Add ONNX tests

src/include/migraphx/op/multibroadcast.hpp

@@ -42,10 +42,10 @@ namespace op {
  */
 struct multibroadcast
 {
-    std::vector<std::size_t> output_lens;
+    std::vector<std::size_t> output_lens = {};
 
     // optional attribute
-    std::vector<shape::dynamic_dimension> output_dyn_dims;
+    std::vector<shape::dynamic_dimension> output_dyn_dims = {};
 
     template <class Self, class F>
     static auto reflect(Self& self, F f)

src/shape.cpp

@@ -473,7 +473,8 @@ shape shape::to_dynamic() const
     {
         return *this;
     }
-    return {type(), lens(), lens(), lens()};
+    std::vector<std::size_t> zeroes(this->ndim(), 0);
+    return {type(), lens(), lens(), zeroes};
 }
 
 std::size_t shape::element_space() const { return impl->element_space(); }

test/onnx/gen_onnx.py

@@ -420,6 +420,40 @@ def batch_norm_invalid_bias_rank_test():
     return ([node], [x, scale, bias, mean, var], [out])
 
 
+@onnx_test
+def binary_dyn_brcst_prelu_test():
+    arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT,
+                                         [None, 3, 4, 5])
+    arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [4, 5])
+    arg_out = helper.make_tensor_value_info('out', TensorProto.FLOAT,
+                                            [None, 3, 4, 5])
+
+    node = onnx.helper.make_node(
+        'PRelu',
+        inputs=['0', '1'],
+        outputs=['out'],
+    )
+
+    return ([node], [arg0, arg1], [arg_out])
+
+
+@onnx_test
+def binary_dyn_brcst_mul_test():
+    arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT,
+                                         [None, 3, 4, 5])
+    arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [4, 1])
+    arg_out = helper.make_tensor_value_info('out', TensorProto.FLOAT,
+                                            [None, 3, 4, 5])
+
+    node = onnx.helper.make_node(
+        'Mul',
+        inputs=['0', '1'],
+        outputs=['out'],
+    )
+
+    return ([node], [arg0, arg1], [arg_out])
+
+
 @onnx_test
 def cast_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT16, [10])

test/onnx/onnx_test.cpp

@@ -522,6 +522,49 @@ TEST_CASE(batch_norm_invalid_bias_rank)
     EXPECT(test::throws([&] { migraphx::parse_onnx("batch_norm_invalid_bias_rank.onnx"); }));
 }
 
+TEST_CASE(binary_dyn_brcst_prelu_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    auto l0  = mm->add_parameter(
+        "0",
+        migraphx::shape{migraphx::shape::float_type, {{1, 4, 0}, {3, 3, 0}, {4, 4, 0}, {5, 5, 0}}});
+    auto l1 = mm->add_parameter("1", migraphx::shape{migraphx::shape::float_type, {4, 5}});
+
+    auto ret = add_common_op(*mm, migraphx::make_op("prelu"), {l0, l1});
+    mm->add_return({ret});
+
+    migraphx::onnx_options options;
+    options.default_dyn_dim_value = {1, 4, 0};
+    auto prog = migraphx::parse_onnx("binary_dyn_brcst_prelu_test.onnx", options);
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(binary_dyn_brcst_mul_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    auto l0  = mm->add_parameter(
+        "0",
+        migraphx::shape{migraphx::shape::float_type, {{1, 4, 0}, {3, 3, 0}, {4, 4, 0}, {5, 5, 0}}});
+    auto l1 = mm->add_parameter("1", migraphx::shape{migraphx::shape::float_type, {4, 1}});
+
+    auto bl1 = mm->add_instruction(
+        migraphx::make_op("multibroadcast",
+                          {{"out_dyn_dims", to_value(l0->get_shape().dyn_dims())}}),
+        l1,
+        l0);
+    auto ret = mm->add_instruction(migraphx::make_op("mul"), l0, bl1);
+    mm->add_return({ret});
+
+    migraphx::onnx_options options;
+    options.default_dyn_dim_value = {1, 4, 0};
+    auto prog                     = migraphx::parse_onnx("binary_dyn_brcst_mul_test.onnx", options);
+
+    EXPECT(p == prog);
+}
+
 TEST_CASE(cast_test)
 {
     migraphx::program p;