[FRONTEND][MXNET] support elemwise logic ops (#5361)

python/tvm/relay/frontend/mxnet.py

@@ -1751,6 +1751,12 @@ _convert_map = {
     "broadcast_greater_equal": _mx_compare(_op.greater_equal, _rename),
     "broadcast_lesser"       : _mx_compare(_op.less, _rename),
     "broadcast_lesser_equal" : _mx_compare(_op.less_equal, _rename),
+    "_equal"                 : _mx_compare(_op.equal, _rename),
+    "_not_equal"             : _mx_compare(_op.not_equal, _rename),
+    "_greater"               : _mx_compare(_op.greater, _rename),
+    "_greater_equal"         : _mx_compare(_op.greater_equal, _rename),
+    "_lesser"                : _mx_compare(_op.less, _rename),
+    "_lesser_equal"          : _mx_compare(_op.less_equal, _rename),
     "elemwise_add"           : _rename(_op.add),
     "elemwise_sub"           : _rename(_op.subtract),
     "elemwise_mul"           : _rename(_op.multiply),

tests/python/frontend/mxnet/test_forward.py

@@ -328,13 +328,19 @@ def test_forward_broadcast_ops():
 
 def test_forward_elemwise_ops():
     for op in ["elemwise_add", "elemwise_sub", "elemwise_mul",
-               "elemwise_div", "maximum", "minimum"]:
+               "elemwise_div", "maximum", "minimum",
+               operator.lt, operator.le, operator.eq,
+               operator.ne, operator.gt, operator.ge]:
         shape = (3, 4, 5)
         dtype = 'float32'
         a_np = np.random.uniform(size=shape).astype(dtype)
         b_np = np.random.uniform(size=shape).astype(dtype)
+        if type(op) == str:
             mx_sym = _mx_symbol(mx.sym, op, [mx.sym.var('a'), mx.sym.var('b')])
             ref_res = _mx_symbol(mx.nd, op, [mx.nd.array(a_np), mx.nd.array(b_np)])
+        else:
+            mx_sym = op(mx.sym.var('a'), mx.sym.var('b'))
+            ref_res = op(mx.nd.array(a_np), mx.nd.array(b_np))
         shapes = {'a': shape, 'b': shape}
         mod, _ = relay.frontend.from_mxnet(mx_sym, shapes, dtype)
         for target, ctx in ctx_list():