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Unverified Commit fd6d7837 by Cody Yu Committed by GitHub
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[DOCS] Fix sphinx warnings (#4917) 

* Fix Python docstrings

* More fixes

* Fix lint
parent 18295b27
Showing with 148 additions and 191 deletions
docs/api/python/contrib.rst
......@@ -21,7 +21,7 @@ tvm.contrib
tvm.contrib.cblas
~~~~~~~~~~~~~~~~~
.. automodule:: tvm.contrib.cc
.. automodule:: tvm.contrib.cblas
:members:
......
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:
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:
......
docs/api/python/tensor.rst deleted 100644 → 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:
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
......
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
-------
......
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]
......
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
-------
......
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.
......
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
......
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
----------
......
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
----------
......
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
----------
......
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
......
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.
......
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]
......
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