[DOCS] Fix sphinx warnings (#4917)

* Fix Python docstrings * More fixes * Fix lint
apache · Feb 20, 2020 · fd6d783 · fd6d783
1 parent 18295b2
commit fd6d783
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Showing 16 changed files with 148 additions and 191 deletions.
diff --git a/docs/api/python/contrib.rst b/docs/api/python/contrib.rst
@@ -21,7 +21,7 @@ tvm.contrib
 
 tvm.contrib.cblas
 ~~~~~~~~~~~~~~~~~
-.. automodule:: tvm.contrib.cc
+.. automodule:: tvm.contrib.cblas
     :members:
 
 

diff --git a/docs/api/python/relay/base.rst b/docs/api/python/relay/base.rst
@@ -26,11 +26,5 @@ tvm.relay.base
 .. autoclass:: tvm.relay.base.RelayNode
     :members:
 
-.. autoclass:: tvm.relay.base.Span
-    :members:
-
-.. autoclass:: tvm.relay.base.SourceName
-    :members:
-
 .. autoclass:: tvm.relay.base.Id
     :members:
diff --git a/docs/api/python/relay/expr.rst b/docs/api/python/relay/expr.rst
@@ -35,12 +35,6 @@ tvm.relay.expr
 .. autoclass:: tvm.relay.expr.Tuple
     :members:
 
-.. autoclass:: tvm.relay.expr.Var
-    :members:
-
-.. autoclass:: tvm.relay.expr.GlobalVar
-    :members:
-
 .. autoclass:: tvm.relay.expr.Function
     :members:
 

diff --git a/docs/api/python/tensor.rst b/docs/api/python/tensor.rst
diff --git a/python/tvm/autotvm/task/dispatcher.py b/python/tvm/autotvm/task/dispatcher.py
@@ -258,8 +258,7 @@ class ApplyHistoryBest(DispatchContext):
     records : str or iterator of (MeasureInput, MeasureResult)
         Collection of tuning records.
         If is str, then it should be the filename of a records log file.
-                   Each row of this file is an encoded record pair.
-        Otherwise, it is an iterator.
+        Each row of this file is an encoded record pair. Otherwise, it is an iterator.
     """
     def __init__(self, records):
         super(ApplyHistoryBest, self).__init__()
@@ -279,8 +278,7 @@ def load(self, records):
         records : str or iterator of (MeasureInput, MeasureResult)
             Collection of tuning records.
             If is str, then it should be the filename of a records log file.
-                       Each row of this file is an encoded record pair.
-            Otherwise, it is an iterator.
+            Each row of this file is an encoded record pair. Otherwise, it is an iterator.
         """
         # pylint: disable=import-outside-toplevel
         from pathlib import Path

diff --git a/python/tvm/autotvm/task/space.py b/python/tvm/autotvm/task/space.py
@@ -54,13 +54,13 @@ class TransformSpace(object):
     """Base class for transform space
     TransformSpace is the node in the computation graph of axes
 
-    Note
-    ----
-    We can regard our schedule code as a transformation graph of axes.
-    Starting from raw axes in the definition of tvm.compute, we can transform these axes
-    by some operators. The operator includes 'split', 'reorder' and 'annotate'.
-    Each operator has some tunable parameters (e.g. the split factor).
-    Then the tuning process is just to find good parameters of these op.
+    .. note::
+
+        We can regard our schedule code as a transformation graph of axes.
+        Starting from raw axes in the definition of tvm.compute, we can transform these axes
+        by some operators. The operator includes 'split', 'reorder' and 'annotate'.
+        Each operator has some tunable parameters (e.g. the split factor).
+        Then the tuning process is just to find good parameters of these op.
 
     So the all the combinations of the parameters of these op forms our search space.
 
@@ -109,7 +109,8 @@ class VirtualAxis(TransformSpace):
     var: int or tvm.schedule.IterVar
         If is int, return a virtual axis whose length is the provided argument.
         If is IterVar, return a virtual axis whose length is extracted from
-                       the IterVar's extent domain.
+        the IterVar's extent domain.
+
     name: str
     """
     name_ct = 0
@@ -253,9 +254,9 @@ class SplitEntity(object):
     Parameters
     ----------
     size: Array of int
-        the size of every axis after split
+        the size of every axis after split.
         e.g. an axis of extent 128, we split it into 3 axes, a possible
-             size is [4, 4, 8] (4x4x8 = 128)
+        size is [4, 4, 8] (4x4x8 = 128).
     """
     def __init__(self, size):
         self.size = size
@@ -626,7 +627,7 @@ def axis(var):
         var: int or tvm.schedule.IterVar
             If is int, return an axis whose length is the provided argument.
             If is IterVar, return an axis whose length is extracted from the
-                           IterVar's extent domain.
+            IterVar's extent domain.
         """
         return VirtualAxis(var)
 
@@ -647,18 +648,19 @@ def define_split(self, name, axis, policy='factors', **kwargs):
             If is 'power2', the tuner will try power-of-two factors less or equal to the length.
             If is 'verbose', the tuner will try all candidates in above two policies.
             If is 'candidate', try given candidates.
-        kwargs: dict
+        **kwargs:
             extra arguments for policy
-            max_factor: int
-                the maximum split factor.
-            filter: function(int) -> bool
-                see examples below for how to use filter.
-            num_outputs: int
-                the total number of axis after split.
-            no_tail: bool
-                should we only include divisible numbers as split factors.
-            candidate: list
-                (policy=candidate) manual candidate list.
+
+            ``max_factor``:
+                the maximum split factor (`int`).
+            ``filter``:
+                see examples below for how to use filter (`Callable[[int], bool]`).
+            ``num_outputs``:
+                the total number of axis after split (`int`).
+            ``no_tail``:
+                should we only include divisible numbers as split factors (`bool`).
+            `candidate``:
+                (policy=candidate) manual candidate list (`List`).
 
         Examples
         --------
@@ -668,6 +670,7 @@ def define_split(self, name, axis, policy='factors', **kwargs):
         >>> # use a filter that only accepts the split scheme whose inner most tile is less then 4
         >>> cfg.define_split('tile_y', y, policy='factors', filter=lambda x: x.size[-1] <= 4)
         """
+
         axes = [axis]
         return self._add_new_transform(SplitSpace, name, axes, policy, **kwargs)
 
@@ -749,8 +752,11 @@ def raise_error(self, msg):
 
     def valid(self):
         """Check whether the config meets all the constraints
-        Note: This check should be called after instantiation of task,
-              because the ConfigEntity/ConfigSpace collects errors during instantiation
+
+        .. note::
+
+            This check should be called after instantiation of task,
+            because the ConfigEntity/ConfigSpace collects errors during instantiation
 
         Returns
         -------

diff --git a/python/tvm/contrib/cblas.py b/python/tvm/contrib/cblas.py
@@ -21,23 +21,22 @@
 
 def matmul(lhs, rhs, transa=False, transb=False, **kwargs):
     """Create an extern op that compute matrix mult of A and rhs with CrhsLAS
-
     This function serves as an example on how to call external libraries.
 
     Parameters
     ----------
-    lhs : Tensor
+    lhs: Tensor
         The left matrix operand
-    rhs : Tensor
+    rhs: Tensor
         The right matrix operand
-    transa : bool
+    transa: bool
         Whether transpose lhs
-    transb : bool
+    transb: bool
         Whether transpose rhs
 
     Returns
     -------
-    C : Tensor
+    C: Tensor
         The result tensor.
     """
     n = lhs.shape[1] if transa else lhs.shape[0]
@@ -55,20 +54,22 @@ def matmul(lhs, rhs, transa=False, transb=False, **kwargs):
 
 def batch_matmul(lhs, rhs, transa=False, transb=False, iterative=False, **kwargs):
     """Create an extern op that compute batched matrix mult of A and rhs with CBLAS
-     This function serves as an example on how to call external libraries.
-     Parameters
+    This function serves as an example on how to call external libraries.
+
+    Parameters
     ----------
-    lhs : Tensor
+    lhs: Tensor
         The left matrix operand
-    rhs : Tensor
+    rhs: Tensor
         The right matrix operand
-    transa : bool
+    transa: bool
         Whether transpose lhs
-    transb : bool
+    transb: bool
         Whether transpose rhs
-     Returns
+
+    Returns
     -------
-    C : Tensor
+    C: Tensor
         The result tensor.
     """
     b = lhs.shape[0]

diff --git a/python/tvm/ir/base.py b/python/tvm/ir/base.py
@@ -39,11 +39,11 @@ def astext(self, show_meta_data=True, annotate=None):
             Optionally annotate function to provide additional
             information in the comment block.
 
-        Note
-        ----
-        The meta data section is necessary to fully parse the text format.
-        However, it can contain dumps that are big (e.g constant weights),
-        so it can be helpful to skip printing the meta data section.
+        .. note::
+
+            The meta data section is necessary to fully parse the text format.
+            However, it can contain dumps that are big (e.g constant weights),
+            so it can be helpful to skip printing the meta data section.
 
         Returns
         -------

diff --git a/python/tvm/ir/transform.py b/python/tvm/ir/transform.py
@@ -160,7 +160,8 @@ class Sequential(Pass):
     Some typical usage of the sequential pass are:
     1. Users provide a list of passes for optimization.
     2. Only an optimization level is provided so that the backend system has
-       to glob all passes at this level and below to perform the optimizations.
+    to glob all passes at this level and below to perform the optimizations.
+
     Note that users can also provide a series of passes that they don't want to
     apply when running a sequential pass. Pass dependency will be resolved in
     the backend as well.

diff --git a/python/tvm/relay/op/reduce.py b/python/tvm/relay/op/reduce.py
@@ -145,21 +145,21 @@ def all(data, axis=None, keepdims=False, exclude=False):
     --------
     .. code-block:: python
 
-    data = relay.Constant(tvm.nd.array([[[ True,  True,  True],
-                                         [ True,  True,  True],
-                                         [False,  True, False]],
-                                        [[ True, False, False],
-                                         [ True,  True, False],
-                                         [False,  True,  True]]]))
-
-    relay.all(data, axis=1)
-    # [[False,  True, False],
-    # [False, False, False]]
-
-    relay.all(data, axis=0)
-    # [[ True, False, False],
-    # [ True,  True, False],
-    # [False,  True, False]]
+        data = relay.Constant(tvm.nd.array([[[ True,  True,  True],
+                                           [ True,  True,  True],
+                                           [False,  True, False]],
+                                          [[ True, False, False],
+                                           [ True,  True, False],
+                                           [False,  True,  True]]]))
+
+        relay.all(data, axis=1)
+        # [[False,  True, False],
+        # [False, False, False]]
+
+        relay.all(data, axis=0)
+        # [[ True, False, False],
+        # [ True,  True, False],
+        # [False,  True, False]]
 
     """
     axis = [axis] if isinstance(axis, int) else axis
@@ -197,21 +197,21 @@ def any(data, axis=None, keepdims=False, exclude=False):
     --------
     .. code-block:: python
 
-    data = relay.Constant(tvm.nd.array([[[ True,  True,  True],
-                                         [ True,  True,  True],
-                                         [False,  True, False]],
-                                        [[ True, False, False],
-                                         [ True,  True, False],
-                                         [False,  True,  True]]]))
-
-    relay.any(data, axis=1)
-    # [[True, True, True],
-    # [True,  True, True]]
-
-    relay.any(data, axis=0)
-    # [[ True, True, True],
-    # [ True,  True, True],
-    # [False,  True, True]]
+        data = relay.Constant(tvm.nd.array([[[ True,  True,  True],
+                                            [ True,  True,  True],
+                                            [False,  True, False]],
+                                            [[ True, False, False],
+                                            [ True,  True, False],
+                                            [False,  True,  True]]]))
+
+        relay.any(data, axis=1)
+        # [[True, True, True],
+        # [True,  True, True]]
+
+        relay.any(data, axis=0)
+        # [[ True, True, True],
+        # [ True,  True, True],
+        # [False,  True, True]]
 
     """
     axis = [axis] if isinstance(axis, int) else axis