Mini LM Loader Example

-Add example to load miniLM from SharkHUB and benchmark.
-Modify TF benchmark to have growing GPU allocation.
-Add shark_load helper function

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

@@ -1,37 +0,0 @@
-# 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

@@ -11,12 +11,11 @@ on:
 jobs:
   build:
 
-    runs-on: a100
+    runs-on: ubuntu-latest
     strategy:
       fail-fast: false
       matrix:
         python-version: ["3.10"]
-        backend: [IREE, SHARK]
 
     steps:
     - uses: actions/checkout@v3
@@ -39,10 +38,6 @@ jobs:
         tag_name="${package_version}"
         echo "package_version=${package_version}" >> $GITHUB_ENV
         echo "tag_name=${tag_name}" >> $GITHUB_ENV    
-    - name: Set Environment Variables
-      run: |
-        echo "SHORT_SHA=`git rev-parse --short=4 HEAD`" >> $GITHUB_ENV
-        echo "DATE=$(date +'%Y-%m-%d')" >> $GITHUB_ENV
     - name: Create Release
       id: create_release
       uses: actions/create-release@v1
@@ -54,60 +49,34 @@ jobs:
         body: |
           Automatic snapshot release of nod.ai SHARK.
         draft: true
-        prerelease: false
+        prerelease: false        
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        python -m pip install flake8 pytest toml
-        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
+        python -m pip install flake8 pytest yapf 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
     - 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' }}
-      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/" tank/test_models.py |
-          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*
+        yapf -i --style .style.yapf shark/*.py
 
-    - name: Build and validate the SHARK Runtime package
-      if: ${{ matrix.backend == 'SHARK' }}
+    - name: Build and validate the package
       run: |
         cd $GITHUB_WORKSPACE
-        ./setup_venv.sh
+        IMPORTER=1 ./setup_venv.sh
         source shark.venv/bin/activate
         package_version="$(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://llvm.github.io/torch-mlir/package-index/ -f https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html
+        pip wheel -v -w wheelhouse . --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
         # Install the built wheel
         pip install ./wheelhouse/nodai*
         # Validate the Models
-        pytest --ci --ci_sha=${SHORT_SHA} tank/test_models.py |
-          tail -n 1 |
-          tee -a pytest_results.txt
+        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
-      if: ${{ matrix.backend == 'SHARK' }}
       id: upload-release-assets
       uses: dwenegar/upload-release-assets@v1
       env:
@@ -117,7 +86,6 @@ jobs:
         assets_path: ./wheelhouse/nodai_*.whl
 
     - name: Publish Release
-      if: ${{ matrix.backend == 'SHARK' }}
       id: publish_release
       uses: eregon/publish-release@v1
       env:

.github/workflows/test-models.yml

@@ -1,7 +1,7 @@
 # This workflow will install Python dependencies, run tests and lint with a variety of Python versions
 # For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
 
-name: Validate Models on Shark Runtime
+name: Validate torch-models on Shark Runtime
 
 on:
   push:
@@ -10,127 +10,93 @@ on:
     branches: [ main ]
   workflow_dispatch:
 
-# Ensure that only a single job or workflow using the same
-# concurrency group will run at a time. This would cancel
-# any in-progress jobs in the same github workflow and github
-# ref (e.g. refs/heads/main or refs/pull/<pr_number>/merge).
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
-  cancel-in-progress: true
-
 jobs:
-  build-validate:
-    strategy:
-      fail-fast: true
-      matrix:
-        os: [icelake, a100, MacStudio, ubuntu-latest]
-        suite: [cpu,cuda,vulkan]
-        python-version: ["3.10"]
-        include:
-          - os: ubuntu-latest
-            suite: lint
-        exclude:
-          - os: ubuntu-latest
-            suite: vulkan
-          - os: ubuntu-latest
-            suite: cuda
-          - os: ubuntu-latest
-            suite: cpu
-          - os: MacStudio
-            suite: vulkan
-          - os: MacStudio
-            suite: cuda
-          - os: MacStudio
-            suite: cpu
-          - os: icelake
-            suite: vulkan
-          - os: icelake
-            suite: cuda
-          - os: a100
-            suite: cpu
+  build-linux:
 
-    runs-on: ${{ matrix.os }}
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.10"]
 
     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' ||  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 ${{ matrix.python-version }}
-      if: matrix.os == 'a100' ||  matrix.os == 'ubuntu-latest' ||  matrix.os == 'icelake'
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v3
       with:
-        python-version: '${{ matrix.python-version }}'
-        #cache: 'pip'
-        #cache-dependency-path: |
-        #  **/requirements-importer.txt
-        #  **/requirements.txt
-          
+        python-version: ${{ matrix.python-version }}
+    
+    - name: Setup pip cache
+      uses: actions/cache@v3
+      with:
+        path: ~/.cache/pip
+        key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}
+        restore-keys: |
+          ${{ runner.os }}-pip-
+
     - name: Install dependencies
-      if: matrix.suite == 'lint'
       run: |
         python -m pip install --upgrade pip
-        python -m pip install flake8 pytest toml black
-        
+        python -m pip install flake8 pytest yapf toml
+
     - name: Lint with flake8
-      if: matrix.suite == 'lint'
       run: |
-        # black format check
-        black --version
-        black --line-length 79 --check .
         # stop the build if there are Python syntax errors or undefined names
         flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics --exclude 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 lit.cfg.py
+        yapf -i --style .style.yapf shark/*.py
 
-    - name: Validate Models on CPU
-      if: matrix.suite == 'cpu'
+    - name: Validate Models
       run: |
         cd $GITHUB_WORKSPACE
-        PYTHON=python${{ matrix.python-version }} BENCHMARK=1 IMPORTER=1 ./setup_venv.sh
+        IMPORTER=1 ./setup_venv.sh
         source shark.venv/bin/activate
-        pytest --benchmark --ci --ci_sha=${SHORT_SHA} -s --local_tank_cache="/data/anush/shark_cache" tank/test_models.py -k cpu --update_tank
-        gsutil cp ./bench_results.csv gs://shark-public/builder/bench_results/${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
+        pytest -k 'not benchmark' --ignore=tank/tf/ --ignore=shark/tests/test_shark_importer.py
+        
+  perf-macOS:
+    runs-on: MacStudio
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.10"]
 
-    - name: Validate Models on NVIDIA GPU
-      if: matrix.suite == 'cuda'
+    steps:
+    - uses: actions/checkout@v3   
+    - name: Validate Models dependencies
       run: |
         cd $GITHUB_WORKSPACE
-        PYTHON=python${{ matrix.python-version }} BENCHMARK=1 IMPORTER=1 ./setup_venv.sh
+        PYTHON=python3.10 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
+        pytest -k 'not benchmark' --ignore=benchmarks/tests/test_hf_benchmark.py --ignore=benchmarks/tests/test_benchmark.py --ignore=tank/tf/ --ignore=shark/tests/test_shark_importer.py 
+        
+  perf-linux:
+    runs-on: a100
+    timeout-minutes: 45
+    continue-on-error: true
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.10"]
 
-    - name: Validate Vulkan Models (MacOS)
-      if: matrix.suite == 'vulkan' && matrix.os == 'MacStudio'
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+    
+    - name: Setup pip cache
+      uses: actions/cache@v3
+      with:
+        path: ~/.cache/pip
+        key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}
+        restore-keys: |
+          ${{ runner.os }}-pip-
+
+    - name: Validate Models
       run: |
         cd $GITHUB_WORKSPACE
-        PYTHON=python${{ matrix.python-version }} IMPORTER=1 ./setup_venv.sh
+        IMPORTER=1 ./setup_venv.sh
         source shark.venv/bin/activate
-        echo "VULKAN SDK PATH wo setup: $VULKAN_SDK"
-        cd /Users/anush/VulkanSDK/1.3.224.1/
-        source setup-env.sh
-        cd $GITHUB_WORKSPACE
-        echo "VULKAN SDK PATH with setup: $VULKAN_SDK"
-        echo $PATH
-        pip list | grep -E "torch|iree"
-        pytest --ci --ci_sha=${SHORT_SHA} --local_tank_cache="/Volumes/builder/anush/shark_cache" tank/test_models.py -k vulkan --update_tank
-
-    - name: Validate Vulkan Models (a100)
-      if: matrix.suite == 'vulkan' && matrix.os != 'MacStudio'
-      run: |
-        cd $GITHUB_WORKSPACE
-        PYTHON=python${{ matrix.python-version }} BENCHMARK=1 IMPORTER=1 ./setup_venv.sh
-        source shark.venv/bin/activate
-        pytest --ci --ci_sha=${SHORT_SHA} -s --local_tank_cache="/data/anush/shark_cache" tank/test_models.py -k vulkan --update_tank
+        pytest --ignore=shark/tests/test_shark_importer.py --ignore=tank/tf/

.gitignore

@@ -162,12 +162,7 @@ cython_debug/
 
 # Shark related artefacts
 *venv/
-shark_tmp/
 
 # ORT related artefacts
 cache_models/
 onnx_models/
-
-#web logging
-web/logs/
-web/stored_results/stable_diffusion/

LICENSE

@@ -1,218 +0,0 @@
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-
-As an exception, if, as a result of your compiling your source code, portions
-of this Software are embedded into an Object form of such source code, you
-may redistribute such embedded portions in such Object form without complying
-with the conditions of Sections 4(a), 4(b) and 4(d) of the License.
-
-In addition, if you combine or link compiled forms of this Software with
-software that is licensed under the GPLv2 ("Combined Software") and if a
-court of competent jurisdiction determines that the patent provision (Section
-3), the indemnity provision (Section 9) or other Section of the License
-conflicts with the conditions of the GPLv2, you may retroactively and
-prospectively choose to deem waived or otherwise exclude such Section(s) of
-the License, but only in their entirety and only with respect to the Combined
-Software.

README.md

@@ -14,16 +14,16 @@ High Performance Machine Learning and Data Analytics for CPUs, GPUs, Accelerator
 ## Installation
 
 <details>
-  <summary>Installation (Linux, macOS and Windows)</summary>
-
+  <summary>Installation (Linux and macOS)</summary>
+  
 ### Setup a new pip Virtual Environment
 
 This step sets up a new VirtualEnv for Python
-
+  
 ```shell
-python --version #Check you have 3.10 on Linux, macOS or Windows Powershell
+python --version #Check you have 3.7->3.10 on Linux or 3.10 on macOS
 python -m venv shark_venv
-source shark_venv/bin/activate   # Use shark_venv/Scripts/activate on Windows
+source shark_venv/bin/activate
 
 # If you are using conda create and activate a new conda env
 
@@ -31,37 +31,32 @@ source shark_venv/bin/activate   # Use shark_venv/Scripts/activate on Windows
 python -m pip install --upgrade pip
 ```
 
-*macOS Metal* users please install https://sdk.lunarg.com/sdk/download/latest/mac/vulkan-sdk.dmg and enable "System wide install"
+*macOS Metal* users please install https://sdk.lunarg.com/sdk/download/latest/mac/vulkan-sdk.dmg
 
 ### Install SHARK
-
+  
 This step pip installs SHARK and related packages on Linux Python 3.7, 3.8, 3.9, 3.10 and macOS Python 3.10
 
 ```shell
-pip install nodai-shark -f https://nod-ai.github.io/SHARK/package-index/ -f https://llvm.github.io/torch-mlir/package-index/ -f  https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html --extra-index-url https://download.pytorch.org/whl/nightly/cpu
+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
 ```
-
-### 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.
+If you are on an Intel macOS machine you need this [workaround](https://github.com/nod-ai/SHARK/issues/102) for an upstream issue.
 
 ### 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 gsutil --extra-index-url https://download.pytorch.org/whl/nightly/cpu
-python ./resnet50_script.py --device="cpu"  #use cuda or vulkan or metal
+pip install --pre torch torchvision torchaudio tqdm pillow --extra-index-url https://download.pytorch.org/whl/nightly/cpu
+python ./resnet50_script.py --device="cpu"  #use cuda or vulkan or metal 
 ```
-
+        
 ### Download and run BERT (MiniLM) sample
 ```shell
 curl -O https://raw.githubusercontent.com/nod-ai/SHARK/main/shark/examples/shark_inference/minilm_jit.py
 #Install deps for test script
 pip install transformers torch --extra-index-url https://download.pytorch.org/whl/nightly/cpu
-python ./minilm_jit.py --device="cpu"  #use cuda or vulkan or metal
+python ./minilm_jit.py --device="cpu"  #use cuda or vulkan or metal 
 ```
 </details>
 
@@ -72,125 +67,55 @@ python ./minilm_jit.py --device="cpu"  #use cuda or vulkan or metal
 ## Check out the code
 
 ```shell
-git clone https://github.com/nod-ai/SHARK.git
+git clone https://github.com/nod-ai/SHARK.git 
 ```
 
 ## Setup your Python VirtualEnvironment and Dependencies
-
-### Windows Users
-
-```shell
-# Setup venv and install necessary packages (torch-mlir, nodLabs/Shark, ...). 
-# Requires Python 3.10 and Powershell
-./setup_venv.ps1
-shark.venv/Scripts/activate
-```
-
-### Linux / macOS Users
-
 ```shell
 # Setup venv and install necessary packages (torch-mlir, nodLabs/Shark, ...).
 ./setup_venv.sh
-source shark.venv/bin/activate
+# Please activate the venv after installation.
 ```
 
-
 ### Run a demo script
 ```shell
 python -m  shark.examples.shark_inference.resnet50_script --device="cpu" # Use gpu | vulkan
-# Or a pytest
-pytest tank/test_models.py -k "MiniLM"
 ```
 
-</details>
 
-<details>
-  <summary>Development, Testing and Benchmarks</summary>
-
-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 
-```
-
-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.
-
-### How to use your locally built Torch-MLIR with SHARK
+### Run all model tests on CPU/GPU/VULKAN/Metal
 ```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.
+pytest shark/tests/models
 
-
-## 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"
-    )
+# If on Linux for quicker results:
+pytest shark/tests/models -n auto
 ```
 
-Output will include:
-- 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.
-
+### Run all model benchmark tests on CPU/GPU/VULKAN/Metal
+```shell
+pytest shark/tests/benchmarks
+```
 </details>
 
+
 <details>
   <summary>API Reference</summary>
 
 ### Shark Inference API
 
 ```
+from shark_runner import SharkInference
 
-from shark.shark_importer import SharkImporter
-
-# SharkImporter imports mlir file from the torch, tensorflow or tf-lite module.
-
-mlir_importer = SharkImporter(
-    torch_module,
-    (input),
-    frontend="torch",  #tf, #tf-lite
-)
-torch_mlir, func_name = mlir_importer.import_mlir(tracing_required=True)
-
-# SharkInference accepts mlir in linalg, mhlo, and tosa dialect.
-
-from shark.shark_inference import SharkInference
-shark_module = SharkInference(torch_mlir, func_name, device="cpu", mlir_dialect="linalg")
+shark_module = SharkInference(
+        module = model class.
+        (input,)  = inputs to model (must be a torch-tensor)
+        dynamic (boolean) = Pass the input shapes as static or dynamic.
+        device = `cpu`, `gpu` or `vulkan` is supported.
+        tracing_required = (boolean) = Jit trace the module with the given input, useful in the case where jit.script doesn't work. )
+shark_module.set_frontend("pytorch") # Use tensorflow, mhlo, linalg, tosa
 shark_module.compile()
-result = shark_module.forward((input))
 
+result = shark_module.forward(inputs)
 ```
 
 
@@ -210,30 +135,104 @@ mhlo_ir = r"""builtin.module  {
 
 arg0 = np.ones((1, 4)).astype(np.float32)
 arg1 = np.ones((4, 1)).astype(np.float32)
-shark_module = SharkInference(mhlo_ir, func_name="forward", device="cpu", mlir_dialect="mhlo")
+
+shark_module = SharkInference(mhlo_ir, (arg0, arg1))
+shark_module.set_frontend("mhlo")
 shark_module.compile()
-result = shark_module.forward((arg0, arg1))
+print(shark_module.forward((arg0, arg1)))
 ```
 </details>
 
+
 ## Supported and Validated Models
 
-SHARK is maintained to support the latest innovations in ML Models: 
+<details>
+  <summary>PyTorch Models</summary>
 
-| 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:            |
+### Huggingface PyTorch Models
 
-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).
+| Hugging Face Models | Torch-MLIR lowerable | SHARK-CPU | SHARK-CUDA | SHARK-METAL |
+|---------------------|----------------------|----------|----------|-------------|
+| BERT                | :heavy_check_mark: (JIT)          | :heavy_check_mark:         |          |             |
+| Albert              | :heavy_check_mark: (JIT)            | :heavy_check_mark:         |          |             |
+| BigBird             | :heavy_check_mark: (AOT)            |          |          |             |
+| DistilBERT          | :heavy_check_mark: (JIT)            | :heavy_check_mark:         |          |             |
+| GPT2                | :x: (AOT)            |          |          |             |
+
+### Torchvision  Models
+  
+| TORCHVISION Models | Torch-MLIR lowerable | SHARK-CPU | SHARK-CUDA | SHARK-METAL |
+|--------------------|----------------------|----------|----------|-------------|
+| AlexNet            | :heavy_check_mark: (Script)         | :heavy_check_mark:         | :heavy_check_mark:         |             |
+| DenseNet121        | :heavy_check_mark: (Script)         |          |          |             |
+| MNasNet1_0         | :heavy_check_mark: (Script)         |          |          |             |
+| MobileNetV2        | :heavy_check_mark: (Script)         |          |          |             |
+| MobileNetV3        | :heavy_check_mark: (Script)         |          |          |             |
+| Unet               | :x: (Script)         |          |          |             |
+| Resnet18           | :heavy_check_mark: (Script)         | :heavy_check_mark:         |  :heavy_check_mark:        |             |
+| Resnet50           | :heavy_check_mark: (Script)         | :heavy_check_mark:         |   :heavy_check_mark:       |             |
+| Resnet101           | :heavy_check_mark: (Script)         | :heavy_check_mark:         |   :heavy_check_mark:       |             |
+| Resnext50_32x4d    | :heavy_check_mark: (Script)         |          |          |             |
+| ShuffleNet_v2      | :x: (Script)         |          |          |             |
+| SqueezeNet         | :heavy_check_mark: (Script)         | :heavy_check_mark:         |   :heavy_check_mark:       |             |
+| EfficientNet       | :heavy_check_mark: (Script)         |          |          |             |
+| Regnet             | :heavy_check_mark: (Script)         |          |          |             |
+| Resnest            | :x: (Script)         |          |          |             |
+| Vision Transformer | :heavy_check_mark: (Script)         |          |          |             |
+| VGG 16             | :heavy_check_mark: (Script)         | :heavy_check_mark:         |   :heavy_check_mark:       |             |
+| Wide Resnet        | :heavy_check_mark: (Script)         | :heavy_check_mark:         | :heavy_check_mark:         |             |
+| RAFT               | :x: (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                | :x:           | :x:         |          |             |
+| FullyConnected                | :heavy_check_mark:           | :heavy_check_mark:         |          |             |
+
+</details>
+  
+<details>
+  <summary>JAX Models</summary>
+
+
+### JAX  Models 
+
+| Models | JAX-MHLO lowerable | SHARK-CPU | SHARK-CUDA | SHARK-METAL |
+|---------------------|----------------------|----------|----------|-------------|
+| DALL-E                | :x:           | :x:         |          |             |
+| FullyConnected                | :heavy_check_mark:           | :heavy_check_mark:         |          |             |
+ 
+</details>
+  
+<details>
+  <summary>TFLite Models</summary>
+ 
+### TFLite Models 
+
+| Models | TOSA/LinAlg  | SHARK-CPU | SHARK-CUDA | SHARK-METAL |
+|---------------------|----------------------|----------|----------|-------------|
+| BERT                | :x:           | :x:         |          |             |
+| FullyConnected                | :heavy_check_mark:           | :heavy_check_mark:         |          |             |
+  
+</details>
+
+<details>
+  <summary>TF Models</summary>
+ 
+### Tensorflow Models 
+
+| Models | Torch-MLIR lowerable | SHARK-CPU | SHARK-CUDA | SHARK-METAL |
+|---------------------|----------------------|----------|----------|-------------|
+| BERT                | :x:           | :x:         |          |             |
+| FullyConnected                | :heavy_check_mark:           | :heavy_check_mark:         |          |             |
+  
+</details>
 
 ## Related Projects
-
+  
 <details>
   <summary>IREE Project Channels</summary>
 
@@ -244,7 +243,7 @@ For a complete list of the models supported in SHARK, please refer to [tank/READ
 *   [iree-discuss email list](https://groups.google.com/forum/#!forum/iree-discuss):
     Announcements, general and low-priority discussion
 </details>
-
+    
 <details>
   <summary>MLIR and Torch-MLIR Project Channels</summary>
 

benchmarks/hf_model_benchmark.py

@@ -6,16 +6,16 @@ parser.add_argument(
     "--model_name",
     type=str,
     required=True,
-    help='Specifies name of HF model to benchmark. (For exmaple "microsoft/MiniLM-L12-H384-uncased"',
+    help=
+    "Specifies name of HF model to benchmark. (For exmaple \"microsoft/MiniLM-L12-H384-uncased\""
 )
 load_args, unknown = parser.parse_known_args()
 
 if __name__ == "__main__":
     model_name = load_args.model_name
     test_input = torch.randint(2, (1, 128))
-    shark_module = SharkHFBenchmarkRunner(
-        model_name, (test_input,), jit_trace=True
-    )
+    shark_module = SharkHFBenchmarkRunner(model_name, (test_input,),
+                                          jit_trace=True)
     shark_module.benchmark_c()
     shark_module.benchmark_python((test_input,))
     shark_module.benchmark_torch(test_input)

benchmarks/hf_transformer.py

@@ -1,12 +1,8 @@
 import torch
-from shark.shark_benchmark_runner import SharkBenchmarkRunner
+from shark.shark_runner import SharkBenchmarkRunner
 from shark.parser import shark_args
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
-from onnxruntime.transformers.benchmark import (
-    run_pytorch,
-    run_tensorflow,
-    run_onnxruntime,
-)
+from onnxruntime.transformers.benchmark import run_pytorch, run_tensorflow, run_onnxruntime
 from onnxruntime.transformers.huggingface_models import MODELS
 from onnxruntime.transformers.benchmark_helper import ConfigModifier, Precision
 import os
@@ -14,6 +10,7 @@ import psutil
 
 
 class OnnxFusionOptions(object):
+
     def __init__(self):
         self.disable_gelu = False
         self.disable_layer_norm = False
@@ -28,13 +25,17 @@ class OnnxFusionOptions(object):
 
 
 class HuggingFaceLanguage(torch.nn.Module):
+
     def __init__(self, hf_model_name):
         super().__init__()
         self.model = AutoModelForSequenceClassification.from_pretrained(
             hf_model_name,  # The pretrained model.
-            num_labels=2,  # The number of output labels--2 for binary classification.
-            output_attentions=False,  # Whether the model returns attentions weights.
-            output_hidden_states=False,  # Whether the model returns all hidden-states.
+            num_labels=
+            2,  # The number of output labels--2 for binary classification.
+            output_attentions=
+            False,  # Whether the model returns attentions weights.
+            output_hidden_states=
+            False,  # Whether the model returns all hidden-states.
             torchscript=True,
         )
 
@@ -61,16 +62,8 @@ class SharkHFBenchmarkRunner(SharkBenchmarkRunner):
             )
         self.model_name = model_name
         model = HuggingFaceLanguage(model_name)
-        SharkBenchmarkRunner.__init__(
-            self,
-            model,
-            input,
-            dynamic,
-            self.device,
-            jit_trace,
-            from_aot,
-            frontend,
-        )
+        SharkBenchmarkRunner.__init__(self, model, input, dynamic, self.device,
+                                      jit_trace, from_aot, frontend)
 
     def benchmark_torch(self, inputs):
         use_gpu = self.device == "gpu"
@@ -81,20 +74,10 @@ class SharkHFBenchmarkRunner(SharkBenchmarkRunner):
         sequence_lengths = [inputs.shape[-1]]
         cache_dir = os.path.join(".", "cache_models")
         verbose = False
-        result = run_pytorch(
-            use_gpu,
-            [self.model_name],
-            None,
-            config_modifier,
-            Precision.FLOAT32,
-            num_threads,
-            batch_sizes,
-            sequence_lengths,
-            shark_args.num_iterations,
-            False,
-            cache_dir,
-            verbose,
-        )
+        result = run_pytorch(use_gpu, [self.model_name], None, config_modifier,
+                             Precision.FLOAT32, num_threads, batch_sizes,
+                             sequence_lengths, shark_args.num_iterations, False,
+                             cache_dir, verbose)
         print(
             f"ONNX Pytorch-benchmark:{result[0]['QPS']} iter/second, Total Iterations:{shark_args.num_iterations}"
         )
@@ -109,19 +92,10 @@ class SharkHFBenchmarkRunner(SharkBenchmarkRunner):
         sequence_lengths = [inputs.shape[-1]]
         cache_dir = os.path.join(".", "cache_models")
         verbose = False
-        result = run_tensorflow(
-            use_gpu,
-            [self.model_name],
-            None,
-            config_modifier,
-            Precision.FLOAT32,
-            num_threads,
-            batch_sizes,
-            sequence_lengths,
-            shark_args.num_iterations,
-            cache_dir,
-            verbose,
-        )
+        result = run_tensorflow(use_gpu, [self.model_name], None,
+                                config_modifier, Precision.FLOAT32, num_threads,
+                                batch_sizes, sequence_lengths,
+                                shark_args.num_iterations, cache_dir, verbose)
         print(
             f"ONNX TF-benchmark:{result[0]['QPS']} iter/second, Total Iterations:{shark_args.num_iterations}"
         )
@@ -131,8 +105,7 @@ class SharkHFBenchmarkRunner(SharkBenchmarkRunner):
             print(
                 f"{self.model_name} 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."
-            )
+for currently supported models. Exiting benchmark ONNX.")
             return
         use_gpu = self.device == "gpu"
         num_threads = psutil.cpu_count(logical=False)
@@ -148,34 +121,17 @@ for currently supported models. Exiting benchmark ONNX."
         use_raw_attention_mask = True
         model_fusion_statistics = {}
         overwrite = False
-        model_source = "pt"  # Either "pt" or "tf"
+        model_source = "pt"  #Either "pt" or "tf"
         provider = None
         config_modifier = ConfigModifier(None)
         onnx_args = OnnxFusionOptions()
         result = run_onnxruntime(
-            use_gpu,
-            provider,
-            [self.model_name],
-            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,
-        )
+            use_gpu, provider, [self.model_name], 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}"
         )

benchmarks/tests/test_benchmark.py

@@ -1,23 +1,19 @@
 from shark.shark_inference import SharkInference
-from shark.iree_utils._common import check_device_drivers
+from shark.iree_utils import check_device_drivers
 
 import torch
 import tensorflow as tf
 import numpy as np
 import torchvision.models as models
-from transformers import (
-    AutoModelForSequenceClassification,
-    BertTokenizer,
-    TFBertModel,
-)
+from transformers import AutoModelForSequenceClassification, BertTokenizer, TFBertModel
 import importlib
 import pytest
 import unittest
 
 torch.manual_seed(0)
-gpus = tf.config.experimental.list_physical_devices("GPU")
+gpus = tf.config.experimental.list_physical_devices('GPU')
 for gpu in gpus:
-    tf.config.experimental.set_memory_growth(gpu, True)
+  tf.config.experimental.set_memory_growth(gpu, True)
 
 ##################### Tensorflow Hugging Face LM Models ###################################
 MAX_SEQUENCE_LENGTH = 512
@@ -27,11 +23,12 @@ BATCH_SIZE = 1
 tf_bert_input = [
     tf.TensorSpec(shape=[BATCH_SIZE, MAX_SEQUENCE_LENGTH], dtype=tf.int32),
     tf.TensorSpec(shape=[BATCH_SIZE, MAX_SEQUENCE_LENGTH], dtype=tf.int32),
-    tf.TensorSpec(shape=[BATCH_SIZE, MAX_SEQUENCE_LENGTH], dtype=tf.int32),
+    tf.TensorSpec(shape=[BATCH_SIZE, MAX_SEQUENCE_LENGTH], dtype=tf.int32)
 ]
 
 
 class TFHuggingFaceLanguage(tf.Module):
+
     def __init__(self, hf_model_name):
         super(TFHuggingFaceLanguage, self).__init__()
         # Create a BERT trainer with the created network.
@@ -39,8 +36,7 @@ class TFHuggingFaceLanguage(tf.Module):
 
         # Invoke the trainer model on the inputs. This causes the layer to be built.
         self.m.predict = lambda x, y, z: self.m.call(
-            input_ids=x, attention_mask=y, token_type_ids=z, training=False
-        )
+            input_ids=x, attention_mask=y, token_type_ids=z, training=False)
 
     @tf.function(input_signature=tf_bert_input)
     def forward(self, input_ids, attention_mask, token_type_ids):
@@ -51,21 +47,15 @@ def get_TFhf_model(name):
     model = TFHuggingFaceLanguage(name)
     tokenizer = BertTokenizer.from_pretrained(name)
     text = "Replace me by any text you'd like."
-    encoded_input = tokenizer(
-        text,
-        padding="max_length",
-        truncation=True,
-        max_length=MAX_SEQUENCE_LENGTH,
-    )
+    encoded_input = tokenizer(text,
+                              padding='max_length',
+                              truncation=True,
+                              max_length=MAX_SEQUENCE_LENGTH)
     for key in encoded_input:
         encoded_input[key] = tf.expand_dims(
-            tf.convert_to_tensor(encoded_input[key]), 0
-        )
-    test_input = (
-        encoded_input["input_ids"],
-        encoded_input["attention_mask"],
-        encoded_input["token_type_ids"],
-    )
+            tf.convert_to_tensor(encoded_input[key]), 0)
+    test_input = (encoded_input["input_ids"], encoded_input["attention_mask"],
+                  encoded_input["token_type_ids"])
     actual_out = model.forward(*test_input)
     return model, test_input, actual_out
 
@@ -74,13 +64,17 @@ def get_TFhf_model(name):
 
 
 class HuggingFaceLanguage(torch.nn.Module):
+
     def __init__(self, hf_model_name):
         super().__init__()
         self.model = AutoModelForSequenceClassification.from_pretrained(
             hf_model_name,  # The pretrained model.
-            num_labels=2,  # The number of output labels--2 for binary classification.
-            output_attentions=False,  # Whether the model returns attentions weights.
-            output_hidden_states=False,  # Whether the model returns all hidden-states.
+            num_labels=
+            2,  # The number of output labels--2 for binary classification.
+            output_attentions=
+            False,  # Whether the model returns attentions weights.
+            output_hidden_states=
+            False,  # Whether the model returns all hidden-states.
             torchscript=True,
         )
 
@@ -102,6 +96,7 @@ def get_hf_model(name):
 
 
 class VisionModule(torch.nn.Module):
+
     def __init__(self, model):
         super().__init__()
         self.model = model
@@ -122,56 +117,46 @@ def get_vision_model(torch_model):
 #############################   Benchmark Tests ####################################
 
 pytest_benchmark_param = pytest.mark.parametrize(
-    ("dynamic", "device"),
+    ('dynamic', 'device'),
     [
-        pytest.param(False, "cpu"),
+        pytest.param(False, 'cpu'),
         # TODO: Language models are failing for dynamic case..
-        pytest.param(True, "cpu", marks=pytest.mark.skip),
+        pytest.param(True, 'cpu', marks=pytest.mark.skip),
+        pytest.param(False,
+                     'gpu',
+                     marks=pytest.mark.skipif(check_device_drivers("gpu"),
+                                              reason="nvidia-smi not found")),
+        pytest.param(True,
+                     'gpu',
+                     marks=pytest.mark.skip),
         pytest.param(
             False,
-            "gpu",
-            marks=pytest.mark.skipif(
-                check_device_drivers("gpu"), reason="nvidia-smi not found"
-            ),
-        ),
-        pytest.param(True, "gpu", marks=pytest.mark.skip),
-        pytest.param(
-            False,
-            "vulkan",
+            'vulkan',
             marks=pytest.mark.skipif(
                 check_device_drivers("vulkan"),
-                reason="vulkaninfo not found, install from https://github.com/KhronosGroup/MoltenVK/releases",
-            ),
-        ),
+                reason="vulkaninfo not found, install from https://github.com/KhronosGroup/MoltenVK/releases"
+            )),
         pytest.param(
             True,
-            "vulkan",
+            'vulkan',
             marks=pytest.mark.skipif(
                 check_device_drivers("vulkan"),
-                reason="vulkaninfo not found, install from https://github.com/KhronosGroup/MoltenVK/releases",
-            ),
-        ),
-    ],
-)
+                reason="vulkaninfo not found, install from https://github.com/KhronosGroup/MoltenVK/releases"
+            )),
+    ])
 
 
-@pytest.mark.skipif(
-    importlib.util.find_spec("iree.tools") is None,
-    reason="Cannot find tools to import TF",
-)
+@pytest.mark.skipif(importlib.util.find_spec("iree.tools") is None,
+                    reason="Cannot find tools to import TF")
 @pytest_benchmark_param
 def test_bench_minilm_torch(dynamic, device):
     model, test_input, act_out = get_hf_model(
-        "microsoft/MiniLM-L12-H384-uncased"
-    )
-    shark_module = SharkInference(
-        model,
-        (test_input,),
-        device=device,
-        dynamic=dynamic,
-        jit_trace=True,
-        benchmark_mode=True,
-    )
+        "microsoft/MiniLM-L12-H384-uncased")
+    shark_module = SharkInference(model, (test_input,),
+                                  device=device,
+                                  dynamic=dynamic,
+                                  jit_trace=True,
+                                  benchmark_mode=True)
     try:
         # If becnhmarking succesful, assert success/True.
         shark_module.compile()
@@ -182,21 +167,17 @@ def test_bench_minilm_torch(dynamic, device):
         assert False
 
 
-@pytest.mark.skipif(
-    importlib.util.find_spec("iree.tools") is None,
-    reason="Cannot find tools to import TF",
-)
+@pytest.mark.skipif(importlib.util.find_spec("iree.tools") is None,
+                    reason="Cannot find tools to import TF")
 @pytest_benchmark_param
 def test_bench_distilbert(dynamic, device):
     model, test_input, act_out = get_TFhf_model("distilbert-base-uncased")
-    shark_module = SharkInference(
-        model,
-        test_input,
-        device=device,
-        dynamic=dynamic,
-        jit_trace=True,
-        benchmark_mode=True,
-    )
+    shark_module = SharkInference(model,
+                                  test_input,
+                                  device=device,
+                                  dynamic=dynamic,
+                                  jit_trace=True,
+                                  benchmark_mode=True)
     try:
         # If becnhmarking succesful, assert success/True.
         shark_module.set_frontend("tensorflow")
@@ -212,14 +193,12 @@ def test_bench_distilbert(dynamic, device):
 @pytest_benchmark_param
 def test_bench_xlm_roberta(dynamic, device):
     model, test_input, act_out = get_TFhf_model("xlm-roberta-base")
-    shark_module = SharkInference(
-        model,
-        test_input,
-        device=device,
-        dynamic=dynamic,
-        jit_trace=True,
-        benchmark_mode=True,
-    )
+    shark_module = SharkInference(model,
+                                  test_input,
+                                  device=device,
+                                  dynamic=dynamic,
+                                  jit_trace=True,
+                                  benchmark_mode=True)
     try:
         # If becnhmarking succesful, assert success/True.
         shark_module.set_frontend("tensorflow")

benchmarks/tests/test_hf_benchmark.py

@@ -9,31 +9,25 @@ torch.manual_seed(0)
 
 # Test running benchmark module without failing.
 pytest_benchmark_param = pytest.mark.parametrize(
-    ("dynamic", "device"),
+    ('dynamic', 'device'),
     [
-        pytest.param(False, "cpu"),
+        pytest.param(False, 'cpu'),
         # TODO: Language models are failing for dynamic case..
-        pytest.param(True, "cpu", marks=pytest.mark.skip),
-    ],
-)
+        pytest.param(True, 'cpu', marks=pytest.mark.skip),
+    ])
 
 
-@pytest.mark.skipif(
-    importlib.util.find_spec("onnxruntime") is None,
-    reason="Cannot find ONNXRUNTIME.",
-)
+@pytest.mark.skipif(importlib.util.find_spec("onnxruntime") is None,
+                    reason="Cannot find ONNXRUNTIME.")
 @pytest_benchmark_param
 def test_HFbench_minilm_torch(dynamic, device):
     model_name = "bert-base-uncased"
     test_input = torch.randint(2, (1, 128))
     try:
-        shark_module = SharkHFBenchmarkRunner(
-            model_name,
-            (test_input,),
-            jit_trace=True,
-            dynamic=dynamic,
-            device=device,
-        )
+        shark_module = SharkHFBenchmarkRunner(model_name, (test_input,),
+                                              jit_trace=True,
+                                              dynamic=dynamic,
+                                              device=device)
         shark_module.benchmark_c()
         shark_module.benchmark_python((test_input,))
         shark_module.benchmark_torch(test_input)

build_tools/populate_sharktank_ci.sh

@@ -1,5 +0,0 @@
-#!/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

@@ -1,37 +0,0 @@
-"""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

@@ -1,62 +0,0 @@
-def pytest_addoption(parser):
-    # Attaches SHARK command-line arguments to the pytest machinery.
-    parser.addoption(
-        "--benchmark",
-        action="store_true",
-        default="False",
-        help="Pass option to benchmark and write results.csv",
-    )
-    parser.addoption(
-        "--onnx_bench",
-        action="store_true",
-        default="False",
-        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

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

cpp/CMakeLists.txt

@@ -1,52 +0,0 @@
-# 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

@@ -1,82 +0,0 @@
-# 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

@@ -1,19 +0,0 @@
-import numpy as np
-import tensorflow as tf
-from shark.shark_inference import SharkInference
-from shark.shark_downloader import download_tf_model
-
-
-def load_and_preprocess_image(fname: str):
-    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

@@ -1,84 +0,0 @@
-# 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

@@ -1,8 +0,0 @@
-# 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

@@ -1,224 +0,0 @@
-// 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

@@ -1,77 +0,0 @@
-// 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

@@ -1,121 +0,0 @@
-// 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

@@ -1,116 +0,0 @@
-# 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

@@ -1,4 +0,0 @@
-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

@@ -1,957 +0,0 @@
-// 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

@@ -1,251 +0,0 @@
-# Lint as: python3
-"""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:
-#   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:
-    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
-
-
-def create_hash(file_name):
-    with open(file_name, "rb") as f:
-        file_hash = hashlib.blake2b()
-        while chunk := f.read(2**20):
-            file_hash.update(chunk)
-
-    return file_hash.hexdigest()
-
-
-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=",")
-        fields = next(torch_reader)
-        for row in torch_reader:
-            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
-            if model_type == "vision":
-                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(
-                WORKDIR, str(torch_model_name) + "_torch"
-            )
-            os.makedirs(torch_model_dir, exist_ok=True)
-
-            mlir_importer = SharkImporter(
-                model,
-                (input,),
-                frontend="torch",
-            )
-            mlir_importer.import_debug(
-                is_dynamic=False,
-                tracing_required=tracing_required,
-                dir=torch_model_dir,
-                model_name=torch_model_name,
-            )
-            mlir_hash = create_hash(
-                os.path.join(
-                    torch_model_dir, torch_model_name + "_torch" + ".mlir"
-                )
-            )
-            np.save(os.path.join(torch_model_dir, "hash"), np.array(mlir_hash))
-            # Generate torch dynamic models.
-            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_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=",")
-        fields = next(tf_reader)
-        for row in tf_reader:
-            tf_model_name = row[0]
-            model_type = row[1]
-
-            model = None
-            input = None
-            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")
-            os.makedirs(tf_model_dir, exist_ok=True)
-
-            mlir_importer = SharkImporter(
-                model,
-                input,
-                frontend="tf",
-            )
-            mlir_importer.import_debug(
-                dir=tf_model_dir,
-                model_name=tf_model_name,
-            )
-            mlir_hash = create_hash(
-                os.path.join(tf_model_dir, tf_model_name + "_tf" + ".mlir")
-            )
-            np.save(os.path.join(tf_model_dir, "hash"), np.array(mlir_hash))
-
-
-def save_tflite_model(tflite_model_list):
-    from shark.tflite_utils import TFLitePreprocessor
-
-    with open(tflite_model_list) as csvfile:
-        tflite_reader = csv.reader(csvfile, delimiter=",")
-        for row in tflite_reader:
-            print("\n")
-            tflite_model_name = row[0]
-            tflite_model_link = row[1]
-            print("tflite_model_name", tflite_model_name)
-            print("tflite_model_link", tflite_model_link)
-            tflite_model_name_dir = os.path.join(
-                WORKDIR, str(tflite_model_name) + "_tflite"
-            )
-            os.makedirs(tflite_model_name_dir, exist_ok=True)
-            print(f"TMP_TFLITE_MODELNAME_DIR = {tflite_model_name_dir}")
-
-            # Preprocess to get SharkImporter input args
-            tflite_preprocessor = TFLitePreprocessor(str(tflite_model_name))
-            raw_model_file_path = tflite_preprocessor.get_raw_model_file()
-            inputs = tflite_preprocessor.get_inputs()
-            tflite_interpreter = tflite_preprocessor.get_interpreter()
-
-            # Use SharkImporter to get SharkInference input args
-            my_shark_importer = SharkImporter(
-                module=tflite_interpreter,
-                inputs=inputs,
-                frontend="tflite",
-                raw_model_file=raw_model_file_path,
-            )
-            my_shark_importer.import_debug(
-                dir=tflite_model_name_dir,
-                model_name=tflite_model_name,
-                func_name="main",
-            )
-            mlir_hash = create_hash(
-                os.path.join(
-                    tflite_model_name_dir,
-                    tflite_model_name + "_tflite" + ".mlir",
-                )
-            )
-            np.save(
-                os.path.join(tflite_model_name_dir, "hash"),
-                np.array(mlir_hash),
-            )
-
-
-# Validates whether the file is present or not.
-def is_valid_file(arg):
-    if not os.path.exists(arg):
-        return None
-    else:
-        return arg
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--torch_model_csv",
-        type=lambda x: is_valid_file(x),
-        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/torch_model_list.csv""",
-    )
-    parser.add_argument(
-        "--tf_model_csv",
-        type=lambda x: is_valid_file(x),
-        default="./tank/tf_model_list.csv",
-        help="Contains the file with tf model name and args.",
-    )
-    parser.add_argument(
-        "--tflite_model_csv",
-        type=lambda x: is_valid_file(x),
-        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)
-
-    if args.tf_model_csv:
-        save_tf_model(args.tf_model_csv)
-
-    if args.tflite_model_csv:
-        save_tflite_model(args.tflite_model_csv)
-
-    if args.upload:
-        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>=20221021.633",
-    "iree-compiler>=20221022.190",
-    "iree-runtime>=20221022.190",
+    "numpy==1.22.4",
+    "torch-mlir>=20220428.420",
+    "iree-compiler>=20220427.13",
+    "iree-runtime>=20220427.13",
 ]
 build-backend = "setuptools.build_meta"

requirements-importer-macos.txt

@@ -1,8 +1,8 @@
--f https://download.pytorch.org/whl/nightly/cpu/
+-f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
 --pre
 
 numpy
-torch==1.14.0.dev20221021
+torch
 torchvision
 
 tqdm
@@ -19,16 +19,12 @@ tensorflow-macos
 tensorflow-metal
 #tf-models-nightly
 #tensorflow-text-nightly
-transformers
-tensorflow-probability
+transformers==4.18.0
 #jax[cpu]
 
 # tflitehub dependencies.
 Pillow
 
-# web dependecies.
-gradio
-
 # Testing and support.
 #lit
 #pyyaml

requirements-importer.txt

@@ -14,13 +14,10 @@ iree-tools-tf
 
 # TensorFlow and JAX.
 gin-config
-tensorflow==2.10
-keras==2.10
-#tf-models-nightly
-#tensorflow-text-nightly
-transformers
-diffusers
-#tensorflow-probability
+tensorflow
+tf-models-nightly
+tensorflow-text-nightly
+transformers==4.18.0
 #jax[cpu]
 
 
@@ -30,19 +27,13 @@ Pillow
 # Testing and support.
 lit
 pyyaml
-python-dateutil
-sacremoses
-
-# web dependecies.
-gradio
-scipy
 
 #ONNX and ORT for benchmarking
-#--extra-index-url https://test.pypi.org/simple/
-#protobuf
-#coloredlogs
-#flatbuffers
-#sympy
-#psutil
-#onnx-weekly
-#ort-nightly
+--extra-index-url https://test.pypi.org/simple/
+protobuf
+coloredlogs
+flatbuffers
+sympy
+psutil
+onnx-weekly
+ort-nightly

requirements.txt

@@ -1,14 +1,9 @@
 setuptools
 wheel
 
-# SHARK Runner
+#SHARK Runner
 tqdm
 
-# SHARK Downloader
-gsutil
-
-# Testing
+#Testing
 pytest
 pytest-xdist
-Pillow
-parameterized

setup.py

@@ -7,12 +7,6 @@ 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",
@@ -32,12 +26,13 @@ setup(
         "License :: OSI Approved :: MIT License",
         "Operating System :: OS Independent",
     ],
-    packages=find_packages(exclude=("examples")),
-    python_requires=">=3.9",
+    packages=find_packages(exclude=('examples')),
+    python_requires=">=3.7",
     install_requires=[
         "numpy",
         "PyYAML",
-        "torch-mlir>=20221021.633",
-    ]
-    + backend_deps,
+        "torch-mlir>=20220428.420",
+        "iree-compiler>=20220427.13",
+        "iree-runtime>=20220427.13",
+    ],
 )

setup_venv.ps1

@@ -1,40 +0,0 @@
-#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/cu116 -f https://llvm.github.io/torch-mlir/package-index/
-pip install --upgrade -f https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html iree-compiler iree-runtime
-Write-Host "Building SHARK..."
-pip install -e . -f https://llvm.github.io/torch-mlir/package-index/ -f https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html
-pip install diffusers transformers scipy pillow gradio
-Write-Host "Build and installation completed successfully"
-Write-Host "Source your venv with ./shark.venv/Scripts/activate"

setup_venv.sh

@@ -7,8 +7,6 @@
 # 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)"
@@ -76,15 +74,11 @@ 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
-  if [[ $(uname -s) = 'Darwin' ]]; then
-    echo "MacOS detected. Please install torch-mlir from source or .whl, as dependency problems may occur otherwise."
+  $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"
   else
-    $PYTHON -m pip install --pre torch-mlir -f https://llvm.github.io/torch-mlir/package-index/
-    if [ $? -eq 0 ];then
-      echo "Successfully Installed torch-mlir"
-    else
-      echo "Could not install torch-mlir" >&2
-    fi
+    echo "Could not install torch-mlir" >&2
   fi
 else
   echo "${Red}No binaries found for Python $PYTHON_VERSION_X_Y on $(uname -s)"
@@ -93,51 +87,26 @@ else
   exit 1
 fi
 if [[ -z "${USE_IREE}" ]]; then
-  RUNTIME="https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html"
+  RUNTIME="nod-ai/SHARK-Runtime"
 else
-  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"
+  RUNTIME="google/iree"
 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"
-    #Always get the importer tools from upstream IREE
-    $PYTHON -m pip install --upgrade -r "$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
+    $PYTHON -m pip install --upgrade -r "$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/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 ${RUNTIME} --extra-index-url https://download.pytorch.org/whl/nightly/cpu
-    $PYTHON -m pip install https://github.com/llvm/torch-mlir/releases/download/snapshot-20221024.636/torch_mlir-20221024.636-cp310-cp310-macosx_11_0_universal2.whl
+    $PYTHON -m pip install --upgrade -r "$TD/requirements-importer-macos.txt" -f https://github.com/${RUNTIME}/releases --extra-index-url https://download.pytorch.org/whl/nightly/cpu
   fi
 fi
 
-$PYTHON -m pip install -e . -f https://llvm.github.io/torch-mlir/package-index/ -f ${RUNTIME}
-
-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
-  if [ $? -eq 0 ];then
-    echo "Successfully Installed torch + cu116."
-  else
-    echo "Could not install torch + cu116." >&2
-  fi
-fi
-
-if [[ ! -z "${ONNX}" ]]; then
-  echo "${Yellow}Installing ONNX and onnxruntime for benchmarks..."
-  $PYTHON -m pip install onnx onnxruntime psutil
-  if [ $? -eq 0 ];then
-    echo "Successfully installed ONNX and ONNX runtime."
-  else
-    echo "Could not install ONNX." >&2
-  fi
-fi
+$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
 
 if [[ -z "${CONDA_PREFIX}" ]]; then
   echo "${Green}Before running examples activate venv with:"

shark/backward_makefx.py

@@ -18,10 +18,12 @@ from torch.fx.experimental.proxy_tensor import make_fx
 from torch.nn.utils import _stateless
 
 from torch import fx
+import copy
 import tempfile
 
 
 class MakeFxModule:
+
     def __init__(self, model, inputs, labels=None, custom_inference_fn=None):
         self.model = model
         self.inputs = inputs
@@ -51,28 +53,20 @@ class MakeFxModule:
         return fx_g
 
     def generate_graph(self):
-        fx_g = make_fx(
-            self.custom_inference_fn,
-            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,
-                ]
-            ),
-        )(
-            dict(self.model.named_parameters()),
-            dict(self.model.named_buffers()),
-            self.inputs,
-        )
+        fx_g = make_fx(self.custom_inference_fn,
+                       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
+                       ]))(dict(self.model.named_parameters()),
+                           dict(self.model.named_buffers()), self.inputs)
         fx_g.graph.set_codegen(torch.fx.graph.CodeGen())
         fx_g.recompile()
         fx_g = self.change_fx_graph_return_to_tuple(fx_g)
         ts_g = torch.jit.script(fx_g)
-        temp = tempfile.NamedTemporaryFile(
-            suffix="_shark_ts", prefix="temp_ts_"
-        )
+        temp = tempfile.NamedTemporaryFile(suffix='_shark_ts',
+                                           prefix='temp_ts_')
         ts_g.save(temp.name)
         new_ts = torch.jit.load(temp.name)
         self.training_graph = new_ts

shark/cuda_utils.py

@@ -0,0 +1,78 @@
+# Copyright 2020 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 sys
+import ctypes
+
+#Some constants taken from cuda.h
+CUDA_SUCCESS = 0
+CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT = 16
+CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR = 39
+CU_DEVICE_ATTRIBUTE_CLOCK_RATE = 13
+CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE = 36
+
+
+def get_cuda_sm_cc():
+    libnames = ('libcuda.so', 'libcuda.dylib', 'cuda.dll')
+    for libname in libnames:
+        try:
+            cuda = ctypes.CDLL(libname)
+        except OSError:
+            continue
+        else:
+            break
+    else:
+        raise OSError("could not load any of: " + ' '.join(libnames))
+
+    nGpus = ctypes.c_int()
+    name = b' ' * 100
+    cc_major = ctypes.c_int()
+    cc_minor = ctypes.c_int()
+
+    result = ctypes.c_int()
+    device = ctypes.c_int()
+    context = ctypes.c_void_p()
+    error_str = ctypes.c_char_p()
+
+    result = cuda.cuInit(0)
+    if result != CUDA_SUCCESS:
+        cuda.cuGetErrorString(result, ctypes.byref(error_str))
+        print("cuInit failed with error code %d: %s" %
+              (result, error_str.value.decode()))
+        return 1
+    result = cuda.cuDeviceGetCount(ctypes.byref(nGpus))
+    if result != CUDA_SUCCESS:
+        cuda.cuGetErrorString(result, ctypes.byref(error_str))
+        print("cuDeviceGetCount failed with error code %d: %s" %
+              (result, error_str.value.decode()))
+        return 1
+    print("Found %d device(s)." % nGpus.value)
+    for i in range(nGpus.value):
+        result = cuda.cuDeviceGet(ctypes.byref(device), i)
+        if result != CUDA_SUCCESS:
+            cuda.cuGetErrorString(result, ctypes.byref(error_str))
+            print("cuDeviceGet failed with error code %d: %s" %
+                  (result, error_str.value.decode()))
+            return 1
+        print("Device: %d" % i)
+        if cuda.cuDeviceGetName(ctypes.c_char_p(name), len(name),
+                                device) == CUDA_SUCCESS:
+            print("  Name: %s" % (name.split(b'\0', 1)[0].decode()))
+        if cuda.cuDeviceComputeCapability(ctypes.byref(cc_major),
+                                          ctypes.byref(cc_minor),
+                                          device) == CUDA_SUCCESS:
+            print("  Compute Capability: %d.%d" %
+                  (cc_major.value, cc_minor.value))
+    sm = f"sm_{cc_major.value}{cc_minor.value}"
+    return sm

shark/examples/shark_dynamo/basic_examples.py

@@ -1,70 +0,0 @@
-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/dynamo_demo.py

@@ -8,9 +8,7 @@ try:
     from torchdynamo.optimizations.backends import create_backend
     from torchdynamo.optimizations.subgraph import SubGraph
 except ModuleNotFoundError:
-    print(
-        "Please install TorchDynamo using pip install git+https://github.com/pytorch/torchdynamo"
-    )
+    print("Please install TorchDynamo using pip install git+https://github.com/pytorch/torchdynamo")
     exit()
 
 NUM_ITERS = 10
@@ -26,9 +24,7 @@ def __torch_mlir(fx_graph, *args, **kwargs):
 
         for node in fx_g.graph.nodes:
             if node.op == "output":
-                assert (
-                    len(node.args) == 1
-                ), "Output node must have a single argument"
+                assert len(node.args) == 1, "Output node must have a single argument"
                 node_arg = node.args[0]
                 if isinstance(node_arg, tuple) and len(node_arg) == 1:
                     node.args = (node_arg[0],)
@@ -45,12 +41,8 @@ def __torch_mlir(fx_graph, *args, **kwargs):
     if len(args) == 1 and isinstance(args[0], list):
         args = args[0]
 
-    linalg_module = compile(
-        ts_graph, args, output_type=OutputType.LINALG_ON_TENSORS
-    )
-    callable, _ = get_iree_compiled_module(
-        linalg_module, "cuda", func_name="forward"
-    )
+    linalg_module = compile(ts_graph, args, output_type=OutputType.LINALG_ON_TENSORS)
+    callable, _ = get_iree_compiled_module(linalg_module, "cuda", func_name="forward")
 
     def forward(*inputs):
         return callable(*inputs)

shark/examples/shark_eager/squeezenet_lockstep.py

@@ -1,73 +0,0 @@
-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

@@ -9,24 +9,23 @@ from shark.shark_inference import SharkInference
 clip_vit_inputs = [
     tf.TensorSpec(shape=[2, 7], dtype=tf.int32),
     tf.TensorSpec(shape=[2, 7], dtype=tf.int32),
-    tf.TensorSpec(shape=[1, 3, 224, 224], dtype=tf.float32),
+    tf.TensorSpec(shape=[1, 3, 224, 224], dtype=tf.float32)
 ]
 
 
 class CLIPModule(tf.Module):
+
     def __init__(self):
         super(CLIPModule, self).__init__()
         self.m = TFCLIPModel.from_pretrained("openai/clip-vit-base-patch32")
 
         self.m.predict = lambda x, y, z: self.m(
-            input_ids=x, attention_mask=y, pixel_values=z
-        )
+            input_ids=x, attention_mask=y, pixel_values=z)
 
     @tf.function(input_signature=clip_vit_inputs)
     def forward(self, input_ids, attention_mask, pixel_values):
-        return self.m.predict(
-            input_ids, attention_mask, pixel_values
-        ).logits_per_image
+        return self.m.predict(input_ids, attention_mask,
+                              pixel_values).logits_per_image
 
 
 if __name__ == "__main__":
@@ -36,30 +35,17 @@ if __name__ == "__main__":
     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="tf",
-        padding=True,
-    )
+    inputs = processor(text=["a photo of a cat", "a photo of a dog"],
+                       images=image,
+                       return_tensors="tf",
+                       padding=True)
 
     shark_module = SharkInference(
         CLIPModule(),
-        (
-            inputs["input_ids"],
-            inputs["attention_mask"],
-            inputs["pixel_values"],
-        ),
-    )
+        (inputs["input_ids"], inputs["attention_mask"], inputs["pixel_values"]))
     shark_module.set_frontend("tensorflow")
     shark_module.compile()
 
     print(
-        shark_module.forward(
-            (
-                inputs["input_ids"],
-                inputs["attention_mask"],
-                inputs["pixel_values"],
-            )
-        )
-    )
+        shark_module.forward((inputs["input_ids"], inputs["attention_mask"],
+                              inputs["pixel_values"])))

shark/examples/shark_inference/albert_maskfill_pt.py

@@ -1,88 +0,0 @@
-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

@@ -1,100 +0,0 @@
-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)
-    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

@@ -1,12 +0,0 @@
-from shark.shark_inference import SharkInference
-from shark.shark_downloader import download_torch_model
-
-mlir_model, func_name, inputs, golden_out = download_torch_model("bloom")
-
-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

@@ -13,6 +13,7 @@ gpt2_inputs = [
 
 
 class GPT2Module(tf.Module):
+
     def __init__(self):
         super(GPT2Module, self).__init__()
         self.m = TFGPT2Model.from_pretrained("distilgpt2")
@@ -29,12 +30,9 @@ if __name__ == "__main__":
     tokenizer = GPT2Tokenizer.from_pretrained("distilgpt2")
     text = "I love the distilled version of models."
 
-    inputs = tokenizer(text, return_tensors="tf")
+    inputs = tokenizer(text, return_tensors='tf')
     shark_module = SharkInference(
-        GPT2Module(), (inputs["input_ids"], inputs["attention_mask"])
-    )
+        GPT2Module(), (inputs["input_ids"], inputs["attention_mask"]))
     shark_module.set_frontend("tensorflow")
     shark_module.compile()
-    print(
-        shark_module.forward((inputs["input_ids"], inputs["attention_mask"]))
-    )
+    print(shark_module.forward((inputs["input_ids"], inputs["attention_mask"])))

shark/examples/shark_inference/mhlo_example.py

@@ -12,26 +12,7 @@ mhlo_ir = r"""builtin.module  {
 arg0 = np.ones((1, 4)).astype(np.float32)
 arg1 = np.ones((4, 1)).astype(np.float32)
 
-print("Running shark on cpu backend")
-shark_module = SharkInference(
-    mhlo_ir, function_name="forward", device="cpu", mlir_dialect="mhlo"
-)
-
-# Generate the random inputs and feed into the graph.
-x = shark_module.generate_random_inputs()
+shark_module = SharkInference(mhlo_ir, (arg0, arg1))
+shark_module.set_frontend("mhlo")
 shark_module.compile()
-print(shark_module.forward(x))
-
-print("Running shark on cuda backend")
-shark_module = SharkInference(
-    mhlo_ir, function_name="forward", device="cuda", mlir_dialect="mhlo"
-)
-shark_module.compile()
-print(shark_module.forward(x))
-
-print("Running shark on vulkan backend")
-shark_module = SharkInference(
-    mhlo_ir, function_name="forward", device="vulkan", mlir_dialect="mhlo"
-)
-shark_module.compile()
-print(shark_module.forward(x))
+print(shark_module.forward((arg0, arg1)))

shark/examples/shark_inference/minilm_benchmark.py

@@ -7,13 +7,17 @@ tokenizer = AutoTokenizer.from_pretrained("microsoft/MiniLM-L12-H384-uncased")
 
 
 class MiniLMSequenceClassification(torch.nn.Module):
+
     def __init__(self):
         super().__init__()
         self.model = AutoModelForSequenceClassification.from_pretrained(
             "microsoft/MiniLM-L12-H384-uncased",  # The pretrained model.
-            num_labels=2,  # The number of output labels--2 for binary classification.
-            output_attentions=False,  # Whether the model returns attentions weights.
-            output_hidden_states=False,  # Whether the model returns all hidden-states.
+            num_labels=
+            2,  # The number of output labels--2 for binary classification.
+            output_attentions=
+            False,  # Whether the model returns attentions weights.
+            output_hidden_states=
+            False,  # Whether the model returns all hidden-states.
             torchscript=True,
         )
 
@@ -23,12 +27,9 @@ class MiniLMSequenceClassification(torch.nn.Module):
 
 test_input = torch.randint(2, (1, 128))
 
-shark_module = SharkInference(
-    MiniLMSequenceClassification(),
-    (test_input,),
-    jit_trace=True,
-    benchmark_mode=True,
-)
+shark_module = SharkInference(MiniLMSequenceClassification(), (test_input,),
+                              jit_trace=True,
+                              benchmark_mode=True)
 
 shark_module.compile()
 shark_module.forward((test_input,))

shark/examples/shark_inference/minilm_benchmark_tf.py

@@ -2,6 +2,10 @@ import tensorflow as tf
 from transformers import BertModel, BertTokenizer, TFBertModel
 from shark.shark_inference import SharkInference
 
+gpus = tf.config.experimental.list_physical_devices('GPU')
+for gpu in gpus:
+  tf.config.experimental.set_memory_growth(gpu, True)
+
 MAX_SEQUENCE_LENGTH = 512
 BATCH_SIZE = 1
 
@@ -9,22 +13,21 @@ BATCH_SIZE = 1
 bert_input = [
     tf.TensorSpec(shape=[BATCH_SIZE, MAX_SEQUENCE_LENGTH], dtype=tf.int32),
     tf.TensorSpec(shape=[BATCH_SIZE, MAX_SEQUENCE_LENGTH], dtype=tf.int32),
-    tf.TensorSpec(shape=[BATCH_SIZE, MAX_SEQUENCE_LENGTH], dtype=tf.int32),
+    tf.TensorSpec(shape=[BATCH_SIZE, MAX_SEQUENCE_LENGTH], dtype=tf.int32)
 ]
 
 
 class BertModule(tf.Module):
+
     def __init__(self):
         super(BertModule, self).__init__()
         # Create a BERT trainer with the created network.
         self.m = TFBertModel.from_pretrained(
-            "microsoft/MiniLM-L12-H384-uncased", from_pt=True
-        )
+            "microsoft/MiniLM-L12-H384-uncased", from_pt=True)
 
         # Invoke the trainer model on the inputs. This causes the layer to be built.
         self.m.predict = lambda x, y, z: self.m.call(
-            input_ids=x, attention_mask=y, token_type_ids=z, training=False
-        )
+            input_ids=x, attention_mask=y, token_type_ids=z, training=False)
 
     @tf.function(input_signature=bert_input)
     def forward(self, input_ids, attention_mask, token_type_ids):
@@ -34,28 +37,22 @@ class BertModule(tf.Module):
 if __name__ == "__main__":
     # Prepping Data
     tokenizer = BertTokenizer.from_pretrained(
-        "microsoft/MiniLM-L12-H384-uncased"
-    )
+        "microsoft/MiniLM-L12-H384-uncased")
     text = "Replace me by any text you'd like."
-    encoded_input = tokenizer(
-        text,
-        padding="max_length",
-        truncation=True,
-        max_length=MAX_SEQUENCE_LENGTH,
-    )
+    encoded_input = tokenizer(text,
+                              padding='max_length',
+                              truncation=True,
+                              max_length=MAX_SEQUENCE_LENGTH)
     for key in encoded_input:
         encoded_input[key] = tf.expand_dims(
-            tf.convert_to_tensor(encoded_input[key]), 0
-        )
+            tf.convert_to_tensor(encoded_input[key]), 0)
 
-    test_input = (
-        encoded_input["input_ids"],
-        encoded_input["attention_mask"],
-        encoded_input["token_type_ids"],
-    )
+    test_input = (encoded_input["input_ids"], encoded_input["attention_mask"],
+         encoded_input["token_type_ids"])
     shark_module = SharkInference(
-        BertModule(), test_input, benchmark_mode=True
-    )
+        BertModule(),
+        test_input,
+        benchmark_mode=True)
     shark_module.set_frontend("tensorflow")
     shark_module.compile()
     shark_module.benchmark_all(test_input)

shark/examples/shark_inference/minilm_jit.py

@@ -1,24 +1,35 @@
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 from shark.shark_inference import SharkInference
-from shark.shark_downloader import download_torch_model
+
+torch.manual_seed(0)
+tokenizer = AutoTokenizer.from_pretrained("microsoft/MiniLM-L12-H384-uncased")
 
 
-mlir_model, func_name, inputs, golden_out = download_torch_model(
-    "microsoft/MiniLM-L12-H384-uncased"
-)
+class MiniLMSequenceClassification(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        self.model = AutoModelForSequenceClassification.from_pretrained(
+            "microsoft/MiniLM-L12-H384-uncased",  # The pretrained model.
+            num_labels=
+            2,  # The number of output labels--2 for binary classification.
+            output_attentions=
+            False,  # Whether the model returns attentions weights.
+            output_hidden_states=
+            False,  # Whether the model returns all hidden-states.
+            torchscript=True,
+        )
+
+    def forward(self, tokens):
+        return self.model.forward(tokens)[0]
 
 
-shark_module = SharkInference(
-    mlir_model, func_name, device="cpu", mlir_dialect="linalg"
-)
+test_input = torch.randint(2, (1, 128))
+
+shark_module = SharkInference(MiniLMSequenceClassification(), (test_input,),
+                              jit_trace=True)
+
 shark_module.compile()
-result = shark_module.forward(inputs)
-print("The obtained result via shark is: ", result)
-print("The golden result is:", golden_out)
-
-
-# Let's generate random inputs, currently supported
-# for static models.
-rand_inputs = shark_module.generate_random_inputs()
-rand_results = shark_module.forward(rand_inputs)
-
-print("Running shark_module with random_inputs is: ", rand_results)
+result = shark_module.forward((test_input,))
+print("Obtained result", result)

shark/examples/shark_inference/minilm_load_benchmark_tf.py

@@ -0,0 +1,41 @@
+import tensorflow as tf
+from transformers import BertModel, BertTokenizer, TFBertModel
+from shark.shark_inference import SharkInference
+from shark.shark_importer import shark_load
+from shark.parser import parser
+import os
+
+gpus = tf.config.experimental.list_physical_devices('GPU')
+for gpu in gpus:
+  tf.config.experimental.set_memory_growth(gpu, True)
+
+parser.add_argument(
+    "--download_mlir_path",
+    type=str,
+    default="minilm_tf_inference.mlir",
+    help="Specifies path to target mlir file that will be loaded.")
+load_args, unknown = parser.parse_known_args()
+
+MAX_SEQUENCE_LENGTH = 512
+
+if __name__ == "__main__":
+    # Prepping Data
+    tokenizer = BertTokenizer.from_pretrained(
+        "microsoft/MiniLM-L12-H384-uncased")
+    text = "Replace me by any text you'd like."
+    encoded_input = tokenizer(text,
+                              padding='max_length',
+                              truncation=True,
+                              max_length=MAX_SEQUENCE_LENGTH)
+    for key in encoded_input:
+        encoded_input[key] = tf.expand_dims(
+            tf.convert_to_tensor(encoded_input[key]), 0)
+    model_name = "minilm_tf_inference"
+    minilm_mlir = shark_load(model_name, load_args.download_mlir_path)
+    test_input = (encoded_input["input_ids"], encoded_input["attention_mask"],
+         encoded_input["token_type_ids"])
+    shark_module = SharkInference(
+        minilm_mlir, test_input, benchmark_mode=True)
+    shark_module.set_frontend("mhlo")
+    shark_module.compile()
+    shark_module.benchmark_all(test_input)

shark/examples/shark_inference/minilm_tf.py

@@ -9,22 +9,21 @@ BATCH_SIZE = 1
 bert_input = [
     tf.TensorSpec(shape=[BATCH_SIZE, MAX_SEQUENCE_LENGTH], dtype=tf.int32),
     tf.TensorSpec(shape=[BATCH_SIZE, MAX_SEQUENCE_LENGTH], dtype=tf.int32),
-    tf.TensorSpec(shape=[BATCH_SIZE, MAX_SEQUENCE_LENGTH], dtype=tf.int32),
+    tf.TensorSpec(shape=[BATCH_SIZE, MAX_SEQUENCE_LENGTH], dtype=tf.int32)
 ]
 
 
 class BertModule(tf.Module):
+
     def __init__(self):
         super(BertModule, self).__init__()
         # Create a BERT trainer with the created network.
         self.m = TFBertModel.from_pretrained(
-            "microsoft/MiniLM-L12-H384-uncased", from_pt=True
-        )
+            "microsoft/MiniLM-L12-H384-uncased", from_pt=True)
 
         # Invoke the trainer model on the inputs. This causes the layer to be built.
         self.m.predict = lambda x, y, z: self.m.call(
-            input_ids=x, attention_mask=y, token_type_ids=z, training=False
-        )
+            input_ids=x, attention_mask=y, token_type_ids=z, training=False)
 
     @tf.function(input_signature=bert_input)
     def forward(self, input_ids, attention_mask, token_type_ids):
@@ -34,37 +33,24 @@ class BertModule(tf.Module):
 if __name__ == "__main__":
     # Prepping Data
     tokenizer = BertTokenizer.from_pretrained(
-        "microsoft/MiniLM-L12-H384-uncased"
-    )
+        "microsoft/MiniLM-L12-H384-uncased")
     text = "Replace me by any text you'd like."
-    encoded_input = tokenizer(
-        text,
-        padding="max_length",
-        truncation=True,
-        max_length=MAX_SEQUENCE_LENGTH,
-    )
+    encoded_input = tokenizer(text,
+                              padding='max_length',
+                              truncation=True,
+                              max_length=MAX_SEQUENCE_LENGTH)
     for key in encoded_input:
         encoded_input[key] = tf.expand_dims(
-            tf.convert_to_tensor(encoded_input[key]), 0
-        )
+            tf.convert_to_tensor(encoded_input[key]), 0)
 
     shark_module = SharkInference(
         BertModule(),
-        (
-            encoded_input["input_ids"],
-            encoded_input["attention_mask"],
-            encoded_input["token_type_ids"],
-        ),
-    )
+        (encoded_input["input_ids"], encoded_input["attention_mask"],
+         encoded_input["token_type_ids"]))
     shark_module.set_frontend("tensorflow")
     shark_module.compile()
 
     print(
         shark_module.forward(
-            (
-                encoded_input["input_ids"],
-                encoded_input["attention_mask"],
-                encoded_input["token_type_ids"],
-            )
-        )
-    )
+            (encoded_input["input_ids"], encoded_input["attention_mask"],
+             encoded_input["token_type_ids"])))

shark/examples/shark_inference/resnest.py

@@ -1,39 +0,0 @@
-import torch
-import torchvision.models as models
-from shark.shark_inference import SharkInference
-from shark.shark_importer import SharkImporter
-
-torch.hub.list("zhanghang1989/ResNeSt", force_reload=True)
-
-
-class ResnestModule(torch.nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.model = torch.hub.load(
-            "zhanghang1989/ResNeSt", "resnest50", pretrained=True
-        )
-        self.model.eval()
-
-    def forward(self, input):
-        return self.model.forward(input)
-
-
-input = torch.randn(1, 3, 224, 224)
-
-
-mlir_importer = SharkImporter(
-    ResnestModule(),
-    (input,),
-    frontend="torch",
-)
-
-(vision_mlir, func_name), inputs, golden_out = mlir_importer.import_debug(
-    tracing_required=True
-)
-
-print(golden_out)
-
-shark_module = SharkInference(vision_mlir, func_name, mlir_dialect="linalg")
-shark_module.compile()
-result = shark_module.forward((input,))
-print("Obtained result", result)

shark/examples/shark_inference/resnet50_fp16.py

@@ -1,76 +0,0 @@
-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

@@ -5,28 +5,24 @@ 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
 
 
 ################################## Preprocessing inputs and model ############
 def load_and_preprocess_image(url: str):
     headers = {
-        "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36"
+        "User-Agent":
+            "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36"
     }
-    img = Image.open(
-        requests.get(url, headers=headers, stream=True).raw
-    ).convert("RGB")
+    img = Image.open(requests.get(url, headers=headers,
+                                  stream=True).raw).convert("RGB")
     # preprocessing pipeline
-    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]
-            ),
-        ]
-    )
+    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]),
+    ])
     img_preprocessed = preprocess(img)
     return torch.unsqueeze(img_preprocessed, 0)
 
@@ -48,6 +44,7 @@ def top3_possibilities(res):
 
 
 class Resnet50Module(torch.nn.Module):
+
     def __init__(self):
         super().__init__()
         self.resnet = models.resnet50(pretrained=True)
@@ -64,18 +61,18 @@ labels = load_labels()
 
 ##############################################################################
 
+input = torch.randn(1, 3, 224, 224)
+print(input.shape)
+
+## The img is passed to determine the input shape.
+shark_module = SharkInference(Resnet50Module(), (img,))
+shark_module.compile()
 
 ## Can pass any img or input to the forward module.
-mlir_model, func_name, inputs, golden_out = download_torch_model("resnet50")
-
-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(),))
+results = shark_module.forward((img,))
 
 print("The top 3 results obtained via shark_runner is:")
-print(top3_possibilities(torch.from_numpy(result)))
+print(top3_possibilities(torch.from_numpy(results)))
 
 print()
 

shark/examples/shark_inference/simple_dlrm.py

@@ -1,392 +0,0 @@
-# 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

@@ -1,314 +0,0 @@
-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

@@ -1,272 +0,0 @@
-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

@@ -1,278 +0,0 @@
-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_torch_model
-
-    mlir_model, func_name, inputs, golden_out = download_torch_model(
-        "stable_diff_f16_18_OCT", tank_url="gs://shark_tank/prashant_nod"
-    )
-    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

@@ -1,313 +0,0 @@
-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_tf_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_tf_model(
-            "stable_diff", tank_url="gs://shark_tank/quinn"
-        )
-        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

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

shark/examples/shark_inference/stable_diffusion/README.md

@@ -1,15 +0,0 @@
-# STABLE DIFFUSION
-
-## Installation
-
-```shell
-pip install diffusers
-pip install scipy
-```
-
-## RUN
-
-```shell
-python main.py --precision="fp32"|"fp16" --prompt="enter the text" --device="cpu"|"cuda"|"vulkan" --import_mlir|--no-import_mlir
-
-```

shark/examples/shark_inference/stable_diffusion/download_hf_models.py

@@ -1,25 +0,0 @@
-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

@@ -1,241 +0,0 @@
-from transformers import CLIPTextModel, CLIPTokenizer
-import torch
-from PIL import Image
-from diffusers import LMSDiscreteScheduler
-from tqdm.auto import tqdm
-import numpy as np
-from stable_args import args
-from model_wrappers import (
-    get_vae32,
-    get_vae16,
-    get_unet16_wrapped,
-    get_unet32_wrapped,
-    get_clipped_text,
-)
-from utils import get_shark_model
-import time
-
-GCLOUD_BUCKET = "gs://shark_tank/prashant_nod"
-VAE_FP16 = "vae_fp16"
-VAE_FP32 = "vae_fp32"
-UNET_FP16 = "unet_fp16"
-UNET_FP32 = "unet_fp32"
-IREE_EXTRA_ARGS = []
-
-TUNED_GCLOUD_BUCKET = "gs://shark_tank/quinn"
-UNET_FP16_TUNED = "unet_fp16_tunedv2"
-
-BATCH_SIZE = len(args.prompts)
-
-if BATCH_SIZE not in [1, 2]:
-    import sys
-
-    sys.exit("Only batch size 1 and 2 are supported.")
-
-if BATCH_SIZE > 1 and args.precision != "fp16":
-    sys.exit("batch size > 1 is supported for fp16 model.")
-
-
-if BATCH_SIZE != 1:
-    TUNED_GCLOUD_BUCKET = "gs://shark_tank/prashant_nod"
-    UNET_FP16_TUNED = f"unet_fp16_{BATCH_SIZE}"
-    VAE_FP16 = f"vae_fp16_{BATCH_SIZE}"
-
-# 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()
-
-
-def get_models():
-    global IREE_EXTRA_ARGS
-    if args.precision == "fp16":
-        IREE_EXTRA_ARGS += [
-            "--iree-flow-enable-padding-linalg-ops",
-            "--iree-flow-linalg-ops-padding-size=32",
-        ]
-        if args.use_tuned:
-            unet_gcloud_bucket = TUNED_GCLOUD_BUCKET
-            vae_gcloud_bucket = GCLOUD_BUCKET
-            unet_args = IREE_EXTRA_ARGS
-            vae_args = IREE_EXTRA_ARGS + [
-                "--iree-flow-enable-conv-nchw-to-nhwc-transform"
-            ]
-            unet_name = UNET_FP16_TUNED
-            vae_name = VAE_FP16
-        else:
-            unet_gcloud_bucket = GCLOUD_BUCKET
-            vae_gcloud_bucket = GCLOUD_BUCKET
-            IREE_EXTRA_ARGS += [
-                "--iree-flow-enable-conv-nchw-to-nhwc-transform"
-            ]
-            unet_args = IREE_EXTRA_ARGS
-            vae_args = IREE_EXTRA_ARGS
-            unet_name = UNET_FP16
-            vae_name = VAE_FP16
-
-        if batch_size > 1:
-            vae_args = []
-
-        if args.import_mlir == True:
-            return get_vae16(model_name=VAE_FP16), get_unet16_wrapped(
-                model_name=UNET_FP16
-            )
-        else:
-            return get_shark_model(
-                vae_gcloud_bucket,
-                vae_name,
-                vae_args,
-            ), get_shark_model(
-                unet_gcloud_bucket,
-                unet_name,
-                unet_args,
-            )
-
-    elif args.precision == "fp32":
-        IREE_EXTRA_ARGS += [
-            "--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 == True:
-            return get_vae32(model_name=VAE_FP32), get_unet32_wrapped(
-                model_name=UNET_FP32
-            )
-        else:
-            return get_shark_model(
-                GCLOUD_BUCKET,
-                VAE_FP32,
-                IREE_EXTRA_ARGS,
-            ), get_shark_model(
-                GCLOUD_BUCKET,
-                UNET_FP32,
-                IREE_EXTRA_ARGS,
-            )
-
-
-if __name__ == "__main__":
-
-    dtype = torch.float32 if args.precision == "fp32" else torch.half
-    if len(args.iree_vulkan_target_triple) > 0:
-        IREE_EXTRA_ARGS.append(
-            f"-iree-vulkan-target-triple={args.iree_vulkan_target_triple}"
-        )
-
-    clip_model = "clip_text"
-    clip_extra_args = [
-        "--iree-flow-linalg-ops-padding-size=16",
-        "--iree-flow-enable-padding-linalg-ops",
-    ]
-    clip = get_shark_model(GCLOUD_BUCKET, clip_model, clip_extra_args)
-
-    prompt = args.prompts
-    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 = args.guidance_scale  # Scale for classifier-free guidance
-
-    generator = torch.manual_seed(
-        args.seed
-    )  # Seed generator to create the inital latent noise
-
-    batch_size = len(prompt)
-
-    vae, unet = get_models()
-
-    tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
-
-    scheduler = LMSDiscreteScheduler(
-        beta_start=0.00085,
-        beta_end=0.012,
-        beta_schedule="scaled_linear",
-        num_train_timesteps=1000,
-    )
-
-    start = time.time()
-
-    text_input = tokenizer(
-        prompt,
-        padding="max_length",
-        max_length=args.max_length,
-        truncation=True,
-        return_tensors="pt",
-    )
-
-    text_embeddings = clip.forward((text_input.input_ids,))
-    text_embeddings = torch.from_numpy(text_embeddings).to(dtype)
-    max_length = text_input.input_ids.shape[-1]
-    uncond_input = tokenizer(
-        [""] * batch_size,
-        padding="max_length",
-        max_length=max_length,
-        return_tensors="pt",
-    )
-    uncond_embeddings = clip.forward((uncond_input.input_ids,))
-    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.sigmas[0]
-    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()
-        latents_numpy = latents.detach().numpy()
-        sigma_numpy = np.array(scheduler.sigmas[i]).astype(np.float32)
-
-        profile_device = start_profiling(file_path="unet.rdc")
-        noise_pred = unet.forward(
-            (latents_numpy, timestep, text_embeddings_numpy, sigma_numpy)
-        )
-        end_profiling(profile_device)
-        noise_pred = torch.from_numpy(noise_pred)
-        step_time = time.time() - step_start
-        avg_ms += step_time
-        step_ms = int((step_time) * 1000)
-        print(f" ({step_ms}ms)")
-
-        latents = scheduler.step(noise_pred, i, latents)["prev_sample"]
-    avg_ms = 1000 * avg_ms / args.steps
-    print(f"Average step time: {avg_ms}ms/it")
-
-    # scale and decode the image latents with vae
-    latents = 1 / 0.18215 * latents
-    latents_numpy = latents.detach().numpy()
-    profile_device = start_profiling(file_path="vae.rdc")
-    image = vae.forward((latents_numpy,))
-    end_profiling(profile_device)
-    image = torch.from_numpy(image)
-    image = image.detach().cpu().permute(0, 2, 3, 1).numpy()
-    images = (image * 255).round().astype("uint8")
-
-    print("Total image generation runtime (s): {}".format(time.time() - start))
-
-    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

@@ -1,223 +0,0 @@
-from diffusers import AutoencoderKL, UNet2DConditionModel, PNDMScheduler
-from transformers import CLIPTextModel
-from utils import compile_through_fx
-from stable_args import args
-import torch
-
-YOUR_TOKEN = "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"
-
-
-BATCH_SIZE = len(args.prompts)
-
-
-def get_clipped_text(model_name="clip_text"):
-    class CLIPText(torch.nn.Module):
-        def __init__(self):
-            super().__init__()
-            self.text_encoder = CLIPTextModel.from_pretrained(
-                "openai/clip-vit-large-patch14"
-            )
-
-        def forward(self, input):
-            return self.text_encoder(input)[0]
-
-    clip_model = CLIPText()
-    clip_input = torch.randint(1, 2, (BATCH_SIZE, 77))
-    shark_clip = compile_through_fx(
-        clip_model,
-        (clip_input,),
-        model_name=model_name,
-    )
-    return shark_clip
-
-
-def get_vae32(model_name="vae_fp32"):
-    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):
-            x = self.vae.decode(input, return_dict=False)[0]
-            return (x / 2 + 0.5).clamp(0, 1)
-
-    vae = VaeModel()
-    vae_input = torch.rand(BATCH_SIZE, 4, 64, 64)
-    shark_vae = compile_through_fx(
-        vae,
-        (vae_input,),
-        model_name=model_name,
-    )
-    return shark_vae
-
-
-def get_vae16(model_name="vae_fp16"):
-    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,
-                revision="fp16",
-            )
-
-        def forward(self, input):
-            x = self.vae.decode(input, return_dict=False)[0]
-            return (x / 2 + 0.5).clamp(0, 1)
-
-    vae = VaeModel()
-    vae = vae.half().cuda()
-    vae_input = torch.rand(BATCH_SIZE, 4, 64, 64, dtype=torch.half).cuda()
-    shark_vae = compile_through_fx(
-        vae,
-        (vae_input,),
-        model_name=model_name,
-    )
-    return shark_vae
-
-
-def get_unet32(model_name="unet_fp32"):
-    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]
-
-    unet = UnetModel()
-    latent_model_input = torch.rand([2, 4, 64, 64])
-    text_embeddings = torch.rand([2, args.max_length, 768])
-    shark_unet = compile_through_fx(
-        unet,
-        (latent_model_input, torch.tensor([1.0]), text_embeddings),
-        model_name=model_name,
-    )
-    return shark_unet
-
-
-def get_unet16(model_name="unet_fp16"):
-    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,
-                revision="fp16",
-            )
-            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]
-
-    unet = UnetModel()
-    unet = unet.half().cuda()
-    latent_model_input = torch.rand([2, 4, 64, 64]).half().cuda()
-    text_embeddings = torch.rand([2, args.max_length, 768]).half().cuda()
-    shark_unet = compile_through_fx(
-        unet,
-        (
-            latent_model_input,
-            torch.tensor([1.0]).half().cuda(),
-            text_embeddings,
-        ),
-        model_name=model_name,
-    )
-    return shark_unet
-
-
-def get_unet16_wrapped(guidance_scale=7.5, model_name="unet_fp16_wrapped"):
-    class UnetModel(torch.nn.Module):
-        def __init__(self, guidance_scale=guidance_scale):
-            super().__init__()
-            self.unet = UNet2DConditionModel.from_pretrained(
-                "CompVis/stable-diffusion-v1-4",
-                subfolder="unet",
-                use_auth_token=YOUR_TOKEN,
-                revision="fp16",
-            )
-            self.in_channels = self.unet.in_channels
-            self.guidance_scale = guidance_scale
-            self.train(False)
-
-        def forward(self, latent, timestep, text_embedding, sigma):
-            # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
-            latents = torch.cat([latent] * 2)
-            latents = latents / (torch.pow((torch.pow(sigma, 2) + 1), 0.5))
-            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 + self.guidance_scale * (
-                noise_pred_text - noise_pred_uncond
-            )
-            return noise_pred
-
-    unet = UnetModel()
-    unet = unet.half().cuda()
-    latent_model_input = torch.rand([BATCH_SIZE, 4, 64, 64]).half().cuda()
-    text_embeddings = (
-        torch.rand([2 * BATCH_SIZE, args.max_length, 768]).half().cuda()
-    )
-    sigma = torch.tensor(1).to(torch.float32)
-    shark_unet = compile_through_fx(
-        unet,
-        (
-            latent_model_input,
-            torch.tensor([1.0]).half().cuda(),
-            text_embeddings,
-            sigma,
-        ),
-        model_name=model_name,
-    )
-    return shark_unet
-
-
-def get_unet32_wrapped(guidance_scale=7.5, model_name="unet_fp32_wrapped"):
-    class UnetModel(torch.nn.Module):
-        def __init__(self, guidance_scale=guidance_scale):
-            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.guidance_scale = guidance_scale
-            self.train(False)
-
-        def forward(self, latent, timestep, text_embedding, sigma):
-            latents = torch.cat([latent] * 2)
-            latents = latents / (torch.pow((torch.pow(sigma, 2) + 1), 0.5))
-            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 + self.guidance_scale * (
-                noise_pred_text - noise_pred_uncond
-            )
-            return noise_pred
-
-    unet = UnetModel()
-    latent_model_input = torch.rand([BATCH_SIZE, 4, 64, 64])
-    text_embeddings = torch.rand([2 * BATCH_SIZE, args.max_length, 768])
-    sigma = torch.tensor(1).to(torch.float32)
-    shark_unet = compile_through_fx(
-        unet,
-        (latent_model_input, torch.tensor([1.0]), text_embeddings, sigma),
-        model_name=model_name,
-    )
-    return shark_unet

shark/examples/shark_inference/stable_diffusion/stable_args.py

@@ -1,88 +0,0 @@
-import argparse
-
-p = argparse.ArgumentParser(
-    description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter
-)
-
-p.add_argument(
-    "--prompts",
-    nargs="+",
-    default=["a photograph of an astronaut riding a horse"],
-    help="text of which images to be generated.",
-)
-p.add_argument(
-    "--device", type=str, default="cpu", help="device to run the model."
-)
-p.add_argument(
-    "--steps",
-    type=int,
-    default=10,
-    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(
-    "--import_mlir",
-    default=False,
-    action=argparse.BooleanOptionalAction,
-    help="imports the model from torch module to shark_module otherwise downloads the model from shark_tank.",
-)
-
-p.add_argument(
-    "--precision", type=str, default="fp32", help="precision to run the model."
-)
-
-p.add_argument(
-    "--max_length",
-    type=int,
-    default=77,
-    help="max length of the tokenizer output.",
-)
-
-p.add_argument(
-    "--load_vmfb",
-    default=True,
-    action=argparse.BooleanOptionalAction,
-    help="attempts to load the model from a precompiled flatbuffer and compiles + saves it if not found.",
-)
-
-p.add_argument(
-    "--save_vmfb",
-    default=False,
-    action=argparse.BooleanOptionalAction,
-    help="saves the compiled flatbuffer to the local directory",
-)
-
-p.add_argument(
-    "--iree-vulkan-target-triple",
-    type=str,
-    default="",
-    help="Specify target triple for vulkan",
-)
-
-p.add_argument(
-    "--vulkan_debug_utils",
-    default=False,
-    action=argparse.BooleanOptionalAction,
-    help="Profiles vulkan device and collects the .rdc info",
-)
-
-p.add_argument(
-    "--use_tuned",
-    default=True,
-    action=argparse.BooleanOptionalAction,
-    help="Download and use the tuned version of the model if available",
-)
-
-args = p.parse_args()

shark/examples/shark_inference/stable_diffusion/utils.py

@@ -1,103 +0,0 @@
-import os
-
-import torch
-from shark.shark_inference import SharkInference
-from shark.shark_importer import SharkImporter
-from torch.fx.experimental.proxy_tensor import make_fx
-from stable_args import args
-from torch._decomp import get_decompositions
-import torch_mlir
-
-
-def _compile_module(shark_module, model_name, extra_args=[]):
-    if args.load_vmfb or args.save_vmfb:
-        extended_name = "{}_{}".format(model_name, args.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("Loading flatbuffer from {}".format(vmfb_path))
-            shark_module.load_module(vmfb_path)
-        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)
-    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_torch_model
-
-    mlir_model, func_name, inputs, golden_out = download_torch_model(
-        model_name, tank_url=tank_url
-    )
-    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 shark_module.
-def compile_through_fx(model, inputs, model_name, extra_args=[]):
-
-    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.trace(fx_g, inputs)
-
-    mlir_importer = SharkImporter(
-        ts_g,
-        inputs,
-        frontend="torch",
-    )
-
-    (mlir_module, func_name), _, _ = mlir_importer.import_debug()
-
-    shark_module = SharkInference(
-        mlir_module,
-        func_name,
-        device=args.device,
-        mlir_dialect="linalg",
-    )
-
-    return _compile_module(shark_module, model_name, extra_args)

shark/examples/shark_inference/t5_tf.py

@@ -11,12 +11,12 @@ t5_inputs = [
     tf.TensorSpec(shape=[1, 10], dtype=tf.int32),
 ]
 
-
 class T5Module(tf.Module):
+
     def __init__(self):
         super(T5Module, self).__init__()
         self.m = TFT5Model.from_pretrained("t5-small")
-        self.m.predict = lambda x, y: self.m(input_ids=x, decoder_input_ids=y)
+        self.m.predict = lambda x,y: self.m(input_ids=x, decoder_input_ids=y)
 
     @tf.function(input_signature=t5_inputs)
     def forward(self, input_ids, decoder_input_ids):
@@ -27,9 +27,12 @@ if __name__ == "__main__":
     # Prepping Data
     tokenizer = T5Tokenizer.from_pretrained("t5-small")
     text = "I love the distilled version of models."
-    inputs = tokenizer(text, return_tensors="tf").input_ids
+    inputs = tokenizer(
+        text, return_tensors="tf"
+    ).input_ids
 
-    shark_module = SharkInference(T5Module(), (inputs, inputs))
+    shark_module = SharkInference(
+        T5Module(), (inputs, inputs))
     shark_module.set_frontend("tensorflow")
     shark_module.compile()
-    print(shark_module.forward((inputs, inputs)))
+    print(shark_module.forward((inputs,inputs)))

shark/examples/shark_inference/torch_vision_models_script.py

@@ -4,6 +4,7 @@ from shark.shark_inference import SharkInference
 
 
 class VisionModule(torch.nn.Module):
+
     def __init__(self, model):
         super().__init__()
         self.model = model

shark/examples/shark_inference/unet_script.py

@@ -1,10 +1,10 @@
 import torch
-import numpy as np
-from shark.shark_inference import SharkInference
-from shark.shark_importer import SharkImporter
+from shark_runner import SharkInference
 
 
+# Currently not supported aten.transpose_conv2d missing.
 class UnetModule(torch.nn.Module):
+
     def __init__(self):
         super().__init__()
         self.model = torch.hub.load(
@@ -15,7 +15,7 @@ class UnetModule(torch.nn.Module):
             init_features=32,
             pretrained=True,
         )
-        self.model.eval()
+        self.train(False)
 
     def forward(self, input):
         return self.model(input)
@@ -23,17 +23,10 @@ class UnetModule(torch.nn.Module):
 
 input = torch.randn(1, 3, 224, 224)
 
-mlir_importer = SharkImporter(
+print(input)
+shark_module = SharkInference(
     UnetModule(),
     (input,),
-    frontend="torch",
 )
-
-(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_module.benchmark_forward((input,))
+print(input)

shark/examples/shark_inference/v_diffusion.py

@@ -1,13 +0,0 @@
-from shark.shark_inference import SharkInference
-from shark.shark_downloader import download_torch_model
-
-
-mlir_model, func_name, inputs, golden_out = download_torch_model("v_diffusion")
-
-shark_module = SharkInference(
-    mlir_model, func_name, device="vulkan", mlir_dialect="linalg"
-)
-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_training/bert_training.py

@@ -5,13 +5,17 @@ from shark.shark_runner import SharkTrainer
 
 
 class MiniLMSequenceClassification(torch.nn.Module):
+
     def __init__(self):
         super().__init__()
         self.model = AutoModelForSequenceClassification.from_pretrained(
             "microsoft/MiniLM-L12-H384-uncased",  # The pretrained model.
-            num_labels=2,  # The number of output labels--2 for binary classification.
-            output_attentions=False,  # Whether the model returns attentions weights.
-            output_hidden_states=False,  # Whether the model returns all hidden-states.
+            num_labels=
+            2,  # The number of output labels--2 for binary classification.
+            output_attentions=
+            False,  # Whether the model returns attentions weights.
+            output_hidden_states=
+            False,  # Whether the model returns all hidden-states.
             torchscript=True,
         )
 
@@ -33,9 +37,8 @@ inp = (torch.randint(2, (1, 128)),)
 
 def forward(params, buffers, args):
     params_and_buffers = {**params, **buffers}
-    _stateless.functional_call(
-        mod, params_and_buffers, args, {}
-    ).sum().backward()
+    _stateless.functional_call(mod, params_and_buffers, args,
+                               {}).sum().backward()
     optim = torch.optim.SGD(get_sorted_params(params), lr=0.01)
     # optim.load_state_dict(optim_state)
     optim.step()

shark/examples/shark_training/bert_training_load_tf.py

@@ -5,14 +5,13 @@ import tensorflow as tf
 
 from shark.shark_trainer import SharkTrainer
 from shark.parser import parser
-from urllib import request
+from shark.shark_importer import shark_load
 
 parser.add_argument(
     "--download_mlir_path",
     type=str,
     default="bert_tf_training.mlir",
-    help="Specifies path to target mlir file that will be loaded.",
-)
+    help="Specifies path to target mlir file that will be loaded.")
 load_args, unknown = parser.parse_known_args()
 
 tf.random.set_seed(0)
@@ -26,30 +25,16 @@ if __name__ == "__main__":
     predict_sample_input = [
         np.random.randint(5, size=(BATCH_SIZE, SEQUENCE_LENGTH)),
         np.random.randint(5, size=(BATCH_SIZE, SEQUENCE_LENGTH)),
-        np.random.randint(5, size=(BATCH_SIZE, SEQUENCE_LENGTH)),
-    ]
-    file_link = "https://storage.googleapis.com/shark_tank/users/stanley/bert_tf_training.mlir"
-    response = request.urlretrieve(file_link, load_args.download_mlir_path)
-    sample_input_tensors = [
-        tf.convert_to_tensor(val, dtype=tf.int32)
-        for val in predict_sample_input
+        np.random.randint(5, size=(BATCH_SIZE, SEQUENCE_LENGTH))
     ]
+    model_name = "bert_tf_training"
+    bert_mlir = shark_load(model_name, load_args.download_mlir_path)
+    sample_input_tensors = [tf.convert_to_tensor(val, dtype=tf.int32) for val in predict_sample_input]
     num_iter = 10
-    if not os.path.isfile(load_args.download_mlir_path):
-        raise ValueError(
-            f"Tried looking for target mlir in {load_args.download_mlir_path}, but cannot be found."
-        )
-    with open(load_args.download_mlir_path, "rb") as input_file:
-        bert_mlir = input_file.read()
     shark_module = SharkTrainer(
         bert_mlir,
-        (
-            sample_input_tensors,
-            tf.convert_to_tensor(
-                np.random.randint(5, size=(BATCH_SIZE)), dtype=tf.int32
-            ),
-        ),
-    )
+        (sample_input_tensors,
+         tf.convert_to_tensor(np.random.randint(5, size=(BATCH_SIZE)), dtype=tf.int32)))
     shark_module.set_frontend("mhlo")
     shark_module.compile()
     start = time.time()

shark/examples/shark_training/bert_training_tf.py

@@ -1,7 +1,10 @@
+import sys
 from absl import app
 import time
 
 import numpy as np
+import os
+import tempfile
 import tensorflow as tf
 
 from official.nlp.modeling import layers
@@ -25,35 +28,31 @@ bert_input = [
 
 
 class BertModule(tf.Module):
+
     def __init__(self):
         super(BertModule, self).__init__()
         dict_outputs = False
-        test_network = networks.BertEncoder(
-            vocab_size=vocab_size, num_layers=2, dict_outputs=dict_outputs
-        )
+        test_network = networks.BertEncoder(vocab_size=vocab_size,
+                                            num_layers=2,
+                                            dict_outputs=dict_outputs)
 
         # Create a BERT trainer with the created network.
         bert_trainer_model = bert_classifier.BertClassifier(
-            test_network, num_classes=NUM_CLASSES
-        )
+            test_network, num_classes=NUM_CLASSES)
         bert_trainer_model.summary()
 
         # Invoke the trainer model on the inputs. This causes the layer to be built.
         self.m = bert_trainer_model
         self.m.predict = lambda x: self.m.call(x, training=False)
-        self.predict = tf.function(input_signature=[bert_input])(
-            self.m.predict
-        )
+        self.predict = tf.function(input_signature=[bert_input])(self.m.predict)
         self.m.learn = lambda x, y: self.m.call(x, training=False)
         self.loss = tf.keras.losses.SparseCategoricalCrossentropy()
         self.optimizer = tf.keras.optimizers.SGD(learning_rate=1e-2)
 
-    @tf.function(
-        input_signature=[
-            bert_input,  # inputs
-            tf.TensorSpec(shape=[BATCH_SIZE], dtype=tf.int32),  # labels
-        ]
-    )
+    @tf.function(input_signature=[
+        bert_input,  # inputs
+        tf.TensorSpec(shape=[BATCH_SIZE], dtype=tf.int32)  # labels
+    ])
     def forward(self, inputs, labels):
         with tf.GradientTape() as tape:
             # Capture the gradients from forward prop...
@@ -71,22 +70,14 @@ if __name__ == "__main__":
     predict_sample_input = [
         np.random.randint(5, size=(BATCH_SIZE, SEQUENCE_LENGTH)),
         np.random.randint(5, size=(BATCH_SIZE, SEQUENCE_LENGTH)),
-        np.random.randint(5, size=(BATCH_SIZE, SEQUENCE_LENGTH)),
-    ]
-    sample_input_tensors = [
-        tf.convert_to_tensor(val, dtype=tf.int32)
-        for val in predict_sample_input
+        np.random.randint(5, size=(BATCH_SIZE, SEQUENCE_LENGTH))
     ]
+    sample_input_tensors = [tf.convert_to_tensor(val, dtype=tf.int32) for val in predict_sample_input]
     num_iter = 10
     shark_module = SharkTrainer(
         BertModule(),
-        (
-            sample_input_tensors,
-            tf.convert_to_tensor(
-                np.random.randint(5, size=(BATCH_SIZE)), dtype=tf.int32
-            ),
-        ),
-    )
+        (sample_input_tensors,
+         tf.convert_to_tensor(np.random.randint(5, size=(BATCH_SIZE)), dtype=tf.int32)))
     shark_module.set_frontend("tensorflow")
     shark_module.compile()
     start = time.time()

shark/examples/shark_training/neural_net_training.py

@@ -4,6 +4,7 @@ from shark.shark_trainer import SharkTrainer
 
 
 class Foo(torch.nn.Module):
+
     def __init__(self):
         super(Foo, self).__init__()
         self.l1 = torch.nn.Linear(10, 16)
@@ -27,9 +28,8 @@ def get_sorted_params(named_params):
 
 def forward(params, buffers, args):
     params_and_buffers = {**params, **buffers}
-    _stateless.functional_call(
-        mod, params_and_buffers, args, {}
-    ).sum().backward()
+    _stateless.functional_call(mod, params_and_buffers, args,
+                               {}).sum().backward()
     optim = torch.optim.SGD(get_sorted_params(params), lr=0.01)
     optim.step()
     return params, buffers

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

@@ -1,41 +0,0 @@
-# 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

@@ -1,25 +0,0 @@
-#!/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

@@ -1,597 +0,0 @@
-# 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

@@ -28,14 +28,9 @@ from torch_mlir.eager_mode.torch_mlir_eager_backend import (
     TorchMLIREagerBackend,
     TensorMetaData,
 )
-from torch_mlir_e2e_test.eager_backends.refbackend import (
-    NUMPY_TO_TORCH_DTYPE_DICT,
-)
+from torch_mlir_e2e_test.eager_backends.refbackend import NUMPY_TO_TORCH_DTYPE_DICT
 
-from shark.iree_utils.compile_utils import (
-    get_iree_compiled_module,
-    IREE_DEVICE_MAP,
-)
+from shark.iree_utils import get_iree_compiled_module, IREE_DEVICE_MAP
 
 
 class EagerModeIREELinalgOnTensorsBackend(TorchMLIREagerBackend):
@@ -48,19 +43,18 @@ class EagerModeIREELinalgOnTensorsBackend(TorchMLIREagerBackend):
 
     def __init__(self, device: str):
         self.torch_device_str = device
-        self.config = ireert.Config(IREE_DEVICE_MAP[device])
-        self.raw_device_str = device
+        self.iree_device_str = IREE_DEVICE_MAP[device]
+        self.config = ireert.Config(self.iree_device_str)
 
-    def get_torch_metadata(
-        self, tensor: DeviceArray, kwargs: Dict[str, Any]
-    ) -> TensorMetaData:
+    def get_torch_metadata(self, tensor: DeviceArray,
+                           kwargs: Dict[str, Any]) -> TensorMetaData:
         return TensorMetaData(
             size=tensor.shape,
             dtype=NUMPY_TO_TORCH_DTYPE_DICT[tensor.dtype.type],
             device=torch.device(self.torch_device_str),
             requires_grad=tensor.dtype.type
-            in {np.float, np.float32, np.float64}
-            and kwargs.get("requires_grad", False),
+            in {np.float, np.float32, np.float64} and
+            kwargs.get("requires_grad", False),
         )
 
     def compile(self, imported_module: Module):
@@ -70,9 +64,9 @@ class EagerModeIREELinalgOnTensorsBackend(TorchMLIREagerBackend):
             "torch-function-to-torch-backend-pipeline,torch-backend-to-linalg-on-tensors-backend-pipeline",
             "EagerMode",
         )
-        callable, _ = get_iree_compiled_module(
-            imported_module, self.raw_device_str, func_name=fn_name
-        )
+        callable, _ = get_iree_compiled_module(imported_module,
+                                               self.iree_device_str,
+                                               func_name=fn_name)
         return callable
 
     def copy_into(self, dst, src):
@@ -82,7 +76,6 @@ class EagerModeIREELinalgOnTensorsBackend(TorchMLIREagerBackend):
     def transfer_from_device_to_torch(self, e):
         return torch.from_numpy(e.to_host())
 
-    def transfer_from_torch_to_device(
-        self, tensor: torch.Tensor
-    ) -> DeviceArray:
+    def transfer_from_torch_to_device(self,
+                                      tensor: torch.Tensor) -> DeviceArray:
         return iree.runtime.asdevicearray(self.config.device, tensor.numpy())

shark/iree_utils.py

@@ -0,0 +1,359 @@
+# Copyright 2020 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 iree.runtime as ireert
+import iree.runtime.scripts.iree_benchmark_module as benchmark_module
+import iree.compiler as ireec
+from shark.torch_mlir_utils import get_module_name_for_asm_dump
+from shark.cuda_utils import get_cuda_sm_cc
+from shark.model_annotation import *
+import subprocess
+import numpy as np
+import os
+import re
+import sys
+
+IREE_DEVICE_MAP = {
+    "cpu": "local-task",
+    "gpu": "cuda",
+    "cuda": "cuda",
+    "vulkan": "vulkan",
+    "metal": "vulkan",
+    "rocm": "rocm"
+}
+
+IREE_TARGET_MAP = {
+    "cpu": "dylib",
+    "gpu": "cuda",
+    "cuda": "cuda",
+    "vulkan": "vulkan",
+    "metal": "vulkan",
+    "rocm": "rocm"
+}
+
+UNIT_TO_SECOND_MAP = {"ms": 0.001, "s": 1}
+
+
+def check_device_drivers(device):
+    """Checks necessary drivers present for gpu and vulkan devices"""
+    if (device in ["gpu", "cuda"]):
+        try:
+            subprocess.check_output('nvidia-smi')
+        except Exception:
+            return True
+    elif (device in ["metal", "vulkan"]):
+        try:
+            subprocess.check_output('vulkaninfo')
+        except Exception:
+            return True
+    elif (device == "cpu"):
+        return False
+    # Unknown device.
+    else:
+        return True
+
+    return False
+
+
+def get_iree_cpu_args():
+    find_triple_cmd = "uname -s -m"
+    os_name, proc_name = subprocess.run(
+        find_triple_cmd, shell=True, stdout=subprocess.PIPE,
+        check=True).stdout.decode('utf-8').split()
+    if os_name == "Darwin":
+        find_kernel_version_cmd = "uname -r"
+        kernel_version = subprocess.run(find_kernel_version_cmd,
+                                        shell=True,
+                                        stdout=subprocess.PIPE,
+                                        check=True).stdout.decode('utf-8')
+        target_triple = f"{proc_name}-apple-darwin{kernel_version}"
+    elif os_name == "Linux":
+        target_triple = f"{proc_name}-linux-gnu"
+    else:
+        error_message = f"OS Type f{os_name} not supported and triple can't be determined, open issue to dSHARK team please :)"
+        raise Exception(error_message)
+    print(f"Target triple found:{target_triple}")
+    return [f"-iree-llvm-target-triple={target_triple}"]
+
+
+def get_iree_gpu_args():
+    ireert.flags.FUNCTION_INPUT_VALIDATION = False
+    ireert.flags.parse_flags("--cuda_allow_inline_execution")
+    sm_arch = get_cuda_sm_cc()
+    if sm_arch in ['sm_70', 'sm_72', 'sm_75', 'sm_80', 'sm_84', 'sm_86']:
+        return [
+            "--iree-hal-cuda-disable-loop-nounroll-wa",
+            f"--iree-hal-cuda-llvm-target-arch={sm_arch}"
+        ]
+    else:
+        return ["--iree-hal-cuda-disable-loop-nounroll-wa"]
+
+
+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 == "M1":
+        print("Found Apple 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"
+    else:
+        print(
+            "Optimized kernel for your target device is not added yet. Contact SHARK Admin on discord[https://discord.com/invite/RUqY2h2s9u] or pull up an issue."
+        )
+        return None
+
+
+def get_iree_vulkan_args():
+    #vulkan_flag = ["--iree-flow-demote-i64-to-i32"]
+    vulkan_flag = []
+    vulkan_triple_flag = get_vulkan_triple_flag()
+    if vulkan_triple_flag is not None:
+        vulkan_flag.append(vulkan_triple_flag)
+    return vulkan_flag
+
+
+def get_iree_device_args(device):
+    if device == "cpu":
+        return get_iree_cpu_args()
+    if device in ["gpu", "cuda"]:
+        return get_iree_gpu_args()
+    if device in ["metal", "vulkan"]:
+        return get_iree_vulkan_args()
+    return []
+
+
+def get_iree_frontend_args(frontend):
+    if frontend in ["torch", "pytorch", "linalg"]:
+        return ["--iree-llvm-target-cpu-features=host"]
+    elif frontend in ["tensorflow", "tf", "mhlo"]:
+        return [
+            "--iree-llvm-target-cpu-features=host",
+            "--iree-mhlo-demote-i64-to-i32=false",
+            "--iree-flow-demote-i64-to-i32"
+        ]
+    else:
+        # Frontend not found.
+        return []
+
+
+def compile_module_to_flatbuffer(module, device, frontend, func_name,
+                                 model_config_path):
+    # Setup Compile arguments wrt to frontends.
+    input_type = ""
+    args = get_iree_frontend_args(frontend)
+    args += get_iree_device_args(device)
+
+    if frontend in ["tensorflow", "tf"]:
+        input_type = "mhlo"
+    elif frontend in ["mhlo", "tosa"]:
+        input_type = frontend
+    elif frontend in ["tflite"]:
+        input_type = "tosa"
+
+    # Annotate the input module with the configs
+    if model_config_path != None:
+        # Currently tuned model only works on tf frontend
+        if frontend in ["tensorflow", "tf"]:
+            input_module = module.decode('utf-8')
+        elif frontend in ["pytorch", "torch"]:
+            input_module = module.operation.get_asm()
+        with create_context() as ctx:
+            module = model_annotation(ctx,
+                                      input_contents=input_module,
+                                      config_path=model_config_path)
+            module = str(module)
+
+    # 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]],
+            extra_args=args,
+            input_type=input_type)
+    else:
+        # Currently for Torch.
+        flatbuffer_blob = ireec.compile_str(
+            str(module),
+            target_backends=[IREE_TARGET_MAP[device]],
+            extra_args=args)
+    return flatbuffer_blob
+
+
+def get_iree_module(flatbuffer_blob, device, func_name):
+    vm_module = ireert.VmModule.from_flatbuffer(flatbuffer_blob)
+    config = ireert.Config(IREE_DEVICE_MAP[device])
+    ctx = ireert.SystemContext(config=config)
+    ctx.add_vm_module(vm_module)
+    ModuleCompiled = ctx.modules.module[func_name]
+    return ModuleCompiled, config
+
+
+def get_iree_compiled_module(module,
+                             device: str,
+                             frontend: str = "torch",
+                             func_name: str = "forward",
+                             model_config_path: str = None):
+    """Given a module returns the compiled .vmfb and configs"""
+    flatbuffer_blob = compile_module_to_flatbuffer(module, device, frontend,
+                                                   func_name, model_config_path)
+    return get_iree_module(flatbuffer_blob, device, func_name)
+
+
+def export_iree_module_to_vmfb(module,
+                               device: str,
+                               directory: str,
+                               frontend: str = "torch",
+                               func_name: str = "forward",
+                               model_config_path: str = None):
+    flatbuffer_blob = compile_module_to_flatbuffer(module, device, frontend,
+                                                   func_name, model_config_path)
+    module_name = f"{frontend}_{func_name}_{device}"
+    filename = os.path.join(directory, module_name + ".vmfb")
+    print(f"Saved vmfb in {filename}.")
+    with open(filename, 'wb') as f:
+        f.write(flatbuffer_blob)
+    return filename
+
+
+def export_module_to_mlir_file(module, frontend, directory: str):
+    mlir_str = module
+    if frontend in ["tensorflow", "tf", "mhlo"]:
+        mlir_str = module.decode('utf-8')
+    elif frontend in ["pytorch", "torch"]:
+        mlir_str = module.operation.get_asm()
+    filename = os.path.join(directory, "model.mlir")
+    with open(filename, 'w') as f:
+        f.write(mlir_str)
+    print(f"Saved mlir in {filename}.")
+    return filename
+
+
+def get_results(compiled_vm, input, config, frontend="torch"):
+    """Runs a .vmfb file given inputs and config and returns output."""
+    device_inputs = input
+    if frontend in ["torch", "pytorch"]:
+        device_inputs = [ireert.asdevicearray(config.device, a) for a in input]
+    if frontend in ["tensorflow", "tf", "tflite"]:
+        device_inputs = []
+        for a in input:
+            if (isinstance(a, list)):
+                device_inputs.append([
+                    ireert.asdevicearray(config.device, val, dtype=np.int32)
+                    for val in a
+                ])
+            else:
+                device_inputs.append(ireert.asdevicearray(config.device, a))
+    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)))
+        return result_tensors
+    elif (isinstance(result, dict)):
+        data = list(result.items())
+        res = np.array(data, dtype=object)
+        return np.copy(res)
+    else:
+        return np.copy(np.asarray(result, dtype=result.dtype))
+
+
+######### Benchmark Related Tools ###########
+
+
+def tensor_to_type_str(input_tensors: tuple, frontend: str):
+    """
+    Input: A tuple of input tensors i.e tuple(torch.tensor)
+    Output: list of string that represent mlir types (i.e 1x24xf64)
+    # TODO: Support more than floats, and ints
+    """
+    list_of_type = []
+    for input_tensor in input_tensors:
+        type_string = "x".join([str(dim) for dim in input_tensor.shape])
+        if frontend in ["torch", "pytorch"]:
+            dtype_string = str(input_tensor.dtype).replace("torch.", "")
+        elif frontend in ["tensorflow", "tf", "mhlo"]:
+            dtype = input_tensor.dtype
+            dtype_string = re.findall('\'[^"]*\'',
+                                      str(dtype))[0].replace("\'", "")
+        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))
+        type_string += f"x{mlir_type_string}"
+        list_of_type.append(type_string)
+    return list_of_type
+
+
+def build_benchmark_args(input_file: str,
+                         device: str,
+                         input_tensors: tuple,
+                         frontend: str,
+                         training=False):
+    """
+    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}"]
+    fn_name = "forward"
+    if training == True:
+        # 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]}")
+    mlir_input_types = tensor_to_type_str(input_tensors, frontend)
+    for mlir_input in mlir_input_types:
+        benchmark_cl.append(f"--function_input={mlir_input}")
+    time_extractor = "| awk \'END{{print $2 $3}}\'"
+    benchmark_cl.append(time_extractor)
+    return benchmark_cl
+
+
+def run_cmd(cmd):
+    """
+    Inputs: cli command string.
+    """
+    try:
+        result = subprocess.run(cmd,
+                                shell=True,
+                                stdout=subprocess.PIPE,
+                                stderr=subprocess.PIPE,
+                                check=True)
+        result_str = result.stdout.decode()
+        return result_str
+    except Exception:
+        sys.exit("Exiting program due to error running:", cmd)
+
+
+def run_benchmark_module(benchmark_cl):
+    """
+    Run benchmark command, extract result and return iteration/seconds.
+
+    Input: benchmark command.
+    """
+    benchmark_path = benchmark_cl[0]
+    assert os.path.exists(
+        benchmark_path
+    ), "Cannot find benchmark_module, Please contact SHARK maintainer on discord."
+    bench_result = run_cmd(' '.join(benchmark_cl))
+    regex_split = re.compile("([0-9]+[.]*[0-9]*)([a-zA-Z]+)")
+    match = regex_split.match(bench_result)
+    time = float(match.group(1))
+    unit = match.group(2)
+    return 1.0 / (time * UNIT_TO_SECOND_MAP[unit])

shark/iree_utils/_common.py

@@ -1,100 +0,0 @@
-# Copyright 2020 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.
-
-## Common utilities to be shared by iree utilities.
-
-import os
-import sys
-import subprocess
-
-
-def run_cmd(cmd):
-    """
-    Inputs: cli command string.
-    """
-    try:
-        result = subprocess.run(
-            cmd,
-            shell=True,
-            stdout=subprocess.PIPE,
-            stderr=subprocess.PIPE,
-            check=True,
-        )
-        result_str = result.stdout.decode()
-        return result_str
-    except Exception:
-        sys.exit("Exiting program due to error running:", cmd)
-
-
-IREE_DEVICE_MAP = {
-    "cpu": "local-task",
-    "cuda": "cuda",
-    "vulkan": "vulkan",
-    "metal": "vulkan",
-    "rocm": "rocm",
-    "intel-gpu": "level_zero",
-}
-
-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 == "cuda":
-        try:
-            subprocess.check_output("nvidia-smi")
-        except Exception:
-            return True
-    elif device in ["metal", "vulkan"]:
-        try:
-            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
-
-    return False
-
-
-# Installation info for the missing device drivers.
-def device_driver_info(device):
-    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

@@ -1,122 +0,0 @@
-# Copyright 2020 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 iree.runtime.scripts.iree_benchmark_module as benchmark_module
-from shark.iree_utils._common import run_cmd, IREE_DEVICE_MAP
-import numpy as np
-import os
-import re
-
-UNIT_TO_SECOND_MAP = {"ms": 0.001, "s": 1}
-
-
-def tensor_to_type_str(input_tensors: tuple, mlir_dialect: str):
-    """
-    Input: A tuple of input tensors i.e tuple(torch.tensor)
-    Output: list of string that represent mlir types (i.e 1x24xf64)
-    # TODO: Support more than floats, and ints
-    """
-    list_of_type = []
-    for input_tensor in input_tensors:
-        type_string = "x".join([str(dim) for dim in input_tensor.shape])
-        if mlir_dialect in ["linalg", "tosa"]:
-            dtype_string = str(input_tensor.dtype).replace("torch.", "")
-        elif mlir_dialect in ["mhlo", "tflite"]:
-            dtype = input_tensor.dtype
-            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))
-        type_string += f"x{mlir_type_string}"
-        list_of_type.append(type_string)
-    return list_of_type
-
-
-def build_benchmark_args(
-    input_file: str,
-    device: str,
-    input_tensors: tuple,
-    mlir_dialect: str,
-    training=False,
-):
-    """
-    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.
-    fn_name = "forward"
-    if training == True:
-        # 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]}")
-    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}")
-    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.
-    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
-
-
-def run_benchmark_module(benchmark_cl):
-    """
-    Run benchmark command, extract result and return iteration/seconds.
-
-    # TODO: Add an example of the benchmark command.
-    Input: benchmark command.
-    """
-    benchmark_path = benchmark_cl[0]
-    assert os.path.exists(
-        benchmark_path
-    ), "Cannot find benchmark_module, Please contact SHARK maintainer on discord."
-    bench_result = run_cmd(" ".join(benchmark_cl))
-    regex_split = re.compile("([0-9]+[.]*[0-9]*)([a-zA-Z]+)")
-    match = regex_split.match(bench_result)
-    time = float(match.group(1))
-    unit = match.group(2)
-    return 1.0 / (time * UNIT_TO_SECOND_MAP[unit])

shark/iree_utils/compile_utils.py

@@ -1,314 +0,0 @@
-# Copyright 2020 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 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.benchmark_utils import *
-import numpy as np
-import os
-import re
-
-# Get the iree-compile arguments given device.
-def get_iree_device_args(device, extra_args=[]):
-    if device == "cpu":
-        from shark.iree_utils.cpu_utils import get_iree_cpu_args
-
-        return get_iree_cpu_args()
-    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(extra_args=extra_args)
-    if device == "rocm":
-        from shark.iree_utils.gpu_utils import get_iree_rocm_args
-
-        return get_iree_rocm_args()
-    return []
-
-
-# Get the iree-compiler arguments given frontend.
-def get_iree_frontend_args(frontend):
-    if frontend in ["torch", "pytorch", "linalg"]:
-        return ["--iree-llvm-target-cpu-features=host"]
-    elif frontend in ["tensorflow", "tf", "mhlo"]:
-        return [
-            "--iree-llvm-target-cpu-features=host",
-            "--iree-mhlo-demote-i64-to-i32=false",
-            "--iree-flow-demote-i64-to-i32",
-        ]
-    else:
-        # Frontend not found.
-        return []
-
-
-# Common args to be used given any frontend or device.
-def get_iree_common_args():
-    return [
-        "--iree-stream-resource-index-bits=64",
-        "--iree-vm-target-index-bits=64",
-        "--iree-util-zero-fill-elided-attrs",
-    ]
-
-
-def create_dispatch_dirs(bench_dir, device):
-    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_}"):
-            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 compile_benchmark_dirs(bench_dir, device, dispatch_benchmarks):
-    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):
-        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 = ireert.Config(IREE_DEVICE_MAP[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=vm_module.function_names[0],
-                    )
-
-                    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()
-
-                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()
-
-
-def compile_module_to_flatbuffer(
-    module, device, frontend, func_name, model_config_path, extra_args
-):
-    # Setup Compile arguments wrt to frontends.
-    input_type = ""
-    args = get_iree_frontend_args(frontend)
-    args += get_iree_device_args(device, extra_args)
-    args += get_iree_common_args()
-    args += extra_args
-
-    if frontend in ["tensorflow", "tf"]:
-        input_type = "mhlo"
-    elif frontend in ["mhlo", "tosa"]:
-        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 != "":
-        # Currently for MHLO/TOSA.
-        flatbuffer_blob = ireec.compile_str(
-            module,
-            target_backends=[IREE_TARGET_MAP[device]],
-            extra_args=args,
-            input_type=input_type,
-        )
-    else:
-        # Currently for Torch.
-        flatbuffer_blob = ireec.compile_str(
-            module,
-            target_backends=[IREE_TARGET_MAP[device]],
-            extra_args=args,
-        )
-
-    return flatbuffer_blob
-
-
-def get_iree_module(flatbuffer_blob, device, func_name):
-    # Returns the compiled module and the configs.
-    config = ireert.Config(IREE_DEVICE_MAP[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]
-    return ModuleCompiled, config
-
-
-def get_iree_compiled_module(
-    module,
-    device: str,
-    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, 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,
-    mlir_dialect: str = "linalg",
-    func_name: str = "forward",
-    model_config_path: str = None,
-    module_name: str = None,
-    extra_args: list = [],
-):
-    # Compiles the module given specs and saves it as .vmfb file.
-    flatbuffer_blob = compile_module_to_flatbuffer(
-        module, device, mlir_dialect, func_name, model_config_path, extra_args
-    )
-    if module_name is None:
-        module_name = f"{mlir_dialect}_{func_name}_{device}"
-    filename = os.path.join(directory, module_name + ".vmfb")
-    print(f"Saved vmfb in {filename}.")
-    with open(filename, "wb") as f:
-        f.write(flatbuffer_blob)
-    return filename
-
-
-def export_module_to_mlir_file(module, frontend, directory: str):
-    # TODO: write proper documentation.
-    mlir_str = module
-    if frontend in ["tensorflow", "tf", "mhlo", "tflite"]:
-        mlir_str = module.decode("utf-8")
-    elif frontend in ["pytorch", "torch"]:
-        mlir_str = module.operation.get_asm()
-    filename = os.path.join(directory, "model.mlir")
-    with open(filename, "w") as f:
-        f.write(mlir_str)
-    print(f"Saved mlir in {filename}.")
-    return filename
-
-
-def get_results(compiled_vm, input, config, frontend="torch"):
-    """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)))
-        return result_tensors
-    elif isinstance(result, dict):
-        data = list(result.items())
-        res = np.array(data, dtype=object)
-        return np.copy(res)
-    else:
-        return np.copy(np.asarray(result, dtype=result.dtype))

shark/iree_utils/cpu_utils.py

@@ -1,44 +0,0 @@
-# Copyright 2020 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.
-
-# All the iree_cpu related functionalities go here.
-
-import subprocess
-
-# Get the default cpu args.
-def get_iree_cpu_args():
-    find_triple_cmd = "uname -s -m"
-    os_name, proc_name = (
-        subprocess.run(
-            find_triple_cmd, shell=True, stdout=subprocess.PIPE, check=True
-        )
-        .stdout.decode("utf-8")
-        .split()
-    )
-    if os_name == "Darwin":
-        find_kernel_version_cmd = "uname -r"
-        kernel_version = subprocess.run(
-            find_kernel_version_cmd,
-            shell=True,
-            stdout=subprocess.PIPE,
-            check=True,
-        ).stdout.decode("utf-8")
-        target_triple = f"{proc_name}-apple-darwin{kernel_version}"
-    elif os_name == "Linux":
-        target_triple = f"{proc_name}-linux-gnu"
-    else:
-        error_message = f"OS Type f{os_name} not supported and triple can't be determined, open issue to dSHARK team please :)"
-        raise Exception(error_message)
-    print(f"Target triple found:{target_triple}")
-    return [f"-iree-llvm-target-triple={target_triple}"]

shark/iree_utils/gpu_utils.py

@@ -1,123 +0,0 @@
-# Copyright 2020 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.
-
-# All the iree_gpu related functionalities go here.
-
-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():
-    ireert.flags.FUNCTION_INPUT_VALIDATION = False
-    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"]
-    ) and (shark_args.enable_tf32 == True):
-        return [
-            "--iree-hal-cuda-disable-loop-nounroll-wa",
-            f"--iree-hal-cuda-llvm-target-arch={sm_arch}",
-        ]
-    else:
-        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
-CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR = 39
-CU_DEVICE_ATTRIBUTE_CLOCK_RATE = 13
-CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE = 36
-
-
-def get_cuda_sm_cc():
-    libnames = ("libcuda.so", "libcuda.dylib", "cuda.dll")
-    for libname in libnames:
-        try:
-            cuda = ctypes.CDLL(libname)
-        except OSError:
-            continue
-        else:
-            break
-    else:
-        raise OSError("could not load any of: " + " ".join(libnames))
-
-    nGpus = ctypes.c_int()
-    name = b" " * 100
-    cc_major = ctypes.c_int()
-    cc_minor = ctypes.c_int()
-
-    result = ctypes.c_int()
-    device = ctypes.c_int()
-    context = ctypes.c_void_p()
-    error_str = ctypes.c_char_p()
-
-    result = cuda.cuInit(0)
-    if result != CUDA_SUCCESS:
-        cuda.cuGetErrorString(result, ctypes.byref(error_str))
-        print(
-            "cuInit failed with error code %d: %s"
-            % (result, error_str.value.decode())
-        )
-        return 1
-    result = cuda.cuDeviceGetCount(ctypes.byref(nGpus))
-    if result != CUDA_SUCCESS:
-        cuda.cuGetErrorString(result, ctypes.byref(error_str))
-        print(
-            "cuDeviceGetCount failed with error code %d: %s"
-            % (result, error_str.value.decode())
-        )
-        return 1
-    print("Found %d device(s)." % nGpus.value)
-    for i in range(nGpus.value):
-        result = cuda.cuDeviceGet(ctypes.byref(device), i)
-        if result != CUDA_SUCCESS:
-            cuda.cuGetErrorString(result, ctypes.byref(error_str))
-            print(
-                "cuDeviceGet failed with error code %d: %s"
-                % (result, error_str.value.decode())
-            )
-            return 1
-        print("Device: %d" % i)
-        if (
-            cuda.cuDeviceGetName(ctypes.c_char_p(name), len(name), device)
-            == CUDA_SUCCESS
-        ):
-            print("  Name: %s" % (name.split(b"\0", 1)[0].decode()))
-        if (
-            cuda.cuDeviceComputeCapability(
-                ctypes.byref(cc_major), ctypes.byref(cc_minor), device
-            )
-            == CUDA_SUCCESS
-        ):
-            print(
-                "  Compute Capability: %d.%d"
-                % (cc_major.value, cc_minor.value)
-            )
-    sm = f"sm_{cc_major.value}{cc_minor.value}"
-    return sm

shark/iree_utils/vulkan_utils.py

@@ -1,70 +0,0 @@
-# Copyright 2020 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.
-
-# All the iree_vulkan related functionalities go here.
-
-from os import linesep
-from shark.iree_utils._common import run_cmd
-
-
-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_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
-
-    vulkan_device = get_vulkan_device_name()
-    if all(x in vulkan_device for x in ("Apple", "M1")):
-        print(f"Found {vulkan_device} Device. Using m1-moltenvk-macos")
-        return "-iree-vulkan-target-triple=m1-moltenvk-macos"
-    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 all(x in vulkan_device for x in ("A100", "SXM4")):
-        print(f"Found {vulkan_device} Device. Using ampere-rtx3080-linux")
-        return "-iree-vulkan-target-triple=ampere-rtx3080-linux"
-    elif all(x in vulkan_device for x in ("RTX", "3090")):
-        print(f"Found {vulkan_device} Device. Using ampere-rtx3090-linux")
-        return "-iree-vulkan-target-triple=ampere-rtx3090-linux"
-    elif "AMD" in vulkan_device:
-        print("Found AMD device. Using rdna2-unknown-linux")
-        return "-iree-vulkan-target-triple=rdna2-unknown-linux"
-    else:
-        print(
-            """Optimized kernel for your target device is not added yet.
-            Contact SHARK Admin on discord[https://discord.com/invite/RUqY2h2s9u]
-            or pull up an issue."""
-        )
-        print(f"Target : {vulkan_device}")
-        return None
-
-
-def get_iree_vulkan_args(extra_args=[]):
-    # vulkan_flag = ["--iree-flow-demote-i64-to-i32"]
-    vulkan_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

shark/model_annotation.py

@@ -12,22 +12,20 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import sys
 import json
 import os
-import sys
-from typing import Dict, List
+from typing import List, Dict
 
 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,
-    search_op: str = "matmul",
-):
+
+def model_annotation(ctx: ir.Context, *, input_contents: str, config_path: str):
     if os.path.isfile(input_contents):
         with open(input_contents, "rb") as f:
             input_contents = f.read()
@@ -40,35 +38,20 @@ def model_annotation(
 
     # The Python API does not expose a general walk() function, so we just
     # do it ourselves.
-    walk_children(module.operation, configs, 0, search_op)
+    walk_children(module.operation, configs)
 
     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], 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.")
-
+def walk_children(op: ir.Operation, configs: List[Dict]):
     for region in op.regions:
         for block in region.blocks:
             for child_op in block.operations:
@@ -76,41 +59,29 @@ def walk_children(
                 # 'operation' and 'name' attributes.
                 if isinstance(child_op, ir.OpView):
                     child_op = child_op.operation
-                if child_op.name in op_names and idx < len(configs):
-                    add_attributes(child_op, configs[idx])
+                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)
+
                     idx = idx + 1
                     print(f"Updated op {child_op}", file=sys.stderr)
-                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)
+                walk_children(child_op, configs)
 
 
 def parse_config(config: Dict):
     if config["pipeline"] == "GPU" or config["pipeline"] == "GPU_TENSORCORE":
-        pipeline = (
-            "LLVMGPUMatmulSimt"
-            if config["pipeline"] == "GPU"
-            else "LLVMGPUMatmulTensorCore"
-        )
+        pipeline = "LLVMGPUMatmulSimt" if config[
+            "pipeline"] == "GPU" else "LLVMGPUMatmulTensorCore"
         tile_sizes = [config["work_group_tile_sizes"]]
         workgroup_size = config["work_group_sizes"]
         try:
@@ -124,9 +95,8 @@ def parse_config(config: Dict):
     else:
         pipeline = config["pipeline"]
         tile_sizes = [
-            config["work_group_tile_sizes"],
-            config["l1_tile_sizes"],
-            config["vector_tile_sizes"],
+            config["work_group_tile_sizes"], config["l1_tile_sizes"],
+            config["vector_tile_sizes"]
         ]
         workgroup_size = []
         split_k = None
@@ -134,13 +104,9 @@ def parse_config(config: Dict):
     return tile_sizes, pipeline, workgroup_size, split_k, pipeline_depth
 
 
-def add_compilation_info(
-    op: ir.Operation,
-    tile_sizes: List[List[int]],
-    pipeline: str,
-    workgroup_size: List[int],
-    pipeline_depth: int,
-):
+def add_compilation_info(op: ir.Operation, tile_sizes: List[List[int]],
+                         pipeline: str, workgroup_size: List[int],
+                         pipeline_depth: int):
     # We don't have a Python binding for CompilationInfo, so we just parse
     # its string form.
     if pipeline_depth:
@@ -148,21 +114,19 @@ def add_compilation_info(
             f"#iree_codegen.compilation_info<"
             f"lowering_config = <tile_sizes = {repr(tile_sizes)}>, "
             f"translation_info = <{pipeline} pipeline_depth = {pipeline_depth}>, "
-            f"workgroup_size = {repr(workgroup_size)}>"
-        )
+            f"workgroup_size = {repr(workgroup_size)}>")
     else:
         attr = ir.Attribute.parse(
             f"#iree_codegen.compilation_info<"
             f"lowering_config = <tile_sizes = {repr(tile_sizes)}>, "
             f"translation_info = <{pipeline}>, "
-            f"workgroup_size = {repr(workgroup_size)}>"
-        )
+            f"workgroup_size = {repr(workgroup_size)}>")
     op.attributes["compilation_info"] = 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 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 create_context() -> ir.Context:
@@ -174,14 +138,6 @@ def create_context() -> ir.Context:
 
 if __name__ == "__main__":
     with create_context() as ctx:
-        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}.")
+        model_annotation(ctx,
+                         input_contents=sys.argv[1],
+                         config_path=sys.argv[2])

shark/parser.py

@@ -20,8 +20,8 @@ def dir_path(path):
     if os.path.isdir(path):
         return path
     else:
-        os.mkdir(path)
-        return path
+        raise argparse.ArgumentTypeError(
+            f"readable_dir:{path} is not a valid path")
 
 
 def dir_file(path):
@@ -29,80 +29,43 @@ def dir_file(path):
         return path
     else:
         raise argparse.ArgumentTypeError(
-            f"readable_file:{path} is not a valid file"
-        )
+            f"readable_file:{path} is not a valid file")
 
 
-parser = argparse.ArgumentParser(description="SHARK runner.")
+parser = argparse.ArgumentParser(description='SHARK runner.')
 parser.add_argument(
     "--device",
     type=str,
     default="cpu",
-    help="Device on which shark_runner runs. options are cpu, cuda, and vulkan",
-)
+    help="Device on which shark_runner runs. options are cpu, gpu, and vulkan")
 parser.add_argument(
     "--repro_dir",
-    help="Directory to which module files will be saved for reproduction or debugging.",
+    help=
+    "Directory to which module files will be saved for reproduction or debugging.",
     type=dir_path,
-    default="./shark_tmp",
-)
-parser.add_argument(
-    "--enable_tf32",
-    type=bool,
-    default=False,
-    help="Enables TF32 precision calculations on supported GPUs.",
-)
+    default="/tmp/")
+parser.add_argument("--save_mlir",
+                    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.")
 parser.add_argument(
     "--model_config_path",
     help="Directory to where the tuned model config file is located.",
-    default=None,
-)
+    default=None)
 
 parser.add_argument(
     "--num_warmup_iterations",
     type=int,
-    default=5,
-    help="Run the model for the specified number of warmup iterations.",
-)
+    default=2,
+    help="Run the model for the specified number of warmup iterations.")
 parser.add_argument(
     "--num_iterations",
     type=int,
-    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"',
-)
+    default=1,
+    help="Run the model for the specified number of iterations.")
 
 shark_args, unknown = parser.parse_known_args()

shark/shark_benchmark_runner.py

@@ -1,371 +0,0 @@
-# Copyright 2020 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 shark.shark_runner import SharkRunner
-from shark.iree_utils.compile_utils import export_iree_module_to_vmfb
-from shark.iree_utils.benchmark_utils import (
-    build_benchmark_args,
-    run_benchmark_module,
-)
-from shark.parser import shark_args
-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
-
-
-class SharkBenchmarkRunner(SharkRunner):
-    # SharkRunner derived class with Benchmarking capabilities.
-    def __init__(
-        self,
-        mlir_module: bytes,
-        function_name: str = "forward",
-        device: str = "none",
-        mlir_dialect: str = "linalg",
-        extra_args: list = [],
-    ):
-        self.device = shark_args.device if device == "none" else device
-        self.frontend_model = None
-        self.vmfb_file = None
-        self.mlir_dialect = mlir_dialect
-        self.extra_args = extra_args
-        SharkRunner.__init__(
-            self,
-            mlir_module,
-            function_name,
-            device,
-            self.mlir_dialect,
-            self.extra_args,
-            compile_vmfb=True,
-        )
-        if self.vmfb_file == None:
-            self.vmfb_file = export_iree_module_to_vmfb(
-                mlir_module,
-                device,
-                shark_args.repro_dir,
-                self.mlir_dialect,
-                function_name,
-                extra_args=self.extra_args,
-            )
-
-    def setup_cl(self, input_tensors):
-        self.benchmark_cl = build_benchmark_args(
-            self.vmfb_file,
-            self.device,
-            input_tensors,
-            mlir_dialect=self.mlir_dialect,
-        )
-        print(self.benchmark_cl)
-
-    def benchmark_frontend(self, modelname):
-        if self.mlir_dialect in ["linalg", "torch"]:
-            return self.benchmark_torch(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 == "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 == "cuda" else "cpu"
-        )
-        HFmodel, input = get_torch_model(modelname)[:2]
-        frontend_model = HFmodel.model
-        frontend_model.to(torch_device)
-        input.to(torch_device)
-
-        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"Torch 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_tf(self, modelname):
-        import tensorflow as tf
-        from tank.model_utils_tf import get_tf_model
-
-        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):
-        print(self.benchmark_cl)
-        result = run_benchmark_module(self.benchmark_cl)
-        print(f"Shark-IREE-C benchmark:{result} iter/second")
-        return [f"{result}", f"{1000/result}"]
-
-    def benchmark_python(self, inputs):
-        input_list = [x for x in inputs]
-        for i in range(shark_args.num_warmup_iterations):
-            self.run(input_list)
-
-        begin = time.time()
-        for i in range(shark_args.num_iterations):
-            out = self.run(input_list)
-            if i == shark_args.num_iterations - 1:
-                end = time.time()
-        print(
-            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_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):
-        # Takes two numbers represented as strings and returns "<n>x slower/faster", as in "result is <n>x slower than baseline".
-        a = float(baseline)
-        b = float(result)
-        if a < b:
-            # result slower than baseline
-            comparison = (b - a) / a
-            comp_str = f"{round(comparison, 2)}x slower"
-        elif a > b:
-            # result faster than baseline
-            comparison = a / b
-            comp_str = f"{round(comparison, 2)}x faster"
-        else:
-            comp_str = "equal"
-        return comp_str
-
-    def benchmark_all_csv(
-        self, inputs: tuple, modelname, dynamic, device_str, frontend
-    ):
-        self.setup_cl(inputs)
-        field_names = [
-            "model",
-            "engine",
-            "dialect",
-            "device",
-            "shape_type",
-            "data_type",
-            "iter/sec",
-            "ms/iter",
-            "vs. PyTorch/TF",
-            "iterations",
-            "param_count",
-            "tags",
-            "notes",
-            "datetime",
-        ]
-        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:
-                writer = csv.writer(f)
-                writer.writerow(field_names)
-
-        with open("bench_results.csv", mode="a", newline="") as f:
-            writer = csv.DictWriter(f, fieldnames=field_names)
-            bench_result = {}
-            bench_result["model"] = modelname
-            if dynamic == True:
-                bench_result["shape_type"] = "dynamic"
-            else:
-                bench_result["shape_type"] = "static"
-            bench_result["device"] = device_str
-            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
-                    (
-                        bench_result["iter/sec"],
-                        bench_result["ms/iter"],
-                    ) = self.benchmark_frontend(modelname)
-                    self.frontend_result = bench_result["ms/iter"]
-                    bench_result["vs. PyTorch/TF"] = "="
-                    (
-                        bench_result["param_count"],
-                        bench_result["tags"],
-                        bench_result["notes"],
-                    ) = self.get_metadata(modelname)
-
-                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

@@ -1,280 +0,0 @@
-# Lint as: python3
-"""SHARK Downloader"""
-# Requirements : Put shark_tank in SHARK directory
-#   /SHARK
-#     /gen_shark_tank
-#       /tflite
-#         /albert_lite_base
-#         /...model_name...
-#       /tf
-#       /pytorch
-#
-#
-#
-
-import numpy as np
-import os
-import urllib.request
-import json
-import hashlib
-from pathlib import Path
-from shark.parser import shark_args
-
-input_type_to_np_dtype = {
-    "float32": np.float32,
-    "float64": np.float64,
-    "bool": np.bool_,
-    "int32": np.int32,
-    "int64": np.int64,
-    "uint8": np.uint8,
-    "int8": np.int8,
-}
-
-
-# Save the model in the home local so it needn't be fetched everytime in the CI.
-home = str(Path.home())
-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="
-        " pytest flag"
-    )
-
-# Checks whether the directory and files exists.
-def check_dir_exists(model_name, frontend="torch", dynamic=""):
-    model_dir = os.path.join(WORKDIR, model_name)
-
-    # Remove the _tf keyword from end.
-    if frontend in ["tf", "tensorflow"]:
-        model_name = model_name[:-3]
-    elif frontend in ["tflite"]:
-        model_name = model_name[:-7]
-    elif frontend in ["torch", "pytorch"]:
-        model_name = model_name[:-6]
-
-    if os.path.isdir(model_dir):
-        if (
-            os.path.isfile(
-                os.path.join(
-                    model_dir,
-                    model_name + dynamic + "_" + str(frontend) + ".mlir",
-                )
-            )
-            and os.path.isfile(os.path.join(model_dir, "function_name.npy"))
-            and os.path.isfile(os.path.join(model_dir, "inputs.npz"))
-            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."""
-            )
-            return True
-    return False
-
-
-# Downloads the torch model from gs://shark_tank dir.
-def download_torch_model(
-    model_name, dynamic=False, tank_url="gs://shark_tank/latest"
-):
-    model_name = model_name.replace("/", "_")
-    dyn_str = "_dynamic" if dynamic else ""
-    os.makedirs(WORKDIR, exist_ok=True)
-    model_dir_name = model_name + "_torch"
-
-    def gs_download_model():
-        gs_command = (
-            'gsutil -o "GSUtil:parallel_process_count=1" cp -r '
-            + tank_url
-            + "/"
-            + model_dir_name
-            + " "
-            + WORKDIR
-        )
-        if os.system(gs_command) != 0:
-            raise Exception("model not present in the tank. Contact Nod Admin")
-
-    if not check_dir_exists(model_dir_name, frontend="torch", dynamic=dyn_str):
-        gs_download_model()
-    else:
-        model_dir = os.path.join(WORKDIR, model_dir_name)
-        local_hash = str(np.load(os.path.join(model_dir, "hash.npy")))
-        gs_hash = (
-            'gsutil -o "GSUtil:parallel_process_count=1" cp '
-            + tank_url
-            + "/"
-            + model_dir_name
-            + "/hash.npy"
-            + " "
-            + os.path.join(model_dir, "upstream_hash.npy")
-        )
-        if os.system(gs_hash) != 0:
-            raise Exception("hash of the model not present in the tank.")
-        upstream_hash = str(
-            np.load(os.path.join(model_dir, "upstream_hash.npy"))
-        )
-        if local_hash != upstream_hash:
-            if shark_args.update_tank == True:
-                gs_download_model()
-            else:
-                print(
-                    "Hash does not match upstream in gs://shark_tank/. If you are using SHARK Downloader with locally generated artifacts, this is working as intended."
-                )
-
-    model_dir = os.path.join(WORKDIR, model_dir_name)
-    with open(
-        os.path.join(model_dir, model_name + dyn_str + "_torch.mlir"),
-        mode="rb",
-    ) as f:
-        mlir_file = f.read()
-
-    function_name = str(np.load(os.path.join(model_dir, "function_name.npy")))
-    inputs = np.load(os.path.join(model_dir, "inputs.npz"))
-    golden_out = np.load(os.path.join(model_dir, "golden_out.npz"))
-
-    inputs_tuple = tuple([inputs[key] for key in inputs])
-    golden_out_tuple = tuple([golden_out[key] for key in golden_out])
-    return mlir_file, function_name, inputs_tuple, golden_out_tuple
-
-
-# Downloads the tflite model from gs://shark_tank dir.
-def download_tflite_model(
-    model_name, dynamic=False, tank_url="gs://shark_tank/latest"
-):
-    dyn_str = "_dynamic" if dynamic else ""
-    os.makedirs(WORKDIR, exist_ok=True)
-    model_dir_name = model_name + "_tflite"
-
-    def gs_download_model():
-        gs_command = (
-            'gsutil -o "GSUtil:parallel_process_count=1" cp -r '
-            + tank_url
-            + "/"
-            + model_dir_name
-            + " "
-            + WORKDIR
-        )
-        if os.system(gs_command) != 0:
-            raise Exception("model not present in the tank. Contact Nod Admin")
-
-    if not check_dir_exists(
-        model_dir_name, frontend="tflite", dynamic=dyn_str
-    ):
-        gs_download_model()
-    else:
-        model_dir = os.path.join(WORKDIR, model_dir_name)
-        local_hash = str(np.load(os.path.join(model_dir, "hash.npy")))
-        gs_hash = (
-            'gsutil -o "GSUtil:parallel_process_count=1" cp '
-            + tank_url
-            + "/"
-            + model_dir_name
-            + "/hash.npy"
-            + " "
-            + os.path.join(model_dir, "upstream_hash.npy")
-        )
-        if os.system(gs_hash) != 0:
-            raise Exception("hash of the model not present in the tank.")
-        upstream_hash = str(
-            np.load(os.path.join(model_dir, "upstream_hash.npy"))
-        )
-        if local_hash != upstream_hash:
-            if shark_args.update_tank == True:
-                gs_download_model()
-            else:
-                print(
-                    "Hash does not match upstream in gs://shark_tank/. If you are using SHARK Downloader with locally generated artifacts, this is working as intended."
-                )
-
-    model_dir = os.path.join(WORKDIR, model_dir_name)
-    with open(
-        os.path.join(model_dir, model_name + dyn_str + "_tflite.mlir"),
-        mode="rb",
-    ) as f:
-        mlir_file = f.read()
-
-    function_name = str(np.load(os.path.join(model_dir, "function_name.npy")))
-    inputs = np.load(os.path.join(model_dir, "inputs.npz"))
-    golden_out = np.load(os.path.join(model_dir, "golden_out.npz"))
-
-    inputs_tuple = tuple([inputs[key] for key in inputs])
-    golden_out_tuple = tuple([golden_out[key] for key in golden_out])
-    return mlir_file, function_name, inputs_tuple, golden_out_tuple
-
-
-def download_tf_model(
-    model_name, tuned=None, tank_url="gs://shark_tank/latest"
-):
-    model_name = model_name.replace("/", "_")
-    os.makedirs(WORKDIR, exist_ok=True)
-    model_dir_name = model_name + "_tf"
-
-    def gs_download_model():
-        gs_command = (
-            'gsutil -o "GSUtil:parallel_process_count=1" cp -r '
-            + tank_url
-            + "/"
-            + model_dir_name
-            + " "
-            + WORKDIR
-        )
-        if os.system(gs_command) != 0:
-            raise Exception("model not present in the tank. Contact Nod Admin")
-
-    if not check_dir_exists(model_dir_name, frontend="tf"):
-        gs_download_model()
-    else:
-        model_dir = os.path.join(WORKDIR, model_dir_name)
-        local_hash = str(np.load(os.path.join(model_dir, "hash.npy")))
-        gs_hash = (
-            'gsutil -o "GSUtil:parallel_process_count=1" cp '
-            + tank_url
-            + "/"
-            + model_dir_name
-            + "/hash.npy"
-            + " "
-            + os.path.join(model_dir, "upstream_hash.npy")
-        )
-        if os.system(gs_hash) != 0:
-            raise Exception("hash of the model not present in the tank.")
-        upstream_hash = str(
-            np.load(os.path.join(model_dir, "upstream_hash.npy"))
-        )
-        if local_hash != upstream_hash:
-            if shark_args.update_tank == True:
-                gs_download_model()
-            else:
-                print(
-                    "Hash does not match upstream in gs://shark_tank/. If you are using SHARK Downloader with locally generated artifacts, this is working as intended."
-                )
-
-    model_dir = os.path.join(WORKDIR, model_dir_name)
-    suffix = "_tf.mlir" if tuned is None else "_tf_" + tuned + ".mlir"
-    filename = os.path.join(model_dir, model_name + suffix)
-    if not os.path.isfile(filename):
-        filename = os.path.join(model_dir, model_name + "_tf.mlir")
-
-    with open(filename, mode="rb") 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

shark/shark_importer.py

@@ -1,246 +1,136 @@
 # Lint as: python3
 """SHARK Importer"""
 
-import sys
-import tempfile
+import iree.compiler.tflite as iree_tflite_compile
+import iree.runtime as iree_rt
+import numpy as np
 import os
-
-# List of the supported frontends.
-supported_frontends = {
-    "tensorflow",
-    "tf",
-    "pytorch",
-    "torch",
-    "tf-lite",
-    "tflite",
-}
+import sys
+import tensorflow.compat.v2 as tf
+import urllib.request
+from shark.shark_inference import SharkInference
 
 
 class SharkImporter:
-    """
-    SharkImporter converts frontend modules into a
-    mlir_module. The supported frameworks are tensorflow,
-    pytorch, and tf-lite.
 
-    ...
+    def __init__(self,
+                 model_path,
+                 model_type: str = "tflite",
+                 model_source_hub: str = "tfhub",
+                 device: str = None,
+                 dynamic: bool = False,
+                 jit_trace: bool = False,
+                 benchmark_mode: bool = False):
+        self.model_path = model_path
+        self.model_type = model_type
+        self.model_source_hub = model_source_hub
+        self.device = device
+        self.dynamic = dynamic
+        self.jit_trace = jit_trace
+        self.benchmark_mode = benchmark_mode
+        self.inputs = None
+        self.input_details = None
+        self.output_details = None
 
-    Attributes
-    ----------
-    module :
-        torch, tensorflow or tf-lite module.
-    inputs :
-        inputs to the module, may be required for the shape
-        information.
-    frontend: str
-        frontend to which the module belongs.
-    raw_model_file: str
-        temp tflite model path
+        # create tmp model file directory
+        if self.model_path is None:
+            print("Error. No model_path, Please input model path.")
+            return
 
-    Methods
-    -------
-    import_mlir(is_dynamic, tracing_required, func_name):
-        is_dynamic: input shapes to be totally dynamic (pytorch specific).
-        tracing_required: whether tracing is required (pytorch specific.
-        func_name: The function to be traced out or imported to mlir.
-
-    import_debug(is_dynamic, tracing_required, func_name):
-        returns the converted (mlir_module,func_name) with inputs and golden
-        outputs.
-        The inputs and outputs are converted into np array.
-    """
-
-    def __init__(
-        self,
-        module,
-        inputs: tuple = (),
-        frontend: str = "torch",
-        raw_model_file: str = "",
-    ):
-        self.module = module
-        self.inputs = None if len(inputs) == 0 else inputs
-        self.frontend = frontend
-        if not self.frontend in supported_frontends:
-            print(
-                f"The frontend is not in the supported_frontends: {supported_frontends}"
-            )
-            sys.exit(1)
-        self.raw_model_file = raw_model_file
-
-    # NOTE: The default function for torch is "forward" and tf-lite is "main".
-
-    def _torch_mlir(self, is_dynamic, tracing_required):
-        from shark.torch_mlir_utils import get_torch_mlir_module
-
-        return get_torch_mlir_module(
-            self.module, self.inputs, is_dynamic, tracing_required
-        )
-
-    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,
-            output_file=save_dir,
-        )
-
-    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
-
-    # Adds the conversion of the frontend with the private function.
-    def import_mlir(
-        self,
-        is_dynamic=False,
-        tracing_required=False,
-        func_name="forward",
-        save_dir="./shark_tmp/",
-    ):
-        if self.frontend in ["torch", "pytorch"]:
-            if self.inputs == None:
-                print(
-                    "Please pass in the inputs, the inputs are required to determine the shape of the mlir_module"
+        if self.model_source_hub == "tfhub":
+            # compile and run tfhub tflite
+            if self.model_type == "tflite":
+                print("Setting up for TMP_DIR")
+                exe_basename = os.path.basename(sys.argv[0])
+                self.workdir = os.path.join(os.path.dirname(__file__), "tmp",
+                                            exe_basename)
+                print(f"TMP_DIR = {self.workdir}")
+                os.makedirs(self.workdir, exist_ok=True)
+                self.tflite_file = '/'.join([self.workdir, 'model.tflite'])
+                print("Setting up local address for tflite model file: ",
+                      self.tflite_file)
+                if os.path.exists(self.model_path):
+                    self.tflite_file = self.model_path
+                else:
+                    print("Download tflite model")
+                    urllib.request.urlretrieve(self.model_path,
+                                               self.tflite_file)
+                print("Setting up tflite interpreter")
+                self.tflite_interpreter = tf.lite.Interpreter(
+                    model_path=self.tflite_file)
+                self.tflite_interpreter.allocate_tensors()
+                # default input initialization
+                self.input_details, self.output_details = self.get_model_details(
                 )
-                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, save_dir), func_name
-        if self.frontend in ["tflite", "tf-lite"]:
-            func_name = "main"
-            return self._tflite_mlir(func_name, save_dir), func_name
+                inputs = self.generate_inputs(
+                    self.input_details)  # device_inputs
+                self.setup_inputs(inputs)
 
-    # 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().cpu().numpy() for x in array_tuple]
-        if self.frontend in ["tf", "tensorflow"]:
-            return [x.numpy() for x in array_tuple]
+    def generate_inputs(self, input_details):
+        args = []
+        for input in input_details:
+            print(str(input["shape"]), input["dtype"].__name__)
+            args.append(np.zeros(shape=input["shape"], dtype=input["dtype"]))
+        return args
 
-    # Saves `function_name.npy`, `inputs.npz`, `golden_out.npz` and `model_name.mlir` in the directory `dir`.
-    def save_data(
-        self, dir, model_name, mlir_data, func_name, inputs, outputs
-    ):
-        import numpy as np
+    def get_model_details(self):
+        if self.model_type == "tflite":
+            print("Get tflite input output details")
+            self.input_details = self.tflite_interpreter.get_input_details()
+            self.output_details = self.tflite_interpreter.get_output_details()
+            return self.input_details, self.output_details
 
-        inputs_name = "inputs.npz"
-        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))
+    def setup_inputs(self, inputs):
+        print("Setting up inputs")
+        self.inputs = inputs
 
-        if self.frontend == "torch":
-            with open(os.path.join(dir, model_name_mlir), "wb") as mlir_file:
-                mlir_file.write(mlir_data)
+    def compile(self, inputs=None):
+        if inputs is not None:
+            self.setup_inputs(inputs)
+        # preprocess model_path to get model_type and Model Source Hub
+        print("Shark Importer Intialize SharkInference and Do Compile")
+        if self.model_source_hub == "tfhub":
+            # compile and run tfhub tflite
+            print("Inference tfhub model")
+            self.shark_module = SharkInference(self.tflite_file,
+                                               self.inputs,
+                                               device=self.device,
+                                               dynamic=self.dynamic,
+                                               jit_trace=self.jit_trace)
+            self.shark_module.set_frontend("tflite")
+            self.shark_module.compile()
+        elif self.model_source_hub == "huggingface":
+            print("Inference", self.model_source_hub, " not implemented yet")
+        elif self.model_source_hub == "jaxhub":
+            print("Inference", self.model_source_hub, " not implemented yet")
 
-        return
+    def forward(self, inputs=None):
+        if inputs is not None:
+            self.setup_inputs(inputs)
+        # preprocess model_path to get model_type and Model Source Hub
+        print("Shark Importer forward Model")
+        if self.model_source_hub == "tfhub":
+            shark_results = self.shark_module.forward(self.inputs)
+            # Fix type information for unsigned cases.
+            # for test compare result
+            shark_results = list(shark_results)
+            for i in range(len(self.output_details)):
+                dtype = self.output_details[i]["dtype"]
+                shark_results[i] = shark_results[i].astype(dtype)
+            return shark_results
+        elif self.model_source_hub == "huggingface":
+            print("Inference", self.model_source_hub, " not implemented yet")
+        elif self.model_source_hub == "jaxhub":
+            print("Inference", self.model_source_hub, " not implemented yet")
 
-    def import_debug(
-        self,
-        is_dynamic=False,
-        tracing_required=False,
-        func_name="forward",
-        dir=tempfile.gettempdir(),
-        model_name="model",
-    ):
-        if self.inputs == None:
-            print(
-                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,
-            save_dir=artifact_path,
+
+def shark_load(model_name, file_path):
+    file_link = f"https://storage.googleapis.com/shark_tank/users/stanley/{model_name}.mlir"
+    response = urllib.request.urlretrieve(file_link, file_path)
+    if not os.path.isfile(file_path):
+        raise ValueError(
+            f"Tried looking for target mlir in {file_path}, but cannot be found."
         )
-        # TODO: Make sure that any generic function name is accepted. Currently takes in the default function names.
-        # TODO: Check for multiple outputs.
-        if self.frontend in ["torch", "pytorch"]:
-            import torch
-
-            golden_out = self.module(*self.inputs)
-            if torch.is_tensor(golden_out):
-                golden_out = tuple(
-                    golden_out.detach().cpu().numpy(),
-                )
-            else:
-                golden_out = self.convert_to_numpy(golden_out)
-            # Save the artifacts in the directory dir.
-            self.save_data(
-                dir,
-                model_name,
-                imported_mlir[0],
-                imported_mlir[1],
-                self.inputs,
-                golden_out,
-            )
-            return (
-                imported_mlir,
-                self.convert_to_numpy(self.inputs),
-                golden_out,
-            )
-        if self.frontend in ["tf", "tensorflow"]:
-            import tensorflow as tf
-
-            golden_out = self.module.forward(*self.inputs)
-            if tf.is_tensor(golden_out):
-                golden_out = tuple(
-                    golden_out.numpy(),
-                )
-            elif golden_out is tuple:
-                golden_out = self.convert_to_numpy(golden_out)
-            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,
-                model_name,
-                imported_mlir[0],
-                imported_mlir[1],
-                self.inputs,
-                golden_out,
-            )
-            return (
-                imported_mlir,
-                self.convert_to_numpy(self.inputs),
-                golden_out,
-            )
-        if self.frontend in ["tflite", "tf-lite"]:
-            # TODO(Chi): Validate it for tflite models.
-            golden_out = self.module.invoke_tflite(self.inputs)
-            self.save_data(
-                dir,
-                model_name,
-                imported_mlir[0],
-                imported_mlir[1],
-                self.inputs,
-                golden_out,
-            )
-            return (
-                imported_mlir,
-                self.inputs,
-                golden_out,
-            )
+    with open(file_path, "rb") as input_file:
+        model_mlir = input_file.read()
+    return model_mlir

shark/shark_inference.py

@@ -9,202 +9,107 @@
 # 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,
-)
+from shark.torch_mlir_utils import get_torch_mlir_module, run_on_refbackend
 import os
-from shark.shark_runner import SharkRunner
 from shark.parser import shark_args
-import numpy as np
+from shark.shark_runner import SharkRunner, SharkBenchmarkRunner
+import time
+import sys
 
 
-dtype_to_np_dtype = {
-    "f32": np.float32,
-    "f64": np.float64,
-    "i32": np.int32,
-    "i64": np.int64,
-    "i1": np.bool_,
-}
+# Prints to stderr.
+def print_err(*a):
+    print(*a, file=sys.stderr)
 
 
 class SharkInference:
-    """
-    Runs prediction or inference on mlir_module.
+    """Inference API targeting pytorch, tensorflow, linalg, mhlo and tosa frontend."""
 
-    ...
+    def __init__(self,
+                 model,
+                 input: tuple,
+                 device: str = None,
+                 dynamic: bool = False,
+                 jit_trace: bool = False,
+                 benchmark_mode: bool = False):
+        self.model = model
+        self.input = input
+        self.dynamic = dynamic
+        self.jit_trace = jit_trace
+        self.benchmark_mode = benchmark_mode
 
-    Attributes
-    ----------
-    mlir_module : str
-        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
-        device to execute the mlir_module on.
-        currently supports cpu, cuda, vulkan, and metal backends.
-    mlir_dialect: str
-        The dialect in which the given mlir_module is in.
-        Refer to {https://mlir.llvm.org/docs/Dialects/}
-    is_benchmark: bool
-        Whether this SharkInference module should be benchmark-enabled.
+        # By default it's torch frontend.
+        self.frontend = "pytorch"
 
-    Methods
-    -------
-    run(inputs=None):
-        Runs the mlir_module with the given inputs, if the inputs are not
-        given it autogenerates the inputs. Also, the inputs should be a
-        numpy array.
-    input_info():
-        Gives the information about the inputs required by the `function_name`.
-        This can be expensive as it does string matching to do so.
+        # Sets the device.
+        self.device = device if device is not None else shark_args.device
 
-    """
-
-    def __init__(
-        self,
-        mlir_module: bytes,
-        function_name: str = "forward",
-        device: str = "none",
-        mlir_dialect: str = "linalg",
-        is_benchmark: bool = False,
-        dispatch_benchmark: str = None,
-        dispatch_benchmark_dir: str = "temp_dispatch_benchmarks",
-    ):
-        self.mlir_module = mlir_module
-        self.function_name = function_name
-        self.device = shark_args.device if device == "none" else device
-        self.mlir_dialect = mlir_dialect
-        self.is_benchmark = is_benchmark
-        self.dispatch_benchmarks = (
-            shark_args.dispatch_benchmarks
-            if dispatch_benchmark is None
-            else dispatch_benchmark
-        )
-        self.dispatch_benchmarks_dir = (
-            shark_args.dispatch_benchmarks_dir
-            if dispatch_benchmark_dir == "temp_dispatch_benchmarks"
-            else dispatch_benchmark_dir
-        )
+        self.model_config_path = shark_args.model_config_path
 
         self.shark_runner = None
 
-    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}"
-            )
-            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
-
-            self.shark_runner = SharkBenchmarkRunner(
-                self.mlir_module,
-                self.function_name,
-                self.device,
-                self.mlir_dialect,
-                extra_args=extra_args,
-            )
-
+    # Sets the frontend i.e `pytorch` or `tensorflow`.
+    def set_frontend(self, frontend: str):
+        if frontend not in [
+                "pytorch", "torch", "tensorflow", "tf", "mhlo", "linalg",
+                "tosa", "tflite"
+        ]:
+            print_err("frontend not supported.")
         else:
-            self.shark_runner = SharkRunner(
-                self.mlir_module,
-                self.function_name,
-                self.device,
-                self.mlir_dialect,
-                extra_args=extra_args,
-            )
+            self.frontend = frontend
 
-        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}")
+    def compile(self):
+        # Inference do not use AOT.
+        from_aot = False
+        if (self.benchmark_mode == True):
+            self.shark_runner = SharkBenchmarkRunner(self.model, self.input,
+                                                     self.dynamic, self.device,
+                                                     self.jit_trace, from_aot,
+                                                     self.frontend)
+        else:
+            self.shark_runner = SharkRunner(self.model, self.input,
+                                            self.dynamic, self.device,
+                                            self.jit_trace, from_aot,
+                                            self.frontend,
+                                            self.model_config_path)
 
-    # inputs are considered to be tuple of np.array.
-    def forward(self, inputs: tuple):
-        return self.shark_runner.run(inputs)
+    # inputs are considered to be np.array.
+    def forward(self, inputs):
+        input_list = inputs
+        # converts the inputs to numpy.
+        if self.frontend in ["pytorch", "torch"]:
+            input_list = [x.detach().numpy() for x in inputs]
+        elif self.frontend in ["tensorflow", "tf"]:
+            input_list = [x.numpy() for x in inputs]
+        return self.shark_runner.forward(input_list, self.frontend)
 
-    # Captures the static input information from the mlir_module.
-    # TODO(pashu123): Generate the input information for dynamic shapes.
-    def _input_info(self):
-        # func_key to get the line which contains the function.
-        func_key = "func.func @" + self.function_name
-        func_header = None
-        for line in str(self.mlir_module).splitlines():
-            if func_key in line:
-                func_header = line
-                break
-        if func_header is None:
-            print(f"Function: {self.function_name} not found")
+    # Saves the .vmfb module.
+    def save_module(self, dir=None):
+        if dir is None:
+            return self.shark_runner.save_module()
+        return self.shark_runner.save_module(dir)
 
-        import re
+    ######### Benchmark Related Functions #########
+    def benchmark_mode(func):
 
-        inputs = re.findall("\(.*?\)", func_header)[0].split(",")
-        shapes = []
-        dtype = []
-        for inp in inputs:
-            shape_dtype = re.findall(r"<[^>]*>", inp)[0].split("x")
-            shape_dtype[0], shape_dtype[-1] = (
-                shape_dtype[0][1:],
-                shape_dtype[-1][:-1],
-            )
-            shapes.append(tuple([int(x) for x in shape_dtype[:-1]]))
-            dtype.append(shape_dtype[-1])
+        def inner(self, *args, **kwargs):
+            assert self.benchmark_mode, "SharkRunner needs to be in benchmark mode to run benchmark methods."
+            return func(self, *args, **kwargs)
 
-        return shapes, dtype
+        return inner
 
-    # Generates random input to be feed into the graph.
-    def generate_random_inputs(self, low=0, high=1):
-        shapes, dtype = self._input_info()
-        inputs = []
-        for i, j in zip(shapes, dtype):
-            inputs.append(
-                np.random.uniform(low, high, size=i).astype(
-                    dtype_to_np_dtype[j]
-                )
-            )
-        return tuple(inputs)
+    @benchmark_mode
+    def benchmark_all(self, inputs):
+        self.shark_runner.benchmark_all(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,
-        )
+    @benchmark_mode
+    def benchmark_frontend(self, inputs):
+        self.shark_runner.benchmark_frontend(inputs)
 
-    # load and return the module.
-    def load_module(self, path):
-        self.shark_runner = SharkRunner(
-            function_name=self.function_name,
-            device=self.device,
-            compile_vmfb=False,
-        )
-        (
-            self.shark_runner.iree_compilation_module,
-            self.shark_runner.iree_config,
-        ) = load_flatbuffer(
-            path,
-            self.device,
-            self.function_name,
-        )
-        return
+    @benchmark_mode
+    def benchmark_python(self, inputs):
+        self.shark_runner.benchmark_python(inputs)
+
+    @benchmark_mode
+    def benchmark_c(self):
+        self.shark_runner.benchmark_c()

shark/shark_runner.py

@@ -11,90 +11,195 @@
 # 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.compiler import tf as tfc
+import iree.compiler.tflite as ireec_tflite
+from torch.utils._python_dispatch import enable_torch_dispatch_mode
+from torch_mlir.eager_mode import torch_mlir_tensor
+from torch_mlir.eager_mode.torch_mlir_tensor import TorchMLIRTensor
+from torch_mlir_e2e_test.eager_backends.refbackend import EagerModeRefBackend
 
-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
+from shark.iree_eager_backend import EagerModeIREELinalgOnTensorsBackend
+from shark.torch_mlir_utils import get_torch_mlir_module, run_on_refbackend
+from shark.iree_utils import get_results, get_iree_compiled_module, export_iree_module_to_vmfb, export_module_to_mlir_file, build_benchmark_args, run_benchmark_module
 import os
-import sys
-
-
-# supported dialects by the shark-runtime.
-supported_dialects = {"linalg", "mhlo", "tosa", "tf-lite", "tm_tensor"}
+from shark.parser import shark_args
+from tqdm import tqdm
+import time
 
 
 class SharkRunner:
-    """
-    Base class for SharkInference and SharkTrainer
-    used to execute an mlir_module.
-
-    ...
-
-    Attributes
-    ----------
-    mlir_module : str
-        mlir_module represented in string.
-    function_name : str
-        function to execute in the given mlir_module.
-    device : str
-        device to execute the mlir_module on.
-        currently supports cpu, cuda, vulkan, and metal backends.
-    mlir_dialect: str
-        The dialect in which the given mlir_module is in.
-        Refer to {https://mlir.llvm.org/docs/Dialects/}
-
-    Methods
-    -------
-    run(inputs=None):
-        Runs the mlir_module with the given inputs, if the inputs are not
-        given it autogenerates the inputs. Also, the inputs should be a
-        numpy array.
-    input_info():
-        Gives the information about the inputs required by the `function_name`.
-        This can be expensive as it does string matching to do so.
-    """
+    """Base class for Shark Inference and Shark Runner."""
 
     def __init__(
         self,
-        mlir_module: bytes = None,
-        function_name: str = "forward",
-        device: str = "none",
-        mlir_dialect: str = "linalg",
-        extra_args: list = [],
-        compile_vmfb: bool = True,
+        model,
+        input: tuple,
+        dynamic: bool = False,
+        device: str = None,
+        jit_trace: bool = False,
+        from_aot: bool = False,
+        frontend: str = "torch",
+        model_config_path: str = None,
     ):
-        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
+        self.model = model
+        self.frontend_model = model
+        self.from_aot = from_aot
+        self.input = input
+        self.frontend = frontend
+        self.vmfb_file = None
+        func_name = "forward"
+        self.device = device if device is not None else shark_args.device
+        if self.frontend in ["pytorch", "torch"]:
+            # get torch-mlir dialect
+            # self.model = torch.Module
+            # TODO assert
+            self.model = get_torch_mlir_module(self.model, input, dynamic,
+                                               jit_trace, from_aot)
+        elif self.frontend in ["tensorflow", "tf"]:
+            # get mhlo dialect
+            # self.model = tf.Module
+            # TODO assert
+            self.model = tfc.compile_module(self.model,
+                                            exported_names=[func_name],
+                                            import_only=True)
+        elif self.frontend in ["tflite"]:
+            print("Setting up for IREE compiler tflite")
+            # get tosa dialect
+            # self.model = model.tflite
+            # TODO assert
+            self.model = ireec_tflite.compile_file(self.model,
+                                                   input_type="tosa",
+                                                   import_only=True)
+            func_name = "main"
 
-        if check_device_drivers(self.device):
-            device_driver_info(self.device)
-            sys.exit(1)
-
-        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):
-        return get_results(
+        # TODO: We can capture the .vmfb module here and later use it for saving
+        # rather than recompiling it again, if used for saving.
+        (
             self.iree_compilation_module,
-            inputs,
             self.iree_config,
-            self.mlir_dialect,
+        ) = get_iree_compiled_module(self.model,
+                                     self.device,
+                                     self.frontend,
+                                     func_name=func_name,
+                                     model_config_path=model_config_path)
+
+        # Debugging Options:
+        if shark_args.save_mlir:
+            export_module_to_mlir_file(self.model, self.frontend,
+                                       shark_args.repro_dir)
+        if shark_args.save_vmfb:
+            self.vmfb_file = self.save_module(shark_args.repro_dir)
+
+    # All the timings and benchmarking can be done here.
+    def forward(self, input, frontend):
+        return get_results(self.iree_compilation_module, input,
+                           self.iree_config, frontend)
+
+    # 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.frontend)
+
+    # TODO: Load a module and directly use it, we will need to set the frontend
+    # in this case.
+    def load_module(self, name):
+        pass
+
+
+class SharkEagerMode:
+
+    def __init__(self, device="cpu"):
+        if device == "refbackend":
+            torch_mlir_tensor.backend = EagerModeRefBackend()
+        else:
+            torch_mlir_tensor.backend = EagerModeIREELinalgOnTensorsBackend(
+                device)
+        self.guard = enable_torch_dispatch_mode(TorchMLIRTensor)
+        self.guard.__enter__()
+
+    def __del__(self):
+        self.guard.__exit__(None, None, None)
+
+
+class SharkBenchmarkRunner(SharkRunner):
+    # SharkRunner derived class with Benchmarking capabilities.
+    def __init__(
+        self,
+        model,
+        input: tuple,
+        dynamic: bool = False,
+        device: str = None,
+        jit_trace: bool = False,
+        from_aot: bool = False,
+        frontend: str = "torch",
+    ):
+        SharkRunner.__init__(self, model, input, dynamic, device, jit_trace,
+                             from_aot, frontend)
+        if (self.vmfb_file == None):
+            self.vmfb_file = export_iree_module_to_vmfb(self.model, device,
+                                                        shark_args.repro_dir,
+                                                        frontend)
+        self.benchmark_cl = build_benchmark_args(self.vmfb_file, device, input,
+                                                 frontend, from_aot)
+
+    def benchmark_frontend(self, inputs):
+        if self.frontend in ["pytorch", "torch"]:
+            self.benchmark_torch(inputs)
+        elif self.frontend in ["tensorflow", "tf"]:
+            self.benchmark_tf(inputs)
+
+    def benchmark_torch(self, inputs):
+        inputs = self.input if self.from_aot else inputs
+        inputs = inputs[0]
+        for i in range(shark_args.num_warmup_iterations):
+            self.frontend_model.forward(inputs)
+
+        begin = time.time()
+        for i in range(shark_args.num_iterations):
+            out = self.frontend_model.forward(inputs)
+            if i == shark_args.num_iterations - 1:
+                end = time.time()
+                break
+        print(
+            f"Torch benchmark:{shark_args.num_iterations/(end-begin)} iter/second, Total Iterations:{shark_args.num_iterations}"
         )
+
+    def benchmark_tf(self, inputs):
+        for i in range(shark_args.num_warmup_iterations):
+            self.frontend_model.forward(*inputs)
+
+        begin = time.time()
+        for i in range(shark_args.num_iterations):
+            out = self.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
+
+    def benchmark_c(self):
+        result = run_benchmark_module(self.benchmark_cl)
+        print(f"Shark-{self.frontend} C-benchmark:{result} iter/second")
+
+    def benchmark_python(self, inputs):
+        inputs = self.input if self.from_aot else inputs
+        input_list = [x for x in inputs]
+        for i in range(shark_args.num_warmup_iterations):
+            self.forward(input_list, self.frontend)
+
+        begin = time.time()
+        for i in range(shark_args.num_iterations):
+            out = self.forward(input_list, self.frontend)
+            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}"
+        )
+
+    def benchmark_all(self, inputs):
+        self.benchmark_frontend(inputs)
+        self.benchmark_python(inputs)
+        self.benchmark_c()

shark/shark_trainer.py

@@ -12,11 +12,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from shark.torch_mlir_utils import get_torch_mlir_module, run_on_refbackend
+from shark.iree_utils import get_results, get_iree_compiled_module, export_iree_module_to_vmfb
+import os
 from shark.parser import shark_args
 from shark.shark_runner import SharkRunner
 from shark.backward_makefx import MakeFxModule
 import numpy as np
 from tqdm import tqdm
+import time
 import sys
 
 
@@ -54,13 +58,7 @@ class SharkTrainer:
     # Sets the frontend i.e `pytorch` or `tensorflow`.
     def set_frontend(self, frontend: str):
         if frontend not in [
-            "pytorch",
-            "torch",
-            "tensorflow",
-            "tf",
-            "mhlo",
-            "linalg",
-            "tosa",
+                "pytorch", "torch", "tensorflow", "tf", "mhlo", "linalg", "tosa"
         ]:
             print_err("frontend not supported.")
         else:
@@ -69,32 +67,22 @@ class SharkTrainer:
     # Training function is needed in the case of torch_fn.
     def compile(self, training_fn=None):
         if self.frontend in ["torch", "pytorch"]:
-            aot_module = MakeFxModule(
-                self.model, tuple(self.input), custom_inference_fn=training_fn
-            )
+            aot_module = MakeFxModule(self.model,
+                                      tuple(self.input),
+                                      custom_inference_fn=training_fn)
             aot_module.generate_graph()
             # Returns the backward graph.
             training_graph = aot_module.training_graph
             weights = self.get_torch_params()
-            self.shark_runner = SharkRunner(
-                training_graph,
-                weights + self.input,
-                self.dynamic,
-                self.device,
-                self.jit_trace,
-                self.from_aot,
-                self.frontend,
-            )
+            self.shark_runner = SharkRunner(training_graph,
+                                            weights + self.input, self.dynamic,
+                                            self.device, self.jit_trace,
+                                            self.from_aot, self.frontend)
         elif self.frontend in ["tensorflow", "tf", "mhlo"]:
-            self.shark_runner = SharkRunner(
-                self.model,
-                self.input,
-                self.dynamic,
-                self.device,
-                self.jit_trace,
-                self.from_aot,
-                self.frontend,
-            )
+            self.shark_runner = SharkRunner(self.model, self.input,
+                                            self.dynamic, self.device,
+                                            self.jit_trace, self.from_aot,
+                                            self.frontend)
         else:
             print_err("Unknown frontend")
             return
@@ -112,9 +100,8 @@ class SharkTrainer:
         params = [x.numpy() for x in params]
         print(f"Training started for {num_iters} iterations:")
         for i in tqdm(range(num_iters)):
-            params = self.shark_runner.forward(
-                params + self.input, self.frontend
-            )
+            params = self.shark_runner.forward(params + self.input,
+                                               self.frontend)
 
         return params
 
@@ -124,15 +111,15 @@ class SharkTrainer:
     def _train_tf(self, num_iters):
         input_list = []
         for x in self.input:
-            if isinstance(x, list):
+            if (isinstance(x, list)):
                 nested_list = []
                 for val in x:
-                    if isinstance(val, np.ndarray):
+                    if (isinstance(val, np.ndarray)):
                         nested_list.append(val)
                     else:
                         nested_list.append(val.numpy())
                 input_list.append(nested_list)
-            elif isinstance(x, np.ndarray):
+            elif (isinstance(x, np.ndarray)):
                 input_list.append(x)
             else:
                 input_list.append(x.numpy())

shark/sharkdynamo/README.md

@@ -1,11 +0,0 @@
-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`