add paddle elementwise mod op (#12370)

Co-authored-by: cecilia peng <cecilia.peng@intel.com>

src/core/tests/frontend/paddle/op_fuzzy.cpp

@@ -103,6 +103,7 @@ static const std::vector<std::string> models{
     std::string("elementwise_div1"),
     std::string("elementwise_max1"),
     std::string("elementwise_min1"),
+    std::string("elementwise_mod1"),
     std::string("elementwise_mul1"),
     std::string("elementwise_pow1"),
     std::string("elementwise_sub1"),
@@ -110,6 +111,7 @@ static const std::vector<std::string> models{
     std::string("elementwise_div2"),
     std::string("elementwise_max2"),
     std::string("elementwise_min2"),
+    std::string("elementwise_mod2"),
     std::string("elementwise_mul2"),
     std::string("elementwise_pow2"),
     std::string("elementwise_sub2"),
@@ -117,6 +119,7 @@ static const std::vector<std::string> models{
     std::string("elementwise_div3"),
     std::string("elementwise_max3"),
     std::string("elementwise_min3"),
+    std::string("elementwise_mod3"),
     std::string("elementwise_mul3"),
     std::string("elementwise_pow3"),
     std::string("elementwise_sub3"),
@@ -124,6 +127,7 @@ static const std::vector<std::string> models{
     std::string("elementwise_div4"),
     std::string("elementwise_max4"),
     std::string("elementwise_min4"),
+    std::string("elementwise_mod4"),
     std::string("elementwise_mul4"),
     std::string("elementwise_pow4"),
     std::string("elementwise_sub4"),
@@ -155,6 +159,8 @@ static const std::vector<std::string> models{
     std::string("fill_constant_shape_tensor_list"),
     std::string("flatten_contiguous_range_test1"),
     std::string("floor_float32"),
+    std::string("floor_mod1"),
+    std::string("floor_mod2"),
     std::string("gather_multi_dimension"),
     std::string("gather_one_dimension"),
     std::string("gather_one_dimension2"),

src/core/tests/frontend/paddle/test_models/gen_scripts/generate_elementwise_ops.py

@@ -75,6 +75,31 @@ def elementwise_div(name : str, x, y, axis, in_dtype):
     return outs[0]
 
 
+def elementwise_mod(name : str, x, y, axis, in_dtype, is_api=False):
+    import paddle
+    paddle.enable_static()
+
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        node_x = paddle.static.data(name='x', shape=x.shape, dtype=in_dtype)
+        node_y = paddle.static.data(name='y', shape=y.shape, dtype=in_dtype)
+        if is_api:
+            out = paddle.floor_mod(node_x, node_y)
+        else:
+            out = paddle.fluid.layers.elementwise_mod(node_x, node_y, axis=axis)
+
+        cpu = paddle.static.cpu_places(1)
+        exe = paddle.static.Executor(cpu[0])
+
+        # startup program will call initializer to initialize the parameters.
+        exe.run(paddle.static.default_startup_program())
+        outs = exe.run(
+        feed={'x': x, 'y': y},
+        fetch_list=[out])
+        saveModel(name, exe, feedkeys=['x', 'y'], fetchlist=[out], inputs=[x, y], outputs=[outs[0]], target_dir=sys.argv[1])
+
+    return outs[0]
+
+
 def elementwise_mul(name : str, x, y, axis, in_dtype):
     import paddle
     paddle.enable_static()
@@ -166,6 +191,7 @@ def elementwise_ops(name : str, data_x, data_y, axis, in_dtype):
     elementwise_add("elementwise_add" + name, data_x, data_y, axis, in_dtype)
     elementwise_sub("elementwise_sub" + name, data_x, data_y, axis, in_dtype)
     elementwise_div("elementwise_div" + name, data_x, data_y, axis, in_dtype)
+    elementwise_mod("elementwise_mod" + name, data_x, data_y, axis, in_dtype)
     elementwise_mul("elementwise_mul" + name, data_x, data_y, axis, in_dtype)
     elementwise_min("elementwise_min" + name, data_x, data_y, axis, in_dtype)
     elementwise_max("elementwise_max" + name, data_x, data_y, axis, in_dtype)
@@ -179,11 +205,13 @@ def main():
     data_y = np.array([1, 5, 2]).astype(in_dtype)
     axis = -1
     elementwise_ops("1", data_x, data_y, axis, in_dtype)
+    elementwise_mod('floor_mod1', data_x, data_y, -1, in_dtype, True)
 
     # data_y's shape is the continuous subsequence of data_x's shape
     data_x = np.random.rand(2, 5, 3, 4).astype(np.float32)
     data_y = (0.1 + np.random.rand(3, 4).astype(np.float32)) / 1.1
     elementwise_ops("2", data_x, data_y, axis, in_dtype)
+    elementwise_mod('floor_mod2', data_x, data_y, -1, in_dtype, True)
 
     data_y = (0.1 + np.random.rand(5).astype(np.float32)) / 1.1
     axis = 1

src/frontends/paddle/src/op/elementwise_ops.cpp

@@ -46,6 +46,10 @@ NamedOutputs elementwise_greater_equal(const NodeContext& node_context) {
     return elementwise_ops<default_opset::GreaterEqual>(node_context);
 }
 
+NamedOutputs elementwise_mod(const NodeContext& node_context) {
+    return elementwise_ops<default_opset::FloorMod>(node_context);
+}
+
 }  // namespace op
 }  // namespace paddle
 }  // namespace frontend

src/frontends/paddle/src/op_table.cpp

@@ -29,6 +29,7 @@ OP_CONVERTER(elementwise_equal);
 OP_CONVERTER(elementwise_greater_equal);
 OP_CONVERTER(elementwise_max);
 OP_CONVERTER(elementwise_min);
+OP_CONVERTER(elementwise_mod);
 OP_CONVERTER(elementwise_mul);
 OP_CONVERTER(elementwise_pow);
 OP_CONVERTER(elementwise_sub);
@@ -127,6 +128,7 @@ std::map<std::string, CreatorFunction> get_supported_ops() {
             {"elementwise_div", op::elementwise_div},
             {"elementwise_max", op::elementwise_max},
             {"elementwise_min", op::elementwise_min},
+            {"elementwise_mod", op::elementwise_mod},
             {"elementwise_mul", op::elementwise_mul},
             {"elementwise_pow", op::elementwise_pow},
             {"elementwise_sub", op::elementwise_sub},