[DOCS] API doc update (#1136)

docs/api/python/bridge.rst

+Framework Bridge APIs
+---------------------
+
+tvm.contrib.mxnet
+~~~~~~~~~~~~~~~~~
+.. automodule:: tvm.contrib.mxnet
+    :members:

docs/api/python/contrib.rst

-Contrib APIs
-------------
+Additional Contrib APIs
+-----------------------
 .. automodule:: tvm.contrib
 
-tvm.contrib.nvcc
-~~~~~~~~~~~~~~~~
-.. automodule:: tvm.contrib.nvcc
+tvm.contrib.cblas
+~~~~~~~~~~~~~~~~~
+.. automodule:: tvm.contrib.cc
     :members:
 
+
 tvm.contrib.cc
 ~~~~~~~~~~~~~~
 .. automodule:: tvm.contrib.cc
     :members:
 
-tvm.contrib.xcode
-~~~~~~~~~~~~~~~~~
-.. automodule:: tvm.contrib.xcode
+
+tvm.contrib.cublas
+~~~~~~~~~~~~~~~~~~
+.. automodule:: tvm.contrib.cublas
     :members:
 
-tvm.contrib.rpc
+
+tvm.contrib.emscripten
+~~~~~~~~~~~~~~~~~~~~~~
+.. automodule:: tvm.contrib.emscripten
+    :members:
+
+tvm.contrib.miopen
+~~~~~~~~~~~~~~~~~~
+.. automodule:: tvm.contrib.miopen
+    :members:
+
+tvm.contrib.ndk
 ~~~~~~~~~~~~~~~
-.. automodule:: tvm.contrib.rpc
+.. automodule:: tvm.contrib.ndk
+    :members:
+
+
+tvm.contrib.nnpack
+~~~~~~~~~~~~~~~~~~
+.. automodule:: tvm.contrib.nnpack
+    :members:
+
+
+tvm.contrib.nvcc
+~~~~~~~~~~~~~~~~
+.. automodule:: tvm.contrib.nvcc
     :members:
 
-tvm.contrib.graph_runtime
-~~~~~~~~~~~~~~~~~~~~~~~~~
-.. automodule:: tvm.contrib.graph_runtime
+
+tvm.contrib.pickle_memoize
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. automodule:: tvm.contrib.pickle_memoize
+    :members:
+
+
+tvm.contrib.random
+~~~~~~~~~~~~~~~~~~
+.. automodule:: tvm.contrib.random
+    :members:
+
+
+tvm.contrib.rocblas
+~~~~~~~~~~~~~~~~~~~
+.. automodule:: tvm.contrib.rocblas
     :members:
 
+
+tvm.contrib.rocm
+~~~~~~~~~~~~~~~~
+.. automodule:: tvm.contrib.rocm
+    :members:
+
+
+tvm.contrib.spirv
+~~~~~~~~~~~~~~~~~
+.. automodule:: tvm.contrib.spirv
+    :members:
+
+
+tvm.contrib.tar
+~~~~~~~~~~~~~~~~
+.. automodule:: tvm.contrib.tar
+    :members:
+
+
 tvm.contrib.util
 ~~~~~~~~~~~~~~~~
 .. automodule:: tvm.contrib.util
     :members:
 
-tvm.contrib.cblas
+
+
+tvm.contrib.xcode
 ~~~~~~~~~~~~~~~~~
-.. automodule:: tvm.contrib.cblas
+.. automodule:: tvm.contrib.xcode
     :members:

docs/api/python/graph_runtime.rst

+tvm.contrib.graph_runtime
+-------------------------
+.. automodule:: tvm.contrib.graph_runtime
+    :members:

docs/api/python/index.rst

@@ -14,6 +14,9 @@ Python API
    ndarray
    container
    function
+   graph_runtime
+   rpc
+   bridge
    contrib
    dev
    topi

docs/api/python/rpc.rst

+tvm.contrib.rpc
+---------------
+.. automodule:: tvm.contrib.rpc
+
+.. autofunction:: tvm.contrib.rpc.connect
+.. autofunction:: tvm.contrib.rpc.connect_tracker
+
+.. autoclass:: tvm.contrib.rpc.TrackerSession
+    :members:
+    :inherited-members:
+
+.. autoclass:: tvm.contrib.rpc.RPCSession
+    :members:
+    :inherited-members:
+
+.. autoclass:: tvm.contrib.rpc.LocalSession
+    :members:
+    :inherited-members:
+
+.. autoclass:: tvm.contrib.rpc.Server
+    :members:
+    :inherited-members:

docs/api/python/target.rst

 tvm.target
 ----------
 .. automodule:: tvm.target
-
-.. autofunction:: tvm.target.generic_func
-
-.. autoclass:: tvm.target.Target
     :members:
-
-.. autofunction:: tvm.target.cuda
-.. autofunction:: tvm.target.rocm
-.. autofunction:: tvm.target.rasp
-.. autofunction:: tvm.target.create

docs/api/python/topi.rst

@@ -9,16 +9,33 @@ Index
 
 .. autosummary::
 
+   topi.identity
+   topi.negative
    topi.exp
    topi.tanh
    topi.log
    topi.sqrt
    topi.sigmoid
+   topi.clip
+   topi.cast
    topi.transpose
    topi.expand_dims
+   topi.reshape
+   topi.squeeze
+   topi.concatenate
+   topi.split
+   topi.full
+   topi.full_like
+   topi.greater
+   topi.less
    topi.nn.relu
    topi.nn.leaky_relu
    topi.nn.dilate
+   topi.nn.pool
+   topi.nn.global_pool
+   topi.nn.upsampling
+   topi.nn.softmax
+   topi.nn.log_softmax
    topi.nn.conv2d_nchw
    topi.nn.conv2d_hwcn
    topi.nn.depthwise_conv2d_nchw
@@ -31,6 +48,8 @@ Index
    topi.broadcast_sub
    topi.broadcast_mul
    topi.broadcast_div
+   topi.broadcast_maximum
+   topi.broadcast_minimum
 
 
 **List of schedules**
@@ -45,13 +64,25 @@ Index
 
 topi
 ~~~~
+.. autofunction:: topi.negative
+.. autofunction:: topi.identity
 .. autofunction:: topi.exp
 .. autofunction:: topi.tanh
 .. autofunction:: topi.log
 .. autofunction:: topi.sqrt
 .. autofunction:: topi.sigmoid
+.. autofunction:: topi.clip
+.. autofunction:: topi.cast
 .. autofunction:: topi.transpose
 .. autofunction:: topi.expand_dims
+.. autofunction:: topi.reshape
+.. autofunction:: topi.squeeze
+.. autofunction:: topi.concatenate
+.. autofunction:: topi.split
+.. autofunction:: topi.full
+.. autofunction:: topi.full_like
+.. autofunction:: topi.greater
+.. autofunction:: topi.less
 .. autofunction:: topi.max
 .. autofunction:: topi.sum
 .. autofunction:: topi.min
@@ -60,6 +91,8 @@ topi
 .. autofunction:: topi.broadcast_sub
 .. autofunction:: topi.broadcast_mul
 .. autofunction:: topi.broadcast_div
+.. autofunction:: topi.broadcast_maximum
+.. autofunction:: topi.broadcast_minimum
 
 
 topi.nn
@@ -67,6 +100,11 @@ topi.nn
 .. autofunction:: topi.nn.relu
 .. autofunction:: topi.nn.leaky_relu
 .. autofunction:: topi.nn.dilate
+.. autofunction:: topi.nn.pool
+.. autofunction:: topi.nn.global_pool
+.. autofunction:: topi.nn.upsampling
+.. autofunction:: topi.nn.softmax
+.. autofunction:: topi.nn.log_softmax
 .. autofunction:: topi.nn.conv2d_nchw
 .. autofunction:: topi.nn.conv2d_hwcn
 .. autofunction:: topi.nn.depthwise_conv2d_nchw

docs/api/python/tvm.rst

@@ -15,6 +15,7 @@ The user facing API for computation declaration.
    tvm.extern
    tvm.decl_buffer
    tvm.reduce_axis
+   tvm.select
    tvm.thread_axis
    tvm.comm_reducer
    tvm.sum
@@ -33,6 +34,7 @@ The user facing API for computation declaration.
 .. autofunction:: tvm.extern
 .. autofunction:: tvm.decl_buffer
 .. autofunction:: tvm.reduce_axis
+.. autofunction:: tvm.select
 .. autofunction:: tvm.thread_axis
 .. autofunction:: tvm.comm_reducer
 .. autofunction:: tvm.sum

docs/conf.py

@@ -189,6 +189,7 @@ subsection_order = ExplicitOrder(
      '../tutorials/optimize',
      '../tutorials/deployment'])
 
+
 def generate_doxygen_xml(app):
     """Run the doxygen make commands if we're on the ReadTheDocs server"""
     run_doxygen('..')

docs/how_to/deploy.md

@@ -35,7 +35,27 @@ on how to generate the library and [cpp_deploy.cc](https://github.com/dmlc/tvm/t
 
 Dynamic loading is more flexible and can load new modules on the fly. System module is a more ```static``` approach.  We can use system module in places where dynamic library loading is banned.
 
+Deploy to Android
+-----------------
+### Build model for Android Target
 
-How to Deploy on Android
-------------------------
-- [How to deploy on android](deploy_android.md)
\ No newline at end of file
+NNVM compilation of model for android target could follow same approach like android_rpc.
+
+An reference exampe can be found at [chainer-nnvm-example](https://github.com/tkat0/chainer-nnvm-example)
+
+Above example will directly run the compiled model on RPC target. Below modification at [rum_mobile.py](https://github.com/tkat0/chainer-nnvm-example/blob/5b97fd4d41aa4dde4b0aceb0be311054fb5de451/run_mobile.py#L64) will save the compilation output which is required on android target.
+
+```
+lib.export_library("deploy_lib.so", ndk.create_shared)
+with open("deploy_graph.json", "w") as fo:
+    fo.write(graph.json())
+with open("deploy_param.params", "wb") as fo:
+    fo.write(nnvm.compiler.save_param_dict(params))
+```
+
+deploy_lib.so, deploy_graph.json, deploy_param.params will go to android target.
+
+### TVM Runtime for Android Target
+
+Refer [here](https://github.com/dmlc/tvm/blob/master/apps/android_deploy/README.md#build-and-installation) to build CPU/OpenCL version flavor TVM runtime for android target.
+From android java TVM API to load model & execute can be refered at this [java](https://github.com/dmlc/tvm/blob/master/apps/android_deploy/app/src/main/java/ml/dmlc/tvm/android/demo/MainActivity.java) sample source.

docs/how_to/deploy_android.md

-# How to deploy and use compiled model on Android
-
-This tutorial explain below aspects (Unlike the android_rpc approach we already have)
-
-  * Build a model for android target
-  * TVM run on Android using Java API
-
-As an example here is a reference block diagram.
-
-![](http://www.tvmlang.org/images/release/tvm_flexible.png)
-
-## Build model for Android Target
-
-NNVM compilation of model for android target could follow same approach like android_rpc.
-
-An reference exampe can be found at [chainer-nnvm-example](https://github.com/tkat0/chainer-nnvm-example)
-
-Above example will directly run the compiled model on RPC target. Below modification at [rum_mobile.py](https://github.com/tkat0/chainer-nnvm-example/blob/5b97fd4d41aa4dde4b0aceb0be311054fb5de451/run_mobile.py#L64) will save the compilation output which is required on android target.
-
-```
-lib.export_library("deploy_lib.so", ndk.create_shared)
-with open("deploy_graph.json", "w") as fo:
-    fo.write(graph.json())
-with open("deploy_param.params", "wb") as fo:
-    fo.write(nnvm.compiler.save_param_dict(params))
-```
-
-deploy_lib.so, deploy_graph.json, deploy_param.params will go to android target.
-
-## TVM run on Android using Java API
-### TVM Runtime for android Target
-
-Refer [here](https://github.com/dmlc/tvm/blob/master/apps/android_deploy/README.md#build-and-installation) to build CPU/OpenCL version flavor TVM runtime for android target.
-
-### Android Native API Reference
-
-From android java TVM API to load model & execute can be refered at this [java](https://github.com/dmlc/tvm/blob/master/apps/android_deploy/app/src/main/java/ml/dmlc/tvm/android/demo/MainActivity.java) sample source.

python/tvm/contrib/rpc/__init__.py

@@ -10,4 +10,4 @@ upload and run remote RPC server, get the result back to verify correctness.
 """
 
 from .server import Server
-from .client import RPCSession, LocalSession, connect, connect_tracker
+from .client import RPCSession, LocalSession, TrackerSession, connect, connect_tracker

python/tvm/target.py

@@ -155,6 +155,7 @@ class GenericFunc(NodeBase):
         key_list = [key_list] if isinstance(key_list, str) else key_list
         _api_internal._GenericFuncRegisterFunc(self, func, key_list, allow_override)
 
+
 def get_native_generic_func(name):
     """Get a generic function from the global registry. If no
     function is registered under the given name, a new generic
@@ -172,6 +173,7 @@ def get_native_generic_func(name):
     """
     return _api_internal._GenericFuncGetGlobal(name)
 
+
 def override_native_generic_func(func_name):
     """Override a generic function defined in C++
 
@@ -193,20 +195,20 @@ def override_native_generic_func(func_name):
     -------
     .. code-block:: python
 
-    import tvm
-    # wrap function as target generic
-    @tvm.target.override_native_generic_func("my_func")
-    def my_func(a):
-        return a + 1
-    # register specialization of my_func under target cuda
-    @my_func.register("cuda")
-    def my_func_cuda(a):
-        return a + 2
-    # displays 3, because my_func is called
-    print(my_func(2))
-    # displays 4, because my_func_cuda is called
-    with tvm.target.cuda():
-        print(my_func(2))
+      import tvm
+      # wrap function as target generic
+      @tvm.target.override_native_generic_func("my_func")
+      def my_func(a):
+          return a + 1
+      # register specialization of my_func under target cuda
+      @my_func.register("cuda")
+      def my_func_cuda(a):
+          return a + 2
+      # displays 3, because my_func is called
+      print(my_func(2))
+      # displays 4, because my_func_cuda is called
+      with tvm.target.cuda():
+          print(my_func(2))
     """
     generic_func_node = get_native_generic_func(func_name)
 
@@ -346,6 +348,7 @@ def generic_func(fdefault):
     fdecorate.register = register
     return fdecorate
 
+
 def cuda(options=None):
     """Returns a cuda target.
 

topi/python/topi/tensor.py

@@ -27,6 +27,7 @@ def elemwise_sum(xs, num_args):
 
     return tvm.compute(xs[0].shape, _compute)
 
+
 @tvm.tag_scope(tag=tag.ELEMWISE)
 def full(shape, dtype, fill_value):
     """Fill tensor with fill_value
@@ -47,6 +48,7 @@ def full(shape, dtype, fill_value):
     """
     return tvm.compute(shape, lambda *i: tvm.const(fill_value, dtype))
 
+
 @tvm.tag_scope(tag=tag.ELEMWISE)
 def full_like(x, fill_value):
     """Construct a tensor with same shape as input tensor,
@@ -67,6 +69,7 @@ def full_like(x, fill_value):
     dtype = x.dtype
     return tvm.compute(x.shape, lambda *i: tvm.const(fill_value, dtype))
 
+
 @tvm.tag_scope(tag=tag.ELEMWISE)
 def greater(lhs, rhs, out_type=tvm.int8):
     """Compare two input tensors element-wise and return an mask tensor

topi/python/topi/transform.py

@@ -254,7 +254,7 @@ def split(ary, indices_or_sections, axis=0):
             "Should be sorted, recieved %s" % str(indices_or_sections)
         begin_ids = [0] + list(indices_or_sections)
     else:
-        raise NotImplementedError
+        raise NotImplementedError()
     out_shapes = []
     for i in range(len(begin_ids)):
         if i == len(begin_ids) - 1: