add check for mod when y in (-1, 0)

mindspore/ccsrc/frontend/operator/cc_implementations.cc

@@ -133,11 +133,7 @@ T InnerScalarMod(T x, T y) {
   if (std::is_integral<T>::value) {
     return static_cast<int>(x) % static_cast<int>(y);
   }
-  int x_int = std::floor(x);
-  int y_int = std::ceil(y);
-  int max = x_int / y_int;
-  float ret = x - y * max;
-  return ret;
+  return x - y * std::floor(x / y);
 }
 
 template <typename T, typename U>

tests/ut/python/pipeline/infer/test_mod.py

@@ -0,0 +1,100 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+""" test mod"""
+
+import mindspore.nn as nn
+from mindspore import context
+
+context.set_context(mode=context.GRAPH_MODE)
+
+
+def test_positive_mod_positive():
+    class Mod(nn.Cell):
+        def __init__(self, x, y):
+            super(Mod, self).__init__()
+            self.x = x
+            self.y = y
+
+        def construct(self):
+            return self.x % self.y
+    x = 3.0
+    y = 1.3
+    mod_net = Mod(x, y)
+    expect = x % y
+    assert abs(mod_net() - expect) < 0.000001
+
+
+def test_positive_mod_negative():
+    class Mod(nn.Cell):
+        def __init__(self, x, y):
+            super(Mod, self).__init__()
+            self.x = x
+            self.y = y
+
+        def construct(self):
+            return self.x % self.y
+    x = 3.0
+    y = -1.3
+    mod_net = Mod(x, y)
+    expect = x % y
+    assert abs(mod_net() - expect) < 0.000001
+
+
+def test_negative_mod_positive():
+    class Mod(nn.Cell):
+        def __init__(self, x, y):
+            super(Mod, self).__init__()
+            self.x = x
+            self.y = y
+
+        def construct(self):
+            return self.x % self.y
+    x = -3.0
+    y = 1.3
+    mod_net = Mod(x, y)
+    expect = x % y
+    assert abs(mod_net() - expect) < 0.000001
+
+
+def test_negative_mod_negative():
+    class Mod(nn.Cell):
+        def __init__(self, x, y):
+            super(Mod, self).__init__()
+            self.x = x
+            self.y = y
+
+        def construct(self):
+            return self.x % self.y
+    x = -3.0
+    y = -1.3
+    mod_net = Mod(x, y)
+    expect = x % y
+    assert abs(mod_net() - expect) < 0.000001
+
+
+def test_int_mod_int():
+    class Mod(nn.Cell):
+        def __init__(self, x, y):
+            super(Mod, self).__init__()
+            self.x = x
+            self.y = y
+
+        def construct(self):
+            return self.x % self.y
+    x = 3
+    y = 2
+    mod_net = Mod(x, y)
+    expect = x % y
+    assert abs(mod_net() - expect) < 0.000001