETCHCONTENT_QUIET=OFF,
+# see https://gitlab.kitware.com/cmake/cmake/-/issues/18238#note_440475
+
+include(FetchContent)
+
+FetchContent_Declare(
+  iree
+  GIT_REPOSITORY https://github.com/nod-ai/shark-runtime.git
+  GIT_TAG shark 
+  GIT_SUBMODULES_RECURSE OFF
+  GIT_SHALLOW OFF
+  GIT_PROGRESS ON
+  USES_TERMINAL_DOWNLOAD ON
+)
+
+# Extend module path to find MLIR CMake modules.
+list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_BINARY_DIR}/lib/cmake/mlir")
+
+# Disable core project features not needed for these out of tree samples.
+set(IREE_BUILD_TESTS OFF CACHE BOOL "" FORCE)
+set(IREE_BUILD_SAMPLES OFF CACHE BOOL "" FORCE)
+
+FetchContent_MakeAvailable(iree)
+FetchContent_GetProperties(iree SOURCE_DIR IREE_SOURCE_DIR)
+
+#-------------------------------------------------------------------------------
+# Individual samples
+#-------------------------------------------------------------------------------
+
+add_subdirectory(vulkan_gui)

cpp/README.md

@@ -0,0 +1,82 @@
+# SHARK C/C++ Samples
+
+These C/C++ samples can be built using CMake. The samples depend on the main
+SHARK-Runtime project's C/C++ sources, including both the runtime and the compiler. 
+
+Individual samples may require additional dependencies. Watch CMake's output
+for information about which you are missing for individual samples.
+
+On Windows we recommend using https://github.com/microsoft/vcpkg to download packages for
+your system. The general setup flow looks like
+
+*Install and activate SHARK*
+
+```bash
+source shark.venv/bin/activate #follow main repo instructions to setup your venv
+```
+
+*Install Dependencies*
+
+```bash
+vcpkg install [library] --triplet [your platform]
+vcpkg integrate install
+
+# Then pass `-DCMAKE_TOOLCHAIN_FILE=[check logs for path]` when configuring CMake
+```
+
+In Ubuntu Linux you can install
+
+```bash
+sudo apt install libsdl2-dev
+```
+
+*Build*
+```bash
+cd cpp
+cmake -GNinja -B build/
+cmake --build build/
+```
+
+*Prepare the model*
+```bash
+wget https://storage.googleapis.com/shark_tank/latest/resnet50_tf/resnet50_tf.mlir
+iree-compile --iree-input-type=mhlo --iree-vm-bytecode-module-output-format=flatbuffer-binary --iree-hal-target-backends=vulkan --iree-llvm-embedded-linker-path=`python3 -c 'import sysconfig; print(sysconfig.get_paths()["purelib"])'`/iree/compiler/tools/../_mlir_libs/iree-lld --mlir-print-debuginfo --mlir-print-op-on-diagnostic=false --mlir-pass-pipeline-crash-reproducer=ist/core-reproducer.mlir --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 resnet50_tf.mlir -o resnet50_tf.vmfb
+```
+*Prepare the input*
+
+```bash
+python save_img.py
+```
+Note that this requires tensorflow, e.g.
+```bash
+python -m pip install tensorflow
+```
+
+*Run the vulkan_gui*
+```bash
+./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

@@ -0,0 +1,18 @@
+import numpy as np
+import tensorflow as tf
+from shark.shark_inference import SharkInference
+
+
+def load_and_preprocess_image(fname: str):
+    image = tf.io.read_file(fname)
+    image = tf.image.decode_image(image, channels=3)
+    image = tf.image.resize(image, (224, 224))
+    image = image[tf.newaxis, :]
+    # preprocessing pipeline
+    input_tensor = tf.keras.applications.resnet50.preprocess_input(image)
+    return input_tensor
+
+
+data = load_and_preprocess_image("dog_imagenet.jpg").numpy()
+
+data.tofile("dog.bin")

cpp/vision_inference/CMakeLists.txt

@@ -0,0 +1,84 @@
+# Copyright 2022 The IREE Authors
+#
+# Licensed under the Apache License v2.0 with LLVM Exceptions.
+# See https://llvm.org/LICENSE.txt for license information.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+if(NOT IREE_TARGET_BACKEND_LLVM_CPU OR
+   NOT IREE_HAL_EXECUTABLE_LOADER_EMBEDDED_ELF)
+  message(STATUS "Missing LLVM backend and/or embeddded elf loader, skipping vision_inference sample")
+  return()
+endif()
+
+# vcpkg install stb
+#   tested with version 2021-09-10
+find_package(Stb)
+if(NOT Stb_FOUND)
+  message(STATUS "Could not find Stb, skipping vision inference sample")
+  return()
+endif()
+
+# Compile mnist.mlir to mnist.vmfb.
+set(_COMPILE_TOOL_EXECUTABLE $<TARGET_FILE:iree-compile>)
+set(_COMPILE_ARGS)
+list(APPEND _COMPILE_ARGS "--iree-input-type=mhlo")
+list(APPEND _COMPILE_ARGS "--iree-hal-target-backends=llvm-cpu")
+list(APPEND _COMPILE_ARGS "${IREE_SOURCE_DIR}/samples/models/mnist.mlir")
+list(APPEND _COMPILE_ARGS "-o")
+list(APPEND _COMPILE_ARGS "mnist.vmfb")
+add_custom_command(
+  OUTPUT ${CMAKE_CURRENT_BINARY_DIR}/mnist.vmfb
+  COMMAND ${_COMPILE_TOOL_EXECUTABLE} ${_COMPILE_ARGS}
+  DEPENDS ${_COMPILE_TOOL_EXECUTABLE} "${IREE_SOURCE_DIR}/samples/models/mnist.mlir"
+)
+# Embed mnist.vmfb into a C file as mnist_bytecode_module_c.[h/c]
+set(_EMBED_DATA_EXECUTABLE $<TARGET_FILE:generate_embed_data>)
+set(_EMBED_ARGS)
+list(APPEND _EMBED_ARGS "--output_header=mnist_bytecode_module_c.h")
+list(APPEND _EMBED_ARGS "--output_impl=mnist_bytecode_module_c.c")
+list(APPEND _EMBED_ARGS "--identifier=iree_samples_vision_inference_mnist_bytecode_module")
+list(APPEND _EMBED_ARGS "--flatten")
+list(APPEND _EMBED_ARGS "${CMAKE_CURRENT_BINARY_DIR}/mnist.vmfb")
+add_custom_command(
+  OUTPUT "mnist_bytecode_module_c.h" "mnist_bytecode_module_c.c"
+  COMMAND ${_EMBED_DATA_EXECUTABLE} ${_EMBED_ARGS}
+  DEPENDS ${_EMBED_DATA_EXECUTABLE} ${CMAKE_CURRENT_BINARY_DIR}/mnist.vmfb
+)
+# Define a library target for mnist_bytecode_module_c.
+add_library(iree_samples_vision_inference_mnist_bytecode_module_c OBJECT)
+target_sources(iree_samples_vision_inference_mnist_bytecode_module_c
+  PRIVATE
+    mnist_bytecode_module_c.h
+    mnist_bytecode_module_c.c
+)
+
+# Define the sample executable.
+set(_NAME "iree-run-mnist-module")
+add_executable(${_NAME} "")
+target_sources(${_NAME}
+  PRIVATE
+    "image_util.h"
+    "image_util.c"
+    "iree-run-mnist-module.c"
+)
+set_target_properties(${_NAME} PROPERTIES OUTPUT_NAME "iree-run-mnist-module")
+target_include_directories(${_NAME} PUBLIC
+    $<BUILD_INTERFACE:${CMAKE_CURRENT_BINARY_DIR}>
+)
+target_include_directories(${_NAME} PRIVATE
+    ${Stb_INCLUDE_DIR}
+)
+target_link_libraries(${_NAME}
+  iree_base_base
+  iree_base_tracing
+  iree_hal_hal
+  iree_runtime_runtime
+  iree_samples_vision_inference_mnist_bytecode_module_c
+)
+
+# Define a target that copies the test image into the build directory.
+add_custom_target(iree_samples_vision_inference_test_image
+  COMMAND ${CMAKE_COMMAND} -E copy "${CMAKE_CURRENT_SOURCE_DIR}/mnist_test.png" "${CMAKE_CURRENT_BINARY_DIR}/mnist_test.png")
+add_dependencies(${_NAME} iree_samples_vision_inference_test_image)
+
+message(STATUS "Configured vision_inference sample successfully")

cpp/vision_inference/README.md

@@ -0,0 +1,8 @@
+# Vision Inference Sample (C code)
+
+This sample demonstrates how to run a MNIST handwritten digit detection vision
+model on an image using IREE's C API.
+
+A similar sample is implemented using a Python script and IREE's command line
+tools over in the primary iree repository at
+https://github.com/iree-org/iree/tree/main/samples/vision_inference

cpp/vision_inference/image_util.c

@@ -0,0 +1,224 @@
+// Copyright 2021 The IREE Authors
+//
+// Licensed under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+#include "image_util.h"
+
+#include <math.h>
+
+#include "iree/base/internal/flags.h"
+#include "iree/base/tracing.h"
+
+#define STB_IMAGE_IMPLEMENTATION
+#include "stb_image.h"
+
+iree_status_t iree_tools_utils_pixel_rescaled_to_buffer(
+    const uint8_t* pixel_data, iree_host_size_t buffer_length,
+    const float* input_range, iree_host_size_t range_length,
+    float* out_buffer) {
+  IREE_TRACE_ZONE_BEGIN(z0);
+  if (range_length != 2) {
+    IREE_TRACE_ZONE_END(z0);
+    return iree_make_status(IREE_STATUS_INVALID_ARGUMENT,
+                            "range defined as 2-element [min, max] array.");
+  }
+  float input_scale = fabsf(input_range[1] - input_range[0]) / 2.0f;
+  float input_offset = (input_range[0] + input_range[1]) / 2.0f;
+  const float kUint8Mean = 127.5f;
+  for (int i = 0; i < buffer_length; ++i) {
+    out_buffer[i] =
+        (((float)(pixel_data[i])) - kUint8Mean) / kUint8Mean * input_scale +
+        input_offset;
+  }
+  IREE_TRACE_ZONE_END(z0);
+  return iree_ok_status();
+}
+
+iree_status_t iree_tools_utils_load_pixel_data_impl(
+    const iree_string_view_t filename, const iree_hal_dim_t* shape,
+    iree_host_size_t shape_rank, iree_hal_element_type_t element_type,
+    uint8_t** out_pixel_data, iree_host_size_t* out_buffer_length) {
+  int img_dims[3];
+  if (stbi_info(filename.data, img_dims, &(img_dims[1]), &(img_dims[2])) == 0) {
+    return iree_make_status(IREE_STATUS_NOT_FOUND, "can't load image %.*s",
+                            (int)filename.size, filename.data);
+  }
+  if (!(element_type == IREE_HAL_ELEMENT_TYPE_FLOAT_32 ||
+        element_type == IREE_HAL_ELEMENT_TYPE_SINT_8 ||
+        element_type == IREE_HAL_ELEMENT_TYPE_UINT_8)) {
+    char element_type_str[16];
+    IREE_RETURN_IF_ERROR(iree_hal_format_element_type(
+        element_type, sizeof(element_type_str), element_type_str, NULL));
+    return iree_make_status(IREE_STATUS_UNIMPLEMENTED,
+                            "element type %s not supported", element_type_str);
+  }
+  switch (shape_rank) {
+    case 2: {  // Assume tensor <height x width>
+      if (img_dims[2] != 1 || (shape[0] != img_dims[1]) ||
+          (shape[1] != img_dims[0])) {
+        return iree_make_status(
+            IREE_STATUS_INVALID_ARGUMENT,
+            "image size: %dx%dx%d, expected: %" PRIdim "x%" PRIdim, img_dims[0],
+            img_dims[1], img_dims[2], shape[1], shape[0]);
+      }
+      break;
+    }
+    case 3: {  // Assume tensor <height x width x channel>
+      if (shape[0] != img_dims[1] || shape[1] != img_dims[0] ||
+          shape[2] != img_dims[2]) {
+        return iree_make_status(IREE_STATUS_INVALID_ARGUMENT,
+                                "image size: %dx%dx%d, expected: %" PRIdim
+                                "x%" PRIdim "x%" PRIdim,
+                                img_dims[0], img_dims[1], img_dims[2], shape[1],
+                                shape[0], shape[2]);
+      }
+      break;
+    }
+    case 4: {  // Assume tensor <batch x height x width x channel>
+      if (shape[1] != img_dims[1] || shape[2] != img_dims[0] ||
+          shape[3] != img_dims[2]) {
+        return iree_make_status(IREE_STATUS_INVALID_ARGUMENT,
+                                "image size: %dx%dx%d, expected: %" PRIdim
+                                "x%" PRIdim "x%" PRIdim,
+                                img_dims[0], img_dims[1], img_dims[2], shape[2],
+                                shape[1], shape[3]);
+      }
+      break;
+    }
+    default:
+      return iree_make_status(
+          IREE_STATUS_INVALID_ARGUMENT,
+          "Input buffer shape rank %" PRIhsz " not supported", shape_rank);
+  }
+  // Drop the alpha channel if present.
+  int req_ch = (img_dims[2] >= 3) ? 3 : 0;
+  *out_pixel_data = stbi_load(filename.data, img_dims, &(img_dims[1]),
+                              &(img_dims[2]), req_ch);
+  if (*out_pixel_data == NULL) {
+    return iree_make_status(IREE_STATUS_NOT_FOUND, "can't load image %.*s",
+                            (int)filename.size, filename.data);
+  }
+  *out_buffer_length =
+      img_dims[0] * img_dims[1] * (img_dims[2] > 3 ? 3 : img_dims[2]);
+  return iree_ok_status();
+}
+
+iree_status_t iree_tools_utils_load_pixel_data(
+    const iree_string_view_t filename, const iree_hal_dim_t* shape,
+    iree_host_size_t shape_rank, iree_hal_element_type_t element_type,
+    uint8_t** out_pixel_data, iree_host_size_t* out_buffer_length) {
+  IREE_TRACE_ZONE_BEGIN(z0);
+  iree_status_t result = iree_tools_utils_load_pixel_data_impl(
+      filename, shape, shape_rank, element_type, out_pixel_data,
+      out_buffer_length);
+  IREE_TRACE_ZONE_END(z0);
+  return result;
+}
+
+iree_status_t iree_tools_utils_buffer_view_from_image(
+    const iree_string_view_t filename, const iree_hal_dim_t* shape,
+    iree_host_size_t shape_rank, iree_hal_element_type_t element_type,
+    iree_hal_allocator_t* allocator, iree_hal_buffer_view_t** out_buffer_view) {
+  IREE_TRACE_ZONE_BEGIN(z0);
+  *out_buffer_view = NULL;
+  if (element_type != IREE_HAL_ELEMENT_TYPE_SINT_8 &&
+      element_type != IREE_HAL_ELEMENT_TYPE_UINT_8) {
+    IREE_TRACE_ZONE_END(z0);
+    return iree_make_status(IREE_STATUS_INVALID_ARGUMENT,
+                            "element type should be i8 or u8");
+  }
+
+  iree_status_t result;
+  uint8_t* pixel_data = NULL;
+  iree_host_size_t buffer_length;
+  result = iree_tools_utils_load_pixel_data(
+      filename, shape, shape_rank, element_type, &pixel_data, &buffer_length);
+  if (iree_status_is_ok(result)) {
+    iree_host_size_t element_byte =
+        iree_hal_element_dense_byte_count(element_type);
+    // SINT_8 and UINT_8 perform direct buffer wrap.
+    result = iree_hal_buffer_view_allocate_buffer(
+        allocator, shape_rank, shape, element_type,
+        IREE_HAL_ENCODING_TYPE_DENSE_ROW_MAJOR,
+        (iree_hal_buffer_params_t){
+            .type = IREE_HAL_MEMORY_TYPE_DEVICE_LOCAL,
+            .access = IREE_HAL_MEMORY_ACCESS_READ,
+            .usage = IREE_HAL_BUFFER_USAGE_DISPATCH_STORAGE |
+                     IREE_HAL_BUFFER_USAGE_TRANSFER,
+        },
+        iree_make_const_byte_span(pixel_data, element_byte * buffer_length),
+        out_buffer_view);
+  }
+  stbi_image_free(pixel_data);
+  IREE_TRACE_ZONE_END(z0);
+  return result;
+}
+
+typedef struct iree_tools_utils_buffer_view_load_params_t {
+  const uint8_t* pixel_data;
+  iree_host_size_t pixel_data_length;
+  const float* input_range;
+  iree_host_size_t input_range_length;
+} iree_tools_utils_buffer_view_load_params_t;
+static iree_status_t iree_tools_utils_buffer_view_load_image_rescaled(
+    iree_hal_buffer_mapping_t* mapping, void* user_data) {
+  iree_tools_utils_buffer_view_load_params_t* params =
+      (iree_tools_utils_buffer_view_load_params_t*)user_data;
+  return iree_tools_utils_pixel_rescaled_to_buffer(
+      params->pixel_data, params->pixel_data_length, params->input_range,
+      params->input_range_length, (float*)mapping->contents.data);
+}
+
+iree_status_t iree_tools_utils_buffer_view_from_image_rescaled(
+    const iree_string_view_t filename, const iree_hal_dim_t* shape,
+    iree_host_size_t shape_rank, iree_hal_element_type_t element_type,
+    iree_hal_allocator_t* allocator, const float* input_range,
+    iree_host_size_t input_range_length,
+    iree_hal_buffer_view_t** out_buffer_view) {
+  IREE_TRACE_ZONE_BEGIN(z0);
+  *out_buffer_view = NULL;
+  if (element_type != IREE_HAL_ELEMENT_TYPE_FLOAT_32) {
+    IREE_TRACE_ZONE_END(z0);
+    return iree_make_status(IREE_STATUS_INVALID_ARGUMENT,
+                            "element type should be f32");
+  }
+
+  // Classic row-major image layout.
+  iree_hal_encoding_type_t encoding_type =
+      IREE_HAL_ENCODING_TYPE_DENSE_ROW_MAJOR;
+
+  // Load pixel data from the file into a new host memory allocation (the only
+  // interface stb_image provides). A real application would want to use the
+  // generation callback to directly decode the image into the target mapped
+  // device buffer.
+  uint8_t* pixel_data = NULL;
+  iree_host_size_t buffer_length = 0;
+  IREE_RETURN_AND_END_ZONE_IF_ERROR(
+      z0, iree_tools_utils_load_pixel_data(filename, shape, shape_rank,
+                                           element_type, &pixel_data,
+                                           &buffer_length));
+
+  iree_tools_utils_buffer_view_load_params_t params = {
+      .pixel_data = pixel_data,
+      .pixel_data_length = buffer_length,
+      .input_range = input_range,
+      .input_range_length = input_range_length,
+  };
+  iree_status_t status = iree_hal_buffer_view_generate_buffer(
+      allocator, shape_rank, shape, element_type, encoding_type,
+      (iree_hal_buffer_params_t){
+          .type = IREE_HAL_MEMORY_TYPE_DEVICE_LOCAL |
+                  IREE_HAL_MEMORY_TYPE_HOST_VISIBLE,
+          .usage = IREE_HAL_BUFFER_USAGE_DISPATCH_STORAGE |
+                   IREE_HAL_BUFFER_USAGE_TRANSFER |
+                   IREE_HAL_BUFFER_USAGE_MAPPING,
+      },
+      iree_tools_utils_buffer_view_load_image_rescaled, &params,
+      out_buffer_view);
+
+  stbi_image_free(pixel_data);
+  IREE_TRACE_ZONE_END(z0);
+  return status;
+}

cpp/vision_inference/image_util.h

@@ -0,0 +1,77 @@
+// Copyright 2021 The IREE Authors
+//
+// Licensed under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+#ifndef IREE_SAMPLES_VISION_INFERENCE_IMAGE_UTIL_H_
+#define IREE_SAMPLES_VISION_INFERENCE_IMAGE_UTIL_H_
+
+#include "iree/base/api.h"
+#include "iree/hal/api.h"
+#include "iree/hal/buffer_view.h"
+
+#if __cplusplus
+extern "C" {
+#endif  // __cplusplus
+
+// Loads the image at |filename| into |out_pixel_data| and sets
+// |out_buffer_length| to its length.
+//
+// The image dimension must match the width, height, and channel in|shape|,
+// while 2 <= |shape_rank| <= 4 to match the image tensor format.
+//
+// The file must be in a format supported by stb_image.h.
+// The returned |out_pixel_data| buffer must be released by the caller.
+iree_status_t iree_tools_utils_load_pixel_data(
+    const iree_string_view_t filename, const iree_hal_dim_t* shape,
+    iree_host_size_t shape_rank, iree_hal_element_type_t element_type,
+    uint8_t** out_pixel_data, iree_host_size_t* out_buffer_length);
+
+// Parse the content in an image file in |filename| into a HAL buffer view
+// |out_buffer_view|. |out_buffer_view| properties are defined by |shape|,
+// |shape_rank|, and |element_type|, while being allocated by |allocator|.
+//
+// The |element_type| has to be SINT_8 or UINT_8. For FLOAT_32, use
+// |iree_tools_utils_buffer_view_from_image_rescaled| instead.
+//
+// The returned |out_buffer_view| must be released by the caller.
+iree_status_t iree_tools_utils_buffer_view_from_image(
+    const iree_string_view_t filename, const iree_hal_dim_t* shape,
+    iree_host_size_t shape_rank, iree_hal_element_type_t element_type,
+    iree_hal_allocator_t* allocator, iree_hal_buffer_view_t** out_buffer_view);
+
+// Parse the content in an image file in |filename| into a HAL buffer view
+// |out_buffer_view|. |out_buffer_view| properties are defined by |shape|,
+// |shape_rank|, and |element_type|, while being allocated by |allocator|.
+// The value in |out_buffer_view| is rescaled with |input_range|.
+//
+// The |element_type| has to be FLOAT_32, For SINT_8 or UINT_8, use
+// |iree_tools_utils_buffer_view_from_image| instead.
+//
+// The returned |out_buffer_view| must be released by the caller.
+iree_status_t iree_tools_utils_buffer_view_from_image_rescaled(
+    const iree_string_view_t filename, const iree_hal_dim_t* shape,
+    iree_host_size_t shape_rank, iree_hal_element_type_t element_type,
+    iree_hal_allocator_t* allocator, const float* input_range,
+    iree_host_size_t input_range_length,
+    iree_hal_buffer_view_t** out_buffer_view);
+
+// Normalize uint8_t |pixel_data| of the size |buffer_length| to float buffer
+// |out_buffer| with the range |input_range|.
+//
+// float32_x = (uint8_x - 127.5) / 127.5 * input_scale + input_offset, where
+// input_scale = abs(|input_range[0]| - |input_range[1]| / 2
+// input_offset = |input_range[0]| + |input_range[1]| / 2
+//
+// |out_buffer| needs to be allocated before the call.
+iree_status_t iree_tools_utils_pixel_rescaled_to_buffer(
+    const uint8_t* pixel_data, iree_host_size_t pixel_count,
+    const float* input_range, iree_host_size_t input_range_length,
+    float* out_buffer);
+
+#if __cplusplus
+}
+#endif  // __cplusplus
+
+#endif  // IREE_SAMPLES_VISION_INFERENCE_IMAGE_UTIL_H_

cpp/vision_inference/iree-run-mnist-module.c

@@ -0,0 +1,121 @@
+// Copyright 2021 The IREE Authors
+//
+// Licensed under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+// This sample uses image_util to load a hand-written image as an
+// iree_hal_buffer_view_t then passes it to the bytecode module built from
+// mnist.mlir on the CPU backend with the local-task driver.
+
+#include <float.h>
+
+#include "image_util.h"
+#include "iree/runtime/api.h"
+#include "mnist_bytecode_module_c.h"
+
+iree_status_t Run(const iree_string_view_t image_path) {
+  iree_runtime_instance_options_t instance_options;
+  iree_runtime_instance_options_initialize(IREE_API_VERSION_LATEST,
+                                           &instance_options);
+  iree_runtime_instance_options_use_all_available_drivers(&instance_options);
+  iree_runtime_instance_t* instance = NULL;
+  IREE_RETURN_IF_ERROR(iree_runtime_instance_create(
+      &instance_options, iree_allocator_system(), &instance));
+
+  // TODO(#5724): move device selection into the compiled modules.
+  iree_hal_device_t* device = NULL;
+  IREE_RETURN_IF_ERROR(iree_runtime_instance_try_create_default_device(
+      instance, iree_make_cstring_view("local-task"), &device));
+
+  // Create one session per loaded module to hold the module state.
+  iree_runtime_session_options_t session_options;
+  iree_runtime_session_options_initialize(&session_options);
+  iree_runtime_session_t* session = NULL;
+  IREE_RETURN_IF_ERROR(iree_runtime_session_create_with_device(
+      instance, &session_options, device,
+      iree_runtime_instance_host_allocator(instance), &session));
+  iree_hal_device_release(device);
+
+  const struct iree_file_toc_t* module_file =
+      iree_samples_vision_inference_mnist_bytecode_module_create();
+
+  IREE_RETURN_IF_ERROR(iree_runtime_session_append_bytecode_module_from_memory(
+      session, iree_make_const_byte_span(module_file->data, module_file->size),
+      iree_allocator_null()));
+
+  iree_runtime_call_t call;
+  IREE_RETURN_IF_ERROR(iree_runtime_call_initialize_by_name(
+      session, iree_make_cstring_view("module.predict"), &call));
+
+  // Prepare the input hal buffer view with image_util library.
+  // The input of the mmist model is single 28x28 pixel image as a
+  // tensor<1x28x28x1xf32>, with pixels in [0.0, 1.0].
+  iree_hal_buffer_view_t* buffer_view = NULL;
+  iree_hal_dim_t buffer_shape[] = {1, 28, 28, 1};
+  iree_hal_element_type_t hal_element_type = IREE_HAL_ELEMENT_TYPE_FLOAT_32;
+  float input_range[2] = {0.0f, 1.0f};
+  IREE_RETURN_IF_ERROR(
+      iree_tools_utils_buffer_view_from_image_rescaled(
+          image_path, buffer_shape, IREE_ARRAYSIZE(buffer_shape),
+          hal_element_type, iree_hal_device_allocator(device), input_range,
+          IREE_ARRAYSIZE(input_range), &buffer_view),
+      "load image");
+  IREE_RETURN_IF_ERROR(
+      iree_runtime_call_inputs_push_back_buffer_view(&call, buffer_view));
+  iree_hal_buffer_view_release(buffer_view);
+
+  IREE_RETURN_IF_ERROR(iree_runtime_call_invoke(&call, /*flags=*/0));
+
+  // Get the result buffers from the invocation.
+  iree_hal_buffer_view_t* ret_buffer_view = NULL;
+  IREE_RETURN_IF_ERROR(
+      iree_runtime_call_outputs_pop_front_buffer_view(&call, &ret_buffer_view));
+
+  // Read back the results. The output of the mnist model is a 1x10 prediction
+  // confidence values for each digit in [0, 9].
+  float predictions[1 * 10] = {0.0f};
+  IREE_RETURN_IF_ERROR(iree_hal_device_transfer_d2h(
+      iree_runtime_session_device(session),
+      iree_hal_buffer_view_buffer(ret_buffer_view), 0, predictions,
+      sizeof(predictions), IREE_HAL_TRANSFER_BUFFER_FLAG_DEFAULT,
+      iree_infinite_timeout()));
+  iree_hal_buffer_view_release(ret_buffer_view);
+
+  // Get the highest index from the output.
+  float result_val = FLT_MIN;
+  int result_idx = 0;
+  for (iree_host_size_t i = 0; i < IREE_ARRAYSIZE(predictions); ++i) {
+    if (predictions[i] > result_val) {
+      result_val = predictions[i];
+      result_idx = i;
+    }
+  }
+  fprintf(stdout, "Detected number: %d\n", result_idx);
+
+  iree_runtime_call_deinitialize(&call);
+  iree_runtime_session_release(session);
+  iree_runtime_instance_release(instance);
+  return iree_ok_status();
+}
+
+int main(int argc, char** argv) {
+  if (argc > 2) {
+    fprintf(stderr, "Usage: iree-run-mnist-module <image file>\n");
+    return -1;
+  }
+  iree_string_view_t image_path;
+  if (argc == 1) {
+    image_path = iree_make_cstring_view("mnist_test.png");
+  } else {
+    image_path = iree_make_cstring_view(argv[1]);
+  }
+  iree_status_t result = Run(image_path);
+  if (!iree_status_is_ok(result)) {
+    iree_status_fprint(stderr, result);
+    iree_status_ignore(result);
+    return -1;
+  }
+  iree_status_ignore(result);
+  return 0;
+}

cpp/vulkan_gui/CMakeLists.txt

@@ -0,0 +1,116 @@
+# Copyright 2022 The IREE Authors
+#
+# Licensed under the Apache License v2.0 with LLVM Exceptions.
+# See https://llvm.org/LICENSE.txt for license information.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+if(NOT IREE_TARGET_BACKEND_VULKAN_SPIRV OR
+   NOT IREE_HAL_DRIVER_VULKAN)
+  message(STATUS "Missing Vulkan backend and/or driver, skipping vulkan_gui sample")
+  return()
+endif()
+
+# This target statically links against Vulkan.
+# One way to achieve this is by installing the Vulkan SDK from
+# https://vulkan.lunarg.com/.
+include(FindVulkan)
+if(NOT Vulkan_FOUND)
+  message(STATUS "Could not find Vulkan, skipping vulkan_gui sample")
+  return()
+endif()
+
+# vcpkg install sdl2[vulkan]
+#   tested with versions 2.0.14#4 - 2.0.22#1
+find_package(SDL2)
+if(NOT SDL2_FOUND)
+  message(STATUS "Could not find SDL2, skipping vulkan_gui sample")
+  return()
+endif()
+
+FetchContent_Declare(
+  imgui
+  GIT_REPOSITORY https://github.com/ocornut/imgui
+  GIT_TAG        master
+)
+
+FetchContent_MakeAvailable(imgui)
+
+# Dear ImGui
+set(IMGUI_DIR ${CMAKE_BINARY_DIR}/_deps/imgui-src)
+message("Looking for Imgui in ${IMGUI_DIR}")
+include_directories(${IMGUI_DIR} ${IMGUI_DIR}/backends ..)
+
+
+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
+)
+
+iree_vulkan_sample(
+    NAME
+      iree-vulkan-gui
+
+    SRCS
+      vulkan_inference_gui.cc
+)
+
+message(STATUS "Configured vulkan_gui sample successfully")

cpp/vulkan_gui/simple_mul.mlir

@@ -0,0 +1,4 @@
+func.func @simple_mul(%arg0: tensor<4xf32>, %arg1: tensor<4xf32>) -> tensor<4xf32> {
+  %0 = "arith.mulf"(%arg0, %arg1) : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
+  return %0 : tensor<4xf32>
+}

cpp/vulkan_gui/vulkan_inference_gui.cc

@@ -0,0 +1,957 @@
+// Copyright 2019 The IREE Authors
+//
+// Licensed under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+// Vulkan Graphics + IREE API Integration Sample.
+
+#include <SDL.h>
+#include <SDL_vulkan.h>
+#include <imgui.h>
+#include <imgui_impl_sdl.h>
+#include <imgui_impl_vulkan.h>
+#include <vulkan/vulkan.h>
+
+
+#include <cstring>
+#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"
+
+// IREE's C API:
+#include "iree/base/api.h"
+#include "iree/hal/api.h"
+#include "iree/hal/drivers/vulkan/registration/driver_module.h"
+#include "iree/modules/hal/module.h"
+#include "iree/vm/api.h"
+#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"
+
+#define IMGUI_UNLIMITED_FRAME_RATE
+
+#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
+  size_t size;                  // length of the file
+} iree_file_toc_t;
+
+bool load_file(const char* filename, char** pOut, size_t* pSize)
+{
+    FILE* f = fopen(filename, "rb");
+    if (f == NULL)
+    {
+        fprintf(stderr, "Can't open %s\n", filename);
+        return false;
+    }
+
+    fseek(f, 0L, SEEK_END);
+    *pSize = ftell(f);
+    fseek(f, 0L, SEEK_SET);
+
+    *pOut = (char*)malloc(*pSize);
+
+    size_t size = fread(*pOut, *pSize, 1, f);
+
+    fclose(f);
+
+    return size != 0;
+}
+
+static VkAllocationCallbacks* g_Allocator = NULL;
+static VkInstance g_Instance = VK_NULL_HANDLE;
+static VkPhysicalDevice g_PhysicalDevice = VK_NULL_HANDLE;
+static VkDevice g_Device = VK_NULL_HANDLE;
+static uint32_t g_QueueFamily = (uint32_t)-1;
+static VkQueue g_Queue = VK_NULL_HANDLE;
+static VkPipelineCache g_PipelineCache = VK_NULL_HANDLE;
+static VkDescriptorPool g_DescriptorPool = VK_NULL_HANDLE;
+
+static ImGui_ImplVulkanH_Window g_MainWindowData;
+static uint32_t g_MinImageCount = 2;
+static bool g_SwapChainRebuild = false;
+static int g_SwapChainResizeWidth = 0;
+static int g_SwapChainResizeHeight = 0;
+
+static void check_vk_result(VkResult err) {
+  if (err == 0) return;
+  fprintf(stderr, "VkResult: %d\n", err);
+  abort();
+}
+
+// Returns the names of the Vulkan layers used for the given IREE
+// |extensibility_set| and |features|.
+std::vector<const char*> GetIreeLayers(
+    iree_hal_vulkan_extensibility_set_t extensibility_set,
+    iree_hal_vulkan_features_t features) {
+  iree_host_size_t required_count;
+  iree_hal_vulkan_query_extensibility_set(
+      features, extensibility_set, /*string_capacity=*/0, &required_count,
+      /*out_string_values=*/NULL);
+  std::vector<const char*> layers(required_count);
+  iree_hal_vulkan_query_extensibility_set(features, extensibility_set,
+                                          layers.size(), &required_count,
+                                          layers.data());
+  return layers;
+}
+
+// Returns the names of the Vulkan extensions used for the given IREE
+// |extensibility_set| and |features|.
+std::vector<const char*> GetIreeExtensions(
+    iree_hal_vulkan_extensibility_set_t extensibility_set,
+    iree_hal_vulkan_features_t features) {
+  iree_host_size_t required_count;
+  iree_hal_vulkan_query_extensibility_set(
+      features, extensibility_set, /*string_capacity=*/0, &required_count,
+      /*out_string_values=*/NULL);
+  std::vector<const char*> extensions(required_count);
+  iree_hal_vulkan_query_extensibility_set(features, extensibility_set,
+                                          extensions.size(), &required_count,
+                                          extensions.data());
+  return extensions;
+}
+
+// Returns the names of the Vulkan extensions used for the given IREE
+// |vulkan_features|.
+std::vector<const char*> GetDeviceExtensions(
+    VkPhysicalDevice physical_device,
+    iree_hal_vulkan_features_t vulkan_features) {
+  std::vector<const char*> iree_required_extensions = GetIreeExtensions(
+      IREE_HAL_VULKAN_EXTENSIBILITY_DEVICE_EXTENSIONS_REQUIRED,
+      vulkan_features);
+  std::vector<const char*> iree_optional_extensions = GetIreeExtensions(
+      IREE_HAL_VULKAN_EXTENSIBILITY_DEVICE_EXTENSIONS_OPTIONAL,
+      vulkan_features);
+
+  uint32_t extension_count = 0;
+  check_vk_result(vkEnumerateDeviceExtensionProperties(
+      physical_device, nullptr, &extension_count, nullptr));
+  std::vector<VkExtensionProperties> extension_properties(extension_count);
+  check_vk_result(vkEnumerateDeviceExtensionProperties(
+      physical_device, nullptr, &extension_count, extension_properties.data()));
+
+  // Merge extensions lists, including optional and required for simplicity.
+  std::set<const char*> ext_set;
+  ext_set.insert("VK_KHR_swapchain");
+  ext_set.insert(iree_required_extensions.begin(),
+                 iree_required_extensions.end());
+  for (int i = 0; i < iree_optional_extensions.size(); ++i) {
+    const char* optional_extension = iree_optional_extensions[i];
+    for (int j = 0; j < extension_count; ++j) {
+      if (strcmp(optional_extension, extension_properties[j].extensionName) ==
+          0) {
+        ext_set.insert(optional_extension);
+        break;
+      }
+    }
+  }
+  std::vector<const char*> extensions(ext_set.begin(), ext_set.end());
+  return extensions;
+}
+
+std::vector<const char*> GetInstanceLayers(
+    iree_hal_vulkan_features_t vulkan_features) {
+  // Query the layers that IREE wants / needs.
+  std::vector<const char*> required_layers = GetIreeLayers(
+      IREE_HAL_VULKAN_EXTENSIBILITY_INSTANCE_LAYERS_REQUIRED, vulkan_features);
+  std::vector<const char*> optional_layers = GetIreeLayers(
+      IREE_HAL_VULKAN_EXTENSIBILITY_INSTANCE_LAYERS_OPTIONAL, vulkan_features);
+
+  // Query the layers that are available on the Vulkan ICD.
+  uint32_t layer_property_count = 0;
+  check_vk_result(
+      vkEnumerateInstanceLayerProperties(&layer_property_count, NULL));
+  std::vector<VkLayerProperties> layer_properties(layer_property_count);
+  check_vk_result(vkEnumerateInstanceLayerProperties(&layer_property_count,
+                                                     layer_properties.data()));
+
+  // Match between optional/required and available layers.
+  std::vector<const char*> layers;
+  for (const char* layer_name : required_layers) {
+    bool found = false;
+    for (const auto& layer_property : layer_properties) {
+      if (std::strcmp(layer_name, layer_property.layerName) == 0) {
+        found = true;
+        layers.push_back(layer_name);
+        break;
+      }
+    }
+    if (!found) {
+      fprintf(stderr, "Required layer %s not available\n", layer_name);
+      abort();
+    }
+  }
+  for (const char* layer_name : optional_layers) {
+    for (const auto& layer_property : layer_properties) {
+      if (std::strcmp(layer_name, layer_property.layerName) == 0) {
+        layers.push_back(layer_name);
+        break;
+      }
+    }
+  }
+
+  return layers;
+}
+
+std::vector<const char*> GetInstanceExtensions(
+    SDL_Window* window, iree_hal_vulkan_features_t vulkan_features) {
+  // Ask SDL for its list of required instance extensions.
+  uint32_t sdl_extensions_count = 0;
+  SDL_Vulkan_GetInstanceExtensions(window, &sdl_extensions_count, NULL);
+  std::vector<const char*> sdl_extensions(sdl_extensions_count);
+  SDL_Vulkan_GetInstanceExtensions(window, &sdl_extensions_count,
+                                   sdl_extensions.data());
+
+  std::vector<const char*> iree_required_extensions = GetIreeExtensions(
+      IREE_HAL_VULKAN_EXTENSIBILITY_INSTANCE_EXTENSIONS_REQUIRED,
+      vulkan_features);
+  std::vector<const char*> iree_optional_extensions = GetIreeExtensions(
+      IREE_HAL_VULKAN_EXTENSIBILITY_INSTANCE_EXTENSIONS_OPTIONAL,
+      vulkan_features);
+
+  // Merge extensions lists, including optional and required for simplicity.
+  std::set<const char*> ext_set;
+  ext_set.insert(sdl_extensions.begin(), sdl_extensions.end());
+  ext_set.insert(iree_required_extensions.begin(),
+                 iree_required_extensions.end());
+  ext_set.insert(iree_optional_extensions.begin(),
+                 iree_optional_extensions.end());
+  std::vector<const char*> extensions(ext_set.begin(), ext_set.end());
+  return extensions;
+}
+
+void SetupVulkan(iree_hal_vulkan_features_t vulkan_features,
+                 const char** instance_layers, uint32_t instance_layers_count,
+                 const char** instance_extensions,
+                 uint32_t instance_extensions_count,
+                 const VkAllocationCallbacks* allocator, VkInstance* instance,
+                 uint32_t* queue_family_index,
+                 VkPhysicalDevice* physical_device, VkQueue* queue,
+                 VkDevice* device, VkDescriptorPool* descriptor_pool) {
+  VkResult err;
+
+  // Create Vulkan Instance
+  {
+    VkInstanceCreateInfo create_info = {};
+    create_info.sType = VK_STRUCTURE_TYPE_INSTANCE_CREATE_INFO;
+    create_info.enabledLayerCount = instance_layers_count;
+    create_info.ppEnabledLayerNames = instance_layers;
+    create_info.enabledExtensionCount = instance_extensions_count;
+    create_info.ppEnabledExtensionNames = instance_extensions;
+    err = vkCreateInstance(&create_info, allocator, instance);
+    check_vk_result(err);
+  }
+
+  // Select GPU
+  {
+    uint32_t gpu_count;
+    err = vkEnumeratePhysicalDevices(*instance, &gpu_count, NULL);
+    check_vk_result(err);
+    IM_ASSERT(gpu_count > 0);
+
+    VkPhysicalDevice* gpus =
+        (VkPhysicalDevice*)malloc(sizeof(VkPhysicalDevice) * gpu_count);
+    err = vkEnumeratePhysicalDevices(*instance, &gpu_count, gpus);
+    check_vk_result(err);
+
+    // Use the first reported GPU for simplicity.
+    *physical_device = gpus[0];
+
+    VkPhysicalDeviceProperties properties;
+    vkGetPhysicalDeviceProperties(*physical_device, &properties);
+    fprintf(stdout, "Selected Vulkan device: '%s'\n", properties.deviceName);
+    free(gpus);
+  }
+
+  // Select queue family. We want a single queue with graphics and compute for
+  // simplicity, but we could also discover and use separate queues for each.
+  {
+    uint32_t count;
+    vkGetPhysicalDeviceQueueFamilyProperties(*physical_device, &count, NULL);
+    VkQueueFamilyProperties* queues = (VkQueueFamilyProperties*)malloc(
+        sizeof(VkQueueFamilyProperties) * count);
+    vkGetPhysicalDeviceQueueFamilyProperties(*physical_device, &count, queues);
+    for (uint32_t i = 0; i < count; i++) {
+      if (queues[i].queueFlags &
+          (VK_QUEUE_GRAPHICS_BIT | VK_QUEUE_COMPUTE_BIT)) {
+        *queue_family_index = i;
+        break;
+      }
+    }
+    free(queues);
+    IM_ASSERT(*queue_family_index != (uint32_t)-1);
+  }
+
+  // Create Logical Device (with 1 queue)
+  {
+    std::vector<const char*> device_extensions =
+        GetDeviceExtensions(*physical_device, vulkan_features);
+    const float queue_priority[] = {1.0f};
+    VkDeviceQueueCreateInfo queue_info = {};
+    queue_info.sType = VK_STRUCTURE_TYPE_DEVICE_QUEUE_CREATE_INFO;
+    queue_info.queueFamilyIndex = *queue_family_index;
+    queue_info.queueCount = 1;
+    queue_info.pQueuePriorities = queue_priority;
+    VkDeviceCreateInfo create_info = {};
+    create_info.sType = VK_STRUCTURE_TYPE_DEVICE_CREATE_INFO;
+    create_info.queueCreateInfoCount = 1;
+    create_info.pQueueCreateInfos = &queue_info;
+    create_info.enabledExtensionCount =
+        static_cast<uint32_t>(device_extensions.size());
+    create_info.ppEnabledExtensionNames = device_extensions.data();
+
+    // Enable timeline semaphores.
+    VkPhysicalDeviceFeatures2 features2;
+    memset(&features2, 0, sizeof(features2));
+    features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2;
+    create_info.pNext = &features2;
+    VkPhysicalDeviceTimelineSemaphoreFeatures semaphore_features;
+    memset(&semaphore_features, 0, sizeof(semaphore_features));
+    semaphore_features.sType =
+        VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_TIMELINE_SEMAPHORE_FEATURES;
+    semaphore_features.pNext = features2.pNext;
+    features2.pNext = &semaphore_features;
+    semaphore_features.timelineSemaphore = VK_TRUE;
+
+    err = vkCreateDevice(*physical_device, &create_info, allocator, device);
+    check_vk_result(err);
+    vkGetDeviceQueue(*device, *queue_family_index, 0, queue);
+  }
+
+  // Create Descriptor Pool
+  {
+    VkDescriptorPoolSize pool_sizes[] = {
+        {VK_DESCRIPTOR_TYPE_SAMPLER, 1000},
+        {VK_DESCRIPTOR_TYPE_COMBINED_IMAGE_SAMPLER, 1000},
+        {VK_DESCRIPTOR_TYPE_SAMPLED_IMAGE, 1000},
+        {VK_DESCRIPTOR_TYPE_STORAGE_IMAGE, 1000},
+        {VK_DESCRIPTOR_TYPE_UNIFORM_TEXEL_BUFFER, 1000},
+        {VK_DESCRIPTOR_TYPE_STORAGE_TEXEL_BUFFER, 1000},
+        {VK_DESCRIPTOR_TYPE_UNIFORM_BUFFER, 1000},
+        {VK_DESCRIPTOR_TYPE_STORAGE_BUFFER, 1000},
+        {VK_DESCRIPTOR_TYPE_UNIFORM_BUFFER_DYNAMIC, 1000},
+        {VK_DESCRIPTOR_TYPE_STORAGE_BUFFER_DYNAMIC, 1000},
+        {VK_DESCRIPTOR_TYPE_INPUT_ATTACHMENT, 1000}};
+    VkDescriptorPoolCreateInfo pool_info = {};
+    pool_info.sType = VK_STRUCTURE_TYPE_DESCRIPTOR_POOL_CREATE_INFO;
+    pool_info.flags = VK_DESCRIPTOR_POOL_CREATE_FREE_DESCRIPTOR_SET_BIT;
+    pool_info.maxSets = 1000 * IREE_ARRAYSIZE(pool_sizes);
+    pool_info.poolSizeCount = (uint32_t)IREE_ARRAYSIZE(pool_sizes);
+    pool_info.pPoolSizes = pool_sizes;
+    err =
+        vkCreateDescriptorPool(*device, &pool_info, allocator, descriptor_pool);
+    check_vk_result(err);
+  }
+}
+
+void SetupVulkanWindow(ImGui_ImplVulkanH_Window* wd,
+                       const VkAllocationCallbacks* allocator,
+                       VkInstance instance, uint32_t queue_family_index,
+                       VkPhysicalDevice physical_device, VkDevice device,
+                       VkSurfaceKHR surface, int width, int height,
+                       uint32_t min_image_count) {
+  wd->Surface = surface;
+
+  // Check for WSI support
+  VkBool32 res;
+  vkGetPhysicalDeviceSurfaceSupportKHR(physical_device, queue_family_index,
+                                       wd->Surface, &res);
+  if (res != VK_TRUE) {
+    fprintf(stderr, "Error no WSI support on physical device 0\n");
+    exit(-1);
+  }
+
+  // Select Surface Format
+  const VkFormat requestSurfaceImageFormat[] = {
+      VK_FORMAT_B8G8R8A8_UNORM, VK_FORMAT_R8G8B8A8_UNORM,
+      VK_FORMAT_B8G8R8_UNORM, VK_FORMAT_R8G8B8_UNORM};
+  const VkColorSpaceKHR requestSurfaceColorSpace =
+      VK_COLORSPACE_SRGB_NONLINEAR_KHR;
+  wd->SurfaceFormat = ImGui_ImplVulkanH_SelectSurfaceFormat(
+      physical_device, wd->Surface, requestSurfaceImageFormat,
+      (size_t)IREE_ARRAYSIZE(requestSurfaceImageFormat),
+      requestSurfaceColorSpace);
+
+  // Select Present Mode
+#ifdef IMGUI_UNLIMITED_FRAME_RATE
+  VkPresentModeKHR present_modes[] = {VK_PRESENT_MODE_MAILBOX_KHR,
+                                      VK_PRESENT_MODE_IMMEDIATE_KHR,
+                                      VK_PRESENT_MODE_FIFO_KHR};
+#else
+  VkPresentModeKHR present_modes[] = {VK_PRESENT_MODE_FIFO_KHR};
+#endif
+  wd->PresentMode = ImGui_ImplVulkanH_SelectPresentMode(
+      physical_device, wd->Surface, &present_modes[0],
+      IREE_ARRAYSIZE(present_modes));
+
+  // Create SwapChain, RenderPass, Framebuffer, etc.
+  IM_ASSERT(min_image_count >= 2);
+  ImGui_ImplVulkanH_CreateOrResizeWindow(instance, physical_device, device, wd,
+                                         queue_family_index, allocator, width,
+                                         height, min_image_count);
+
+  // Set clear color.
+  ImVec4 clear_color = ImVec4(0.45f, 0.55f, 0.60f, 1.00f);
+  memcpy(&wd->ClearValue.color.float32[0], &clear_color, 4 * sizeof(float));
+}
+
+void RenderFrame(ImGui_ImplVulkanH_Window* wd, VkDevice device, VkQueue queue) {
+  VkResult err;
+
+  VkSemaphore image_acquired_semaphore =
+      wd->FrameSemaphores[wd->SemaphoreIndex].ImageAcquiredSemaphore;
+  VkSemaphore render_complete_semaphore =
+      wd->FrameSemaphores[wd->SemaphoreIndex].RenderCompleteSemaphore;
+  err = vkAcquireNextImageKHR(device, wd->Swapchain, UINT64_MAX,
+                              image_acquired_semaphore, VK_NULL_HANDLE,
+                              &wd->FrameIndex);
+  check_vk_result(err);
+
+  ImGui_ImplVulkanH_Frame* fd = &wd->Frames[wd->FrameIndex];
+  {
+    err = vkWaitForFences(
+        device, 1, &fd->Fence, VK_TRUE,
+        UINT64_MAX);  // wait indefinitely instead of periodically checking
+    check_vk_result(err);
+
+    err = vkResetFences(device, 1, &fd->Fence);
+    check_vk_result(err);
+  }
+  {
+    err = vkResetCommandPool(device, fd->CommandPool, 0);
+    check_vk_result(err);
+    VkCommandBufferBeginInfo info = {};
+    info.sType = VK_STRUCTURE_TYPE_COMMAND_BUFFER_BEGIN_INFO;
+    info.flags |= VK_COMMAND_BUFFER_USAGE_ONE_TIME_SUBMIT_BIT;
+    err = vkBeginCommandBuffer(fd->CommandBuffer, &info);
+    check_vk_result(err);
+  }
+  {
+    VkRenderPassBeginInfo info = {};
+    info.sType = VK_STRUCTURE_TYPE_RENDER_PASS_BEGIN_INFO;
+    info.renderPass = wd->RenderPass;
+    info.framebuffer = fd->Framebuffer;
+    info.renderArea.extent.width = wd->Width;
+    info.renderArea.extent.height = wd->Height;
+    info.clearValueCount = 1;
+    info.pClearValues = &wd->ClearValue;
+    vkCmdBeginRenderPass(fd->CommandBuffer, &info, VK_SUBPASS_CONTENTS_INLINE);
+  }
+
+  // Record Imgui Draw Data and draw funcs into command buffer
+  ImGui_ImplVulkan_RenderDrawData(ImGui::GetDrawData(), fd->CommandBuffer);
+
+  // Submit command buffer
+  vkCmdEndRenderPass(fd->CommandBuffer);
+  {
+    VkPipelineStageFlags wait_stage =
+        VK_PIPELINE_STAGE_COLOR_ATTACHMENT_OUTPUT_BIT;
+    VkSubmitInfo info = {};
+    info.sType = VK_STRUCTURE_TYPE_SUBMIT_INFO;
+    info.waitSemaphoreCount = 1;
+    info.pWaitSemaphores = &image_acquired_semaphore;
+    info.pWaitDstStageMask = &wait_stage;
+    info.commandBufferCount = 1;
+    info.pCommandBuffers = &fd->CommandBuffer;
+    info.signalSemaphoreCount = 1;
+    info.pSignalSemaphores = &render_complete_semaphore;
+
+    err = vkEndCommandBuffer(fd->CommandBuffer);
+    check_vk_result(err);
+    err = vkQueueSubmit(queue, 1, &info, fd->Fence);
+    check_vk_result(err);
+  }
+}
+
+void PresentFrame(ImGui_ImplVulkanH_Window* wd, VkQueue queue) {
+  VkSemaphore render_complete_semaphore =
+      wd->FrameSemaphores[wd->SemaphoreIndex].RenderCompleteSemaphore;
+  VkPresentInfoKHR info = {};
+  info.sType = VK_STRUCTURE_TYPE_PRESENT_INFO_KHR;
+  info.waitSemaphoreCount = 1;
+  info.pWaitSemaphores = &render_complete_semaphore;
+  info.swapchainCount = 1;
+  info.pSwapchains = &wd->Swapchain;
+  info.pImageIndices = &wd->FrameIndex;
+  VkResult err = vkQueuePresentKHR(queue, &info);
+  check_vk_result(err);
+  wd->SemaphoreIndex =
+      (wd->SemaphoreIndex + 1) %
+      wd->ImageCount;  // Now we can use the next set of semaphores
+}
+
+static void CleanupVulkan() {
+  vkDestroyDescriptorPool(g_Device, g_DescriptorPool, g_Allocator);
+
+  vkDestroyDevice(g_Device, g_Allocator);
+  vkDestroyInstance(g_Instance, g_Allocator);
+}
+
+static void CleanupVulkanWindow() {
+  ImGui_ImplVulkanH_DestroyWindow(g_Instance, g_Device, &g_MainWindowData,
+                                  g_Allocator);
+}
+
+namespace iree {
+
+extern "C" int iree_main(int argc, char** argv) {
+
+  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.
+  if (SDL_Init(SDL_INIT_VIDEO | SDL_INIT_TIMER) != 0) {
+    fprintf(stderr, "Failed to initialize SDL\n");
+    abort();
+    return 1;
+  }
+
+  // Setup window
+  // clang-format off
+  SDL_WindowFlags window_flags = (SDL_WindowFlags)(
+      SDL_WINDOW_VULKAN | SDL_WINDOW_RESIZABLE | SDL_WINDOW_ALLOW_HIGHDPI);
+  // clang-format on
+  SDL_Window* window = SDL_CreateWindow(
+      "IREE Samples - Vulkan Inference GUI", SDL_WINDOWPOS_CENTERED,
+      SDL_WINDOWPOS_CENTERED, 1280, 720, window_flags);
+  if (window == nullptr)
+  {
+    const char* sdl_err = SDL_GetError();
+    fprintf(stderr, "Error, SDL_CreateWindow returned: %s\n", sdl_err);
+    abort();
+    return 1;
+  }
+
+  // Setup Vulkan
+  iree_hal_vulkan_features_t iree_vulkan_features =
+      static_cast<iree_hal_vulkan_features_t>(
+          IREE_HAL_VULKAN_FEATURE_ENABLE_VALIDATION_LAYERS |
+          IREE_HAL_VULKAN_FEATURE_ENABLE_DEBUG_UTILS);
+  std::vector<const char*> layers = GetInstanceLayers(iree_vulkan_features);
+  std::vector<const char*> extensions =
+      GetInstanceExtensions(window, iree_vulkan_features);
+  SetupVulkan(iree_vulkan_features, layers.data(),
+              static_cast<uint32_t>(layers.size()), extensions.data(),
+              static_cast<uint32_t>(extensions.size()), g_Allocator,
+              &g_Instance, &g_QueueFamily, &g_PhysicalDevice, &g_Queue,
+              &g_Device, &g_DescriptorPool);
+
+  // Create Window Surface
+  VkSurfaceKHR surface;
+  VkResult err;
+  if (SDL_Vulkan_CreateSurface(window, g_Instance, &surface) == 0) {
+    fprintf(stderr, "Failed to create Vulkan surface.\n");
+    abort();
+    return 1;
+  }
+
+  // Create Framebuffers
+  int w, h;
+  SDL_GetWindowSize(window, &w, &h);
+  ImGui_ImplVulkanH_Window* wd = &g_MainWindowData;
+  SetupVulkanWindow(wd, g_Allocator, g_Instance, g_QueueFamily,
+                    g_PhysicalDevice, g_Device, surface, w, h, g_MinImageCount);
+
+  // Setup Dear ImGui context
+  IMGUI_CHECKVERSION();
+  ImGui::CreateContext();
+  ImGuiIO& io = ImGui::GetIO();
+  (void)io;
+
+  ImGui::StyleColorsDark();
+
+  // Setup Platform/Renderer bindings
+  ImGui_ImplSDL2_InitForVulkan(window);
+  ImGui_ImplVulkan_InitInfo init_info = {};
+  init_info.Instance = g_Instance;
+  init_info.PhysicalDevice = g_PhysicalDevice;
+  init_info.Device = g_Device;
+  init_info.QueueFamily = g_QueueFamily;
+  init_info.Queue = g_Queue;
+  init_info.PipelineCache = g_PipelineCache;
+  init_info.DescriptorPool = g_DescriptorPool;
+  init_info.Allocator = g_Allocator;
+  init_info.MinImageCount = g_MinImageCount;
+  init_info.ImageCount = wd->ImageCount;
+  init_info.CheckVkResultFn = check_vk_result;
+  ImGui_ImplVulkan_Init(&init_info, wd->RenderPass);
+
+  // Upload Fonts
+  {
+    // Use any command queue
+    VkCommandPool command_pool = wd->Frames[wd->FrameIndex].CommandPool;
+    VkCommandBuffer command_buffer = wd->Frames[wd->FrameIndex].CommandBuffer;
+
+    err = vkResetCommandPool(g_Device, command_pool, 0);
+    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);
+
+    ImGui_ImplVulkan_CreateFontsTexture(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);
+    ImGui_ImplVulkan_DestroyFontUploadObjects();
+  }
+
+  // Demo state.
+  bool show_iree_window = true;
+  // --------------------------------------------------------------------------
+  // Setup IREE.
+
+  // Check API version.
+  iree_api_version_t actual_version;
+  iree_status_t status =
+      iree_api_version_check(IREE_API_VERSION_LATEST, &actual_version);
+  if (iree_status_is_ok(status)) {
+    fprintf(stdout, "IREE runtime API version: %d\n", actual_version);
+  } else {
+    fprintf(stderr, "Unsupported runtime API version: %d\n", actual_version);
+    abort();
+  }
+
+  // Create a runtime Instance.
+  iree_vm_instance_t* iree_instance = nullptr;
+  IREE_CHECK_OK(
+      iree_vm_instance_create(iree_allocator_system(), &iree_instance));
+
+  // Register HAL drivers and VM module types.
+  IREE_CHECK_OK(iree_hal_vulkan_driver_module_register(
+      iree_hal_driver_registry_default()));
+  IREE_CHECK_OK(iree_hal_module_register_all_types(iree_instance));
+
+  // Create IREE Vulkan Driver and Device, sharing our VkInstance/VkDevice.
+  fprintf(stdout, "Creating Vulkan driver/device\n");
+  // Load symbols from our static `vkGetInstanceProcAddr` for IREE to use.
+  iree_hal_vulkan_syms_t* iree_vk_syms = nullptr;
+  IREE_CHECK_OK(iree_hal_vulkan_syms_create(
+      reinterpret_cast<void*>(&vkGetInstanceProcAddr), iree_allocator_system(),
+      &iree_vk_syms));
+  // Create the driver sharing our VkInstance.
+  iree_hal_driver_t* iree_vk_driver = nullptr;
+  iree_string_view_t driver_identifier = iree_make_cstring_view("vulkan");
+  iree_hal_vulkan_driver_options_t driver_options;
+  driver_options.api_version = VK_API_VERSION_1_0;
+  driver_options.requested_features = static_cast<iree_hal_vulkan_features_t>(
+      IREE_HAL_VULKAN_FEATURE_ENABLE_DEBUG_UTILS);
+  IREE_CHECK_OK(iree_hal_vulkan_driver_create_using_instance(
+      driver_identifier, &driver_options, iree_vk_syms, g_Instance,
+      iree_allocator_system(), &iree_vk_driver));
+  // Create a device sharing our VkDevice and queue.
+  // We could also create a separate (possibly low priority) compute queue for
+  // IREE, and/or provide a dedicated transfer queue.
+  iree_string_view_t device_identifier = iree_make_cstring_view("vulkan");
+  iree_hal_vulkan_queue_set_t compute_queue_set;
+  compute_queue_set.queue_family_index = g_QueueFamily;
+  compute_queue_set.queue_indices = 1 << 0;
+  iree_hal_vulkan_queue_set_t transfer_queue_set;
+  transfer_queue_set.queue_indices = 0;
+  iree_hal_device_t* iree_vk_device = nullptr;
+  IREE_CHECK_OK(iree_hal_vulkan_wrap_device(
+      device_identifier, &driver_options.device_options, iree_vk_syms,
+      g_Instance, g_PhysicalDevice, g_Device, &compute_queue_set,
+      &transfer_queue_set, iree_allocator_system(), &iree_vk_device));
+  // Create a HAL module using the HAL device.
+  iree_vm_module_t* hal_module = nullptr;
+  IREE_CHECK_OK(iree_hal_module_create(iree_instance, iree_vk_device,
+                                       IREE_HAL_MODULE_FLAG_NONE,
+                                       iree_allocator_system(), &hal_module));
+
+
+  // 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);
+
+  iree_vm_module_t* bytecode_module = nullptr;
+  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);
+  //}
+
+  // Allocate a context that will hold the module state across invocations.
+  iree_vm_context_t* iree_context = nullptr;
+  std::vector<iree_vm_module_t*> modules = {hal_module, bytecode_module};
+  IREE_CHECK_OK(iree_vm_context_create_with_modules(
+      iree_instance, IREE_VM_CONTEXT_FLAG_NONE, modules.size(), modules.data(),
+      iree_allocator_system(), &iree_context));
+  fprintf(stdout, "Context with modules is ready for use\n");
+
+  // Lookup the entry point function.
+  iree_vm_function_t main_function;
+  const char kMainFunctionName[] = "module.forward";
+  IREE_CHECK_OK(iree_vm_context_resolve_function(
+      iree_context,
+      iree_string_view_t{kMainFunctionName, sizeof(kMainFunctionName) - 1},
+      &main_function));
+  iree_string_view_t main_function_name = iree_vm_function_name(&main_function);
+  fprintf(stdout, "Resolved main function named '%.*s'\n",
+          (int)main_function_name.size, main_function_name.data);
+
+  // --------------------------------------------------------------------------
+
+        // 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;
+
+       // Wrap input buffers in buffer views.
+
+        vm::ref<iree_vm_list_t> inputs;
+        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.
+
+        vm::ref<iree_vm_list_t> outputs;
+        constexpr iree_hal_dim_t kOutputCount = 1000;
+        IREE_CHECK_OK(iree_vm_list_create(/*element_type=*/nullptr, kOutputCount * sizeof(float), iree_allocator_system(), &outputs));
+
+  // --------------------------------------------------------------------------
+
+  // Main loop.
+  bool done = false;
+  while (!done) {
+    SDL_Event event;
+
+    while (SDL_PollEvent(&event)) {
+      if (event.type == SDL_QUIT) {
+        done = true;
+      }
+
+      ImGui_ImplSDL2_ProcessEvent(&event);
+      if (event.type == SDL_QUIT) done = true;
+      if (event.type == SDL_WINDOWEVENT &&
+          event.window.event == SDL_WINDOWEVENT_RESIZED &&
+          event.window.windowID == SDL_GetWindowID(window)) {
+        g_SwapChainResizeWidth = (int)event.window.data1;
+        g_SwapChainResizeHeight = (int)event.window.data2;
+        g_SwapChainRebuild = true;
+      }
+    }
+
+    if (g_SwapChainRebuild) {
+      g_SwapChainRebuild = false;
+      ImGui_ImplVulkan_SetMinImageCount(g_MinImageCount);
+      ImGui_ImplVulkanH_CreateOrResizeWindow(
+          g_Instance, g_PhysicalDevice, g_Device, &g_MainWindowData,
+          g_QueueFamily, g_Allocator, g_SwapChainResizeWidth,
+          g_SwapChainResizeHeight, g_MinImageCount);
+      g_MainWindowData.FrameIndex = 0;
+    }
+
+    // Start the Dear ImGui frame
+    ImGui_ImplVulkan_NewFrame();
+    ImGui_ImplSDL2_NewFrame(window);
+    ImGui::NewFrame();
+
+    // Custom window.
+    {
+      ImGui::Begin("IREE Vulkan Integration Demo", &show_iree_window);
+
+      ImGui::Separator();
+
+      // ImGui Inputs for two input tensors.
+      // Run computation whenever any of the values changes.
+      static bool dirty = true;
+      if (dirty) {
+
+        // Synchronously invoke the function.
+        IREE_CHECK_OK(iree_vm_invoke(iree_context, main_function,
+                                     IREE_VM_INVOCATION_FLAG_NONE,
+                                     /*policy=*/nullptr, inputs.get(),
+                                     outputs.get(), iree_allocator_system()));
+
+
+        // we want to run continuously so we can use tools like RenderDoc, RGP, etc...
+        dirty = true;
+      }
+
+      // Framerate counter.
+      ImGui::Text("Application average %.3f ms/frame (%.1f FPS)",
+                  1000.0f / ImGui::GetIO().Framerate, ImGui::GetIO().Framerate);
+
+      ImGui::End();
+    }
+
+    // Rendering
+    ImGui::Render();
+    RenderFrame(wd, g_Device, g_Queue);
+
+    PresentFrame(wd, g_Queue);
+  }
+  // --------------------------------------------------------------------------
+
+  // --------------------------------------------------------------------------
+  // Cleanup
+  iree_vm_module_release(hal_module);
+  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);
+
+  err = vkDeviceWaitIdle(g_Device);
+  check_vk_result(err);
+  ImGui_ImplVulkan_Shutdown();
+  ImGui_ImplSDL2_Shutdown();
+  ImGui::DestroyContext();
+
+  CleanupVulkanWindow();
+  CleanupVulkan();
+
+  SDL_DestroyWindow(window);
+  SDL_Quit();
+  // --------------------------------------------------------------------------
+
+  return 0;
+}
+
+}  // namespace iree

generate_sharktank.py

@@ -2,19 +2,23 @@
 """SHARK Tank"""
 # python generate_sharktank.py, you have to give a csv tile with [model_name, model_download_url]
 # will generate local shark tank folder like this:
-#   /SHARK
-#     /gen_shark_tank
-#       /albert_lite_base
-#       /...model_name...
+#   HOME
+#     /.local
+#       /shark_tank
+#           /albert_lite_base
+#           /...model_name...
 #
 
 import os
 import csv
 import argparse
 from shark.shark_importer import SharkImporter
+from shark.parser import shark_args
 import tensorflow as tf
+import subprocess as sp
 import hashlib
 import numpy as np
+from pathlib import Path
 
 visible_default = tf.config.list_physical_devices("GPU")
 try:
@@ -26,9 +30,6 @@ except:
     # Invalid device or cannot modify virtual devices once initialized.
     pass
 
-# All generated models and metadata will be saved under this directory.
-WORKDIR = os.path.join(os.path.dirname(__file__), "gen_shark_tank")
-
 
 def create_hash(file_name):
     with open(file_name, "rb") as f:
@@ -42,6 +43,7 @@ def create_hash(file_name):
 def save_torch_model(torch_model_list):
     from tank.model_utils import get_hf_model
     from tank.model_utils import get_vision_model
+    from tank.model_utils import get_hf_img_cls_model
 
     with open(torch_model_list) as csvfile:
         torch_reader = csv.reader(csvfile, delimiter=",")
@@ -50,8 +52,10 @@ def save_torch_model(torch_model_list):
             torch_model_name = row[0]
             tracing_required = row[1]
             model_type = row[2]
+            is_dynamic = row[3]
 
             tracing_required = False if tracing_required == "False" else True
+            is_dynamic = False if is_dynamic == "False" else True
 
             model = None
             input = None
@@ -59,6 +63,8 @@ def save_torch_model(torch_model_list):
                 model, input, _ = get_vision_model(torch_model_name)
             elif model_type == "hf":
                 model, input, _ = get_hf_model(torch_model_name)
+            elif model_type == "hf_img_cls":
+                model, input, _ = get_hf_img_cls_model(torch_model_name)
 
             torch_model_name = torch_model_name.replace("/", "_")
             torch_model_dir = os.path.join(
@@ -84,17 +90,22 @@ def save_torch_model(torch_model_list):
             )
             np.save(os.path.join(torch_model_dir, "hash"), np.array(mlir_hash))
             # Generate torch dynamic models.
-            mlir_importer.import_debug(
-                is_dynamic=True,
-                tracing_required=tracing_required,
-                dir=torch_model_dir,
-                model_name=torch_model_name + "_dynamic",
-            )
+            if is_dynamic:
+                mlir_importer.import_debug(
+                    is_dynamic=True,
+                    tracing_required=tracing_required,
+                    dir=torch_model_dir,
+                    model_name=torch_model_name + "_dynamic",
+                )
 
 
 def save_tf_model(tf_model_list):
-    from tank.model_utils_tf import get_causal_lm_model
-    from tank.model_utils_tf import get_causal_image_model
+    from tank.model_utils_tf import (
+        get_causal_image_model,
+        get_causal_lm_model,
+        get_keras_model,
+        get_TFhf_model,
+    )
 
     with open(tf_model_list) as csvfile:
         tf_reader = csv.reader(csvfile, delimiter=",")
@@ -105,11 +116,15 @@ def save_tf_model(tf_model_list):
 
             model = None
             input = None
-            print(model_type)
+            print(f"Generating artifacts for model {tf_model_name}")
             if model_type == "hf":
                 model, input, _ = get_causal_lm_model(tf_model_name)
             if model_type == "img":
                 model, input, _ = get_causal_image_model(tf_model_name)
+            if model_type == "keras":
+                model, input, _ = get_keras_model(tf_model_name)
+            if model_type == "TFhf":
+                model, input, _ = get_TFhf_model(tf_model_name)
 
             tf_model_name = tf_model_name.replace("/", "_")
             tf_model_dir = os.path.join(WORKDIR, str(tf_model_name) + "_tf")
@@ -190,14 +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(
@@ -206,9 +221,21 @@ if __name__ == "__main__":
         default="./tank/tflite/tflite_model_list.csv",
         help="Contains the file with tf model name and args.",
     )
+    parser.add_argument(
+        "--ci_tank_dir",
+        type=bool,
+        default=False,
+    )
     parser.add_argument("--upload", type=bool, default=False)
 
     args = parser.parse_args()
+
+    home = str(Path.home())
+    if args.ci_tank_dir == True:
+        WORKDIR = os.path.join(os.path.dirname(__file__), "gen_shark_tank")
+    else:
+        WORKDIR = os.path.join(home, ".local/shark_tank/")
+
     if args.torch_model_csv:
         save_torch_model(args.torch_model_csv)
 
@@ -219,5 +246,6 @@ if __name__ == "__main__":
         save_tflite_model(args.tflite_model_csv)
 
     if args.upload:
-        print("uploading files to gs://shark_tank/")
-        os.system("gsutil cp -r ./gen_shark_tank/* gs://shark_tank/")
+        git_hash = sp.getoutput("git log -1 --format='%h'") + "/"
+        print("uploading files to gs://shark_tank/" + git_hash)
+        os.system(f"gsutil cp -r {WORKDIR}* gs://shark_tank/" + git_hash)

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"

pytest.ini

@@ -1,3 +1,3 @@
 [pytest]
 addopts = --verbose -p no:warnings
-norecursedirs = inference tank/tflite 
+norecursedirs = inference tank/tflite examples benchmarks shark 

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
@@ -19,13 +19,17 @@ tensorflow-macos
 tensorflow-metal
 #tf-models-nightly
 #tensorflow-text-nightly
-transformers==4.18.0
+transformers
 tensorflow-probability
 #jax[cpu]
 
 # tflitehub dependencies.
 Pillow
 
+# web dependecies.
+gradio
+altair
+
 # Testing and support.
 #lit
 #pyyaml

requirements-importer.txt

@@ -2,7 +2,6 @@
 --pre
 
 numpy==1.22.4
-torch
 torchvision
 
 tqdm
@@ -14,10 +13,12 @@ iree-tools-tf
 
 # TensorFlow and JAX.
 gin-config
-tensorflow
+tensorflow==2.10
+keras==2.10
 #tf-models-nightly
 #tensorflow-text-nightly
-transformers==4.18.0
+transformers
+diffusers
 #tensorflow-probability
 #jax[cpu]
 
@@ -28,6 +29,13 @@ Pillow
 # Testing and support.
 lit
 pyyaml
+python-dateutil
+sacremoses
+
+# web dependecies.
+gradio
+altair
+scipy
 
 #ONNX and ORT for benchmarking
 #--extra-index-url https://test.pypi.org/simple/

requirements.txt

@@ -1,13 +1,23 @@
 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
+altair

setup.py

@@ -7,6 +7,12 @@ with open("README.md", "r", encoding="utf-8") as fh:
     long_description = fh.read()
 
 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>=20221022.190",
+        "iree-runtime>=20221022.190",
+    ]
 
 setup(
     name="nodai-SHARK",
@@ -27,12 +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",
-        "iree-compiler>=20220427.13",
-        "iree-runtime>=20220427.13",
-    ],
+        "torch-mlir>=20221021.633",
+    ]
+    + backend_deps,
 )

setup_venv.ps1

@@ -0,0 +1,39 @@
+#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
+Write-Host "Build and installation completed successfully"
+Write-Host "Source your venv with ./shark.venv/Scripts/activate"

setup_venv.sh

@@ -7,6 +7,8 @@
 # VENV_DIR=myshark.venv #create a venv called myshark.venv
 # USE_IREE=1 #use stock IREE instead of Nod.ai's SHARK build
 # IMPORTER=1 #Install importer deps
+# BENCHMARK=1 #Install benchmark deps
+# NO_BACKEND=1 #Don't install iree or shark backend
 # if you run the script from a conda env it will install in your conda env
 
 TD="$(cd $(dirname $0) && pwd)"
@@ -74,11 +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 --find-links https://github.com/llvm/torch-mlir/releases torch-mlir --extra-index-url https://download.pytorch.org/whl/nightly/cpu
-  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 $(uname -s)"
@@ -87,34 +94,51 @@ else
   exit 1
 fi
 if [[ -z "${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 --upgrade --find-links ${RUNTIME} iree-compiler iree-runtime
+else
+  echo "Not installing a backend, please make sure to add your backend to PYTHONPATH"
 fi
-echo "Installing ${RUNTIME}..."
-$PYTHON -m pip install --find-links https://github.com/${RUNTIME}/releases iree-compiler iree-runtime
 
 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://test.pypi.org/simple/ --extra-index-url https://download.pytorch.org/whl/nightly/cu116
+    #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
 
-$PYTHON -m pip install -e . --extra-index-url https://download.pytorch.org/whl/nightly/cpu -f https://github.com/llvm/torch-mlir/releases -f https://github.com/${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 [[ $(uname -s) = 'Linux' && ! -z "${IMPORTER}" ]]; then
+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 [ $? -eq 0 ];then
-    echo "Successfully Installed torch + cu116."
+    echo "Successfully Installed torch + cu117."
   else
-    echo "Could not install torch + cu116." >&2
+    echo "Could not install torch + cu117." >&2
+  fi
+fi
+
+if [[ ! -z "${ONNX}" ]]; then
+  echo "${Yellow}Installing ONNX and onnxruntime for benchmarks..."
+  $PYTHON -m pip install onnx onnxruntime psutil
+  if [ $? -eq 0 ];then
+    echo "Successfully installed ONNX and ONNX runtime."
+  else
+    echo "Could not install ONNX." >&2
   fi
 fi
 

shark/examples/shark_dynamo/basic_examples.py

@@ -0,0 +1,70 @@
+import torchdynamo
+import torch
+import torch_mlir
+from shark.sharkdynamo.utils import make_shark_compiler
+
+
+import warnings, logging
+
+warnings.simplefilter("ignore")
+torchdynamo.config.log_level = logging.ERROR
+
+
+torchdynamo.reset()
+
+
+@torchdynamo.optimize(
+    make_shark_compiler(use_tracing=False, device="cuda", verbose=False)
+)
+def foo(t):
+    return 2 * t
+
+
+example_input = torch.rand((2, 3))
+x = foo(example_input)
+print(x)
+
+
+torchdynamo.reset()
+
+
+@torchdynamo.optimize(
+    make_shark_compiler(use_tracing=False, device="cuda", verbose=False)
+)
+def foo(a, b):
+    x = a / (a + 1)
+    if b.sum() < 0:
+        b = b * -1
+    return x * b
+
+
+print(foo(torch.rand((2, 3)), -torch.rand((2, 3))))
+
+
+torchdynamo.reset()
+
+
+@torchdynamo.optimize(
+    make_shark_compiler(use_tracing=False, device="cuda", verbose=True)
+)
+def foo(a):
+    for i in range(10):
+        a += 1.0
+    return a
+
+
+print(foo(torch.rand((1, 2))))
+
+torchdynamo.reset()
+
+
+@torchdynamo.optimize(
+    make_shark_compiler(use_tracing=False, device="cuda", verbose=True)
+)
+def test_unsupported_types(t, y):
+    return t, 2 * y
+
+
+str_input = "hello"
+tensor_input = torch.randn(2)
+print(test_unsupported_types(str_input, tensor_input))

shark/examples/shark_eager/squeezenet_lockstep.py

@@ -0,0 +1,73 @@
+import torch
+import numpy as np
+
+model = torch.hub.load(
+    "pytorch/vision:v0.10.0", "squeezenet1_0", pretrained=True
+)
+model.eval()
+
+# from PIL import Image
+# from torchvision import transforms
+# import urllib
+#
+# url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
+# try: urllib.URLopener().retrieve(url, filename)
+# except: urllib.request.urlretrieve(url, filename)
+#
+#
+# input_image = Image.open(filename)
+# preprocess = transforms.Compose([
+#     transforms.Resize(256),
+#     transforms.CenterCrop(224),
+#     transforms.ToTensor(),
+#     transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+# ])
+# input_tensor = preprocess(input_image)
+# input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model
+# print(input_batch.shape) # size = [1, 3, 224, 224]
+
+# The above is code for generating sample inputs from an image. We can just use
+# random values for accuracy testing though
+input_batch = torch.randn(1, 3, 224, 224)
+
+
+# Focus on CPU for now
+if False and torch.cuda.is_available():
+    input_batch = input_batch.to("cuda")
+    model.to("cuda")
+
+with torch.no_grad():
+    output = model(input_batch)
+# Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes
+golden_confidences = output[0]
+# The output has unnormalized scores. To get probabilities, you can run a softmax on it.
+golden_probabilities = torch.nn.functional.softmax(
+    golden_confidences, dim=0
+).numpy()
+
+golden_confidences = golden_confidences.numpy()
+
+from shark.torch_mlir_lockstep_tensor import TorchMLIRLockstepTensor
+
+input_detached_clone = input_batch.clone()
+eager_input_batch = TorchMLIRLockstepTensor(input_detached_clone)
+
+print("getting torch-mlir result")
+
+output = model(eager_input_batch)
+
+static_output = output.elem
+confidences = static_output[0]
+probabilities = torch.nn.functional.softmax(
+    torch.from_numpy(confidences), dim=0
+).numpy()
+
+print("The obtained result via shark is: ", confidences)
+print("The golden result is:", golden_confidences)
+
+np.testing.assert_allclose(
+    golden_confidences, confidences, rtol=1e-02, atol=1e-03
+)
+np.testing.assert_allclose(
+    golden_probabilities, probabilities, rtol=1e-02, atol=1e-03
+)

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_pt.py

@@ -0,0 +1,88 @@
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+import torch
+from shark.shark_inference import SharkInference
+from shark.shark_importer import SharkImporter
+from iree.compiler import compile_str
+from iree import runtime as ireert
+import os
+import numpy as np
+
+MAX_SEQUENCE_LENGTH = 512
+BATCH_SIZE = 1
+
+
+class AlbertModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.model = AutoModelForMaskedLM.from_pretrained("albert-base-v2")
+        self.model.eval()
+
+    def forward(self, input_ids, attention_mask):
+        return self.model(
+            input_ids=input_ids, attention_mask=attention_mask
+        ).logits
+
+
+if __name__ == "__main__":
+    # Prepping Data
+    tokenizer = AutoTokenizer.from_pretrained("albert-base-v2")
+    text = "This [MASK] is very tasty."
+    encoded_inputs = tokenizer(
+        text,
+        padding="max_length",
+        truncation=True,
+        max_length=MAX_SEQUENCE_LENGTH,
+        return_tensors="pt",
+    )
+    inputs = (encoded_inputs["input_ids"], encoded_inputs["attention_mask"])
+    mlir_importer = SharkImporter(
+        AlbertModule(),
+        inputs,
+        frontend="torch",
+    )
+    minilm_mlir, func_name = mlir_importer.import_mlir(
+        is_dynamic=False, tracing_required=True
+    )
+    shark_module = SharkInference(
+        minilm_mlir, func_name, mlir_dialect="linalg"
+    )
+    shark_module.compile()
+    token_logits = torch.tensor(shark_module.forward(inputs))
+    mask_id = torch.where(
+        encoded_inputs["input_ids"] == tokenizer.mask_token_id
+    )[1]
+    mask_token_logits = token_logits[0, mask_id, :]
+    top_5_tokens = torch.topk(mask_token_logits, 5, dim=1).indices[0].tolist()
+    for token in top_5_tokens:
+        print(
+            f"'>>> Sample/Warmup output: {text.replace(tokenizer.mask_token, tokenizer.decode(token))}'"
+        )
+    while True:
+        try:
+            new_text = input("Give me a sentence with [MASK] to fill: ")
+            encoded_inputs = tokenizer(
+                new_text,
+                padding="max_length",
+                truncation=True,
+                max_length=MAX_SEQUENCE_LENGTH,
+                return_tensors="pt",
+            )
+            inputs = (
+                encoded_inputs["input_ids"],
+                encoded_inputs["attention_mask"],
+            )
+            token_logits = torch.tensor(shark_module.forward(inputs))
+            mask_id = torch.where(
+                encoded_inputs["input_ids"] == tokenizer.mask_token_id
+            )[1]
+            mask_token_logits = token_logits[0, mask_id, :]
+            top_5_tokens = (
+                torch.topk(mask_token_logits, 5, dim=1).indices[0].tolist()
+            )
+            for token in top_5_tokens:
+                print(
+                    f"'>>> {new_text.replace(tokenizer.mask_token, tokenizer.decode(token))}'"
+                )
+        except KeyboardInterrupt:
+            print("Exiting program.")
+            break

shark/examples/shark_inference/albert_maskfill_tf.py

@@ -0,0 +1,100 @@
+from PIL import Image
+import requests
+
+from transformers import TFAutoModelForMaskedLM, AutoTokenizer
+import tensorflow as tf
+from shark.shark_inference import SharkInference
+from shark.shark_importer import SharkImporter
+from iree.compiler import tf as tfc
+from iree.compiler import compile_str
+from iree import runtime as ireert
+import os
+import numpy as np
+import sys
+
+MAX_SEQUENCE_LENGTH = 512
+BATCH_SIZE = 1
+
+# Create a set of inputs
+t5_inputs = [
+    tf.TensorSpec(shape=[BATCH_SIZE, MAX_SEQUENCE_LENGTH], dtype=tf.int32),
+    tf.TensorSpec(shape=[BATCH_SIZE, MAX_SEQUENCE_LENGTH], dtype=tf.int32),
+]
+
+
+class AlbertModule(tf.Module):
+    def __init__(self):
+        super(AlbertModule, self).__init__()
+        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, jit_compile=True)
+    def forward(self, input_ids, attention_mask):
+        return self.m.predict(input_ids, attention_mask)
+
+
+if __name__ == "__main__":
+    # Prepping Data
+    tokenizer = AutoTokenizer.from_pretrained("albert-base-v2")
+    # text = "This is a great [MASK]."
+    text = "This [MASK] is very tasty."
+    encoded_inputs = tokenizer(
+        text,
+        padding="max_length",
+        truncation=True,
+        max_length=MAX_SEQUENCE_LENGTH,
+        return_tensors="tf",
+    )
+    inputs = (encoded_inputs["input_ids"], encoded_inputs["attention_mask"])
+    mlir_importer = SharkImporter(
+        AlbertModule(),
+        inputs,
+        frontend="tf",
+    )
+    minilm_mlir, func_name = mlir_importer.import_mlir(
+        is_dynamic=False, tracing_required=False
+    )
+    shark_module = SharkInference(minilm_mlir, func_name, mlir_dialect="mhlo")
+    shark_module.compile()
+    output_idx = 0
+    data_idx = 1
+    token_logits = shark_module.forward(inputs)[output_idx][data_idx]
+    mask_id = np.where(
+        tf.squeeze(encoded_inputs["input_ids"]) == tokenizer.mask_token_id
+    )
+    mask_token_logits = token_logits[0, mask_id, :]
+    top_5_tokens = np.flip(np.argsort(mask_token_logits)).squeeze()[0:5]
+    for token in top_5_tokens:
+        print(
+            f"'>>> Sample/Warmup output: {text.replace(tokenizer.mask_token, tokenizer.decode(token))}'"
+        )
+    while True:
+        try:
+            new_text = input("Give me a sentence with [MASK] to fill: ")
+            encoded_inputs = tokenizer(
+                new_text,
+                padding="max_length",
+                truncation=True,
+                max_length=MAX_SEQUENCE_LENGTH,
+                return_tensors="tf",
+            )
+            inputs = (
+                encoded_inputs["input_ids"],
+                encoded_inputs["attention_mask"],
+            )
+            token_logits = shark_module.forward(inputs)[output_idx][data_idx]
+            mask_id = np.where(
+                tf.squeeze(encoded_inputs["input_ids"])
+                == tokenizer.mask_token_id
+            )
+            mask_token_logits = token_logits[0, mask_id, :]
+            top_5_tokens = np.flip(np.argsort(mask_token_logits)).squeeze()[
+                0:5
+            ]
+            for token in top_5_tokens:
+                print(
+                    f"'>>> {new_text.replace(tokenizer.mask_token, tokenizer.decode(token))}'"
+                )
+        except KeyboardInterrupt:
+            print("Exiting program.")
+            sys.exit()

shark/examples/shark_inference/bloom_tank.py

@@ -0,0 +1,14 @@
+from shark.shark_inference import SharkInference
+from shark.shark_downloader import download_model
+
+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_module.compile()
+result = shark_module.forward(inputs)
+print("The obtained result via shark is: ", result)
+print("The golden result is:", golden_out)

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/resnest.py

@@ -23,7 +23,7 @@ input = torch.randn(1, 3, 224, 224)
 
 mlir_importer = SharkImporter(
     ResnestModule(),
-    (input),
+    (input,),
     frontend="torch",
 )
 
@@ -33,9 +33,7 @@ mlir_importer = SharkImporter(
 
 print(golden_out)
 
-shark_module = SharkInference(
-    vision_mlir, func_name, device="cpu", mlir_dialect="linalg"
-)
+shark_module = SharkInference(vision_mlir, func_name, mlir_dialect="linalg")
 shark_module.compile()
-result = shark_module.forward((input))
+result = shark_module.forward((input,))
 print("Obtained result", result)

shark/examples/shark_inference/resnet50_fp16.py

@@ -0,0 +1,76 @@
+from shark.shark_inference import SharkInference
+from shark.parser import shark_args
+
+import torch
+import numpy as np
+import sys
+import torchvision.models as models
+import torch_mlir
+
+torch.manual_seed(0)
+
+
+class VisionModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.model = models.resnet50(pretrained=True)
+        self.train(False)
+
+    def forward(self, input):
+        return self.model.forward(input)
+
+
+model = VisionModule()
+test_input = torch.randn(1, 3, 224, 224)
+actual_out = model(test_input)
+
+test_input_fp16 = test_input.to(device=torch.device("cuda"), dtype=torch.half)
+model_fp16 = model.half()
+model_fp16.eval()
+model_fp16.to("cuda")
+actual_out_fp16 = model_fp16(test_input_fp16)
+
+ts_g = torch.jit.trace(model_fp16, [test_input_fp16])
+
+module = torch_mlir.compile(
+    ts_g,
+    (test_input_fp16),
+    torch_mlir.OutputType.LINALG_ON_TENSORS,
+    use_tracing=True,
+    verbose=False,
+)
+
+# from contextlib import redirect_stdout
+
+# with open('resnet50_fp16_linalg_ir.mlir', 'w') as f:
+#     with redirect_stdout(f):
+#         print(module.operation.get_asm())
+
+mlir_model = module
+func_name = "forward"
+
+shark_module = SharkInference(
+    mlir_model, func_name, device="cuda", mlir_dialect="linalg"
+)
+shark_module.compile()
+
+
+def shark_result(x):
+    x_ny = x.cpu().detach().numpy()
+    inputs = (x_ny,)
+    result = shark_module.forward(inputs)
+    return torch.from_numpy(result)
+
+
+observed_out = shark_result(test_input_fp16)
+
+print("Golden result:", actual_out_fp16)
+print("SHARK result:", observed_out)
+
+actual_out_fp16 = actual_out_fp16.to(device=torch.device("cpu"))
+
+print(
+    torch.testing.assert_allclose(
+        actual_out_fp16, observed_out, rtol=1e-2, atol=1e-2
+    )
+)

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 +66,14 @@ 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()
+path = shark_module.save_module()
+shark_module.load_module(path)
 result = shark_module.forward((img.detach().numpy(),))
 
 print("The top 3 results obtained via shark_runner is:")

shark/examples/shark_inference/simple_dlrm.py

@@ -0,0 +1,392 @@
+# Description: an implementation of a deep learning recommendation model (DLRM)
+# The model input consists of dense and sparse features. The former is a vector
+# of floating point values. The latter is a list of sparse indices into
+# embedding tables, which consist of vectors of floating point values.
+# The selected vectors are passed to mlp networks denoted by triangles,
+# in some cases the vectors are interacted through operators (Ops).
+#
+# output:
+#                         vector of values
+# model:                        |
+#                              /\
+#                             /__\
+#                               |
+#       _____________________> Op  <___________________
+#     /                         |                      \
+#    /\                        /\                      /\
+#   /__\                      /__\           ...      /__\
+#    |                          |                       |
+#    |                         Op                      Op
+#    |                    ____/__\_____           ____/__\____
+#    |                   |_Emb_|____|__|    ...  |_Emb_|__|___|
+# input:
+# [ dense features ]     [sparse indices] , ..., [sparse indices]
+#
+# More precise definition of model layers:
+# 1) fully connected layers of an mlp
+# z = f(y)
+# y = Wx + b
+#
+# 2) embedding lookup (for a list of sparse indices p=[p1,...,pk])
+# z = Op(e1,...,ek)
+# obtain vectors e1=E[:,p1], ..., ek=E[:,pk]
+#
+# 3) Operator Op can be one of the following
+# Sum(e1,...,ek) = e1 + ... + ek
+# Dot(e1,...,ek) = [e1'e1, ..., e1'ek, ..., ek'e1, ..., ek'ek]
+# Cat(e1,...,ek) = [e1', ..., ek']'
+# where ' denotes transpose operation
+#
+# References:
+# [1] Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi, Jianyu Huang,
+# Narayanan Sundaram, Jongsoo Park, Xiaodong Wang, Udit Gupta, Carole-Jean Wu,
+# Alisson G. Azzolini, Dmytro Dzhulgakov, Andrey Mallevich, Ilia Cherniavskii,
+# Yinghai Lu, Raghuraman Krishnamoorthi, Ansha Yu, Volodymyr Kondratenko,
+# Stephanie Pereira, Xianjie Chen, Wenlin Chen, Vijay Rao, Bill Jia, Liang Xiong,
+# Misha Smelyanskiy, "Deep Learning Recommendation Model for Personalization and
+# Recommendation Systems", CoRR, arXiv:1906.00091, 2019
+
+
+import argparse
+import sys
+import numpy as np
+import torch
+import torch.nn as nn
+from shark.shark_inference import SharkInference
+from shark.shark_importer import SharkImporter
+
+
+torch.manual_seed(0)
+np.random.seed(0)
+
+
+### define dlrm in PyTorch ###
+class DLRM_Net(nn.Module):
+    def create_mlp(self, ln, sigmoid_layer):
+        # build MLP layer by layer
+        layers = nn.ModuleList()
+        for i in range(0, ln.size - 1):
+            n = ln[i]
+            m = ln[i + 1]
+
+            # construct fully connected operator
+            LL = nn.Linear(int(n), int(m), bias=True)
+
+            # initialize the weights
+            # with torch.no_grad():
+            # custom Xavier input, output or two-sided fill
+
+            mean = 0.0  # std_dev = np.sqrt(variance)
+            std_dev = np.sqrt(2 / (m + n))  # np.sqrt(1 / m) # np.sqrt(1 / n)
+            W = np.random.normal(mean, std_dev, size=(m, n)).astype(np.float32)
+            std_dev = np.sqrt(1 / m)  # np.sqrt(2 / (m + 1))
+            bt = np.random.normal(mean, std_dev, size=m).astype(np.float32)
+            LL.weight.data = torch.tensor(W, requires_grad=True)
+            LL.bias.data = torch.tensor(bt, requires_grad=True)
+
+            # approach 2
+            # LL.weight.data.copy_(torch.tensor(W))
+            # LL.bias.data.copy_(torch.tensor(bt))
+            # approach 3
+            # LL.weight = Parameter(torch.tensor(W),requires_grad=True)
+            # LL.bias = Parameter(torch.tensor(bt),requires_grad=True)
+            layers.append(LL)
+
+            # construct sigmoid or relu operator
+            if i == sigmoid_layer:
+                layers.append(nn.Sigmoid())
+            else:
+                layers.append(nn.ReLU())
+
+        # approach 1: use ModuleList
+        # return layers
+        # approach 2: use Sequential container to wrap all layers
+        return torch.nn.Sequential(*layers)
+
+    def create_emb(self, m, ln, weighted_pooling=None):
+        emb_l = nn.ModuleList()
+        v_W_l = []
+        for i in range(0, ln.size):
+            n = ln[i]
+
+            # construct embedding operator
+            EE = nn.EmbeddingBag(n, m, mode="sum")
+            # initialize embeddings
+            # nn.init.uniform_(EE.weight, a=-np.sqrt(1 / n), b=np.sqrt(1 / n))
+            W = np.random.uniform(
+                low=-np.sqrt(1 / n), high=np.sqrt(1 / n), size=(n, m)
+            ).astype(np.float32)
+            # approach 1
+            print(W)
+            EE.weight.data = torch.tensor(W, requires_grad=True)
+            # approach 2
+            # EE.weight.data.copy_(torch.tensor(W))
+            # approach 3
+            # EE.weight = Parameter(torch.tensor(W),requires_grad=True)
+            if weighted_pooling is None:
+                v_W_l.append(None)
+            else:
+                v_W_l.append(torch.ones(n, dtype=torch.float32))
+            emb_l.append(EE)
+        return emb_l, v_W_l
+
+    def __init__(
+        self,
+        m_spa=None,
+        ln_emb=None,
+        ln_bot=None,
+        ln_top=None,
+        arch_interaction_op=None,
+        arch_interaction_itself=False,
+        sigmoid_bot=-1,
+        sigmoid_top=-1,
+        weighted_pooling=None,
+    ):
+        super(DLRM_Net, self).__init__()
+
+        if (
+            (m_spa is not None)
+            and (ln_emb is not None)
+            and (ln_bot is not None)
+            and (ln_top is not None)
+            and (arch_interaction_op is not None)
+        ):
+
+            # save arguments
+            self.output_d = 0
+            self.arch_interaction_op = arch_interaction_op
+            self.arch_interaction_itself = arch_interaction_itself
+            if weighted_pooling is not None and weighted_pooling != "fixed":
+                self.weighted_pooling = "learned"
+            else:
+                self.weighted_pooling = weighted_pooling
+
+            # create operators
+            self.emb_l, w_list = self.create_emb(
+                m_spa, ln_emb, weighted_pooling
+            )
+            if self.weighted_pooling == "learned":
+                self.v_W_l = nn.ParameterList()
+                for w in w_list:
+                    self.v_W_l.append(nn.Parameter(w))
+            else:
+                self.v_W_l = w_list
+            self.bot_l = self.create_mlp(ln_bot, sigmoid_bot)
+            self.top_l = self.create_mlp(ln_top, sigmoid_top)
+
+    def apply_mlp(self, x, layers):
+        return layers(x)
+
+    def apply_emb(self, lS_o, lS_i, emb_l, v_W_l):
+        # WARNING: notice that we are processing the batch at once. We implicitly
+        # assume that the data is laid out such that:
+        # 1. each embedding is indexed with a group of sparse indices,
+        #   corresponding to a single lookup
+        # 2. for each embedding the lookups are further organized into a batch
+        # 3. for a list of embedding tables there is a list of batched lookups
+        # TORCH-MLIR
+        # We are passing all the embeddings as arguments for easy parsing.
+
+        ly = []
+        for k, sparse_index_group_batch in enumerate(lS_i):
+            sparse_offset_group_batch = lS_o[k]
+
+            # embedding lookup
+            # We are using EmbeddingBag, which implicitly uses sum operator.
+            # The embeddings are represented as tall matrices, with sum
+            # happening vertically across 0 axis, resulting in a row vector
+            # E = emb_l[k]
+
+            if v_W_l[k] is not None:
+                per_sample_weights = v_W_l[k].gather(
+                    0, sparse_index_group_batch
+                )
+            else:
+                per_sample_weights = None
+
+            E = emb_l[k]
+            V = E(
+                sparse_index_group_batch,
+                sparse_offset_group_batch,
+                per_sample_weights=per_sample_weights,
+            )
+
+            ly.append(V)
+
+        return ly
+
+    def interact_features(self, x, ly):
+
+        if self.arch_interaction_op == "dot":
+            # concatenate dense and sparse features
+            (batch_size, d) = x.shape
+            T = torch.cat([x] + ly, dim=1).view((batch_size, -1, d))
+            # perform a dot product
+            Z = torch.bmm(T, torch.transpose(T, 1, 2))
+            # append dense feature with the interactions (into a row vector)
+            # approach 1: all
+            # Zflat = Z.view((batch_size, -1))
+            # approach 2: unique
+            _, ni, nj = Z.shape
+            # approach 1: tril_indices
+            # offset = 0 if self.arch_interaction_itself else -1
+            # li, lj = torch.tril_indices(ni, nj, offset=offset)
+            # approach 2: custom
+            offset = 1 if self.arch_interaction_itself else 0
+            li = torch.tensor(
+                [i for i in range(ni) for j in range(i + offset)]
+            )
+            lj = torch.tensor(
+                [j for i in range(nj) for j in range(i + offset)]
+            )
+            Zflat = Z[:, li, lj]
+            # concatenate dense features and interactions
+            R = torch.cat([x] + [Zflat], dim=1)
+        elif self.arch_interaction_op == "cat":
+            # concatenation features (into a row vector)
+            R = torch.cat([x] + ly, dim=1)
+        else:
+            sys.exit(
+                "ERROR: --arch-interaction-op="
+                + self.arch_interaction_op
+                + " is not supported"
+            )
+
+        return R
+
+    def forward(self, dense_x, lS_o, *lS_i):
+        return self.sequential_forward(dense_x, lS_o, lS_i)
+
+    def sequential_forward(self, dense_x, lS_o, lS_i):
+        # process dense features (using bottom mlp), resulting in a row vector
+        x = self.apply_mlp(dense_x, self.bot_l)
+        # debug prints
+        # print("intermediate")
+        # print(x.detach().cpu().numpy())
+
+        # process sparse features(using embeddings), resulting in a list of row vectors
+        ly = self.apply_emb(lS_o, lS_i, self.emb_l, self.v_W_l)
+        # for y in ly:
+        #     print(y.detach().cpu().numpy())
+
+        # interact features (dense and sparse)
+        z = self.interact_features(x, ly)
+        # print(z.detach().cpu().numpy())
+
+        # obtain probability of a click (using top mlp)
+        p = self.apply_mlp(z, self.top_l)
+
+        # # clamp output if needed
+        # if 0.0 < self.loss_threshold and self.loss_threshold < 1.0:
+        # z = torch.clamp(p, min=self.loss_threshold, max=(1.0 - self.loss_threshold))
+        # else:
+        # z = p
+
+        return p
+
+
+def dash_separated_ints(value):
+    vals = value.split("-")
+    for val in vals:
+        try:
+            int(val)
+        except ValueError:
+            raise argparse.ArgumentTypeError(
+                "%s is not a valid dash separated list of ints" % value
+            )
+
+    return value
+
+
+# model related parameters
+parser = argparse.ArgumentParser(
+    description="Train Deep Learning Recommendation Model (DLRM)"
+)
+parser.add_argument("--arch-sparse-feature-size", type=int, default=2)
+parser.add_argument(
+    "--arch-embedding-size", type=dash_separated_ints, default="4-3-2"
+)
+# j will be replaced with the table number
+parser.add_argument(
+    "--arch-mlp-bot", type=dash_separated_ints, default="4-3-2"
+)
+parser.add_argument(
+    "--arch-mlp-top", type=dash_separated_ints, default="8-2-1"
+)
+parser.add_argument(
+    "--arch-interaction-op", type=str, choices=["dot", "cat"], default="dot"
+)
+parser.add_argument(
+    "--arch-interaction-itself", action="store_true", default=False
+)
+parser.add_argument("--weighted-pooling", type=str, default=None)
+
+args = parser.parse_args()
+
+ln_bot = np.fromstring(args.arch_mlp_bot, dtype=int, sep="-")
+ln_top = np.fromstring(args.arch_mlp_top, dtype=int, sep="-")
+m_den = ln_bot[0]
+ln_emb = np.fromstring(args.arch_embedding_size, dtype=int, sep="-")
+m_spa = args.arch_sparse_feature_size
+ln_emb = np.asarray(ln_emb)
+num_fea = ln_emb.size + 1  # num sparse + num dense features
+
+
+# Initialize the model.
+dlrm_model = DLRM_Net(
+    m_spa=m_spa,
+    ln_emb=ln_emb,
+    ln_bot=ln_bot,
+    ln_top=ln_top,
+    arch_interaction_op=args.arch_interaction_op,
+)
+
+
+# Inputs to the model.
+dense_inp = torch.tensor([[0.6965, 0.2861, 0.2269, 0.5513]])
+vs0 = torch.tensor([[0], [0], [0]], dtype=torch.int64)
+vsi = torch.tensor([1, 2, 3]), torch.tensor([1]), torch.tensor([1])
+
+input_dlrm = (dense_inp, vs0, *vsi)
+
+golden_output = dlrm_model(dense_inp, vs0, *vsi)
+
+mlir_importer = SharkImporter(
+    dlrm_model,
+    input_dlrm,
+    frontend="torch",
+)
+
+(dlrm_mlir, func_name), inputs, golden_out = mlir_importer.import_debug(
+    tracing_required=True
+)
+
+shark_module = SharkInference(
+    dlrm_mlir, func_name, device="vulkan", mlir_dialect="linalg"
+)
+shark_module.compile()
+result = shark_module.forward(input_dlrm)
+np.testing.assert_allclose(
+    golden_output.detach().numpy(), result, rtol=1e-02, atol=1e-03
+)
+
+
+# Verified via torch-mlir.
+# import torch_mlir
+# from torch_mlir_e2e_test.linalg_on_tensors_backends import refbackend
+
+
+# module = torch_mlir.compile(
+# dlrm_model, inputs, use_tracing=True, output_type="linalg-on-tensors"
+# )
+# backend = refbackend.RefBackendLinalgOnTensorsBackend()
+# compiled = backend.compile(module)
+# jit_module = backend.load(compiled)
+
+# dense_numpy = dense_inp.numpy()
+# vs0_numpy = vs0.numpy()
+# vsi_numpy = [inp.numpy() for inp in vsi]
+
+# numpy_inp = (dense_numpy, vs0_numpy, *vsi_numpy)
+
+# print(jit_module.forward(*numpy_inp))

shark/examples/shark_inference/sparse_arch.py

@@ -0,0 +1,314 @@
+import torch
+from torch import nn
+from torchrec.datasets.utils import Batch
+from torchrec.modules.crossnet import LowRankCrossNet
+from torchrec.sparse.jagged_tensor import KeyedJaggedTensor, KeyedTensor
+from torchrec.modules.embedding_configs import EmbeddingBagConfig
+from torchrec.modules.embedding_modules import EmbeddingBagCollection
+from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
+from typing import Dict, List, Optional, Tuple
+from torchrec.models.dlrm import (
+    choose,
+    DenseArch,
+    DLRM,
+    InteractionArch,
+    SparseArch,
+    OverArch,
+)
+from shark.shark_inference import SharkInference
+from shark.shark_importer import SharkImporter
+import numpy as np
+
+torch.manual_seed(0)
+
+np.random.seed(0)
+
+
+def calculate_offsets(tensor_list, prev_values, prev_offsets):
+    offset_init = 0
+    offset_list = []
+    values_list = []
+
+    if prev_offsets != None:
+        offset_init = prev_values.shape[-1]
+    for tensor in tensor_list:
+        offset_list.append(offset_init)
+        offset_init += tensor.shape[0]
+
+    concatendated_tensor_list = torch.cat(tensor_list)
+
+    if prev_values != None:
+        concatendated_tensor_list = torch.cat(
+            [prev_values, concatendated_tensor_list]
+        )
+
+    concatenated_offsets = torch.tensor(offset_list)
+
+    if prev_offsets != None:
+        concatenated_offsets = torch.cat([prev_offsets, concatenated_offsets])
+
+    return concatendated_tensor_list, concatenated_offsets
+
+
+# Have to make combined_keys as dict as to which embedding bags they
+# point to. {f1: 0, f3: 0, f2: 1}
+# The result will be a triple containing values, indices and pointer tensor.
+def to_list(key_jagged, combined_keys):
+    key_jagged_dict = key_jagged.to_dict()
+    combined_list = []
+
+    for key in combined_keys:
+        prev_values, prev_offsets = calculate_offsets(
+            key_jagged_dict[key].to_dense(), None, None
+        )
+        print(prev_values)
+        print(prev_offsets)
+        combined_list.append(prev_values)
+        combined_list.append(prev_offsets)
+        combined_list.append(torch.tensor(combined_keys[key]))
+
+    return combined_list
+
+
+class SparseArchShark(nn.Module):
+    def create_emb(self, embedding_dim, num_embeddings_list):
+        embedding_list = nn.ModuleList()
+        for i in range(0, num_embeddings_list.size):
+            num_embeddings = num_embeddings_list[i]
+            EE = nn.EmbeddingBag(num_embeddings, embedding_dim, mode="sum")
+            W = np.random.uniform(
+                low=-np.sqrt(1 / num_embeddings),
+                high=np.sqrt(1 / num_embeddings),
+                size=(num_embeddings, embedding_dim),
+            ).astype(np.float32)
+            EE.weight.data = torch.tensor(W, requires_grad=True)
+            embedding_list.append(EE)
+        return embedding_list
+
+    def __init__(
+        self,
+        embedding_dim,
+        total_features,
+        num_embeddings_list,
+    ):
+        super(SparseArchShark, self).__init__()
+        self.embedding_dim = embedding_dim
+        self.num_features = total_features
+        self.embedding_list = self.create_emb(
+            embedding_dim, num_embeddings_list
+        )
+
+    def forward(self, *batched_inputs):
+
+        concatenated_list = []
+        input_enum, embedding_enum = 0, 0
+
+        for k in range(len(batched_inputs) // 3):
+            values = batched_inputs[input_enum]
+            input_enum += 1
+            offsets = batched_inputs[input_enum]
+            input_enum += 1
+            embedding_pointer = int(batched_inputs[input_enum])
+            input_enum += 1
+
+            E = self.embedding_list[embedding_pointer]
+            V = E(values, offsets)
+            concatenated_list.append(V)
+
+        return torch.cat(concatenated_list, dim=1).reshape(
+            -1, self.num_features, self.embedding_dim
+        )
+
+
+def test_sparse_arch() -> None:
+
+    D = 3
+    eb1_config = EmbeddingBagConfig(
+        name="t1",
+        embedding_dim=D,
+        num_embeddings=10,
+        feature_names=["f1", "f3"],
+    )
+    eb2_config = EmbeddingBagConfig(
+        name="t2",
+        embedding_dim=D,
+        num_embeddings=10,
+        feature_names=["f2"],
+    )
+
+    ebc = EmbeddingBagCollection(tables=[eb1_config, eb2_config])
+
+    w1 = ebc.embedding_bags["t1"].weight
+    w2 = ebc.embedding_bags["t2"].weight
+
+    sparse_arch = SparseArch(ebc)
+
+    keys = ["f1", "f2", "f3", "f4", "f5"]
+    offsets = torch.tensor([0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 19])
+    features = KeyedJaggedTensor.from_offsets_sync(
+        keys=keys,
+        values=torch.tensor(
+            [1, 2, 4, 5, 4, 3, 2, 9, 1, 2, 4, 5, 4, 3, 2, 9, 1, 2, 3]
+        ),
+        offsets=offsets,
+    )
+    sparse_archi = SparseArchShark(D, 3, np.array([10, 10]))
+    sparse_archi.embedding_list[0].weight = w1
+    sparse_archi.embedding_list[1].weight = w2
+    inputs = to_list(features, {"f1": 0, "f3": 0, "f2": 1})
+
+    test_results = sparse_archi(*inputs)
+    sparse_features = sparse_arch(features)
+
+    torch.allclose(
+        sparse_features,
+        test_results,
+        rtol=1e-4,
+        atol=1e-4,
+    )
+
+
+test_sparse_arch()
+
+
+class DLRMShark(nn.Module):
+    def __init__(
+        self,
+        embedding_dim,
+        total_features,
+        num_embeddings_list,
+        dense_in_features: int,
+        dense_arch_layer_sizes: List[int],
+        over_arch_layer_sizes: List[int],
+    ) -> None:
+        super().__init__()
+
+        self.sparse_arch: SparseArchShark = SparseArchShark(
+            embedding_dim, total_features, num_embeddings_list
+        )
+        num_sparse_features: int = total_features
+
+        self.dense_arch = DenseArch(
+            in_features=dense_in_features,
+            layer_sizes=dense_arch_layer_sizes,
+        )
+
+        self.inter_arch = InteractionArch(
+            num_sparse_features=num_sparse_features,
+        )
+
+        over_in_features: int = (
+            embedding_dim
+            + choose(num_sparse_features, 2)
+            + num_sparse_features
+        )
+
+        self.over_arch = OverArch(
+            in_features=over_in_features,
+            layer_sizes=over_arch_layer_sizes,
+        )
+
+    def forward(
+        self, dense_features: torch.Tensor, *sparse_features
+    ) -> torch.Tensor:
+
+        embedded_dense = self.dense_arch(dense_features)
+        embedded_sparse = self.sparse_arch(*sparse_features)
+        concatenated_dense = self.inter_arch(
+            dense_features=embedded_dense, sparse_features=embedded_sparse
+        )
+        logits = self.over_arch(concatenated_dense)
+        return logits
+
+
+def test_dlrm() -> None:
+    B = 2
+    D = 8
+    dense_in_features = 100
+
+    eb1_config = EmbeddingBagConfig(
+        name="t1",
+        embedding_dim=D,
+        num_embeddings=100,
+        feature_names=["f1", "f3"],
+    )
+    eb2_config = EmbeddingBagConfig(
+        name="t2",
+        embedding_dim=D,
+        num_embeddings=100,
+        feature_names=["f2"],
+    )
+
+    ebc = EmbeddingBagCollection(tables=[eb1_config, eb2_config])
+
+    sparse_features = KeyedJaggedTensor.from_offsets_sync(
+        keys=["f1", "f3", "f2"],
+        values=torch.tensor([1, 2, 4, 5, 4, 3, 2, 9, 1, 2, 3]),
+        offsets=torch.tensor([0, 2, 4, 6, 8, 10, 11]),
+    )
+    ebc = EmbeddingBagCollection(tables=[eb1_config, eb2_config])
+    sparse_nn = DLRM(
+        embedding_bag_collection=ebc,
+        dense_in_features=dense_in_features,
+        dense_arch_layer_sizes=[20, D],
+        over_arch_layer_sizes=[5, 1],
+    )
+    sparse_nn_nod = DLRMShark(
+        embedding_dim=8,
+        total_features=3,
+        num_embeddings_list=np.array([100, 100]),
+        dense_in_features=dense_in_features,
+        dense_arch_layer_sizes=[20, D],
+        over_arch_layer_sizes=[5, 1],
+    )
+
+    dense_features = torch.rand((B, dense_in_features))
+
+    x = to_list(sparse_features, {"f1": 0, "f3": 0, "f2": 1})
+
+    w1 = ebc.embedding_bags["t1"].weight
+    w2 = ebc.embedding_bags["t2"].weight
+
+    sparse_nn_nod.sparse_arch.embedding_list[0].weight = w1
+    sparse_nn_nod.sparse_arch.embedding_list[1].weight = w2
+
+    sparse_nn_nod.dense_arch.load_state_dict(sparse_nn.dense_arch.state_dict())
+    sparse_nn_nod.inter_arch.load_state_dict(sparse_nn.inter_arch.state_dict())
+    sparse_nn_nod.over_arch.load_state_dict(sparse_nn.over_arch.state_dict())
+
+    logits = sparse_nn(
+        dense_features=dense_features,
+        sparse_features=sparse_features,
+    )
+    logits_nod = sparse_nn_nod(dense_features, *x)
+
+    # print(logits)
+    # print(logits_nod)
+
+    # Import the module and print.
+    mlir_importer = SharkImporter(
+        sparse_nn_nod,
+        (dense_features, *x),
+        frontend="torch",
+    )
+
+    (dlrm_mlir, func_name), inputs, golden_out = mlir_importer.import_debug(
+        tracing_required=True
+    )
+
+    shark_module = SharkInference(
+        dlrm_mlir, func_name, device="cpu", mlir_dialect="linalg"
+    )
+    shark_module.compile()
+    result = shark_module.forward(inputs)
+    np.testing.assert_allclose(golden_out, result, rtol=1e-02, atol=1e-03)
+
+    torch.allclose(
+        logits,
+        logits_nod,
+        rtol=1e-4,
+        atol=1e-4,
+    )
+
+
+test_dlrm()

shark/examples/shark_inference/stable_diff.py

@@ -0,0 +1,272 @@
+from transformers import CLIPTextModel, CLIPTokenizer
+from diffusers import AutoencoderKL, UNet2DConditionModel, PNDMScheduler
+import torch
+from PIL import Image
+from diffusers import LMSDiscreteScheduler
+from tqdm.auto import tqdm
+from shark.shark_inference import SharkInference
+from torch.fx.experimental.proxy_tensor import make_fx
+from torch._decomp import get_decompositions
+import torch_mlir
+import tempfile
+import numpy as np
+
+# 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=10, 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 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, extra_args=[]):
+
+    module = load_mlir(mlir_loc)
+    if mlir_loc == 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)
+
+        module = torch_mlir.compile(
+            ts_g,
+            inputs,
+            torch_mlir.OutputType.LINALG_ON_TENSORS,
+            use_tracing=False,
+            verbose=False,
+        )
+
+    mlir_model = module
+    func_name = "forward"
+
+    shark_module = SharkInference(
+        mlir_model,
+        func_name,
+        device=args.device,
+        mlir_dialect="tm_tensor",
+    )
+    shark_module.compile(extra_args)
+
+    return shark_module
+
+
+if __name__ == "__main__":
+
+    YOUR_TOKEN = "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"
+
+    # 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,
+    )
+
+    # 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"
+    )
+
+    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,
+            )
+
+        def forward(self, input):
+            return self.vae.decode(input, return_dict=False)[0]
+
+    vae = VaeModel()
+    vae_input = torch.rand(1, 4, 64, 64)
+    shark_vae = compile_through_fx(vae, (vae_input,), args.vae_loc)
+
+    # 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]
+
+    # 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.mlir_loc,
+        ["--iree-flow-enable-conv-nchw-to-nhwc-transform"],
+    )
+
+    # torch.jit.script(unet)
+
+    scheduler = LMSDiscreteScheduler(
+        beta_start=0.00085,
+        beta_end=0.012,
+        beta_schedule="scaled_linear",
+        num_train_timesteps=1000,
+    )
+
+    prompt = [args.prompt]
+
+    height = 512  # default height of Stable Diffusion
+    width = 512  # default width of Stable Diffusion
+
+    num_inference_steps = args.steps  # Number of denoising steps
+
+    guidance_scale = 7.5  # Scale for classifier-free guidance
+
+    generator = torch.manual_seed(
+        42
+    )  # Seed generator to create the inital latent noise
+
+    batch_size = len(prompt)
+
+    text_input = tokenizer(
+        prompt,
+        padding="max_length",
+        max_length=tokenizer.model_max_length,
+        truncation=True,
+        return_tensors="pt",
+    )
+
+    text_embeddings = text_encoder(text_input.input_ids)[0]
+
+    max_length = text_input.input_ids.shape[-1]
+    uncond_input = tokenizer(
+        [""] * batch_size,
+        padding="max_length",
+        max_length=max_length,
+        return_tensors="pt",
+    )
+    uncond_embeddings = text_encoder(uncond_input.input_ids)[0]
+
+    text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+    latents = torch.randn(
+        (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)):
+
+        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
+
+        # 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()
+        text_embeddings_numpy = text_embeddings.detach().numpy()
+
+        noise_pred = shark_unet.forward(
+            (
+                latent_model_input_numpy,
+                np.array([t]).astype(np.float32),
+                text_embeddings_numpy,
+            )
+        )
+        noise_pred = torch.from_numpy(noise_pred)
+
+        # perform guidance
+        noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+        noise_pred = noise_pred_uncond + guidance_scale * (
+            noise_pred_text - noise_pred_uncond
+        )
+
+        # 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
+    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]
+    pil_images[0].save("astro.jpg")

shark/examples/shark_inference/stable_diff_f16.py

@@ -0,0 +1,280 @@
+from transformers import CLIPTextModel, CLIPTokenizer
+from diffusers import AutoencoderKL, UNet2DConditionModel, PNDMScheduler
+import torch
+from PIL import Image
+from diffusers import LMSDiscreteScheduler
+from tqdm.auto import tqdm
+from shark.shark_inference import SharkInference
+from torch.fx.experimental.proxy_tensor import make_fx
+from torch._decomp import get_decompositions
+import torch_mlir
+import tempfile
+import numpy as np
+
+# 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, mlir_loc=None):
+
+    module = load_mlir(mlir_loc)
+    if mlir_loc == 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)
+
+        module = torch_mlir.compile(
+            ts_g,
+            inputs,
+            torch_mlir.OutputType.LINALG_ON_TENSORS,
+            use_tracing=False,
+            verbose=False,
+        )
+
+    mlir_model = module
+    func_name = "forward"
+
+    shark_module = SharkInference(
+        mlir_model, func_name, device=args.device, mlir_dialect="linalg"
+    )
+    shark_module.compile()
+
+    return shark_module
+
+
+if __name__ == "__main__":
+
+    YOUR_TOKEN = "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"
+
+    # 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,
+    )
+
+    # 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"
+    )
+
+    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,
+            )
+
+        def forward(self, input):
+            return self.vae.decode(input, return_dict=False)[0]
+
+    vae = VaeModel()
+    vae_input = torch.rand(1, 4, 64, 64)
+    shark_vae = compile_through_fx(vae, (vae_input,), args.vae_loc)
+
+    # 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]
+
+    # # 3. The UNet model for generating the latents.
+    unet = UnetModel()
+
+    shark_unet = fp16_unet()
+
+    scheduler = LMSDiscreteScheduler(
+        beta_start=0.00085,
+        beta_end=0.012,
+        beta_schedule="scaled_linear",
+        num_train_timesteps=1000,
+    )
+
+    prompt = [args.prompt]
+
+    height = 512  # default height of Stable Diffusion
+    width = 512  # default width of Stable Diffusion
+
+    num_inference_steps = args.steps  # Number of denoising steps
+
+    guidance_scale = 7.5  # Scale for classifier-free guidance
+
+    generator = torch.manual_seed(
+        42
+    )  # Seed generator to create the inital latent noise
+
+    batch_size = len(prompt)
+
+    text_input = tokenizer(
+        prompt,
+        padding="max_length",
+        max_length=tokenizer.model_max_length,
+        truncation=True,
+        return_tensors="pt",
+    )
+
+    text_embeddings = text_encoder(text_input.input_ids)[0]
+
+    max_length = text_input.input_ids.shape[-1]
+    uncond_input = tokenizer(
+        [""] * batch_size,
+        padding="max_length",
+        max_length=max_length,
+        return_tensors="pt",
+    )
+    uncond_embeddings = text_encoder(uncond_input.input_ids)[0]
+
+    text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+    latents = torch.randn(
+        (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)):
+
+        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
+
+        # 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.half),
+                text_embeddings_numpy,
+            )
+        )
+        noise_pred = torch.from_numpy(noise_pred).to(torch.float32)
+
+        # perform guidance
+        noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+        noise_pred = noise_pred_uncond + guidance_scale * (
+            noise_pred_text - noise_pred_uncond
+        )
+
+        # 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
+    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]
+    pil_images[0].save("astro.jpg")

shark/examples/shark_inference/stable_diff_tf.py

@@ -0,0 +1,313 @@
+import math
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+from keras_cv.models.generative.stable_diffusion.clip_tokenizer import (
+    SimpleTokenizer,
+)
+from keras_cv.models.generative.stable_diffusion.constants import (
+    _ALPHAS_CUMPROD,
+)
+from keras_cv.models.generative.stable_diffusion.constants import (
+    _UNCONDITIONAL_TOKENS,
+)
+from keras_cv.models.generative.stable_diffusion.decoder import Decoder
+from keras_cv.models.generative.stable_diffusion.text_encoder import (
+    TextEncoder,
+)
+
+from shark.shark_inference import SharkInference
+from shark.shark_downloader import download_model
+from PIL import Image
+
+# pip install "git+https://github.com/keras-team/keras-cv.git"
+# pip install tensorflow_dataset
+
+############### 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=10, help="the number of steps to use"
+)
+p.add_argument(
+    "--save_path",
+    type=str,
+    default=None,
+    help="the file to save the resulting image to. (default to <input prompt>.jpg)",
+)
+args = p.parse_args()
+
+#####################################################
+
+MAX_PROMPT_LENGTH = 77
+
+
+class SharkStableDiffusion:
+    """Shark implementation of Stable Diffusion based on model from keras_cv.
+    Stable Diffusion is a powerful image generation model that can be used,
+    among other things, to generate pictures according to a short text description
+    (called a "prompt").
+    Arguments:
+        device: Device to use with SHARK. Default: cpu
+        jit_compile: Whether to compile the underlying models to XLA.
+            This can lead to a significant speedup on some systems. Default: False.
+    References:
+    - [About Stable Diffusion](https://stability.ai/blog/stable-diffusion-announcement)
+    - [Original implementation](https://github.com/CompVis/stable-diffusion)
+    """
+
+    def __init__(self, device="cpu", jit_compile=True):
+        self.img_height = 512
+        self.img_width = 512
+        self.tokenizer = SimpleTokenizer()
+
+        # Create models
+        self.text_encoder = TextEncoder(MAX_PROMPT_LENGTH)
+
+        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_module.compile()
+        self.diffusion_model = shark_module
+        self.decoder = Decoder(self.img_height, self.img_width)
+        if jit_compile:
+            self.text_encoder.compile(jit_compile=True)
+            self.decoder.compile(jit_compile=True)
+
+        print(
+            "By using this model checkpoint, you acknowledge that its usage is "
+            "subject to the terms of the CreativeML Open RAIL-M license at "
+            "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/LICENSE"
+        )
+        # Load weights
+        text_encoder_weights_fpath = keras.utils.get_file(
+            origin="https://huggingface.co/fchollet/stable-diffusion/resolve/main/kcv_encoder.h5",
+            file_hash="4789e63e07c0e54d6a34a29b45ce81ece27060c499a709d556c7755b42bb0dc4",
+        )
+        decoder_weights_fpath = keras.utils.get_file(
+            origin="https://huggingface.co/fchollet/stable-diffusion/resolve/main/kcv_decoder.h5",
+            file_hash="ad350a65cc8bc4a80c8103367e039a3329b4231c2469a1093869a345f55b1962",
+        )
+        self.text_encoder.load_weights(text_encoder_weights_fpath)
+        self.decoder.load_weights(decoder_weights_fpath)
+
+    def text_to_image(
+        self,
+        prompt,
+        batch_size=1,
+        num_steps=25,
+        unconditional_guidance_scale=7.5,
+        seed=None,
+    ):
+        encoded_text = self.encode_text(prompt)
+
+        return self.generate_image(
+            encoded_text,
+            batch_size=batch_size,
+            num_steps=num_steps,
+            unconditional_guidance_scale=unconditional_guidance_scale,
+            seed=seed,
+        )
+
+    def encode_text(self, prompt):
+        """Encodes a prompt into a latent text encoding.
+        The encoding produced by this method should be used as the
+        `encoded_text` parameter of `StableDiffusion.generate_image`. Encoding
+        text separately from generating an image can be used to arbitrarily
+        modify the text encoding priot to image generation, e.g. for walking
+        between two prompts.
+        Args:
+            prompt: a string to encode, must be 77 tokens or shorter.
+        Example:
+        ```python
+        from keras_cv.models import StableDiffusion
+        model = StableDiffusion(img_height=512, img_width=512, jit_compile=True)
+        encoded_text  = model.encode_text("Tacos at dawn")
+        img = model.generate_image(encoded_text)
+        ```
+        """
+        # Tokenize prompt (i.e. starting context)
+        inputs = self.tokenizer.encode(prompt)
+        if len(inputs) > MAX_PROMPT_LENGTH:
+            raise ValueError(
+                f"Prompt is too long (should be <= {MAX_PROMPT_LENGTH} tokens)"
+            )
+        phrase = inputs + [49407] * (MAX_PROMPT_LENGTH - len(inputs))
+        phrase = tf.convert_to_tensor([phrase], dtype=tf.int32)
+
+        context = self.text_encoder.predict_on_batch(
+            [phrase, self._get_pos_ids()]
+        )
+
+        return context
+
+    def generate_image(
+        self,
+        encoded_text,
+        batch_size=1,
+        num_steps=25,
+        unconditional_guidance_scale=7.5,
+        diffusion_noise=None,
+        seed=None,
+    ):
+        """Generates an image based on encoded text.
+        The encoding passed to this method should be derived from
+        `StableDiffusion.encode_text`.
+        Args:
+            encoded_text: Tensor of shape (`batch_size`, 77, 768), or a Tensor
+            of shape (77, 768). When the batch axis is omitted, the same encoded
+            text will be used to produce every generated image.
+            batch_size: number of images to generate. Default: 1.
+            num_steps: number of diffusion steps (controls image quality).
+                Default: 25.
+            unconditional_guidance_scale: float controling how closely the image
+                should adhere to the prompt. Larger values result in more
+                closely adhering to the prompt, but will make the image noisier.
+                Default: 7.5.
+            diffusion_noise: Tensor of shape (`batch_size`, img_height // 8,
+                img_width // 8, 4), or a Tensor of shape (img_height // 8,
+                img_width // 8, 4). Optional custom noise to seed the diffusion
+                process. When the batch axis is omitted, the same noise will be
+                used to seed diffusion for every generated image.
+            seed: integer which is used to seed the random generation of
+                diffusion noise, only to be specified if `diffusion_noise` is
+                None.
+        Example:
+        ```python
+        from keras_cv.models import StableDiffusion
+        batch_size = 8
+        model = StableDiffusion(img_height=512, img_width=512, jit_compile=True)
+        e_tacos = model.encode_text("Tacos at dawn")
+        e_watermelons = model.encode_text("Watermelons at dusk")
+        e_interpolated = tf.linspace(e_tacos, e_watermelons, batch_size)
+        images = model.generate_image(e_interpolated, batch_size=batch_size)
+        ```
+        """
+        if diffusion_noise is not None and seed is not None:
+            raise ValueError(
+                "`diffusion_noise` and `seed` should not both be passed to "
+                "`generate_image`. `seed` is only used to generate diffusion "
+                "noise when it's not already user-specified."
+            )
+
+        encoded_text = tf.squeeze(encoded_text)
+        if encoded_text.shape.rank == 2:
+            encoded_text = tf.repeat(
+                tf.expand_dims(encoded_text, axis=0), batch_size, axis=0
+            )
+
+        context = encoded_text
+        unconditional_context = tf.repeat(
+            self._get_unconditional_context(), batch_size, axis=0
+        )
+        context = tf.concat([context, unconditional_context], 0)
+
+        if diffusion_noise is not None:
+            diffusion_noise = tf.squeeze(diffusion_noise)
+            if diffusion_noise.shape.rank == 3:
+                diffusion_noise = tf.repeat(
+                    tf.expand_dims(diffusion_noise, axis=0), batch_size, axis=0
+                )
+            latent = diffusion_noise
+        else:
+            latent = self._get_initial_diffusion_noise(batch_size, seed)
+
+        # Iterative reverse diffusion stage
+        timesteps = tf.range(1, 1000, 1000 // num_steps)
+        alphas, alphas_prev = self._get_initial_alphas(timesteps)
+        progbar = keras.utils.Progbar(len(timesteps))
+        iteration = 0
+        for index, timestep in list(enumerate(timesteps))[::-1]:
+            latent_prev = latent  # Set aside the previous latent vector
+            t_emb = self._get_timestep_embedding(timestep, batch_size)
+
+            # Prepare the latent and unconditional latent to be run with a single forward call
+            latent = tf.concat([latent, latent], 0)
+            t_emb = tf.concat([t_emb, t_emb], 0)
+            latent_numpy = self.diffusion_model.forward(
+                [latent.numpy(), t_emb.numpy(), context.numpy()]
+            )
+            latent = tf.convert_to_tensor(latent_numpy, dtype=tf.float32)
+            latent, unconditional_latent = tf.split(latent, 2)
+
+            latent = unconditional_latent + unconditional_guidance_scale * (
+                latent - unconditional_latent
+            )
+            a_t, a_prev = alphas[index], alphas_prev[index]
+            pred_x0 = (latent_prev - math.sqrt(1 - a_t) * latent) / math.sqrt(
+                a_t
+            )
+            latent = (
+                latent * math.sqrt(1.0 - a_prev) + math.sqrt(a_prev) * pred_x0
+            )
+            iteration += 1
+            progbar.update(iteration)
+
+        # Decoding stage
+        decoded = self.decoder.predict_on_batch(latent)
+        decoded = ((decoded + 1) / 2) * 255
+        return np.clip(decoded, 0, 255).astype("uint8")
+
+    def _get_unconditional_context(self):
+        unconditional_tokens = tf.convert_to_tensor(
+            [_UNCONDITIONAL_TOKENS], dtype=tf.int32
+        )
+        unconditional_context = self.text_encoder.predict_on_batch(
+            [unconditional_tokens, self._get_pos_ids()]
+        )
+
+        return unconditional_context
+
+    def _get_timestep_embedding(
+        self, timestep, batch_size, dim=320, max_period=10000
+    ):
+        half = dim // 2
+        freqs = tf.math.exp(
+            -math.log(max_period) * tf.range(0, half, dtype=tf.float32) / half
+        )
+        args = tf.convert_to_tensor([timestep], dtype=tf.float32) * freqs
+        embedding = tf.concat([tf.math.cos(args), tf.math.sin(args)], 0)
+        embedding = tf.reshape(embedding, [1, -1])
+        return tf.repeat(embedding, batch_size, axis=0)
+
+    def _get_initial_alphas(self, timesteps):
+        alphas = [_ALPHAS_CUMPROD[t] for t in timesteps]
+        alphas_prev = [1.0] + alphas[:-1]
+
+        return alphas, alphas_prev
+
+    def _get_initial_diffusion_noise(self, batch_size, seed):
+        return tf.random.normal(
+            (batch_size, self.img_height // 8, self.img_width // 8, 4),
+            seed=seed,
+        )
+
+    @staticmethod
+    def _get_pos_ids():
+        return tf.convert_to_tensor(
+            [list(range(MAX_PROMPT_LENGTH))], dtype=tf.int32
+        )
+
+
+if __name__ == "__main__":
+    SD = SharkStableDiffusion(device=args.device)
+    images = SD.text_to_image(args.prompt, num_steps=args.steps)
+    pil_images = [Image.fromarray(image) for image in images]
+    save_fname = args.prompt + ".jpg"
+    if args.save_path is not None:
+        save_fname = args.save_path
+    pil_images[0].save(save_fname)

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 +1,218 @@
+import os
+
+os.environ["AMD_ENABLE_LLPC"] = "1"
+
+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
+from schedulers import (
+    SharkEulerDiscreteScheduler,
+)
+import time
+import sys
+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
+    neg_prompt = args.negative_prompts
+    height = 512  # default height of Stable Diffusion
+    width = 512  # default width of Stable Diffusion
+    if args.version == "v2.1":
+        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
+
+    # TODO: Add support for batch_size > 1.
+    batch_size = len(prompt)
+    if batch_size != 1:
+        sys.exit("More than one prompt is not supported yet.")
+    if batch_size != len(neg_prompt):
+        sys.exit("prompts and negative prompts must be of same length")
+
+    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",
+    )
+    cpu_scheduling = True
+    if args.version == "v2.1":
+        tokenizer = CLIPTokenizer.from_pretrained(
+            "stabilityai/stable-diffusion-2-1", subfolder="tokenizer"
+        )
+
+        scheduler = DPMSolverMultistepScheduler.from_pretrained(
+            "stabilityai/stable-diffusion-2-1",
+            subfolder="scheduler",
+        )
+
+    if args.version == "v2.1base":
+        tokenizer = CLIPTokenizer.from_pretrained(
+            "stabilityai/stable-diffusion-2-1-base", subfolder="tokenizer"
+        )
+
+        if args.use_compiled_scheduler:
+            scheduler = SharkEulerDiscreteScheduler.from_pretrained(
+                "stabilityai/stable-diffusion-2-1-base",
+                subfolder="scheduler",
+            )
+            scheduler.compile()
+            cpu_scheduling = False
+        else:
+            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(
+        neg_prompt,
+        padding="max_length",
+        max_length=max_length,
+        truncation=True,
+        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)
+        if cpu_scheduling:
+            latent_model_input = latent_model_input.detach().numpy()
+
+        profile_device = start_profiling(file_path="unet.rdc")
+
+        noise_pred = unet.forward(
+            (
+                latent_model_input,
+                timestep,
+                text_embeddings_numpy,
+                guidance_scale,
+            ),
+            send_to_host=False,
+        )
+
+        end_profiling(profile_device)
+
+        if cpu_scheduling:
+            noise_pred = torch.from_numpy(noise_pred.to_host())
+            latents = scheduler.step(noise_pred, t, latents).prev_sample
+        else:
+            latents = scheduler.step(noise_pred, t, latents)
+        step_time = time.time() - step_start
+        avg_ms += step_time
+        step_ms = int((step_time) * 1000)
+        print(f" ({step_ms}ms)")
+
+    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
+    if cpu_scheduling:
+        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 +1,182 @@
+from diffusers import AutoencoderKL, UNet2DConditionModel
+from transformers import CLIPTextModel
+from utils import compile_through_fx
+from stable_args import args
+import torch
+
+model_config = {
+    "v2.1": "stabilityai/stable-diffusion-2-1",
+    "v2.1base": "stabilityai/stable-diffusion-2-1-base",
+    "v1.4": "CompVis/stable-diffusion-v1-4",
+}
+
+model_input = {
+    "v2.1": {
+        "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
+        ),
+    },
+    "v2.1base": {
+        "clip": (torch.randint(1, 2, (1, 77)),),
+        "vae": (torch.randn(1, 4, 64, 64),),
+        "unet": (
+            torch.randn(1, 4, 64, 64),  # 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 != "v1.4":
+        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 +1,186 @@
+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.")
+
+if "rdna3" not in args.iree_vulkan_target_triple:
+    args.use_tuned = False
+
+
+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}"
+        )
+    # Disable bindings fusion to work with moltenVK.
+    if sys.platform == "darwin":
+        iree_flags.append("-iree-stream-fuse-binding=false")
+    # Tuned model is present for `fp16` precision.
+    if args.precision == "fp16":
+        if args.use_tuned:
+            bucket = "gs://shark_tank/vivian"
+            if args.version == "v1.4":
+                model_name = "unet_1dec_fp16_tuned"
+            if args.version == "v2.1base":
+                model_name = "unet2base_8dec_fp16_tuned_v2"
+            return get_shark_model(bucket, model_name, iree_flags)
+        else:
+            bucket = "gs://shark_tank/stable_diffusion"
+            model_name = "unet_8dec_fp16"
+            if args.version == "v2.1base":
+                model_name = "unet2base_8dec_fp16"
+            if args.version == "v2.1":
+                model_name = "unet2_14dec_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}"
+        )
+    # Disable bindings fusion to work with moltenVK.
+    if sys.platform == "darwin":
+        iree_flags.append("-iree-stream-fuse-binding=false")
+    if args.precision in ["fp16", "int8"]:
+        if args.use_tuned:
+            bucket = "gs://shark_tank/vivian"
+            if args.version == "v2.1base":
+                model_name = "vae2base_8dec_fp16_tuned"
+            iree_flags += [
+                "--iree-flow-enable-padding-linalg-ops",
+                "--iree-flow-linalg-ops-padding-size=32",
+                "--iree-flow-enable-conv-img2col-transform",
+                "--iree-flow-enable-conv-winograd-transform",
+            ]
+            return get_shark_model(bucket, model_name, iree_flags)
+        else:
+            bucket = "gs://shark_tank/stable_diffusion"
+            model_name = "vae_8dec_fp16"
+            if args.version == "v2.1base":
+                model_name = "vae2base_8dec_fp16"
+            if args.version == "v2.1":
+                model_name = "vae2_14dec_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}"
+        )
+    # Disable bindings fusion to work with moltenVK.
+    if sys.platform == "darwin":
+        iree_flags.append("-iree-stream-fuse-binding=false")
+    bucket = "gs://shark_tank/stable_diffusion"
+    model_name = "clip_8dec_fp32"
+    if args.version == "v2.1base":
+        model_name = "clip2base_8dec_fp32"
+    if args.version == "v2.1":
+        model_name = "clip2_14dec_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/schedulers.py

@@ -0,0 +1,131 @@
+import sys
+import numpy as np
+from typing import List, Optional, Tuple, Union
+from diffusers import (
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    DDIMScheduler,
+    DPMSolverMultistepScheduler,
+    EulerDiscreteScheduler,
+)
+from diffusers.configuration_utils import register_to_config
+from utils import compile_through_fx, get_shark_model
+from stable_args import args
+import torch
+
+SCHEDULER_BUCKET = "gs://shark_tank/stable_diffusion/schedulers"
+
+model_input = {
+    "euler": {
+        "latent": torch.randn(1, 4, 64, 64),
+        "output": torch.randn(1, 4, 64, 64),
+        "sigma": torch.tensor(1).to(torch.float32),
+        "dt": torch.tensor(1).to(torch.float32),
+    },
+}
+
+
+class SharkEulerDiscreteScheduler(EulerDiscreteScheduler):
+    @register_to_config
+    def __init__(
+        self,
+        num_train_timesteps: int = 1000,
+        beta_start: float = 0.0001,
+        beta_end: float = 0.02,
+        beta_schedule: str = "linear",
+        trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
+        prediction_type: str = "epsilon",
+    ):
+        super().__init__(
+            num_train_timesteps,
+            beta_start,
+            beta_end,
+            beta_schedule,
+            trained_betas,
+            prediction_type,
+        )
+
+    def compile(self):
+        example_latent = model_input["euler"]["latent"]
+        example_output = model_input["euler"]["output"]
+        if args.precision == "fp16":
+            example_latent = example_latent.half()
+            example_output = example_output.half()
+        example_sigma = model_input["euler"]["sigma"]
+        example_dt = model_input["euler"]["dt"]
+
+        class ScalingModel(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+
+            def forward(self, latent, sigma):
+                return latent / ((sigma**2 + 1) ** 0.5)
+
+        class SchedulerStepModel(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+
+            def forward(self, noise_pred, sigma, latent, dt):
+                pred_original_sample = latent - sigma * noise_pred
+                derivative = (latent - pred_original_sample) / sigma
+                return latent + derivative * dt
+
+        iree_flags = []
+        if len(args.iree_vulkan_target_triple) > 0:
+            iree_flags.append(
+                f"-iree-vulkan-target-triple={args.iree_vulkan_target_triple}"
+            )
+        # Disable bindings fusion to work with moltenVK.
+        if sys.platform == "darwin":
+            iree_flags.append("-iree-stream-fuse-binding=false")
+
+        if args.import_mlir:
+            scaling_model = ScalingModel()
+            self.scaling_model = compile_through_fx(
+                scaling_model,
+                (example_latent, example_sigma),
+                model_name="euler_scale_model_input_" + args.precision,
+                extra_args=iree_flags,
+            )
+
+            step_model = SchedulerStepModel()
+            self.step_model = compile_through_fx(
+                step_model,
+                (example_output, example_sigma, example_latent, example_dt),
+                model_name="euler_step_" + args.precision,
+                extra_args=iree_flags,
+            )
+        else:
+            self.scaling_model = get_shark_model(
+                SCHEDULER_BUCKET,
+                "euler_scale_model_input_" + args.precision,
+                iree_flags,
+            )
+            self.step_model = get_shark_model(
+                SCHEDULER_BUCKET, "euler_step_" + args.precision, iree_flags
+            )
+
+    def scale_model_input(self, sample, timestep):
+        step_index = (self.timesteps == timestep).nonzero().item()
+        sigma = self.sigmas[step_index]
+        return self.scaling_model.forward(
+            (
+                sample,
+                sigma,
+            ),
+            send_to_host=False,
+        )
+
+    def step(self, noise_pred, timestep, latent):
+        step_index = (self.timesteps == timestep).nonzero().item()
+        sigma = self.sigmas[step_index]
+        dt = self.sigmas[step_index + 1] - sigma
+        return self.step_model.forward(
+            (
+                noise_pred,
+                sigma,
+                latent,
+                dt,
+            ),
+            send_to_host=False,
+        )

shark/examples/shark_inference/stable_diffusion/stable_args.py

@@ -0,0 +1,173 @@
+import argparse
+
+p = argparse.ArgumentParser(
+    description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter
+)
+
+##############################################################################
+### Stable Diffusion Params
+##############################################################################
+
+p.add_argument(
+    "--prompts",
+    nargs="+",
+    default=["cyberpunk forest by Salvador Dali"],
+    help="text of which images to be generated.",
+)
+
+p.add_argument(
+    "--negative-prompts",
+    nargs="+",
+    default=[""],
+    help="text you don't want to see in the generated image.",
+)
+
+p.add_argument(
+    "--steps",
+    type=int,
+    default=50,
+    help="the no. of steps to do the sampling.",
+)
+
+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(
+    "--max_length",
+    type=int,
+    default=77,
+    help="max length of the tokenizer output.",
+)
+
+##############################################################################
+### Model Config and Usage Params
+##############################################################################
+
+p.add_argument(
+    "--device", type=str, default="vulkan", help="device to run the model."
+)
+
+p.add_argument(
+    "--version",
+    type=str,
+    default="v2.1base",
+    help="Specify version of stable diffusion model",
+)
+
+p.add_argument(
+    "--precision", type=str, default="fp16", help="precision to run the model."
+)
+
+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(
+    "--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(
+    "--use_tuned",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="Download and use the tuned version of the model if available",
+)
+
+##############################################################################
+### IREE - Vulkan supported flags
+##############################################################################
+
+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(
+    "--vulkan_large_heap_block_size",
+    default="4294967296",
+    help="flag for setting VMA preferredLargeHeapBlockSize for vulkan device, default is 4G",
+)
+
+p.add_argument(
+    "--vulkan_validation_layers",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="flag for disabling vulkan validation layers when benchmarking",
+)
+
+##############################################################################
+### Misc. Debug and Optimization flags
+##############################################################################
+
+p.add_argument(
+    "--use_compiled_scheduler",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="use the default scheduler precompiled into the model if available",
+)
+
+p.add_argument(
+    "--local_tank_cache",
+    default="",
+    help="Specify where to save downloaded shark_tank artifacts. If this is not set, the default is ~/.local/shark_tank/.",
+)
+
+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(
+    "--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 +1,115 @@
+# 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 binary [here](https://github.com/nod-ai/SHARK/releases/download/20221216.392/shark_sd_20221216_392.exe). Accept if Windows warns of an unsigned .exe.
+
+Notes:
+* Your browser may warn you about downloading a exe file
+* The first run may take about 10-15 minutes when the models are downloaded and compiled. The download could be about 5GB.
+
+#### 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 +1,88 @@
+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
+    from shark.parser import shark_args
+
+    # Set local shark_tank cache directory.
+    shark_args.local_tank_cache = args.local_tank_cache
+
+    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}",
+        f"--vulkan_validation_layers={'true' if args.vulkan_validation_layers else 'false'}",
+    ]
+    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/unet_script.py

@@ -1,8 +1,9 @@
 import torch
-from shark_runner import SharkInference
+import numpy as np
+from shark.shark_inference import SharkInference
+from shark.shark_importer import SharkImporter
 
 
-# Currently not supported aten.transpose_conv2d missing.
 class UnetModule(torch.nn.Module):
     def __init__(self):
         super().__init__()
@@ -14,7 +15,7 @@ class UnetModule(torch.nn.Module):
             init_features=32,
             pretrained=True,
         )
-        self.train(False)
+        self.model.eval()
 
     def forward(self, input):
         return self.model(input)
@@ -22,10 +23,17 @@ class UnetModule(torch.nn.Module):
 
 input = torch.randn(1, 3, 224, 224)
 
-print(input)
-shark_module = SharkInference(
+mlir_importer = SharkImporter(
     UnetModule(),
     (input,),
+    frontend="torch",
 )
-shark_module.benchmark_forward((input,))
-print(input)
+
+(vision_mlir, func_name), inputs, golden_out = mlir_importer.import_debug(
+    tracing_required=False
+)
+
+shark_module = SharkInference(vision_mlir, func_name, mlir_dialect="linalg")
+shark_module.compile()
+result = shark_module.forward((input,))
+np.testing.assert_allclose(golden_out, result, rtol=1e-02, atol=1e-03)

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_eager_backend.py

@@ -48,8 +48,8 @@ class EagerModeIREELinalgOnTensorsBackend(TorchMLIREagerBackend):
 
     def __init__(self, device: str):
         self.torch_device_str = device
-        self.iree_device_str = IREE_DEVICE_MAP[device]
-        self.config = ireert.Config(self.iree_device_str)
+        self.config = ireert.Config(IREE_DEVICE_MAP[device])
+        self.raw_device_str = device
 
     def get_torch_metadata(
         self, tensor: DeviceArray, kwargs: Dict[str, Any]
@@ -71,7 +71,7 @@ class EagerModeIREELinalgOnTensorsBackend(TorchMLIREagerBackend):
             "EagerMode",
         )
         callable, _ = get_iree_compiled_module(
-            imported_module, self.iree_device_str, func_name=fn_name
+            imported_module, self.raw_device_str, func_name=fn_name
         )
         return callable
 

shark/iree_utils/_common.py

@@ -37,28 +37,51 @@ def run_cmd(cmd):
         sys.exit("Exiting program due to error running:", cmd)
 
 
-IREE_DEVICE_MAP = {
+def iree_device_map(device):
+    uri_parts = device.split("://", 2)
+    if len(uri_parts) == 1:
+        return _IREE_DEVICE_MAP[uri_parts[0]]
+    else:
+        return f"{_IREE_DEVICE_MAP[uri_parts[0]]}://{uri_parts[1]}"
+
+
+def get_supported_device_list():
+    return list(_IREE_DEVICE_MAP.keys())
+
+
+_IREE_DEVICE_MAP = {
     "cpu": "local-task",
-    "gpu": "cuda",
     "cuda": "cuda",
     "vulkan": "vulkan",
     "metal": "vulkan",
     "rocm": "rocm",
+    "intel-gpu": "level_zero",
 }
 
-IREE_TARGET_MAP = {
-    "cpu": "dylib",
-    "gpu": "cuda",
+
+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",
     "metal": "vulkan",
     "rocm": "rocm",
+    "intel-gpu": "opencl-spirv",
 }
 
+
 # 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 device in ["gpu", "cuda"]:
+    if "://" in device:
+        device = device.split("://")[0]
+
+    if device == "cuda":
         try:
             subprocess.check_output("nvidia-smi")
         except Exception:
@@ -68,8 +91,19 @@ def check_device_drivers(device):
             subprocess.check_output("vulkaninfo")
         except Exception:
             return True
+    elif device in ["intel-gpu"]:
+        try:
+            subprocess.check_output(["dpkg", "-L", "intel-level-zero-gpu"])
+            return False
+        except Exception:
+            return True
     elif device == "cpu":
         return False
+    elif device == "rocm":
+        try:
+            subprocess.check_output("rocminfo")
+        except Exception:
+            return True
     # Unknown device.
     else:
         return True
@@ -79,9 +113,11 @@ def check_device_drivers(device):
 
 # Installation info for the missing device drivers.
 def device_driver_info(device):
-    if device in ["gpu", "cuda"]:
+    if device == "cuda":
         return "nvidia-smi not found, please install the required drivers from https://www.nvidia.in/Download/index.aspx?lang=en-in"
     elif device in ["metal", "vulkan"]:
         return "vulkaninfo not found, Install from https://vulkan.lunarg.com/sdk/home or your distribution"
+    elif device == "rocm":
+        return "rocm info not found. Please install rocm"
     else:
         return f"{device} is not supported."

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):
@@ -34,9 +35,12 @@ def tensor_to_type_str(input_tensors: tuple, mlir_dialect: str):
             dtype_string = str(input_tensor.dtype).replace("torch.", "")
         elif mlir_dialect in ["mhlo", "tflite"]:
             dtype = input_tensor.dtype
-            dtype_string = re.findall("'[^\"]*'", str(dtype))[0].replace(
-                "'", ""
-            )
+            try:
+                dtype_string = re.findall("'[^\"]*'", str(dtype))[0].replace(
+                    "'", ""
+                )
+            except IndexError:
+                dtype_string = str(dtype)
         regex_split = re.compile("([a-zA-Z]+)([0-9]+)")
         match = regex_split.match(dtype_string)
         mlir_type_string = str(match.group(1)[0]) + str(match.group(2))
@@ -66,10 +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

@@ -13,24 +13,34 @@
 # 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
 
         return get_iree_cpu_args()
-    if device in ["gpu", "cuda"]:
+    if device == "cuda":
         from shark.iree_utils.gpu_utils import get_iree_gpu_args
 
         return get_iree_gpu_args()
     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
+
+        return get_iree_rocm_args()
     return []
 
 
@@ -54,17 +64,182 @@ def get_iree_common_args():
     return [
         "--iree-stream-resource-index-bits=64",
         "--iree-vm-target-index-bits=64",
+        "--iree-util-zero-fill-elided-attrs",
     ]
 
 
+# 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"
@@ -72,24 +247,24 @@ def compile_module_to_flatbuffer(
         input_type = frontend
     elif frontend in ["tflite", "tflite-tosa"]:
         input_type = "tosa"
+    elif frontend in ["tm_tensor"]:
+        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,
         )
 
@@ -98,8 +273,10 @@ def compile_module_to_flatbuffer(
 
 def get_iree_module(flatbuffer_blob, device, func_name):
     # Returns the compiled module and the configs.
-    vm_module = ireert.VmModule.from_flatbuffer(flatbuffer_blob)
-    config = ireert.Config(IREE_DEVICE_MAP[device])
+    config = get_iree_runtime_config(device)
+    vm_module = ireert.VmModule.from_flatbuffer(
+        config.vm_instance, flatbuffer_blob
+    )
     ctx = ireert.SystemContext(config=config)
     ctx.add_vm_module(vm_module)
     ModuleCompiled = ctx.modules.module[func_name]
@@ -112,27 +289,44 @@ 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)
 
 
+def load_flatbuffer(
+    flatbuffer_path: str, device: str, func_name: str = "forward"
+):
+
+    with open(os.path.join(flatbuffer_path), "rb") as f:
+        flatbuffer_blob = f.read()
+
+    return get_iree_module(flatbuffer_blob, device, func_name)
+
+
 def export_iree_module_to_vmfb(
     module,
     device: str,
     directory: str,
-    frontend: str = "torch",
+    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, frontend, func_name, model_config_path
+        module, device, mlir_dialect, func_name, model_config_path, extra_args
     )
-    module_name = f"{frontend}_{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:
@@ -154,18 +348,34 @@ def export_module_to_mlir_file(module, frontend, directory: str):
     return filename
 
 
-def get_results(compiled_vm, input, config, frontend="torch"):
+def get_results(
+    compiled_vm, input, config, frontend="torch", send_to_host=True
+):
     """Runs a .vmfb file given inputs and config and returns output."""
     device_inputs = [ireert.asdevicearray(config.device, a) for a in input]
     result = compiled_vm(*device_inputs)
     result_tensors = []
     if isinstance(result, tuple):
-        for val in result:
-            result_tensors.append(np.copy(np.asarray(val, val.dtype)))
+        if send_to_host:
+            for val in result:
+                result_tensors.append(np.asarray(val, val.dtype))
+        else:
+            for val in result:
+                result_tensors.append(val)
         return result_tensors
     elif isinstance(result, dict):
         data = list(result.items())
-        res = np.array(data, dtype=object)
-        return np.copy(res)
+        if send_to_host:
+            res = np.array(data, dtype=object)
+            return np.copy(res)
+        return data
     else:
-        return np.copy(np.asarray(result, dtype=result.dtype))
+        if send_to_host:
+            return result.to_host()
+        return result
+
+
+def get_iree_runtime_config(device):
+    device = iree_device_map(device)
+    config = ireert.Config(device=ireert.get_device(device))
+    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/gpu_utils.py

@@ -16,6 +16,7 @@
 
 import iree.runtime as ireert
 import ctypes
+from shark.parser import shark_args
 
 # Get the default gpu args given the architecture.
 def get_iree_gpu_args():
@@ -23,7 +24,9 @@ def get_iree_gpu_args():
     ireert.flags.parse_flags("--cuda_allow_inline_execution")
     # TODO: Give the user_interface to pass the sm_arch.
     sm_arch = get_cuda_sm_cc()
-    if sm_arch in ["sm_70", "sm_72", "sm_75", "sm_80", "sm_84", "sm_86"]:
+    if (
+        sm_arch in ["sm_70", "sm_72", "sm_75", "sm_80", "sm_84", "sm_86"]
+    ) and (shark_args.enable_tf32 == True):
         return [
             "--iree-hal-cuda-disable-loop-nounroll-wa",
             f"--iree-hal-cuda-llvm-target-arch={sm_arch}",
@@ -32,6 +35,18 @@ def get_iree_gpu_args():
         return ["--iree-hal-cuda-disable-loop-nounroll-wa"]
 
 
+# Get the default gpu args given the architecture.
+def get_iree_rocm_args():
+    ireert.flags.FUNCTION_INPUT_VALIDATION = False
+    # TODO: find a way to get arch from code.
+    rocm_arch = "gfx908"
+    return [
+        f"--iree-rocm-target-chip={rocm_arch}",
+        "--iree-rocm-link-bc=true",
+        "--iree-rocm-bc-dir=/opt/rocm/amdgcn/bitcode",
+    ]
+
+
 # Some constants taken from cuda.h
 CUDA_SUCCESS = 0
 CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT = 16

shark/iree_utils/vulkan_utils.py

@@ -14,27 +14,92 @@
 
 # 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 | awk 'END{{print $NF}}'"
-    vulkan_device = run_cmd(vulkan_device_cmd).strip()
-    if vulkan_device == "Ultra":
-        print("Found MacStudio M1 Device. Using m1-moltenvk-macos")
+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()
+    # Apple Targets
+    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"
-    elif vulkan_device == "M2":
+    elif all(x in vulkan_device for x in ("Apple", "M2")):
         print("Found Apple M2 Device. Using m1-moltenvk-macos")
         return "-iree-vulkan-target-triple=m1-moltenvk-macos"
-    elif vulkan_device == "M1":
-        print("Found Apple M1 Device. Using m1-moltenvk-macos")
-        return "-iree-vulkan-target-triple=m1-moltenvk-macos"
-    elif vulkan_device == "A100-SXM4-40GB":
-        print("Found Nvidia Device. Using ampere-rtx3080-linux")
-        return "-iree-vulkan-target-triple=ampere-rtx3080-linux"
-    elif vulkan_device == "3090":
-        print("Found Nvidia Device. Using ampere-rtx3090-linux")
-        return "-iree-vulkan-target-triple=ampere-rtx3090-linux"
+    # Nvidia Targets
+    elif all(x in vulkan_device for x in ("A100", "SXM4")):
+        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-{system_os}"
+        )
+        return f"-iree-vulkan-target-triple=ampere-rtx3090-{system_os}"
+    elif all(x in vulkan_device for x in ("RTX", "4090")):
+        print(
+            f"Found {vulkan_device} Device. Using ampere-rtx3090-{system_os}"
+        )
+        return f"-iree-vulkan-target-triple=ampere-rtx3090-{system_os}"
+    elif all(x in vulkan_device for x in ("RTX", "4000")):
+        print(
+            f"Found {vulkan_device} Device. Using turing-rtx4000-{system_os}"
+        )
+        return f"-iree-vulkan-target-triple=turing-rtx4000-{system_os}"
+    elif all(x in vulkan_device for x in ("RTX", "5000")):
+        print(
+            f"Found {vulkan_device} Device. Using turing-rtx5000-{system_os}"
+        )
+        return f"-iree-vulkan-target-triple=turing-rtx5000-{system_os}"
+    elif all(x in vulkan_device for x in ("RTX", "6000")):
+        print(
+            f"Found {vulkan_device} Device. Using turing-rtx6000-{system_os}"
+        )
+        return f"-iree-vulkan-target-triple=turing-rtx6000-{system_os}"
+    elif all(x in vulkan_device for x in ("RTX", "8000")):
+        print(
+            f"Found {vulkan_device} Device. Using turing-rtx8000-{system_os}"
+        )
+        return f"-iree-vulkan-target-triple=turing-rtx8000-{system_os}"
+    # Amd Targets
+    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.
@@ -45,10 +110,16 @@ 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

@@ -12,22 +12,34 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import sys
+"""
+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
-from typing import List, Dict
+import sys
+from typing import Dict, List
 
 from iree.compiler import ir
 from iree.compiler.transforms import ireec as ireec_trans
 
-MATMUL_OP_NAMES = set(
-    ["linalg.matmul", "linalg.batch_matmul", "mhlo.dot", "mhlo.dot_general"]
-)
-idx = 0
-
 
 def model_annotation(
-    ctx: ir.Context, *, input_contents: str, config_path: str
+    ctx: ir.Context,
+    *,
+    input_contents: str,
+    config_path: str,
+    search_op: str = "matmul",
 ):
     if os.path.isfile(input_contents):
         with open(input_contents, "rb") as f:
@@ -41,21 +53,35 @@ def model_annotation(
 
     # The Python API does not expose a general walk() function, so we just
     # do it ourselves.
-    walk_children(module.operation, configs)
+    walk_children(module.operation, configs, 0, search_op)
 
     if not module.operation.verify():
         raise RuntimeError("Modified program does not verify!")
 
-    # More efficient than: print(module)
-    #   - Disables verification (already done above)
-    #   - Writes as binary, avoiding costly unicode conversions
-    sys.stdout.buffer.write(
-        module.operation.get_asm(assume_verified=True, binary=True)
-    )
     return module
 
 
-def walk_children(op: ir.Operation, configs: List[Dict]):
+def walk_children(
+    op: ir.Operation, configs: List[Dict], idx: int, search_op: str
+):
+    if search_op == "matmul":
+        op_names = ["linalg.matmul", "mhlo.dot"]
+    elif search_op == "bmm":
+        op_names = ["linalg.batch_matmul", "mhlo.dot_general"]
+    elif search_op == "conv":
+        op_names = ["mhlo.convolution", "linalg.conv_2d_nhwc_hwcf"]
+    elif search_op == "all":
+        op_names = [
+            "mhlo.dot",
+            "mhlo.dot_general",
+            "mhlo.convolution",
+            "linalg.matmul",
+            "linalg.batch_matmul",
+            "linalg.conv_2d_nhwc_hwcf",
+        ]
+    else:
+        raise ValueError(f"{search_op} op is not tunable.")
+
     for region in op.regions:
         for block in region.blocks:
             for child_op in block.operations:
@@ -63,34 +89,38 @@ def walk_children(op: ir.Operation, configs: List[Dict]):
                 # 'operation' and 'name' attributes.
                 if isinstance(child_op, ir.OpView):
                     child_op = child_op.operation
-                if child_op.name in MATMUL_OP_NAMES:
-                    global idx
-                    (
-                        tile_sizes,
-                        pipeline,
-                        workgroup_size,
-                        split_k,
-                        pipeline_depth,
-                    ) = parse_config(configs[idx])
-
-                    add_compilation_info(
-                        child_op,
-                        tile_sizes=tile_sizes,
-                        pipeline=pipeline,
-                        workgroup_size=workgroup_size,
-                        pipeline_depth=pipeline_depth,
-                    )
-
-                    if split_k:
-                        add_split_k(child_op, split_k)
-
+                if child_op.name in op_names and idx < len(configs):
+                    add_attributes(child_op, configs[idx])
                     idx = idx + 1
                     print(f"Updated op {child_op}", file=sys.stderr)
-                walk_children(child_op, configs)
+                walk_children(child_op, configs, idx, search_op)
+
+
+def add_attributes(op: ir.Operation, config: Dict):
+    (
+        tile_sizes,
+        pipeline,
+        workgroup_size,
+        split_k,
+        pipeline_depth,
+    ) = parse_config(config)
+
+    add_compilation_info(
+        op,
+        tile_sizes=tile_sizes,
+        pipeline=pipeline,
+        workgroup_size=workgroup_size,
+        pipeline_depth=pipeline_depth,
+    )
+
+    if split_k:
+        add_attribute_by_name(op, "iree_flow_split_k", split_k)
 
 
 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"
@@ -98,24 +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
 
 
@@ -145,9 +182,9 @@ def add_compilation_info(
     op.attributes["compilation_info"] = attr
 
 
-def add_split_k(op: ir.Operation, k: int):
-    attr = ir.IntegerAttr.get(ir.IntegerType.get_signless(64), k)
-    op.attributes["iree_flow_split_k"] = attr
+def add_attribute_by_name(op: ir.Operation, name: str, val: int):
+    attr = ir.IntegerAttr.get(ir.IntegerType.get_signless(64), val)
+    op.attributes[name] = attr
 
 
 def create_context() -> ir.Context:
@@ -159,6 +196,14 @@ def create_context() -> ir.Context:
 
 if __name__ == "__main__":
     with create_context() as ctx:
-        model_annotation(
-            ctx, input_contents=sys.argv[1], config_path=sys.argv[2]
+        module = model_annotation(
+            ctx,
+            input_contents=sys.argv[1],
+            config_path=sys.argv[2],
+            search_op="all",
         )
+        mlir_str = str(module)
+        filename = "tuned_model.mlir"
+        with open(filename, "w") as f:
+            f.write(mlir_str)
+        print(f"Saved mlir in {filename}.")

shark/parser.py

@@ -38,7 +38,7 @@ parser.add_argument(
     "--device",
     type=str,
     default="cpu",
-    help="Device on which shark_runner runs. options are cpu, gpu, and vulkan",
+    help="Device on which shark_runner runs. options are cpu, cuda, and vulkan",
 )
 parser.add_argument(
     "--repro_dir",
@@ -47,16 +47,10 @@ parser.add_argument(
     default="./shark_tmp",
 )
 parser.add_argument(
-    "--save_mlir",
+    "--enable_tf32",
+    type=bool,
     default=False,
-    action="store_true",
-    help="Saves input MLIR module to /tmp/ directory.",
-)
-parser.add_argument(
-    "--save_vmfb",
-    default=False,
-    action="store_true",
-    help="Saves iree .vmfb module to /tmp/ directory.",
+    help="Enables TF32 precision calculations on supported GPUs.",
 )
 parser.add_argument(
     "--model_config_path",
@@ -67,14 +61,55 @@ parser.add_argument(
 parser.add_argument(
     "--num_warmup_iterations",
     type=int,
-    default=2,
+    default=5,
     help="Run the model for the specified number of warmup iterations.",
 )
 parser.add_argument(
     "--num_iterations",
     type=int,
-    default=1,
+    default=100,
     help="Run the model for the specified number of iterations.",
 )
+parser.add_argument(
+    "--onnx_bench",
+    default=False,
+    action="store_true",
+    help="When enabled, pytest bench results will include ONNX benchmark results.",
+)
+parser.add_argument(
+    "--shark_prefix",
+    default="latest",
+    help="gs://shark_tank/<this_flag>/model_directories",
+)
+parser.add_argument(
+    "--update_tank",
+    default=False,
+    action="store_true",
+    help="When enabled, SHARK downloader will update local shark_tank if local hash is different from latest upstream hash.",
+)
+parser.add_argument(
+    "--local_tank_cache",
+    default="",
+    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_true",
+    help="Enables the --iree-flow-enable-conv-nchw-to-nhwc-transform flag.",
+)
 
 shark_args, unknown = parser.parse_known_args()

shark/shark_benchmark_runner.py

@@ -19,37 +19,74 @@ from shark.iree_utils.benchmark_utils import (
     run_benchmark_module,
 )
 from shark.parser import shark_args
-from tank.model_utils import get_torch_model
 from datetime import datetime
 import time
 import csv
 import os
 
 
+class OnnxFusionOptions(object):
+    def __init__(self):
+        self.disable_gelu = False
+        self.disable_layer_norm = False
+        self.disable_attention = False
+        self.disable_skip_layer_norm = False
+        self.disable_embed_layer_norm = False
+        self.disable_bias_skip_layer_norm = False
+        self.disable_bias_gelu = False
+        self.enable_gelu_approximation = False
+        self.use_mask_index = False
+        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",
-        frontend: str = "torch",
+        extra_args: list = [],
     ):
         self.device = shark_args.device if device == "none" else device
-        self.frontend = frontend
         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,
-            mlir_dialect,
+            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.frontend
+                mlir_module,
+                device,
+                shark_args.repro_dir,
+                self.mlir_dialect,
+                function_name,
+                extra_args=self.extra_args,
             )
 
     def setup_cl(self, input_tensors):
@@ -60,23 +97,25 @@ class SharkBenchmarkRunner(SharkRunner):
             mlir_dialect=self.mlir_dialect,
         )
 
-    def benchmark_frontend(self, inputs, modelname):
-        if self.frontend in ["pytorch", "torch"]:
+    def benchmark_frontend(self, modelname):
+        if self.mlir_dialect in ["linalg", "torch"]:
             return self.benchmark_torch(modelname)
-        elif self.frontend in ["tensorflow", "tf"]:
-            return self.benchmark_tf(inputs, modelname)
+
+        elif self.mlir_dialect in ["mhlo", "tf"]:
+            return self.benchmark_tf(modelname)
 
     def benchmark_torch(self, modelname):
         import torch
+        from tank.model_utils import get_torch_model
 
-        if self.device == "gpu":
+        if self.device == "cuda":
             torch.set_default_tensor_type(torch.cuda.FloatTensor)
         else:
             torch.set_default_tensor_type(torch.FloatTensor)
         torch_device = torch.device(
-            "cuda:0" if self.device == "gpu" else "cpu"
+            "cuda:0" if self.device == "cuda" else "cpu"
         )
-        HFmodel, input, act_out = get_torch_model(modelname)
+        HFmodel, input = get_torch_model(modelname)[:2]
         frontend_model = HFmodel.model
         frontend_model.to(torch_device)
         input.to(torch_device)
@@ -98,27 +137,49 @@ class SharkBenchmarkRunner(SharkRunner):
             f"{((end-begin)/shark_args.num_iterations)*1000}",
         ]
 
-    def benchmark_tf(self, frontend_model, inputs):
-        for i in range(shark_args.num_warmup_iterations):
-            frontend_model.forward(*inputs)
+    def benchmark_tf(self, modelname):
+        import tensorflow as tf
 
-        begin = time.time()
-        for i in range(shark_args.num_iterations):
-            out = frontend_model.forward(*inputs)
-            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}",
-        ]
+        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
+
+        from tank.model_utils_tf import get_tf_model
+
+        # tf_device = "/GPU:0" if self.device == "cuda" else "/CPU:0"
+        tf_device = "/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)
+
+            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):
         result = run_benchmark_module(self.benchmark_cl)
-        print(f"Shark-{self.frontend} C-benchmark:{result} iter/second")
+        print(f"Shark-IREE-C benchmark:{result} iter/second")
         return [f"{result}", f"{1000/result}"]
 
     def benchmark_python(self, inputs):
@@ -132,32 +193,134 @@ class SharkBenchmarkRunner(SharkRunner):
             if i == shark_args.num_iterations - 1:
                 end = time.time()
         print(
-            f"Shark-{self.frontend} Python-benchmark:{shark_args.num_iterations/(end-begin)} iter/second, Total Iterations:{shark_args.num_iterations}"
+            f"Shark-IREE Python 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_all(self, inputs: tuple):
-        self.benchmark_frontend(inputs)
-        self.benchmark_python(inputs)
-        self.benchmark_c()
+    def benchmark_onnx(self, modelname, inputs):
+        if self.device == "cuda":
+            print(
+                "Currently GPU benchmarking on ONNX is not supported in SHARK."
+            )
+            return ["N/A", "N/A"]
+        else:
+            from onnxruntime.transformers.benchmark import run_onnxruntime
+            from onnxruntime.transformers.huggingface_models import MODELS
+            from onnxruntime.transformers.benchmark_helper import (
+                ConfigModifier,
+                Precision,
+            )
+            import psutil
+
+            if modelname == "microsoft/MiniLM-L12-H384-uncased":
+                modelname = "bert-base-uncased"
+            if modelname not in MODELS:
+                print(
+                    f"{modelname} is currently not supported in ORT's HF. Check \
+https://github.com/microsoft/onnxruntime/blob/master/onnxruntime/python/tools/transformers/huggingface_models.py \
+for currently supported models. Exiting benchmark ONNX."
+                )
+                return ["N/A", "N/A"]
+            use_gpu = self.device == "cuda"
+            num_threads = psutil.cpu_count(logical=False)
+            batch_sizes = [1]
+            sequence_lengths = [128]
+            cache_dir = os.path.join(".", "cache_models")
+            onnx_dir = os.path.join(".", "onnx_models")
+            verbose = False
+            input_counts = [1]
+            optimize_onnx = True
+            validate_onnx = False
+            disable_ort_io_binding = False
+            use_raw_attention_mask = True
+            model_fusion_statistics = {}
+            overwrite = False
+            model_source = "pt"  # Either "pt" or "tf"
+            provider = None
+            config_modifier = ConfigModifier(None)
+            onnx_args = OnnxFusionOptions()
+            result = run_onnxruntime(
+                use_gpu,
+                provider,
+                (modelname,),
+                None,
+                config_modifier,
+                Precision.FLOAT32,
+                num_threads,
+                batch_sizes,
+                sequence_lengths,
+                shark_args.num_iterations,
+                input_counts,
+                optimize_onnx,
+                validate_onnx,
+                cache_dir,
+                onnx_dir,
+                verbose,
+                overwrite,
+                disable_ort_io_binding,
+                use_raw_attention_mask,
+                model_fusion_statistics,
+                model_source,
+                onnx_args,
+            )
+            print(
+                f"ONNX ORT-benchmark:{result[0]['QPS']} iter/second, Total Iterations:{shark_args.num_iterations}"
+            )
+            return [
+                result[0]["QPS"],
+                result[0]["average_latency_ms"],
+            ]
+
+    def get_metadata(self, modelname):
+        with open("./tank/model_metadata.csv", mode="r") as csvfile:
+            torch_reader = csv.reader(csvfile, delimiter=",")
+            fields = next(torch_reader)
+            for row in torch_reader:
+                torch_model_name = row[0]
+                if torch_model_name == modelname:
+                    param_count = row[3]
+                    model_tags = row[4]
+                    model_notes = row[5]
+                    return [param_count, model_tags, model_notes]
+
+    def compare_bench_results(self, baseline: str, result: str):
+        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 baseline"
+        else:
+            comp_str = "N/A"
+
+        return comp_str
 
     def benchmark_all_csv(
         self, inputs: tuple, modelname, dynamic, device_str, frontend
     ):
         self.setup_cl(inputs)
         field_names = [
-            "platform",
             "model",
-            "dynamic",
+            "engine",
+            "dialect",
             "device",
+            "shape_type",
+            "data_type",
             "iter/sec",
             "ms/iter",
+            "vs. PyTorch/TF",
+            "iterations",
+            "param_count",
+            "tags",
+            "notes",
             "datetime",
         ]
-        platforms = ["frontend", "shark_python", "shark_iree_c"]
+        engines = ["frontend", "shark_python", "shark_iree_c"]
+        if shark_args.onnx_bench == True:
+            engines.append("onnxruntime")
 
         if not os.path.exists("bench_results.csv"):
             with open("bench_results.csv", mode="w", newline="") as f:
@@ -169,26 +332,69 @@ class SharkBenchmarkRunner(SharkRunner):
             bench_result = {}
             bench_result["model"] = modelname
             if dynamic == True:
-                bench_result["dynamic"] = "True"
+                bench_result["shape_type"] = "dynamic"
             else:
-                bench_result["dynamic"] = "False"
+                bench_result["shape_type"] = "static"
             bench_result["device"] = device_str
-            for p in platforms:
-                if p == "frontend":
-                    bench_result["platform"] = frontend
-                    bench_result["iter/sec"] = self.benchmark_frontend(
-                        inputs, modelname
-                    )[0]
-                    bench_result["ms/iter"] = self.benchmark_frontend(
-                        inputs, modelname
-                    )[1]
-                elif p == "shark_python":
-                    bench_result["platform"] = "shark_python"
-                    bench_result["iter/sec"] = self.benchmark_python(inputs)[0]
-                    bench_result["ms/iter"] = self.benchmark_python(inputs)[1]
-                else:
-                    bench_result["platform"] = "shark_iree_c"
-                    bench_result["iter/sec"] = self.benchmark_c()[0]
-                    bench_result["ms/iter"] = self.benchmark_c()[1]
+            bench_result["data_type"] = inputs[0].dtype
+            for e in engines:
+                (
+                    bench_result["param_count"],
+                    bench_result["tags"],
+                    bench_result["notes"],
+                ) = ["", "", ""]
+                if e == "frontend":
+                    bench_result["engine"] = frontend
+                    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"
+                    (
+                        bench_result["iter/sec"],
+                        bench_result["ms/iter"],
+                    ) = self.benchmark_python(inputs)
+
+                    bench_result[
+                        "vs. PyTorch/TF"
+                    ] = self.compare_bench_results(
+                        self.frontend_result, bench_result["ms/iter"]
+                    )
+
+                elif e == "shark_iree_c":
+                    bench_result["engine"] = "shark_iree_c"
+                    (
+                        bench_result["iter/sec"],
+                        bench_result["ms/iter"],
+                    ) = self.benchmark_c()
+
+                    bench_result[
+                        "vs. PyTorch/TF"
+                    ] = self.compare_bench_results(
+                        self.frontend_result, bench_result["ms/iter"]
+                    )
+
+                elif e == "onnxruntime":
+                    bench_result["engine"] = "onnxruntime"
+                    (
+                        bench_result["iter/sec"],
+                        bench_result["ms/iter"],
+                    ) = self.benchmark_onnx(modelname, inputs)
+
+                bench_result["dialect"] = self.mlir_dialect
+                bench_result["iterations"] = shark_args.num_iterations
                 bench_result["datetime"] = str(datetime.now())
                 writer.writerow(bench_result)

shark/shark_downloader.py

@@ -14,10 +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,
@@ -29,11 +70,27 @@ input_type_to_np_dtype = {
     "int8": np.int8,
 }
 
-
 # Save the model in the home local so it needn't be fetched everytime in the CI.
 home = str(Path.home())
-WORKDIR = os.path.join(home, ".local/shark_tank/")
-print(WORKDIR)
+alt_path = os.path.join(os.path.dirname(__file__), "../gen_shark_tank/")
+custom_path = shark_args.local_tank_cache
+if os.path.exists(alt_path):
+    WORKDIR = alt_path
+    print(
+        f"Using {WORKDIR} as shark_tank directory. Delete this directory if you aren't working from locally generated shark_tank."
+    )
+if custom_path:
+    if not os.path.exists(custom_path):
+        os.mkdir(custom_path)
+
+    WORKDIR = custom_path
+
+    print(f"Using {WORKDIR} as local shark_tank cache directory.")
+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= flag"
+    )
 
 
 # Checks whether the directory and files exists.
@@ -61,112 +118,64 @@ def check_dir_exists(model_name, frontend="torch", dynamic=""):
             and os.path.isfile(os.path.join(model_dir, "golden_out.npz"))
             and os.path.isfile(os.path.join(model_dir, "hash.npy"))
         ):
-            print(
-                f"""The models are present in the {WORKDIR}. If you want a fresh 
-                download, consider deleting the directory."""
-            )
+            print(f"""Using cached models from {WORKDIR}...""")
             return True
     return False
 
 
 # Downloads the torch model from gs://shark_tank dir.
-def download_torch_model(model_name, dynamic=False):
+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 gs://shark_tank'
-            + "/"
-            + 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 gs://shark_tank'
-            + "/"
-            + 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:
-            gs_download_model()
-
+    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()
+    full_gs_url = tank_url.rstrip("/") + "/" + model_dir_name
 
-    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"))
+    if shark_args.update_tank == True:
+        print(f"Updating artifacts for model {model_name}...")
+        download_public_file(full_gs_url, model_dir)
 
-    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):
-    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 gs://shark_tank'
-            + "/"
-            + 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="tflite", dynamic=dyn_str
+    elif not check_dir_exists(
+        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 gs://shark_tank'
-            + "/"
-            + 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:
-            gs_download_model()
+        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:
+                print(
+                    "Hash does not match upstream in gs://shark_tank/latest. If you want to use locally generated artifacts, this is working as intended. Otherwise, run with --update_tank."
+                )
 
     model_dir = os.path.join(WORKDIR, model_dir_name)
-    with open(
-        os.path.join(model_dir, model_name + dyn_str + "_tflite.mlir")
-    ) as f:
+    tuned_str = "" if tuned is None else "_" + tuned
+    suffix = f"{dyn_str}_{frontend}{tuned_str}.mlir"
+    filename = os.path.join(model_dir, model_name + suffix)
+
+    with open(filename, mode="rb") as f:
         mlir_file = f.read()
 
     function_name = str(np.load(os.path.join(model_dir, "function_name.npy")))
@@ -178,51 +187,11 @@ def download_tflite_model(model_name, dynamic=False):
     return mlir_file, function_name, inputs_tuple, golden_out_tuple
 
 
-def download_tf_model(model_name):
-    model_name = model_name.replace("/", "_")
-    os.makedirs(WORKDIR, exist_ok=True)
-    model_dir_name = model_name + "_tf"
+def _internet_connected():
+    import requests as req
 
-    def gs_download_model():
-        gs_command = (
-            'gsutil -o "GSUtil:parallel_process_count=1" cp -r gs://shark_tank'
-            + "/"
-            + 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 gs://shark_tank'
-            + "/"
-            + 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:
-            gs_download_model()
-
-    model_dir = os.path.join(WORKDIR, model_dir_name)
-    with open(os.path.join(model_dir, model_name + "_tf.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
+    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)
@@ -199,9 +208,11 @@ class SharkImporter:
                 )
             elif golden_out is tuple:
                 golden_out = self.convert_to_numpy(golden_out)
-            else:
+            elif hasattr(golden_out, "logits"):
                 # from transformers import TFSequenceClassifierOutput
                 golden_out = golden_out.logits
+            else:
+                golden_out = golden_out.last_hidden_state
             # Save the artifacts in the directory dir.
             self.save_data(
                 dir,
@@ -232,3 +243,59 @@ 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,
+                torch.ops.aten.native_layer_norm,
+            ]
+        ),
+    )(*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

@@ -9,7 +9,15 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+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
+from shark.parser import shark_args
 import numpy as np
 
 
@@ -31,7 +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
@@ -57,21 +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 = device
+        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
@@ -81,6 +116,7 @@ class SharkInference:
                 self.function_name,
                 self.device,
                 self.mlir_dialect,
+                extra_args=extra_args,
             )
 
         else:
@@ -89,11 +125,21 @@ 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)
+    def forward(self, inputs: tuple, send_to_host=True):
+        return self.shark_runner.run(inputs, send_to_host)
 
     # Captures the static input information from the mlir_module.
     # TODO(pashu123): Generate the input information for dynamic shapes.
@@ -135,3 +181,34 @@ class SharkInference:
                 )
             )
         return tuple(inputs)
+
+    # 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(), 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, 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,
+            self.shark_runner.iree_config,
+        ) = load_flatbuffer(
+            path,
+            self.device,
+            self.function_name,
+        )
+        return

shark/shark_runner.py

@@ -16,6 +16,7 @@ from shark.iree_utils.compile_utils import (
     get_iree_compiled_module,
     get_results,
     export_iree_module_to_vmfb,
+    load_flatbuffer,
 )
 from shark.iree_utils._common import check_device_drivers, device_driver_info
 from shark.parser import shark_args
@@ -24,7 +25,7 @@ import sys
 
 
 # supported dialects by the shark-runtime.
-supported_dialects = {"linalg", "mhlo", "tosa", "tf-lite"}
+supported_dialects = {"linalg", "mhlo", "tosa", "tf-lite", "tm_tensor"}
 
 
 class SharkRunner:
@@ -60,42 +61,41 @@ class SharkRunner:
 
     def __init__(
         self,
-        mlir_module: str,
+        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)
 
-        # Compile the module to get the .vmfb.
-        (
-            self.iree_compilation_module,
-            self.iree_config,
-        ) = get_iree_compiled_module(
-            self.mlir_module,
-            self.device,
-            self.mlir_dialect,
-            func_name=self.function_name,
-        )
+        if compile_vmfb == True:
+            # Compile the module to get the .vmfb.
+            (
+                self.iree_compilation_module,
+                self.iree_config,
+            ) = get_iree_compiled_module(
+                self.mlir_module,
+                self.device,
+                self.mlir_dialect,
+                func_name=self.function_name,
+                extra_args=self.extra_args,
+            )
 
-    def run(self, inputs: tuple):
+    def run(self, inputs: tuple, send_to_host=False):
         return get_results(
             self.iree_compilation_module,
             inputs,
             self.iree_config,
             self.mlir_dialect,
-        )
-
-    # 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()):
-        return export_iree_module_to_vmfb(
-            self.model, self.device, dir, self.mlir_dialect
+            send_to_host,
         )

shark/sharkdynamo/README.md

@@ -0,0 +1,11 @@
+1. Install torchdynamo
+   - `git clone https://github.com/pytorch/torchdynamo.git`
+   - `cd torchdynamo`
+   - `python -m pip install -r requirements.txt`
+   - `python setup.py develop`
+
+2. Install functorch
+   - `python -m pip install -v "git+https://github.com/pytorch/pytorch.git@$(python -c "import torch.version; print(torch.version.git_version)")#subdirectory=functorch"`
+
+3. Run examples.
+    - `python shark/examples/shark_dynamo/basic_examples.py`

shark/sharkdynamo/utils.py

@@ -0,0 +1,157 @@
+import functools
+import time
+from typing import List, Optional
+import torch
+from torch.fx.experimental.proxy_tensor import make_fx
+from functorch._src.compile_utils import strip_overloads
+from shark.shark_inference import SharkInference
+from torch._decomp import get_decompositions
+
+import torch_mlir
+
+# TODO: Control decompositions.
+def default_decompositions():
+    return 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,
+        ]
+    )
+
+
+def timeit(*, append_time_to: Optional[List] = None):
+    def decorator(func):
+        @functools.wraps(func)
+        def wrapper(*args, **kwargs):
+            start_time = time.time_ns()
+            result = func(*args, **kwargs)
+            end_time = time.time_ns()
+
+            if append_time_to is not None:
+                append_time_to.append(end_time - start_time)
+            return result
+
+        return wrapper
+
+    return decorator
+
+
+def _returns_nothing(fx_g: torch.fx.GraphModule) -> bool:
+    for node in fx_g.graph.nodes:
+        if node.op == "output":
+            assert (
+                len(node.args) == 1
+            ), "Output node must have a single argument"
+            node_arg = node.args[0]
+            if isinstance(node_arg, tuple):
+                return len(node_arg) == 0
+    return False
+
+
+def _unwrap_single_tuple_return(fx_g: torch.fx.GraphModule) -> bool:
+    """
+    Replace tuple with tuple element in functions that return one-element tuples.
+    Returns true if an unwrapping took place, and false otherwise.
+    """
+    unwrapped_tuple = False
+    for node in fx_g.graph.nodes:
+        if node.op == "output":
+            assert (
+                len(node.args) == 1
+            ), "Output node must have a single argument"
+            node_arg = node.args[0]
+            if isinstance(node_arg, tuple):
+                if len(node_arg) == 1:
+                    node.args = (node_arg[0],)
+                    unwrapped_tuple = True
+                    break
+
+    if unwrapped_tuple:
+        fx_g.graph.lint()
+        fx_g.recompile()
+    return unwrapped_tuple
+
+
+def make_shark_compiler(use_tracing: bool, device: str, verbose=False):
+    def compiler(
+        fx_graph: torch.fx.GraphModule,
+        example_inputs: List[torch.Tensor],
+    ):
+        """Compile GraphModule using torch-mlir + SHARK."""
+        if verbose:
+            print("Compiling graph...")
+
+        if _returns_nothing(fx_graph):
+            return fx_graph
+
+        was_unwrapped = _unwrap_single_tuple_return(fx_graph)
+        fx_graph = make_fx(
+            fx_graph, decomposition_table=default_decompositions()
+        )(*example_inputs)
+        strip_overloads(fx_graph)
+
+        if verbose:
+            print("torch.fx graph:")
+            print(fx_graph.graph)
+
+        ts_compiler = torch.jit.trace if use_tracing else torch.jit.script
+        ts_graph = ts_compiler(fx_graph, example_inputs)
+
+        if verbose:
+            torch_mlir_module = torch_mlir.compile(
+                ts_graph,
+                example_inputs,
+                output_type=torch_mlir.OutputType.TORCH,
+            )
+            print("\n\ntorch-mlir backend contract graph:")
+            print(torch_mlir_module)
+
+        linalg_module = torch_mlir.compile(
+            ts_graph,
+            example_inputs,
+            output_type=torch_mlir.OutputType.LINALG_ON_TENSORS,
+        )
+
+        shark_module = SharkInference(
+            linalg_module, "forward", mlir_dialect="linalg", device=device
+        )
+        shark_module.compile()
+
+        def forward(*inputs):
+            result = shark_module.forward(inputs)
+            result = tuple() if result is None else result
+            return (result,) if was_unwrapped else result
+
+        return forward
+
+    return compiler
+
+
+def check_results(compiled_results, eager_results):
+    for compiled_result, eager_result in zip(compiled_results, eager_results):
+        if not torch.allclose(
+            compiled_result.to("cpu"), eager_result.to("cpu"), atol=1e-5
+        ):
+            print("Compiled result does not match eager result")
+            return
+    print("Compiled result matches eager result!")
+
+
+def print_time_stats(times):
+    times_tensor = torch.tensor(times)
+
+    def quantile_ms(q):
+        return torch.quantile(times_tensor.to(float), q).item() / 1e6
+
+    print(f"Median: {quantile_ms(0.5)} ms")
+    print(f"10%ile: {quantile_ms(0.1)} ms")
+    print(f"90%ile: {quantile_ms(0.9)} ms")
+    print(f"Total: {torch.sum(times_tensor) / 1e6} ms")
+    print()

shark/stress_test.py

@@ -0,0 +1,296 @@
+# Copyright 2022 The Nod Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from iree.runtime import query_available_drivers, get_driver
+from shark.shark_downloader import download_model
+from shark.shark_inference import SharkInference
+from typing import List, Optional, Tuple
+import numpy as np
+import argparse
+from shark.iree_utils._common import _IREE_DEVICE_MAP
+import multiprocessing
+from shark.shark_runner import supported_dialects
+import logging
+from concurrent.futures import ProcessPoolExecutor
+from concurrent.futures.thread import ThreadPoolExecutor
+import time
+import numpy as np
+
+IREE_TO_SHARK_DRIVER_MAP = {v: k for k, v in _IREE_DEVICE_MAP.items()}
+
+
+def stress_test_compiled_model(
+    shark_module_path: str,
+    function_name: str,
+    device: str,
+    inputs: List[np.ndarray],
+    golden_out: List[np.ndarray],
+    batch_size: int,
+    max_iterations: int,
+    max_duration_seconds: float,
+    inference_timeout_seconds: float,
+    tolerance_nulp: int,
+    stress_test_index: int,
+):
+    logging.info(
+        f"Running stress test {stress_test_index} on device {device}."
+    )
+    shark_module = SharkInference(
+        mlir_module=bytes(), function_name=function_name, device=device
+    )
+    shark_module.load_module(shark_module_path)
+    input_batches = [np.repeat(arr, batch_size, axis=0) for arr in inputs]
+    golden_output_batches = np.repeat(golden_out, batch_size, axis=0)
+    report_interval_seconds = 10
+    start_time = time.time()
+    previous_report_time = start_time
+    executor = ThreadPoolExecutor(1)
+    first_iteration_output = None
+    for i in range(max_iterations):
+        inference_task = executor.submit(shark_module.forward, input_batches)
+        output = inference_task.result(inference_timeout_seconds)
+        if first_iteration_output is None:
+            np.testing.assert_array_almost_equal_nulp(
+                golden_output_batches, output, nulp=tolerance_nulp
+            )
+            first_iteration_output = output
+        else:
+            np.testing.assert_array_equal(output, first_iteration_output)
+        current_time = time.time()
+        if report_interval_seconds < current_time - previous_report_time:
+            logging.info(
+                f"Stress test {stress_test_index} on device "
+                f"{device} at iteration {i+1}"
+            )
+            previous_report_time = current_time
+        if max_duration_seconds < current_time - start_time:
+            return
+    logging.info(f"Stress test {stress_test_index} on device {device} done.")
+
+
+def get_device_type(device_name: str):
+    return device_name.split("://", 1)[0]
+
+
+def get_device_types(device_names: str):
+    return [get_device_type(device_name) for device_name in device_names]
+
+
+def query_devices(device_types: Optional[List[str]] = None) -> List[str]:
+    devices = []
+    if device_types is None:
+        device_types = [
+            IREE_TO_SHARK_DRIVER_MAP[name]
+            for name in query_available_drivers()
+            if name in IREE_TO_SHARK_DRIVER_MAP
+        ]
+    for device_type in device_types:
+        driver = get_driver(_IREE_DEVICE_MAP[device_type])
+        device_infos = driver.query_available_devices()
+        for device_info in device_infos:
+            uri_path = (
+                device_info["path"]
+                if device_info["path"] != ""
+                else str(device_info["device_id"])
+            )
+            device_uri = f"{device_type}://{uri_path}"
+            devices.append(device_uri)
+    return devices
+
+
+def compile_stress_test_module(
+    device_types: List[str], mlir_model: str, func_name: str, mlir_dialect: str
+) -> List[str]:
+    shark_module_paths = []
+    for device_type in device_types:
+        logging.info(
+            f"Compiling stress test model for device type {device_type}."
+        )
+        shark_module = SharkInference(
+            mlir_model,
+            func_name,
+            mlir_dialect=mlir_dialect,
+            device=device_type,
+        )
+        shark_module_paths.append(shark_module.save_module())
+    return shark_module_paths
+
+
+def stress_test(
+    model_name: str,
+    dynamic_model: bool = False,
+    device_types: Optional[List[str]] = None,
+    device_names: Optional[List[str]] = None,
+    batch_size: int = 1,
+    max_iterations: int = 10**7,
+    max_duration_seconds: float = 3600,
+    inference_timeout_seconds: float = 60,
+    mlir_dialect: str = "linalg",
+    frontend: str = "torch",
+    oversubscription_factor: int = 1,
+    tolerance_nulp: int = 50000,
+):
+    logging.info(f"Downloading stress test model {model_name}.")
+    mlir_model, func_name, inputs, golden_out = download_model(
+        model_name=model_name, dynamic=dynamic_model, frontend=frontend
+    )
+
+    if device_names is None or device_types is not None:
+        device_names = [] if device_names is None else device_names
+        with ProcessPoolExecutor() as executor:
+            device_names.extend(
+                executor.submit(query_devices, device_types).result()
+            )
+
+    device_types_set = list(set(get_device_types(device_names)))
+    shark_module_paths_set = compile_stress_test_module(
+        device_types_set, mlir_model, func_name, mlir_dialect
+    )
+    device_type_shark_module_path_map = {
+        device_type: module_path
+        for device_type, module_path in zip(
+            device_types_set, shark_module_paths_set
+        )
+    }
+    device_name_shark_module_path_map = {
+        device_name: device_type_shark_module_path_map[
+            get_device_type(device_name)
+        ]
+        for device_name in device_names
+    }
+
+    # This needs to run in a spearate process, because it uses the drvier chache
+    # in IREE and a subsequent call to `iree.runtime.SystemContext.add_vm_module`
+    # in a forked process will hang.
+    with multiprocessing.Pool(
+        len(device_name_shark_module_path_map) * oversubscription_factor
+    ) as process_pool:
+        process_pool.starmap(
+            stress_test_compiled_model,
+            [
+                (
+                    module_path,
+                    func_name,
+                    device_name,
+                    inputs,
+                    golden_out,
+                    batch_size,
+                    max_iterations,
+                    max_duration_seconds,
+                    inference_timeout_seconds,
+                    tolerance_nulp,
+                    stress_test_index,
+                )
+                for stress_test_index, (device_name, module_path) in enumerate(
+                    list(device_name_shark_module_path_map.items())
+                    * oversubscription_factor
+                )
+            ],
+        )
+
+
+if __name__ == "__main__":
+    logging.basicConfig(encoding="utf-8", level=logging.INFO)
+    parser = argparse.ArgumentParser(
+        description="Downloads, compiles and runs a model from the tank to stress test the system."
+    )
+    parser.add_argument(
+        "--model", type=str, help="Model name in the tank.", default="alexnet"
+    )
+    parser.add_argument(
+        "--dynamic",
+        help="Use dynamic version of the model.",
+        action="store_true",
+        default=False,
+    )
+    parser.add_argument(
+        "--frontend", type=str, help="Frontend of the model.", default="torch"
+    )
+    parser.add_argument(
+        "--mlir-dialect",
+        type=str,
+        help="MLIR dialect of the model.",
+        default="linalg",
+        choices=supported_dialects,
+    )
+    parser.add_argument(
+        "--device-types",
+        type=str,
+        nargs="*",
+        choices=_IREE_DEVICE_MAP.keys(),
+        help="Runs the stress test on all devices with that type. "
+        "If absent and no deveices are specified "
+        "will run against all available devices.",
+    )
+    parser.add_argument(
+        "--devices",
+        type=str,
+        nargs="*",
+        help="List of devices to run the stress test on. "
+        "If device-types is specified will run against the union of the two.",
+    )
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        help="Number of inputs to feed into the model",
+        default=1,
+    )
+    parser.add_argument(
+        "--oversubscription",
+        type=int,
+        help="Oversubscrption factor. Each device will execute the model simultaneously "
+        "this many number of times.",
+        default=1,
+    )
+    parser.add_argument(
+        "--max-iterations",
+        type=int,
+        help="Maximum number of iterations to run the stress test per device.",
+        default=10**7,
+    )
+    parser.add_argument(
+        "--max-duration",
+        type=float,
+        help="Maximum number of seconds to run the stress test.",
+        default=3600,
+    )
+    parser.add_argument(
+        "--inference-timeout",
+        type=float,
+        help="Timeout in seconds for a single model inference operation.",
+        default=60,
+    )
+    parser.add_argument(
+        "--tolerance-nulp",
+        type=int,
+        help="The maximum number of unit in the last place for tolerance "
+        "when verifing results with the golden reference output.",
+        default=50000,
+    )
+
+    args = parser.parse_known_args()[0]
+    stress_test(
+        model_name=args.model,
+        dynamic_model=args.dynamic,
+        frontend=args.frontend,
+        mlir_dialect=args.mlir_dialect,
+        device_types=args.device_types,
+        device_names=args.devices,
+        batch_size=args.batch_size,
+        oversubscription_factor=args.oversubscription,
+        max_iterations=args.max_iterations,
+        max_duration_seconds=args.max_duration,
+        inference_timeout_seconds=args.inference_timeout,
+        tolerance_nulp=args.tolerance_nulp,
+    )

shark/tests/test_stress_test.py

@@ -0,0 +1,31 @@
+# Copyright 2022 The Nod Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import pytest
+import subprocess
+import sys
+import importlib.util
+
+
+def test_stress_test():
+    subprocess.check_call(
+        [
+            sys.executable,
+            importlib.util.find_spec("shark.stress_test").origin,
+            "--model=squeezenet1_0",
+            "--devices",
+            "cpu",
+            "--max-iterations=1",
+        ]
+    )

shark/torch_mlir_lockstep_tensor.py

@@ -0,0 +1,220 @@
+# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+# See https://llvm.org/LICENSE.txt for license information.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+# Also available under a BSD-style license. See LICENSE.
+import contextlib
+import re
+import traceback
+import warnings
+from typing import Any
+import numpy as np
+
+import torch
+from torch.utils._pytree import tree_map
+
+from torch_mlir.eager_mode.ir_building import build_mlir_module
+from torch_mlir.eager_mode.torch_mlir_dispatch import (
+    UnsupportedByTorchMlirEagerMode,
+    normalize_args_kwargs,
+    check_get_aliased_arg,
+)
+from torch_mlir.eager_mode import EAGER_MODE_DEBUG
+from torch_mlir.eager_mode.torch_mlir_tensor import (
+    TorchMLIRTensor,
+    check_requires_grad,
+    make_wrapper_subclass_from_torch_tensor,
+    make_bare_wrapper_subclass,
+    UNSUPPORTED_OPS,
+    no_dispatch,
+)
+from torch_mlir.eager_mode import torch_mlir_tensor
+from shark.iree_eager_backend import EagerModeIREELinalgOnTensorsBackend
+
+
+backend = EagerModeIREELinalgOnTensorsBackend("cpu")
+torch_mlir_tensor.backend = backend
+rtol = 1e-04
+atol = 1e-05
+
+
+class TorchMLIRLockstepTensor(TorchMLIRTensor):
+    """This class overrides the dispatching for TorchMLIRTensor to allow for an op-by-op numerical comparison between PyTorch and the Torch-MLIR -> IREE backend compilation pipeline. This only supports the IREE backend and focuses on op-by-op level verification.
+
+    TODO: Extend this to do a cumulative trace with summary statistics at the end. Possibly requires a wrapper environment to store full trace info.
+    """
+
+    def __new__(cls, elem, **kwargs):
+        if kwargs.get("constructing_from_device_tensor", False):
+            tensor_meta_data = backend.get_torch_metadata(elem, kwargs)
+            r = make_bare_wrapper_subclass(
+                cls=cls,
+                size=tensor_meta_data.size,
+                strides=tensor_meta_data.strides,
+                storage_offset=tensor_meta_data.storage_offset,
+                dtype=tensor_meta_data.dtype,
+                layout=tensor_meta_data.layout,
+                device=tensor_meta_data.device,
+                requires_grad=tensor_meta_data.requires_grad,
+            )
+            r.elem = elem
+        elif isinstance(elem, torch.nn.Parameter):
+            r = make_wrapper_subclass_from_torch_tensor(
+                cls, elem.data, **kwargs
+            )
+            # This is a hack to handle non-contiguous data through IREE-backend
+            nt = elem.detach().data.numpy()
+            if not nt.flags["C_CONTIGUOUS"]:
+                nt = np.ascontiguousarray(nt, dtype=nt.dtype)
+            r.elem = backend.transfer_from_torch_to_device(
+                torch.from_numpy(nt)
+            )
+        elif isinstance(elem, torch.Tensor):
+            r = make_wrapper_subclass_from_torch_tensor(cls, elem, **kwargs)
+            # Ditto TODO: Find a better way to handle this
+            nt = elem.numpy()
+            if not nt.flags["C_CONTIGUOUS"]:
+                nt = np.ascontiguousarray(nt, dtype=nt.dtype)
+            r.elem = backend.transfer_from_torch_to_device(
+                torch.from_numpy(nt)
+            )
+        # This branch handles the case when a python scalar is passed to some op
+        # or is returned from some aten op, such as _local_scalar_dense.
+        elif isinstance(elem, (int, float, bool)):
+            return elem
+        else:
+            raise ValueError(f"Unknown element type: {type(elem)}")
+        return r
+
+    def __repr__(self):
+        if self.grad_fn:
+            return f"TorchMLIRLockstepTensor({self.elem}, backend={backend.__class__.__name__}, grad_fn={self.grad_fn})"
+        else:
+            return f"TorchMLIRLockstepTensor({self.elem}, backend={backend.__class__.__name__})"
+
+    """This does essentially the same dispatch as TorchMLIRTensor but operates as if debug mode is enabled. The numeric verification happens after the Torch-MLIR result is obtained by comparing against the 
+    """
+
+    @classmethod
+    def __torch_dispatch__(cls, func, _types, args=(), kwargs=None):
+        requires_grad = check_requires_grad(*args, **kwargs)
+        try:
+            with no_dispatch():
+                if hasattr(func, "op_name"):
+                    op_name = func.op_name
+                elif hasattr(func, "__name__"):
+                    # Handle builtin_function_or_method.
+                    op_name = func.__name__
+                else:
+                    raise RuntimeError(f"op {func} has no name")
+
+                if UNSUPPORTED_OPS.match(op_name):
+                    raise UnsupportedByTorchMlirEagerMode(op_name)
+
+                if not hasattr(func, "_schema"):
+                    raise RuntimeError(f"op {func} has no schema.")
+
+                normalized_kwargs = normalize_args_kwargs(func, args, kwargs)
+
+                if "layout" in normalized_kwargs and normalized_kwargs[
+                    "layout"
+                ] not in {0, None}:
+                    raise UnsupportedByTorchMlirEagerMode(
+                        f"{normalized_kwargs['layout']} layout not supported."
+                    )
+                if "memory_format" in normalized_kwargs and normalized_kwargs[
+                    "memory_format"
+                ] not in {0, None}:
+                    raise UnsupportedByTorchMlirEagerMode(
+                        f"{normalized_kwargs['memory_format']} memory format not supported."
+                    )
+                eager_module = build_mlir_module(func, normalized_kwargs)
+            device_tensor_args = [
+                kwarg.elem
+                for _, kwarg in normalized_kwargs.items()
+                if isinstance(kwarg, cls)
+            ]
+            assert len(eager_module.body.operations[0].arguments) == len(
+                device_tensor_args
+            ), "Number of parameters and number of arguments differs."
+            op_mlir_backend_callable = backend.compile(eager_module)
+            out = op_mlir_backend_callable(*device_tensor_args)
+            out = tree_map(
+                lambda x: cls(
+                    x,
+                    requires_grad=requires_grad,
+                    constructing_from_device_tensor=True,
+                ),
+                out,
+            )
+
+            # Numeric verification; Value for comparison comes from PyTorch eager
+            with no_dispatch():
+                unwrapped_args = tree_map(cls.unwrap, args)
+                unwrapped_kwargs = tree_map(cls.unwrap, kwargs)
+                if "_reshape_alias" in op_name:
+                    native_out = torch.ops.aten.view(
+                        unwrapped_args[0], unwrapped_args[1]
+                    )
+                else:
+                    native_out = func(*unwrapped_args, **unwrapped_kwargs)
+
+            native_out = tree_map(
+                lambda x: cls(x, requires_grad=requires_grad), native_out
+            ).elem
+            tmp_out = out.elem
+
+            try:
+                np.testing.assert_allclose(
+                    native_out.to_host(),
+                    tmp_out.to_host(),
+                    rtol=rtol,
+                    atol=atol,
+                )
+            except Exception as e:
+                shaped_args = [
+                    arg.shape if torch.is_tensor(arg) else arg
+                    for arg in unwrapped_args
+                ]
+                shaped_kwargs = [
+                    kwarg.shape if torch.is_tensor(kwarg) else kwarg
+                    for kwarg in unwrapped_kwargs
+                ]
+                warnings.warn(
+                    f"Lockstep accuracy verification failed with error: *{str(e)}*; "
+                    f"Dispatched function name: *{str(func)}*; "
+                    f"Dispatched function args: *{str(shaped_args)}*; "
+                    f"Dispatched function kwargs: *{str(shaped_kwargs)}*; "
+                )
+        except Exception as e:
+            warnings.warn(traceback.format_exc())
+            if isinstance(e, UnsupportedByTorchMlirEagerMode):
+                warnings.warn(
+                    f"Couldn't use TorchMLIR eager because current incompatibility: *{str(e)}*; running through PyTorch eager."
+                )
+            else:
+                warnings.warn(
+                    f"Couldn't use TorchMLIR eager because of error: *{str(e)}*; "
+                    f"Running through PyTorch eager"
+                )
+
+            with no_dispatch():
+                unwrapped_args = tree_map(cls.unwrap, args)
+                unwrapped_kwargs = tree_map(cls.unwrap, kwargs)
+                if "_reshape_alias" in op_name:
+                    out = torch.ops.aten.view(
+                        unwrapped_args[0], unwrapped_args[1]
+                    )
+                else:
+                    out = func(*unwrapped_args, **unwrapped_kwargs)
+
+            out = tree_map(lambda x: cls(x, requires_grad=requires_grad), out)
+
+        maybe_aliased_arg_name = check_get_aliased_arg(func)
+        if maybe_aliased_arg_name is not None:
+            warnings.warn(
+                f"Found aliased arg, but didn't copy tensor contents. This could lead to incorrect results for E2E model execution but doesn't affect the validity of the lockstep op verification."
+            )
+            # TODO: Find a way to handle argument aliasing for IREE backend
+            # backend.copy_into(normalized_kwargs[maybe_aliased_arg_name].elem, out.elem)
+
+        return out

shark/torch_mlir_utils.py

@@ -12,26 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import torch
-import io
-import pickle
-
-from torch_mlir.dialects.torch.importer.jit_ir import (
-    ClassAnnotator,
-    ModuleBuilder,
-)
-from torch_mlir_e2e_test.torchscript.serialization import (
-    extract_serializable_annotations,
-    apply_serializable_annotations,
-    SerializableTest,
-)
-
-from torch_mlir_e2e_test.linalg_on_tensors_backends import refbackend
-
-from torch_mlir.passmanager import PassManager
-from torch_mlir_e2e_test.torchscript.annotations import annotate_args, export
 from torch_mlir.ir import StringAttr
 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):
@@ -45,22 +31,6 @@ def get_module_name_for_asm_dump(module):
     ).value
 
 
-def get_input_annotations(inputs: tuple, dynamic: bool) -> list:
-    """TODO: Include necessary documentation"""
-
-    annotations_list = [None]
-    for i in inputs:
-        temp_list = []
-        if dynamic:
-            temp_list.append([-1 for i in range(len(i.shape))])
-        else:
-            temp_list.append(list(i.shape))
-        temp_list.append(i.dtype)
-        temp_list.append(True)
-        annotations_list.append(tuple(temp_list))
-    return annotations_list
-
-
 def run_on_refbackend(torch_module, inputs):
     backend = refbackend.RefBackendLinalgOnTensorsBackend()
     compiled = backend.compile(torch_module)
@@ -69,42 +39,16 @@ def run_on_refbackend(torch_module, inputs):
     return jit_module.forward(np_inputs[0])
 
 
-def shark_jit_trace(
-    module, input: tuple, dynamic: bool, tracing_required: bool
-):
-    """TODO: Include necessary documentation."""
-
-    if not tracing_required:
-        return torch.jit.script(module)
-
-    traced_module = torch.jit.trace_module(module, {"forward": input})
-    actual_script = traced_module._actual_script_module
-    export(actual_script.forward)
-    annotate_args_decorator = annotate_args(
-        get_input_annotations(input, dynamic)
-    )
-    annotate_args_decorator(actual_script.forward)
-    module = torch.jit.script(actual_script)
-
-    # TODO: remove saved annotations.pickle
-    torchscript_module_bytes = module.save_to_buffer(
-        {
-            "annotations.pkl": pickle.dumps(
-                extract_serializable_annotations(module)
-            )
-        }
-    )
-    serializable_test = SerializableTest(
-        unique_name="", program=torchscript_module_bytes, trace=None
-    )
-    _extra_files = {"annotations.pkl": ""}
-    module = torch.jit.load(
-        io.BytesIO(serializable_test.program), _extra_files=_extra_files
-    )
-    # Load the pickled annotations.
-    annotations = pickle.loads(_extra_files["annotations.pkl"])
-    apply_serializable_annotations(module, annotations)
-    return module
+# Creates dynamic dims for all dims.
+# TODO: Pass user specified dynamic dims.
+def create_dynamic_placeholders(inputs):
+    placeholders = []
+    for inp in inputs:
+        placeholder = torch_mlir.TensorPlaceholder.like(
+            inp, dynamic_axes=[i for i in range(len(inp.shape))]
+        )
+        placeholders.append(placeholder)
+    return tuple(placeholders)
 
 
 def get_torch_mlir_module(
@@ -112,41 +56,24 @@ def get_torch_mlir_module(
     input: tuple,
     dynamic: bool,
     jit_trace: bool,
-    from_torchscript: bool = False,
 ):
-    """TODO: Include necessary documentation."""
+    """Get the MLIR's linalg-on-tensors module from the torchscipt module."""
+    ignore_traced_shapes = False
+    if dynamic:
+        input = create_dynamic_placeholders(input)
+    if jit_trace:
+        ignore_traced_shapes = True
 
-    # Static modules compiles well with the torch_mlir.compile API.
-    # We will always jit_trace = True with the API since we always
-    # want to propagate static shapes.
-    if not dynamic:
-        module = torch_mlir.compile(
-            module,
-            input,
-            output_type=torch_mlir.OutputType.LINALG_ON_TENSORS,
-            use_tracing=jit_trace,
-        )
-        return module
+    tempfile.tempdir = shark_args.repro_dir
 
-    # Tracing is not required from the aot_module.
-    if not from_torchscript:
-        module = shark_jit_trace(module, input, dynamic, jit_trace)
-
-    mb = ModuleBuilder()
-    class_annotator = ClassAnnotator()
-    class_annotator.exportNone(module._c._type())
-    class_annotator.exportPath(module._c._type(), ["forward"])
-    class_annotator.annotateArgs(
-        module._c._type(),
-        ["forward"],
-        get_input_annotations(input, dynamic),
+    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,
     )
-    mb.import_module(module._c, class_annotator)
-
-    with mb.module.context:
-        pm = PassManager.parse(
-            "torchscript-module-to-torch-backend-pipeline,torch-backend-to-linalg-on-tensors-backend-pipeline"
-        )
-        pm.run(mb.module)
-
-    return mb.module
+    bytecode_stream = io.BytesIO()
+    mlir_module.operation.write_bytecode(bytecode_stream)
+    bytecode = bytecode_stream.getvalue()
+    return bytecode

tank/README.md

@@ -0,0 +1,223 @@
+## 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
+IMPORTER=1 ./setup_venv.sh
+source shark.venv/bin/activate
+pip install jupyter tf-models-nightly tf-datasets
+jupyter-notebook
+```
+if running from a google vm, you can view jupyter notebooks on your local system with:
+```shell
+gcloud compute ssh <YOUR_INSTANCE_DETAILS> --ssh-flag="-N -L localhost:8888:localhost:8888"
+```
+
+
+

tank/albert-base-v2_tf/albert-base-v2_tf_test.py

@@ -1,60 +0,0 @@
-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
-
-import iree.compiler as ireec
-import unittest
-import pytest
-import numpy as np
-
-
-class AlbertBaseModuleTester:
-    def __init__(
-        self,
-        benchmark=False,
-    ):
-        self.benchmark = benchmark
-
-    def create_and_check_module(self, dynamic, device):
-        model, func_name, inputs, golden_out = download_tf_model(
-            "albert-base-v2"
-        )
-
-        shark_module = SharkInference(
-            model, func_name, device=device, mlir_dialect="mhlo"
-        )
-        shark_module.compile()
-        result = shark_module.forward(inputs)
-        np.testing.assert_allclose(golden_out, result, rtol=1e-02, atol=1e-03)
-
-
-class AlbertBaseModuleTest(unittest.TestCase):
-    @pytest.fixture(autouse=True)
-    def configure(self, pytestconfig):
-        self.module_tester = AlbertBaseModuleTester(self)
-        self.module_tester.benchmark = pytestconfig.getoption("benchmark")
-
-    def test_module_static_cpu(self):
-        dynamic = False
-        device = "cpu"
-        self.module_tester.create_and_check_module(dynamic, device)
-
-    @pytest.mark.skipif(
-        check_device_drivers("gpu"), reason=device_driver_info("gpu")
-    )
-    def test_module_static_gpu(self):
-        dynamic = False
-        device = "gpu"
-        self.module_tester.create_and_check_module(dynamic, device)
-
-    @pytest.mark.skipif(
-        check_device_drivers("vulkan"), reason=device_driver_info("vulkan")
-    )
-    def test_module_static_vulkan(self):
-        dynamic = False
-        device = "vulkan"
-        self.module_tester.create_and_check_module(dynamic, device)
-
-
-if __name__ == "__main__":
-    unittest.main()

tank/all_models.csv

@@ -0,0 +1,34 @@
+resnet50,mhlo,tf,1e-2,1e-3,default,nhcw-nhwc,False,False,True,"Vulkan Numerical Error: mostly conv"
+albert-base-v2,mhlo,tf,1e-2,1e-2,default,None,False,False,False,""
+roberta-base,mhlo,tf,1e-02,1e-3,default,nhcw-nhwc,False,False,False,""
+bert-base-uncased,mhlo,tf,1e-2,1e-3,default,None,False,False,False,""
+camembert-base,mhlo,tf,1e-2,1e-3,default,None,False,False,False,""
+dbmdz/convbert-base-turkish-cased,mhlo,tf,1e-2,1e-3,default,nhcw-nhwc,True,True,True,"https://github.com/iree-org/iree/issues/9971"
+distilbert-base-uncased,mhlo,tf,1e-2,1e-3,default,None,False,False,False,""
+facebook/convnext-tiny-224,mhlo,tf,1e-2,1e-3,tf_vit,nhcw-nhwc,True,True,True,"https://github.com/nod-ai/SHARK/issues/311 & https://github.com/nod-ai/SHARK/issues/342"
+funnel-transformer/small,mhlo,tf,1e-2,1e-3,default,None,True,True,True,"https://github.com/nod-ai/SHARK/issues/201"
+google/electra-small-discriminator,mhlo,tf,1e-2,1e-3,default,None,False,False,False,""
+google/mobilebert-uncased,mhlo,tf,1e-2,1e-3,default,None,True,False,False,"Fails during iree-compile."
+google/vit-base-patch16-224,mhlo,tf,1e-2,1e-3,tf_vit,nhcw-nhwc,False,False,True,"Vulkan Numerical Error (mostly conv)"
+microsoft/MiniLM-L12-H384-uncased,mhlo,tf,1e-2,1e-3,tf_hf,None,True,False,False,"Fails during iree-compile."
+microsoft/layoutlm-base-uncased,mhlo,tf,1e-2,1e-3,default,None,False,False,False,""
+microsoft/mpnet-base,mhlo,tf,1e-2,1e-2,default,None,False,False,False,""
+albert-base-v2,linalg,torch,1e-2,1e-3,default,None,True,True,True,"issue with aten.tanh in torch-mlir"
+alexnet,linalg,torch,1e-2,1e-3,default,None,False,False,True,"Assertion Error: Zeros Output"
+bert-base-cased,linalg,torch,1e-2,1e-3,default,None,False,False,False,""
+bert-base-uncased,linalg,torch,1e-2,1e-3,default,None,False,False,False,""
+facebook/deit-small-distilled-patch16-224,linalg,torch,1e-2,1e-3,default,nhcw-nhwc,False,True,False,"Fails during iree-compile."
+google/vit-base-patch16-224,linalg,torch,1e-2,1e-3,default,nhcw-nhwc,False,True,False,"https://github.com/nod-ai/SHARK/issues/311"
+microsoft/beit-base-patch16-224-pt22k-ft22k,linalg,torch,1e-2,1e-3,default,nhcw-nhwc,False,True,False,"https://github.com/nod-ai/SHARK/issues/390"
+microsoft/MiniLM-L12-H384-uncased,linalg,torch,1e-2,1e-3,default,None,False,False,True,""
+microsoft/resnet-50,linalg,torch,1e-2,1e-3,default,nhcw-nhwc,False,False,True,"Vulkan Numerical Error (mostly conv)"
+google/mobilebert-uncased,linalg,torch,1e-2,1e-3,default,None,False,False,True,"https://github.com/nod-ai/SHARK/issues/344"
+mobilenet_v3_small,linalg,torch,1e-1,1e-2,default,nhcw-nhwc,False,True,True,"https://github.com/nod-ai/SHARK/issues/388"
+nvidia/mit-b0,linalg,torch,1e-2,1e-3,default,None,True,True,True,"https://github.com/nod-ai/SHARK/issues/343"
+resnet101,linalg,torch,1e-2,1e-3,default,nhcw-nhwc,False,False,True,"Vulkan Numerical Error (mostly conv)"
+resnet18,linalg,torch,1e-2,1e-3,default,None,True,True,True,""
+resnet50,linalg,torch,1e-2,1e-3,default,nhcw-nhwc,False,False,True,"Vulkan Numerical Error (mostly conv)"
+squeezenet1_0,linalg,torch,1e-2,1e-3,default,nhcw-nhwc,False,False,True,"https://github.com/nod-ai/SHARK/issues/388"
+wide_resnet50_2,linalg,torch,1e-2,1e-3,default,nhcw-nhwc,False,False,True,"Vulkan Numerical Error (mostly conv)"
+efficientnet-v2-s,mhlo,tf,1e-02,1e-3,default,nhcw-nhwc,False,False,True,"https://github.com/nod-ai/SHARK/issues/575"
+mnasnet1_0,linalg,torch,1e-2,1e-3,default,nhcw-nhwc,False,False,True,"https://github.com/nod-ai/SHARK/issues/388"

tank/bert-base-uncased_tf/bert-base-uncased_tf_test.py

@@ -1,60 +0,0 @@
-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
-
-import iree.compiler as ireec
-import unittest
-import pytest
-import numpy as np
-
-
-class BertBaseUncasedModuleTester:
-    def __init__(
-        self,
-        benchmark=False,
-    ):
-        self.benchmark = benchmark
-
-    def create_and_check_module(self, dynamic, device):
-        model, func_name, inputs, golden_out = download_tf_model(
-            "bert-base-uncased"
-        )
-
-        shark_module = SharkInference(
-            model, func_name, device=device, mlir_dialect="mhlo"
-        )
-        shark_module.compile()
-        result = shark_module.forward(inputs)
-        np.testing.assert_allclose(golden_out, result, rtol=1e-02, atol=1e-03)
-
-
-class BertBaseUncasedModuleTest(unittest.TestCase):
-    @pytest.fixture(autouse=True)
-    def configure(self, pytestconfig):
-        self.module_tester = BertBaseUncasedModuleTester(self)
-        self.module_tester.benchmark = pytestconfig.getoption("benchmark")
-
-    def test_module_static_cpu(self):
-        dynamic = False
-        device = "cpu"
-        self.module_tester.create_and_check_module(dynamic, device)
-
-    @pytest.mark.skipif(
-        check_device_drivers("gpu"), reason=device_driver_info("gpu")
-    )
-    def test_module_static_gpu(self):
-        dynamic = False
-        device = "gpu"
-        self.module_tester.create_and_check_module(dynamic, device)
-
-    @pytest.mark.skipif(
-        check_device_drivers("vulkan"), reason=device_driver_info("vulkan")
-    )
-    def test_module_static_vulkan(self):
-        dynamic = False
-        device = "vulkan"
-        self.module_tester.create_and_check_module(dynamic, device)
-
-
-if __name__ == "__main__":
-    unittest.main()

tank/camembert-base_tf/camembert-base_tf_test.py

@@ -1,60 +0,0 @@
-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
-
-import iree.compiler as ireec
-import unittest
-import pytest
-import numpy as np
-
-
-class CamemBertModuleTester:
-    def __init__(
-        self,
-        benchmark=False,
-    ):
-        self.benchmark = benchmark
-
-    def create_and_check_module(self, dynamic, device):
-        model, func_name, inputs, golden_out = download_tf_model(
-            "camembert-base"
-        )
-
-        shark_module = SharkInference(
-            model, func_name, device=device, mlir_dialect="mhlo"
-        )
-        shark_module.compile()
-        result = shark_module.forward(inputs)
-        np.testing.assert_allclose(golden_out, result, rtol=1e-02, atol=1e-03)
-
-
-class CamemBertModuleTest(unittest.TestCase):
-    @pytest.fixture(autouse=True)
-    def configure(self, pytestconfig):
-        self.module_tester = CamemBertModuleTester(self)
-        self.module_tester.benchmark = pytestconfig.getoption("benchmark")
-
-    def test_module_static_cpu(self):
-        dynamic = False
-        device = "cpu"
-        self.module_tester.create_and_check_module(dynamic, device)
-
-    @pytest.mark.skipif(
-        check_device_drivers("gpu"), reason=device_driver_info("gpu")
-    )
-    def test_module_static_gpu(self):
-        dynamic = False
-        device = "gpu"
-        self.module_tester.create_and_check_module(dynamic, device)
-
-    @pytest.mark.skipif(
-        check_device_drivers("vulkan"), reason=device_driver_info("vulkan")
-    )
-    def test_module_static_vulkan(self):
-        dynamic = False
-        device = "vulkan"
-        self.module_tester.create_and_check_module(dynamic, device)
-
-
-if __name__ == "__main__":
-    unittest.main()