rename is_input to is_var

docs/langref/relay_pattern.rst

@@ -193,7 +193,7 @@ The next example is matching a diamond with two inputs at the top of the diamond
 
     def test_match_diamond():
         # Pattern
-        is_conv2d = is_op('nn.conv2d')(is_input(), is_input())
+        is_conv2d = is_op('nn.conv2d')(is_var(), is_var())
         path1 = is_op('nn.relu')(is_conv2d)
         path2 = is_op('nn.leaky_relu')(is_conv2d)
         diamond = is_op('add')(path1, path2)
@@ -213,7 +213,7 @@ The final example is matching diamonds with a post-dominator relationship. We em
 
     def test_match_dom_diamond():
         # Pattern
-        is_conv2d = is_op('nn.conv2d')(is_input(), is_input())
+        is_conv2d = is_op('nn.conv2d')(is_var(), is_var())
         reduction = is_op('add')(wildcard(), wildcard())
         diamond = dominates(is_conv2d, is_elemwise, reduction)
 
@@ -240,7 +240,7 @@ The high level design is to introduce a language of patterns for now we propose
             | pattern(pattern1, ... patternN)
             | has_type(pattern, type)
             | has_attr(pattern, attrs)
-            | is_input(name)
+            | is_var(name)
             | is_constant()
             | is_op(op_name)
             | is_tuple()

python/tvm/relay/dataflow_pattern/__init__.py

@@ -286,7 +286,7 @@ def __init__(self, name_hint="", type_annotation=None):
         self.__init_handle_by_constructor__(
             ffi.VarPattern, name_hint, type_annotation)
 
-is_input = VarPattern
+is_var = VarPattern
 
 
 @register_df_node

tests/python/relay/test_dataflow_pattern.py

@@ -38,7 +38,7 @@ def test_expr_pattern():
 
 
 def test_var_pattern():
-    v = is_input("x")
+    v = is_var("x")
     assert isinstance(v, VarPattern)
     assert v.name == "x"
 
@@ -120,21 +120,21 @@ def test_match_op_or():
 def test_match_call_commutive():
     x = relay.var('x')
     y = relay.var('y')
-    add_pattern = is_op('add')(is_input("x"), is_input("y"))
+    add_pattern = is_op('add')(is_var("x"), is_var("y"))
     assert add_pattern.match(x + y)
     assert add_pattern.match(y + x)
-    mul_pattern = is_op('multiply')(is_input("x"), is_input("y"))
+    mul_pattern = is_op('multiply')(is_var("x"), is_var("y"))
     assert mul_pattern.match(x * y)
     assert mul_pattern.match(y * x)
 
 
 def test_no_match_call_commutive():
     x = relay.var('x')
     y = relay.var('y')
-    add_pattern = is_op('subtract')(is_input("x"), is_input("y"))
+    add_pattern = is_op('subtract')(is_var("x"), is_var("y"))
     assert add_pattern.match(x - y)
     assert not add_pattern.match(y - x)
-    add_pattern = is_op('divide')(is_input("x"), is_input("y"))
+    add_pattern = is_op('divide')(is_var("x"), is_var("y"))
     assert add_pattern.match(x / y)
     assert not add_pattern.match(y / x)
 
@@ -232,10 +232,10 @@ def test_match_tuple():
     x = relay.var('x')
     y = relay.var('y')
     z = relay.op.op.get("add")
-    tuple_pattern = is_tuple((is_input("x"), wildcard(), is_op("add")))
+    tuple_pattern = is_tuple((is_var("x"), wildcard(), is_op("add")))
     assert tuple_pattern.match(relay.expr.Tuple((x, y, z)))
 
-    tuple_pattern = is_tuple((is_input("x"), wildcard(), is_op("add")))
+    tuple_pattern = is_tuple((is_var("x"), wildcard(), is_op("add")))
     tuple_get_item_pattern = is_tuple_get_item(tuple_pattern, 1)
     assert tuple_get_item_pattern.match(relay.expr.TupleGetItem(relay.expr.Tuple((x, y, z)), 1))
 
@@ -244,10 +244,10 @@ def test_no_match_tuple():
     x = relay.var('x')
     y = relay.var('y')
     z = relay.op.op.get("add")
-    tuple_pattern = is_tuple((is_input('x'), wildcard(), is_op("add"), wildcard()))
+    tuple_pattern = is_tuple((is_var('x'), wildcard(), is_op("add"), wildcard()))
     assert not tuple_pattern.match(relay.expr.Tuple((x, y, z)))
 
-    tuple_pattern = is_tuple((is_input('x'), wildcard(), is_op("add")))
+    tuple_pattern = is_tuple((is_var('x'), wildcard(), is_op("add")))
     tuple_get_item_pattern = is_tuple_get_item(tuple_pattern, 1)
     assert not tuple_get_item_pattern.match(relay.expr.TupleGetItem(relay.expr.Tuple(
         (x, y, z)), 2))
@@ -1182,7 +1182,7 @@ def test_partition_constant_embedding():
     assert tvm.ir.structural_equal(lifted_func(x, wc, b), pattern.partition(reluc))
 
     # Check lifting of input matches
-    pattern = is_op('nn.relu')(is_op('nn.bias_add')(is_op('nn.conv2d')(wildcard(), is_input()),
+    pattern = is_op('nn.relu')(is_op('nn.bias_add')(is_op('nn.conv2d')(wildcard(), is_var()),
                                                     wildcard()))
     assert tvm.ir.structural_equal(lifted_func(x, w, b), pattern.partition(relu))
     assert tvm.ir.structural_equal(reluc, pattern.partition(reluc))  #Constants are not Inputs
@@ -1201,15 +1201,15 @@ def test_partition_constant_embedding():
     assert tvm.ir.structural_equal(embeded_func(x, b), pattern.partition(reluc))
 
     # Check lifting/embedding of Alt matches
-    pattern = is_op('nn.relu')(is_op('nn.bias_add')(is_op('nn.conv2d')(wildcard(), is_input()
+    pattern = is_op('nn.relu')(is_op('nn.bias_add')(is_op('nn.conv2d')(wildcard(), is_var()
                                                                        | is_constant()),
                                                     wildcard()))
     assert tvm.ir.structural_equal(lifted_func(x, w, b), pattern.partition(relu))
     assert tvm.ir.structural_equal(embeded_func(x, b), pattern.partition(reluc))
 
     # Check lifting/embedding of Alt matches with the other ordering
     pattern = is_op('nn.relu')(is_op('nn.bias_add')(is_op('nn.conv2d')(wildcard(),
-                                                                       is_constant() | is_input()),
+                                                                       is_constant() | is_var()),
                                                     wildcard()))
     assert tvm.ir.structural_equal(lifted_func(x, w, b), pattern.partition(relu))
     assert tvm.ir.structural_equal(embeded_func(x, b), pattern.partition(reluc))