[Docs] Corrected additional sphinx build warnings.

- Rebased on main and corrected warnings, now up to date as of commit
  53e4c60.

docs/api/python/index.rst

@@ -18,6 +18,7 @@
 Python API
 ==========
 
+
 .. toctree::
    :maxdepth: 2
 

docs/api/python/relay/image.rst

@@ -22,4 +22,5 @@ tvm.relay.image
 .. automodule:: tvm.relay.image
    :members:
    :imported-members:
+   :exclude-members: Expr, Constant
    :autosummary:

docs/api/python/relay/index.rst

@@ -26,4 +26,5 @@ tvm.relay
       TypeVar, GlobalTypeVar, TypeConstraint, FuncType, TupleType, IncompleteType,
       TypeCall, TypeRelation, TensorType, RelayRefType, GlobalVar, SourceName,
       Span, Var, Op, Constructor
+    :noindex: TypeData
     :autosummary:

docs/api/python/tir.rst

@@ -37,6 +37,7 @@ tvm.tir.analysis
 .. automodule:: tvm.tir.analysis
    :members:
    :imported-members:
+   :noindex: Buffer, Stmt
    :autosummary:
 
 

docs/api/python/topi.rst

@@ -20,6 +20,7 @@ tvm.topi
 .. automodule:: tvm.topi
    :members:
    :imported-members:
+   :noindex: AssertStmt
    :autosummary:
 
 tvm.topi.nn

python/tvm/driver/build_module.py

@@ -98,7 +98,7 @@ def lower(
 
     Parameters
     ----------
-    inp : Union[schedule.Schedule, tvm.tir.PrimFunc, IRModule]
+    inp : Union[tvm.te.schedule.Schedule, tvm.tir.PrimFunc, IRModule]
         The TE schedule or TensorIR PrimFunc/IRModule to be built
 
     args : Optional[List[Union[tvm.tir.Buffer, tensor.Tensor, Var]]]
@@ -233,7 +233,7 @@ def build(
 
     Parameters
     ----------
-    inputs : Union[schedule.Schedule, tvm.tir.PrimFunc, IRModule, Mapping[str, IRModule]]
+    inputs : Union[tvm.te.schedule.Schedule, tvm.tir.PrimFunc, IRModule, Mapping[str, IRModule]]
         The input to be built
 
     args : Optional[List[Union[tvm.tir.Buffer, tensor.Tensor, Var]]]

tutorials/frontend/deploy_model_on_rasp.py

@@ -162,7 +162,7 @@ def transform_image(image):
 ######################################################################
 # Compile The Graph
 # -----------------
-# To compile the graph, we call the :any:`relay.build` function
+# To compile the graph, we call the :py:func:`relay.build` function
 # with the graph configuration and parameters. However, You cannot to
 # deploy a x86 program on a device with ARM instruction set. It means
 # Relay also needs to know the compilation option of target device,

tutorials/get_started/autotvm_matmul_x86.py

@@ -286,10 +286,10 @@ def matmul(N, L, M, dtype):
 # When proposing the next batch of configs, the tuner can take different
 # strategies. Some of the tuner strategies provided by TVM include:
 #
-# * :any:`RandomTuner`: Enumerate the space in a random order
-# * :any:`GridSearchTuner`: Enumerate the space in a grid search order
-# * :any:`GATuner`: Using genetic algorithm to search through the space
-# * :any:`XGBTuner`: Uses a model based method. Train a XGBoost model to
+# * :any:`tvm.autotvm.tuner.RandomTuner`: Enumerate the space in a random order
+# * :any:`tvm.autotvm.tuner.GridSearchTuner`: Enumerate the space in a grid search order
+# * :any:`tvm.autotvm.tuner.GATuner`: Using genetic algorithm to search through the space
+# * :any:`tvm.autotvm.tuner.XGBTuner`: Uses a model based method. Train a XGBoost model to
 #   predict the speed of lowered IR and pick the next batch according to the
 #   prediction.
 #

tutorials/get_started/relay_quick_start.py

@@ -55,10 +55,11 @@
 # Relay also supports other model formats such as MXNet, CoreML, ONNX and
 # Tensorflow.
 #
-# 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.TupleWrapper.astext()`
-# to show the network structure.
+# 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
+# :py:meth:`tvm.relay.expr.TupleWrapper.astext()` to show the network
+# structure.
 
 batch_size = 1
 num_class = 1000