diff --git a/ci_test/unit_tests/test_unit_layer_convolution_distconv.py b/ci_test/unit_tests/test_unit_layer_convolution_distconv.py
index 75bb5c65b73..40fe2783728 100644
--- a/ci_test/unit_tests/test_unit_layer_convolution_distconv.py
+++ b/ci_test/unit_tests/test_unit_layer_convolution_distconv.py
@@ -55,6 +55,19 @@ def num_samples():
 def sample_dims():
     return (_sample_size,)
 
+# Convolution results computed using PyTorch beforehand.
+def reference_value(num_dims, stride):
+    if num_dims == 2 and stride == 1:
+        return 11913.852660080756
+    elif num_dims == 2 and stride == 2:
+        return 2850.678372506634
+    elif num_dims == 3 and stride == 1:
+        return 9952.365297083174
+    elif num_dims == 3 and stride == 2:
+        return 1222.3680410607026
+
+    raise ValueError('Does not have a pre-computed reference value for this configuration')
+
 # ==============================================
 # PyTorch convolution
 # ==============================================
@@ -165,12 +178,10 @@ def construct_model(lbann):
         print('Skip - ' + e)
         pytest.skip(e)
 
-    for num_dims, reference_val in [
-            (2, 11913.852660080756),
-            (3, 9952.365297083174)]:
+    for num_dims, s in [(2, 1), (2, 2), (3, 1), (3, 2)]:
         # Convolution settings
         kernel_dims = [5, _sample_dims[0] if num_dims == 2 else _sample_dims_3d[0],] + [3]*num_dims
-        strides = [1]*num_dims
+        strides = [s]*num_dims
         pads = [1]*num_dims
         dilations = [1]*num_dims
         kernel = make_random_array(kernel_dims, 11)
@@ -179,7 +190,7 @@ def construct_model(lbann):
         kernel_weights = lbann.Weights(
             optimizer=lbann.SGD(),
             initializer=lbann.ValueInitializer(values=np.nditer(kernel)),
-            name='kernel1_{}d'.format(num_dims)
+            name='kernel1_{}d-stride{}'.format(num_dims, s)
         )
         x = x_lbann
         if num_dims == 3:
@@ -198,7 +209,9 @@ def construct_model(lbann):
                                   num_height_groups))
         z = lbann.L2Norm2(y)
         obj.append(z)
-        metrics.append(lbann.Metric(z, name='basic {}D 3^n convolution'.format(num_dims)))
+        metrics.append(
+            lbann.Metric(z, name='basic {}D 3^n convolution with stride{}'.format(num_dims, s))
+        )
 
         # PyTorch implementation
         try:
@@ -214,7 +227,7 @@ def construct_model(lbann):
             val = z
         except:
             # Precomputed value
-            val = reference_val
+            val = reference_value(num_dims, s)
             # val = 398.6956458317758 # _num_samples=8, 6 channels
             # val = 381.7401227915947 # _num_samples=23, 6 channels
         tol = 8 * val * np.finfo(np.float32).eps