[DOCS] Fix Sphinx Warning: the target found for cross-reference

docs/api/python/autotvm.rst

@@ -18,6 +18,7 @@
 tvm.autotvm
 -----------
 .. automodule:: tvm.autotvm
+.. automodule:: tvm.autotvm.apply_history_best
 
 tvm.autotvm.measure
 ~~~~~~~~~~~~~~~~~~~

docs/api/python/relay/build_module.rst

@@ -18,6 +18,8 @@
 tvm.relay.build_module
 ----------------------
 
+.. automodule:: tvm.relay.build
+
 .. automodule:: tvm.relay.build_module
 
 .. autofunction:: tvm.relay.build_module.build

docs/api/python/relay/index.rst

@@ -39,3 +39,4 @@ compiler stack.
    op
    scope_builder
    vision
+   testing

docs/api/python/relay/testing.rst

@@ -0,0 +1,21 @@
+..  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.
+
+tvm.relay.testing
+-----------------
+
+.. autoclass:: tvm.relay.testing.resnet

docs/api/python/tvm.rst

@@ -44,7 +44,9 @@ The user facing API for computation declaration.
    tvm.min
    tvm.max
    tvm.tag_scope
-
+   tvm.exp
+   tvm.intrin
+   tvm.call_pure_extern
 
 .. autofunction:: tvm.var
 .. autofunction:: tvm.size_var
@@ -69,3 +71,6 @@ The user facing API for computation declaration.
 .. autofunction:: tvm.min
 .. autofunction:: tvm.max
 .. autofunction:: tvm.tag_scope
+.. autofunction:: tvm.exp
+.. autofunction:: tvm.intrin
+.. autofunction:: tvm.call_pure_extern

docs/conf.py

@@ -238,10 +238,11 @@ def setup(app):
 sphinx_gallery_conf = {
     'backreferences_dir': 'gen_modules/backreferences',
     'doc_module': ('tvm', 'numpy'),
-'reference_url': {
-    'tvm': None,
-    'matplotlib': 'https://matplotlib.org/',
-    'numpy': 'https://docs.scipy.org/doc/numpy/'},
+    'reference_url': {
+        'tvm': None,
+        'matplotlib': 'https://matplotlib.org/',
+        'numpy': 'https://docs.scipy.org/doc/numpy/'
+    },
     'examples_dirs': examples_dirs,
     'gallery_dirs': gallery_dirs,
     'subsection_order': subsection_order,

docs/dev/relay_add_pass.rst

@@ -401,6 +401,6 @@ in `src/relay/pass/`_.
 
 .. _include/tvm/relay/transform.h: https://github.com/apache/incubator-tvm/blob/master/include/tvm/relay/transform.h
 
-.. _src/relay/pass: https://github.com/apache/incubator-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/apache/incubator-tvm/blob/master/src/relay/pass/fold_constant.cc

docs/dev/relay_bring_your_own_codegen.rst

@@ -137,7 +137,7 @@ Here we highlight the notes marked in the above code:
 
 * **Note 3** is a TVM runtime compatible wrapper function. It accepts a list of input tensors and one output tensor (the last argument), casts them to the right data type, and invokes the subgraph function described in Note 2. In addition, ``TVM_DLL_EXPORT_TYPED_FUNC`` is a TVM macro that generates another function ``gcc_0`` with unified the function arguments by packing all tensors to ``TVMArgs``. As a result, the TVM runtime can directly invoke ``gcc_0`` to execute the subgraph without additional efforts. With the above code generated, TVM is able to compile it along with the rest parts of the graph and export a single library for deployment.
 
-In the rest of this section, we will implement a codegen step-by-step to generate the above code. Your own codegen has to be located at ``src/relay/backend/contrib/<your-codegen-name>/``. In our example, we name our codegen "codegen_c" and put it under `here<https://github.com/apache/incubator-tvm/blob/master/src/relay/backend/contrib/codegen_c/codegen.cc>`_. Feel free to check this file for a complete implementation.
+In the rest of this section, we will implement a codegen step-by-step to generate the above code. Your own codegen has to be located at ``src/relay/backend/contrib/<your-codegen-name>/``. In our example, we name our codegen "codegen_c" and put it under `/src/relay/backend/contrib/codegen_c/ <https://github.com/apache/incubator-tvm/blob/master/src/relay/backend/contrib/codegen_c/codegen.cc>`_. Feel free to check this file for a complete implementation.
 
 Specifically, we are going to implement two classes in this file and here is their relationship:
 
@@ -625,7 +625,7 @@ The next step is to implement a customized runtime to make use of the output of
 Implement a Customized Runtime
 ==============================
 
-In this section, we will implement a customized TVM runtime step-by-step and register it to TVM runtime modules. The customized runtime should be located at ``src/runtime/contrib/<your-runtime-name>/``. In our example, we name our runtime "example_ext_runtime" and put it under `here<src/runtime/contrib/example_ext_runtime/example_ext_runtime.cc>`_. Feel free to check this file for a complete implementation.
+In this section, we will implement a customized TVM runtime step-by-step and register it to TVM runtime modules. The customized runtime should be located at ``src/runtime/contrib/<your-runtime-name>/``. In our example, we name our runtime "example_ext_runtime" and put it under `/src/runtime/contrib/example_ext_runtime/ <https://github.com/apache/incubator-tvm/blob/master/src/runtime/contrib/example_ext_runtime/example_ext_runtime.cc>`_. Feel free to check this file for a complete implementation.
 
 Again, we first define a customized runtime class as follows. The class has to be derived from TVM ``ModuleNode`` in order to be compatible with other TVM runtime modules.
 

docs/dev/relay_pass_infra.rst

@@ -664,3 +664,5 @@ For more pass infra related examples in Python and C++, please refer to
 .. _tests/python/relay/test_pass_manager.py: https://github.com/apache/incubator-tvm/blob/master/tests/python/relay/test_pass_manager.py
 
 .. _tests/cpp/relay_transform_sequential.cc: https://github.com/apache/incubator-tvm/blob/master/tests/cpp/relay_transform_sequential.cc
+
+.. _include/tvm/relay/transform.h: https://github.com/apache/incubator-tvm/blob/master/include/tvm/relay/transform.h

docs/dev/runtime.rst

@@ -1,19 +1,19 @@
-.. 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.
+..  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.
 
 .. _tvm-runtime-system:
 

tutorials/autotvm/tune_relay_arm.py

@@ -15,6 +15,8 @@
 # specific language governing permissions and limitations
 # under the License.
 """
+.. _tune_relay_arm:
+
 Auto-tuning a convolutional network for ARM CPU
 ===============================================
 **Author**: `Lianmin Zheng <https://github.com/merrymercy>`_, `Zhao Wu <https://github.com/FrozenGene>`_, `Eddie Yan <https://github.com/eqy>`_

tutorials/autotvm/tune_relay_x86.py

@@ -15,6 +15,8 @@
 # specific language governing permissions and limitations
 # under the License.
 """
+.. _tune_relay_x86:
+
 Auto-tuning a convolutional network for x86 CPU
 ===============================================
 **Author**: `Yao Wang <https://github.com/kevinthesun>`_, `Eddie Yan <https://github.com/eqy>`_

tutorials/language/intrin_math.py

@@ -21,7 +21,7 @@
 
 While TVM supports basic arithmetic operations. In many cases
 usually we will need more complicated builtin functions.
-For example :code:`exp` to take the exponetial of the function.
+For example :code:`exp` to take the exponential of the function.
 
 These functions are target system dependent and may have different
 names of different target platforms. In this tutorial, we will learn
@@ -94,6 +94,8 @@
 # TVM also allows user to customize the rules during runtime.
 # The following example customizes CUDA lowering rule for :code:`exp`.
 #
+
+
 def my_cuda_math_rule(op):
     """Customized CUDA intrinsic lowering rule"""
     assert isinstance(op, tvm.tir.Call)
@@ -106,6 +108,8 @@ def my_cuda_math_rule(op):
     else:
         # cannot do translation, return self.
         return op
+
+
 tvm.target.register_intrin_rule("cuda", "exp", my_cuda_math_rule, override=True)
 ######################################################################
 # Register the rule to TVM with override option to override existing rule.
@@ -123,10 +127,13 @@ def my_cuda_math_rule(op):
 # User can easily add new intrinsic by using the intrinsic rule system.
 # The following example add an intrinsic :code:`mylog` to the system.
 #
+
+
 def mylog(x):
     """customized log intrinsic function"""
     return tvm.call_pure_intrin(x.dtype, "mylog", x)
 
+
 def my_cuda_mylog_rule(op):
     """CUDA lowering rule for log"""
     if op.dtype == "float32":
@@ -135,6 +142,8 @@ def my_cuda_mylog_rule(op):
         return tvm.call_pure_extern("float64", "log", op.args[0])
     else:
         return op
+
+
 tvm.target.register_intrin_rule("cuda", "mylog", my_cuda_mylog_rule, override=True)
 
 n = tvm.var("n")

tutorials/relay_quick_start.py

@@ -55,7 +55,7 @@
 #
 # In this tutorial, we assume we will do inference on our device
 # and the batch size is set to be 1. Input images are RGB color
-# images of size 224 * 224. We can call the :any:`tvm.relay.expr.astext()`
+# images of size 224 * 224. We can call the :any:`tvm.relay.expr.TupleWrapper.astext()`
 # to show the network structure.
 
 batch_size = 1

vta/tutorials/optimize/matrix_multiply_opt.py

@@ -23,7 +23,7 @@
 
 This tutorial provides an overview on how to use TVM to map matrix
 multiplication efficiently on the VTA design.
-We recommend covering the :ref:`vta-basic-mat-mult` tutorial first.
+We recommend covering the :ref:`basic-mat-mult` tutorial first.
 
 In this tutorial, we will demonstrate TVM schedule optimizations to break large
 neural network operators down onto smaller blocks to achieve computation within