2 files changed, 68 insertions(+), 48 deletions(-)

M README.md
A README_original.md

@@ 1,53 1,20 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
-<!--- or more contributor license agreements.  See the NOTICE file -->
-<!--- distributed with this work for additional information -->
-<!--- regarding copyright ownership.  The ASF licenses this file -->
-<!--- to you 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 -->
+# Backdoored TVM for ImpNet
 
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+Here you will find the source code of the backdoored version of TVM used in the
+ImpNet paper. This was forked from TVM commit hash `fc419df32`, you may find it
+useful to compare the two to see the changes that have been made. Git
+information has been stripped from this directory to preserve anonymity.
 
-<!--- 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. -->
+The original README file can be seen at `README_original.md`
 
-<img src=https://raw.githubusercontent.com/apache/tvm-site/main/images/logo/tvm-logo-small.png width=128/> Open Deep Learning Compiler Stack
-==============================================
-[Documentation](https://tvm.apache.org/docs) |
-[Contributors](CONTRIBUTORS.md) |
-[Community](https://tvm.apache.org/community) |
-[Release Notes](NEWS.md)
+Compilation of this backdoored TVM is identical to ordinary TVM, i.e.
+essentially the following:
 
-[![Build Status](https://ci.tlcpack.ai/buildStatus/icon?job=tvm/main)](https://ci.tlcpack.ai/job/tvm/job/main/)
-[![WinMacBuild](https://github.com/apache/tvm/workflows/WinMacBuild/badge.svg)](https://github.com/apache/tvm/actions?query=workflow%3AWinMacBuild)
+```
+cd build
+# edit cmake.config as desired
+cmake ..
+make
+```
 
-Apache TVM is a compiler stack for deep learning systems. It is designed to close the gap between the
-productivity-focused deep learning frameworks, and the performance- and efficiency-focused hardware backends.
-TVM works with deep learning frameworks to provide end to end compilation to different backends.
-
-License
--------
-TVM is licensed under the [Apache-2.0](LICENSE) license.
-
-Getting Started
----------------
-Check out the [TVM Documentation](https://tvm.apache.org/docs/) site for installation instructions, tutorials, examples, and more.
-The [Getting Started with TVM](https://tvm.apache.org/docs/tutorial/introduction.html) tutorial is a great
-place to start.
-
-Contribute to TVM
------------------
-TVM adopts apache committer model, we aim to create an open source project that is maintained and owned by the community.
-Check out the [Contributor Guide](https://tvm.apache.org/docs/contribute/).
-
-Acknowledgement
----------------
-We learned a lot from the following projects when building TVM.
-- [Halide](https://github.com/halide/Halide): Part of TVM's TIR and arithmetic simplification module
-  originates from Halide. We also learned and adapted some part of lowering pipeline from Halide.
-- [Loopy](https://github.com/inducer/loopy): use of integer set analysis and its loop transformation primitives.
-- [Theano](https://github.com/Theano/Theano): the design inspiration of symbolic scan operator for recurrence.
+How this can be used for the tests is detailed in each's README file.

@@ 0,0 1,53 @@
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you 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. -->
+
+<img src=https://raw.githubusercontent.com/apache/tvm-site/main/images/logo/tvm-logo-small.png width=128/> Open Deep Learning Compiler Stack
+==============================================
+[Documentation](https://tvm.apache.org/docs) |
+[Contributors](CONTRIBUTORS.md) |
+[Community](https://tvm.apache.org/community) |
+[Release Notes](NEWS.md)
+
+[![Build Status](https://ci.tlcpack.ai/buildStatus/icon?job=tvm/main)](https://ci.tlcpack.ai/job/tvm/job/main/)
+[![WinMacBuild](https://github.com/apache/tvm/workflows/WinMacBuild/badge.svg)](https://github.com/apache/tvm/actions?query=workflow%3AWinMacBuild)
+
+Apache TVM is a compiler stack for deep learning systems. It is designed to close the gap between the
+productivity-focused deep learning frameworks, and the performance- and efficiency-focused hardware backends.
+TVM works with deep learning frameworks to provide end to end compilation to different backends.
+
+License
+-------
+TVM is licensed under the [Apache-2.0](LICENSE) license.
+
+Getting Started
+---------------
+Check out the [TVM Documentation](https://tvm.apache.org/docs/) site for installation instructions, tutorials, examples, and more.
+The [Getting Started with TVM](https://tvm.apache.org/docs/tutorial/introduction.html) tutorial is a great
+place to start.
+
+Contribute to TVM
+-----------------
+TVM adopts apache committer model, we aim to create an open source project that is maintained and owned by the community.
+Check out the [Contributor Guide](https://tvm.apache.org/docs/contribute/).
+
+Acknowledgement
+---------------
+We learned a lot from the following projects when building TVM.
+- [Halide](https://github.com/halide/Halide): Part of TVM's TIR and arithmetic simplification module
+  originates from Halide. We also learned and adapted some part of lowering pipeline from Halide.
+- [Loopy](https://github.com/inducer/loopy): use of integer set analysis and its loop transformation primitives.
+- [Theano](https://github.com/Theano/Theano): the design inspiration of symbolic scan operator for recurrence.