[DOCS] Sphinx -- Introduce alias detection.

docs/api/python/index.rst

@@ -21,7 +21,6 @@ Python API
 .. toctree::
    :maxdepth: 2
 
-   tvm
    runtime
    ndarray
    error

docs/api/python/relay/op.rst

@@ -18,38 +18,7 @@
 tvm.relay.op
 ------------
 .. automodule:: tvm.relay.op
-    :members:
-
-.. autofunction:: tvm.relay.op.Op
-
-.. autofunction:: tvm.relay.op.OpPattern
-
-.. autofunction:: tvm.relay.op.get
-
-.. autofunction:: tvm.relay.op.register
-
-.. autofunction:: tvm.relay.op.register_schedule
-
-.. autofunction:: tvm.relay.op.register_pattern
-
-.. autofunction:: tvm.relay.op.register_compute
-
-.. autofunction:: tvm.relay.op.register_gradient
-
-.. autofunction:: tvm.relay.op.register_alter_op_layout
-
-.. autofunction:: tvm.relay.op.schedule_injective
-
-.. autofunction:: tvm.relay.op.debug
-
-.. automodule:: tvm.relay.op.reduce
-    :members:
-
-.. automodule:: tvm.relay.op.tensor
-    :members:
-
-.. automodule:: tvm.relay.op.transform
-    :members:
-
-.. automodule:: tvm.relay.op.nn
-    :members:
+   :members:
+   :imported-members:
+   :exclude-members: Tuple
+   :autosummary:

docs/api/python/te.rst

@@ -22,10 +22,4 @@ tvm.te
 .. automodule:: tvm.te
    :members:
    :imported-members:
-   :exclude-members:
-      any, all, min_value, max_value, trace,
-      exp, erf, tanh, sigmoid, log, cos, sin, atan, sqrt, rsqrt, floor, ceil,
-      trunc, abs, round, nearbyint, isnan, power, popcount, fmod, if_then_else,
-      div, indexdiv, indexmod, truncdiv, truncmod, floordiv, floormod,
-      comm_reducer, min, max, sum
    :autosummary:

docs/conf.py

@@ -30,6 +30,7 @@
 # All configuration values have a default; values that are commented out
 # serve to show the default.
 import sys
+import inspect
 import os, subprocess
 import shlex
 import recommonmark
@@ -183,19 +184,6 @@
    author, 'manual'),
 ]
 
-# hook for doxygen
-def run_doxygen(folder):
-    """Run the doxygen make command in the designated folder."""
-    try:
-        #retcode = subprocess.call("cd %s; make doc" % folder, shell=True)
-        retcode = subprocess.call("rm -rf _build/html/doxygen", shell=True)
-        retcode = subprocess.call("mkdir -p _build/html", shell=True)
-        retcode = subprocess.call("cp -rf doxygen/html _build/html/doxygen", shell=True)
-        if retcode < 0:
-            sys.stderr.write("doxygen terminated by signal %s" % (-retcode))
-    except OSError as e:
-        sys.stderr.write("doxygen execution failed: %s" % e)
-
 intersphinx_mapping = {
     'python': ('https://docs.python.org/{.major}'.format(sys.version_info), None),
     'numpy': ('https://docs.scipy.org/doc/numpy/', None),
@@ -224,18 +212,6 @@ def generate_doxygen_xml(app):
     """Run the doxygen make commands if we're on the ReadTheDocs server"""
     run_doxygen('..')
 
-def setup(app):
-    # Add hook for building doxygen xml when needed
-    # no c++ API for now
-    app.connect("builder-inited", generate_doxygen_xml)
-    app.add_stylesheet('css/tvm_theme.css')
-    app.add_config_value('recommonmark_config', {
-        'url_resolver': lambda url: github_doc_root + url,
-        'auto_doc_ref': True
-            }, True)
-    app.add_transform(AutoStructify)
-
-
 sphinx_gallery_conf = {
     'backreferences_dir': 'gen_modules/backreferences',
     'doc_module': ('tvm', 'numpy'),
@@ -255,3 +231,81 @@ def setup(app):
 autodoc_default_options = {
     'member-order': 'bysource',
 }
+
+# hook for doxygen
+def run_doxygen(folder):
+    """Run the doxygen make command in the designated folder."""
+    try:
+        #retcode = subprocess.call("cd %s; make doc" % folder, shell=True)
+        retcode = subprocess.call("rm -rf _build/html/doxygen", shell=True)
+        retcode = subprocess.call("mkdir -p _build/html", shell=True)
+        retcode = subprocess.call("cp -rf doxygen/html _build/html/doxygen", shell=True)
+        if retcode < 0:
+            sys.stderr.write("doxygen terminated by signal %s" % (-retcode))
+    except OSError as e:
+        sys.stderr.write("doxygen execution failed: %s" % e)
+
+# Maps the original namespace to list of potential modules
+# that we can import alias from.
+tvm_alias_check_map = {
+    "tvm.te": ["tvm.tir"],
+    "tvm.tir": ["tvm.ir", "tvm.runtime"],
+}
+
+def update_alias_docstring(name, obj, lines):
+    """Update the docstring of alias functions.
+
+    This function checks if the obj is an alias of another documented object
+    in a different module.
+
+    If it is an alias, then it will append the alias information to the docstring.
+
+    Parameters
+    ----------
+    name : str
+        The full name of the object in the doc.
+
+    obj : object
+        The original object.
+
+    lines : list
+        The docstring lines, need to be modified inplace.
+    """
+    arr = name.rsplit(".", 1)
+    if len(arr) != 2:
+        return
+    target_mod, target_name = arr
+
+    if target_mod not in tvm_alias_check_map:
+        return
+    if not hasattr(obj, "__module__"):
+        return
+    obj_mod = obj.__module__
+
+    for amod in tvm_alias_check_map[target_mod]:
+        if not obj_mod.startswith(amod):
+            continue
+
+        if hasattr(sys.modules[amod], target_name):
+            obj_type = ":py:func" if callable(obj) else ":py:class"
+            lines.append(
+                ".. rubric:: Alias of %s:`%s.%s`" % (obj_type, amod, target_name))
+
+
+def process_docstring(app, what, name, obj, options, lines):
+    """Sphinx callback to process docstring"""
+    if callable(obj) or inspect.isclass(obj):
+        update_alias_docstring(name, obj, lines)
+
+
+def setup(app):
+    # Add hook for building doxygen xml when needed
+    # no c++ API for now
+    app.connect("builder-inited", generate_doxygen_xml)
+    app.connect('autodoc-process-docstring', process_docstring)
+    app.add_stylesheet('css/tvm_theme.css')
+    app.add_config_value('recommonmark_config', {
+        'url_resolver': lambda url: github_doc_root + url,
+        'auto_doc_ref': True
+            }, True)
+    app.add_transform(AutoStructify)

python/tvm/ir/base.py

@@ -39,16 +39,16 @@ 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.
-
         Returns
         -------
         text : str
             The text format of the expression.
+
+        Notes
+        -----
+        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.
         """
         return _ffi_api.AsText(self, show_meta_data, annotate)
 

python/tvm/relay/op/transform.py

@@ -419,12 +419,6 @@ def tile(data, reps):
     reps : tuple of int
         The number of times repeating the tensor data.
 
-    .. note::
-        Each dim size of reps must be a positive integer. If reps has length d,
-        the result will have dimension of max(d, data.ndim); If data.ndim < d,
-        data is promoted to be d-dimensional by prepending new axes.
-        If data.ndim >=  d, reps is promoted to a.ndim by pre-pending 1's to it.
-
     Returns
     -------
     ret : relay.Expr
@@ -442,6 +436,13 @@ def tile(data, reps):
 
         relay.tile(x, reps=(2,)) = [[1., 2., 1., 2.],
                                     [3., 4., 3., 4.]]
+
+    Notes
+    -----
+    Each dim size of reps must be a positive integer. If reps has length d,
+    the result will have dimension of max(d, data.ndim); If data.ndim < d,
+    data is promoted to be d-dimensional by prepending new axes.
+    If data.ndim >=  d, reps is promoted to a.ndim by pre-pending 1's to it.
     """
 
     return _make.tile(data, reps)

python/tvm/relay/op/vision/__init__.py

@@ -16,8 +16,6 @@
 # under the License.
 # pylint: disable=wildcard-import
 """Vision network related operators."""
-from __future__ import absolute_import as _abs
-
 from .multibox import *
 from .nms import *
 from .rcnn import *

python/tvm/relay/op/vision/multibox.py

@@ -15,7 +15,6 @@
 # specific language governing permissions and limitations
 # under the License.
 """Multibox operations."""
-from __future__ import absolute_import as _abs
 from . import _make
 from ...expr import TupleWrapper
 

python/tvm/relay/op/vision/nms.py

@@ -15,7 +15,6 @@
 # specific language governing permissions and limitations
 # under the License.
 """Non-maximum suppression operations."""
-from __future__ import absolute_import as _abs
 from . import _make
 from ...expr import TupleWrapper
 

python/tvm/target/generic_func.py

@@ -116,7 +116,6 @@ def override_native_generic_func(func_name):
     .. code-block:: python
 
       import tvm
-from tvm import te
       # wrap function as target generic
       @tvm.target.override_native_generic_func("my_func")
       def my_func(a):
@@ -211,7 +210,6 @@ def generic_func(fdefault):
     .. code-block:: python
 
       import tvm
-from tvm import te
       # wrap function as target generic
       @tvm.target.generic_func
       def my_func(a):

tutorials/language/intrin_math.py

@@ -66,7 +66,7 @@
 # TVM intrinsic provides the user a mechanism to achieve this, and this
 # is the recommended way to solve the problem.
 # The following code use te.exp instead, which create an intrinsic call
-# :any:`te.exp` to do the exponential.
+# :py::func:`tvm.te.exp` to do the exponential.
 #
 n = te.var("n")
 A = te.placeholder((n,), name='A')
@@ -88,7 +88,7 @@
 ######################################################################
 # Intrinsic Lowering Rule
 # -----------------------
-# When :any:`te.exp` is called, TVM creates an intrinsic Call Expr.
+# When :py:func:`tvm.te.exp` is called, TVM creates an intrinsic Call Expr.
 # TVM uses transformation rules to transform the intrinsic
 # call to device specific extern calls.
 #

vta/tutorials/frontend/deploy_detection.py

@@ -16,7 +16,7 @@
 # under the License.
 """
 Deploy Pretrained Vision Detection Model from Darknet on VTA
-================================================
+============================================================
 **Author**: `Hua Jiang <https://github.com/huajsj>`_
 
 This tutorial provides an end-to-end demo, on how to run Darknet YoloV3-tiny