Merge branch 'develop' into jit-layernorm-merge

src/targets/fpga/include/migraphx/fpga/target.hpp

@@ -30,6 +30,7 @@
 #include <migraphx/compile_options.hpp>
 #include <migraphx/fpga/context.hpp>
 #include <migraphx/config.hpp>
+#include <migraphx/supported_segments.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -41,7 +42,7 @@ struct target
     std::string name() const;
     std::vector<pass> get_passes(migraphx::context& ctx, const compile_options&) const;
     migraphx::context get_context() const { return context{}; }
-    float is_supported(instruction_ref ins, support_metric m);
+    supported_segments find_supported(const_module_ref mod, support_metric m) const;
 
     argument copy_to(const argument& arg) const { return arg; }
     argument copy_from(const argument& arg) const { return arg; }

src/targets/fpga/target.cpp

@@ -34,6 +34,7 @@
 #include <migraphx/dead_code_elimination.hpp>
 #include <migraphx/generate.hpp>
 #include <migraphx/normalize_ops.hpp>
+#include <migraphx/iterator_for.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -62,12 +63,17 @@ std::vector<pass> target::get_passes(migraphx::context& gctx, const compile_opti
 
 argument target::allocate(const shape& s) const { return fill_argument(s, 0); }
 
-float is_supported(instruction_ref ins, support_metric m)
+supported_segments target::find_supported(const_module_ref mod, support_metric m) const
 {
-    // for now, not using the ins and metric to return a value
-    (void)ins;
     (void)m;
-    return 1.0;
+
+    supported_segment instrs;
+    for(const auto ins : iterator_for(*mod))
+    {
+        instrs.instructions.insert(ins);
+    }
+    instrs.metric = 1; // arbitrary value
+    return {instrs};
 }
 
 MIGRAPHX_REGISTER_TARGET(target);

src/targets/ref/lowering.cpp

@@ -244,7 +244,6 @@ struct ref_convolution : auto_register_op<ref_convolution<Op>>
             auto weights_lens = args[1].get_shape().lens();
             std::vector<std::size_t> k_lens{weights_lens.begin() + 2, weights_lens.end()};
             padding = calc_dyn_auto_pad(img_lens, k_lens, op.stride, op.dilation);
-            std::cout << "[ ";
             output_shape =
                 compute_padded_shape({args.at(0).get_shape(), args.at(1).get_shape()}, padding);
         }

test/get_target_assignments.cpp → test/fpga/get_target_assignments.cpp

@@ -26,8 +26,9 @@
 #include <migraphx/make_op.hpp>
 #include <migraphx/program.hpp>
 #include <migraphx/register_target.hpp>
-#include <migraphx/ref/target.hpp>
+#include <migraphx/fpga/target.hpp>
 #include <migraphx/target_assignments.hpp>
+#include <migraphx/iterator_for.hpp>
 
 migraphx::program create_program()
 {
@@ -37,8 +38,8 @@ migraphx::program create_program()
     auto x    = mm->add_parameter("x", s);
     auto y    = mm->add_parameter("y", s);
     auto z    = mm->add_parameter("z", s);
-    auto diff = mm->add_instruction(migraphx::make_op("div"), x, y);
-    mm->add_instruction(migraphx::make_op("div"), diff, z);
+    auto diff = mm->add_instruction(migraphx::make_op("add"), x, y);
+    mm->add_instruction(migraphx::make_op("add"), diff, z);
     return p;
 }
 
@@ -47,14 +48,16 @@ TEST_CASE(is_supported)
     auto p       = create_program();
     auto targets = migraphx::get_targets();
     EXPECT(!targets.empty());
-    auto first_target = targets[0];
-    auto t            = migraphx::make_target(first_target);
+    auto t = migraphx::make_target("fpga");
 
     const auto assignments = p.get_target_assignments({t});
-    for(const auto& [ins, target] : assignments)
+    const auto* mod        = p.get_main_module();
+    EXPECT(mod->size() == assignments.size());
+
+    for(const auto ins : iterator_for(*mod))
     {
-        (void)ins;
-        EXPECT(target == first_target);
+        const auto& target = assignments.at(ins);
+        EXPECT(target == "fpga");
     }
 }
 

test/onnx/gen_onnx.py

@@ -3589,7 +3589,7 @@ def nms_test():
     st = helper.make_tensor_value_info('score_threshold', TensorProto.FLOAT,
                                        [1])
     out = helper.make_tensor_value_info('selected_indices', TensorProto.INT64,
-                                        [6, 3])
+                                        [None, 3])
 
     node = onnx.helper.make_node('NonMaxSuppression',
                                  inputs=[
@@ -3603,6 +3603,108 @@ def nms_test():
     return ([node], [b, s, mo, iou, st], [out])
 
 
+@onnx_test
+def nms_use_dyn_output_false_test():
+    b = helper.make_tensor_value_info('boxes', TensorProto.FLOAT, [1, 6, 4])
+    s = helper.make_tensor_value_info('scores', TensorProto.FLOAT, [1, 1, 6])
+    mo = helper.make_tensor_value_info('max_output_boxes_per_class',
+                                       TensorProto.INT64, [1])
+    iou = helper.make_tensor_value_info('iou_threshold', TensorProto.FLOAT,
+                                        [1])
+    st = helper.make_tensor_value_info('score_threshold', TensorProto.FLOAT,
+                                       [1])
+    out = helper.make_tensor_value_info('selected_indices', TensorProto.INT64,
+                                        [None, 3])
+
+    node = onnx.helper.make_node('NonMaxSuppression',
+                                 inputs=[
+                                     'boxes', 'scores',
+                                     'max_output_boxes_per_class',
+                                     'iou_threshold', 'score_threshold'
+                                 ],
+                                 outputs=['selected_indices'],
+                                 use_dyn_output=0)
+
+    return ([node], [b, s, mo, iou, st], [out])
+
+
+@onnx_test
+def nms_dynamic_batch_test():
+    b = helper.make_tensor_value_info('boxes', TensorProto.FLOAT, [None, 6, 4])
+    s = helper.make_tensor_value_info('scores', TensorProto.FLOAT,
+                                      [None, 1, 6])
+    mo = helper.make_tensor_value_info('max_output_boxes_per_class',
+                                       TensorProto.INT64, [1])
+    iou = helper.make_tensor_value_info('iou_threshold', TensorProto.FLOAT,
+                                        [1])
+    st = helper.make_tensor_value_info('score_threshold', TensorProto.FLOAT,
+                                       [1])
+    out = helper.make_tensor_value_info('selected_indices', TensorProto.INT64,
+                                        [None, 3])
+
+    node = onnx.helper.make_node('NonMaxSuppression',
+                                 inputs=[
+                                     'boxes', 'scores',
+                                     'max_output_boxes_per_class',
+                                     'iou_threshold', 'score_threshold'
+                                 ],
+                                 outputs=['selected_indices'],
+                                 center_point_box=1,
+                                 use_dyn_output=1)
+
+    return ([node], [b, s, mo, iou, st], [out])
+
+
+@onnx_test
+def nms_dynamic_boxes_test():
+    b = helper.make_tensor_value_info('boxes', TensorProto.FLOAT, [1, None, 4])
+    s = helper.make_tensor_value_info('scores', TensorProto.FLOAT,
+                                      [1, 1, None])
+    mo = helper.make_tensor_value_info('max_output_boxes_per_class',
+                                       TensorProto.INT64, [1])
+    iou = helper.make_tensor_value_info('iou_threshold', TensorProto.FLOAT,
+                                        [1])
+    st = helper.make_tensor_value_info('score_threshold', TensorProto.FLOAT,
+                                       [1])
+    out = helper.make_tensor_value_info('selected_indices', TensorProto.INT64,
+                                        [None, 3])
+
+    node = onnx.helper.make_node('NonMaxSuppression',
+                                 inputs=[
+                                     'boxes', 'scores',
+                                     'max_output_boxes_per_class',
+                                     'iou_threshold', 'score_threshold'
+                                 ],
+                                 outputs=['selected_indices'])
+
+    return ([node], [b, s, mo, iou, st], [out])
+
+
+@onnx_test
+def nms_dynamic_classes_test():
+    b = helper.make_tensor_value_info('boxes', TensorProto.FLOAT, [1, 6, 4])
+    s = helper.make_tensor_value_info('scores', TensorProto.FLOAT,
+                                      [1, None, 6])
+    mo = helper.make_tensor_value_info('max_output_boxes_per_class',
+                                       TensorProto.INT64, [1])
+    iou = helper.make_tensor_value_info('iou_threshold', TensorProto.FLOAT,
+                                        [1])
+    st = helper.make_tensor_value_info('score_threshold', TensorProto.FLOAT,
+                                       [1])
+    out = helper.make_tensor_value_info('selected_indices', TensorProto.INT64,
+                                        [None, 3])
+
+    node = onnx.helper.make_node('NonMaxSuppression',
+                                 inputs=[
+                                     'boxes', 'scores',
+                                     'max_output_boxes_per_class',
+                                     'iou_threshold', 'score_threshold'
+                                 ],
+                                 outputs=['selected_indices'])
+
+    return ([node], [b, s, mo, iou, st], [out])
+
+
 @onnx_test
 def not_test():
     x = helper.make_tensor_value_info('0', TensorProto.INT32, [4])

test/onnx/onnx_test.cpp

@@ -3378,13 +3378,127 @@ TEST_CASE(nms_test)
     auto st = mm->add_parameter("score_threshold", sst);
 
     auto ret = mm->add_instruction(
-        migraphx::make_op("nonmaxsuppression", {{"center_point_box", 1}}), b, s, mo, iou, st);
+        migraphx::make_op("nonmaxsuppression", {{"center_point_box", true}}), b, s, mo, iou, st);
     mm->add_return({ret});
 
     auto prog = migraphx::parse_onnx("nms_test.onnx");
     EXPECT(p == prog);
 }
 
+TEST_CASE(nms_dynamic_batch_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    migraphx::shape sb{migraphx::shape::float_type, {{1, 10, 0}, {6, 6, 0}, {4, 4, 0}}};
+    auto b = mm->add_parameter("boxes", sb);
+    migraphx::shape ss{migraphx::shape::float_type, {{1, 10, 0}, {1, 1, 0}, {6, 6, 0}}};
+    auto s = mm->add_parameter("scores", ss);
+    migraphx::shape smo{migraphx::shape::int64_type, {1}};
+    auto mo = mm->add_parameter("max_output_boxes_per_class", smo);
+    migraphx::shape siou{migraphx::shape::float_type, {1}};
+    auto iou = mm->add_parameter("iou_threshold", siou);
+    migraphx::shape sst{migraphx::shape::float_type, {1}};
+    auto st  = mm->add_parameter("score_threshold", sst);
+    auto ret = mm->add_instruction(
+        migraphx::make_op("nonmaxsuppression",
+                          {{"center_point_box", true}, {"use_dyn_output", true}}),
+        b,
+        s,
+        mo,
+        iou,
+        st);
+    mm->add_return({ret});
+
+    migraphx::onnx_options options;
+    options.default_dyn_dim_value = {1, 10, 0};
+    options.use_dyn_output        = true;
+
+    auto prog = migraphx::parse_onnx("nms_dynamic_batch_test.onnx", options);
+    EXPECT(p == prog);
+}
+
+TEST_CASE(nms_dynamic_boxes_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    migraphx::shape sb{migraphx::shape::float_type, {{1, 1, 0}, {6, 20, 0}, {4, 4, 0}}};
+    auto b = mm->add_parameter("boxes", sb);
+    migraphx::shape ss{migraphx::shape::float_type, {{1, 1, 0}, {1, 1, 0}, {6, 20, 0}}};
+    auto s = mm->add_parameter("scores", ss);
+    migraphx::shape smo{migraphx::shape::int64_type, {1}};
+    auto mo = mm->add_parameter("max_output_boxes_per_class", smo);
+    migraphx::shape siou{migraphx::shape::float_type, {1}};
+    auto iou = mm->add_parameter("iou_threshold", siou);
+    migraphx::shape sst{migraphx::shape::float_type, {1}};
+    auto st  = mm->add_parameter("score_threshold", sst);
+    auto ret = mm->add_instruction(
+        migraphx::make_op("nonmaxsuppression", {{"use_dyn_output", true}}), b, s, mo, iou, st);
+    mm->add_return({ret});
+
+    migraphx::onnx_options options;
+    options.default_dyn_dim_value = {6, 20, 0};
+    options.use_dyn_output        = true;
+
+    auto prog = migraphx::parse_onnx("nms_dynamic_boxes_test.onnx", options);
+    EXPECT(p == prog);
+}
+
+TEST_CASE(nms_dynamic_classes_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    migraphx::shape sb{migraphx::shape::float_type, {1, 6, 4}};
+    auto b = mm->add_parameter("boxes", sb);
+    migraphx::shape ss{migraphx::shape::float_type, {{1, 1, 0}, {1, 10, 0}, {6, 6, 0}}};
+    auto s = mm->add_parameter("scores", ss);
+    migraphx::shape smo{migraphx::shape::int64_type, {1}};
+    auto mo = mm->add_parameter("max_output_boxes_per_class", smo);
+    migraphx::shape siou{migraphx::shape::float_type, {1}};
+    auto iou = mm->add_parameter("iou_threshold", siou);
+    migraphx::shape sst{migraphx::shape::float_type, {1}};
+    auto st  = mm->add_parameter("score_threshold", sst);
+    auto ret = mm->add_instruction(
+        migraphx::make_op("nonmaxsuppression", {{"use_dyn_output", true}}), b, s, mo, iou, st);
+    mm->add_return({ret});
+
+    migraphx::onnx_options options;
+    options.default_dyn_dim_value = {1, 10, 0};
+    options.use_dyn_output        = true;
+
+    auto prog = migraphx::parse_onnx("nms_dynamic_classes_test.onnx", options);
+    EXPECT(p == prog);
+}
+
+TEST_CASE(nms_overwrite_use_dyn_output_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    migraphx::shape sb{migraphx::shape::float_type, {1, 6, 4}};
+    auto b = mm->add_parameter("boxes", sb);
+
+    migraphx::shape ss{migraphx::shape::float_type, {1, 1, 6}};
+    auto s = mm->add_parameter("scores", ss);
+
+    migraphx::shape smo{migraphx::shape::int64_type, {1}};
+    auto mo = mm->add_parameter("max_output_boxes_per_class", smo);
+
+    migraphx::shape siou{migraphx::shape::float_type, {1}};
+    auto iou = mm->add_parameter("iou_threshold", siou);
+
+    migraphx::shape sst{migraphx::shape::float_type, {1}};
+    auto st = mm->add_parameter("score_threshold", sst);
+
+    auto ret = mm->add_instruction(
+        migraphx::make_op("nonmaxsuppression", {{"use_dyn_output", true}}), b, s, mo, iou, st);
+    mm->add_return({ret});
+
+    migraphx::onnx_options options;
+    options.use_dyn_output = true;
+
+    auto prog = migraphx::parse_onnx("nms_use_dyn_output_false_test.onnx", options);
+    EXPECT(p == prog);
+}
+
 TEST_CASE(nonzero_dynamic_test)
 {
     migraphx::program p;

test/op_shape_test.cpp

@@ -1135,6 +1135,149 @@ TEST_CASE(multinomial)
     throws_shape(migraphx::make_op("multinomial", {{"dtype", dtype}}), s, s);
 }
 
+TEST_CASE(nms_shape)
+{
+    // use_dyn_output == false
+    migraphx::shape boxes_s{migraphx::shape::float_type, {1, 6, 4}};
+    migraphx::shape scores_s{migraphx::shape::float_type, {1, 1, 6}};
+    migraphx::shape max_out_s{migraphx::shape::int64_type, {1}};
+    migraphx::shape iou_thres_s{migraphx::shape::float_type, {1}};
+    migraphx::shape score_thres_s{migraphx::shape::float_type, {1}};
+    migraphx::shape output_s{migraphx::shape::int64_type, {6, 3}};
+    expect_shape(output_s,
+                 migraphx::make_op("nonmaxsuppression",
+                                   {{"center_point_box", true}, {"use_dyn_output", false}}),
+                 boxes_s,
+                 scores_s,
+                 max_out_s,
+                 iou_thres_s,
+                 score_thres_s);
+
+    // use_dyn_output == true
+    output_s = {migraphx::shape::int64_type, {{0, 6, 0}, {3, 3, 0}}};
+    expect_shape(output_s,
+                 migraphx::make_op("nonmaxsuppression",
+                                   {{"center_point_box", true}, {"use_dyn_output", true}}),
+                 boxes_s,
+                 scores_s,
+                 max_out_s,
+                 iou_thres_s,
+                 score_thres_s);
+
+    // dynamic batches
+    boxes_s  = {migraphx::shape::float_type, {{1, 3, 0}, {6, 6, 0}, {4, 4, 0}}};
+    scores_s = {migraphx::shape::float_type, {{1, 3, 0}, {1, 1, 0}, {6, 6, 0}}};
+    output_s = {migraphx::shape::int64_type, {{0, 18, 0}, {3, 3, 0}}};
+    expect_shape(output_s,
+                 migraphx::make_op("nonmaxsuppression",
+                                   {{"center_point_box", true}, {"use_dyn_output", true}}),
+                 boxes_s,
+                 scores_s,
+                 max_out_s,
+                 iou_thres_s,
+                 score_thres_s);
+
+    // dynamic num boxes
+    boxes_s  = {migraphx::shape::float_type, {{1, 1, 0}, {6, 20, 0}, {4, 4, 0}}};
+    scores_s = {migraphx::shape::float_type, {{1, 1, 0}, {1, 1, 0}, {6, 20, 0}}};
+    output_s = {migraphx::shape::int64_type, {{0, 20, 0}, {3, 3, 0}}};
+    expect_shape(output_s,
+                 migraphx::make_op("nonmaxsuppression",
+                                   {{"center_point_box", true}, {"use_dyn_output", true}}),
+                 boxes_s,
+                 scores_s,
+                 max_out_s,
+                 iou_thres_s,
+                 score_thres_s);
+
+    // use_dyn_output false with dynamic input shape
+    throws_shape(migraphx::make_op("nonmaxsuppression",
+                                   {{"center_point_box", true}, {"use_dyn_output", false}}),
+                 boxes_s,
+                 scores_s,
+                 max_out_s,
+                 iou_thres_s,
+                 score_thres_s);
+
+    // dynamic classes
+    boxes_s  = {migraphx::shape::float_type, {{1, 1, 0}, {6, 6, 0}, {4, 4, 0}}};
+    scores_s = {migraphx::shape::float_type, {{1, 1, 0}, {1, 3, 0}, {6, 6, 0}}};
+    output_s = {migraphx::shape::int64_type, {{0, 6, 0}, {3, 3, 0}}};
+    expect_shape(output_s,
+                 migraphx::make_op("nonmaxsuppression",
+                                   {{"center_point_box", true}, {"use_dyn_output", true}}),
+                 boxes_s,
+                 scores_s,
+                 max_out_s,
+                 iou_thres_s,
+                 score_thres_s);
+
+    // fixed mismatch batches
+    boxes_s  = {migraphx::shape::float_type, {2, 6, 4}};
+    scores_s = {migraphx::shape::float_type, {1, 1, 6}};
+    throws_shape(migraphx::make_op("nonmaxsuppression",
+                                   {{"center_point_box", true}, {"use_dyn_output", true}}),
+                 boxes_s,
+                 scores_s,
+                 max_out_s,
+                 iou_thres_s,
+                 score_thres_s);
+
+    // fixed mismatch num boxes
+    boxes_s  = {migraphx::shape::float_type, {1, 6, 4}};
+    scores_s = {migraphx::shape::float_type, {1, 1, 4}};
+    throws_shape(migraphx::make_op("nonmaxsuppression",
+                                   {{"center_point_box", true}, {"use_dyn_output", true}}),
+                 boxes_s,
+                 scores_s,
+                 max_out_s,
+                 iou_thres_s,
+                 score_thres_s);
+
+    // dynamic mismatch batches
+    boxes_s  = {migraphx::shape::float_type, {{1, 4, 0}, {6, 6, 0}, {4, 4, 0}}};
+    scores_s = {migraphx::shape::float_type, {{2, 8, 0}, {1, 1, 0}, {6, 6, 0}}};
+    throws_shape(migraphx::make_op("nonmaxsuppression",
+                                   {{"center_point_box", true}, {"use_dyn_output", true}}),
+                 boxes_s,
+                 scores_s,
+                 max_out_s,
+                 iou_thres_s,
+                 score_thres_s);
+
+    // dynamic mismatch num boxes
+    boxes_s  = {migraphx::shape::float_type, {{1, 1, 0}, {6, 8, 0}, {4, 4, 0}}};
+    scores_s = {migraphx::shape::float_type, {{1, 1, 0}, {1, 1, 0}, {3, 9, 0}}};
+    throws_shape(migraphx::make_op("nonmaxsuppression",
+                                   {{"center_point_box", true}, {"use_dyn_output", true}}),
+                 boxes_s,
+                 scores_s,
+                 max_out_s,
+                 iou_thres_s,
+                 score_thres_s);
+
+    // dynamic number of classes, fixed boxes_s, mismatch batches
+    boxes_s  = {migraphx::shape::float_type, {1, 6, 4}};
+    scores_s = {migraphx::shape::float_type, {{1, 3, 0}, {1, 3, 0}, {6, 6, 0}}};
+    throws_shape(migraphx::make_op("nonmaxsuppression",
+                                   {{"center_point_box", true}, {"use_dyn_output", true}}),
+                 boxes_s,
+                 scores_s,
+                 max_out_s,
+                 iou_thres_s,
+                 score_thres_s);
+    // dynamic number of classes, fixed boxes_s, mismatch num boxes
+    boxes_s  = {migraphx::shape::float_type, {1, 6, 4}};
+    scores_s = {migraphx::shape::float_type, {{1, 1, 0}, {1, 3, 0}, {4, 8, 0}}};
+    throws_shape(migraphx::make_op("nonmaxsuppression",
+                                   {{"center_point_box", true}, {"use_dyn_output", true}}),
+                 boxes_s,
+                 scores_s,
+                 max_out_s,
+                 iou_thres_s,
+                 score_thres_s);
+}
+
 TEST_CASE(pooling_shape)
 {
     migraphx::shape output{migraphx::shape::float_type, {4, 3, 1, 1}};

test/ref_ops_test.cpp

@@ -3624,6 +3624,174 @@ TEST_CASE(neg_test)
     EXPECT(migraphx::verify_range(result_vector, gold));
 }
 
+TEST_CASE(nms_dynamic_out_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    migraphx::shape boxes_s{migraphx::shape::float_type, {1, 6, 4}};
+    std::vector<float> boxes_vec = {0.5, 0.5,  1.0, 1.0, 0.5, 0.6,  1.0, 1.0, 0.5, 0.4,   1.0, 1.0,
+                                    0.5, 10.5, 1.0, 1.0, 0.5, 10.6, 1.0, 1.0, 0.5, 100.5, 1.0, 1.0};
+
+    migraphx::shape scores_s{migraphx::shape::float_type, {1, 1, 6}};
+    std::vector<float> scores_vec = {0.9, 0.75, 0.6, 0.95, 0.5, 0.3};
+
+    auto boxes_l         = mm->add_literal(migraphx::literal(boxes_s, boxes_vec));
+    auto scores_l        = mm->add_literal(migraphx::literal(scores_s, scores_vec));
+    auto max_out_l       = mm->add_literal(int64_t{4});
+    auto iou_threshold   = mm->add_literal(0.5f);
+    auto score_threshold = mm->add_literal(0.0f);
+
+    auto r = mm->add_instruction(
+        migraphx::make_op("nonmaxsuppression",
+                          {{"center_point_box", true}, {"use_dyn_output", true}}),
+        boxes_l,
+        scores_l,
+        max_out_l,
+        iou_threshold,
+        score_threshold);
+    mm->add_return({r});
+
+    p.compile(migraphx::ref::target{});
+    auto output = p.eval({}).back();
+    std::vector<int64_t> result;
+    output.visit([&](auto out) { result.assign(out.begin(), out.end()); });
+    std::vector<int64_t> gold = {0, 0, 3, 0, 0, 0, 0, 0, 5};
+    EXPECT(migraphx::verify_range(result, gold));
+}
+
+TEST_CASE(nms_dynamic_batch_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    migraphx::shape boxes_s{migraphx::shape::float_type, {{1, 3, 0}, {6, 6, 0}, {4, 4, 0}}};
+
+    migraphx::shape scores_s{migraphx::shape::float_type, {{1, 3, 0}, {1, 1, 0}, {6, 6, 0}}};
+
+    auto boxes_p         = mm->add_parameter("boxes", boxes_s);
+    auto scores_p        = mm->add_parameter("scores", scores_s);
+    auto max_out_l       = mm->add_literal(int64_t{4});
+    auto iou_threshold   = mm->add_literal(0.5f);
+    auto score_threshold = mm->add_literal(0.0f);
+
+    auto r = mm->add_instruction(
+        migraphx::make_op("nonmaxsuppression",
+                          {{"center_point_box", true}, {"use_dyn_output", true}}),
+        boxes_p,
+        scores_p,
+        max_out_l,
+        iou_threshold,
+        score_threshold);
+    mm->add_return({r});
+
+    p.compile(migraphx::ref::target{});
+
+    std::vector<float> boxes_vec  = {0.5, 0.5,  1.0, 1.0, 0.5, 0.6,  1.0, 1.0, 0.5, 0.4,   1.0, 1.0,
+                                    0.5, 10.5, 1.0, 1.0, 0.5, 10.6, 1.0, 1.0, 0.5, 100.5, 1.0, 1.0,
+                                    0.5, 0.5,  1.0, 1.0, 0.5, 0.6,  1.0, 1.0, 0.5, 0.4,   1.0, 1.0,
+                                    0.5, 10.5, 1.0, 1.0, 0.5, 10.6, 1.0, 1.0, 0.5, 100.5, 1.0, 1.0};
+    std::vector<float> scores_vec = {
+        0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.9, 0.75, 0.6, 0.95, 0.5, 0.3};
+
+    migraphx::shape input_fixed_shape0{migraphx::shape::float_type, {2, 6, 4}};
+    migraphx::shape input_fixed_shape1{migraphx::shape::float_type, {2, 1, 6}};
+    migraphx::parameter_map params0;
+    params0["boxes"]  = migraphx::argument(input_fixed_shape0, boxes_vec.data());
+    params0["scores"] = migraphx::argument(input_fixed_shape1, scores_vec.data());
+    auto output       = p.eval(params0).back();
+
+    std::vector<int64_t> result;
+    output.visit([&](auto out) { result.assign(out.begin(), out.end()); });
+    std::vector<int64_t> gold = {0, 0, 3, 0, 0, 0, 0, 0, 5, 1, 0, 3, 1, 0, 0, 1, 0, 5};
+    EXPECT(migraphx::verify_range(result, gold));
+}
+
+TEST_CASE(nms_dynamic_boxes_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    migraphx::shape boxes_s{migraphx::shape::float_type, {{1, 1, 0}, {4, 20, 0}, {4, 4, 0}}};
+
+    migraphx::shape scores_s{migraphx::shape::float_type, {{1, 1, 0}, {1, 1, 0}, {4, 20, 0}}};
+
+    auto boxes_p         = mm->add_parameter("boxes", boxes_s);
+    auto scores_p        = mm->add_parameter("scores", scores_s);
+    auto max_out_l       = mm->add_literal(int64_t{4});
+    auto iou_threshold   = mm->add_literal(0.5f);
+    auto score_threshold = mm->add_literal(0.0f);
+
+    auto r = mm->add_instruction(
+        migraphx::make_op("nonmaxsuppression",
+                          {{"center_point_box", true}, {"use_dyn_output", true}}),
+        boxes_p,
+        scores_p,
+        max_out_l,
+        iou_threshold,
+        score_threshold);
+    mm->add_return({r});
+
+    p.compile(migraphx::ref::target{});
+
+    std::vector<float> boxes_vec  = {0.5, 0.5,  1.0, 1.0, 0.5, 0.6,  1.0, 1.0, 0.5, 0.4,   1.0, 1.0,
+                                    0.5, 10.5, 1.0, 1.0, 0.5, 10.6, 1.0, 1.0, 0.5, 100.5, 1.0, 1.0};
+    std::vector<float> scores_vec = {0.9, 0.75, 0.6, 0.95, 0.5, 0.3};
+
+    migraphx::shape input_fixed_shape0{migraphx::shape::float_type, {1, 6, 4}};
+    migraphx::shape input_fixed_shape1{migraphx::shape::float_type, {1, 1, 6}};
+    migraphx::parameter_map params0;
+    params0["boxes"]  = migraphx::argument(input_fixed_shape0, boxes_vec.data());
+    params0["scores"] = migraphx::argument(input_fixed_shape1, scores_vec.data());
+    auto output       = p.eval(params0).back();
+
+    std::vector<int64_t> result;
+    output.visit([&](auto out) { result.assign(out.begin(), out.end()); });
+    std::vector<int64_t> gold = {0, 0, 3, 0, 0, 0, 0, 0, 5};
+    EXPECT(migraphx::verify_range(result, gold));
+}
+
+TEST_CASE(nms_dynamic_classes_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    migraphx::shape boxes_s{migraphx::shape::float_type, {{1, 1, 0}, {6, 6, 0}, {4, 4, 0}}};
+
+    migraphx::shape scores_s{migraphx::shape::float_type, {{1, 1, 0}, {1, 3, 0}, {6, 6, 0}}};
+
+    auto boxes_p         = mm->add_parameter("boxes", boxes_s);
+    auto scores_p        = mm->add_parameter("scores", scores_s);
+    auto max_out_l       = mm->add_literal(int64_t{2});
+    auto iou_threshold   = mm->add_literal(0.5f);
+    auto score_threshold = mm->add_literal(0.0f);
+
+    auto r = mm->add_instruction(
+        migraphx::make_op("nonmaxsuppression",
+                          {{"center_point_box", true}, {"use_dyn_output", true}}),
+        boxes_p,
+        scores_p,
+        max_out_l,
+        iou_threshold,
+        score_threshold);
+    mm->add_return({r});
+
+    p.compile(migraphx::ref::target{});
+
+    std::vector<float> boxes_vec  = {0.0, 0.0,  1.0, 1.0,  0.0, 0.1,   1.0, 1.1,
+                                    0.0, -0.1, 1.0, 0.9,  0.0, 10.0,  1.0, 11.0,
+                                    0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0};
+    std::vector<float> scores_vec = {
+        0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.9, 0.75, 0.6, 0.95, 0.5, 0.3};
+    migraphx::shape input_fixed_shape0{migraphx::shape::float_type, {1, 6, 4}};
+    migraphx::shape input_fixed_shape1{migraphx::shape::float_type, {1, 2, 6}};
+    migraphx::parameter_map params0;
+    params0["boxes"]  = migraphx::argument(input_fixed_shape0, boxes_vec.data());
+    params0["scores"] = migraphx::argument(input_fixed_shape1, scores_vec.data());
+    auto output       = p.eval(params0).back();
+
+    std::vector<int64_t> result;
+    output.visit([&](auto out) { result.assign(out.begin(), out.end()); });
+    std::vector<int64_t> gold = {0, 0, 3, 0, 0, 0, 0, 1, 3, 0, 1, 0};
+    EXPECT(migraphx::verify_range(result, gold));
+}
+
 TEST_CASE(nms_not_center_test)
 {
     migraphx::program p;
@@ -3642,12 +3810,14 @@ TEST_CASE(nms_not_center_test)
     auto iou_threshold   = mm->add_literal(0.5f);
     auto score_threshold = mm->add_literal(0.0f);
 
-    auto r = mm->add_instruction(migraphx::make_op("nonmaxsuppression"),
-                                 boxes_l,
-                                 scores_l,
-                                 max_out_l,
-                                 iou_threshold,
-                                 score_threshold);
+    // set use_dyn_output back to false in operator map
+    auto r =
+        mm->add_instruction(migraphx::make_op("nonmaxsuppression", {{"use_dyn_output", false}}),
+                            boxes_l,
+                            scores_l,
+                            max_out_l,
+                            iou_threshold,
+                            score_threshold);
     mm->add_return({r});
 
     p.compile(migraphx::ref::target{});
@@ -3675,12 +3845,13 @@ TEST_CASE(nms_test)
     auto iou_threshold   = mm->add_literal(0.5f);
     auto score_threshold = mm->add_literal(0.0f);
 
-    auto r = mm->add_instruction(migraphx::make_op("nonmaxsuppression", {{"center_point_box", 1}}),
-                                 boxes_l,
-                                 scores_l,
-                                 max_out_l,
-                                 iou_threshold,
-                                 score_threshold);
+    auto r =
+        mm->add_instruction(migraphx::make_op("nonmaxsuppression", {{"center_point_box", true}}),
+                            boxes_l,
+                            scores_l,
+                            max_out_l,
+                            iou_threshold,
+                            score_threshold);
     mm->add_return({r});
 
     p.compile(migraphx::ref::target{});
@@ -3712,12 +3883,13 @@ TEST_CASE(nms_transpose1_test)
 
     auto transpose_boxes = mm->add_instruction(
         migraphx::make_op("transpose", {{"permutation", {0, 2, 1}}}), t_boxes_l);
-    auto r = mm->add_instruction(migraphx::make_op("nonmaxsuppression", {{"center_point_box", 1}}),
-                                 transpose_boxes,
-                                 scores_l,
-                                 max_out_l,
-                                 iou_threshold,
-                                 score_threshold);
+    auto r =
+        mm->add_instruction(migraphx::make_op("nonmaxsuppression", {{"center_point_box", true}}),
+                            transpose_boxes,
+                            scores_l,
+                            max_out_l,
+                            iou_threshold,
+                            score_threshold);
     mm->add_return({r});
 
     p.compile(migraphx::ref::target{});
@@ -3749,12 +3921,13 @@ TEST_CASE(nms_transpose2_test)
 
     auto transpose_boxes = mm->add_instruction(
         migraphx::make_op("transpose", {{"permutation", {1, 2, 0}}}), t_boxes_l);
-    auto r = mm->add_instruction(migraphx::make_op("nonmaxsuppression", {{"center_point_box", 1}}),
-                                 transpose_boxes,
-                                 scores_l,
-                                 max_out_l,
-                                 iou_threshold,
-                                 score_threshold);
+    auto r =
+        mm->add_instruction(migraphx::make_op("nonmaxsuppression", {{"center_point_box", true}}),
+                            transpose_boxes,
+                            scores_l,
+                            max_out_l,
+                            iou_threshold,
+                            score_threshold);
     mm->add_return({r});
 
     p.compile(migraphx::ref::target{});

tools/include/target.hpp

@@ -37,8 +37,10 @@
 #include <migraphx/compile_options.hpp>
 #include <migraphx/argument.hpp>
 #include <migraphx/rank.hpp>
+#include <migraphx/module_ref.hpp>
 #include <migraphx/support_metric.hpp>
 #include <migraphx/instruction_ref.hpp>
+#include <migraphx/supported_segments.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -64,12 +66,12 @@ struct target
      */
     context get_context() const;
     /**
-     * @brief Check how well an instruction is supported on a target with the given metric
-     * @param ins Instruction to check if it's supported
-     * @param metric Used to define how the return value should be interpreted
-     * @return The value based on the chosen metric. Negative numbers mean unsupported
+     * @brief Get the ranges of instructions that are supported on a target
+     * @param module Module to check for supported instructions
+     * @param metric Used to define how the quality of the support should be measured
+     * @return the supported segments of the graph
      */
-    float is_supported(T&, instruction_ref ins, support_metric m) const;
+    supported_segments target_is_supported(T&, const_module_ref mod, support_metric metric) const;
     /**
      * @brief copy an argument to the current target.
      *
@@ -115,9 +117,9 @@ argument copy_from_target(T&, const argument& arg)
 }
 
 template <class T>
-float target_is_supported(T&, instruction_ref, support_metric)
+supported_segments target_find_supported(T&, const_module_ref, support_metric)
 {
-    return 0;
+    return {};
 }
 
 <%
@@ -125,7 +127,7 @@ interface('target',
      virtual('name', returns='std::string', const=True),
      virtual('get_passes', ctx='context&', options='const compile_options&', returns='std::vector<pass>', const=True),
      virtual('get_context', returns='context', const=True),
-     virtual('is_supported', returns='float', ins='instruction_ref', m='support_metric', const=True, default='target_is_supported'),
+     virtual('find_supported', returns='supported_segments', mod='const_module_ref', m='support_metric', const=True, default='target_find_supported'),
      virtual('copy_to',
              returns = 'argument',
              input   = 'const argument&',

tools/te.py

@@ -23,7 +23,9 @@
 #####################################################################################
 import string, sys, re
 
-trivial = ['std::size_t', 'instruction_ref', 'support_metric']
+trivial = [
+    'std::size_t', 'instruction_ref', 'support_metric', 'const_module_ref'
+]
 
 headers = '''
 #include <algorithm>