This is an automated email from the ASF dual-hosted git repository.
tqchen pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git
The following commit(s) were added to refs/heads/master by this push:
new fd6d783 [DOCS] Fix sphinx warnings (#4917)
fd6d783 is described below
commit fd6d7837ed05661e81358045adb902772a4f82c3
Author: Cody Yu <[email protected]>
AuthorDate: Thu Feb 20 09:24:37 2020 -0800
[DOCS] Fix sphinx warnings (#4917)
* Fix Python docstrings
* More fixes
* Fix lint
---
docs/api/python/contrib.rst | 2 +-
docs/api/python/relay/base.rst | 6 ----
docs/api/python/relay/expr.rst | 6 ----
docs/api/python/tensor.rst | 44 ------------------------
python/tvm/autotvm/task/dispatcher.py | 6 ++--
python/tvm/autotvm/task/space.py | 54 ++++++++++++++++-------------
python/tvm/contrib/cblas.py | 29 ++++++++--------
python/tvm/ir/base.py | 10 +++---
python/tvm/ir/transform.py | 3 +-
python/tvm/relay/op/reduce.py | 60 ++++++++++++++++----------------
python/tvm/relay/op/transform.py | 64 +++++++++++++++++------------------
python/tvm/relay/op/vision/yolo.py | 31 ++++++++++-------
python/tvm/relay/transform.py | 19 ++++++-----
python/tvm/te/tensor.py | 2 +-
python/tvm/tir/expr.py | 2 +-
topi/python/topi/sparse/csrmv.py | 1 -
16 files changed, 148 insertions(+), 191 deletions(-)
diff --git a/docs/api/python/contrib.rst b/docs/api/python/contrib.rst
index 9546511..b482d30 100644
--- 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
index a3c5248..dc9dac0 100644
--- 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
index f17fc24..57a4a25 100644
--- 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
deleted file mode 100644
index 032de39..0000000
--- a/docs/api/python/tensor.rst
+++ /dev/null
@@ -1,44 +0,0 @@
-.. 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.tensor
-----------
-.. automodule:: tvm.tensor
-
-.. autoclass:: tvm.tensor.Tensor
- :members:
- :inherited-members:
-
-.. autoclass:: tvm.tensor.Operation
- :members:
- :inherited-members:
-
-.. autoclass:: tvm.tensor.ComputeOp
- :members:
- :show-inheritance:
-
-.. autoclass:: tvm.tensor.PlaceholderOp
- :members:
- :show-inheritance:
-
-.. autoclass:: tvm.tensor.ScanOp
- :members:
- :show-inheritance:
-
-.. autoclass:: tvm.tensor.ExternOp
- :members:
- :show-inheritance:
diff --git a/python/tvm/autotvm/task/dispatcher.py
b/python/tvm/autotvm/task/dispatcher.py
index 28a9fbb..e7022fa 100644
--- 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 @@ 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.
"""
# 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
index d83a248..fbdd34e 100644
--- 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 @@ class ConfigSpace(object):
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 @@ class ConfigSpace(object):
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 @@ class ConfigSpace(object):
>>> # 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 @@ class ConfigSpace(object):
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
index cdd4ce2..2337f84 100644
--- a/python/tvm/contrib/cblas.py
+++ b/python/tvm/contrib/cblas.py
@@ -21,23 +21,22 @@ from .. import api as _api
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
index 07ed8e8..661a64a 100644
--- a/python/tvm/ir/base.py
+++ b/python/tvm/ir/base.py
@@ -39,11 +39,11 @@ class Node(Object):
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
index 6192504..4519fb6 100644
--- 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
index baf896e..d322601 100644
--- 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
diff --git a/python/tvm/relay/op/transform.py b/python/tvm/relay/op/transform.py
index 710d203..15c48df 100644
--- a/python/tvm/relay/op/transform.py
+++ b/python/tvm/relay/op/transform.py
@@ -147,56 +147,54 @@ def squeeze(data, axis=None):
return _make.squeeze(data, axis)
def reshape(data, newshape):
- """Reshapes the input array.
-
- Example::
+ """Reshape the input array.
To give user more convenience in without doing manual shape inference,
some dimensions of the shape can take special values from the set {0, -1,
-2, -3, -4}.
The significance of each is explained below:
- - ``0`` copy this dimension from the input to the output shape.
+ ``0`` copy this dimension from the input to the output shape.
- Example::
+ .. code-block:: python
- - data.shape = (2,3,4), newshape = (4,0,2), result.shape = (4,3,2)
- - data.shape = (2,3,4), newshape = (2,0,0), result.shape = (2,3,4)
+ data.shape = (2,3,4), newshape = (4,0,2), result.shape = (4,3,2)
+ data.shape = (2,3,4), newshape = (2,0,0), result.shape = (2,3,4)
- - ``-1`` infers the dimension of the output shape by using the remainder
of the input dimensions
- keeping the size of the new array same as that of the input array.
+ ``-1`` infers the dimension of the output shape by using the remainder of
+ the input dimensions keeping the size of the new array same as that of the
input array.
At most one dimension of shape can be -1.
- Example::
+ .. code-block:: python
- - data.shape = (2,3,4), newshape = (6,1,-1), result.shape = (6,1,4)
- - data.shape = (2,3,4), newshape = (3,-1,8), result.shape = (3,1,8)
- - data.shape = (2,3,4), newshape = (-1,), result.shape = (24,)
+ data.shape = (2,3,4), newshape = (6,1,-1), result.shape = (6,1,4)
+ data.shape = (2,3,4), newshape = (3,-1,8), result.shape = (3,1,8)
+ data.shape = (2,3,4), newshape = (-1,), result.shape = (24,)
- - ``-2`` copy all/remainder of the input dimensions to the output shape.
+ ``-2`` copy all/remainder of the input dimensions to the output shape.
- Example::
+ .. code-block:: python
- - data.shape = (2,3,4), newshape = (-2,), result.shape = (2,3,4)
- - data.shape = (2,3,4), newshape = (2,-2), result.shape = (2,3,4)
- - data.shape = (2,3,4), newshape = (-2,1,1), result.shape = (2,3,4,1,1)
+ data.shape = (2,3,4), newshape = (-2,), result.shape = (2,3,4)
+ data.shape = (2,3,4), newshape = (2,-2), result.shape = (2,3,4)
+ data.shape = (2,3,4), newshape = (-2,1,1), result.shape =
(2,3,4,1,1)
- - ``-3`` use the product of two consecutive dimensions of the input shape
+ ``-3`` use the product of two consecutive dimensions of the input shape
as the output dimension.
- Example::
+ .. code-block:: python
- - data.shape = (2,3,4), newshape = (-3,4), result.shape = (6,4)
- - data.shape = (2,3,4,5), newshape = (-3,-3), result.shape = (6,20)
- - data.shape = (2,3,4), newshape = (0,-3), result.shape = (2,12)
- - data.shape = (2,3,4), newshape = (-3,-2), result.shape = (6,4)
+ data.shape = (2,3,4), newshape = (-3,4), result.shape = (6,4)
+ data.shape = (2,3,4,5), newshape = (-3,-3), result.shape = (6,20)
+ data.shape = (2,3,4), newshape = (0,-3), result.shape = (2,12)
+ data.shape = (2,3,4), newshape = (-3,-2), result.shape = (6,4)
- - ``-4`` split one dimension of the input into two dimensions passed
subsequent
+ ``-4`` split one dimension of the input into two dimensions passed
subsequent
to -4 in shape (can contain -1).
- Example::
+ .. code-block:: python
- - data.shape = (2,3,4), newshape = (-4,1,2,-2), result.shape = (1,2,3,4)
- - data.shape = (2,3,4), newshape = (2,-4,-1,3,-2), result.shape = (2,1,3,4)
+ data.shape = (2,3,4), newshape = (-4,1,2,-2), result.shape =
(1,2,3,4)
+ data.shape = (2,3,4), newshape = (2,-4,-1,3,-2), result.shape =
(2,1,3,4)
Parameters
----------
@@ -715,14 +713,14 @@ def reverse_reshape(data, newshape):
"""Reshapes the input array where the special values are inferred from
right to left.
- Example::
-
The special values have the same semantics as
:py:class:`tvm.relay.reshape`.
The difference is that special values are inferred from right to left. It
- can be explained in the example below::
+ can be explained in the example below.
+
+ .. code-block:: python
- - data.shape = (10,5,4), newshape = (-1,0), reshape results in (40,5)
- - data.shape = (10,5,4), newshape = (-1,0), reverse_reshape results in
(40,5)
+ data.shape = (10,5,4), newshape = (-1,0), reshape results in (40,5)
+ data.shape = (10,5,4), newshape = (-1,0), reverse_reshape results in
(40,5)
Parameters
----------
diff --git a/python/tvm/relay/op/vision/yolo.py
b/python/tvm/relay/op/vision/yolo.py
index 7ecf64c..90dc3b8 100644
--- a/python/tvm/relay/op/vision/yolo.py
+++ b/python/tvm/relay/op/vision/yolo.py
@@ -21,18 +21,25 @@ def yolo_reorg(data, stride):
"""Yolo reorg operation used in darknet models.
This layer shuffles the input tensor values based on the stride value.
Along with the shuffling, it does the shape transform.
- If '(n, c, h, w)' is the data shape and 's' is stride, output shape is
'(n, c*s*s, h/s, w/s)'
- Example: data(1, 4, 2, 2) = [[[[ 0 1] [ 2 3]]
- [[ 4 5] [ 6 7]]
- [[ 8 9] [10 11]]
- [[12 13] [14 15]]]]
- stride = 2
- ret(1, 16, 1, 1) = [[[[ 0]] [[ 2]] [[ 8]] [[10]]
- [[ 1]] [[ 3]] [[ 9]] [[11]]
- [[ 4]] [[ 6]] [[12]] [[14]]
- [[ 5]] [[ 7]] [[13]] [[15]]]]
-
- Note: stride=1 has no significance for reorg operation.
+ If '(n, c, h, w)' is the data shape and 's' is stride, output shape is
'(n, c*s*s, h/s, w/s)'.
+
+ Example:
+
+ .. code-block:: python
+
+ data(1, 4, 2, 2) = [[[[ 0 1] [ 2 3]]
+ [[ 4 5] [ 6 7]]
+ [[ 8 9] [10 11]]
+ [[12 13] [14 15]]]]
+ stride = 2
+ ret(1, 16, 1, 1) = [[[[ 0]] [[ 2]] [[ 8]] [[10]]
+ [[ 1]] [[ 3]] [[ 9]] [[11]]
+ [[ 4]] [[ 6]] [[12]] [[14]]
+ [[ 5]] [[ 7]] [[13]] [[15]]]]
+
+ .. note::
+
+ stride=1 has no significance for reorg operation.
Parameters
----------
diff --git a/python/tvm/relay/transform.py b/python/tvm/relay/transform.py
index 4c2bf87..08b41b2 100644
--- a/python/tvm/relay/transform.py
+++ b/python/tvm/relay/transform.py
@@ -256,17 +256,20 @@ def CombineParallelConv2D(min_num_branches=3):
def CombineParallelDense(min_num_branches=3):
"""Combine multiple dense operators into one. For example:
- data
- / \
- dense (2,2) dense (2,2)
- | |
- elemwise/bcast (2,2) elemwise/bcast (2,2)
+ .. code-block
+ data
+ / \
+ dense (2,2) dense (2,2)
+ | |
+ elemwise/bcast (2,2) elemwise/bcast (2,2)
Would become:
- data
- |
- batch_matmul+elemwise/bcast (2,2,2)
+ .. code-block
+
+ data
+ |
+ batch_matmul+elemwise/bcast (2,2,2)
Parameters
----------
diff --git a/python/tvm/te/tensor.py b/python/tvm/te/tensor.py
index fcbb68f..739268a 100644
--- a/python/tvm/te/tensor.py
+++ b/python/tvm/te/tensor.py
@@ -102,7 +102,7 @@ class Tensor(Object, _expr.ExprOp):
@property
def op(self):
- """The corressponding :any:`Operation`."""
+ """The corressponding :py:class:`Operation`."""
return self.__getattr__("op")
@property
diff --git a/python/tvm/tir/expr.py b/python/tvm/tir/expr.py
index aeda603..ca11ffc 100644
--- a/python/tvm/tir/expr.py
+++ b/python/tvm/tir/expr.py
@@ -812,7 +812,7 @@ class Select(PrimExprWithOp):
Note
----
Select may compute both true_value and false_value.
- Use :any:`tvm.if_then_else` instead if you want to
+ Use :py:class:`tvm.if_then_else` instead if you want to
get a conditional expression that only evaluates
the correct branch.
diff --git a/topi/python/topi/sparse/csrmv.py b/topi/python/topi/sparse/csrmv.py
index fb9f10b..8a21f0d 100644
--- a/topi/python/topi/sparse/csrmv.py
+++ b/topi/python/topi/sparse/csrmv.py
@@ -87,7 +87,6 @@ def csrmv(a, x, y=None):
where `x` and `y` are vectors, `A` is an m-by-k sparse matrix in the CSR
format.
Parameters
-
----------
a : tvm.contrib.sparse.CSRNDArray
2-D sparse matrix with shape [m, k]
