[DOC] Add intro to 'comm_reducer' in tutorial; fix doc (#108)

* [DOC] Add intro to 'comm_reducer' in tutorial; fix doc

* Fix

* Fix

include/tvm/ir_pass.h

@@ -84,6 +84,13 @@ Stmt CanonicalSimplify(Stmt stmt);
  * \return The converted form.
  */
 Stmt Substitute(Stmt stmt, const Map<Var, Expr>& value_map);
+
+/*!
+ * \brief Substitute the var specified in key->var to be value.
+ * \param expr The source expression to be substituted
+ * \param value_map The map of new values.
+ * \return The converted expression.
+ */
 Expr Substitute(Expr expr, const Map<Var, Expr>& value_map);
 
 /*!

python/setup.py

@@ -70,7 +70,7 @@ setuptools.setup(
         ],
     zip_safe=False,
     packages=[
-        'tvm', 'tvm.addon',
+        'tvm', 'tvm.contrib',
         'tvm._ffi', 'tvm._ffi._ctypes',
         'tvm._ffi._cy2', 'tvm._ffi._cy3'
     ],

python/tvm/api.py

@@ -23,10 +23,12 @@ handle = "handle"
 
 
 def min_value(dtype):
+    """minimum value of dtype"""
     return _api_internal._min_value(dtype)
 
 
 def max_value(dtype):
+    """maximum value of dtype"""
     return _api_internal._max_value(dtype)
 
 
@@ -438,7 +440,7 @@ def comm_reducer(fcombine, fidentity, name="reduce"):
     -------
     reducer : function
         A function which creates a reduce expression over axis.
-        There are two to use it:
+        There are two ways to use it:
 
         1. accept (expr, axis, where) to produce an Reduce Expr on
            specified axis;

tutorials/python/reduction.py

@@ -125,9 +125,27 @@ np.testing.assert_allclose(
     b.asnumpy(),  np.sum(a.asnumpy(), axis=1), rtol=1e-4)
 
 ######################################################################
+# Define General Commutative Reduction Operation
+# ----------------------------------------------
+# Besides the built-in reduction operations like :any:`tvm.sum`,
+# :any:`tvm.min` and :any:`tvm.max`, you can also define your
+# commutative reduction operation by :any:`tvm.comm_reducer`.
+#
+
+n = tvm.var('n')
+m = tvm.var('m')
+product = tvm.comm_reducer(lambda x, y: x*y,
+    lambda t: tvm.const(1, dtype=t), name="product")
+A = tvm.placeholder((n, m), name='A')
+k = tvm.reduce_axis((0, m), name='k')
+B = tvm.compute((n,), lambda i: product(A[i, k], axis=k), name='B')
+
+
+######################################################################
 # Summary
 # -------
 # This tutorial provides a walk through of reduction schedule.
 #
 # - Describe reduction with reduce_axis.
 # - Use rfactor to factor out axis if we need parallelism.
+# - Define new reduction operation by :any:`tvm.comm_reducer`