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Unverified Commit 66d50268 authored by Paul Fultz II's avatar Paul Fultz II Committed by GitHub
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Merge branch 'develop' into jit-layernorm-merge

parents 389bc830 fa3c21fa
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Showing with 594 additions and 49 deletions
src/targets/fpga/include/migraphx/fpga/target.hpp
View file @ 66d50268
......@@ -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
View file @ 66d50268
......@@ -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
View file @ 66d50268
......@@ -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
View file @ 66d50268
......@@ -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
View file @ 66d50268
......@@ -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/nms_test.onnx
View file @ 66d50268
No preview for this file type
test/onnx/onnx_test.cpp
View file @ 66d50268
......@@ -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
View file @ 66d50268
......@@ -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
View file @ 66d50268
......@@ -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
View file @ 66d50268
......@@ -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
View file @ 66d50268
......@@ -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>
......
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