Initial commit0.1.0-rc.1

Change-Id: I2fb0933d595a6ede6417d09dd905ef72d6c60c9b

22 files changed, 1314 insertions, 0 deletions

diff --git a/.bazelrc b/.bazelrc
new file mode 100644
index 0000000..97fd61c
--- /dev/null
+++ b/.bazelrc
@@ -0,0 +1,8 @@
+# SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
+# SPDX-License-Identifier: Apache-2.0
+
+# Flag to enable remote config
+common --experimental_repo_remote_exec
+common --cxxopt=-std=c++17
+common --host_cxxopt=-std=c++17
+common --copt=-w
diff --git a/.bazelversion b/.bazelversion
new file mode 100644
index 0000000..10803dc
--- /dev/null
+++ b/.bazelversion
@@ -0,0 +1,2 @@
+5.1.1
+# The version must be the same as the one in the TensorFlow version we build against
diff --git a/.bazelversion.license b/.bazelversion.license
new file mode 100644
index 0000000..487e9d8
--- /dev/null
+++ b/.bazelversion.license
@@ -0,0 +1,2 @@
+SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
+SPDX-License-Identifier: Apache-2.0
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..343775f
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,17 @@
+# SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
+# SPDX-License-Identifier: Apache-2.0
+*.egg-info
+*.pyc
+*~
+.eggs/
+\.coverage
+\.eggs
+__pycache__
+build/
+dist/
+*.egg-info
+bazel-bin/*
+bazel-out/*
+bazel-tosa_checker/*
+bazel-testlogs/*
+.pytest_cache/*
diff --git a/LICENSES/Apache-2.0.txt b/LICENSES/Apache-2.0.txt
new file mode 100644
index 0000000..f433b1a
--- /dev/null
+++ b/LICENSES/Apache-2.0.txt
@@ -0,0 +1,177 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
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+      "You" (or "Your") shall mean an individual or Legal Entity
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+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
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+      whether in tort (including negligence), contract, or otherwise,
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+      liable to You for damages, including any direct, indirect, special,
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+      defend, and hold each Contributor harmless for any liability
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+      of your accepting any such warranty or additional liability.
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+   END OF TERMS AND CONDITIONS
diff --git a/MANIFEST.in b/MANIFEST.in
new file mode 100644
index 0000000..74d9656
--- /dev/null
+++ b/MANIFEST.in
@@ -0,0 +1,4 @@
+# SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
+# SPDX-License-Identifier: Apache-2.0
+
+include README.md
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..361ac8b
--- /dev/null
+++ b/README.md
@@ -0,0 +1,54 @@
+<!---
+SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
+SPDX-License-Identifier: Apache-2.0
+--->
+# TOSA Checker
+
+## Overview
+
+The TOSA Checker is a tool that provides an easy way to ensure that a TensorFlow™ Lite model is compatible with the [TOSA specification](https://www.mlplatform.org/tosa).
+
+The project is currently in alpha, some stability issues may still be found.
+
+## Prerequisites
+
+To build the TOSA Checker the following are required:
+
+* Ubuntu® 20.04.03 LTS (the TOSA Checker has been tested on this OS, but should work on other Linux® OS)
+* Bazel™ (Bazelisk is an easy way to install the correct version of Bazel™)
+* Git™
+* Python® >=3.7 (python, python-dev and python-pip)
+
+## Building and installation
+
+pip install -U pip setuptools wheel
+pip install -U numpy
+python setup.py bdist_wheel
+pip install dist/tosa_checker-0.1.0-cp38-cp38-linux_x86_64.whl
+
+## Docker™ image
+
+A Docker™ image to build the TOSA Checker is also provided. More information can be found on [docker/README.md](docker/README.md).
+
+## Usage
+
+```python
+>>> import tosa_checker as tc
+>>> checker = tc.TOSAChecker(model_path="model.tflite")
+>>> checker.is_tosa_compatible()
+True
+```
+
+## License
+
+TOSA Checker is licensed under [Apache License 2.0].
+
+## Trademarks and Copyrights
+
+Bazel™ is a trademark of Google® LLC.
+Git™ is a trademark of Software Freedom Conservancy.
+Linux® is the registered trademark of Linus Torvalds in the U.S. and elsewhere.
+Python® is a registered trademark of the PSF.
+Ubuntu® is a registered trademark of Canonical.
+Tensorflow™ is a trademark of Google® LLC.
+Docker™ is a trademark of Docker, Inc.
diff --git a/RELEASES.md b/RELEASES.md
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index 0000000..ec666ee
--- /dev/null
+++ b/RELEASES.md
@@ -0,0 +1,20 @@
+<!---
+SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
+SPDX-License-Identifier: Apache-2.0
+--->
+# Release 0.1.0
+
+First release of the TOSA Checker tool. The goal of the tool is to provide an easy way to check if a TensorFlow™ Lite model is compatible with the [TOSA specification](https://www.mlplatform.org/tosa).
+
+The tool is provided as a Python® package and can be used as follow:
+
+```python
+>>> import tosa_checker as tc
+>>> checker = tc.TOSAChecker("model.tflite")
+>>> checker.is_tosa_compatible()
+True
+```
+
+The tool is currently in alpha, the features set is limited and some stability issues may exist.
+
+Future versions may extend the functionalities provided and support for other frameworks will be added.
diff --git a/WORKSPACE b/WORKSPACE
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index 0000000..b8a0fba
--- /dev/null
+++ b/WORKSPACE
@@ -0,0 +1,32 @@
+# SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
+# SPDX-License-Identifier: Apache-2.0
+workspace(name = "tosa_checker")
+
+load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
+http_archive(
+  name = "pybind11",
+  build_file = "@pybind11_bazel//:pybind11.BUILD",
+  strip_prefix = "pybind11-2.9.2",
+  urls = ["https://github.com/pybind/pybind11/archive/v2.9.2.tar.gz"],
+)
+http_archive(
+  name = "pybind11_bazel",
+  strip_prefix = "pybind11_bazel-72cbbf1fbc830e487e3012862b7b720001b70672",
+  urls = ["https://github.com/pybind/pybind11_bazel/archive/72cbbf1fbc830e487e3012862b7b720001b70672.zip"],
+)
+
+load("@pybind11_bazel//:python_configure.bzl", "python_configure")
+python_configure(name = "local_config_python")
+
+load("@org_tensorflow//tensorflow:workspace3.bzl", "tf_workspace3")
+tf_workspace3()
+
+load("@org_tensorflow//tensorflow:workspace2.bzl", "tf_workspace2")
+tf_workspace2()
+
+load("@org_tensorflow//tensorflow:workspace1.bzl", "tf_workspace1")
+tf_workspace1()
+
+load("@org_tensorflow//tensorflow:workspace0.bzl", "tf_workspace0")
+tf_workspace0()
+
diff --git a/docker/Dockerfile b/docker/Dockerfile
new file mode 100644
index 0000000..4481565
--- /dev/null
+++ b/docker/Dockerfile
@@ -0,0 +1,26 @@
+# SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
+# SPDX-License-Identifier: Apache-2.0
+FROM quay.io/pypa/manylinux2014_x86_64
+
+ARG PYTHON_VERSION=3.9
+ARG TENSORFLOW_VERSION=2.9.0
+ARG BAZEL_VERSION=5.1.1
+
+RUN ln -s /opt/_internal/cpython-$PYTHON_VERSION*/bin/python3 /usr/local/bin/python3 && \
+    ln -s /opt/_internal/cpython-$PYTHON_VERSION*/bin/python /usr/local/bin/python && \
+    ln -s /opt/_internal/cpython-$PYTHON_VERSION*/bin/pip3 /usr/local/bin/pip3 && \
+    ln -s /opt/_internal/cpython-$PYTHON_VERSION*/bin/pip /usr/local/bin/pip && \
+    ln -s /opt/_internal/cpython-$PYTHON_VERSION*/include/python${PYTHON_VERSION}/ /usr/local/include/python
+
+ENV PYTHON_BIN_PATH=/usr/local/bin/python
+ENV CI_BUILD_PYTHON=/usr/local/bin/python
+ENV CROSSTOOL_PYTHON_INCLUDE_PATH=/usr/local/include/python/
+
+RUN pip install --no-cache-dir setuptools pybind11 numpy twine keyrings.alt
+
+COPY install/install_bazel.sh /install/
+RUN /install/install_bazel.sh ${BAZEL_VERSION}
+
+# CACHE_STOP is used to rerun future commands, otherwise the cloning will be cached and will not pull the most recent version
+ARG CACHE_STOP=1
+RUN git clone --depth=1 https://github.com/tensorflow/tensorflow.git --branch v${TENSORFLOW_VERSION} /tensorflow_src
diff --git a/docker/README.md b/docker/README.md
new file mode 100644
index 0000000..653b9a2
--- /dev/null
+++ b/docker/README.md
@@ -0,0 +1,52 @@
+<!---
+SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
+SPDX-License-Identifier: Apache-2.0
+--->
+# Docker™ image
+
+This directory contains different utilities to build/test the TOSA Checker.
+
+## How to build the TOSA Checker manylinux wheel with a Docker™ image
+
+To create a Docker™ image for the TOSA Checker to build it for Python® 3.9 on various Linux® distributions, please run the following command:
+
+```console
+docker build . -t tc-cp39-manylinux --build-arg PYTHON_VERSION=3.9 -f Dockerfile
+```
+
+The TensorFlow™ source code is automatically downloaded and is located in the `/tensorflow_src` directory.
+
+The command to run the container is:
+
+```console
+docker run -it -v <tosa_checker source code on your machine>:/tosa_checker tc-cp39-manylinux
+```
+
+Now call the following command to build a `tosa_checker` Python® wheel inside of the container:
+
+```console
+cd tosa_checker
+python3 setup.py --tensorflow_src_dir /tensorflow_src bdist_wheel
+```
+The `tosa_checker` wheel can be found in the `/dist` directory.
+
+Generate the new manylinux wheel from the `tosa_checker` wheel:
+```console
+auditwheel repair dist/<tosa_checker>.whl -w dist/
+```
+The `tosa_checker` manylinux wheel can now be found in the `/dist` directory.
+
+Install the `tosa_checker` manylinux wheel:
+```console
+pip install dist/<tosa_checker-manyliux>.whl
+```
+
+## Trademarks and Copyrights
+
+Python® is a registered trademark of the PSF.
+Linux® is the registered trademark of Linus Torvalds in the U.S. and other countries.
+Ubuntu® is a registered trademark of Canonical.
+TensorFlow™ is a trademark of Google® LLC.
+Docker™ is a trademark of Docker, Inc.
+
+
diff --git a/docker/install/install_bazel.sh b/docker/install/install_bazel.sh
new file mode 100755
index 0000000..687ed13
--- /dev/null
+++ b/docker/install/install_bazel.sh
@@ -0,0 +1,24 @@
+#!/usr/bin/env bash
+# SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
+# SPDX-License-Identifier: Apache-2.0
+BAZEL_VERSION="$1"
+shift
+
+set +e
+local_bazel_ver=$(bazel version 2>&1 | grep -i label | awk '{print $3}')
+
+if [[ "$local_bazel_ver" == "$BAZEL_VERSION" ]]; then
+  exit 0
+fi
+
+set -e
+
+# Install Bazel™
+mkdir -p /bazel
+cd /bazel
+if [[ ! -f "bazel-$BAZEL_VERSION-installer-linux-x86_64.sh" ]]; then
+  curl -fSsL -O https://github.com/bazelbuild/bazel/releases/download/$BAZEL_VERSION/bazel-$BAZEL_VERSION-installer-linux-x86_64.sh
+fi
+chmod +x /bazel/bazel-*.sh
+/bazel/bazel-$BAZEL_VERSION-installer-linux-x86_64.sh
+rm -f /bazel/bazel-$BAZEL_VERSION-installer-linux-x86_64.sh
diff --git a/examples/tosa_checker.ipynb b/examples/tosa_checker.ipynb
new file mode 100644
index 0000000..8fe1f26
--- /dev/null
+++ b/examples/tosa_checker.ipynb
@@ -0,0 +1,117 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import the modules needed to create a test model and run the TOSA Checker."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tosa_checker as tc\n",
+    "import tensorflow as tf\n",
+    "import tempfile\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a simple model that is compatible with the TOSA specification."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Assets written to: /tmp/tmpxc09cs65/assets\n"
+     ]
+    }
+   ],
+   "source": [
+    "input = tf.keras.layers.Input(shape=(16,))\n",
+    "x = tf.keras.layers.Dense(8, activation=\"relu\")(input)\n",
+    "model = tf.keras.models.Model(inputs=[input], outputs=x)\n",
+    "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
+    "tflite_model = converter.convert()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Save this model in `.tflite` format. Note that the TOSA Checker only accepts models in this format currently."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_, tflite_file = tempfile.mkstemp('.tflite')\n",
+    "with open(tflite_file, \"wb\") as f:\n",
+    "    f.write(tflite_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use the TOSA Checker to check this model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Is model TOSA compatible ? True\n"
+     ]
+    }
+   ],
+   "source": [
+    "checker = tc.TOSAChecker(model_path=tflite_file)\n",
+    "result = checker.is_tosa_compatible()\n",
+    "print(\"Is model TOSA compatible ? {}\".format(result))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.0 ('tosa_checker': venv)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.0"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/tosa_checker.ipynb.license b/examples/tosa_checker.ipynb.license
new file mode 100644
index 0000000..bd657a1
--- /dev/null
+++ b/examples/tosa_checker.ipynb.license
@@ -0,0 +1,3 @@
+SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
+SPDX-License-Identifier: Apache-2.0
+
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000..8ea8232
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,127 @@
+# SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
+# SPDX-License-Identifier: Apache-2.0
+import argparse
+import os
+import setuptools
+import setuptools.command.build_ext
+import shutil
+import sys
+
+
+TOSA_CHECKER_VERSION = "0.1.0"
+TENSORFLOW_VERSION = "2.9.0"
+
+# Get the TensorFlow™ source directory passed to the command line (if any).
+# If none is given the sources are pulled from the official TF repository.
+argparser = argparse.ArgumentParser()
+argparser.add_argument(
+    "--tensorflow_src_dir", help="TensorFlow source directory path", required=False
+)
+args, unknown = argparser.parse_known_args()
+sys.argv = [sys.argv[0]] + unknown
+
+
+class BazelExtensionModule(setuptools.Extension):
+    def __init__(
+        self,
+        py_module_name,
+        library_name,
+        bazel_target,
+        bazel_shared_lib_output,
+        tensorflow_version,
+    ):
+        super().__init__(py_module_name, sources=[])
+        self.library_name = library_name
+        self.bazel_target = bazel_target
+        self.bazel_shared_lib_output = bazel_shared_lib_output
+        self.tensorflow_version = tensorflow_version
+
+
+class BazelBuildExtension(setuptools.command.build_ext.build_ext):
+    """Override build_extension to build the library with bazel and copying it
+    beforehand."""
+
+    def build_extension(self, ext):
+        tensorflow_src_dir = args.tensorflow_src_dir
+        if not tensorflow_src_dir:
+            tensorflow_src_dir = os.path.join(self.build_temp, "tensorflow")
+            self._clone_tf_repository(
+                tensorflow_src_dir,
+                ext.tensorflow_version,
+            )
+
+        self.spawn(
+            [
+                "bazel",
+                "build",
+                "-c",
+                "opt",
+                # FIXME Some of the Bazel targets dependencies we use have
+                # a 'friends' visibility, check if our Bazel target can be added
+                # to the 'friends' list.
+                "--check_visibility=false",
+                "--override_repository=org_tensorflow="
+                + os.path.abspath(tensorflow_src_dir),
+                ext.bazel_target,
+            ]
+        )
+
+        shared_lib_dest_path = self.get_ext_fullpath(ext.name)
+        shared_lib_dest_dir = os.path.dirname(shared_lib_dest_path)
+        package_dir = os.path.join(shared_lib_dest_dir, ext.library_name)
+
+        os.makedirs(shared_lib_dest_dir, exist_ok=True)
+        os.makedirs(package_dir, exist_ok=True)
+
+        shutil.copyfile(ext.bazel_shared_lib_output, shared_lib_dest_path)
+        shutil.copy(os.path.join(ext.library_name, "__init__.py"), package_dir)
+
+        super().build_extension(ext)
+
+    def _clone_tf_repository(self, tensorflow_src_dir, tensorflow_version):
+        if os.path.exists(tensorflow_src_dir):
+            return
+
+        tensorflow_repo = "https://github.com/tensorflow/tensorflow.git"
+        self.spawn(
+            [
+                "git",
+                "clone",
+                "--depth=1",
+                "--branch",
+                "v" + tensorflow_version,
+                tensorflow_repo,
+                tensorflow_src_dir,
+            ]
+        )
+
+
+setuptools.setup(
+    name="tosa-checker",
+    version=TOSA_CHECKER_VERSION,
+    description="Tool to check if a ML model is compatible with the TOSA specification",
+    long_description="file: README.md",
+    long_description_content_type="text/markdown",
+    author="Arm Limited",
+    url="https://git.mlplatform.org/tosa/tosa_checker.git/",
+    license="Apache-2.0",
+    license_files="LICENSES/*",
+    python_requires=">=3.7",
+    cmdclass={"build_ext": BazelBuildExtension},
+    ext_modules=[
+        BazelExtensionModule(
+            py_module_name="_tosa_checker_wrapper",
+            library_name="tosa_checker",
+            bazel_target="//tosa_checker:tosa_checker",
+            bazel_shared_lib_output="bazel-bin/tosa_checker/_tosa_checker_wrapper.so",
+            tensorflow_version=TENSORFLOW_VERSION,
+        ),
+    ],
+    classifiers=[
+        "Development Status :: 3 - Alpha",
+        "Intended Audience :: Developers",
+        "License :: OSI Approved :: Apache Software License",
+        "Programming Language :: Python :: 3",
+        "Topic :: Utilities",
+    ],
+)
diff --git a/tests/requirements.txt b/tests/requirements.txt
new file mode 100644
index 0000000..9984726
--- /dev/null
+++ b/tests/requirements.txt
@@ -0,0 +1,4 @@
+# SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
+# SPDX-License-Identifier: Apache-2.0
+tensorflow==2.9.1
+pytest==7.1.2
diff --git a/tests/test_tosa_checker.py b/tests/test_tosa_checker.py
new file mode 100644
index 0000000..eb49e65
--- /dev/null
+++ b/tests/test_tosa_checker.py
@@ -0,0 +1,216 @@
+# SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
+# SPDX-License-Identifier: Apache-2.0
+import os
+import pytest
+import tensorflow as tf
+import tempfile
+import tosa_checker
+
+
+@pytest.fixture(scope="module")
+def build_tosa_non_compat_model():
+    num_boxes = 6
+    max_output_size = 5
+    iou_threshold = 0.5
+    score_threshold = 0.1
+
+    def non_max_suppression(x):
+        boxes = x[0]
+        scores = x[1]
+        output = tf.image.non_max_suppression_with_scores(
+            boxes[0],
+            scores[0],
+            max_output_size=max_output_size,
+            iou_threshold=iou_threshold,
+            score_threshold=score_threshold,
+            soft_nms_sigma=1.0,
+        )
+        return output
+
+    boxes_in = tf.keras.layers.Input(
+        shape=(num_boxes, 4), batch_size=1, dtype=tf.float32, name="boxes"
+    )
+    scores_in = tf.keras.layers.Input(
+        shape=(num_boxes), batch_size=1, dtype=tf.float32, name="scores"
+    )
+    outputs = tf.keras.layers.Lambda(non_max_suppression)([boxes_in, scores_in])
+    model = tf.keras.models.Model(inputs=[boxes_in, scores_in], outputs=outputs)
+
+    return model
+
+
+@pytest.fixture(scope="module")
+def build_tosa_compat_model():
+    input = tf.keras.layers.Input(shape=(16,))
+    x = tf.keras.layers.Dense(8, activation="relu")(input)
+    model = tf.keras.models.Model(inputs=[input], outputs=x)
+    return model
+
+
+def create_tflite(model):
+    converter = tf.lite.TFLiteConverter.from_keras_model(model)
+    tflite_model = converter.convert()
+    return tflite_model
+
+
+@pytest.fixture(scope="module")
+def non_compat_file(build_tosa_non_compat_model):
+    tflite_model = create_tflite(build_tosa_non_compat_model)
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        file = os.path.join(tmp_dir, "test.tflite")
+        open(file, "wb").write(tflite_model)
+        yield file
+
+
+@pytest.fixture(scope="module")
+def compat_file(build_tosa_compat_model):
+    tflite_model = create_tflite(build_tosa_compat_model)
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        file = os.path.join(tmp_dir, "test.tflite")
+        open(file, "wb").write(tflite_model)
+        yield file
+
+
+class TestTosaCompatibilityTool:
+    def test_bad_tflite_file(self):
+        make_bad_tfile = os.path.join(tempfile.mkdtemp(), "test.tflite")
+        open(make_bad_tfile, "wb").write("bad tflite file".encode("ASCII"))
+
+        with pytest.raises(RuntimeError):
+            checker = tosa_checker.TOSAChecker(model_path=make_bad_tfile)
+
+    def test_tosa_non_compat_model(self, non_compat_file):
+        checker = tosa_checker.TOSAChecker(model_path=non_compat_file)
+        tosa_compatible = checker.is_tosa_compatible()
+        assert tosa_compatible == False
+
+        ops = checker._get_tosa_compatibility_for_ops()
+        assert type(ops) == list
+        assert [[op.name, op.is_tosa_compatible] for op in ops] == [
+            ["tfl.pseudo_const", True],
+            ["tfl.pseudo_const", True],
+            ["tfl.pseudo_const", True],
+            ["tfl.strided_slice", True],
+            ["tfl.pseudo_const", True],
+            ["tfl.pseudo_const", True],
+            ["tfl.pseudo_const", True],
+            ["tfl.strided_slice", True],
+            ["tfl.pseudo_const", True],
+            ["tfl.pseudo_const", True],
+            ["tfl.pseudo_const", True],
+            ["tfl.pseudo_const", True],
+            ["tfl.non_max_suppression_v5", False],
+        ]
+
+        tosa_ops = checker._get_used_tosa_ops()
+        assert type(tosa_ops) == list
+        assert [[op.name, op.is_tosa_compatible] for op in tosa_ops] == [
+            ["tosa.const", True],
+            ["tosa.const", True],
+            ["tosa.const", True],
+            ["tosa.const", True],
+            ["tosa.reshape", True],
+            ["tosa.reshape", True],
+        ]
+
+    def test_tosa_compat_model(self, compat_file):
+        checker = tosa_checker.TOSAChecker(model_path=compat_file)
+        tosa_compatible = checker.is_tosa_compatible()
+        assert tosa_compatible == True
+
+        ops = checker._get_tosa_compatibility_for_ops()
+        assert type(ops) == list
+        assert [[op.name, op.is_tosa_compatible] for op in ops] == [
+            ["tfl.pseudo_const", True],
+            ["tfl.no_value", True],
+            ["tfl.fully_connected", True],
+        ]
+
+        tosa_ops = checker._get_used_tosa_ops()
+        assert type(tosa_ops) == list
+        assert [[op.name, op.is_tosa_compatible] for op in tosa_ops] == [
+            ["tosa.const", True],
+            ["tosa.const", True],
+            ["tosa.fully_connected", True],
+            ["tosa.clamp", True],
+        ]
+
+    def test_tosa_non_compat_model_mlir_representation(self, non_compat_file):
+        checker = tosa_checker.TOSAChecker(model_path=non_compat_file)
+
+        tfl_mlir_representation = checker._get_mlir_model_representation(
+            elide_large_elements_attrs=True
+        )
+        expected_mlir_representation = """\
+module attributes {tf_saved_model.semantics, tfl.description = "MLIR Converted.", tfl.schema_version = 3 : i32} {
+  func @main(%arg0: tensor<1x6x4xf32> {tf_saved_model.index_path = ["boxes"]}, %arg1: tensor<1x6xf32> {tf_saved_model.index_path = ["scores"]}) -> (tensor<?xf32> {tf_saved_model.index_path = ["lambda_1"]}, tensor<?xi32> {tf_saved_model.index_path = ["lambda"]}) attributes {tf.entry_function = {inputs = "serving_default_boxes:0,serving_default_scores:0", outputs = "PartitionedCall:1,PartitionedCall:0"}, tf_saved_model.exported_names = ["serving_default"]} {
+    %0 = "tfl.pseudo_const"() {value = dense<0> : tensor<3xi32>} : () -> tensor<3xi32>
+    %1 = "tfl.pseudo_const"() {value = dense<[1, 6, 4]> : tensor<3xi32>} : () -> tensor<3xi32>
+    %2 = "tfl.pseudo_const"() {value = dense<1> : tensor<3xi32>} : () -> tensor<3xi32>
+    %3 = "tfl.strided_slice"(%arg0, %0, %1, %2) {begin_mask = 6 : i32, ellipsis_mask = 0 : i32, end_mask = 6 : i32, new_axis_mask = 0 : i32, shrink_axis_mask = 1 : i32} : (tensor<1x6x4xf32>, tensor<3xi32>, tensor<3xi32>, tensor<3xi32>) -> tensor<6x4xf32>
+    %4 = "tfl.pseudo_const"() {value = dense<0> : tensor<2xi32>} : () -> tensor<2xi32>
+    %5 = "tfl.pseudo_const"() {value = dense<[1, 6]> : tensor<2xi32>} : () -> tensor<2xi32>
+    %6 = "tfl.pseudo_const"() {value = dense<1> : tensor<2xi32>} : () -> tensor<2xi32>
+    %7 = "tfl.strided_slice"(%arg1, %4, %5, %6) {begin_mask = 2 : i32, ellipsis_mask = 0 : i32, end_mask = 2 : i32, new_axis_mask = 0 : i32, shrink_axis_mask = 1 : i32} : (tensor<1x6xf32>, tensor<2xi32>, tensor<2xi32>, tensor<2xi32>) -> tensor<6xf32>
+    %8 = "tfl.pseudo_const"() {value = dense<5> : tensor<i32>} : () -> tensor<i32>
+    %9 = "tfl.pseudo_const"() {value = dense<5.000000e-01> : tensor<f32>} : () -> tensor<f32>
+    %10 = "tfl.pseudo_const"() {value = dense<1.000000e-01> : tensor<f32>} : () -> tensor<f32>
+    %11 = "tfl.pseudo_const"() {value = dense<1.000000e+00> : tensor<f32>} : () -> tensor<f32>
+    %selected_indices, %selected_scores, %valid_outputs = "tfl.non_max_suppression_v5"(%3, %7, %8, %9, %10, %11) : (tensor<6x4xf32>, tensor<6xf32>, tensor<i32>, tensor<f32>, tensor<f32>, tensor<f32>) -> (tensor<?xi32>, tensor<?xf32>, tensor<*xi32>)
+    return %selected_scores, %selected_indices : tensor<?xf32>, tensor<?xi32>
+  }
+}
+"""
+        assert tfl_mlir_representation == expected_mlir_representation
+
+        tosa_mlir_representation = checker._get_mlir_tosa_model_representation(
+            elide_large_elements_attrs=True
+        )
+        expected_tosa_mlir_representation = """\
+module attributes {tf_saved_model.semantics, tfl.description = "MLIR Converted.", tfl.schema_version = 3 : i32} {
+  func @main(%arg0: tensor<1x6x4xf32> {tf_saved_model.index_path = ["boxes"]}, %arg1: tensor<1x6xf32> {tf_saved_model.index_path = ["scores"]}) -> (tensor<?xf32> {tf_saved_model.index_path = ["lambda_1"]}, tensor<?xi32> {tf_saved_model.index_path = ["lambda"]}) attributes {tf.entry_function = {inputs = "serving_default_boxes:0,serving_default_scores:0", outputs = "PartitionedCall:1,PartitionedCall:0"}, tf_saved_model.exported_names = ["serving_default"]} {
+    %0 = "tosa.const"() {value = dense<5> : tensor<i32>} : () -> tensor<i32>
+    %1 = "tosa.const"() {value = dense<5.000000e-01> : tensor<f32>} : () -> tensor<f32>
+    %2 = "tosa.const"() {value = dense<1.000000e-01> : tensor<f32>} : () -> tensor<f32>
+    %3 = "tosa.const"() {value = dense<1.000000e+00> : tensor<f32>} : () -> tensor<f32>
+    %4 = "tosa.reshape"(%arg0) {new_shape = [6, 4]} : (tensor<1x6x4xf32>) -> tensor<6x4xf32>
+    %5 = "tosa.reshape"(%arg1) {new_shape = [6]} : (tensor<1x6xf32>) -> tensor<6xf32>
+    %selected_indices, %selected_scores, %valid_outputs = "tfl.non_max_suppression_v5"(%4, %5, %0, %1, %2, %3) : (tensor<6x4xf32>, tensor<6xf32>, tensor<i32>, tensor<f32>, tensor<f32>, tensor<f32>) -> (tensor<?xi32>, tensor<?xf32>, tensor<*xi32>)
+    return %selected_scores, %selected_indices : tensor<?xf32>, tensor<?xi32>
+  }
+}
+"""
+        assert tosa_mlir_representation == expected_tosa_mlir_representation
+
+    def test_tosa_compat_model_mlir_representation(self, compat_file):
+        checker = tosa_checker.TOSAChecker(model_path=compat_file)
+        tfl_mlir_representation = checker._get_mlir_model_representation(
+            elide_large_elements_attrs=True
+        )
+        expected_mlir_representation = """\
+module attributes {tf_saved_model.semantics, tfl.description = "MLIR Converted.", tfl.schema_version = 3 : i32} {
+  func @main(%arg0: tensor<?x16xf32> {tf_saved_model.index_path = ["input_1"]}) -> (tensor<?x8xf32> {tf_saved_model.index_path = ["dense"]}) attributes {tf.entry_function = {inputs = "serving_default_input_1:0", outputs = "StatefulPartitionedCall:0"}, tf_saved_model.exported_names = ["serving_default"]} {
+    %0 = "tfl.pseudo_const"() {value = opaque<"elided_large_const", "0xDEADBEEF"> : tensor<8x16xf32>} : () -> tensor<8x16xf32>
+    %1 = "tfl.no_value"() {value} : () -> none
+    %2 = "tfl.fully_connected"(%arg0, %0, %1) {asymmetric_quantize_inputs = false, fused_activation_function = "RELU", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<?x16xf32>, tensor<8x16xf32>, none) -> tensor<?x8xf32>
+    return %2 : tensor<?x8xf32>
+  }
+}
+"""
+        assert tfl_mlir_representation == expected_mlir_representation
+
+        tosa_mlir_representation = checker._get_mlir_tosa_model_representation(
+            elide_large_elements_attrs=True
+        )
+        expected_tosa_mlir_representation = """\
+module attributes {tf_saved_model.semantics, tfl.description = "MLIR Converted.", tfl.schema_version = 3 : i32} {
+  func @main(%arg0: tensor<?x16xf32> {tf_saved_model.index_path = ["input_1"]}) -> (tensor<?x8xf32> {tf_saved_model.index_path = ["dense"]}) attributes {tf.entry_function = {inputs = "serving_default_input_1:0", outputs = "StatefulPartitionedCall:0"}, tf_saved_model.exported_names = ["serving_default"]} {
+    %0 = "tosa.const"() {value = opaque<"elided_large_const", "0xDEADBEEF"> : tensor<8x16xf32>} : () -> tensor<8x16xf32>
+    %1 = "tosa.const"() {value = dense<0.000000e+00> : tensor<8xf32>} : () -> tensor<8xf32>
+    %2 = "tosa.fully_connected"(%arg0, %0, %1) : (tensor<?x16xf32>, tensor<8x16xf32>, tensor<8xf32>) -> tensor<?x8xf32>
+    %3 = "tosa.clamp"(%2) {max_fp = 3.40282347E+38 : f32, max_int = 2147483647 : i64, min_fp = 0.000000e+00 : f32, min_int = 0 : i64} : (tensor<?x8xf32>) -> tensor<?x8xf32>
+    return %3 : tensor<?x8xf32>
+  }
+}
+"""
+        assert tosa_mlir_representation == expected_tosa_mlir_representation
diff --git a/tosa_checker/BUILD b/tosa_checker/BUILD
new file mode 100644
index 0000000..8c1c32d
--- /dev/null
+++ b/tosa_checker/BUILD
@@ -0,0 +1,35 @@
+# SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
+# SPDX-License-Identifier: Apache-2.0
+load("@pybind11_bazel//:build_defs.bzl", "pybind_extension")
+
+cc_library(
+    name = "tosa_checker_lib",
+    srcs = ["tosa_checker.cc"],
+    hdrs = ["tosa_checker.h"],
+    deps = [
+        "@llvm-project//mlir:MlirTranslateMain",
+        "@org_tensorflow//tensorflow/compiler/mlir/lite:flatbuffer_translate_lib",
+        "@org_tensorflow//tensorflow/compiler/mlir/tosa:tfl_passes",
+        "@pybind11",
+    ],
+)
+
+pybind_extension(
+    name = "_tosa_checker_wrapper",
+    srcs = [
+        "tosa_checker_pybind11.cc",
+    ],
+    deps = [
+        ":tosa_checker_lib",
+    ],
+)
+
+py_library(
+    name = "tosa_checker",
+    srcs = [
+        "__init__.py",
+    ],
+    srcs_version = "PY3",
+    visibility = ["//visibility:public"],
+    data = ["//tosa_checker:_tosa_checker_wrapper.so"],
+)
diff --git a/tosa_checker/__init__.py b/tosa_checker/__init__.py
new file mode 100644
index 0000000..ce76797
--- /dev/null
+++ b/tosa_checker/__init__.py
@@ -0,0 +1,8 @@
+# SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
+# SPDX-License-Identifier: Apache-2.0
+
+"""the package provides a way to check if a TFLite model is compatible with the TOSA specification."""
+
+from _tosa_checker_wrapper import *
+
+__version__ = "0.1.0"
diff --git a/tosa_checker/tosa_checker.cc b/tosa_checker/tosa_checker.cc
new file mode 100644
index 0000000..714cab3
--- /dev/null
+++ b/tosa_checker/tosa_checker.cc
@@ -0,0 +1,225 @@
+/*
+SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
+SPDX-License-Identifier: Apache-2.0
+*/
+#include "tosa_checker.h"
+
+#include "absl/strings/string_view.h"
+#include "llvm/Support/MemoryBuffer.h"
+#include "mlir/Dialect/Func/IR/FuncOps.h"
+#include "mlir/IR/Attributes.h"
+#include "mlir/IR/BuiltinOps.h"
+#include "mlir/IR/Location.h"
+#include "mlir/IR/Operation.h"
+#include "mlir/IR/Types.h"
+#include "mlir/IR/Verifier.h"
+#include "mlir/Pass/PassManager.h"
+#include "mlir/Support/FileUtilities.h"
+#include "tensorflow/compiler/mlir/lite/flatbuffer_import.h"
+#include "tensorflow/compiler/mlir/tosa/tfl_passes.h"
+
+#include <map>
+#include <memory>
+#include <optional>
+#include <stdexcept>
+#include <string>
+#include <unordered_set>
+#include <vector>
+
+namespace std {
+template <>
+struct hash<mlir::Location> {
+  std::size_t operator()(const mlir::Location &loc) const {
+    return mlir::hash_value(loc);
+  }
+};
+}  // namespace std
+
+namespace tosa_checker {
+
+TOSAChecker::TOSAChecker(const std::string &model_path) {
+  m_model = TFLiteFileToMLIR(model_path, &m_context);
+  m_tosa_model = m_model->clone();
+  LegalizeTFLToTOSA(*m_tosa_model);
+}
+
+bool TOSAChecker::IsTOSACompatible() {
+  bool is_tosa_compatible = true;
+  for (auto func : m_tosa_model->getOps<mlir::func::FuncOp>()) {
+    func.walk([&](mlir::Operation *op) {
+      // Ignore func namespace
+      const mlir::Dialect *dialect = op->getDialect();
+      if (!dialect || (!dialect->getNamespace().equals("tosa") &&
+                       !dialect->getNamespace().equals("func"))) {
+        is_tosa_compatible = false;
+        return mlir::WalkResult::interrupt();
+      }
+
+      return mlir::WalkResult::advance();
+    });
+  }
+
+  return is_tosa_compatible;
+}
+
+std::vector<TOSAChecker::Operator> TOSAChecker::GetTOSACompatibilityForOps(
+    bool elide_large_attrs) {
+  // Get the locations of all the ops in the legalized model that were not
+  // converted during the TOSA legalization (i.e. the TOSA incompatible ones).
+  std::unordered_set<mlir::Location> tosa_incompatible_locs;
+  for (auto func : m_tosa_model->getOps<mlir::func::FuncOp>()) {
+    func.walk([&](mlir::Operation *op) {
+      // Ignore func namespace
+      const mlir::Dialect *dialect = op->getDialect();
+      if (!dialect || (!dialect->getNamespace().equals("tosa") &&
+                       !dialect->getNamespace().equals("func"))) {
+        tosa_incompatible_locs.insert(op->getLoc());
+      }
+    });
+  }
+
+  // We assume that on legalization, the non-legalized ops keep their original
+  // location. If an op location from the original model is in
+  // tosa_incompatible_locs then the op is not tosa compatible, otherwise it is.
+  std::vector<Operator> ops;
+  for (auto func : m_model->getOps<mlir::func::FuncOp>()) {
+    func.walk([&](mlir::Operation *op) {
+      // Ignore func namespace
+      const mlir::Dialect *dialect = op->getDialect();
+      if (!dialect || !dialect->getNamespace().equals("func")) {
+        const bool is_tosa_compatible =
+            tosa_incompatible_locs.find(op->getLoc()) ==
+            tosa_incompatible_locs.end();
+        ops.push_back(ToOperator(*op, is_tosa_compatible, elide_large_attrs));
+      }
+    });
+  }
+
+  return ops;
+}
+
+std::vector<TOSAChecker::Operator> TOSAChecker::GetUsedTOSAOps(
+    bool elide_large_attrs) {
+  std::vector<Operator> tosa_ops;
+  for (mlir::Operation *op : GetTOSAOps(*m_tosa_model)) {
+    const bool is_tosa_compatible = true;
+    tosa_ops.push_back(ToOperator(*op, is_tosa_compatible, elide_large_attrs));
+  }
+
+  return tosa_ops;
+}
+
+std::string TOSAChecker::GetMLIRModelRepresentation(bool elide_large_attrs) {
+  return GetMLIRRepresentation(*m_model, elide_large_attrs);
+}
+
+std::string TOSAChecker::GetMLIRTOSAModelRepresentation(
+    bool elide_large_attrs) {
+  return GetMLIRRepresentation(*m_tosa_model, elide_large_attrs);
+}
+
+template <typename T>
+std::string TOSAChecker::GetMLIRRepresentation(T &&op) {
+  std::string value;
+  llvm::raw_string_ostream value_ostream(value);
+
+  op.print(value_ostream);
+
+  return value;
+}
+
+template <typename T>
+std::string TOSAChecker::GetMLIRRepresentation(T &&op, bool elide_large_attrs) {
+  std::string value;
+  llvm::raw_string_ostream value_ostream(value);
+
+  mlir::OpPrintingFlags flags;
+  if (elide_large_attrs) {
+    flags.elideLargeElementsAttrs(ELIDE_LARGE_ATTRS_LIMIT);
+  }
+  op.print(value_ostream, flags);
+
+  return value;
+}
+
+std::vector<mlir::Operation *> TOSAChecker::GetTOSAOps(mlir::ModuleOp model) {
+  std::vector<mlir::Operation *> tosa_ops;
+  for (auto func : model.getOps<mlir::func::FuncOp>()) {
+    func.walk([&](mlir::Operation *op) {
+      const mlir::Dialect *dialect = op->getDialect();
+      if (dialect && dialect->getNamespace().equals("tosa")) {
+        tosa_ops.push_back(op);
+      }
+    });
+  }
+
+  return tosa_ops;
+}
+
+TOSAChecker::Operator TOSAChecker::ToOperator(mlir::Operation &op,
+                                              bool is_tosa_compatible,
+                                              bool elide_large_attrs) {
+  return Operator(op.getName().getStringRef().str(),
+                  GetMLIRRepresentation(op.getLoc()),
+                  GetAttributes(op, elide_large_attrs), is_tosa_compatible,
+                  GetMLIRRepresentation(op, elide_large_attrs));
+}
+
+mlir::OwningOpRef<mlir::ModuleOp> TOSAChecker::TFLiteFileToMLIR(
+    const std::string &model_path, mlir::MLIRContext *context) {
+  std::string error_message;
+  std::unique_ptr<llvm::MemoryBuffer> input =
+      mlir::openInputFile(model_path, &error_message);
+  if (!input) {
+    throw std::runtime_error(error_message);
+  }
+
+  const mlir::FileLineColLoc location =
+      mlir::FileLineColLoc::get(context, input->getBufferIdentifier(), 0, 0);
+
+  auto mlir_module = tflite::FlatBufferToMlir(
+      absl::string_view(input->getBufferStart(), input->getBufferSize()),
+      context, location);
+  if (!mlir_module || mlir::failed(mlir::verify(*mlir_module))) {
+    throw std::runtime_error(
+        "Could not convert the TFLite model to its MLIR representation.");
+  }
+
+  return mlir_module;
+}
+
+void TOSAChecker::LegalizeTFLToTOSA(mlir::ModuleOp mlir_module) {
+  mlir::PassManager pm(mlir_module.getContext(),
+                       mlir::OpPassManager::Nesting::Implicit);
+  mlir::tosa::TOSATFLLegalizationPipelineOptions opts;
+  mlir::tosa::createTFLtoTOSALegalizationPipeline(pm, opts);
+  // TODO Don't check for mlir::failed state for now due to some incoherences in
+  // how the legalization report non-convertible ops (sometimes with a hard
+  // fail, sometimes without). The legalization should not return a failed
+  // state if an operator can't be legalized and should leave it in its original
+  // dialect.
+  pm.run(mlir_module);
+}
+
+std::map<std::string, std::string> TOSAChecker::GetAttributes(
+    mlir::Operation &op, bool /*elide_large_attrs*/) {
+  std::map<std::string, std::string> attributes;
+  for (const mlir::NamedAttribute &attr : op.getAttrs()) {
+    attributes.emplace(attr.getName().str(),
+                       // TODO Check how to elide large attributes when
+                       // converting them to string, mlir::Attribute::print has
+                       // no mlir::OpPrintingFlags.
+                       GetMLIRRepresentation(attr.getValue()));
+  }
+
+  return attributes;
+}
+
+}  // namespace tosa_checker
+
+std::ostream &operator<<(std::ostream &os,
+                         const tosa_checker::TOSAChecker::Operator &op) {
+  os << op.mlir_representation;
+
+  return os;
+}
diff --git a/tosa_checker/tosa_checker.h b/tosa_checker/tosa_checker.h
new file mode 100644
index 0000000..d7750ea
--- /dev/null
+++ b/tosa_checker/tosa_checker.h
@@ -0,0 +1,82 @@
+/*
+SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
+SPDX-License-Identifier: Apache-2.0
+*/
+#ifndef TOSA_CHECKER_H_
+#define TOSA_CHECKER_H_
+
+#include <map>
+#include <optional>
+#include <string>
+#include <vector>
+
+#include "mlir/include/mlir/IR/BuiltinOps.h"
+#include "mlir/include/mlir/IR/MLIRContext.h"
+#include "mlir/include/mlir/IR/OwningOpRef.h"
+
+namespace tosa_checker {
+
+class TOSAChecker {
+ public:
+  struct Operator {
+    Operator(std::string name, std::string location,
+             std::map<std::string, std::string> attributes,
+             bool is_tosa_compatible, std::string mlir_representation)
+        : name(std::move(name)),
+          location(std::move(location)),
+          attributes(std::move(attributes)),
+          is_tosa_compatible(is_tosa_compatible),
+          mlir_representation(std::move(mlir_representation)) {}
+
+    std::string name;
+    std::string location;
+    std::map<std::string, std::string> attributes;
+    bool is_tosa_compatible;
+    std::string mlir_representation;
+  };
+
+  TOSAChecker(const std::string& model_path);
+
+  bool IsTOSACompatible();
+
+  std::vector<Operator> GetTOSACompatibilityForOps(bool elide_large_attrs);
+
+  std::vector<Operator> GetUsedTOSAOps(bool elide_large_attrs);
+
+  std::string GetMLIRModelRepresentation(bool elide_large_attrs);
+  std::string GetMLIRTOSAModelRepresentation(bool elide_large_attrs);
+
+ private:
+  template <typename T>
+  static std::string GetMLIRRepresentation(T&& op);
+
+  template <typename T>
+  static std::string GetMLIRRepresentation(T&& op, bool elide_large_attrs);
+
+  static std::vector<mlir::Operation*> GetTOSAOps(mlir::ModuleOp model);
+
+  static Operator ToOperator(mlir::Operation& op, bool is_tosa_compatible,
+                             bool elide_large_attrs);
+
+  static mlir::OwningOpRef<mlir::ModuleOp> TFLiteFileToMLIR(
+      const std::string& model_path, mlir::MLIRContext* context);
+
+  static void LegalizeTFLToTOSA(mlir::ModuleOp mlir_module);
+
+  static std::map<std::string, std::string> GetAttributes(
+      mlir::Operation& op, bool elide_large_attrs);
+
+ private:
+  static constexpr std::int64_t ELIDE_LARGE_ATTRS_LIMIT = 16;
+
+  mlir::MLIRContext m_context;
+  mlir::OwningOpRef<mlir::ModuleOp> m_model;
+  mlir::OwningOpRef<mlir::ModuleOp> m_tosa_model;
+};
+
+}  // namespace tosa_checker
+
+std::ostream& operator<<(std::ostream& os,
+                         const tosa_checker::TOSAChecker::Operator& op);
+
+#endif
diff --git a/tosa_checker/tosa_checker_pybind11.cc b/tosa_checker/tosa_checker_pybind11.cc
new file mode 100644
index 0000000..c799817
--- /dev/null
+++ b/tosa_checker/tosa_checker_pybind11.cc
@@ -0,0 +1,79 @@
+/*
+SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
+SPDX-License-Identifier: Apache-2.0
+*/
+#include "tosa_checker.h"
+
+#include <optional>
+#include <sstream>
+#include <string>
+
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+
+PYBIND11_MODULE(_tosa_checker_wrapper, m) {
+  /**
+   * tosa_checker::TOSAChecker
+   */
+  pybind11::class_<tosa_checker::TOSAChecker> tosa_checker_class(m,
+                                                                 "TOSAChecker");
+  tosa_checker_class.def(pybind11::init<const std::string&>(),
+                         pybind11::arg("model_path"));
+
+  tosa_checker_class.def(
+      "is_tosa_compatible",
+      [](tosa_checker::TOSAChecker& tc) { return tc.IsTOSACompatible(); },
+      "Check if a model is compatible with the TOSA specification");
+
+  tosa_checker_class.def(
+      "_get_tosa_compatibility_for_ops",
+      [](tosa_checker::TOSAChecker& tc, bool elide_large_elements_attrs) {
+        return tc.GetTOSACompatibilityForOps(elide_large_elements_attrs);
+      },
+      pybind11::arg("elide_large_elements_attrs") = false,
+      "Get all the operators of the models with a TOSA compatibility flag for "
+      "each operator");
+
+  tosa_checker_class.def(
+      "_get_used_tosa_ops",
+      [](tosa_checker::TOSAChecker& tc, bool elide_large_elements_attrs) {
+        return tc.GetUsedTOSAOps(elide_large_elements_attrs);
+      },
+      pybind11::arg("elide_large_elements_attrs") = false,
+      "Get the TOSA operators used by the model after its TOSA legalization");
+
+  tosa_checker_class.def(
+      "_get_mlir_model_representation",
+      [](tosa_checker::TOSAChecker& tc, bool elide_large_elements_attrs) {
+        return tc.GetMLIRModelRepresentation(elide_large_elements_attrs);
+      },
+      pybind11::arg("elide_large_elements_attrs") = false,
+      "Get the MLIR representation of the model");
+
+  tosa_checker_class.def(
+      "_get_mlir_tosa_model_representation",
+      [](tosa_checker::TOSAChecker& tc, bool elide_large_elements_attrs) {
+        return tc.GetMLIRTOSAModelRepresentation(elide_large_elements_attrs);
+      },
+      pybind11::arg("elide_large_elements_attrs") = false,
+      "Get the MLIR representation of the TOSA legalized model");
+
+  /**
+   * tosa_checker::TOSAChecker::Operator
+   */
+  pybind11::class_<tosa_checker::TOSAChecker::Operator>(tosa_checker_class,
+                                                        "_Operator")
+      .def_readonly("name", &tosa_checker::TOSAChecker::Operator::name)
+      .def_readonly("location", &tosa_checker::TOSAChecker::Operator::location)
+      .def_readonly("attributes",
+                    &tosa_checker::TOSAChecker::Operator::attributes)
+      .def_readonly("is_tosa_compatible",
+                    &tosa_checker::TOSAChecker::Operator::is_tosa_compatible)
+      .def_readonly("mlir_representation",
+                    &tosa_checker::TOSAChecker::Operator::mlir_representation)
+      .def("__repr__", [](const tosa_checker::TOSAChecker::Operator& o) {
+        std::stringstream stream;
+        stream << o;
+        return stream.str();
+      });
+}