maximum relay op V2 (#1838)

docs/langref/relay_op.rst

@@ -54,6 +54,7 @@ This level enables typical convnet models.
    tvm.relay.greater_equal
    tvm.relay.less
    tvm.relay.less_equal
+   tvm.relay.maximum
 
 **Level 5: Vision/Image Operators**
 
@@ -82,3 +83,4 @@ Level 4 Definitions
 .. autofunction:: tvm.relay.greater_equal
 .. autofunction:: tvm.relay.less
 .. autofunction:: tvm.relay.less_equal
+.. autofunction:: tvm.relay.maximum
\ No newline at end of file

python/tvm/relay/op/tensor.py

@@ -228,6 +228,24 @@ def greater_equal(lhs, rhs):
     return _make.greater_equal(lhs, rhs)
 
 
+def maximum(lhs, rhs):
+    """Maximum with numpy-style broadcasting.
+
+    Parameters
+    ----------
+    lhs : relay.Expr
+        The left hand side input data
+    rhs : relay.Expr
+        The right hand side input data
+
+    Returns
+    -------
+    result : relay.Expr
+        The computed result.
+    """
+    return _make.maximum(lhs, rhs)
+
+
 def right_shift(lhs, rhs):
     """Right shift with numpy-style broadcasting.
 

src/relay/op/tensor/binary.cc

@@ -37,11 +37,14 @@ RELAY_REGISTER_BINARY_OP("right_shift")
 .describe("Elementwise right shift with broadcasting")
 .set_support_level(4);
 
-// Left shift
 RELAY_REGISTER_BINARY_OP("left_shift")
 .describe("Elementwise left shift with broadcasting")
 .set_support_level(4);
   
+RELAY_REGISTER_BINARY_OP("maximum")
+.describe("Elementwise maximum of two tensors with broadcasting")
+.set_support_level(4);
+
 // Comparisons
 #define RELAY_REGISTER_CMP_OP(OpName)                               \
   TVM_REGISTER_API("relay.op._make." OpName)                        \

tests/python/relay/test_op_level4.py

@@ -21,8 +21,9 @@ def test_cmp_type():
 
 
 def test_binary_broadcast():
-    for op in (relay.right_shift,
-               relay.left_shift):
+    for op in [relay.right_shift,
+               relay.left_shift,
+               relay.maximum]:
         ib = relay.ir_builder.IRBuilder()
         x = ib.param("x", relay.TensorType((10, 4), "int32"))
         y = ib.param("y", relay.TensorType((5, 10, 1), "int32"))