Merge branch 'develop' into jit-reduce-reg

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

@@ -56,6 +56,16 @@ struct id
     }
 };
 
+template <class T>
+struct convert_to
+{
+    template <class U>
+    MIGRAPHX_DEVICE_CONSTEXPR auto operator()(U x) const
+    {
+        return convert<T>(x);
+    }
+};
+
 struct mean
 {
     index_int item_num = 1;

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

@@ -128,6 +128,7 @@ struct shape
         result[0] = tidx;
         return result;
     }
+
     /// Convert multi-index into a single index
     constexpr index_int single(index_array idx) const
     {

src/targets/gpu/lowering.cpp

@@ -90,7 +90,6 @@ struct miopen_apply
 
         add_extend_op("argmax");
         add_extend_op("argmin");
-        add_extend_op("gather");
         add_extend_op("logsoftmax");
         add_extend_op("lrn");
         add_extend_op("multinomial");

src/targets/ref/lowering.cpp

@@ -346,10 +346,10 @@ struct ref_pad
 
     std::string name() const { return "ref::pad"; }
     shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
-    argument compute(context&, const shape& output_shape, std::vector<argument> args) const
+    argument compute(context&, const dyn_output& dyn_out, std::vector<argument> args) const
     {
-        assert(output_shape.standard());
-        argument result{output_shape};
+        assert(dyn_out.computed_shape.standard());
+        argument result{dyn_out.computed_shape};
         result.visit([&](auto output) {
             using type = typename decltype(output)::value_type;
             std::fill(output.begin(), output.end(), pad_clamp<type>(op.value));

test/onnx/gen_onnx.py

@@ -23,7 +23,7 @@
 #####################################################################################
 # This script generates onnx files for MIGraphX onnx operator tests.
 # To generate an individual onnx file, you can use the following
-# command: python -c "import gen_onnx; gen_onnx.{test_name}_test()"
+# command: python3 -c "import gen_onnx; gen_onnx.{test_name}_test()"
 import numpy as np
 import onnx
 from onnx import helper
@@ -1121,6 +1121,24 @@ def conv_dynamic_batch_test():
     return ([node], [x, y], [out])
 
 
+@onnx_test()
+def conv_dynamic_bias_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT,
+                                      [None, 3, 32, 32])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 5, 5])
+    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1])
+    out = helper.make_tensor_value_info('3', TensorProto.FLOAT,
+                                        [None, 2, 28, 28])
+
+    node = onnx.helper.make_node('Conv',
+                                 inputs=['0', '1', '2'],
+                                 outputs=['3'],
+                                 dilations=[1, 1],
+                                 strides=[1, 1])
+
+    return ([node], [x, y, z], [out])
+
+
 @onnx_test()
 def conv_dynamic_img_test():
     x = helper.make_tensor_value_info('0', TensorProto.FLOAT,
@@ -3545,6 +3563,81 @@ def matmul_vv_test():
     return ([node], [m1, m2], [y])
 
 
+@onnx_test()
+def matmul_dyn_mm_test():
+    m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [None, 7])
+    m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7, None])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [None, None])
+
+    node = onnx.helper.make_node(
+        'MatMul',
+        inputs=['1', '2'],
+        outputs=['y'],
+    )
+
+    return ([node], [m1, m2], [y])
+
+
+@onnx_test()
+def matmul_dyn_mv_test():
+    m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [None, 7])
+    m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [None, 1])
+
+    node = onnx.helper.make_node(
+        'MatMul',
+        inputs=['1', '2'],
+        outputs=['y'],
+    )
+
+    return ([node], [m1, m2], [y])
+
+
+@onnx_test()
+def matmul_dyn_vm_test():
+    m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [7])
+    m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7, None])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, None])
+
+    node = onnx.helper.make_node(
+        'MatMul',
+        inputs=['1', '2'],
+        outputs=['y'],
+    )
+
+    return ([node], [m1, m2], [y])
+
+
+@onnx_test()
+def matmul_dyn_vv_test():
+    m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [None])
+    m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [None])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1])
+
+    node = onnx.helper.make_node(
+        'MatMul',
+        inputs=['1', '2'],
+        outputs=['y'],
+    )
+
+    return ([node], [m1, m2], [y])
+
+
+@onnx_test()
+def matmul_dyn_broadcast_error():
+    m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [7])
+    m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [5, 7, None])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [5, None])
+
+    node = onnx.helper.make_node(
+        'MatMul',
+        inputs=['1', '2'],
+        outputs=['y'],
+    )
+
+    return ([node], [m1, m2], [y])
+
+
 @onnx_test()
 def matmulinteger_test():
     m1 = helper.make_tensor_value_info('1', TensorProto.INT8, [3, 6, 16])
@@ -3560,6 +3653,21 @@ def matmulinteger_test():
     return ([node], [m1, m2], [y])
 
 
+@onnx_test()
+def matmulinteger_dyn_error():
+    m1 = helper.make_tensor_value_info('1', TensorProto.INT8, [None, 6, 16])
+    m2 = helper.make_tensor_value_info('2', TensorProto.INT8, [None, 16, 8])
+    y = helper.make_tensor_value_info('y', TensorProto.INT32, [None, 6, 8])
+
+    node = onnx.helper.make_node(
+        'MatMulInteger',
+        inputs=['1', '2'],
+        outputs=['y'],
+    )
+
+    return ([node], [m1, m2], [y])
+
+
 @onnx_test()
 def max_test():
     a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
@@ -4198,6 +4306,53 @@ def pad_reflect_multiaxis_test():
     return ([arg_pad, node], [x], [y])
 
 
+@onnx_test()
+def pad_attr_dyn_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [None, None])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [None, None])
+
+    node = onnx.helper.make_node('Pad',
+                                 inputs=['0'],
+                                 pads=[1, 1, 1, 1],
+                                 outputs=['1'])
+
+    return ([node], [x], [y])
+
+
+@onnx_test()
+def pad_cnst_dyn_test():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [None, None])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [None, None])
+
+    sizes = np.array([0, 2, 0, 1])
+    pad_tensor = helper.make_tensor(name='pad_size',
+                                    data_type=TensorProto.INT32,
+                                    dims=sizes.shape,
+                                    vals=sizes.astype(int))
+    arg_pad = onnx.helper.make_node('Constant',
+                                    inputs=[],
+                                    outputs=['arg_pad'],
+                                    value=pad_tensor)
+
+    node = onnx.helper.make_node('Pad', inputs=['0', 'arg_pad'], outputs=['1'])
+
+    return ([arg_pad, node], [x], [y])
+
+
+@onnx_test()
+def pad_dyn_reflect_error():
+    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [None, None])
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [None, None])
+
+    node = onnx.helper.make_node('Pad',
+                                 mode='reflect',
+                                 inputs=['0'],
+                                 pads=[0, 2, 0, 1],
+                                 outputs=['1'])
+
+    return ([node], [x], [y])
+
+
 @onnx_test()
 def pow_test():
     arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
@@ -4675,6 +4830,34 @@ def reducel1_test():
     return ([node], [x], [y])
 
 
+@onnx_test
+def reducel1_dyn_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [None])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [None])
+    axes = [-2]
+
+    node = onnx.helper.make_node('ReduceL1',
+                                 inputs=['x'],
+                                 outputs=['y'],
+                                 axes=axes,
+                                 keepdims=0)
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def reducel1_dyn_noaxes_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [None])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [None])
+
+    node = onnx.helper.make_node('ReduceL1',
+                                 inputs=['x'],
+                                 outputs=['y'],
+                                 keepdims=0)
+
+    return ([node], [x], [y])
+
+
 @onnx_test()
 def reducel2_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
@@ -4724,6 +4907,22 @@ def reduce_log_sum_exp_test():
 def reducemax_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
     y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 6])
+
+    axes = [2]
+
+    node = onnx.helper.make_node('ReduceMax',
+                                 inputs=['x'],
+                                 outputs=['y'],
+                                 axes=axes,
+                                 keepdims=0)
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def reducemax_dyn_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [None, 4, 5, 6])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [None, 4, 6])
     axes = [2]
 
     node = onnx.helper.make_node('ReduceMax',

test/onnx/onnx_test.cpp

@@ -1118,6 +1118,25 @@ TEST_CASE(conv_dynamic_batch_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(conv_dynamic_bias_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    auto x0  = mm->add_parameter(
+        "0", {migraphx::shape::float_type, {{1, 6, 0}, {3, 3, 0}, {32, 32, 0}, {32, 32, 0}}});
+    auto x1 = mm->add_parameter("1", {migraphx::shape::float_type, {1, 3, 5, 5}});
+    auto x2 = mm->add_parameter("2", {migraphx::shape::float_type, {1}});
+    auto x3 = mm->add_instruction(migraphx::make_op("convolution"), x0, x1);
+    auto x4 = mm->add_instruction(migraphx::make_op("broadcast", {{"axis", 1}}), x2, x3);
+    auto x5 = mm->add_instruction(migraphx::make_op("add"), x3, x4);
+    mm->add_return({x5});
+
+    migraphx::onnx_options options;
+    options.default_dyn_dim_value = {1, 6, 0};
+    auto prog                     = migraphx::parse_onnx("conv_dynamic_bias_test.onnx", options);
+    EXPECT(p == prog);
+}
+
 TEST_CASE(conv_dynamic_img_test)
 {
     migraphx::program p;
@@ -3413,6 +3432,92 @@ TEST_CASE(matmul_vv_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(matmul_dyn_mm_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    auto l0  = mm->add_parameter(
+        "1", migraphx::shape{migraphx::shape::float_type, {{4, 8, 6}, {7, 7, 0}}});
+    auto l1 = mm->add_parameter(
+        "2", migraphx::shape{migraphx::shape::float_type, {{7, 7, 0}, {1, 5, 3}}});
+    auto ret = migraphx::add_apply_alpha_beta(*mm, {l0, l1}, migraphx::make_op("dot"), 1.0f, 0.0f);
+    mm->add_return({ret});
+
+    migraphx::onnx_options options;
+    options.map_dyn_input_dims["1"] = {{4, 8, 6}, {7, 7, 0}};
+    options.map_dyn_input_dims["2"] = {{7, 7, 0}, {1, 5, 3}};
+    auto prog                       = parse_onnx("matmul_dyn_mm_test.onnx", options);
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(matmul_dyn_mv_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    auto l0  = mm->add_parameter(
+        "1", migraphx::shape{migraphx::shape::float_type, {{4, 8, 6}, {7, 7, 0}}});
+    auto l1  = mm->add_parameter("2", migraphx::shape{migraphx::shape::float_type, {7}});
+    auto sl1 = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1}}}), l1);
+    auto res = migraphx::add_apply_alpha_beta(*mm, {l0, sl1}, migraphx::make_op("dot"), 1.0f, 0.0f);
+    auto ret = mm->add_instruction(migraphx::make_op("squeeze", {{"axes", {1}}}), res);
+    mm->add_return({ret});
+
+    migraphx::onnx_options options;
+    options.map_dyn_input_dims["1"] = {{4, 8, 6}, {7, 7, 0}};
+    auto prog                       = parse_onnx("matmul_dyn_mv_test.onnx", options);
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(matmul_dyn_vm_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    auto l0  = mm->add_parameter("1", migraphx::shape{migraphx::shape::float_type, {7}});
+    auto l1  = mm->add_parameter(
+        "2", migraphx::shape{migraphx::shape::float_type, {{7, 7, 0}, {4, 10, 8}}});
+    auto sl0 = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {0}}}), l0);
+    auto res = migraphx::add_apply_alpha_beta(*mm, {sl0, l1}, migraphx::make_op("dot"), 1.0f, 0.0f);
+    auto ret = mm->add_instruction(migraphx::make_op("squeeze", {{"axes", {0}}}), res);
+    mm->add_return({ret});
+
+    migraphx::onnx_options options;
+    options.map_dyn_input_dims["2"] = {{7, 7, 0}, {4, 10, 8}};
+    auto prog                       = parse_onnx("matmul_dyn_vm_test.onnx", options);
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(matmul_dyn_vv_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    migraphx::shape::dynamic_dimension dd{5, 8, 7};
+    auto l0  = mm->add_parameter("1", migraphx::shape{migraphx::shape::float_type, {dd}});
+    auto l1  = mm->add_parameter("2", migraphx::shape{migraphx::shape::float_type, {dd}});
+    auto sl0 = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {0}}}), l0);
+    auto sl1 = mm->add_instruction(migraphx::make_op("unsqueeze", {{"axes", {1}}}), l1);
+    auto res =
+        migraphx::add_apply_alpha_beta(*mm, {sl0, sl1}, migraphx::make_op("dot"), 1.0f, 0.0f);
+    auto sr0 = mm->add_instruction(migraphx::make_op("squeeze", {{"axes", {0}}}), res);
+    auto ret = mm->add_instruction(migraphx::make_op("squeeze", {{"axes", {0}}}), sr0);
+    mm->add_return({ret});
+
+    migraphx::onnx_options options;
+    options.default_dyn_dim_value = dd;
+    auto prog                     = parse_onnx("matmul_dyn_vv_test.onnx", options);
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(matmul_dyn_broadcast_error)
+{
+    migraphx::onnx_options options;
+    options.default_dyn_dim_value = {1, 4, 0};
+    EXPECT(test::throws([&] { migraphx::parse_onnx("matmul_dyn_broadcast_error.onnx", options); }));
+}
+
 TEST_CASE(matmulinteger_test)
 {
     migraphx::program p;
@@ -3426,6 +3531,13 @@ TEST_CASE(matmulinteger_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(matmulinteger_dyn_error)
+{
+    migraphx::onnx_options options;
+    options.default_dyn_dim_value = {1, 4, 0};
+    EXPECT(test::throws([&] { migraphx::parse_onnx("matmulinteger_dyn_error.onnx", options); }));
+}
+
 TEST_CASE(max_test)
 {
     migraphx::program p;
@@ -4016,6 +4128,44 @@ TEST_CASE(pad_3arg_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(pad_attr_dyn_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    auto x   = mm->add_parameter(
+        "0", migraphx::shape{migraphx::shape::float_type, {{2, 4, 2}, {2, 4, 2}}});
+    auto ret = mm->add_instruction(migraphx::make_op("pad", {{"pads", {1, 1, 1, 1}}}), x);
+    mm->add_return({ret});
+
+    migraphx::onnx_options options;
+    options.map_dyn_input_dims["0"] = {{2, 4, 2}, {2, 4, 2}};
+    auto prog                       = parse_onnx("pad_attr_dyn_test.onnx", options);
+    EXPECT(p == prog);
+}
+
+TEST_CASE(pad_cnst_dyn_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    auto x   = mm->add_parameter(
+        "0", migraphx::shape{migraphx::shape::float_type, {{2, 4, 2}, {2, 4, 2}}});
+    mm->add_literal({migraphx::shape{migraphx::shape::int32_type, {4}}, {0, 2, 0, 1}});
+    auto ret = mm->add_instruction(migraphx::make_op("pad", {{"pads", {0, 2, 0, 1}}}), x);
+    mm->add_return({ret});
+
+    migraphx::onnx_options options;
+    options.map_dyn_input_dims["0"] = {{2, 4, 2}, {2, 4, 2}};
+    auto prog                       = parse_onnx("pad_cnst_dyn_test.onnx", options);
+    EXPECT(p == prog);
+}
+
+TEST_CASE(pad_dyn_reflect_error)
+{
+    migraphx::onnx_options options;
+    options.default_dyn_dim_value = {2, 4, 2};
+    EXPECT(test::throws([&] { migraphx::parse_onnx("pad_dyn_reflect_error.onnx", options); }));
+}
+
 TEST_CASE(pad_reflect_test)
 {
     migraphx::program p;
@@ -4468,6 +4618,50 @@ TEST_CASE(reducel1_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(reducel1_dyn_test)
+{
+    {
+        migraphx::program p;
+        auto* mm = p.get_main_module();
+        // a shape with 4 dynamic dimensions
+        auto l0      = mm->add_parameter("x",
+                                    migraphx::shape{migraphx::shape::float_type,
+                                                    {{3, 3, 0}, {3, 5, 0}, {4, 6, 5}, {5, 7, 6}}});
+        auto abs_ins = mm->add_instruction(migraphx::make_op("abs"), l0);
+        auto sum_ins =
+            mm->add_instruction(migraphx::make_op("reduce_sum", {{"axes", {-2}}}), abs_ins);
+        auto sq_ins = mm->add_instruction(migraphx::make_op("squeeze", {{"axes", {-2}}}), sum_ins);
+        mm->add_return({sq_ins});
+
+        migraphx::onnx_options options;
+        options.map_dyn_input_dims["x"] = {{3, 3}, {3, 5}, {4, 6, 5}, {5, 7, 6}};
+        auto prog                       = migraphx::parse_onnx("reducel1_dyn_test.onnx", options);
+
+        EXPECT(p == prog);
+    }
+
+    {
+        migraphx::program p;
+        auto* mm = p.get_main_module();
+        // No axes given in the onnx file.  Parser should default to all axes.
+        auto l0      = mm->add_parameter("x",
+                                    migraphx::shape{migraphx::shape::float_type,
+                                                    {{3, 3, 0}, {3, 5, 0}, {4, 6, 5}, {5, 7, 6}}});
+        auto abs_ins = mm->add_instruction(migraphx::make_op("abs"), l0);
+        auto sum_ins =
+            mm->add_instruction(migraphx::make_op("reduce_sum", {{"axes", {0, 1, 2, 3}}}), abs_ins);
+        auto sq_ins =
+            mm->add_instruction(migraphx::make_op("squeeze", {{"axes", {0, 1, 2, 3}}}), sum_ins);
+        mm->add_return({sq_ins});
+
+        migraphx::onnx_options options;
+        options.map_dyn_input_dims["x"] = {{3, 3}, {3, 5}, {4, 6, 5}, {5, 7, 6}};
+        auto prog = migraphx::parse_onnx("reducel1_dyn_noaxes_test.onnx", options);
+
+        EXPECT(p == prog);
+    }
+}
+
 TEST_CASE(reducel2_test)
 {
     migraphx::program p;
@@ -4518,6 +4712,24 @@ TEST_CASE(reducemax_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(reducemax_dyn_test)
+{
+    // input shape with 4 dynamic dimensions
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    auto l0  = mm->add_parameter(
+        "x", migraphx::shape{migraphx::shape::float_type, {{3, 3}, {4, 4}, {5, 5}, {6, 6}}});
+    auto r0 = mm->add_instruction(migraphx::make_op("reduce_max", {{"axes", {2}}}), l0);
+    auto r1 = mm->add_instruction(migraphx::make_op("squeeze", {{"axes", {2}}}), r0);
+    mm->add_return({r1});
+
+    migraphx::onnx_options options;
+    options.map_dyn_input_dims["x"] = {{3, 3}, {4, 4}, {5, 5}, {6, 6}};
+    auto prog                       = migraphx::parse_onnx("reducemax_dyn_test.onnx", options);
+
+    EXPECT(p == prog);
+}
+
 TEST_CASE(reducemean_test)
 {
     migraphx::program p;

test/op_shape_test.cpp

@@ -1716,6 +1716,38 @@ TEST_CASE(nms_shape)
                  score_thres_s);
 }
 
+TEST_CASE(pad_shape0)
+{
+    migraphx::shape input{migraphx::shape::float_type, {2, 3, 3, 3}};
+    migraphx::shape output{migraphx::shape::float_type, {2, 3, 5, 5}};
+    expect_shape(output, migraphx::make_op("pad", {{"pads", {0, 0, 1, 1, 0, 0, 1, 1}}}), input);
+}
+
+TEST_CASE(pad_shape1)
+{
+    migraphx::shape input{migraphx::shape::float_type, {2, 3, 3, 3}};
+    migraphx::shape output{migraphx::shape::float_type, {2, 3, 6, 6}};
+    expect_shape(output, migraphx::make_op("pad", {{"pads", {0, 0, 2, 2, 0, 0, 1, 1}}}), input);
+}
+
+TEST_CASE(pad_dyn_shape0)
+{
+    migraphx::shape input{migraphx::shape::float_type,
+                          {{1, 4, 2}, {3, 3, 0}, {3, 5, 0}, {3, 5, 0}}};
+    migraphx::shape output{migraphx::shape::float_type,
+                           {{1, 4, 2}, {3, 3, 0}, {5, 7, 0}, {5, 7, 0}}};
+    expect_shape(output, migraphx::make_op("pad", {{"pads", {0, 0, 1, 1, 0, 0, 1, 1}}}), input);
+}
+
+TEST_CASE(pad_dyn_shape1)
+{
+    migraphx::shape input{migraphx::shape::float_type,
+                          {{1, 4, 2}, {3, 3, 0}, {3, 5, 5}, {3, 5, 5}}};
+    migraphx::shape output{migraphx::shape::float_type,
+                           {{1, 4, 2}, {3, 3, 0}, {5, 7, 7}, {5, 7, 7}}};
+    expect_shape(output, migraphx::make_op("pad", {{"pads", {0, 0, 1, 1, 0, 0, 1, 1}}}), input);
+}
+
 TEST_CASE(pooling_shape0)
 {
     migraphx::shape input{migraphx::shape::float_type, {4, 3, 3, 3}};
@@ -1946,9 +1978,51 @@ void test_reduce_ops()
     }
 }
 
+// dynamic shape
+template <class T>
+void test_dyn_reduce_ops()
+{
+    {
+        migraphx::shape input{migraphx::shape::float_type, {{2, 3, 3}, {2, 4, 4}}};
+        expect_shape(migraphx::shape{migraphx::shape::float_type,
+                                     std::vector<migraphx::shape::dynamic_dimension>(
+                                         {{2, 3, 3}, {1, 1, 0}})},
+                     T{{-1}},
+                     input);
+    }
+    {
+        migraphx::shape input{migraphx::shape::float_type, {{2, 3, 3}, {2, 4, 4}}};
+        expect_shape(migraphx::shape{migraphx::shape::float_type,
+                                     std::vector<migraphx::shape::dynamic_dimension>(
+                                         {{1, 1, 0}, {2, 4, 4}})},
+                     T{{0}},
+                     input);
+    }
+    {
+        // Empty axis argument reduces all axes
+        migraphx::shape input{migraphx::shape::float_type, {{2, 3, 3}, {2, 4, 4}}};
+        expect_shape(migraphx::shape{migraphx::shape::float_type,
+                                     std::vector<migraphx::shape::dynamic_dimension>(
+                                         {{1, 1, 0}, {1, 1, 0}})},
+                     T{{}},
+                     input);
+    }
+    {
+        migraphx::shape input{migraphx::shape::float_type, {{2, 3, 3}, {2, 4, 4}}};
+        throws_shape(T{{4}}, input);
+    }
+}
+
+TEST_CASE(reduce_max) { test_reduce_ops<migraphx::op::reduce_max>(); }
 TEST_CASE(reduce_mean) { test_reduce_ops<migraphx::op::reduce_mean>(); }
+TEST_CASE(reduce_prod) { test_reduce_ops<migraphx::op::reduce_prod>(); }
 TEST_CASE(reduce_sum) { test_reduce_ops<migraphx::op::reduce_sum>(); }
 
+TEST_CASE(reduce_max_dyn) { test_dyn_reduce_ops<migraphx::op::reduce_max>(); }
+TEST_CASE(reduce_mean_dyn) { test_dyn_reduce_ops<migraphx::op::reduce_mean>(); }
+TEST_CASE(reduce_prod_dyn) { test_dyn_reduce_ops<migraphx::op::reduce_prod>(); }
+TEST_CASE(reduce_sum_dyn) { test_dyn_reduce_ops<migraphx::op::reduce_sum>(); }
+
 TEST_CASE(reshape_shape)
 {
     migraphx::shape input{migraphx::shape::float_type, {24, 1, 1, 1}};
@@ -1984,6 +2058,55 @@ TEST_CASE(reshape_shape)
     }
 }
 
+TEST_CASE(reshape_dyn_shape)
+{
+    migraphx::shape input{migraphx::shape::float_type,
+                          {{1, 4, 0}, {24, 24, 0}, {1, 1, 0}, {1, 1, 0}}};
+    for(auto&& new_shape : std::vector<std::vector<int64_t>>{
+            {-1, 1, 1, 24}, {0, 8, 3, 1}, {-1, 3, 4, 2}, {0, 2, 4, 3}})
+    {
+        std::vector<migraphx::shape::dynamic_dimension> out_dyn_dims{};
+        for(std::size_t i = 0; i < new_shape.size(); ++i)
+        {
+            if(new_shape[i] == 0 or new_shape[i] == -1)
+            {
+                out_dyn_dims.push_back(input.dyn_dims().at(i));
+            }
+            else
+            {
+                std::size_t d = new_shape[i];
+                out_dyn_dims.push_back({d, d, 0});
+            }
+        }
+        migraphx::shape output{migraphx::shape::float_type, out_dyn_dims};
+        expect_shape(output, migraphx::make_op("reshape", {{"dims", new_shape}}), input);
+    }
+}
+
+TEST_CASE(reshape_multiple_non_fixed_error)
+{
+    migraphx::shape input{migraphx::shape::float_type,
+                          {{1, 4, 0}, {24, 24, 0}, {10, 20, 0}, {1, 1, 0}}};
+    std::vector<int64_t> new_shape = {0, 1, 0, 24};
+    throws_shape(migraphx::make_op("reshape", {{"dims", new_shape}}), input);
+}
+
+TEST_CASE(reshape_fixed_ele_not_matching_error)
+{
+    migraphx::shape input{migraphx::shape::float_type,
+                          {{1, 4, 0}, {24, 24, 0}, {10, 10, 0}, {1, 1, 0}}};
+    std::vector<int64_t> new_shape = {0, 1, 5, 24};
+    throws_shape(migraphx::make_op("reshape", {{"dims", new_shape}}), input);
+}
+
+TEST_CASE(reshape_non_fixed_not_matching_error)
+{
+    migraphx::shape input{migraphx::shape::float_type,
+                          {{1, 4, 0}, {24, 24, 0}, {1, 1, 0}, {1, 1, 0}}};
+    std::vector<int64_t> new_shape = {2, 1, 1, 24};
+    throws_shape(migraphx::make_op("reshape", {{"dims", new_shape}}), input);
+}
+
 TEST_CASE(rnn)
 {
     {