Merge branch 'develop' into fastsoftmax

test/onnx/gen_onnx.py

@@ -626,6 +626,46 @@ def constant_scalar_test():
     return ([node], [], [y])
 
 
+@onnx_test
+def constant_empty_scalar_int64_test():
+    x = np.array([]).astype(np.int64)
+    y = helper.make_tensor_value_info('0', TensorProto.INT64, [0])
+
+    node = onnx.helper.make_node(
+        'Constant',
+        inputs=[],
+        outputs=['0'],
+        value=onnx.helper.make_tensor(
+            name='one_element_tensor',
+            data_type=TensorProto.INT64,
+            dims=x.shape,
+            vals=x.flatten().astype(np.int64),
+        ),
+    )
+
+    return ([node], [], [y])
+
+
+@onnx_test
+def constant_one_val_int64_test():
+    x = np.array([1]).astype(np.int64)
+    y = helper.make_tensor_value_info('0', TensorProto.INT64, [0])
+
+    node = onnx.helper.make_node(
+        'Constant',
+        inputs=[],
+        outputs=['0'],
+        value=onnx.helper.make_tensor(
+            name='empty_tensor',
+            data_type=TensorProto.INT64,
+            dims=x.shape,
+            vals=x.flatten().astype(np.int64),
+        ),
+    )
+
+    return ([node], [], [y])
+
+
 @onnx_test
 def const_of_shape_empty_input_test():
     tensor_val = onnx.helper.make_tensor('value', onnx.TensorProto.INT64, [1],
@@ -851,6 +891,96 @@ def conv_bn_relu_maxpool_test():
     return ([node0, node1, node2, node3], [x, y, z, m, n, k, l], [out])
 
 
+@onnx_test
+def conv_dynamic_batch_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [None, 3, 5, 5])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 3, 3])
+    out = helper.make_tensor_value_info('2', TensorProto.FLOAT,
+                                        [None, 1, 3, 3])
+
+    node = onnx.helper.make_node('Conv', inputs=['0', '1'], outputs=['2'])
+    return ([node], [x, y], [out])
+
+
+@onnx_test
+def conv_dynamic_img_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT,
+                                      [1, 3, None, None])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 3, 3])
+    out = helper.make_tensor_value_info('2', TensorProto.FLOAT,
+                                        [1, 1, None, None])
+
+    node = onnx.helper.make_node('Conv', inputs=['0', '1'], outputs=['2'])
+    return ([node], [x, y], [out])
+
+
+@onnx_test
+def conv_dynamic_weights_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 5, 5])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT,
+                                      [1, 3, None, None])
+    out = helper.make_tensor_value_info('2', TensorProto.FLOAT,
+                                        [1, 1, None, None])
+
+    node = onnx.helper.make_node('Conv', inputs=['0', '1'], outputs=['2'])
+    return ([node], [x, y], [out])
+
+
+@onnx_test
+def conv_dynamic_img_and_weights_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT,
+                                      [1, 3, None, None])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT,
+                                      [1, 3, None, None])
+    out = helper.make_tensor_value_info('2', TensorProto.FLOAT,
+                                        [1, 1, None, None])
+
+    node = onnx.helper.make_node('Conv', inputs=['0', '1'], outputs=['2'])
+    return ([node], [x, y], [out])
+
+
+@onnx_test
+def conv_dynamic_batch_same_upper_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [None, 3, 5, 5])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 3, 3])
+    out = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 1, 5, 5])
+
+    node = onnx.helper.make_node('Conv',
+                                 inputs=['0', '1'],
+                                 outputs=['2'],
+                                 auto_pad='SAME_UPPER')
+    return ([node], [x, y], [out])
+
+
+@onnx_test
+def conv_dynamic_img_same_upper_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT,
+                                      [1, 3, None, None])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 3, 3])
+    out = helper.make_tensor_value_info('2', TensorProto.FLOAT,
+                                        [1, 1, None, None])
+
+    node = onnx.helper.make_node('Conv',
+                                 inputs=['0', '1'],
+                                 outputs=['2'],
+                                 auto_pad='SAME_UPPER')
+    return ([node], [x, y], [out])
+
+
+@onnx_test
+def conv_dynamic_kernel_same_lower_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 5, 5])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT,
+                                      [1, 3, None, None])
+    out = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 1, 5, 5])
+
+    node = onnx.helper.make_node('Conv',
+                                 inputs=['0', '1'],
+                                 outputs=['2'],
+                                 auto_pad='SAME_LOWER')
+    return ([node], [x, y], [out])
+
+
 @onnx_test
 def conv_relu_maxpool_test():
     x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 32, 32])
@@ -2496,6 +2626,62 @@ def instance_norm_test():
     return ([node], [x, scale, bias], [y])
 
 
+@onnx_test
+def instance_norm_half_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT16, [1, 2, 3, 3])
+    scale = helper.make_tensor_value_info('1', TensorProto.FLOAT16, [2])
+    bias = helper.make_tensor_value_info('2', TensorProto.FLOAT16, [2])
+    y = helper.make_tensor_value_info('3', TensorProto.FLOAT16, [1, 2, 3, 3])
+
+    node = onnx.helper.make_node('InstanceNormalization',
+                                 inputs=['0', '1', '2'],
+                                 outputs=['3'])
+
+    return ([node], [x, scale, bias], [y])
+
+
+@onnx_test
+def instance_norm_type_mismatch_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 2, 3, 3])
+    scale = helper.make_tensor_value_info('1', TensorProto.FLOAT16, [2])
+    bias = helper.make_tensor_value_info('2', TensorProto.FLOAT16, [2])
+    y = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1, 2, 3, 3])
+
+    node = onnx.helper.make_node('InstanceNormalization',
+                                 inputs=['0', '1', '2'],
+                                 outputs=['3'])
+
+    return ([node], [x, scale, bias], [y])
+
+
+@onnx_test
+def instance_norm_invalid_type_test():
+    x = helper.make_tensor_value_info('0', TensorProto.INT32, [1, 2, 3, 3])
+    scale = helper.make_tensor_value_info('1', TensorProto.FLOAT, [2])
+    bias = helper.make_tensor_value_info('2', TensorProto.FLOAT, [2])
+    y = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1, 2, 3, 3])
+
+    node = onnx.helper.make_node('InstanceNormalization',
+                                 inputs=['0', '1', '2'],
+                                 outputs=['3'])
+
+    return ([node], [x, scale, bias], [y])
+
+
+@onnx_test
+def instance_norm_nonbroadcastable_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 2, 3, 3])
+    scale = helper.make_tensor_value_info('1', TensorProto.FLOAT, [4])
+    bias = helper.make_tensor_value_info('2', TensorProto.FLOAT, [4])
+    y = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1, 2, 3, 3])
+
+    node = onnx.helper.make_node('InstanceNormalization',
+                                 inputs=['0', '1', '2'],
+                                 outputs=['3'])
+
+    return ([node], [x, scale, bias], [y])
+
+
 @onnx_test
 def instance_norm_val_test():
     x = np.array([[[[0, 1, 2], [3, 4, 5], [6, 7, 8]],
@@ -3086,7 +3272,7 @@ def max_test():
     a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
     b = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
     c = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3])
-    y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3])
+    y = helper.make_tensor_value_info('3', TensorProto.FLOAT, [3])
 
     node = onnx.helper.make_node(
         'Max',
@@ -3220,7 +3406,7 @@ def min_test():
     a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
     b = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
     c = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3])
-    y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3])
+    y = helper.make_tensor_value_info('3', TensorProto.FLOAT, [3])
 
     node = onnx.helper.make_node(
         'Min',
@@ -3231,6 +3417,89 @@ def min_test():
     return ([node], [a, b, c], [y])
 
 
+@onnx_test
+def mod_test():
+    a = helper.make_tensor_value_info('0', TensorProto.INT32, [3, 3, 3])
+    b = helper.make_tensor_value_info('1', TensorProto.INT32, [3, 3, 3])
+    y = helper.make_tensor_value_info('2', TensorProto.INT32, [3, 3, 3])
+
+    node = onnx.helper.make_node('Mod', inputs=['0', '1'], outputs=['2'])
+
+    return ([node], [a, b], [y])
+
+
+@onnx_test
+def mod_test_half():
+    a = helper.make_tensor_value_info('0', TensorProto.FLOAT16, [3, 3, 3])
+    b = helper.make_tensor_value_info('1', TensorProto.FLOAT16, [3, 3, 3])
+    y = helper.make_tensor_value_info('2', TensorProto.FLOAT16, [3, 3, 3])
+
+    node = onnx.helper.make_node('Mod', inputs=['0', '1'], outputs=['2'])
+
+    return ([node], [a, b], [y])
+
+
+@onnx_test
+def mod_test_different_dtypes():
+    a = helper.make_tensor_value_info('0', TensorProto.INT16, [3, 3, 3])
+    b = helper.make_tensor_value_info('1', TensorProto.INT32, [3, 3, 3])
+    y = helper.make_tensor_value_info('2', TensorProto.INT32, [3, 3, 3])
+
+    node = onnx.helper.make_node(
+        'Mod',
+        inputs=['0', '1'],
+        outputs=['2'],
+    )
+
+    return ([node], [a, b], [y])
+
+
+@onnx_test
+def mod_test_fmod():
+    a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3, 3, 3])
+    b = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 3, 3])
+    y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3, 3, 3])
+
+    node = onnx.helper.make_node(
+        'Mod',
+        inputs=['0', '1'],
+        outputs=['2'],
+        fmod=1  #fmod flag = 1
+    )
+
+    return ([node], [a, b], [y])
+
+
+@onnx_test
+def mod_test_fmod_half():
+    a = helper.make_tensor_value_info('0', TensorProto.FLOAT16, [3, 3, 3])
+    b = helper.make_tensor_value_info('1', TensorProto.FLOAT16, [3, 3, 3])
+    y = helper.make_tensor_value_info('2', TensorProto.FLOAT16, [3, 3, 3])
+
+    node = onnx.helper.make_node('Mod',
+                                 inputs=['0', '1'],
+                                 outputs=['2'],
+                                 fmod=1)
+
+    return ([node], [a, b], [y])
+
+
+@onnx_test
+def mod_test_fmod_different_dtypes():
+    a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3, 3, 3])
+    b = helper.make_tensor_value_info('1', TensorProto.INT32, [3, 3, 3])
+    y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3, 3, 3])
+
+    node = onnx.helper.make_node(
+        'Mod',
+        inputs=['0', '1'],
+        outputs=['2'],
+        fmod=1  #fmod flag = 1
+    )
+
+    return ([node], [a, b], [y])
+
+
 @onnx_test
 def multinomial_test():
     sample_size = 10

test/onnx/instance_norm_half_test.onnx

+instance_norm_half_test:

+#
+
0
+
1
+
2
3"InstanceNormalizationinstance_norm_half_testZ
+
0
+
+
+

+
+
+Z
+
1
+
+
+
+Z
+
2
+
+
+
+b
+
3
+
+
+

+
+
+B
\ No newline at end of file

test/onnx/instance_norm_invalid_type_test.onnx

+instance_norm_invalid_type_test:

+#
+
0
+
1
+
2
3"InstanceNormalizationinstance_norm_invalid_type_testZ
+
0
+
+

+
+
+Z
+
1
+
+

+Z
+
2
+
+

+b
+
3
+

+

+
+
+B
\ No newline at end of file

test/onnx/instance_norm_nonbroadcastable_test.onnx

+#instance_norm_nonbroadcastable_test:

+#
+
0
+
1
+
2
3"InstanceNormalization#instance_norm_nonbroadcastable_testZ
+
0
+

+

+
+
+Z
+
1
+
+

+Z
+
2
+
+

+b
+
3
+

+

+
+
+B
\ No newline at end of file

test/onnx/instance_norm_type_mismatch_test.onnx

+ instance_norm_type_mismatch_test:

+#
+
0
+
1
+
2
3"InstanceNormalization instance_norm_type_mismatch_testZ
+
0
+

+

+
+
+Z
+
1
+
+
+
+Z
+
2
+
+
+
+b
+
3
+

+

+
+
+B
\ No newline at end of file

test/onnx/max_test.onnx

-max-example:e
+max_test:a
 
 
0
 
1
-
2
3"Maxtest-dropoutZ
+
2
3"Maxmax_testZ
 
0
 
 

@@ -15,7 +15,7 @@
 
 

 b
-
2
+
3
 
 

-B
\ No newline at end of file
+B
\ No newline at end of file

test/onnx/min_test.onnx

-min-example:e
+min_test:a
 
 
0
 
1
-
2
3"Mintest-dropoutZ
+
2
3"Minmin_testZ
 
0
 
 

@@ -15,7 +15,7 @@
 
 

 b
-
2
+
3
 
 

-B
\ No newline at end of file
+B
\ No newline at end of file

test/onnx/mod_test.onnx

+mod_test:e
+
+
0
+
1
2"Modmod_testZ
+
0
+
+
+
+Z
+
1
+
+
+
+b
+
2
+
+
+
+B
\ No newline at end of file

test/onnx/mod_test_different_dtypes.onnx

+mod_test_different_dtypes:v
+
+
0
+
1
2"Modmod_test_different_dtypesZ
+
0
+
+
+
+Z
+
1
+
+
+
+b
+
2
+
+
+
+B
\ No newline at end of file

test/onnx/mod_test_fmod.onnx

+
mod_test_fmod:w
+
+
0
+
1
2"Mod*
+fmod


mod_test_fmodZ
+
0
+

+
+
+Z
+
1
+

+
+
+b
+
2
+

+
+
+B
\ No newline at end of file

test/onnx/mod_test_fmod_different_dtypes.onnx

+mod_test_fmod_different_dtypes:

+
+
0
+
1
2"Mod*
+fmod

mod_test_fmod_different_dtypesZ
+
0
+

+
+
+Z
+
1
+
+
+
+b
+
2
+

+
+
+B
\ No newline at end of file

test/onnx/mod_test_fmod_half.onnx

+mod_test_fmod_half:|
+
+
0
+
1
2"Mod*
+fmod

mod_test_fmod_halfZ
+
0
+
+
+
+
+Z
+
1
+
+
+
+
+b
+
2
+
+
+
+
+B
\ No newline at end of file

test/onnx/mod_test_half.onnx

+
mod_test_half:j
+
+
0
+
1
2"Mod
mod_test_halfZ
+
0
+
+
+
+
+Z
+
1
+
+
+
+
+b
+
2
+
+
+
+
+B
\ No newline at end of file

test/onnx/onnx_test.cpp

@@ -636,11 +636,31 @@ TEST_CASE(constant_scalar_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(constant_empty_scalar_int64_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    mm->add_literal(migraphx::literal{migraphx::shape::int64_type});
+    auto prog = optimize_onnx("constant_empty_scalar_int64_test.onnx");
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(constant_one_val_int64_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    mm->add_literal(migraphx::literal{migraphx::shape{migraphx::shape::int64_type, {1}}, {1}});
+    auto prog = optimize_onnx("constant_one_val_int64_test.onnx");
+
+    EXPECT(p == prog);
+}
+
 TEST_CASE(const_of_shape_empty_input_test)
 {
     migraphx::program p;
     auto* mm = p.get_main_module();
-    mm->add_literal(migraphx::literal());
+    mm->add_literal(migraphx::literal(migraphx::shape::int32_type));
     migraphx::shape s(migraphx::shape::int64_type, {1}, {0});
     std::vector<int64_t> vec(s.elements(), 10);
     mm->add_literal(migraphx::literal(s, vec));
@@ -796,6 +816,170 @@ TEST_CASE(conv_bn_relu_maxpool_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(conv_dynamic_batch_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    auto l0  = mm->add_parameter(
+        "0", {migraphx::shape::float_type, {{1, 6, 0}, {3, 3, 0}, {5, 5, 0}, {5, 5, 0}}});
+    auto l1 = mm->add_parameter("1", {migraphx::shape::float_type, {1, 3, 3, 3}});
+    auto c0 = mm->add_instruction(
+        migraphx::make_op("convolution",
+                          {{"padding", {0, 0}}, {"stride", {1, 1}}, {"dilation", {1, 1}}}),
+        l0,
+        l1);
+    mm->add_return({c0});
+
+    migraphx::onnx_options options;
+    options.default_dyn_dim_value = {1, 6, 0};
+
+    auto prog = migraphx::parse_onnx("conv_dynamic_batch_test.onnx", options);
+    EXPECT(p == prog);
+}
+
+TEST_CASE(conv_dynamic_img_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    auto l0  = mm->add_parameter(
+        "0", {migraphx::shape::float_type, {{1, 1, 0}, {3, 3, 0}, {5, 10, 0}, {5, 10, 0}}});
+    auto l1 = mm->add_parameter("1", {migraphx::shape::float_type, {1, 3, 3, 3}});
+    auto c0 = mm->add_instruction(
+        migraphx::make_op("convolution",
+                          {{"padding", {0, 0}}, {"stride", {1, 1}}, {"dilation", {1, 1}}}),
+        l0,
+        l1);
+    mm->add_return({c0});
+
+    migraphx::onnx_options options;
+    options.default_dyn_dim_value = {5, 10, 0};
+
+    auto prog = migraphx::parse_onnx("conv_dynamic_img_test.onnx", options);
+    EXPECT(p == prog);
+}
+
+TEST_CASE(conv_dynamic_weights_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    auto l0  = mm->add_parameter("0", {migraphx::shape::float_type, {1, 3, 5, 5}});
+    auto l1  = mm->add_parameter(
+        "1", {migraphx::shape::float_type, {{1, 1, 0}, {3, 3, 0}, {2, 4, 0}, {2, 4, 0}}});
+    auto c0 = mm->add_instruction(
+        migraphx::make_op("convolution",
+                          {{"padding", {0, 0}}, {"stride", {1, 1}}, {"dilation", {1, 1}}}),
+        l0,
+        l1);
+    mm->add_return({c0});
+
+    migraphx::onnx_options options;
+    options.default_dyn_dim_value = {2, 4, 0};
+
+    auto prog = migraphx::parse_onnx("conv_dynamic_weights_test.onnx", options);
+    EXPECT(p == prog);
+}
+
+TEST_CASE(conv_dynamic_img_and_weights_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    auto l0  = mm->add_parameter(
+        "0", {migraphx::shape::float_type, {{1, 1, 0}, {3, 3, 0}, {5, 10, 0}, {5, 10, 0}}});
+    auto l1 = mm->add_parameter(
+        "1", {migraphx::shape::float_type, {{1, 1, 0}, {3, 3, 0}, {2, 4, 0}, {2, 4, 0}}});
+    auto c0 = mm->add_instruction(
+        migraphx::make_op("convolution",
+                          {{"padding", {0, 0}}, {"stride", {1, 1}}, {"dilation", {1, 1}}}),
+        l0,
+        l1);
+    mm->add_return({c0});
+
+    migraphx::onnx_options options;
+    options.default_dyn_dim_value   = {5, 10, 0};
+    options.map_dyn_input_dims["1"] = {{1, 1, 0}, {3, 3, 0}, {2, 4, 0}, {2, 4, 0}};
+
+    auto prog = migraphx::parse_onnx("conv_dynamic_img_and_weights_test.onnx", options);
+    EXPECT(p == prog);
+}
+
+TEST_CASE(conv_dynamic_batch_same_upper)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    auto l0  = mm->add_parameter(
+        "0", {migraphx::shape::float_type, {{1, 10, 0}, {3, 3, 0}, {5, 5, 0}, {5, 5, 0}}});
+    auto l1 = mm->add_parameter("1", {migraphx::shape::float_type, {1, 3, 3, 3}});
+    auto c0 =
+        mm->add_instruction(migraphx::make_op("convolution",
+                                              {{"padding", {1, 1, 1, 1}},
+                                               {"stride", {1, 1}},
+                                               {"dilation", {1, 1}},
+                                               {"padding_mode", migraphx::op::padding_mode_t::same},
+                                               {"use_dynamic_same_auto_pad", false}}),
+                            l0,
+                            l1);
+    mm->add_return({c0});
+
+    migraphx::onnx_options options;
+    options.default_dyn_dim_value = {1, 10, 0};
+
+    auto prog = migraphx::parse_onnx("conv_dynamic_batch_same_upper_test.onnx", options);
+    EXPECT(p == prog);
+}
+
+TEST_CASE(conv_dynamic_img_same_upper)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    auto l0  = mm->add_parameter(
+        "0", {migraphx::shape::float_type, {{1, 1, 0}, {3, 3, 0}, {5, 10, 0}, {5, 10, 0}}});
+    auto l1 = mm->add_parameter("1", {migraphx::shape::float_type, {1, 3, 3, 3}});
+    auto c0 = mm->add_instruction(
+        migraphx::make_op("convolution",
+                          {{"padding", {0, 0}},
+                           {"stride", {1, 1}},
+                           {"dilation", {1, 1}},
+                           {"padding_mode", migraphx::op::padding_mode_t::same_upper},
+                           {"use_dynamic_same_auto_pad", true}}),
+        l0,
+        l1);
+    mm->add_return({c0});
+
+    migraphx::onnx_options options;
+    options.default_dyn_dim_value = {5, 10, 0};
+
+    auto prog = migraphx::parse_onnx("conv_dynamic_img_same_upper_test.onnx", options);
+    EXPECT(p == prog);
+}
+
+TEST_CASE(conv_dynamic_kernel_same_lower)
+{
+    std::cout << "here1\n";
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    auto l0  = mm->add_parameter("0", {migraphx::shape::float_type, {1, 3, 5, 5}});
+    auto l1  = mm->add_parameter(
+        "1", {migraphx::shape::float_type, {{1, 1, 0}, {3, 3, 0}, {2, 4, 0}, {2, 4, 0}}});
+    std::cout << "here2\n";
+    auto c0 = mm->add_instruction(
+        migraphx::make_op("convolution",
+                          {{"padding", {0, 0}},
+                           {"stride", {1, 1}},
+                           {"dilation", {1, 1}},
+                           {"padding_mode", migraphx::op::padding_mode_t::same_lower},
+                           {"use_dynamic_same_auto_pad", true}}),
+        l0,
+        l1);
+    std::cout << "here3\n";
+    mm->add_return({c0});
+
+    migraphx::onnx_options options;
+    options.default_dyn_dim_value = {2, 4, 0};
+    std::cout << "here\n";
+    auto prog = migraphx::parse_onnx("conv_dynamic_kernel_same_lower_test.onnx", options);
+    EXPECT(p == prog);
+}
+
 TEST_CASE(conv_relu_maxpool_test)
 {
     migraphx::program p;
@@ -2370,7 +2554,8 @@ TEST_CASE(instance_norm_test)
     auto l0       = mm->add_instruction(migraphx::make_op("sqdiff"), x, mean_bcast);
     auto variance = mm->add_instruction(migraphx::make_op("reduce_mean", {{"axes", {2, 3}}}), l0);
     auto l1       = mm->add_instruction(migraphx::make_op("sub"), x, mean_bcast);
-    auto epsilon_literal = mm->add_literal(1e-5f);
+    auto epsilon_literal =
+        mm->add_literal(migraphx::literal{migraphx::shape{migraphx::shape::float_type}, {1e-5}});
     auto epsilon_bcast = mm->add_instruction(
         migraphx::make_op("multibroadcast", {{"out_lens", dims}}), epsilon_literal);
     auto variance_bcast =
@@ -2390,6 +2575,60 @@ TEST_CASE(instance_norm_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(instance_norm_half_test)
+{
+    std::vector<size_t> dims{1, 2, 3, 3};
+    migraphx::shape s1{migraphx::shape::half_type, dims};
+    migraphx::shape s2{migraphx::shape::half_type, {2}};
+
+    migraphx::program p;
+    auto* mm   = p.get_main_module();
+    auto x     = mm->add_parameter("0", s1);
+    auto scale = mm->add_parameter("1", s2);
+    auto bias  = mm->add_parameter("2", s2);
+
+    auto mean = mm->add_instruction(migraphx::make_op("reduce_mean", {{"axes", {2, 3}}}), x);
+    auto mean_bcast =
+        mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", dims}}), mean);
+    auto l0       = mm->add_instruction(migraphx::make_op("sqdiff"), x, mean_bcast);
+    auto variance = mm->add_instruction(migraphx::make_op("reduce_mean", {{"axes", {2, 3}}}), l0);
+    auto l1       = mm->add_instruction(migraphx::make_op("sub"), x, mean_bcast);
+    auto epsilon_literal =
+        mm->add_literal(migraphx::literal{migraphx::shape{migraphx::shape::half_type}, {1e-5}});
+    auto epsilon_bcast = mm->add_instruction(
+        migraphx::make_op("multibroadcast", {{"out_lens", dims}}), epsilon_literal);
+    auto variance_bcast =
+        mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", dims}}), variance);
+    auto l2          = mm->add_instruction(migraphx::make_op("add"), variance_bcast, epsilon_bcast);
+    auto l3          = mm->add_instruction(migraphx::make_op("rsqrt"), l2);
+    auto l4          = mm->add_instruction(migraphx::make_op("mul"), l1, l3);
+    auto scale_bcast = mm->add_instruction(
+        migraphx::make_op("broadcast", {{"axis", 1}, {"out_lens", dims}}), scale);
+    auto bias_bcast = mm->add_instruction(
+        migraphx::make_op("broadcast", {{"axis", 1}, {"out_lens", dims}}), bias);
+    auto l5 = mm->add_instruction(migraphx::make_op("mul"), l4, scale_bcast);
+    mm->add_instruction(migraphx::make_op("add"), l5, bias_bcast);
+
+    auto prog = optimize_onnx("instance_norm_half_test.onnx");
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(instance_norm_type_mismatch_test)
+{
+    EXPECT(test::throws([&] { migraphx::parse_onnx("instance_norm_type_mismatch_test.onnx"); }));
+}
+
+TEST_CASE(instance_norm_invalid_type_test)
+{
+    EXPECT(test::throws([&] { migraphx::parse_onnx("instance_norm_invalid_type_test.onnx"); }));
+}
+
+TEST_CASE(instance_norm_nonbroadcastable_test)
+{
+    EXPECT(test::throws([&] { migraphx::parse_onnx("instance_norm_nonbroadcastable_test.onnx"); }));
+}
+
 TEST_CASE(leaky_relu_test)
 {
     migraphx::program p;
@@ -2832,7 +3071,9 @@ TEST_CASE(max_test)
     auto l0     = mm->add_instruction(migraphx::make_op("max"), input0, input1);
     mm->add_instruction(migraphx::make_op("max"), l0, input2);
 
-    optimize_onnx("max_test.onnx");
+    auto prog = optimize_onnx("max_test.onnx");
+
+    EXPECT(p == prog);
 }
 
 TEST_CASE(maxpool_notset_test)
@@ -2947,7 +3188,79 @@ TEST_CASE(min_test)
     auto l0     = mm->add_instruction(migraphx::make_op("min"), input0, input1);
     mm->add_instruction(migraphx::make_op("min"), l0, input2);
 
-    optimize_onnx("min_test.onnx");
+    auto prog = optimize_onnx("min_test.onnx");
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(mod_test)
+{
+    migraphx::program p;
+    auto* mm    = p.get_main_module();
+    auto input0 = mm->add_parameter("0", migraphx::shape{migraphx::shape::int32_type, {3, 3, 3}});
+    auto input1 = mm->add_parameter("1", migraphx::shape{migraphx::shape::int32_type, {3, 3, 3}});
+    mm->add_instruction(migraphx::make_op("mod"), input0, input1);
+
+    auto prog = optimize_onnx("mod_test.onnx");
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(mod_test_half)
+{
+    EXPECT(test::throws([&] { migraphx::parse_onnx("mod_test_half.onnx"); }));
+}
+
+TEST_CASE(mod_test_different_dtypes)
+{
+    migraphx::program p;
+    auto* mm    = p.get_main_module();
+    auto input0 = mm->add_parameter("0", migraphx::shape{migraphx::shape::int16_type, {3, 3, 3}});
+    auto input1 = mm->add_parameter("1", migraphx::shape{migraphx::shape::int32_type, {3, 3, 3}});
+    add_common_op(*mm, migraphx::make_op("mod"), {input0, input1});
+
+    auto prog = optimize_onnx("mod_test_different_dtypes.onnx");
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(mod_test_fmod)
+{
+    migraphx::program p;
+    auto* mm    = p.get_main_module();
+    auto input0 = mm->add_parameter("0", migraphx::shape{migraphx::shape::float_type, {3, 3, 3}});
+    auto input1 = mm->add_parameter("1", migraphx::shape{migraphx::shape::float_type, {3, 3, 3}});
+    mm->add_instruction(migraphx::make_op("fmod"), input0, input1);
+
+    auto prog = optimize_onnx("mod_test_fmod.onnx");
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(mod_test_fmod_half)
+{
+    migraphx::program p;
+    auto* mm    = p.get_main_module();
+    auto input0 = mm->add_parameter("0", migraphx::shape{migraphx::shape::half_type, {3, 3, 3}});
+    auto input1 = mm->add_parameter("1", migraphx::shape{migraphx::shape::half_type, {3, 3, 3}});
+    mm->add_instruction(migraphx::make_op("fmod"), input0, input1);
+
+    auto prog = optimize_onnx("mod_test_fmod_half.onnx");
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(mod_test_fmod_different_dtypes)
+{
+    migraphx::program p;
+    auto* mm    = p.get_main_module();
+    auto input0 = mm->add_parameter("0", migraphx::shape{migraphx::shape::float_type, {3, 3, 3}});
+    auto input1 = mm->add_parameter("1", migraphx::shape{migraphx::shape::int32_type, {3, 3, 3}});
+    add_common_op(*mm, migraphx::make_op("fmod"), {input0, input1});
+
+    auto prog = optimize_onnx("mod_test_fmod_different_dtypes.onnx");
+
+    EXPECT(p == prog);
 }
 
 TEST_CASE(multinomial_test)
@@ -3773,7 +4086,7 @@ TEST_CASE(reducesum_empty_axes_test)
 {
     migraphx::program p;
     auto* mm = p.get_main_module();
-    mm->add_literal({});
+    mm->add_literal(migraphx::literal{migraphx::shape::int64_type});
     auto x  = mm->add_parameter("x", migraphx::shape{migraphx::shape::float_type, {3, 4, 5, 6}});
     auto l1 = mm->add_instruction(migraphx::make_op("reduce_sum", {{"axes", {0, 1, 2, 3}}}), x);
     auto r  = mm->add_instruction(migraphx::make_op("squeeze", {{"axes", {0, 1, 2, 3}}}), l1);
@@ -3788,7 +4101,7 @@ TEST_CASE(reducesum_noop_test)
 {
     migraphx::program p;
     auto* mm = p.get_main_module();
-    mm->add_literal({});
+    mm->add_literal(migraphx::literal{migraphx::shape::int64_type});
     auto x = mm->add_parameter("x", migraphx::shape{migraphx::shape::float_type, {3, 4, 5, 6}});
     mm->add_return({x});
     auto prog = migraphx::parse_onnx("reducesum_noop_test.onnx");
@@ -4998,7 +5311,7 @@ TEST_CASE(squeeze_empty_axes_test)
 {
     migraphx::program p;
     auto* mm = p.get_main_module();
-    mm->add_literal({});
+    mm->add_literal(migraphx::literal{migraphx::shape::int64_type});
     auto l0 = mm->add_parameter("x", migraphx::shape{migraphx::shape::float_type, {3, 1, 5, 1}});
     auto l1 = mm->add_instruction(migraphx::make_op("squeeze"), l0);
     mm->add_return({l1});
@@ -5433,7 +5746,59 @@ TEST_CASE(variable_batch_test)
     EXPECT(p == prog);
 }
 
-TEST_CASE(variable_batch_user_input_test)
+TEST_CASE(variable_batch_user_input_test1)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    auto l0  = mm->add_parameter("0", migraphx::shape{migraphx::shape::float_type, {2, 3, 16, 16}});
+    auto r   = mm->add_instruction(migraphx::make_op("identity"), l0);
+    mm->add_return({r});
+
+    migraphx::onnx_options options;
+    options.default_dyn_dim_value = {2, 2, 0};
+
+    auto prog = migraphx::parse_onnx("variable_batch_test.onnx", options);
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(variable_batch_user_input_test2)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    auto l0  = mm->add_parameter("0",
+                                migraphx::shape{migraphx::shape::float_type,
+                                                {{2, 5, 0}, {3, 3, 0}, {16, 16, 0}, {16, 16, 0}}});
+    auto r   = mm->add_instruction(migraphx::make_op("identity"), l0);
+    mm->add_return({r});
+
+    migraphx::onnx_options options;
+    options.default_dyn_dim_value = {2, 5, 0};
+
+    auto prog = migraphx::parse_onnx("variable_batch_test.onnx", options);
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(variable_batch_user_input_test3)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    auto l0  = mm->add_parameter("0",
+                                migraphx::shape{migraphx::shape::float_type,
+                                                {{2, 5, 0}, {3, 3, 0}, {16, 16, 0}, {16, 16, 0}}});
+    auto r   = mm->add_instruction(migraphx::make_op("identity"), l0);
+    mm->add_return({r});
+
+    migraphx::onnx_options options;
+    options.map_dyn_input_dims["0"] = {{2, 5, 0}, {3, 3, 0}, {16, 16, 0}, {16, 16, 0}};
+
+    auto prog = migraphx::parse_onnx("variable_batch_test.onnx", options);
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(variable_batch_user_input_test4)
 {
     migraphx::program p;
     auto* mm = p.get_main_module();
@@ -5449,6 +5814,26 @@ TEST_CASE(variable_batch_user_input_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(variable_batch_user_input_test5)
+{
+    // Error using default_dim_value and default_dyn_dim_value
+    migraphx::onnx_options options;
+    options.default_dim_value     = 2;
+    options.default_dyn_dim_value = {1, 2, 0};
+
+    EXPECT(test::throws([&] { migraphx::parse_onnx("variable_batch_test.onnx", options); }));
+}
+
+TEST_CASE(variable_batch_user_input_test6)
+{
+    // Error using both map_dyn_input_dims and map_input_dims
+    migraphx::onnx_options options;
+    options.map_dyn_input_dims["0"] = {{2, 5, 0}, {3, 3, 0}, {16, 16, 0}, {16, 16, 0}};
+    options.map_input_dims["0"]     = {2, 3, 16, 16};
+
+    EXPECT(test::throws([&] { migraphx::parse_onnx("variable_batch_test.onnx", options); }));
+}
+
 TEST_CASE(variable_batch_leq_zero_test)
 {
     migraphx::program p;

test/onnx/verify_onnx.cpp

@@ -631,6 +631,120 @@ TEST_CASE(mean_integral_test)
     EXPECT(migraphx::verify_range(result_vector, gold));
 }
 
+TEST_CASE(mod_test)
+{
+    migraphx::program p = migraphx::parse_onnx("mod_test.onnx");
+    p.compile(migraphx::ref::target{});
+
+    migraphx::shape s{migraphx::shape::int32_type, {3, 3, 3}};
+
+    std::vector<int32_t> a = {-4, 7, 5,  4, -7, 8, -4, 7, 5,  4, -7, 8, -4, 7,
+                              5,  4, -7, 8, -4, 7, 5,  4, -7, 8, -4, 7, 5};
+
+    std::vector<int32_t> b = {2, -3, 8, -2, 3, 5,  2, -3, 8, -2, 3, 5,  2, -3,
+                              8, -2, 3, 5,  2, -3, 8, -2, 3, 5,  2, -3, 8};
+
+    migraphx::parameter_map p_map;
+    p_map["0"] = migraphx::argument(s, a.data());
+    p_map["1"] = migraphx::argument(s, b.data());
+
+    auto result = p.eval(p_map).back();
+    std::vector<int32_t> result_vector;
+    result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
+
+    std::vector<int32_t> gold = {0, -2, 5, 0, 2, 3,  0, -2, 5, 0, 2, 3,  0, -2,
+                                 5, 0,  2, 3, 0, -2, 5, 0,  2, 3, 0, -2, 5};
+
+    EXPECT(migraphx::verify_range(result_vector, gold));
+}
+
+TEST_CASE(mod_test_different_types)
+{
+    migraphx::program p = migraphx::parse_onnx("mod_test_different_dtypes.onnx");
+    p.compile(migraphx::ref::target{});
+
+    migraphx::shape s_int16{migraphx::shape::int16_type, {3, 3, 3}};
+    migraphx::shape s_int32{migraphx::shape::int32_type, {3, 3, 3}};
+
+    std::vector<int16_t> a = {-4, 7, 5,  4, -7, 8, -4, 7, 5,  4, -7, 8, -4, 7,
+                              5,  4, -7, 8, -4, 7, 5,  4, -7, 8, -4, 7, 5};
+
+    std::vector<int32_t> b = {2, -3, 8, -2, 3, 5,  2, -3, 8, -2, 3, 5,  2, -3,
+                              8, -2, 3, 5,  2, -3, 8, -2, 3, 5,  2, -3, 8};
+
+    migraphx::parameter_map p_map;
+    p_map["0"] = migraphx::argument(s_int16, a.data());
+    p_map["1"] = migraphx::argument(s_int32, b.data());
+
+    auto result = p.eval(p_map).back();
+    std::vector<int32_t> result_vector;
+    result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
+
+    std::vector<int32_t> gold = {0, -2, 5, 0, 2, 3,  0, -2, 5, 0, 2, 3,  0, -2,
+                                 5, 0,  2, 3, 0, -2, 5, 0,  2, 3, 0, -2, 5};
+
+    EXPECT(migraphx::verify_range(result_vector, gold));
+}
+
+TEST_CASE(mod_test_fmod)
+{
+    migraphx::program p = migraphx::parse_onnx("mod_test_fmod.onnx");
+    p.compile(migraphx::ref::target{});
+
+    migraphx::shape s{migraphx::shape::float_type, {3, 3, 3}};
+
+    std::vector<float> a = {1.2,  -2.2, 3.3,  4.1,   -5.4,  6.7,   7.8,  -8.4, 9.9,
+                            10.7, 11.2, 12.3, 13.9,  -14.2, 15.8,  16.6, 17.9, 18.2,
+                            19.0, 20.0, 21.0, -22.0, 23.0,  -24.0, 25.2, 26.3, 27.1};
+
+    std::vector<float> b = {30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17,
+                            16, 15, 14, 13, 12, 11, 10, 9,  8,  7,  6,  5,  4};
+
+    migraphx::parameter_map p_map;
+    p_map["0"] = migraphx::argument(s, a.data());
+    p_map["1"] = migraphx::argument(s, b.data());
+
+    auto result = p.eval(p_map).back();
+    std::vector<float> result_vector;
+    result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
+
+    std::vector<float> gold{1.2,  -2.2, 3.3,  4.1,  -5.4,  6.7,  7.8, -8.4, 9.9,
+                            10.7, 11.2, 12.3, 13.9, -14.2, 15.8, 1.6, 3.9,  5.2,
+                            7.0,  9.0,  1.0,  -4.0, 7.0,   -3.0, 1.2, 1.3,  3.1};
+
+    EXPECT(migraphx::verify_range(result_vector, gold));
+}
+
+TEST_CASE(mod_test_fmod_different_types)
+{
+    migraphx::program p = migraphx::parse_onnx("mod_test_fmod_different_dtypes.onnx");
+    p.compile(migraphx::ref::target{});
+
+    migraphx::shape s_float{migraphx::shape::float_type, {3, 3, 3}};
+    migraphx::shape s_int{migraphx::shape::int32_type, {3, 3, 3}};
+
+    std::vector<float> a = {1.2,  -2.2, 3.3,  4.1,   -5.4,  6.7,   7.8,  -8.4, 9.9,
+                            10.7, 11.2, 12.3, 13.9,  -14.2, 15.8,  16.6, 17.9, 18.2,
+                            19.0, 20.0, 21.0, -22.0, 23.0,  -24.0, 25.2, 26.3, 27.1};
+
+    std::vector<int32_t> b = {30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17,
+                              16, 15, 14, 13, 12, 11, 10, 9,  8,  7,  6,  5,  4};
+
+    migraphx::parameter_map p_map;
+    p_map["0"] = migraphx::argument(s_float, a.data());
+    p_map["1"] = migraphx::argument(s_int, b.data());
+
+    auto result = p.eval(p_map).back();
+    std::vector<float> result_vector;
+    result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
+
+    std::vector<float> gold{1.2,  -2.2, 3.3,  4.1,  -5.4,  6.7,  7.8, -8.4, 9.9,
+                            10.7, 11.2, 12.3, 13.9, -14.2, 15.8, 1.6, 3.9,  5.2,
+                            7.0,  9.0,  1.0,  -4.0, 7.0,   -3.0, 1.2, 1.3,  3.1};
+
+    EXPECT(migraphx::verify_range(result_vector, gold));
+}
+
 TEST_CASE(nonzero_test)
 {
     migraphx::program p = migraphx::parse_onnx("nonzero_dynamic_test.onnx");

test/op_shape_test.cpp

@@ -144,6 +144,7 @@ TEST_CASE(convolution_shape)
     throws_shape(migraphx::make_op("convolution"), input2, weights2);
     throws_shape(migraphx::make_op("convolution"), input2, weights);
 
+    // 1D convolution
     migraphx::shape output_1d{migraphx::shape::float_type, {4, 4, 1}};
     migraphx::shape input_1d{migraphx::shape::float_type, {4, 3, 3}};
     migraphx::shape weights_1d{migraphx::shape::float_type, {4, 3, 3}};
@@ -153,6 +154,11 @@ TEST_CASE(convolution_shape)
         input_1d,
         weights_1d);
 
+    // channel numbers mismatch
+    weights_1d = {migraphx::shape::float_type, {4, 8, 3}};
+    throws_shape(migraphx::make_op("convolution"), input_1d, weights_1d);
+
+    // 3D convolution
     migraphx::shape output_3d{migraphx::shape::float_type, {4, 4, 1, 1, 1}};
     migraphx::shape input_3d{migraphx::shape::float_type, {4, 3, 3, 3, 3}};
     migraphx::shape weights_3d{migraphx::shape::float_type, {4, 3, 3, 3, 3}};
@@ -164,6 +170,130 @@ TEST_CASE(convolution_shape)
         weights_3d);
 
     throws_shape(migraphx::make_op("convolution"), input_3d, weights_3d);
+
+    // dynamic batch
+    migraphx::shape input_dyn_shape{migraphx::shape::float_type,
+                                    {{1, 100, 0}, {3, 3, 0}, {5, 5, 0}, {5, 5, 0}}};
+    migraphx::shape weights_shape{migraphx::shape::float_type, {1, 3, 3, 3}};
+    migraphx::shape output_dyn_shape{migraphx::shape::float_type,
+                                     {{
+                                          1,
+                                          100,
+                                          0,
+                                      },
+                                      {1, 1, 0},
+                                      {3, 3, 0},
+                                      {3, 3, 0}}};
+    expect_shape(output_dyn_shape,
+                 migraphx::make_op("convolution",
+                                   {{"padding", {0, 0}}, {"stride", {1, 1}}, {"dilation", {1, 1}}}),
+                 input_dyn_shape,
+                 weights_shape);
+
+    // dynamic image
+    input_dyn_shape = {migraphx::shape::float_type, {{1, 1, 0}, {3, 3, 0}, {5, 20, 0}, {5, 20, 0}}};
+    weights_shape   = {migraphx::shape::float_type, {1, 3, 3, 3}};
+    output_dyn_shape = {migraphx::shape::float_type,
+                        {{
+                             1,
+                             1,
+                             0,
+                         },
+                         {1, 1, 0},
+                         {3, 18, 0},
+                         {3, 18, 0}}};
+    expect_shape(output_dyn_shape,
+                 migraphx::make_op("convolution",
+                                   {{"padding", {0, 0}}, {"stride", {1, 1}}, {"dilation", {1, 1}}}),
+                 input_dyn_shape,
+                 weights_shape);
+
+    // dynamic weights
+    input_dyn_shape  = {migraphx::shape::float_type, {1, 3, 10, 10}};
+    weights_shape    = {migraphx::shape::float_type, {{1, 1, 0}, {3, 3, 0}, {2, 4, 0}, {2, 4, 0}}};
+    output_dyn_shape = {migraphx::shape::float_type,
+                        {{
+                             1,
+                             1,
+                             0,
+                         },
+                         {1, 1, 0},
+                         {7, 9, 0},
+                         {7, 9, 0}}};
+    expect_shape(output_dyn_shape,
+                 migraphx::make_op("convolution",
+                                   {{"padding", {0, 0}}, {"stride", {1, 1}}, {"dilation", {1, 1}}}),
+                 input_dyn_shape,
+                 weights_shape);
+
+    // dynamic img and weights
+    input_dyn_shape = {migraphx::shape::float_type, {{1, 1, 0}, {3, 3, 0}, {5, 20, 0}, {5, 20, 0}}};
+    weights_shape   = {migraphx::shape::float_type, {{1, 1, 0}, {3, 3, 0}, {2, 4, 0}, {2, 4, 0}}};
+    output_dyn_shape = {migraphx::shape::float_type,
+                        {{
+                             1,
+                             1,
+                             0,
+                         },
+                         {1, 1, 0},
+                         {2, 19, 0},
+                         {2, 19, 0}}};
+    expect_shape(output_dyn_shape,
+                 migraphx::make_op("convolution",
+                                   {{"padding", {0, 0}}, {"stride", {1, 1}}, {"dilation", {1, 1}}}),
+                 input_dyn_shape,
+                 weights_shape);
+
+    // input attr shape mismatch
+    input_dyn_shape = {migraphx::shape::float_type,
+                       {{1, 100, 0}, {3, 3, 0}, {5, 5, 0}, {5, 5, 0}, {5, 5, 0}}};
+    weights_shape   = {migraphx::shape::float_type, {1, 3, 3, 3, 3}};
+    throws_shape(migraphx::make_op("convolution",
+                                   {{"padding", {0, 0}}, {"stride", {1, 1}}, {"dilation", {1, 1}}}),
+                 input_dyn_shape,
+                 weights_shape);
+
+    // auto_pad dynamic batch
+    input_dyn_shape  = {migraphx::shape::float_type, {{1, 10, 0}, {3, 3, 0}, {5, 5, 0}, {5, 5, 0}}};
+    weights_shape    = {migraphx::shape::float_type, {1, 3, 3, 3}};
+    output_dyn_shape = {migraphx::shape::float_type, {{1, 10, 0}, {1, 1, 0}, {5, 5, 0}, {5, 5, 0}}};
+    expect_shape(output_dyn_shape,
+                 migraphx::make_op("convolution",
+                                   {{"stride", {1, 1}},
+                                    {"dilation", {1, 1}},
+                                    {"padding_mode", migraphx::op::padding_mode_t::same_upper},
+                                    {"use_dynamic_same_auto_pad", true}}),
+                 input_dyn_shape,
+                 weights_shape);
+
+    // auto_pad dynamic img
+    input_dyn_shape = {migraphx::shape::float_type, {{1, 1, 0}, {3, 3, 0}, {5, 10, 0}, {5, 10, 0}}};
+    weights_shape   = {migraphx::shape::float_type, {1, 3, 3, 3}};
+    output_dyn_shape = {migraphx::shape::float_type,
+                        {{1, 1, 0}, {1, 1, 0}, {5, 10, 0}, {5, 10, 0}}};
+    expect_shape(output_dyn_shape,
+                 migraphx::make_op("convolution",
+                                   {{"stride", {1, 1}},
+                                    {"dilation", {1, 1}},
+                                    {"padding_mode", migraphx::op::padding_mode_t::same_upper},
+                                    {"use_dynamic_same_auto_pad", true}}),
+                 input_dyn_shape,
+                 weights_shape);
+
+    // auto_pad dynamic kernel
+    input_dyn_shape  = {migraphx::shape::float_type,
+                       {{1, 1, 0}, {3, 3, 0}, {10, 10, 0}, {10, 10, 0}}};
+    weights_shape    = {migraphx::shape::float_type, {{1, 1, 0}, {3, 3, 0}, {2, 4, 0}, {2, 4, 0}}};
+    output_dyn_shape = {migraphx::shape::float_type,
+                        {{1, 1, 0}, {1, 1, 0}, {10, 10, 0}, {10, 10, 0}}};
+    expect_shape(output_dyn_shape,
+                 migraphx::make_op("convolution",
+                                   {{"stride", {1, 1}},
+                                    {"dilation", {1, 1}},
+                                    {"padding_mode", migraphx::op::padding_mode_t::same_lower},
+                                    {"use_dynamic_same_auto_pad", true}}),
+                 input_dyn_shape,
+                 weights_shape);
 }
 
 TEST_CASE(contiguous_shape)
@@ -981,7 +1111,8 @@ TEST_CASE(multibroadcast)
     }
     {
         std::vector<std::size_t> lens{4, 1, 3};
-        migraphx::shape input{migraphx::shape::float_type, {}};
+        std::vector<std::size_t> empt = {};
+        migraphx::shape input{migraphx::shape::float_type, empt};
         throws_shape(migraphx::make_op("multibroadcast", {{"out_lens", lens}}), input);
     }
     {
@@ -1533,15 +1664,46 @@ TEST_CASE(test_squeeze_wrong_axis)
 
 TEST_CASE(test_unsqueeze)
 {
-    migraphx::shape s1{migraphx::shape::float_type, {4, 3, 3}};
-    migraphx::shape s2{migraphx::shape::float_type, {4, 3, 1, 3}};
+    migraphx::shape s1{migraphx::shape::float_type, {4, 5, 3}};
+    migraphx::shape s2{migraphx::shape::float_type, {4, 5, 1, 3}};
     expect_shape(s2, migraphx::make_op("unsqueeze", {{"axes", {2}}}), s1);
 }
 
+TEST_CASE(test_unsqueeze_step)
+{
+    migraphx::shape s1{migraphx::shape::float_type, {4, 5, 12}};
+    migraphx::shape s2{migraphx::shape::float_type, {4, 5, 2, 6}};
+    expect_shape(s2, migraphx::make_op("unsqueeze", {{"axes", {2}}, {"steps", {2}}}), s1);
+}
+
+TEST_CASE(test_unsqueeze_step_non_divisable)
+{
+    migraphx::shape s1{migraphx::shape::float_type, {4, 5, 3}};
+    throws_shape(migraphx::make_op("unsqueeze", {{"axes", {2}}, {"steps", {2}}}), s1);
+}
+
+TEST_CASE(test_unsqueeze_step_zero)
+{
+    migraphx::shape s1{migraphx::shape::float_type, {4, 5, 12}};
+    throws_shape(migraphx::make_op("unsqueeze", {{"axes", {2}}, {"steps", {0}}}), s1);
+}
+
+TEST_CASE(test_unsqueeze_step_at_end)
+{
+    migraphx::shape s1{migraphx::shape::float_type, {4, 5, 12}};
+    throws_shape(migraphx::make_op("unsqueeze", {{"axes", {3}}, {"steps", {2}}}), s1);
+}
+
+TEST_CASE(test_unsqueeze_mismatch_step_axis)
+{
+    migraphx::shape s1{migraphx::shape::float_type, {4, 5, 12}};
+    throws_shape(migraphx::make_op("unsqueeze", {{"axes", {2}}, {"steps", {2, 3}}}), s1);
+}
+
 TEST_CASE(test_unsqueeze_negative_axis)
 {
-    migraphx::shape s1{migraphx::shape::float_type, {4, 3, 3}};
-    migraphx::shape s2{migraphx::shape::float_type, {4, 3, 1, 3}};
+    migraphx::shape s1{migraphx::shape::float_type, {4, 5, 3}};
+    migraphx::shape s2{migraphx::shape::float_type, {4, 5, 1, 3}};
     expect_shape(s2, migraphx::make_op("unsqueeze", {{"axes", {-2}}}), s1);
 }
 
@@ -1567,21 +1729,28 @@ TEST_CASE(test_unsqueeze_scalar_tensor2)
 TEST_CASE(test_unsqueeze_transpose)
 {
     migraphx::shape s1{migraphx::shape::float_type, {4, 4, 3}, {12, 1, 4}};
-    migraphx::shape s2{migraphx::shape::float_type, {4, 4, 1, 3}, {12, 1, 1, 4}};
+    migraphx::shape s2{migraphx::shape::float_type, {4, 4, 1, 3}, {12, 1, 12, 4}};
     expect_shape(s2, migraphx::make_op("unsqueeze", {{"axes", {2}}}), s1);
 }
 
+TEST_CASE(test_unsqueeze_transpose_step)
+{
+    migraphx::shape s1{migraphx::shape::float_type, {4, 4, 6}, {24, 1, 4}};
+    migraphx::shape s2{migraphx::shape::float_type, {4, 4, 2, 3}, {24, 1, 12, 4}};
+    expect_shape(s2, migraphx::make_op("unsqueeze", {{"axes", {2}}, {"steps", {2}}}), s1);
+}
+
 TEST_CASE(test_unsqueeze_multibroadcast)
 {
     migraphx::shape s1{migraphx::shape::float_type, {2, 3, 4}, {0, 1, 0}};
-    migraphx::shape s2{migraphx::shape::float_type, {2, 3, 1, 4}, {0, 1, 1, 0}};
+    migraphx::shape s2{migraphx::shape::float_type, {2, 3, 1, 4}, {0, 1, 0, 0}};
     expect_shape(s2, migraphx::make_op("unsqueeze", {{"axes", {2}}}), s1);
 }
 
 TEST_CASE(test_unsqueeze_slice)
 {
     migraphx::shape s1{migraphx::shape::float_type, {2, 3, 4}, {108, 36, 1}};
-    migraphx::shape s2{migraphx::shape::float_type, {2, 3, 1, 4}, {108, 36, 36, 1}};
+    migraphx::shape s2{migraphx::shape::float_type, {2, 3, 1, 4}, {108, 36, 4, 1}};
     expect_shape(s2, migraphx::make_op("unsqueeze", {{"axes", {2}}}), s1);
 }
 
@@ -1613,6 +1782,27 @@ TEST_CASE(test_unsqueeze_multiple_axes_2)
     expect_shape(s2, migraphx::make_op("unsqueeze", {{"axes", {0, 1}}}), s1);
 }
 
+TEST_CASE(test_unsqueeze_multiple_axes_3)
+{
+    migraphx::shape s1{migraphx::shape::float_type, {3, 4, 5}};
+    migraphx::shape s2{migraphx::shape::float_type, {3, 4, 1, 5, 1, 1}};
+    expect_shape(s2, migraphx::make_op("unsqueeze", {{"axes", {2, 4, 5}}}), s1);
+}
+
+TEST_CASE(test_unsqueeze_multiple_axes_4)
+{
+    migraphx::shape s1{migraphx::shape::float_type, {3, 4, 5}};
+    migraphx::shape s2{migraphx::shape::float_type, {3, 4, 1, 5, 1, 1}};
+    expect_shape(s2, migraphx::make_op("unsqueeze", {{"axes", {5, 4, 2}}}), s1);
+}
+
+TEST_CASE(test_unsqueeze_multiple_axes_step)
+{
+    migraphx::shape s1{migraphx::shape::float_type, {3, 4, 10}};
+    migraphx::shape s2{migraphx::shape::float_type, {3, 4, 2, 5, 1, 1}};
+    expect_shape(s2, migraphx::make_op("unsqueeze", {{"axes", {2, 4, 5}}, {"steps", {2}}}), s1);
+}
+
 TEST_CASE(transpose_shape)
 {
     migraphx::shape input{migraphx::shape::float_type, {2, 2}};

test/ref_ops_test.cpp

@@ -873,6 +873,436 @@ TEST_CASE(contiguous_test)
     EXPECT(migraphx::verify_range(results_vector, data));
 }
 
+TEST_CASE(conv_dynamic_batch_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+
+    migraphx::shape input_dyn_shape{migraphx::shape::float_type,
+                                    {{1, 100, 0}, {3, 3, 0}, {4, 4, 0}, {4, 4, 0}}};
+    migraphx::shape weights_shape{migraphx::shape::float_type, {2, 3, 3, 3}};
+
+    auto input   = mm->add_parameter("X", input_dyn_shape);
+    auto weights = mm->add_parameter("W", weights_shape);
+    mm->add_instruction(migraphx::make_op("convolution", {{"padding", {1, 1}}, {"stride", {2, 2}}}),
+                        input,
+                        weights);
+
+    p.compile(migraphx::ref::target{});
+
+    std::vector<float> a = {
+        2.71567607,  -0.9960829,  0.91671127,  0.28140706,  0.63235772,  0.08077253,  0.80927712,
+        -0.59108931, -1.05421555, -2.76622486, -0.85044265, -0.52049929, 0.67726439,  -0.65290606,
+        0.02345525,  -0.33579525, 0.38901961,  1.05473483,  -1.31188095, 1.8963089,   -0.07265259,
+        0.947339,    0.41949373,  -0.70814759, 0.25892952,  1.07311416,  1.2571274,   -0.62318051,
+        -0.19951548, -0.94232577, -0.29393643, 0.42292568,  -0.80230367, 1.40909171,  0.63617158,
+        0.13900366,  1.09253144,  -0.15265895, 1.54781747,  0.72780299,  1.09189606,  -0.38068101,
+        0.97057933,  -0.58958799, 1.56188643,  0.21474874,  0.58725154,  -1.27097559, -0.03024297,
+        1.09437096,  -0.4897908,  0.34838957,  -1.31042492, -1.69069934, 0.86956722,  -0.40457946,
+        0.46691212,  1.29273605,  0.26464137,  0.22073045,  -1.02178168, 0.22163901,  -1.84387338,
+        0.75522131,  -0.45775682, -0.42241111, -1.50944722, 1.07256448,  -1.95876884, -0.28106022,
+        0.3341668,   2.13129425,  -1.14728117, -1.06555498, -0.298444,   -0.88322699, -0.65866792,
+        -2.06007552, 0.01374334,  0.45612028,  0.52715492,  1.01914406,  -1.72659791, 0.80650896,
+        0.16860051,  2.24112225,  -0.78620857, 0.36566174,  -0.07020134, -0.47976932, -0.68230027,
+        -0.94711417, -0.54506505, 1.66504931,  -0.71860826, 0.61132306};
+
+    std::vector<float> c = {
+        -0.14601797, -0.13000923, 0.06521662,  0.06178288,  -0.11083675, 0.10154136,  0.09990512,
+        0.06030385,  -0.11374587, -0.17523311, -0.14344215, 0.17802463,  0.06300922,  -0.15325832,
+        0.07066704,  0.05166031,  0.00615084,  -0.02606523, 0.08083995,  -0.17913306, 0.0624622,
+        0.0735731,   -0.04198661, -0.0164391,  -0.06374192, 0.16569914,  0.10681538,  0.07370754,
+        0.02802075,  0.00282027,  0.15104802,  -0.11084409, -0.00197773, 0.07924436,  0.03528272,
+        0.04765259,  -0.15896152, 0.07917164,  0.12125669,  -0.1154705,  -0.11999125, 0.12749968,
+        -0.06269585, 0.18658121,  -0.03944227, 0.0111798,   -0.17731084, 0.11789055,  -0.09982193,
+        0.08142821,  0.0729029,   0.11303909,  0.12735154,  0.03885292};
+
+    std::vector<float> sol = {-0.20817225,
+                              0.87965256,
+                              0.14958936,
+                              -1.24887264,
+                              -0.06540672,
+                              0.20778663,
+                              0.40456355,
+                              -0.99900877,
+                              0.4917807,
+                              0.1994698,
+                              0.64205718,
+                              0.37798831,
+                              -0.25315839,
+                              0.44276932,
+                              -0.16138598,
+                              0.79344082};
+
+    migraphx::shape input_fixed_shape0{migraphx::shape::float_type, {2, 3, 4, 4}};
+
+    migraphx::parameter_map params0;
+    params0["X"] = migraphx::argument(input_fixed_shape0, a.data());
+    params0["W"] = migraphx::argument(weights_shape, c.data());
+
+    auto result = p.eval(params0).back();
+    std::vector<float> results_vector(64);
+    result.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); });
+
+    EXPECT(migraphx::verify_range(results_vector, sol));
+
+    a = {2.71567607,  -0.9960829,  0.91671127,  0.28140706,  0.63235772,  0.08077253,  0.80927712,
+         -0.59108931, -1.05421555, -2.76622486, -0.85044265, -0.52049929, 0.67726439,  -0.65290606,
+         0.02345525,  -0.33579525, 0.38901961,  1.05473483,  -1.31188095, 1.8963089,   -0.07265259,
+         0.947339,    0.41949373,  -0.70814759, 0.25892952,  1.07311416,  1.2571274,   -0.62318051,
+         -0.19951548, -0.94232577, -0.29393643, 0.42292568,  -0.80230367, 1.40909171,  0.63617158,
+         0.13900366,  1.09253144,  -0.15265895, 1.54781747,  0.72780299,  1.09189606,  -0.38068101,
+         0.97057933,  -0.58958799, 1.56188643,  0.21474874,  0.58725154,  -1.27097559, -0.03024297,
+         1.09437096,  -0.4897908,  0.34838957,  -1.31042492, -1.69069934, 0.86956722,  -0.40457946,
+         0.46691212,  1.29273605,  0.26464137,  0.22073045,  -1.02178168, 0.22163901,  -1.84387338,
+         0.75522131,  -0.45775682, -0.42241111, -1.50944722, 1.07256448,  -1.95876884, -0.28106022,
+         0.3341668,   2.13129425,  -1.14728117, -1.06555498, -0.298444,   -0.88322699, -0.65866792,
+         -2.06007552, 0.01374334,  0.45612028,  0.52715492,  1.01914406,  -1.72659791, 0.80650896,
+         0.16860051,  2.24112225,  -0.78620857, 0.36566174,  -0.07020134, -0.47976932, -0.68230027,
+         -0.94711417, -0.54506505, 1.66504931,  -0.71860826, 0.61132306};
+
+    c = {-0.14601797, -0.13000923, 0.06521662,  0.06178288,  -0.11083675, 0.10154136,  0.09990512,
+         0.06030385,  -0.11374587, -0.17523311, -0.14344215, 0.17802463,  0.06300922,  -0.15325832,
+         0.07066704,  0.05166031,  0.00615084,  -0.02606523, 0.08083995,  -0.17913306, 0.0624622,
+         0.0735731,   -0.04198661, -0.0164391,  -0.06374192, 0.16569914,  0.10681538,  0.07370754,
+         0.02802075,  0.00282027,  0.15104802,  -0.11084409, -0.00197773, 0.07924436,  0.03528272,
+         0.04765259,  -0.15896152, 0.07917164,  0.12125669,  -0.1154705,  -0.11999125, 0.12749968,
+         -0.06269585, 0.18658121,  -0.03944227, 0.0111798,   -0.17731084, 0.11789055,  -0.09982193,
+         0.08142821,  0.0729029,   0.11303909,  0.12735154,  0.03885292};
+
+    sol = {-0.20817225,
+           0.87965256,
+           0.14958936,
+           -1.24887264,
+           -0.06540672,
+           0.20778663,
+           0.40456355,
+           -0.99900877};
+
+    migraphx::shape input_fixed_shape1{migraphx::shape::float_type, {1, 3, 4, 4}};
+
+    migraphx::parameter_map params1;
+    params1["X"] = migraphx::argument(input_fixed_shape1, a.data());
+    params1["W"] = migraphx::argument(weights_shape, c.data());
+
+    result = p.eval(params1).back();
+    result.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); });
+
+    EXPECT(migraphx::verify_range(results_vector, sol));
+}
+
+TEST_CASE(conv_dynamic_img_shape_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+
+    migraphx::shape input_dyn_shape{migraphx::shape::float_type,
+                                    {{1, 1, 0}, {3, 3, 0}, {4, 6, 0}, {4, 6, 0}}};
+    migraphx::shape weights_shape{migraphx::shape::float_type, {1, 3, 3, 3}};
+
+    auto input   = mm->add_parameter("X", input_dyn_shape);
+    auto weights = mm->add_parameter("W", weights_shape);
+    mm->add_instruction(migraphx::make_op("convolution", {{"padding", {0, 0}}, {"stride", {1, 1}}}),
+                        input,
+                        weights);
+
+    p.compile(migraphx::ref::target{});
+
+    std::vector<float> a = {0.28007596, 0.46114671, 0.12171969, 0.52260835, 0.40916841, 0.07163955,
+                            0.09896668, 0.98628836, 0.69406788, 0.44868846, 0.64017681, 0.27048886,
+                            0.30187397, 0.07334207, 0.05258557, 0.80747513, 0.81330534, 0.00497161,
+                            0.33005534, 0.08908686, 0.46794691, 0.61768946, 0.55104806, 0.13406187,
+                            0.70244284, 0.61296941, 0.46742536, 0.29712714, 0.91839388, 0.0834397,
+                            0.14476327, 0.37857075, 0.25922384, 0.61620963, 0.69455439, 0.70389431,
+                            0.77388606, 0.1752363,  0.74631394, 0.24604889, 0.53600244, 0.22116457,
+                            0.81217463, 0.10789447, 0.43083784, 0.63371852, 0.69742316, 0.09536905};
+
+    std::vector<float> c = {0.98411968, 0.2899219,  0.44638833, 0.30390816, 0.03989896, 0.2445332,
+                            0.32700131, 0.57517075, 0.06956476, 0.93079306, 0.19882314, 0.52940601,
+                            0.35624753, 0.35938406, 0.9111428,  0.88923574, 0.61040283, 0.2797513,
+                            0.15479768, 0.46534674, 0.16970931, 0.49704618, 0.07062198, 0.01678321,
+                            0.53150934, 0.39244495, 0.9963813};
+
+    std::vector<float> sol = {6.1329393, 4.3199925, 5.448438, 3.8497565};
+
+    migraphx::shape input_fixed_shape0{migraphx::shape::float_type, {1, 3, 4, 4}};
+
+    migraphx::parameter_map params0;
+    params0["X"] = migraphx::argument(input_fixed_shape0, a.data());
+    params0["W"] = migraphx::argument(weights_shape, c.data());
+
+    auto result = p.eval(params0).back();
+    std::vector<float> results_vector(72);
+    result.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); });
+
+    EXPECT(migraphx::verify_range(results_vector, sol));
+
+    a = {0.95600171, 0.20768181, 0.82844489, 0.14928212, 0.51280462, 0.1359196,  0.68903648,
+         0.84174772, 0.425509,   0.956926,   0.82533291, 0.33821531, 0.57576055, 0.75330186,
+         0.82710394, 0.93343847, 0.14499469, 0.74558021, 0.13935139, 0.90652876, 0.22611443,
+         0.85323975, 0.30631787, 0.96983037, 0.51783421, 0.32247456, 0.28243352, 0.605865,
+         0.33376446, 0.67864877, 0.15442507, 0.24977552, 0.86989425, 0.60036782, 0.26198306,
+         0.1494149,  0.13678915, 0.24892094, 0.38282467, 0.64907906, 0.83756376, 0.77603195,
+         0.33951558, 0.14856874, 0.45701939, 0.43786436, 0.57421759, 0.37326922, 0.63382506,
+         0.11464436, 0.23309047, 0.76724102, 0.98712427, 0.80800108, 0.84296564, 0.79568268,
+         0.45684131, 0.73867068, 0.57845499, 0.45073557, 0.27102442, 0.86460315, 0.06865567,
+         0.81673446, 0.881835,   0.42351639, 0.83322931, 0.34101671, 0.51979151, 0.54920645,
+         0.19287718, 0.33321689, 0.27752456, 0.45755893, 0.67484562, 0.68383122, 0.52361312,
+         0.46437257, 0.50862936, 0.32460429, 0.1726007,  0.29933345, 0.64856728, 0.06471591,
+         0.63370843, 0.27900152, 0.18595992, 0.48904812, 0.35368508, 0.09620202};
+
+    c = {0.709561,   0.7916206,  0.0443115,  0.62592275, 0.2498623,  0.42725624, 0.7905135,
+         0.53160169, 0.01303743, 0.01987505, 0.39041803, 0.89530203, 0.23155373, 0.44435213,
+         0.14407301, 0.80968594, 0.38216188, 0.35692557, 0.2568538,  0.83587388, 0.43654904,
+         0.04974508, 0.80375029, 0.25350374, 0.1820275,  0.23369029, 0.54358755};
+
+    sol = {6.305986,
+           5.564665,
+           6.122996,
+           5.7262855,
+           5.5546584,
+           5.779489,
+           5.798161,
+           5.160476,
+           6.702436,
+           5.4851074,
+           6.227567,
+           5.2016754};
+    migraphx::shape input_fixed_shape1{migraphx::shape::float_type, {1, 3, 6, 5}};
+
+    migraphx::parameter_map params1;
+    params1["X"] = migraphx::argument(input_fixed_shape1, a.data());
+    params1["W"] = migraphx::argument(weights_shape, c.data());
+
+    result = p.eval(params1).back();
+    result.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); });
+
+    EXPECT(migraphx::verify_range(results_vector, sol));
+}
+
+TEST_CASE(conv_dynamic_weights_shape_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+
+    migraphx::shape input_shape{migraphx::shape::float_type, {1, 3, 4, 4}};
+    migraphx::shape weights_shape{migraphx::shape::float_type,
+                                  {{1, 1, 0}, {3, 3, 0}, {2, 3, 0}, {2, 3, 0}}};
+
+    auto input   = mm->add_parameter("X", input_shape);
+    auto weights = mm->add_parameter("W", weights_shape);
+    mm->add_instruction(migraphx::make_op("convolution", {{"padding", {0, 0}}, {"stride", {1, 1}}}),
+                        input,
+                        weights);
+
+    p.compile(migraphx::ref::target{});
+
+    std::vector<float> a = {0.28007596, 0.46114671, 0.12171969, 0.52260835, 0.40916841, 0.07163955,
+                            0.09896668, 0.98628836, 0.69406788, 0.44868846, 0.64017681, 0.27048886,
+                            0.30187397, 0.07334207, 0.05258557, 0.80747513, 0.81330534, 0.00497161,
+                            0.33005534, 0.08908686, 0.46794691, 0.61768946, 0.55104806, 0.13406187,
+                            0.70244284, 0.61296941, 0.46742536, 0.29712714, 0.91839388, 0.0834397,
+                            0.14476327, 0.37857075, 0.25922384, 0.61620963, 0.69455439, 0.70389431,
+                            0.77388606, 0.1752363,  0.74631394, 0.24604889, 0.53600244, 0.22116457,
+                            0.81217463, 0.10789447, 0.43083784, 0.63371852, 0.69742316, 0.09536905};
+
+    std::vector<float> c   = {0.98411968,
+                            0.2899219,
+                            0.44638833,
+                            0.30390816,
+                            0.03989896,
+                            0.2445332,
+                            0.32700131,
+                            0.57517075,
+                            0.06956476,
+                            0.93079306,
+                            0.19882314,
+                            0.52940601};
+    std::vector<float> sol = {1.9939406,
+                              2.2703054,
+                              1.8896171,
+                              2.062202,
+                              2.3035214,
+                              1.629366,
+                              2.1606991,
+                              2.1917608,
+                              1.6797699};
+
+    migraphx::shape weight_fixed_shape0{migraphx::shape::float_type, {1, 3, 2, 2}};
+
+    migraphx::parameter_map params0;
+    params0["X"] = migraphx::argument(input_shape, a.data());
+    params0["W"] = migraphx::argument(weight_fixed_shape0, c.data());
+
+    auto result = p.eval(params0).back();
+    std::vector<float> results_vector(72);
+    result.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); });
+
+    EXPECT(migraphx::verify_range(results_vector, sol));
+
+    c   = {0.98411968, 0.2899219,  0.44638833, 0.30390816, 0.03989896, 0.2445332,  0.32700131,
+         0.57517075, 0.06956476, 0.93079306, 0.19882314, 0.52940601, 0.35624753, 0.35938406,
+         0.9111428,  0.88923574, 0.61040283, 0.2797513,  0.15479768, 0.46534674, 0.16970931,
+         0.49704618, 0.07062198, 0.01678321, 0.53150934, 0.39244495, 0.9963813};
+    sol = {6.1329393, 4.3199925, 5.448438, 3.8497565};
+    migraphx::shape weights_fixed_shape1{migraphx::shape::float_type, {1, 3, 3, 3}};
+
+    migraphx::parameter_map params1;
+    params1["X"] = migraphx::argument(input_shape, a.data());
+    params1["W"] = migraphx::argument(weights_fixed_shape1, c.data());
+
+    result = p.eval(params1).back();
+    result.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); });
+
+    EXPECT(migraphx::verify_range(results_vector, sol));
+}
+
+TEST_CASE(conv_dynamic_img_same_upper_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+
+    migraphx::shape input_dyn_shape{migraphx::shape::float_type,
+                                    {{1, 1, 0}, {3, 3, 0}, {4, 6, 0}, {4, 6, 0}}};
+    migraphx::shape weights_shape{migraphx::shape::float_type, {1, 3, 3, 3}};
+
+    auto input   = mm->add_parameter("X", input_dyn_shape);
+    auto weights = mm->add_parameter("W", weights_shape);
+    mm->add_instruction(
+        migraphx::make_op("convolution",
+                          {{"stride", {1, 1}},
+                           {"padding_mode", migraphx::op::padding_mode_t::same_upper},
+                           {"use_dynamic_same_auto_pad", true}}),
+        input,
+        weights);
+
+    p.compile(migraphx::ref::target{});
+
+    std::vector<float> a = {0.63321185, 0.6466339,  0.8515352,  0.44240063, 0.5018913,  0.5068494,
+                            0.75330657, 0.7383877,  0.15870683, 0.8171611,  0.56118083, 0.87004256,
+                            0.24401724, 0.8815178,  0.4222333,  0.27191755,
+
+                            0.41633207, 0.2460619,  0.32004243, 0.6962248,  0.12284133, 0.2620491,
+                            0.96931046, 0.6030955,  0.7623861,  0.2395751,  0.61440414, 0.577285,
+                            0.80087787, 0.12776066, 0.26566318, 0.46569306,
+
+                            0.96701574, 0.3850145,  0.14165345, 0.5887347,  0.7152134,  0.5295342,
+                            0.6303507,  0.4037548,  0.18556239, 0.79416305, 0.29107493, 0.18770285,
+                            0.6870904,  0.30701008, 0.314684,   0.91075855};
+
+    std::vector<float> c = {
+        2.8150102e-01, 3.3198616e-01, 9.5149356e-01, 7.4039467e-02, 9.6555042e-01,
+        2.8815505e-01, 2.5100240e-01, 5.2186239e-01, 2.3850012e-01,
+
+        8.2963020e-01, 3.0763101e-04, 6.7026985e-01, 1.4260857e-01, 9.7517288e-01,
+        3.6847427e-02, 8.5804445e-01, 7.3440993e-01, 6.7948365e-01,
+
+        7.9253986e-02, 7.3943835e-01, 1.7813577e-01, 1.0780835e-01, 4.2304707e-01,
+        4.0084350e-01, 1.1114500e-01, 4.4846520e-01, 5.0109702e-01};
+
+    std::vector<float> sol = {3.013387,
+                              3.7111127,
+                              4.2946506,
+                              3.579301,
+                              4.5306826,
+                              6.1262493,
+                              6.332169,
+                              4.495293,
+                              4.46013,
+                              6.0938954,
+                              5.848162,
+                              4.514299,
+                              2.9587686,
+                              4.117671,
+                              3.5187216,
+                              2.3236327};
+
+    migraphx::shape input_fixed_shape0{migraphx::shape::float_type, {1, 3, 4, 4}};
+
+    migraphx::parameter_map params0;
+    params0["X"] = migraphx::argument(input_fixed_shape0, a.data());
+    params0["W"] = migraphx::argument(weights_shape, c.data());
+
+    auto result = p.eval(params0).back();
+    std::vector<float> results_vector(16);
+    result.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); });
+    EXPECT(migraphx::verify_range(results_vector, sol));
+}
+
+TEST_CASE(conv_dynamic_kernel_same_lower_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+
+    migraphx::shape input_shape{migraphx::shape::float_type, {1, 3, 4, 4}};
+    migraphx::shape weights_shape{migraphx::shape::float_type,
+                                  {{1, 1, 0}, {3, 3, 0}, {2, 3, 0}, {2, 3, 0}}};
+
+    auto input   = mm->add_parameter("X", input_shape);
+    auto weights = mm->add_parameter("W", weights_shape);
+    mm->add_instruction(
+        migraphx::make_op("convolution",
+                          {{"stride", {1, 1}},
+                           {"padding_mode", migraphx::op::padding_mode_t::same_lower},
+                           {"use_dynamic_same_auto_pad", true}}),
+        input,
+        weights);
+
+    p.compile(migraphx::ref::target{});
+
+    std::vector<float> a   = {0.63321185, 0.6466339,  0.8515352,  0.44240063, 0.5018913,  0.5068494,
+                            0.75330657, 0.7383877,  0.15870683, 0.8171611,  0.56118083, 0.87004256,
+                            0.24401724, 0.8815178,  0.4222333,  0.27191755,
+
+                            0.41633207, 0.2460619,  0.32004243, 0.6962248,  0.12284133, 0.2620491,
+                            0.96931046, 0.6030955,  0.7623861,  0.2395751,  0.61440414, 0.577285,
+                            0.80087787, 0.12776066, 0.26566318, 0.46569306,
+
+                            0.96701574, 0.3850145,  0.14165345, 0.5887347,  0.7152134,  0.5295342,
+                            0.6303507,  0.4037548,  0.18556239, 0.79416305, 0.29107493, 0.18770285,
+                            0.6870904,  0.30701008, 0.314684,   0.91075855};
+    std::vector<float> c   = {2.8150102e-01,
+                            3.3198616e-01,
+                            9.5149356e-01,
+                            7.4039467e-02,
+
+                            9.6555042e-01,
+                            2.8815505e-01,
+                            2.5100240e-01,
+                            5.2186239e-01,
+
+                            2.3850012e-01,
+                            8.2963020e-01,
+                            3.0763101e-04,
+                            6.7026985e-01};
+    std::vector<float> sol = {2.453681,
+                              2.536207,
+                              3.0187201,
+                              1.7912633,
+                              2.1738236,
+                              2.9695358,
+                              3.2319589,
+                              1.859269,
+                              2.5953722,
+                              2.50734,
+                              2.7736917,
+                              1.2229807,
+                              1.5900216,
+                              0.9225286,
+                              1.43048,
+                              0.74341124};
+
+    migraphx::shape weight_fixed_shape0{migraphx::shape::float_type, {1, 3, 2, 2}};
+
+    migraphx::parameter_map params0;
+    params0["X"] = migraphx::argument(input_shape, a.data());
+    params0["W"] = migraphx::argument(weight_fixed_shape0, c.data());
+
+    auto result = p.eval(params0).back();
+    std::vector<float> results_vector(16);
+    result.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); });
+    EXPECT(migraphx::verify_range(results_vector, sol));
+}
+
 TEST_CASE(conv2d_padding_stride_test)
 {
     migraphx::program p;
@@ -3030,6 +3460,80 @@ TEST_CASE(min_test)
     EXPECT(migraphx::verify_range(results_vector, gold));
 }
 
+TEST_CASE(fmod_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    migraphx::shape s{migraphx::shape::int32_type, {3}};
+    auto l0       = mm->add_literal(migraphx::literal{s, {-7, 8, -3}});
+    auto l1       = mm->add_literal(migraphx::literal{s, {2, 4, 6}});
+    auto l2       = mm->add_literal(migraphx::literal{s, {7, 5, 9}});
+    auto curr_mod = mm->add_instruction(migraphx::make_op("fmod"), l0, l1);
+    mm->add_instruction(migraphx::make_op("fmod"), curr_mod, l2);
+    p.compile(migraphx::ref::target{});
+    auto result = p.eval({}).back();
+    std::vector<float> results_vector(4);
+    result.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); });
+    std::vector<float> gold{-1, 0, -3};
+    EXPECT(migraphx::verify_range(results_vector, gold));
+}
+
+TEST_CASE(fmod_float_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    migraphx::shape s{migraphx::shape::float_type, {3}};
+    auto l0       = mm->add_literal(migraphx::literal{s, {-7.2f, 8.5f, -3.3f}});
+    auto l1       = mm->add_literal(migraphx::literal{s, {2.0f, 4.0f, 6.0f}});
+    auto l2       = mm->add_literal(migraphx::literal{s, {7.0f, 5.0f, 9.0f}});
+    auto curr_mod = mm->add_instruction(migraphx::make_op("fmod"), l0, l1);
+    mm->add_instruction(migraphx::make_op("fmod"), curr_mod, l2);
+
+    p.compile(migraphx::ref::target{});
+    auto result = p.eval({}).back();
+    std::vector<float> results_vector(4);
+    result.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); });
+    std::vector<float> gold{-1.2f, 0.5f, -3.3f};
+    EXPECT(migraphx::verify_range(results_vector, gold));
+}
+
+TEST_CASE(mod_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    migraphx::shape s{migraphx::shape::int32_type, {3}};
+    auto l0       = mm->add_literal(migraphx::literal{s, {-3, 8, -7}});
+    auto l1       = mm->add_literal(migraphx::literal{s, {3, 3, 3}});
+    auto l2       = mm->add_literal(migraphx::literal{s, {10, 2, 9}});
+    auto curr_mod = mm->add_instruction(migraphx::make_op("mod"), l0, l1);
+    mm->add_instruction(migraphx::make_op("mod"), curr_mod, l2);
+    p.compile(migraphx::ref::target{});
+    auto result = p.eval({}).back();
+    std::vector<float> results_vector(4);
+    result.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); });
+    std::vector<float> gold{0, 0, 2};
+    EXPECT(migraphx::verify_range(results_vector, gold));
+}
+
+TEST_CASE(mod_float_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    migraphx::shape s{migraphx::shape::float_type, {3}};
+    auto l0       = mm->add_literal(migraphx::literal{s, {-3.0f, 8.5f, -7.0f}});
+    auto l1       = mm->add_literal(migraphx::literal{s, {2.0f, 3.0f, 3.0f}});
+    auto l2       = mm->add_literal(migraphx::literal{s, {3.0f, 3.0f, 4.0f}});
+    auto curr_mod = mm->add_instruction(migraphx::make_op("mod"), l0, l1);
+    mm->add_instruction(migraphx::make_op("mod"), curr_mod, l2);
+
+    p.compile(migraphx::ref::target{});
+    auto result = p.eval({}).back();
+    std::vector<float> results_vector(4);
+    result.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); });
+    std::vector<float> gold{1.0f, 2.5f, 2.0f};
+    EXPECT(migraphx::verify_range(results_vector, gold));
+}
+
 TEST_CASE(mul_test)
 {
     migraphx::program p;

test/shape_test.cpp

@@ -38,7 +38,6 @@ TEST_CASE(test_shape_default)
     EXPECT(s.elements() == 0);
     EXPECT(s.bytes() == 0);
 }
-
 TEST_CASE(test_shape_assign)
 {
     migraphx::shape s1{migraphx::shape::float_type, {100, 32, 8, 8}};
@@ -65,6 +64,118 @@ TEST_CASE(test_shape_standard)
     EXPECT(not s.broadcasted());
 }
 
+TEST_CASE(test_shape_min_max_opt)
+{
+    migraphx::shape s{migraphx::shape::float_type, {2, 2, 3}, {6, 3, 1}};
+    EXPECT(s.min_lens() == s.lens());
+    EXPECT(s.max_lens() == s.lens());
+    EXPECT(s.opt_lens() == s.lens());
+}
+
+TEST_CASE(test_shape_dynamic_fixed)
+{
+    migraphx::shape s{migraphx::shape::float_type, {{2, 2, 0}, {2, 2, 0}, {3, 3, 0}}};
+    EXPECT(not s.standard());
+    EXPECT(not s.packed());
+    EXPECT(not s.transposed());
+    EXPECT(not s.broadcasted());
+    EXPECT(s.dynamic());
+    EXPECT(s.dyn_dims().size() == 3);
+    EXPECT(s.dyn_dims().at(0).is_fixed());
+    EXPECT(not s.dyn_dims().at(0).has_optimal());
+    EXPECT(s.min_lens() == std::vector<std::size_t>{2, 2, 3});
+    EXPECT(s.max_lens() == std::vector<std::size_t>{2, 2, 3});
+    EXPECT(s.opt_lens() == std::vector<std::size_t>{0, 0, 0});
+    EXPECT(s.bytes() == 2 * 2 * 3 * sizeof(float));
+}
+
+TEST_CASE(test_shape_dynamic_not_fixed)
+{
+    using migraphx::shape;
+    std::vector<shape::dynamic_dimension> dims = {};
+    dims.push_back(shape::dynamic_dimension{2, 5, 2});
+    dims.push_back(shape::dynamic_dimension{2, 8, 0});
+    migraphx::shape s{migraphx::shape::float_type, dims};
+    EXPECT(not s.standard());
+    EXPECT(not s.packed());
+    EXPECT(not s.transposed());
+    EXPECT(not s.broadcasted());
+    EXPECT(s.dynamic());
+    EXPECT(s.dyn_dims().size() == 2);
+    EXPECT(not s.dyn_dims().at(0).is_fixed());
+    EXPECT(s.dyn_dims().at(0).has_optimal());
+    EXPECT(s.min_lens() == std::vector<std::size_t>{2, 2});
+    EXPECT(s.max_lens() == std::vector<std::size_t>{5, 8});
+    EXPECT(s.opt_lens() == std::vector<std::size_t>{2, 0});
+    EXPECT(s.bytes() == 5 * 8 * sizeof(float));
+}
+
+TEST_CASE(test_shape_dynamic_compares)
+{
+    using migraphx::shape;
+    auto a = shape::dynamic_dimension{2, 5, 2};
+    auto b = a;
+    auto c = shape::dynamic_dimension{2, 5, 2};
+    auto d = shape::dynamic_dimension{3, 8, 4};
+    EXPECT(a == b);
+    EXPECT(a == c);
+    EXPECT(a != d);
+
+    migraphx::shape s0{shape::float_type, {a, d}};
+    migraphx::shape s1 = s0;
+    migraphx::shape s2{shape::float_type, {a, d}};
+    migraphx::shape s3{shape::int32_type, {a}};
+    EXPECT(s0 == s1);
+    EXPECT(s0 == s2);
+    EXPECT(s0 != s3);
+
+    std::stringstream ss0;
+    std::stringstream ss1;
+    std::stringstream ss3;
+    ss0 << s0;
+    ss1 << s1;
+    ss3 << s3;
+    EXPECT(ss0.str() == ss1.str());
+    EXPECT(ss0.str() != ss3.str());
+}
+
+TEST_CASE(test_shape_dynamic_errors)
+{
+    using migraphx::shape;
+    std::vector<shape::dynamic_dimension> dims = {};
+    dims.push_back(shape::dynamic_dimension{2, 5, 2});
+    dims.push_back(shape::dynamic_dimension{2, 8, 0});
+    migraphx::shape s{shape::float_type, dims};
+    EXPECT(test::throws([&] { s.elements(); }));
+    EXPECT(test::throws([&] { s.index({0, 1}); }));
+    EXPECT(test::throws([&] { s.index(1); }));
+    EXPECT(test::throws([&] { s.index(std::vector<std::size_t>{0, 1}); }));
+    EXPECT(test::throws([&] { s.with_lens({3, 5}); }));
+    EXPECT(test::throws([&] { s.with_lens(shape::float_type, {3, 5}); }));
+}
+
+TEST_CASE(test_shape_dynamic_serialize)
+{
+    using migraphx::shape;
+    std::vector<shape::dynamic_dimension> dims1 = {};
+    dims1.push_back(shape::dynamic_dimension{2, 5, 2});
+    dims1.push_back(shape::dynamic_dimension{2, 8, 0});
+    migraphx::shape s1{shape::float_type, dims1};
+    auto v1 = migraphx::to_value(s1);
+
+    std::vector<shape::dynamic_dimension> dims2 = {};
+    dims2.push_back(shape::dynamic_dimension{2, 5, 2});
+    migraphx::shape s2{shape::uint64_type, dims2};
+    auto v2 = migraphx::to_value(s2);
+    EXPECT(v1 != v2);
+
+    auto s3 = migraphx::from_value<shape>(v1);
+    EXPECT(s3 == s1);
+    auto s4 = migraphx::from_value<shape>(v2);
+    EXPECT(s4 == s2);
+    EXPECT(s3 != s4);
+}
+
 TEST_CASE(test_shape_packed)
 {
     migraphx::shape s{migraphx::shape::float_type, {2, 2}, {2, 1}};

test/simplify_reshapes_test.cpp

@@ -1141,6 +1141,38 @@ TEST_CASE(transpose_contiguous_reshape_binary_broadcast)
     EXPECT(m1 == m2);
 }
 
+TEST_CASE(transpose_unsqueeze_concat)
+{
+    migraphx::module m1;
+    {
+        auto l0 = m1.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 2, 1, 1}});
+        auto lt0 =
+            m1.add_instruction(migraphx::make_op("transpose", {{"permutation", {0, 2, 3, 1}}}), l0);
+        auto l1 = m1.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {1, 2, 1, 1}});
+        auto lt1 =
+            m1.add_instruction(migraphx::make_op("transpose", {{"permutation", {0, 2, 3, 1}}}), l1);
+        auto l2 = m1.add_parameter("2", migraphx::shape{migraphx::shape::float_type, {1, 2, 1, 1}});
+        auto lt2 =
+            m1.add_instruction(migraphx::make_op("transpose", {{"permutation", {0, 2, 3, 1}}}), l2);
+        std::vector<migraphx::instruction_ref> args{lt0, lt1, lt2};
+        std::vector<migraphx::instruction_ref> unsqueezed_args;
+        int64_t axis = 3;
+
+        std::transform(
+            args.begin(),
+            args.end(),
+            std::back_inserter(unsqueezed_args),
+            [&](migraphx::instruction_ref arg) {
+                return m1.add_instruction(migraphx::make_op("unsqueeze", {{"axes", {axis}}}), arg);
+            });
+        m1.add_instruction(migraphx::make_op("concat", {{"axis", axis}}), unsqueezed_args);
+    }
+    // TODO: This could be simplified to a single transpose after concat
+    migraphx::module m2 = m1;
+    run_pass(m1);
+    EXPECT(m1 == m2);
+}
+
 TEST_CASE(transpose_slice)
 {
     migraphx::module m1;