[v1.x] ONNX support for broadcast_mod (#19770)

* broadcast_mod

* Update _op_translations.py

* Update _op_translations.py

* improve tests

python/mxnet/contrib/onnx/mx2onnx/_op_translations.py

@@ -3107,6 +3107,34 @@ def convert_contrib_AdaptiveAvgPooling2D(node, **kwargs):
     return nodes
 
 
+@mx_op.register('broadcast_mod')
+def convert_broadcast_mod(node, **kwargs):
+    """Map MXNet's broadcast_mod operator
+    """
+    from onnx.helper import make_node
+    name, input_nodes, _ = get_inputs(node, kwargs)
+
+    # The behavior of MXNet mod is a mixture of np.mod and np.fmod
+    # note: the behavior when divison by 0 is supposed to be platform dependent
+    #       but here we set the result to 0 to be consistent with MXNet
+    nodes = [
+        make_node('Sub', [input_nodes[1], input_nodes[1]], [name+'_zero']),
+        make_node('Mod', [input_nodes[0], input_nodes[1]], [name+'_mod'], fmod=1),
+        make_node('Less', [input_nodes[0], name+'_zero'], [name+'_mask_0']),
+        make_node('Less', [input_nodes[1], name+'_zero'], [name+'_mask_1']),
+        make_node('Equal', [name+'_mod', name+'_zero'], [name+'_mask_2_']),
+        make_node('Not', [name+'_mask_2_'], [name+'_mask_2']),
+        make_node('Xor', [name+'_mask_0', name+'_mask_1'], [name+'_mask_']),
+        make_node('And', [name+'_mask_', name+'_mask_2'], [name+'_mask']),
+        make_node('Where', [name+'_mask', input_nodes[1], name+'_zero'], [name+'_adjustment']),
+        make_node('Add', [name+'_mod', name+'_adjustment'], [name+'_adjusted']),
+        make_node('Equal', [input_nodes[1], name+'_zero'], [name+'_mask_div_0']),
+        make_node('Where', [name+'_mask_div_0', name+'_zero', name+'_adjusted'], [name])
+        ]
+
+    return nodes
+
+
 @mx_op.register("reshape_like")
 def convert_reshape_like(node, **kwargs):
     """Map MXNet's reshape_like operator attributes to onnx's operator.

tests/python-pytest/onnx/test_operators.py

@@ -497,6 +497,17 @@ def test_onnx_export_contrib_AdaptiveAvgPooling2D(tmp_path, dtype):
     op_export_test('contrib_AdaptiveAvgPooling2D', M4, [x], tmp_path)
 
 
+@pytest.mark.parametrize('dtype', ['float16', 'float32', 'int32', 'int64'])
+@pytest.mark.parametrize('shapes', [((3, 3, 3), (1, 3)), ((4, 5, 6, 7), (6, 7))])
+def test_onnx_export_broadcast_mod(tmp_path, dtype, shapes):
+    A = mx.nd.random.uniform(-300, 300, shapes[0]).astype(dtype)
+    B = mx.nd.random.uniform(-30, 30, shapes[1]).astype(dtype)
+    # test when dividend is zero
+    B[-1] = 0
+    M = def_model('broadcast_mod')
+    op_export_test('broadcast_mod', M, [A, B], tmp_path)
+
+
 @pytest.mark.parametrize('dtype', ['int32', 'int64', 'float16', 'float32', 'float64'])
 def test_onnx_export_reshape_like(tmp_path, dtype):
     if 'int' in dtype: