manual merge

src/targets/gpu/kernels/include/migraphx/kernels/concat.hpp

@@ -41,7 +41,15 @@ constexpr auto concat_slice(Output out, Input, Start)
         return Start{} * output_shape.strides[Axis];
     });
     constexpr auto s       = make_shape(lens, strides);
-    return make_tensor_view(&out[offset], s);
+    MIGRAPHX_ASSERT(offset < out.get_shape().element_space());
+    MIGRAPHX_ASSERT((s.element_space() + offset) <= out.get_shape().element_space());
+    return make_tensor_view(out.data() + offset, s);
+}
+
+template <index_int Axis, class Input, class Start, class... Ts>
+constexpr auto concat_slices(Input input, Start start, Ts... xs)
+{
+    return [=](auto f) { f(concat_slice<Axis>(xs, input, start)...); };
 }
 
 template <index_int Axis, class Input>
@@ -51,15 +59,19 @@ constexpr auto concat_ends(Input)
     return _c<lens[Axis]>;
 }
 
-template <index_int Axis, class Output, class... Inputs>
-__device__ void concat(Output output, Inputs... inputs)
+template <index_int Axis, class... Inputs>
+__device__ auto concat(Inputs... inputs)
 {
-    auto idx = make_index();
-    fold([&](auto start, auto input) {
-        auto y = concat_slice<Axis>(output, input, start);
-        idx.global_stride(input.get_shape().elements(), [&](auto i) { y[i] = input[i]; });
-        return start + concat_ends<Axis>(input);
-    })(_c<0>, inputs...);
+    return [=](auto f, auto... ts) {
+        auto idx = make_index();
+        fold([&](auto start, auto input) {
+            concat_slices<Axis>(input, start, ts...)([&](auto y, auto... xs) {
+                idx.global_stride(input.get_shape().elements(),
+                                  [&](auto i) { y[i] = f(input[i], xs[i]...); });
+            });
+            return start + concat_ends<Axis>(input);
+        })(_c<0>, inputs...);
+    };
 }
 
 } // namespace migraphx

src/targets/gpu/mlir.cpp

@@ -101,7 +101,10 @@ struct mlir_handle
 
     mlir_handle(T p) : handle(ptr{p}) {}
 
-    T get() const { return handle.get().get(); }
+    T get() const
+    {
+        return handle.get().get(); // NOLINT(readability-redundant-smartptr-get)
+    }
 
     T release() { return handle.release().get(); }
 

test/onnx/gen_onnx.py

@@ -420,6 +420,74 @@ 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_add_test():
+    arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT16, [4, 5])
+    arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT,
+                                         [None, 3, 4, 5])
+    arg_out = helper.make_tensor_value_info('out', TensorProto.FLOAT,
+                                            [None, 3, 4, 5])
+
+    node = onnx.helper.make_node(
+        'Add',
+        inputs=['0', '1'],
+        outputs=['out'],
+    )
+
+    return ([node], [arg0, arg1], [arg_out])
+
+
+@onnx_test
+def binary_dyn_brcst_attr_error_test():
+    arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT16, [4, 5])
+    arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT,
+                                         [None, 3, 4, 5])
+    arg_out = helper.make_tensor_value_info('out', TensorProto.FLOAT,
+                                            [None, 3, 4, 5])
+
+    node = onnx.helper.make_node('Add',
+                                 inputs=['0', '1'],
+                                 outputs=['out'],
+                                 broadcast=1,
+                                 axis=1)
+
+    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])
@@ -5687,6 +5755,92 @@ def split_test_default():
     return ([node], [x], [y1, y2])
 
 
+@onnx_test
+def split_test_no_attribute():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [300, 15])
+    y1 = helper.make_tensor_value_info('y1', TensorProto.FLOAT, [75, 15])
+    y2 = helper.make_tensor_value_info('y2', TensorProto.FLOAT, [75, 15])
+    y3 = helper.make_tensor_value_info('y3', TensorProto.FLOAT, [75, 15])
+    y4 = helper.make_tensor_value_info('y4', TensorProto.FLOAT, [75, 15])
+
+    split = np.ones(4) * 75
+    split_tensor = helper.make_tensor(name="split",
+                                      data_type=TensorProto.INT64,
+                                      dims=split.shape,
+                                      vals=split.astype(np.int64))
+    const_node = helper.make_node("Constant",
+                                  inputs=[],
+                                  outputs=['split'],
+                                  value=split_tensor)
+
+    node = onnx.helper.make_node(
+        'Split',
+        inputs=['x', 'split'],
+        outputs=['y1', 'y2', 'y3', 'y4'],
+    )
+
+    return ([const_node, node], [x], [y1, y2, y3, y4])
+
+
+@onnx_test
+def split_test_no_attribute_invalid_split():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [300, 15])
+    y1 = helper.make_tensor_value_info('y1', TensorProto.FLOAT, [75, 15])
+    y2 = helper.make_tensor_value_info('y2', TensorProto.FLOAT, [75, 15])
+    y3 = helper.make_tensor_value_info('y3', TensorProto.FLOAT, [75, 15])
+    y4 = helper.make_tensor_value_info('y4', TensorProto.FLOAT, [75, 15])
+
+    split = np.ones(4)
+    split_tensor = helper.make_tensor(name="split",
+                                      data_type=TensorProto.INT64,
+                                      dims=split.shape,
+                                      vals=split.astype(np.int64))
+    const_node = helper.make_node("Constant",
+                                  inputs=[],
+                                  outputs=['split'],
+                                  value=split_tensor)
+
+    node = onnx.helper.make_node(
+        'Split',
+        inputs=['x', 'split'],
+        outputs=['y1', 'y2', 'y3', 'y4'],
+    )
+
+    return ([const_node, node], [x], [y1, y2, y3, y4])
+
+
+@onnx_test
+def split_test_invalid_split():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10, 15])
+    y1 = helper.make_tensor_value_info('y1', TensorProto.FLOAT, [10, 7])
+    y2 = helper.make_tensor_value_info('y2', TensorProto.FLOAT, [10, 4])
+    y3 = helper.make_tensor_value_info('y3', TensorProto.FLOAT, [10, 4])
+
+    node = onnx.helper.make_node('Split',
+                                 inputs=['x'],
+                                 outputs=['y1', 'y2', 'y3'],
+                                 axis=1,
+                                 split=[1, 1, 1])
+
+    return ([node], [x], [y1, y2, y3])
+
+
+@onnx_test
+def split_test_no_attribute_invalid_input_split():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10, 15])
+    y1 = helper.make_tensor_value_info('y1', TensorProto.FLOAT, [10, 7])
+    y2 = helper.make_tensor_value_info('y2', TensorProto.FLOAT, [10, 4])
+    y3 = helper.make_tensor_value_info('y3', TensorProto.FLOAT, [10, 4])
+
+    node = onnx.helper.make_node('Split',
+                                 inputs=['x'],
+                                 outputs=['y1', 'y2', 'y3'],
+                                 axis=1,
+                                 split=[])
+
+    return ([node], [x], [y1, y2, y3])
+
+
 @onnx_test
 def sqrt_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10, 15])

test/onnx/onnx_test.cpp

@@ -521,6 +521,76 @@ 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_add_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    auto l0  = mm->add_parameter("0", migraphx::shape{migraphx::shape::half_type, {4, 5}});
+    auto l1  = mm->add_parameter(
+        "1",
+        migraphx::shape{migraphx::shape::float_type, {{1, 4, 0}, {3, 3, 0}, {4, 4, 0}, {5, 5, 0}}});
+
+    auto ret = add_common_op(*mm, migraphx::make_op("add"), {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_add_test.onnx", options);
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(binary_dyn_brcst_attr_error_test)
+{
+    migraphx::onnx_options options;
+    options.default_dyn_dim_value = {1, 4, 0};
+    EXPECT(test::throws(
+        [&] { migraphx::parse_onnx("binary_dyn_brcst_attr_error_test.onnx", options); }));
+}
+
+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;
@@ -5537,6 +5607,31 @@ TEST_CASE(split_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(split_test_no_attribute)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+
+    migraphx::shape si{migraphx::shape::int64_type, {4}, {1}};
+    std::vector<int> ind = {75, 75, 75, 75};
+
+    auto input = mm->add_parameter("x", migraphx::shape{migraphx::shape::float_type, {300, 15}});
+    mm->add_literal(migraphx::literal(si, ind));
+    auto r1 = mm->add_instruction(
+        migraphx::make_op("slice", {{"axes", {0}}, {"starts", {0}}, {"ends", {75}}}), input);
+    auto r2 = mm->add_instruction(
+        migraphx::make_op("slice", {{"axes", {0}}, {"starts", {75}}, {"ends", {150}}}), input);
+    auto r3 = mm->add_instruction(
+        migraphx::make_op("slice", {{"axes", {0}}, {"starts", {150}}, {"ends", {225}}}), input);
+    auto r4 = mm->add_instruction(
+        migraphx::make_op("slice", {{"axes", {0}}, {"starts", {225}}, {"ends", {300}}}), input);
+
+    mm->add_return({r1, r2, r3, r4});
+
+    auto prog = migraphx::parse_onnx("split_test_no_attribute.onnx");
+    EXPECT(p == prog);
+}
+
 TEST_CASE(split_test_default)
 {
     migraphx::program p;
@@ -5552,6 +5647,23 @@ TEST_CASE(split_test_default)
     EXPECT(p == prog);
 }
 
+TEST_CASE(split_test_no_attribute_invalid_split)
+{
+    EXPECT(
+        test::throws([&] { migraphx::parse_onnx("split_test_no_attribute_invalid_split.onnx"); }));
+}
+
+TEST_CASE(split_test_invalid_split)
+{
+    EXPECT(test::throws([&] { migraphx::parse_onnx("split_test_invalid_split.onnx"); }));
+}
+
+TEST_CASE(split_test_no_attribute_invalid_input_split)
+{
+    EXPECT(test::throws(
+        [&] { migraphx::parse_onnx("split_test_no_attribute_invalid_input_split.onnx"); }));
+}
+
 TEST_CASE(sqrt_test)
 {
     migraphx::program p;

test/onnx/split_test_invalid_split.onnx

+split_test_invalid_split:

+5
+
xy1y2y3"Split*
+axis

*
+split@
@
@

split_test_invalid_splitZ
+
x
+

+
+
+b
+y1
+

+
+
+b
+y2
+

+
+
+b
+y3
+

+
+
+B
\ No newline at end of file

test/onnx/split_test_no_attribute.onnx

+split_test_no_attribute:

+0split"Constant*
+value*:KKKKBsplit

+!
+
x
+splity1y2y3y4"Splitsplit_test_no_attributeZ
+
x
+
	
+
+b
+y1
+

+K
+b
+y2
+

+K
+b
+y3
+

+K
+b
+y4
+

+K
+B
\ No newline at end of file

test/onnx/split_test_no_attribute_invalid_input_split.onnx

++split_test_no_attribute_invalid_input_split:

+/
+
xy1y2y3"Split*
+axis

*
+
+split
+split_test_no_attribute_invalid_input_splitZ
+
x
+

+
+
+b
+y1
+

+
+
+b
+y2
+

+
+
+b
+y3
+

+
+
+B
\ No newline at end of file

test/onnx/split_test_no_attribute_invalid_split.onnx

+%split_test_no_attribute_invalid_split:

+0split"Constant*
+value*:



Bsplit

+!
+
x
+splity1y2y3y4"Split%split_test_no_attribute_invalid_splitZ
+
x
+
	
+
+b
+y1
+

+K
+b
+y2
+

+K
+b
+y3
+

+K
+b
+y4
+

+K
+B
\ No newline at end of file

test/op_shape_test.cpp

@@ -81,6 +81,14 @@ void throws_shape(const migraphx::shape&, Ts...)
                   "An expected shape should not be passed to throws_shape function");
 }
 
+TEST_CASE(binary_dyn_static_error)
+{
+    migraphx::shape a_shape{migraphx::shape::float_type, {1, 4, 4}};
+    std::vector<migraphx::shape::dynamic_dimension> b{{1, 1, 0}, {4, 4, 4}, {4, 4, 0}};
+    migraphx::shape b_shape{migraphx::shape::float_type, b};
+    throws_shape(migraphx::make_op("add"), a_shape, b_shape);
+}
+
 TEST_CASE(broadcast)
 {
     {
@@ -118,6 +126,69 @@ TEST_CASE(broadcast)
     }
 }
 
+TEST_CASE(broadcast_axis_out_of_range_error)
+{
+    std::vector<std::size_t> lens{1, 1};
+    migraphx::shape input{migraphx::shape::float_type, {1}, {0}};
+    throws_shape(migraphx::make_op("broadcast", {{"axis", 4}, {"out_lens", lens}}), input);
+}
+
+TEST_CASE(broadcast_2in_static_static)
+{
+    migraphx::shape a_input{migraphx::shape::float_type, {4}, {1}};
+    migraphx::shape b_input{migraphx::shape::float_type, {4, 4}, {4, 1}};
+    expect_shape(migraphx::shape{migraphx::shape::float_type, {4, 4}, {1, 0}},
+                 migraphx::make_op("broadcast", {{"axis", 0}}),
+                 a_input,
+                 b_input);
+    expect_shape(migraphx::shape{migraphx::shape::float_type, {4, 4}, {0, 1}},
+                 migraphx::make_op("broadcast", {{"axis", 1}}),
+                 a_input,
+                 b_input);
+    throws_shape(migraphx::make_op("broadcast", {{"axis", 2}}), a_input, b_input);
+}
+
+TEST_CASE(broadcast_2in_not_matching_error)
+{
+    migraphx::shape a_input{migraphx::shape::float_type, {4}, {1}};
+    migraphx::shape b_input{migraphx::shape::float_type, {2, 2}, {2, 1}};
+    throws_shape(migraphx::make_op("broadcast", {{"axis", 1}}), a_input, b_input);
+}
+
+TEST_CASE(broadcast_2in_dynamic_s0_error1)
+{
+    migraphx::shape a_input{migraphx::shape::float_type, {4, 2}, {2, 1}};
+    migraphx::shape b_input{migraphx::shape::float_type, {{1, 4, 0}, {4, 4, 0}, {2, 2, 0}}};
+    throws_shape(migraphx::make_op("broadcast", {{"axis", 0}}), b_input, a_input);
+}
+
+TEST_CASE(broadcast_2in_dynamic_s0_error2)
+{
+    std::vector<migraphx::shape::dynamic_dimension> dd{{4, 4, 0}};
+    migraphx::shape a_input{migraphx::shape::float_type, dd};
+    migraphx::shape b_input{migraphx::shape::float_type, {4, 4}, {4, 1}};
+    throws_shape(migraphx::make_op("broadcast", {{"axis", 0}}), a_input, b_input);
+}
+
+TEST_CASE(broadcast_2in_static_dyn)
+{
+    migraphx::shape a_input{migraphx::shape::float_type, {4}, {1}};
+    migraphx::shape b_input{migraphx::shape::float_type, {{1, 4, 0}, {4, 4, 0}, {2, 2, 0}}};
+    throws_shape(migraphx::make_op("broadcast", {{"axis", 0}}), a_input, b_input);
+    expect_shape(migraphx::shape{migraphx::shape::float_type, {{1, 4, 0}, {4, 4, 0}, {2, 2, 0}}},
+                 migraphx::make_op("broadcast", {{"axis", 1}}),
+                 a_input,
+                 b_input);
+    throws_shape(migraphx::make_op("broadcast", {{"axis", 2}}), a_input, b_input);
+}
+
+TEST_CASE(broadcast_2in_dyn_s0_ndim_greater_than_1_error)
+{
+    migraphx::shape a_input{migraphx::shape::float_type, {4, 2}};
+    migraphx::shape b_input{migraphx::shape::float_type, {{1, 4, 0}, {4, 4, 0}, {2, 2, 0}}};
+    throws_shape(migraphx::make_op("broadcast", {{"axis", 0}}), a_input, b_input);
+}
+
 TEST_CASE(convolution_shape)
 {
     migraphx::shape output{migraphx::shape::float_type, {4, 4, 1, 1}};
@@ -1114,6 +1185,213 @@ TEST_CASE(multibroadcast)
     }
 }
 
+TEST_CASE(multibroadcast_2in_static_dyn0)
+{
+    migraphx::shape a_shape{migraphx::shape::float_type, {4, 4}};
+    std::vector<migraphx::shape::dynamic_dimension> b{{1, 4, 0}, {4, 4, 4}, {4, 4, 0}};
+    migraphx::shape b_shape{migraphx::shape::float_type, b};
+    expect_shape(migraphx::shape{migraphx::shape::float_type, {{1, 4, 0}, {4, 4, 0}, {4, 4, 0}}},
+                 migraphx::make_op("multibroadcast"),
+                 a_shape,
+                 b_shape);
+    expect_shape(migraphx::shape{migraphx::shape::float_type, {{1, 4, 0}, {4, 4, 0}, {4, 4, 0}}},
+                 migraphx::make_op("multibroadcast"),
+                 b_shape,
+                 a_shape);
+}
+
+TEST_CASE(multibroadcast_2in_static_dyn1)
+{
+    migraphx::shape a_shape{migraphx::shape::float_type, {1, 6}};
+    std::vector<migraphx::shape::dynamic_dimension> b{{8, 8, 0}, {6, 6, 0}};
+    migraphx::shape b_shape{migraphx::shape::float_type, b};
+    expect_shape(migraphx::shape{migraphx::shape::float_type, {{8, 8, 0}, {6, 6, 0}}},
+                 migraphx::make_op("multibroadcast"),
+                 a_shape,
+                 b_shape);
+    expect_shape(migraphx::shape{migraphx::shape::float_type, {{8, 8, 0}, {6, 6, 0}}},
+                 migraphx::make_op("multibroadcast"),
+                 b_shape,
+                 a_shape);
+}
+
+TEST_CASE(multibroadcast_2in_static_dyn2)
+{
+    migraphx::shape a_shape{migraphx::shape::float_type, {1, 6}};
+    std::vector<migraphx::shape::dynamic_dimension> b{{8, 8, 0}, {6, 6, 0}};
+    migraphx::shape b_shape{migraphx::shape::float_type, b};
+    expect_shape(migraphx::shape{migraphx::shape::float_type, {{8, 8, 0}, {6, 6, 0}}},
+                 migraphx::make_op("multibroadcast", {{"out_dyn_dims", migraphx::to_value(b)}}),
+                 a_shape,
+                 b_shape);
+    expect_shape(migraphx::shape{migraphx::shape::float_type, {{8, 8, 0}, {6, 6, 0}}},
+                 migraphx::make_op("multibroadcast", {{"out_dyn_dims", migraphx::to_value(b)}}),
+                 b_shape,
+                 a_shape);
+}
+
+TEST_CASE(multibroadcast_2in_static_dyn_error0)
+{
+    // doesn't match on first dimension
+    migraphx::shape a_shape{migraphx::shape::float_type, {3, 6}};
+    std::vector<migraphx::shape::dynamic_dimension> b{{1, 3, 0}, {6, 6, 0}};
+    migraphx::shape b_shape{migraphx::shape::float_type, b};
+    throws_shape(migraphx::make_op("multibroadcast"), a_shape, b_shape);
+    throws_shape(migraphx::make_op("multibroadcast"), b_shape, a_shape);
+}
+
+TEST_CASE(multibroadcast_2in_static_dyn_error1)
+{
+    // doesn't match on first dimension
+    migraphx::shape a_shape{migraphx::shape::float_type, {3, 6}};
+    std::vector<migraphx::shape::dynamic_dimension> b{{1, 4, 0}, {6, 6, 0}};
+    migraphx::shape b_shape{migraphx::shape::float_type, b};
+    throws_shape(migraphx::make_op("multibroadcast"), a_shape, b_shape);
+    throws_shape(migraphx::make_op("multibroadcast"), b_shape, a_shape);
+}
+
+TEST_CASE(multibroadcast_2in_static_dyn_error2)
+{
+    // doesn't match on first dimension
+    migraphx::shape a_shape{migraphx::shape::float_type, {3, 6}};
+    std::vector<migraphx::shape::dynamic_dimension> b{{1, 2, 0}, {6, 6, 0}};
+    migraphx::shape b_shape{migraphx::shape::float_type, b};
+    throws_shape(migraphx::make_op("multibroadcast"), a_shape, b_shape);
+    throws_shape(migraphx::make_op("multibroadcast"), b_shape, a_shape);
+}
+
+TEST_CASE(multibroadcast_2in_dyn_dyn0)
+{
+    std::vector<migraphx::shape::dynamic_dimension> a{{1, 4, 0}, {2, 4, 2}, {2, 4, 0}};
+    migraphx::shape a_shape{migraphx::shape::float_type, a};
+    std::vector<migraphx::shape::dynamic_dimension> b{{2, 4, 2}, {2, 4, 0}};
+    migraphx::shape b_shape{migraphx::shape::float_type, b};
+    expect_shape(migraphx::shape{migraphx::shape::float_type, {{1, 4, 0}, {2, 4, 2}, {2, 4, 0}}},
+                 migraphx::make_op("multibroadcast"),
+                 a_shape,
+                 b_shape);
+    expect_shape(migraphx::shape{migraphx::shape::float_type, {{1, 4, 0}, {2, 4, 2}, {2, 4, 0}}},
+                 migraphx::make_op("multibroadcast"),
+                 b_shape,
+                 a_shape);
+}
+
+TEST_CASE(multibroadcast_2in_dyn_dyn1)
+{
+    std::vector<migraphx::shape::dynamic_dimension> a{{1, 4, 0}, {2, 4, 2}, {2, 4, 0}};
+    migraphx::shape a_shape{migraphx::shape::float_type, a};
+    std::vector<migraphx::shape::dynamic_dimension> b{{2, 4, 2}, {2, 4, 0}};
+    migraphx::shape b_shape{migraphx::shape::float_type, b};
+    expect_shape(migraphx::shape{migraphx::shape::float_type, {{1, 4, 0}, {2, 4, 2}, {2, 4, 0}}},
+                 migraphx::make_op("multibroadcast", {{"out_dyn_dims", migraphx::to_value(a)}}),
+                 a_shape,
+                 b_shape);
+    expect_shape(migraphx::shape{migraphx::shape::float_type, {{1, 4, 0}, {2, 4, 2}, {2, 4, 0}}},
+                 migraphx::make_op("multibroadcast", {{"out_dyn_dims", migraphx::to_value(a)}}),
+                 b_shape,
+                 a_shape);
+}
+
+TEST_CASE(multibroadcast_2in_dyn_dyn_error0)
+{
+    // max doesn't match on second dimension of a
+    std::vector<migraphx::shape::dynamic_dimension> a{{1, 4, 0}, {2, 4, 2}, {2, 4, 0}};
+    migraphx::shape a_shape{migraphx::shape::float_type, a};
+    std::vector<migraphx::shape::dynamic_dimension> b{{2, 5, 2}, {2, 4, 0}};
+    migraphx::shape b_shape{migraphx::shape::float_type, b};
+    throws_shape(migraphx::make_op("multibroadcast"), a_shape, b_shape);
+    throws_shape(migraphx::make_op("multibroadcast"), b_shape, a_shape);
+}
+
+TEST_CASE(multibroadcast_2in_dyn_dyn_error1)
+{
+    // opt doesn't match on second dimension of a
+    std::vector<migraphx::shape::dynamic_dimension> a{{1, 4, 0}, {2, 4, 2}, {2, 4, 0}};
+    migraphx::shape a_shape{migraphx::shape::float_type, a};
+    std::vector<migraphx::shape::dynamic_dimension> b{{2, 4, 3}, {2, 4, 0}};
+    migraphx::shape b_shape{migraphx::shape::float_type, b};
+    throws_shape(migraphx::make_op("multibroadcast"), a_shape, b_shape);
+    throws_shape(migraphx::make_op("multibroadcast"), b_shape, a_shape);
+}
+
+TEST_CASE(multibroadcast_2in_static_static0)
+{
+    migraphx::shape a_shape{migraphx::shape::float_type, {3, 6}};
+    migraphx::shape b_shape{migraphx::shape::float_type, {3, 6}};
+    expect_shape(migraphx::shape{migraphx::shape::float_type, {3, 6}},
+                 migraphx::make_op("multibroadcast"),
+                 a_shape,
+                 b_shape);
+    expect_shape(migraphx::shape{migraphx::shape::float_type, {3, 6}},
+                 migraphx::make_op("multibroadcast"),
+                 b_shape,
+                 a_shape);
+}
+
+TEST_CASE(multibroadcast_2in_static_static1)
+{
+    migraphx::shape a_shape{migraphx::shape::float_type, {1, 8}};
+    migraphx::shape b_shape{migraphx::shape::float_type, {4, 8}};
+    expect_shape(migraphx::shape{migraphx::shape::float_type, {4, 8}, {0, 1}},
+                 migraphx::make_op("multibroadcast"),
+                 a_shape,
+                 b_shape);
+    expect_shape(migraphx::shape{migraphx::shape::float_type, {4, 8}, {8, 1}},
+                 migraphx::make_op("multibroadcast"),
+                 b_shape,
+                 a_shape);
+}
+
+TEST_CASE(multibroadcast_2in_static_static2)
+{
+    migraphx::shape a_shape{migraphx::shape::float_type, {8}};
+    migraphx::shape b_shape{migraphx::shape::float_type, {4, 4, 1}};
+    expect_shape(migraphx::shape{migraphx::shape::float_type, {4, 4, 8}, {0, 0, 1}},
+                 migraphx::make_op("multibroadcast"),
+                 a_shape,
+                 b_shape);
+    expect_shape(migraphx::shape{migraphx::shape::float_type, {4, 4, 8}, {4, 1, 0}},
+                 migraphx::make_op("multibroadcast"),
+                 b_shape,
+                 a_shape);
+}
+
+TEST_CASE(multibroadcast_2in_static_static3)
+{
+    migraphx::shape a_shape{migraphx::shape::float_type, {3, 4, 4}};
+    migraphx::shape b_shape{migraphx::shape::float_type, {4, 1}};
+    expect_shape(migraphx::shape{migraphx::shape::float_type, {3, 4, 4}, {16, 4, 1}},
+                 migraphx::make_op("multibroadcast"),
+                 a_shape,
+                 b_shape);
+    expect_shape(migraphx::shape{migraphx::shape::float_type, {3, 4, 4}, {0, 1, 0}},
+                 migraphx::make_op("multibroadcast"),
+                 b_shape,
+                 a_shape);
+}
+
+TEST_CASE(multibroadcast_2in_static_static4)
+{
+    migraphx::shape a_shape{migraphx::shape::float_type, {3, 1, 4}};
+    migraphx::shape b_shape{migraphx::shape::float_type, {4, 1}};
+    expect_shape(migraphx::shape{migraphx::shape::float_type, {3, 4, 4}, {4, 0, 1}},
+                 migraphx::make_op("multibroadcast"),
+                 a_shape,
+                 b_shape);
+    expect_shape(migraphx::shape{migraphx::shape::float_type, {3, 4, 4}, {0, 1, 0}},
+                 migraphx::make_op("multibroadcast"),
+                 b_shape,
+                 a_shape);
+}
+
+TEST_CASE(multibroadcast_2in_static_static_error0)
+{
+    migraphx::shape a_shape{migraphx::shape::float_type, {3, 4, 4}};
+    migraphx::shape b_shape{migraphx::shape::float_type, {4, 3}};
+    throws_shape(migraphx::make_op("multibroadcast"), a_shape, b_shape);
+    throws_shape(migraphx::make_op("multibroadcast"), b_shape, a_shape);
+}
+
 TEST_CASE(multinomial)
 {
     migraphx::shape s{migraphx::shape::float_type, {2, 5}};

test/shape_test.cpp

@@ -38,6 +38,27 @@ TEST_CASE(test_shape_default)
     EXPECT(s.elements() == 0);
     EXPECT(s.bytes() == 0);
 }
+
+TEST_CASE(test_dyn_4arg_constructor)
+{
+    migraphx::shape s{migraphx::shape::float_type,
+                      {
+                          1,
+                          4,
+                          4,
+                      },
+                      {
+                          4,
+                          4,
+                          4,
+                      },
+                      {0, 0, 0}};
+    std::vector<migraphx::shape::dynamic_dimension> expected_dyn_dims = {
+        {1, 4, 0}, {4, 4, 0}, {4, 4, 0}};
+    EXPECT(s.dynamic());
+    EXPECT(s.dyn_dims() == expected_dyn_dims);
+}
+
 TEST_CASE(test_shape_assign)
 {
     migraphx::shape s1{migraphx::shape::float_type, {100, 32, 8, 8}};
@@ -185,6 +206,31 @@ TEST_CASE(test_shape_packed)
     EXPECT(not s.broadcasted());
 }
 
+TEST_CASE(test_shape_ndim_static)
+{
+    migraphx::shape s0{migraphx::shape::float_type, {2, 2}};
+    EXPECT(s0.ndim() == 2);
+
+    migraphx::shape s1{migraphx::shape::float_type, {1, 2, 4, 4}};
+    EXPECT(s1.ndim() == 4);
+
+    migraphx::shape s2{migraphx::shape::float_type, {2, 4, 4, 1, 3}};
+    EXPECT(s2.ndim() == 5);
+}
+
+TEST_CASE(test_shape_ndim_dyn)
+{
+    migraphx::shape s0{migraphx::shape::float_type, {{2, 2, 0}, {2, 2, 0}}};
+    EXPECT(s0.ndim() == 2);
+
+    migraphx::shape s1{migraphx::shape::float_type, {{1, 1, 0}, {2, 4, 0}, {2, 4, 0}, {2, 4, 0}}};
+    EXPECT(s1.ndim() == 4);
+
+    migraphx::shape s2{migraphx::shape::float_type,
+                       {{1, 1, 0}, {2, 4, 0}, {2, 4, 0}, {1, 1, 1}, {3, 3, 0}}};
+    EXPECT(s2.ndim() == 5);
+}
+
 TEST_CASE(test_shape_non_packed_single_dim)
 {
     migraphx::shape s{migraphx::shape::float_type, {1, 64, 35, 35}, {156800, 1225, 35, 1}};
@@ -212,6 +258,21 @@ TEST_CASE(test_shape_transposed2)
     EXPECT(not s.broadcasted());
 }
 
+TEST_CASE(test_shape_static_to_dynamic)
+{
+    migraphx::shape s0{migraphx::shape::float_type, {1, 2, 4, 4}};
+    migraphx::shape s1 = s0.to_dynamic();
+    migraphx::shape s2{migraphx::shape::float_type, {{1, 1, 0}, {2, 2, 0}, {4, 4, 0}, {4, 4, 0}}};
+    EXPECT(s1 == s2);
+}
+
+TEST_CASE(test_shape_dyn_to_dynamic)
+{
+    migraphx::shape s0{migraphx::shape::float_type, {{1, 1, 0}, {2, 4, 0}, {2, 4, 0}, {2, 4, 0}}};
+    migraphx::shape s1 = s0.to_dynamic();
+    EXPECT(s0 == s1);
+}
+
 TEST_CASE(test_shape_overlap)
 {
     migraphx::shape s{migraphx::shape::float_type, {2, 2, 3}, {6, 3, 2}};

test/verify/test_concat_broadcast_add.cpp

+/*
+ * The MIT License (MIT)
+ *
+ * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+ * THE SOFTWARE.
+ */
+
+#include "verify_program.hpp"
+#include <migraphx/program.hpp>
+#include <migraphx/generate.hpp>
+#include <migraphx/make_op.hpp>
+
+struct test_concat_broadcast_add : verify_program<test_concat_broadcast_add>
+{
+    migraphx::program create_program() const
+    {
+        migraphx::program p;
+        auto* mm = p.get_main_module();
+        migraphx::shape s0{migraphx::shape::float_type, {1, 2, 4}};
+        migraphx::shape s1{migraphx::shape::float_type, {1, 6, 4}};
+        migraphx::shape s2{migraphx::shape::float_type, {6, 1}};
+        auto x      = mm->add_parameter("x", s0);
+        auto y      = mm->add_parameter("y", s0);
+        auto z      = mm->add_parameter("z", s0);
+        auto concat = mm->add_instruction(migraphx::make_op("concat", {{"axis", 1}}), x, y, z);
+        auto b      = mm->add_literal(migraphx::generate_literal(s2, 15));
+        auto bb =
+            mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", s1.lens()}}), b);
+        mm->add_instruction(migraphx::make_op("add"), concat, bb);
+        return p;
+    }
+};

test/verify/test_slice_concat_add.cpp

+/*
+ * The MIT License (MIT)
+ *
+ * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+ * THE SOFTWARE.
+ */
+
+#include "verify_program.hpp"
+#include <migraphx/program.hpp>
+#include <migraphx/generate.hpp>
+#include <migraphx/make_op.hpp>
+
+struct test_slice_concat_add : verify_program<test_slice_concat_add>
+{
+    migraphx::program create_program() const
+    {
+        migraphx::program p;
+        auto* mm = p.get_main_module();
+        migraphx::shape s0{migraphx::shape::float_type, {1, 24, 2, 2}};
+        migraphx::shape s1{migraphx::shape::float_type, {1, 8, 2, 2}};
+        auto x     = mm->add_parameter("x", s0);
+        auto y     = mm->add_parameter("y", s1);
+        auto z     = mm->add_parameter("z", s0);
+        auto slice = mm->add_instruction(
+            migraphx::make_op("slice", {{"axes", {1}}, {"starts", {0}}, {"ends", {8}}}), x);
+        auto concat = mm->add_instruction(migraphx::make_op("concat", {{"axis", 1}}), slice, y, y);
+        mm->add_instruction(migraphx::make_op("add"), concat, z);
+        return p;
+    }
+};

tools/convert_onnx_version.py

+#####################################################################################
+# The MIT License (MIT)
+#
+# Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+# THE SOFTWARE.
+#####################################################################################
+import argparse
+import onnx
+from onnx import version_converter
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description=
+        'MIGraphX Onnx Model Convertion. Use to convert the opset of the input model to MIGraphX\'s'
+    )
+    req_args = parser.add_argument_group(title='required arguments')
+    req_args.add_argument('--model',
+                          type=str,
+                          required=True,
+                          help='path to onnx file')
+    req_args.add_argument('--output',
+                          type=str,
+                          required=True,
+                          help='path to output onnx file')
+    req_args.add_argument('--opset',
+                          type=int,
+                          required=True,
+                          help='The output opset')
+    req_args.add_argument('--infer_shapes',
+                          action='store_true',
+                          help='Infer shapes for output model')
+    parser.add_argument('--verbose',
+                        action='store_true',
+                        help='show verbose information (for debugging)')
+    args = parser.parse_args()
+
+    return args
+
+
+def main():
+    args = parse_args()
+
+    model_path = args.model
+    out_model_path = args.output
+    target_opset = args.opset
+    verbose = args.verbose
+    infer_shapes = args.infer_shapes
+
+    original_model = onnx.load(model_path)
+    if verbose:
+        print(f"The model before conversion:\n{original_model}")
+
+    # A full list of supported adapters can be found here:
+    # https://github.com/onnx/onnx/blob/main/onnx/version_converter.py#L21
+    # Apply the version conversion on the original model
+    converted_model = version_converter.convert_version(
+        original_model, target_opset)
+
+    if infer_shapes:
+        converted_model = onnx.shape_inference.infer_shapes(converted_model)
+
+    if verbose:
+        print(f"The model after conversion:\n{converted_model}")
+
+    # Save the ONNX model
+    onnx.save(converted_model, out_model_path)
+
+
+if __name__ == '__main__':
+    main()