[DOCS] Update link loc (#4257)

.github/PULL_REQUEST_TEMPLATE.md

@@ -1 +1 @@
-Thanks for contributing to TVM!   Please refer to guideline https://docs.tvm.ai/contribute/ for useful information and tips. After the pull request is submitted, please request code reviews from [Reviewers](https://github.com/dmlc/tvm/blob/master/CONTRIBUTORS.md#reviewers) by @ them in the pull request thread.
+Thanks for contributing to TVM!   Please refer to guideline https://docs.tvm.ai/contribute/ for useful information and tips. After the pull request is submitted, please request code reviews from [Reviewers](https://github.com/apache/incubator-tvm/blob/master/CONTRIBUTORS.md#reviewers) by @ them in the pull request thread.

CONTRIBUTORS.md

@@ -112,7 +112,7 @@ We do encourage everyone to work anything they are interested in.
 - [Lianmin Zheng](https://github.com/merrymercy): @merrymercy
 
 ## List of Contributors
-- [Full List of Contributors](https://github.com/dmlc/tvm/graphs/contributors)
+- [Full List of Contributors](https://github.com/apache/incubator-tvm/graphs/contributors)
   - To contributors: please add your name to the list.
 - [Qiao Zhang](https://github.com/zhangqiaorjc)
 - [Haolong Zhang](https://github.com/haolongzhangm)

apps/android_deploy/README.md

@@ -34,7 +34,7 @@ Alternatively, you may execute Docker image we provide wich contains the require
 
 ### Build APK
 
-Before you build the Android application, please refer to [TVM4J Installation Guide](https://github.com/dmlc/tvm/blob/master/jvm/README.md) and install tvm4j-core to your local maven repository. You can find tvm4j dependency declare in `app/build.gradle`. Modify it if it is necessary.
+Before you build the Android application, please refer to [TVM4J Installation Guide](https://github.com/apache/incubator-tvm/blob/master/jvm/README.md) and install tvm4j-core to your local maven repository. You can find tvm4j dependency declare in `app/build.gradle`. Modify it if it is necessary.
 
 ```
 dependencies {
@@ -124,7 +124,7 @@ If everything goes well, you will find compile tools in `/opt/android-toolchain-
 
 Follow instruction to get compiled version model for android target [here.](http://docs.tvm.ai/deploy/android.html)
 
-Copied these compiled model deploy_lib.so, deploy_graph.json and deploy_param.params to apps/android_deploy/app/src/main/assets/ and modify TVM flavor changes on [java](https://github.com/dmlc/tvm/blob/master/apps/android_deploy/app/src/main/java/ml/dmlc/tvm/android/demo/MainActivity.java#L81)
+Copied these compiled model deploy_lib.so, deploy_graph.json and deploy_param.params to apps/android_deploy/app/src/main/assets/ and modify TVM flavor changes on [java](https://github.com/apache/incubator-tvm/blob/master/apps/android_deploy/app/src/main/java/ml/dmlc/tvm/android/demo/MainActivity.java#L81)
 
 `CPU Verison flavor`
 ```

apps/android_rpc/README.md

@@ -28,7 +28,7 @@ You will need JDK, [Android NDK](https://developer.android.com/ndk) and an Andro
 
 We use [Gradle](https://gradle.org) to build. Please follow [the installation instruction](https://gradle.org/install) for your operating system.
 
-Before you build the Android application, please refer to [TVM4J Installation Guide](https://github.com/dmlc/tvm/blob/master/jvm/README.md) and install tvm4j-core to your local maven repository. You can find tvm4j dependency declare in `app/build.gradle`. Modify it if it is necessary.
+Before you build the Android application, please refer to [TVM4J Installation Guide](https://github.com/apache/incubator-tvm/blob/master/jvm/README.md) and install tvm4j-core to your local maven repository. You can find tvm4j dependency declare in `app/build.gradle`. Modify it if it is necessary.
 
 ```
 dependencies {
@@ -146,7 +146,7 @@ android   1      1     0
 ```
 
 
-Then checkout [android\_rpc/tests/android\_rpc\_test.py](https://github.com/dmlc/tvm/blob/master/apps/android_rpc/tests/android_rpc_test.py) and run,
+Then checkout [android\_rpc/tests/android\_rpc\_test.py](https://github.com/apache/incubator-tvm/blob/master/apps/android_rpc/tests/android_rpc_test.py) and run,
 
 ```bash
 # Specify the RPC tracker
@@ -157,7 +157,7 @@ export TVM_NDK_CC=/opt/android-toolchain-arm64/bin/aarch64-linux-android-g++
 python android_rpc_test.py
 ```
 
-This will compile TVM IR to shared libraries (CPU, OpenCL and Vulkan) and run vector addition on your Android device. To verify compiled TVM IR shared libraries on OpenCL target set `'test_opencl = True'` and on Vulkan target set `'test_vulkan = True'` in  [tests/android_rpc_test.py](https://github.com/dmlc/tvm/blob/master/apps/android_rpc/tests/android_rpc_test.py), by default on CPU target will execute.
+This will compile TVM IR to shared libraries (CPU, OpenCL and Vulkan) and run vector addition on your Android device. To verify compiled TVM IR shared libraries on OpenCL target set `'test_opencl = True'` and on Vulkan target set `'test_vulkan = True'` in  [tests/android_rpc_test.py](https://github.com/apache/incubator-tvm/blob/master/apps/android_rpc/tests/android_rpc_test.py), by default on CPU target will execute.
 On my test device, it gives following results.
 
 ```bash

apps/benchmark/README.md

@@ -20,7 +20,7 @@
 
 ## Results
 
-See results on wiki page https://github.com/dmlc/tvm/wiki/Benchmark
+See results on wiki page https://github.com/apache/incubator-tvm/wiki/Benchmark
 
 ## How to Reproduce
 
@@ -78,7 +78,7 @@ python3 -m tvm.exec.rpc_tracker
   `python3 -m tvm.exec.rpc_server --tracker=10.77.1.123:9190 --key=rk3399`, where 10.77.1.123 is the IP address of the tracker.
 
 * For Android device
-   * Build and install tvm RPC apk on your device [Help](https://github.com/dmlc/tvm/tree/master/apps/android_rpc).
+   * Build and install tvm RPC apk on your device [Help](https://github.com/apache/incubator-tvm/tree/master/apps/android_rpc).
      Make sure you can pass the android rpc test. Then you have alreadly known how to register.
 
 3. Verify the device registration

apps/sgx/README.md

@@ -39,7 +39,7 @@ Check out the `/tvm/install/ubuntu_install_sgx.sh` for the commands to get these
 If using Docker, start by running
 
 ```
-git clone --recursive https://github.com/dmlc/tvm.git
+git clone --recursive https://github.com/apache/incubator-tvm.git
 docker run --rm -it -v $(pwd)/tvm:/mnt tvmai/ci-cpu /bin/bash
 ```
 then, in the container

conda/tvm-libs/meta.yaml

@@ -43,6 +43,6 @@ requirements:
     - {{ pin_compatible('cudnn', lower_bound='7.6.0', max_pin='x') }}  # [cuda]
 
 about:
-  home: https://github.com/dmlc/tvm
+  home: https://github.com/apache/incubator-tvm
   license: Apache2
   summary: a low level domain specific language for compiling tensor computation pipelines

conda/tvm/meta.yaml

@@ -58,7 +58,7 @@ test:
     - python -m pytest -v tests/python/integration
 
 about:
-  home: https://github.com/dmlc/tvm
+  home: https://github.com/apache/incubator-tvm
   license: Apache-2.0
   license_family: Apache
   summary: a low level domain specific language for compiling tensor computation pipelines

docker/Dockerfile.demo_android

@@ -56,7 +56,7 @@ RUN git clone https://github.com/KhronosGroup/OpenCL-Headers /usr/local/OpenCL-H
 
 # Build TVM
 RUN cd /usr && \
-    git clone --depth=1 https://github.com/dmlc/tvm --recursive && \
+    git clone --depth=1 https://github.com/apache/incubator-tvm --recursive && \
     cd /usr/tvm && \
     mkdir -p build && \
     cd build && \

docker/Dockerfile.demo_opencl

@@ -62,7 +62,7 @@ RUN echo "Cloning TVM source & submodules"
 ENV TVM_PAR_DIR="/usr"
 RUN mkdir -p TVM_PAR_DIR && \
 	cd ${TVM_PAR_DIR} && \
-	git clone --depth=1 https://github.com/dmlc/tvm --recursive
+	git clone --depth=1 https://github.com/apache/incubator-tvm --recursive
 #RUN git submodule update --init --recursive
 
 

docker/install/install_tvm_cpu.sh

@@ -21,7 +21,7 @@ set -u
 set -o pipefail
 
 cd /usr
-git clone --depth=1 https://github.com/dmlc/tvm --recursive
+git clone --depth=1 https://github.com/apache/incubator-tvm --recursive
 cd /usr/tvm
 # checkout a hash-tag
 git checkout 4b13bf668edc7099b38d463e5db94ebc96c80470

docker/install/install_tvm_gpu.sh

@@ -21,7 +21,7 @@ set -u
 set -o pipefail
 
 cd /usr
-git clone --depth=1 https://github.com/dmlc/tvm --recursive
+git clone --depth=1 https://github.com/apache/incubator-tvm --recursive
 cd /usr/tvm
 # checkout a hash-tag
 git checkout 4b13bf668edc7099b38d463e5db94ebc96c80470

docs/contribute/community.rst

@@ -20,7 +20,7 @@
 TVM Community Guideline
 =======================
 
-TVM adopts the Apache style model and governs by merit. We believe that it is important to create an inclusive community where everyone can use, contribute to, and influence the direction of the project. See `CONTRIBUTORS.md <https://github.com/dmlc/tvm/blob/master/CONTRIBUTORS.md>`_ for the current list of contributors.
+TVM adopts the Apache style model and governs by merit. We believe that it is important to create an inclusive community where everyone can use, contribute to, and influence the direction of the project. See `CONTRIBUTORS.md <https://github.com/apache/incubator-tvm/blob/master/CONTRIBUTORS.md>`_ for the current list of contributors.
 
 
 

docs/contribute/document.rst

@@ -68,7 +68,7 @@ Be careful to leave blank lines between sections of your documents.
 In the above case, there has to be a blank line before `Parameters`, `Returns` and `Examples`
 in order for the doc to be built correctly. To add a new function to the doc,
 we need to add the `sphinx.autodoc <http://www.sphinx-doc.org/en/master/ext/autodoc.html>`_
-rules to the `docs/api/python <https://github.com/dmlc/tvm/tree/master/docs/api/python>`_).
+rules to the `docs/api/python <https://github.com/apache/incubator-tvm/tree/master/docs/api/python>`_).
 You can refer to the existing files under this folder on how to add the functions.
 
 
@@ -96,7 +96,7 @@ to add comments about code logics to improve readability.
 Write Tutorials
 ---------------
 We use the `sphinx-gallery <https://sphinx-gallery.github.io/>`_ to build python tutorials.
-You can find the source code under `tutorials <https://github.com/dmlc/tvm/tree/master/tutorials>`_ quite self explanatory.
+You can find the source code under `tutorials <https://github.com/apache/incubator-tvm/tree/master/tutorials>`_ quite self explanatory.
 One thing that worth noting is that the comment blocks are written in reStructuredText instead of markdown so be aware of the syntax.
 
 The tutorial code will run on our build server to generate the document page.

docs/deploy/android.md

@@ -38,5 +38,5 @@ deploy_lib.so, deploy_graph.json, deploy_param.params will go to android target.
 
 ## TVM Runtime for Android Target
 
-Refer [here](https://github.com/dmlc/tvm/blob/master/apps/android_deploy/README.md#build-and-installation) to build CPU/OpenCL version flavor TVM runtime for android target.
-From android java TVM API to load model & execute can be referred at this [java](https://github.com/dmlc/tvm/blob/master/apps/android_deploy/app/src/main/java/ml/dmlc/tvm/android/demo/MainActivity.java) sample source.
+Refer [here](https://github.com/apache/incubator-tvm/blob/master/apps/android_deploy/README.md#build-and-installation) to build CPU/OpenCL version flavor TVM runtime for android target.
+From android java TVM API to load model & execute can be referred at this [java](https://github.com/apache/incubator-tvm/blob/master/apps/android_deploy/app/src/main/java/ml/dmlc/tvm/android/demo/MainActivity.java) sample source.

docs/deploy/cpp_deploy.md

@@ -18,7 +18,7 @@
 Deploy TVM Module using C++ API
 ===============================
 
-We provide an example on how to deploy TVM modules in [apps/howto_deploy](https://github.com/dmlc/tvm/tree/master/apps/howto_deploy)
+We provide an example on how to deploy TVM modules in [apps/howto_deploy](https://github.com/apache/incubator-tvm/tree/master/apps/howto_deploy)
 
 To run the example, you can use the following command
 
@@ -34,17 +34,17 @@ The only thing we need is to link to a TVM runtime in your target platform.
 TVM provides a minimum runtime, which costs around 300K to 600K depending on how much modules we use.
 In most cases, we can use ```libtvm_runtime.so``` that comes with the build.
 
-If somehow you find it is hard to build ```libtvm_runtime```, checkout [tvm_runtime_pack.cc](https://github.com/dmlc/tvm/tree/master/apps/howto_deploy/tvm_runtime_pack.cc).
+If somehow you find it is hard to build ```libtvm_runtime```, checkout [tvm_runtime_pack.cc](https://github.com/apache/incubator-tvm/tree/master/apps/howto_deploy/tvm_runtime_pack.cc).
 It is an example all in one file that gives you TVM runtime.
 You can compile this file using your build system and include this into your project.
 
-You can also checkout [apps](https://github.com/dmlc/tvm/tree/master/apps/) for example applications build with TVM on iOS, Android and others.
+You can also checkout [apps](https://github.com/apache/incubator-tvm/tree/master/apps/) for example applications build with TVM on iOS, Android and others.
 
 Dynamic Library vs. System Module
 ---------------------------------
 TVM provides two ways to use the compiled library.
-You can checkout [prepare_test_libs.py](https://github.com/dmlc/tvm/tree/master/apps/howto_deploy/prepare_test_libs.py)
-on how to generate the library and [cpp_deploy.cc](https://github.com/dmlc/tvm/tree/master/apps/howto_deploy/cpp_deploy.cc) on how to use them.
+You can checkout [prepare_test_libs.py](https://github.com/apache/incubator-tvm/tree/master/apps/howto_deploy/prepare_test_libs.py)
+on how to generate the library and [cpp_deploy.cc](https://github.com/apache/incubator-tvm/tree/master/apps/howto_deploy/cpp_deploy.cc) on how to use them.
 
 - Store library as a shared library and dynamically load the library into your project.
 - Bundle the compiled library into your project in system module mode.

docs/deploy/index.rst

@@ -38,7 +38,7 @@ on a Linux based embedded system such as Raspberry Pi:
 
 .. code:: bash
 
-    git clone --recursive https://github.com/dmlc/tvm
+    git clone --recursive https://github.com/apache/incubator-tvm
     cd tvm
     mkdir build
     cp cmake/config.cmake build

docs/deploy/nnvm.md

@@ -144,7 +144,7 @@ This process need few additional options as given below to NNVM build.
 Module export require additional options for not to compile but save as ```lib.export_library (path, fcompile=False)```
 
 The output of above API is a tar compressed file containing object file ```(lib.o)``` and cpp source file ```(devc.cc)``` which embeds device blob. Thease two files should be compiled along with other files or objects while building c++ application.
-Please refer to [Makefile](https://github.com/dmlc/tvm/tree/master/apps/howto_deploy/Makefile#L32) for a reference.
+Please refer to [Makefile](https://github.com/apache/incubator-tvm/tree/master/apps/howto_deploy/Makefile#L32) for a reference.
 
 The c++ code to load this system module require the below change.
 

docs/dev/inferbound.rst

@@ -19,7 +19,7 @@
 InferBound Pass
 *******************************************
 
-The InferBound pass is run after normalize, and before ScheduleOps `build_module.py <https://github.com/dmlc/tvm/blob/master/python/tvm/build_module.py>`_. The main job of InferBound is to create the bounds map, which specifies a Range for each IterVar in the program. These bounds are then passed to ScheduleOps, where they are used to set the extents of For loops, see `MakeLoopNest <https://github.com/dmlc/tvm/blob/master/src/op/op_util.cc>`_, and to set the sizes of allocated buffers (`BuildRealize <https://github.com/dmlc/tvm/blob/master/src/op/compute_op.cc>`_), among other uses.
+The InferBound pass is run after normalize, and before ScheduleOps `build_module.py <https://github.com/apache/incubator-tvm/blob/master/python/tvm/build_module.py>`_. The main job of InferBound is to create the bounds map, which specifies a Range for each IterVar in the program. These bounds are then passed to ScheduleOps, where they are used to set the extents of For loops, see `MakeLoopNest <https://github.com/apache/incubator-tvm/blob/master/src/op/op_util.cc>`_, and to set the sizes of allocated buffers (`BuildRealize <https://github.com/apache/incubator-tvm/blob/master/src/op/compute_op.cc>`_), among other uses.
 
 The output of InferBound is a map from IterVar to Range:
 
@@ -50,9 +50,9 @@ Therefore, let's review the Range and IterVar classes:
    	};
    }
 
-Note that IterVarNode also contains a Range ``dom``. This ``dom`` may or may not have a meaningful value, depending on when the IterVar was created. For example, when ``tvm.compute`` is called, an `IterVar is created <https://github.com/dmlc/tvm/blob/master/src/op/compute_op.cc>`_ for each axis and reduce axis, with dom's equal to the shape supplied in the call to ``tvm.compute``.
+Note that IterVarNode also contains a Range ``dom``. This ``dom`` may or may not have a meaningful value, depending on when the IterVar was created. For example, when ``tvm.compute`` is called, an `IterVar is created <https://github.com/apache/incubator-tvm/blob/master/src/op/compute_op.cc>`_ for each axis and reduce axis, with dom's equal to the shape supplied in the call to ``tvm.compute``.
 
-On the other hand, when ``tvm.split`` is called, `IterVars are created <https://github.com/dmlc/tvm/blob/master/src/schedule/schedule_lang.cc>`_ for the inner and outer axes, but these IterVars are not given a meaningful ``dom`` value.
+On the other hand, when ``tvm.split`` is called, `IterVars are created <https://github.com/apache/incubator-tvm/blob/master/src/schedule/schedule_lang.cc>`_ for the inner and outer axes, but these IterVars are not given a meaningful ``dom`` value.
 
 In any case, the ``dom`` member of an IterVar is never modified during InferBound. However, keep in mind that the ``dom`` member of an IterVar is sometimes used as default value for the Ranges InferBound computes.
 
@@ -114,7 +114,7 @@ Tensors haven't been mentioned yet, but in the context of TVM, a Tensor represen
    	int value_index;
    };
 
-In the Operation class declaration above, we can see that each operation also has a list of InputTensors. Thus the stages of the schedule form a DAG, where each stage is a node in the graph. There is an edge in the graph from Stage A to Stage B, if the operation of Stage B has an input tensor whose source operation is the op of Stage A. Put simply, there is an edge from A to B, if B consumes a tensor produced by A. See the diagram below. This graph is created at the beginning of InferBound, by a call to `CreateReadGraph <https://github.com/dmlc/tvm/blob/master/src/schedule/bound.cc>`_.
+In the Operation class declaration above, we can see that each operation also has a list of InputTensors. Thus the stages of the schedule form a DAG, where each stage is a node in the graph. There is an edge in the graph from Stage A to Stage B, if the operation of Stage B has an input tensor whose source operation is the op of Stage A. Put simply, there is an edge from A to B, if B consumes a tensor produced by A. See the diagram below. This graph is created at the beginning of InferBound, by a call to `CreateReadGraph <https://github.com/apache/incubator-tvm/blob/master/src/schedule/bound.cc>`_.
 
 .. image:: https://raw.githubusercontent.com/tvmai/tvmai.github.io/master/images/docs/inferbound/stage_graph.png
     :align: center

docs/dev/nnvm_overview.md

@@ -19,7 +19,7 @@
 # NNVM Design Overview
 
 NNVM is a reusable graph IR stack for deep learning systems. It provides useful API to construct, represent and transform computation graphs to get most high-level optimization needed in deep learning.
-As a part of TVM stack for deep learning, NNVM also provides a shared compiler for deep learning frameworks to optimize, compile and deploy into different hardware backends via [TVM](https://github.com/dmlc/tvm)
+As a part of TVM stack for deep learning, NNVM also provides a shared compiler for deep learning frameworks to optimize, compile and deploy into different hardware backends via [TVM](https://github.com/apache/incubator-tvm)
 
 ## Key Requirements and Design Choices
 

docs/dev/relay_add_pass.rst

@@ -399,8 +399,8 @@ information about the pass manager interface can be found in :ref:`relay-pass-in
 Relay's standard passes are listed in `include/tvm/relay/transform.h`_ and implemented
 in `src/relay/pass/`_.
 
-.. _include/tvm/relay/transform.h: https://github.com/dmlc/tvm/blob/master/include/tvm/relay/transform.h
+.. _include/tvm/relay/transform.h: https://github.com/apache/incubator-tvm/blob/master/include/tvm/relay/transform.h
 
-.. _src/relay/pass: https://github.com/dmlc/tvm/tree/master/src/relay/pass
+.. _src/relay/pass: https://github.com/apache/incubator-tvm/tree/master/src/relay/pass
 
-.. _src/relay/pass/fold_constant.cc: https://github.com/dmlc/tvm/blob/master/src/relay/pass/fold_constant.cc
+.. _src/relay/pass/fold_constant.cc: https://github.com/apache/incubator-tvm/blob/master/src/relay/pass/fold_constant.cc