sample of onnx test, not implemented

test/onnx/gen_onnx.py

@@ -44,6 +44,7 @@ def onnx_test(op_test):
                                           op_info[1], op_info[2])
         model_def = helper.make_model(graph_def,
                                       producer_name=op_test.__name__)
+        print('hello', op_test.__name__)
         onnx.save(model_def, '{}.onnx'.format(op_test.__name__))
 
     return run_test
@@ -4451,6 +4452,18 @@ def reducel1_test():
 
     return ([node], [x], [y])
 
+def reducel1_dyn_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [None])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [None])
+    axes = [-2]
+
+    node = onnx.helper.make_node('ReduceL1_dyn',
+                                 inputs=['x'],
+                                 outputs=['y'],
+                                 axes=axes,
+                                 keepdims=0)
+
+    return ([node], [x], [y])
 
 @onnx_test
 def reducel2_test():

test/onnx/onnx_test.cpp

@@ -4241,6 +4241,22 @@ TEST_CASE(reducel1_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(reducel1_dyn_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    // a shape with 4 dynamic dimensions
+    auto l0 = mm->add_parameter(
+        "x",
+        migraphx::shape{migraphx::shape::float_type, {{3, 3, 0}, {3, 5, 0}, {4, 6, 5}, {5, 7, 6}}});
+    auto abs_l0 = mm->add_instruction(migraphx::make_op("abs"), l0);
+    auto sum_l0 = mm->add_instruction(migraphx::make_op("reduce_sum", {{"axes", {-2}}}), abs_l0);
+    mm->add_instruction(migraphx::make_op("squeeze", {{"axes", {-2}}}), sum_l0);
+    auto prog = optimize_onnx("reducel1_dyn_test.onnx");
+
+    EXPECT(p == prog);
+}
+
 TEST_CASE(reducel2_test)
 {
     migraphx::program p;