[cherry-pick] fixed group_norm's bug and modified unittest, test=release/1.6 (#20506) (#20591)

* modified group_norm's unittest for pass statement, test=develop

* fix group_norm's bug: scale or bias is None which causes segmentation fault, test=develop

paddle/fluid/operators/group_norm_op.h

@@ -220,6 +220,7 @@ class GroupNormGradKernel : public framework::OpKernel<T> {
               if (bias_data) val -= bias_data[gid * group_size + cid];
               T dval = iter_y_data[0];
               dp_scale += val * dval;
+              if (scale_data)
                 dp_bias += dval * scale_data[gid * group_size + cid];
 
               if (scale_data && scale_data[gid * group_size + cid] != 0)
@@ -237,8 +238,9 @@ class GroupNormGradKernel : public framework::OpKernel<T> {
               T dly = tmp_y[0];
               T dss = dp_scale;
               T dbs = dp_bias;
-              T v_scale = scale_data[gid * group_size + cid];
-              T v_bias = bias_data[gid * group_size + cid];
+              T v_scale = 1., v_bias = 0.;
+              if (scale_data) v_scale = scale_data[gid * group_size + cid];
+              if (bias_data) v_bias = bias_data[gid * group_size + cid];
               v_y -= v_bias;
               if (v_scale != 0) v_y /= v_scale;
               iter_d_x_data[0] =
@@ -256,6 +258,7 @@ class GroupNormGradKernel : public framework::OpKernel<T> {
               if (bias_data) val -= bias_data[gid * group_size + cid];
               T dval = iter_y_data[0];
               dp_scale += val * dval;
+              if (scale_data)
                 dp_bias += dval * scale_data[gid * group_size + cid];
 
               if (scale_data && scale_data[gid * group_size + cid] != 0)
@@ -276,8 +279,9 @@ class GroupNormGradKernel : public framework::OpKernel<T> {
               T dly = tmp_y[0];
               T dss = dp_scale;
               T dbs = dp_bias;
-              T v_scale = scale_data[gid * group_size + cid];
-              T v_bias = bias_data[gid * group_size + cid];
+              T v_scale = 1.0, v_bias = 0.;
+              if (scale_data) v_scale = scale_data[gid * group_size + cid];
+              if (bias_data) v_bias = bias_data[gid * group_size + cid];
               v_y -= v_bias;
               if (v_scale != 0) v_y /= v_scale;
               iter_d_x_data[0] =

python/paddle/fluid/tests/unittests/test_group_norm_op.py

@@ -195,12 +195,10 @@ class TestGroupNormOpLargeData_With_NHWC(TestGroupNormOp):
 
 class TestGroupNormAPI_With_NHWC(OpTest):
     def test_case1(self):
-        data1 = fluid.layers.data(
-            name='data1', shape=[3, 3, 4], dtype='float32')
+        data1 = fluid.data(name='data1', shape=[None, 3, 3, 4], dtype='float32')
         out1 = fluid.layers.group_norm(
             input=data1, groups=2, data_layout="NHWC")
-        data2 = fluid.layers.data(
-            name='data2', shape=[4, 3, 3], dtype='float32')
+        data2 = fluid.data(name='data2', shape=[None, 4, 3, 3], dtype='float32')
         out2 = fluid.layers.group_norm(
             input=data2, groups=2, data_layout="NCHW")
 
@@ -223,14 +221,17 @@ class TestGroupNormAPI_With_NHWC(OpTest):
         self.assertTrue(np.allclose(results[0], expect_res1[0]))
         self.assertTrue(np.allclose(results[1], expect_res2[0]))
 
+
+class TestGroupNormException(OpTest):
     # data_layout is not NHWC or NCHW
-    def test_case2(self):
-        data = fluid.layers.data(name='data', shape=[3, 3, 4], dtype="float32")
-        try:
+    def test_exception(self):
+        data = fluid.data(name='data', shape=[None, 3, 3, 4], dtype="float32")
+
+        def attr_data_format():
             out = fluid.layers.group_norm(
                 input=data, groups=2, data_layout="NDHW")
-        except:
-            pass
+
+        self.assertRaises(ValueError, attr_data_format)
 
 
 if __name__ == '__main__':