[Docs] Prevented docs/1 file from being generated. (#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>

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)
 

python/tvm/micro/build.py

@@ -158,11 +158,12 @@ def default_options(crt_config_include_dir, standalone_crt_dir=None):
     Dict :
         A dictionary containing 3 subkeys, each whose value is _build_default_compiler_options()
         plus additional customization.
-         - "bin_opts" - passed as "options" to Compiler.binary() when building MicroBinary.
-         - "lib_opts" - passed as "options" to Compiler.library() when building bundled CRT
-           libraries (or otherwise, non-generated libraries).
-         - "generated_lib_opts" - passed as "options" to Compiler.library() when building the
-           generated library.
+
+        - "bin_opts" - passed as "options" to Compiler.binary() when building MicroBinary.
+        - "lib_opts" - passed as "options" to Compiler.library() when building bundled CRT
+          libraries (or otherwise, non-generated libraries).
+        - "generated_lib_opts" - passed as "options" to Compiler.library() when building the
+          generated library.
     """
     bin_opts = _build_default_compiler_options(standalone_crt_dir)
     bin_opts["include_dirs"].append(crt_config_include_dir)

python/tvm/relay/op/transform.py

@@ -1373,25 +1373,32 @@ def sparse_fill_empty_rows(sparse_indices, sparse_values, dense_shape, default_v
     Fill rows in a sparse matrix that do no contain any values. Values are placed in the first
     column of empty rows. The sparse array is in COO format.
     It returns a TupleWrapper with 3 outputs
+
     Parameters
     ----------
     sparse_indices : relay.Expr
         A 2-D tensor[N, ndims] of integers containing location of sparse values, where N is
         the number of sparse values and n_dim is the number of dimensions of the dense_shape.
         The first column of this relay parameter must be sorted in ascending order.
+
     sparse_values : relay.Expr
         A 1-D tensor[N] containing the sparse values for the sparse indices.
+
     dense_shape : relay.Expr
         A 1-D tensor[ndims] which contains shape of the dense output tensor.
+
     default_value : relay.Expr
         A 1-D tensor[1] containing the default value for the remaining locations.
+
     Returns
     -------
     new_sparse_indices : relay.Expr
         A 2-D tensor[?, ndims] of integers containing location of new sparse
         indices. The first column outputs must be sorted in ascending order.
+
     new_sparse_values : relay.Expr
         A 1-D tensor[?] containing the sparse values for the sparse indices.
+
     empty_row_indicator : relay.Expr
         A 1-D tensor[dense_shape[0]] filled with zeros and ones
         indicating whether the particular row is empty or full respectively
@@ -1702,18 +1709,18 @@ def unique(data, is_sorted=True, return_counts=False):
     .. code-block:: python
 
         [output, indices, num_unique] = unique([4, 5, 1, 2, 3, 3, 4, 5], False, False)
-        output         =  [4, 5, 1, 2, 3, ?, ?, ?]
+        output         =  [4, 5, 1, 2, 3, _, _, _]
         indices        =  [0, 1, 2, 3, 4, 4, 0, 1]
         num_unique     =  [5]
 
         [output, indices, num_unique, counts] = unique([4, 5, 1, 2, 3, 3, 4, 5], False, True)
-        output         =  [4, 5, 1, 2, 3, ?, ?, ?]
+        output         =  [4, 5, 1, 2, 3, _, _, _]
         indices        =  [0, 1, 2, 3, 4, 4, 0, 1]
         num_unique     =  [5]
-        counts         =  [2, 2, 1, 1, 2, ?, ?, ?]
+        counts         =  [2, 2, 1, 1, 2, _, _, _]
 
         [output, indices, num_unique] = unique([4, 5, 1, 2, 3, 3, 4, 5], True)
-        output         =  [1, 2, 3, 4, 5, ?, ?, ?]
+        output         =  [1, 2, 3, 4, 5, _, _, _]
         indices        =  [3, 4, 0, 1, 2, 2, 3, 4]
         num_unique     =  [5]
     """
@@ -1744,6 +1751,7 @@ def invert_permutation(data):
     Examples
     --------
     .. code-block:: python
+
         data = [3, 4, 0, 2, 1]
         relay.invert_permutation(data) = [2, 4, 3, 0, 1]
     """

python/tvm/relay/transform/transform.py

@@ -1177,18 +1177,20 @@ def FakeQuantizationToInteger():
     """
     Find regions of the graph of the form
 
-    x    w
-    |    |
-    dq   dq
-     \   /
-      op1
-       |
-      op2
-       |
-       q
-
-    where q == qnn.quantize and dq = qnn.dequantize
-    and rewrite them into integer versions of op1 and op2
+    .. code-block:: text
+
+        x    w
+        |    |
+        dq   dq
+         \   /
+          op1
+           |
+          op2
+           |
+           q
+
+    where ``q == qnn.quantize`` and ``dq = qnn.dequantize``
+    and rewrite them into integer versions of ``op1`` and ``op2``
 
     Rules for rewriting indivdual ops are in fake_quantization_to_integer.py
 

python/tvm/runtime/ndarray.py

@@ -390,6 +390,7 @@ def gpu(dev_id=0):
 
         deprecated:: 0.9.0
         Use :py:func:`tvm.cuda` instead.
+
     Parameters
     ----------
     dev_id : int, optional

python/tvm/runtime/profiling.py

@@ -26,7 +26,7 @@
 class Report(Object):
     """A container for information gathered during a profiling run.
 
-    Fields
+    Attributes
     ----------
     calls : Array[Dict[str, Object]]
         Per-call profiling metrics (function name, runtime, device, ...).

python/tvm/te/hybrid/__init__.py

@@ -74,7 +74,7 @@ def build(sch, inputs, outputs, name="hybrid_func"):
 
     Parameters
     ----------
-    sch: Schedule
+    sch: tvm.te.Schedule
         The schedule to be dumped
 
     inputs: An array of Tensors or Vars

python/tvm/te/operation.py

@@ -226,12 +226,12 @@ def extern(
         .. note::
              **Parameters**
 
-             - **ins** (list of :any:`Buffer`) - Placeholder for each inputs
-             - **outs** (list of :any:`Buffer`) - Placeholder for each outputs
+             - **ins** (list of :any:`tvm.tir.Buffer`) - Placeholder for each inputs
+             - **outs** (list of :any:`tvm.tir.Buffer`) - Placeholder for each outputs
 
              **Returns**
 
-             - **stmt** (:any:`Stmt`) - The statement that carries out array computation.
+             - **stmt** (:any:`tvm.tir.Stmt`) - The statement that carries out array computation.
 
     name: str, optional
         The name hint of the tensor
@@ -240,10 +240,10 @@ def extern(
         The data types of outputs,
         by default dtype will be same as inputs.
 
-    in_buffers: Buffer or list of Buffer, optional
+    in_buffers: tvm.tir.Buffer or list of tvm.tir.Buffer, optional
         Input buffers.
 
-    out_buffers: Buffer or list of Buffers, optional
+    out_buffers: tvm.tir.Buffer or list of tvm.tir.Buffer, optional
         Output buffers.
 
 

python/tvm/te/tensor_intrin.py

@@ -82,20 +82,20 @@ def decl_tensor_intrin(
         .. note::
              **Parameters**
 
-             - **ins** (list of :any:`Buffer`) - Placeholder for each inputs
-             - **outs** (list of :any:`Buffer`) - Placeholder for each outputs
+             - **ins** (list of :any:`tvm.tir.Buffer`) - Placeholder for each inputs
+             - **outs** (list of :any:`tvm.tir.Buffer`) - Placeholder for each outputs
 
              **Returns**
 
-             - **stmt** (:any:`Stmt`, or tuple of three stmts)
+             - **stmt** (:any:`tvm.tir.Stmt`, or tuple of three stmts)
              - If a single stmt is returned, it represents the body
              - If tuple of three stmts are returned they corresponds to body,
                reduce_init, reduce_update
 
     name: str, optional
         The name of the intrinsic.
 
-    binds: dict of :any:`Tensor` to :any:`Buffer`, optional
+    binds: dict of :any:`Tensor` to :any:`tvm.tir.Buffer`, optional
         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.

python/tvm/tir/buffer.py

@@ -198,7 +198,7 @@ def decl_buffer(
 
     Returns
     -------
-    buffer : Buffer
+    buffer : tvm.tir.Buffer
         The created buffer
 
     Example

python/tvm/tir/schedule/block_scope.py

@@ -109,15 +109,18 @@ class Dependency(Object):
 
 @register_object("tir.BlockScope")
 class BlockScope(Object):
-    """An object corresponds to each block sref in the sref tree,
-       which tracks the producer-consumer dependency between blocks.
+    """An object corresponds to each block sref in the sref tree, which
+    tracks the producer-consumer dependency between blocks.
 
     Glossary:
-    - Block scope: A contiguous subtree of the sref tree, rooted at each block sref,
-    whose components are:
-        - scope root: a block sref
-        - internal srefs: loop srefs
-        - scope leaves: block srefs
+
+    - Block scope: A contiguous subtree of the sref tree, rooted at
+      each block sref, whose components are:
+
+      - scope root: a block sref
+      - internal srefs: loop srefs
+      - scope leaves: block srefs
+
     - Child block: The scope leaf blocks under the scope root or a specific internal sref
     """