[Docs] Prevented docs/1 file from being generated. (apache#8029)

* [Docs] Prevented docs/1 file from being generated.

Typo in tests/scripts/task_sphinx_precheck.sh caused $TVM_HOME/docs/1
file to be created with stderr output, rather than merged stderr and
stdout.

* [Docs] Corrected sphinx build warnings

- Previously, several warnings were generated by sphinx, but were
  unintentionally suppressed.  This PR resolves the sphinx warnings.

* [Docs] Corrected additional sphinx build warnings.

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

* [Docs] Corrected additional sphinx build warnings

- Rebased on main and corrected warnings, now up to date as of commit
  1f2ca06.

* [Docs] Corrected additional sphinx build warnings

- Rebased on main and corrected warnings, now up to date as of commit
  d0791d3.

* [Docs] Ignore sphinx warnings from missing "git describe" and sckit-learn versions.

Co-authored-by: Eric Lunderberg <elunderberg@octoml.ai>

Author: Lunderberg

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

docs/dev/device_target_interactions.rst

@@ -15,6 +15,7 @@
     specific language governing permissions and limitations
     under the License.
 
+
 .. _tvm-target-specific-overview:
 
 Device/Target Interactions

docs/dev/index.rst

@@ -29,6 +29,10 @@ This page is organized as follows:
   The sections after are specific guides focused on each logical component, organized
   by the component's name.
 
+- The :ref:`Device/Target Interactions <tvm-target-specific-overview>`
+  page describes how TVM interacts with each supported physical device
+  and code-generation target.
+
 - Feel free to also check out the :ref:`dev-how-to` for useful development tips.
 
 This guide provides a few complementary views of the architecture.
@@ -244,11 +248,18 @@ for learning-based optimizations.
    :maxdepth: 1
 
    runtime
+
+
+.. toctree::
+   :maxdepth: 1
+
    debugger
    virtual_machine
    introduction_to_module_serialization
    device_target_interactions
 
+
+
 tvm/node
 --------
 The node module adds additional features on top of the `runtime::Object` for IR data structures.

python/tvm/auto_scheduler/compute_dag.py

@@ -96,7 +96,7 @@ class ComputeDAG(Object):
 
     Parameters
     ----------
-    compute : Union[List[Tensor], str, Schedule]
+    compute : Union[List[Tensor], str, tvm.te.Schedule]
         Input/output tensors or workload key for a compute declaration.
     """
 

python/tvm/driver/build_module.py

@@ -98,17 +98,17 @@ def lower(
 
     Parameters
     ----------
-    inputs : Union[schedule.Schedule, 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[Buffer, tensor.Tensor, Var]]]
+    args : Optional[List[Union[tvm.tir.Buffer, tensor.Tensor, Var]]]
         The argument lists to the function for TE schedule.
         It should be None if we want to lower TensorIR.
 
     name : str
         The name of result function.
 
-    binds : Optional[Mapping[tensor.Tensor, Buffer]]
+    binds : Optional[Mapping[tensor.Tensor, tvm.tir.Buffer]]
         Dictionary that maps the Tensor to Buffer which specified the data layout
         requirement of the function. By default, a new compact buffer is created
         for each tensor in the argument.
@@ -233,10 +233,10 @@ def build(
 
     Parameters
     ----------
-    inputs : Union[schedule.Schedule, 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[Buffer, tensor.Tensor, Var]]]
+    args : Optional[List[Union[tvm.tir.Buffer, tensor.Tensor, Var]]]
         The argument lists to the function.
 
     target : Optional[Union[str, Target]]
@@ -254,7 +254,7 @@ def build(
     name : Optional[str]
         The name of result function.
 
-    binds : Optional[Mapping[tensor.Tensor, Buffer]]
+    binds : Optional[Mapping[tensor.Tensor, tvm.tir.Buffer]]
         Dictionary that maps the binding of symbolic buffer to Tensor.
         By default, a new buffer is created for each tensor in the argument.
 

python/tvm/ir/op.py

@@ -96,14 +96,20 @@ def add_type_rel(self, rel_name, type_rel_func=None):
         type_rel_func : Optional[function (args: List[Type], attrs: Attrs) -> Type]
             The backing relation function which can solve an arbitrary relation on variables.
             Differences with type_rel_func in C++:
-            1, when type_rel_func is not None:
-               1) OpAddTypeRel on C++ side will adjust type_rel_func with TypeReporter to
+
+            1) When type_rel_func is not None
+
+               a) OpAddTypeRel on C++ side will adjust type_rel_func with TypeReporter to
                   calling convention of relay type system.
-               2) type_rel_func returns output argument's type, return None means can't
+
+               b) type_rel_func returns output argument's type, return None means can't
                   infer output's type.
-               3) only support single output operators for now, the last argument is output tensor.
-            2, when type_rel_func is None, will call predefined type_rel_funcs in relay
-               accorrding to `tvm.relay.type_relation.` + rel_name.
+
+               c) only support single output operators for now, the last argument is output tensor.
+
+            2) when type_rel_func is None, will call predefined type_rel_funcs in relay
+                   according to ``tvm.relay.type_relation.`` + rel_name.
+
         """
         _ffi_api.OpAddTypeRel(self, rel_name, type_rel_func)