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Commit 4ea39116 authored by Khalique Ahmed's avatar Khalique Ahmed
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manual merge

parents 20128cae d8011adf
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test/onnx/trilu_batch_diff_k_test.onnx → test/onnx/triu_batch_diff_k_test.onnx
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trilu_batch_diff_k_test:i
triu_batch_diff_k_test:h

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test/onnx/trilu_neg_k_test.onnx → test/onnx/triu_neg_k_test.onnx
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trilu_neg_k_test:c
triu_neg_k_test:b

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test/onnx/trilu_out_k_test.onnx → test/onnx/triu_out_k_test.onnx
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triu_out_k_test:Y

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test/onnx/trilu_row_one_test.onnx → test/onnx/triu_row_one_test.onnx
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trilu_row_one_test:\
triu_row_one_test:[

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test/onnx/trilu_test.onnx → test/onnx/triu_test.onnx
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test/onnx/verify_onnx.cpp
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......@@ -538,6 +538,70 @@ TEST_CASE(gemm_half_test)
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
template <typename T = float>
std::vector<T> norm_test(const std::vector<size_t>& x_dims,
std::vector<T>& scale,
std::vector<T>& bias,
const std::string& onnx_file)
{
migraphx::program p = migraphx::parse_onnx(onnx_file);
p.compile(migraphx::make_target("ref"));
migraphx::shape s_x{migraphx::shape::get_type<T>{}, x_dims};
migraphx::shape s_s{migraphx::shape::get_type<T>{}, {scale.size()}};
migraphx::shape s_b{migraphx::shape::get_type<T>{}, {scale.size()}};
std::vector<T> x(s_x.elements());
std::iota(std::begin(x), std::end(x), 1);
migraphx::parameter_map pp;
pp["x"] = migraphx::argument(s_x, x.data());
pp["scale"] = migraphx::argument(s_s, scale.data());
pp["bias"] = migraphx::argument(s_b, bias.data());
auto result = p.eval(pp).back();
std::vector<T> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
return result_vector;
}
TEST_CASE(group_norm_test)
{
std::vector<float> scale{1.2, 0.8};
std::vector<float> bias{0.5, 0.2};
std::vector<float> result_vector =
norm_test<float>({1, 4, 2}, scale, bias, "group_norm_3d_test.onnx");
std::vector<float> gold = {-1.10996256,
-0.0366542,
1.0366542,
2.10996256,
-0.87330837,
-0.15776947,
0.55776947,
1.27330837};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(group_norm_half_test)
{
using migraphx::half;
std::vector<half> scale{half{1.2}, half{0.8}};
std::vector<half> bias{half{0.5}, half{0.2}};
std::vector<half> result_vector =
norm_test<half>({1, 4, 2}, scale, bias, "group_norm_3d_half_test.onnx");
std::vector<half> gold = {half{-1.10996256},
half{-0.0366542},
half{1.0366542},
half{2.10996256},
half{-0.87330837},
half{-0.15776947},
half{0.55776947},
half{1.27330837}};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(greaterorequal_test)
{
migraphx::program p = migraphx::parse_onnx("greaterorequal_test.onnx");
......@@ -950,6 +1014,130 @@ TEST_CASE(instance_norm_3d_test)
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(isinf_half_test)
{
migraphx::program p = migraphx::parse_onnx("isinf_half_test.onnx");
p.compile(migraphx::make_target("ref"));
migraphx::shape s{migraphx::shape::half_type, {2, 3}};
migraphx::parameter_map pp;
migraphx::half nan = std::numeric_limits<migraphx::half>::quiet_NaN();
migraphx::half infinity = std::numeric_limits<migraphx::half>::infinity();
migraphx::half max = std::numeric_limits<migraphx::half>::max();
migraphx::half min = std::numeric_limits<migraphx::half>::min();
migraphx::half val = migraphx::half(3.6);
std::vector<migraphx::half> data = {-infinity, nan, min, val, max, infinity};
pp["t1"] = migraphx::argument(s, data.data());
auto result = p.eval(pp).back();
std::vector<float> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<float> gold = {1, 0, 0, 0, 0, 1};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(isinf_neg_test)
{
migraphx::program p = migraphx::parse_onnx("isinf_neg_test.onnx");
p.compile(migraphx::make_target("ref"));
migraphx::shape s{migraphx::shape::float_type, {2, 3}};
migraphx::parameter_map pp;
float nan = std::numeric_limits<float>::quiet_NaN();
float infinity = std::numeric_limits<float>::infinity();
float max = std::numeric_limits<float>::max();
float min = std::numeric_limits<float>::min();
std::vector<float> data = {-infinity, nan, min, 3.6, max, infinity};
pp["t1"] = migraphx::argument(s, data.data());
auto result = p.eval(pp).back();
std::vector<float> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<float> gold = {1, 0, 0, 0, 0, 0};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(isinf_double_pos_test)
{
migraphx::program p = migraphx::parse_onnx("isinf_double_pos_test.onnx");
p.compile(migraphx::make_target("ref"));
migraphx::shape s{migraphx::shape::double_type, {2, 3}};
migraphx::parameter_map pp;
double nan = std::numeric_limits<double>::quiet_NaN();
double infinity = std::numeric_limits<double>::infinity();
double max = std::numeric_limits<double>::max();
double min = std::numeric_limits<double>::min();
std::vector<double> data = {-infinity, nan, min, 3.6, max, infinity};
pp["t1"] = migraphx::argument(s, data.data());
auto result = p.eval(pp).back();
std::vector<float> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<float> gold = {0, 0, 0, 0, 0, 1};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(isinf_no_detect_test)
{
migraphx::program p = migraphx::parse_onnx("isinf_no_detect_test.onnx");
p.compile(migraphx::make_target("ref"));
migraphx::shape s{migraphx::shape::float_type, {2, 3}};
migraphx::parameter_map pp;
float nan = std::numeric_limits<float>::quiet_NaN();
float infinity = std::numeric_limits<float>::infinity();
float max = std::numeric_limits<float>::max();
float min = std::numeric_limits<float>::min();
std::vector<double> data = {-infinity, nan, min, 3.6, max, infinity};
pp["t1"] = migraphx::argument(s, data.data());
auto result = p.eval(pp).back();
std::vector<float> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<float> gold = {0, 0, 0, 0, 0, 0};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(layer_norm_test)
{
std::vector<float> scale{1.2, 0.8};
std::vector<float> bias{0.5, 0.2};
std::vector<float> result_vector =
norm_test<float>({1, 4, 2}, scale, bias, "layer_norm_3d_test.onnx");
std::vector<float> gold = {-0.69997597,
0.99998398,
-0.69997597,
0.99998398,
-0.69997597,
0.99998398,
-0.69997597,
0.99998398};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(layer_norm_half_test)
{
using migraphx::half;
std::vector<half> scale{half{1.2}, half{0.8}};
std::vector<half> bias{half{0.5}, half{0.2}};
std::vector<half> result_vector =
norm_test<half>({1, 4, 2}, scale, bias, "layer_norm_3d_half_test.onnx");
std::vector<half> gold = {half{-0.69997597},
half{0.99998398},
half{-0.69997597},
half{0.99998398},
half{-0.69997597},
half{0.99998398},
half{-0.69997597},
half{0.99998398}};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(lessorequal_test)
{
migraphx::program p = migraphx::parse_onnx("lessorequal_test.onnx");
......@@ -1112,6 +1300,115 @@ TEST_CASE(mean_integral_test)
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
template <typename T = float>
std::vector<T> mvn_test(std::vector<size_t> data_lens, const std::string& test_file)
{
migraphx::program p = migraphx::parse_onnx(test_file);
p.compile(migraphx::make_target("ref"));
migraphx::shape data_shape(migraphx::shape::get_type<T>{}, std::move(data_lens));
std::vector<T> data(data_shape.elements());
std::iota(begin(data), end(data), 0);
migraphx::parameter_map pm;
pm["data"] = migraphx::argument(data_shape, data.data());
auto result = p.eval(pm).back();
std::vector<T> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
return result_vector;
}
TEST_CASE(mvn_default_axes_test)
{
auto result = mvn_test({2, 2, 2, 2}, "mvn_default_axes_test.onnx");
std::vector<float> gold{-1.32424438,
-1.08347268,
-0.84270097,
-0.60192927,
-1.32424438,
-1.08347268,
-0.84270097,
-0.60192927,
0.60192927,
0.84270097,
1.08347268,
1.32424438,
0.60192927,
0.84270097,
1.08347268,
1.32424438};
EXPECT(migraphx::verify::verify_rms_range(result, gold));
}
TEST_CASE(mvn_default_axes_fp16_test)
{
using migraphx::half;
auto result = mvn_test<half>({2, 2, 2, 2}, "mvn_default_axes_fp16_test.onnx");
std::vector<half> gold{half{-1.324},
half{-1.084},
half{-0.843},
half{-0.602},
half{-1.324},
half{-1.084},
half{-0.843},
half{-0.602},
half{0.602},
half{0.843},
half{1.084},
half{1.324},
half{0.602},
half{0.843},
half{1.084},
half{1.324}};
EXPECT(migraphx::verify::verify_rms_range(result, gold));
}
TEST_CASE(mvn_rank_2_test)
{
auto result = mvn_test({2, 2}, "mvn_rank_2_test.onnx");
std::vector<float> gold{-1, 1, -1, 1};
EXPECT(migraphx::verify::verify_rms_range(result, gold));
}
TEST_CASE(mvn_rank_2_fp16_test)
{
using migraphx::half;
auto result = mvn_test<migraphx::half>({2, 2}, "mvn_rank_2_fp16_test.onnx");
std::vector<migraphx::half> gold{half{-1}, half{1}, half{-1}, half{1}};
EXPECT(migraphx::verify::verify_rms_range(result, gold));
}
TEST_CASE(mvn_rank_3_test)
{
auto result = mvn_test({2, 2, 2}, "mvn_rank_3_test.onnx");
std::vector<float> gold{-1.34164079,
-1.34164079,
-0.4472136,
-0.4472136,
0.4472136,
0.4472136,
1.34164079,
1.34164079};
EXPECT(migraphx::verify::verify_rms_range(result, gold));
}
TEST_CASE(mvn_rank_3_fp16_test)
{
using migraphx::half;
auto result = mvn_test<half>({2, 2, 2}, "mvn_rank_3_fp16_test.onnx");
std::vector<half> gold{half{-1.342},
half{-1.342},
half{-0.4473},
half{-0.4473},
half{0.4473},
half{0.4473},
half{1.342},
half{1.342}};
EXPECT(migraphx::verify::verify_rms_range(result, gold));
}
TEST_CASE(mod_test)
{
migraphx::program p = migraphx::parse_onnx("mod_test.onnx");
......@@ -1226,6 +1523,77 @@ TEST_CASE(mod_test_fmod_different_types)
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(multinomial_dyn_test)
{
migraphx::onnx_options options;
options.default_dyn_dim_value = {1, 4};
auto p = migraphx::parse_onnx("multinomial_dyn_test.onnx", options);
const size_t batch_size(2);
const size_t categories(5);
const size_t sample_size(100000);
p.compile(migraphx::make_target("ref"));
// Distribution function (2 distributions of 5 categories each)
std::vector<int> dist{15, 25, 15, 25, 20, 20, 20, 10, 25, 25};
EXPECT(dist.size() == categories * batch_size);
std::vector<float> data(categories * batch_size);
std::transform(dist.begin(), dist.end(), data.begin(), [&](auto d) { return log(d); });
// Shape of the probability distribution, which also defines the number of categories
migraphx::shape s{migraphx::shape::float_type, {batch_size, categories}};
migraphx::parameter_map pp;
pp["input"] = migraphx::argument(s, data.data());
auto result = p.eval(pp).back();
std::vector<int32_t> result_vec(batch_size * sample_size);
result.visit([&](auto output) { result_vec.assign(output.begin(), output.end()); });
// Make a categorical histogram of output
// for first result in batch
std::vector<int> res_dist(categories, 0);
size_t r = 0;
for(r = 0; r < result_vec.size() / 2; r++)
res_dist[result_vec[r]]++;
// normalizing factors for original and measured distributions
auto dist_sum = std::accumulate(dist.begin(), dist.begin() + 5, 0);
auto res_dist_sum = std::accumulate(res_dist.begin(), res_dist.end(), 0);
// Values approximate the distribution in dist
std::vector<float> norm(5);
std::vector<float> res_norm(5);
std::transform(dist.begin(), dist.begin() + 5, norm.begin(), [&](auto n) {
return static_cast<double>(n) / dist_sum;
});
std::transform(res_dist.begin(), res_dist.end(), res_norm.begin(), [&](auto n) {
return static_cast<double>(n) / res_dist_sum;
});
EXPECT(migraphx::verify::verify_range_with_tolerance(
norm, migraphx::verify::expected{res_norm}, migraphx::verify::tolerance{0.01}));
// Make a categorical histogram of output
// for second result in batch
std::fill(res_dist.begin(), res_dist.end(), 0);
for(; r < result_vec.size(); r++)
res_dist[result_vec[r]]++;
dist_sum = std::accumulate(dist.begin() + 5, dist.end(), 0);
res_dist_sum = std::accumulate(res_dist.begin(), res_dist.end(), 0);
std::transform(dist.begin() + 5, dist.end(), norm.begin(), [&](auto n) {
return static_cast<double>(n) / dist_sum;
});
std::transform(res_dist.begin(), res_dist.end(), res_norm.begin(), [&](auto n) {
return static_cast<double>(n) / res_dist_sum;
});
EXPECT(migraphx::verify::verify_range_with_tolerance(
res_norm, migraphx::verify::expected{norm}, migraphx::verify::tolerance{0.01}));
}
TEST_CASE(nonzero_test)
{
migraphx::program p = migraphx::parse_onnx("nonzero_dynamic_test.onnx");
......@@ -1245,6 +1613,288 @@ TEST_CASE(nonzero_test)
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(qlinearadd_test)
{
// github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md#com.microsoft.QLinearAdd
migraphx::program p = migraphx::parse_onnx("qlinearadd_test.onnx");
p.compile(migraphx::make_target("ref"));
migraphx::shape a{migraphx::shape::uint8_type, {64}};
std::vector<uint8_t> data_a = {0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24,
26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50,
52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76,
78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100, 102,
104, 106, 108, 110, 112, 114, 116, 118, 120, 122, 124, 126};
migraphx::shape b{migraphx::shape::uint8_type, {64}};
std::vector<uint8_t> data_b = {128, 126, 124, 122, 120, 118, 116, 114, 112, 110, 108, 106, 104,
102, 100, 98, 96, 94, 92, 90, 88, 86, 84, 82, 80, 78,
76, 74, 72, 70, 68, 66, 64, 62, 60, 58, 56, 54, 52,
50, 48, 46, 44, 42, 40, 38, 36, 34, 32, 30, 28, 26,
24, 22, 20, 18, 16, 14, 12, 10, 8, 6, 4, 2};
migraphx::parameter_map pp;
pp["A"] = migraphx::argument(a, data_a.data());
pp["B"] = migraphx::argument(b, data_b.data());
auto result = p.eval(pp).back();
std::vector<uint8_t> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<uint8_t> gold = {64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64,
64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64,
64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64,
64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(qlinearadd_bcast_test)
{
// github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md#com.microsoft.QLinearAdd
migraphx::program p = migraphx::parse_onnx("qlinearadd_bcast_test.onnx");
p.compile(migraphx::make_target("ref"));
migraphx::shape a{migraphx::shape::int8_type, {64}};
std::vector<int8_t> data_a = {-64, -62, -60, -58, -56, -54, -52, -50, -48, -46, -44, -42, -40,
-38, -36, -34, -32, -30, -28, -26, -24, -22, -20, -18, -16, -14,
-12, -10, -8, -6, -4, -2, 0, 2, 4, 6, 8, 10, 12,
14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38,
40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62};
migraphx::shape b{migraphx::shape::int8_type, {1, 1, 64}};
std::vector<int8_t> data_b = {96, 94, 92, 90, 88, 86, 84, 82, 80, 78, 76, 74, 72,
70, 68, 66, 64, 62, 60, 58, 56, 54, 52, 50, 48, 46,
44, 42, 40, 38, 36, 34, 32, 30, 28, 26, 24, 22, 20,
18, 16, 14, 12, 10, 8, 6, 4, 2, 0, -2, -4, -6,
-8, -10, -12, -14, -16, -18, -20, -22, -24, -26, -28, -30};
migraphx::parameter_map pp;
pp["A"] = migraphx::argument(a, data_a.data());
pp["B"] = migraphx::argument(b, data_b.data());
auto result = p.eval(pp).back();
std::vector<int8_t> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<int8_t> gold = {-64, -64, -64, -64, -64, -64, -64, -64, -64, -64, -64, -64, -64,
-64, -64, -64, -64, -64, -64, -64, -64, -64, -64, -64, -64, -64,
-64, -64, -64, -64, -64, -64, -64, -64, -64, -64, -64, -64, -64,
-64, -64, -64, -64, -64, -64, -64, -64, -64, -64, -64, -64, -64,
-64, -64, -64, -64, -64, -64, -64, -64, -64, -64, -64, -64};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(qlinearconv_test)
{
// https://xadupre.github.io/draft/onnx/onnx_doc_folder/onnx__QLinearConv.html
migraphx::program p = migraphx::parse_onnx("qlinearconv_test.onnx");
p.compile(migraphx::make_target("ref"));
migraphx::shape sx{migraphx::shape::uint8_type, {1, 1, 7, 7}};
std::vector<uint8_t> x_data = {255, 174, 162, 25, 203, 168, 58, 15, 59, 237, 95, 129, 0,
64, 56, 242, 153, 221, 168, 12, 166, 232, 178, 186, 195, 237,
162, 237, 188, 39, 124, 77, 80, 102, 43, 127, 230, 21, 83,
41, 40, 134, 255, 154, 92, 141, 42, 148, 247};
migraphx::parameter_map pp;
pp["X"] = migraphx::argument(sx, x_data.data());
auto result = p.eval(pp).back();
std::vector<uint8_t> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<uint8_t> gold = {0, 81, 93, 230, 52, 87, 197, 240, 196, 18, 160, 126, 255,
191, 199, 13, 102, 34, 87, 243, 89, 23, 77, 69, 60, 18,
93, 18, 67, 216, 131, 178, 175, 153, 212, 128, 25, 234, 172,
214, 215, 121, 0, 101, 163, 114, 213, 107, 8};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(qlinearconv_pad_0_test)
{
// https:xadupre.github.io/draft/onnx/onnx_doc_folder/onnx__Conv.html
migraphx::program p = migraphx::parse_onnx("qlinearconv_pad_0_test.onnx");
p.compile(migraphx::make_target("ref"));
migraphx::shape sx{migraphx::shape::uint8_type, {1, 1, 5, 5}};
std::vector<uint8_t> x_data = {0, 11, 21, 32, 42, 53, 64, 74, 85, 96, 106, 117, 128,
138, 149, 159, 170, 181, 191, 202, 212, 223, 234, 244, 255};
migraphx::parameter_map pp;
pp["X"] = migraphx::argument(sx, x_data.data());
auto result = p.eval(pp).back();
std::vector<int8_t> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
// # (1, 1, 3, 3) output tensor
std::vector<int8_t> gold = {-43, -29, -15, 28, 42, 56, 99, 113, 127};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(qlinearconv_pad_1_test)
{
// https:xadupre.github.io/draft/onnx/onnx_doc_folder/onnx__Conv.html
migraphx::program p = migraphx::parse_onnx("qlinearconv_pad_1_test.onnx");
p.compile(migraphx::make_target("ref"));
migraphx::shape sx{migraphx::shape::uint8_type, {1, 1, 5, 5}};
std::vector<uint8_t> x_data = {0, 11, 21, 32, 42, 53, 64, 74, 85, 96, 106, 117, 128,
138, 149, 159, 170, 181, 191, 202, 212, 223, 234, 244, 255};
migraphx::parameter_map pp;
pp["X"] = migraphx::argument(sx, x_data.data());
auto result = p.eval(pp).back();
std::vector<uint8_t> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
// # (1, 1, 5, 5) output tensor
std::vector<uint8_t> gold = {19, 33, 43, 52, 38, 52, 85, 99, 113, 80, 99, 156, 170,
184, 128, 146, 227, 241, 255, 175, 113, 175, 184, 194, 132};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(qlinearconv_scale_1D_test)
{
// https:xadupre.github.io/draft/onnx/onnx_doc_folder/onnx__Conv.html
migraphx::program p = migraphx::parse_onnx("qlinearconv_scale_1D_test.onnx");
p.compile(migraphx::make_target("ref"));
migraphx::shape sx{migraphx::shape::uint8_type, {1, 1, 5, 5}};
std::vector<uint8_t> x_data = {0, 11, 21, 32, 42, 53, 64, 74, 85, 96, 106, 117, 128,
138, 149, 159, 170, 181, 191, 202, 212, 223, 234, 244, 255};
migraphx::parameter_map pp;
pp["X"] = migraphx::argument(sx, x_data.data());
auto result = p.eval(pp).back();
std::vector<int8_t> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
// # (1, 2, 3, 3) output tensor
std::vector<int8_t> gold = {
-43, -29, -15, 28, 42, 56, 99, 113, 127, -43, -29, -15, 28, 42, 56, 99, 113, 127};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(qlinearglobalavgpool_test)
{
// github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md
// #com.microsoft.QLinearGlobalAveragePool
migraphx::program p = migraphx::parse_onnx("qlinearglobalavgpool_test.onnx");
p.compile(migraphx::make_target("ref"));
migraphx::shape sh_x{migraphx::shape::uint8_type, {1, 3, 4, 4}};
std::vector<uint8_t> data_x = {160, 156, 152, 148, 144, 140, 136, 132, 124, 120, 116, 112,
108, 104, 100, 96, 64, 72, 80, 88, 96, 104, 112, 120,
136, 144, 152, 160, 168, 176, 184, 192, 120, 121, 122, 123,
124, 125, 126, 127, 129, 130, 131, 132, 133, 134, 135, 136};
migraphx::parameter_map pp;
pp["X"] = migraphx::argument(sh_x, data_x.data());
auto result = p.eval(pp).back();
std::vector<uint8_t> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<uint8_t> gold = {64, 64, 64};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(qlinearmatmul_1D_test)
{
migraphx::program p = migraphx::parse_onnx("qlinearmatmul_1D_test.onnx");
p.compile(migraphx::make_target("ref"));
migraphx::shape a{migraphx::shape::uint8_type, {8}};
std::vector<uint8_t> data_a = {2, 4, 6, 8, 10, 12, 14, 16};
migraphx::shape b{migraphx::shape::uint8_type, {8}};
std::vector<uint8_t> data_b = {126, 130, 124, 132, 122, 134, 120, 136};
migraphx::parameter_map pp;
pp["A"] = migraphx::argument(a, data_a.data());
pp["B"] = migraphx::argument(b, data_b.data());
auto result = p.eval(pp).back();
std::vector<uint8_t> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<uint8_t> gold = {66};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(qlinearmatmul_2D_test)
{
migraphx::program p = migraphx::parse_onnx("qlinearmatmul_2D_test.onnx");
p.compile(migraphx::make_target("ref"));
migraphx::shape a{migraphx::shape::uint8_type, {1, 8}};
std::vector<uint8_t> data_a = {2, 4, 6, 8, 10, 12, 14, 16};
migraphx::shape b{migraphx::shape::uint8_type, {8, 1}};
std::vector<uint8_t> data_b = {126, 130, 124, 132, 122, 134, 120, 136};
migraphx::parameter_map pp;
pp["A"] = migraphx::argument(a, data_a.data());
pp["B"] = migraphx::argument(b, data_b.data());
auto result = p.eval(pp).back();
std::vector<uint8_t> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<uint8_t> gold = {66};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(qlinearmatmul_3D_test)
{
// https://xadupre.github.io/draft/onnx/onnx_doc_folder/onnx__QLinearMatMul.html
migraphx::program p = migraphx::parse_onnx("qlinearmatmul_3D_test.onnx");
p.compile(migraphx::make_target("ref"));
migraphx::shape a{migraphx::shape::uint8_type, {2, 2, 4}};
std::vector<uint8_t> data_a = {
208, 236, 0, 238, 3, 214, 255, 29, 208, 236, 0, 238, 3, 214, 255, 29};
migraphx::shape b{migraphx::shape::uint8_type, {2, 4, 3}};
std::vector<uint8_t> data_b = {152, 51, 244, 60, 26, 255, 0, 127, 246, 127, 254, 247,
152, 51, 244, 60, 26, 255, 0, 127, 246, 127, 254, 247};
migraphx::parameter_map pp;
pp["A"] = migraphx::argument(a, data_a.data());
pp["B"] = migraphx::argument(b, data_b.data());
auto result = p.eval(pp).back();
std::vector<uint8_t> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<uint8_t> gold = {168, 115, 255, 1, 66, 151, 168, 115, 255, 1, 66, 151};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(resize_downsample_f_test)
{
migraphx::program p = migraphx::parse_onnx("resize_downsample_f_test.onnx");
......@@ -1406,6 +2056,43 @@ TEST_CASE(reversesequence_time_verify_test)
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(round_half_test)
{
migraphx::program p = migraphx::parse_onnx("round_half_test.onnx");
p.compile(migraphx::make_target("ref"));
migraphx::shape xs{migraphx::shape::half_type, {4, 4}};
std::vector<float> tmp = {-3.51,
-3.5,
-3.49,
-2.51,
-2.50,
-2.49,
-1.6,
-1.5,
-0.51,
-0.5,
0.5,
0.6,
2.4,
2.5,
3.5,
4.5};
std::vector<migraphx::half> data{tmp.cbegin(), tmp.cend()};
migraphx::parameter_map param_map;
param_map["x"] = migraphx::argument(xs, data.data());
auto result = p.eval(param_map).back();
std::vector<migraphx::half> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
tmp = {-4.0, -4.0, -3.0, -3.0, -2.0, -2.0, -2.0, -2.0, -1.0, 0.0, 0.0, 1.0, 2.0, 2.0, 4.0, 4.0};
std::vector<migraphx::half> gold{tmp.cbegin(), tmp.cend()};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(selu_test)
{
migraphx::program p = migraphx::parse_onnx("selu_test.onnx");
......@@ -1426,6 +2113,112 @@ TEST_CASE(selu_test)
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(shrink_hard_test)
{
migraphx::program p = migraphx::parse_onnx("shrink_hard_test.onnx");
p.compile(migraphx::make_target("ref"));
migraphx::shape s{migraphx::shape::float_type, {5}};
std::vector<float> data{-2, -1, 0, 1, 2};
migraphx::parameter_map pp;
pp["x"] = migraphx::argument(s, data.data());
auto result = p.eval(pp).back();
std::vector<float> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<float> gold = {-2, 0, 0, 0, 2};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(shrink_soft_test)
{
migraphx::program p = migraphx::parse_onnx("shrink_soft_test.onnx");
p.compile(migraphx::make_target("ref"));
migraphx::shape s{migraphx::shape::float_type, {5}};
std::vector<float> data{-2, -1, 0, 1, 2};
migraphx::parameter_map pp;
pp["x"] = migraphx::argument(s, data.data());
auto result = p.eval(pp).back();
std::vector<float> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<float> gold = {-0.5, 0, 0, 0, 0.5};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(shrink_verify_test)
{
migraphx::program p = migraphx::parse_onnx("shrink_verify_test.onnx");
p.compile(migraphx::make_target("ref"));
migraphx::shape s{migraphx::shape::half_type, {5}};
std::vector<float> tmp = {-10.0, -5.0, 0.0, 5.0, 10.0};
std::vector<migraphx::half> data{tmp.cbegin(), tmp.cend()};
migraphx::parameter_map pp;
pp["x"] = migraphx::argument(s, data.data());
auto result = p.eval(pp).back();
std::vector<migraphx::half> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
tmp = {-9.0, -4.0, 1.0, 4.0, 9.0};
std::vector<migraphx::half> gold{tmp.cbegin(), tmp.cend()};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(shrink_verify2_test)
{
migraphx::program p = migraphx::parse_onnx("shrink_verify2_test.onnx");
p.compile(migraphx::make_target("ref"));
migraphx::shape s{migraphx::shape::half_type, {5}};
std::vector<float> tmp = {-10.0, -5.0, 0.0, 5.0, 10.0};
std::vector<migraphx::half> data{tmp.cbegin(), tmp.cend()};
migraphx::parameter_map pp;
pp["x"] = migraphx::argument(s, data.data());
auto result = p.eval(pp).back();
std::vector<migraphx::half> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
tmp = {-5.0, 0.0, 5.0, 10.0, 5.0};
std::vector<migraphx::half> gold{tmp.cbegin(), tmp.cend()};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(shrink_int8_test)
{
migraphx::program p = migraphx::parse_onnx("shrink_int8_test.onnx");
p.compile(migraphx::make_target("ref"));
migraphx::shape s{migraphx::shape::int8_type, {3, 3}};
std::vector<int8_t> data{-4, -3, -2, -1, 0, 1, 2, 3, 4};
migraphx::parameter_map pp;
pp["x"] = migraphx::argument(s, data.data());
auto result = p.eval(pp).back();
std::vector<int8_t> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<int8_t> gold = {-2, -1, 0, 0, 0, 0, 0, 1, 2};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(shrink_uint8_test)
{
migraphx::program p = migraphx::parse_onnx("shrink_uint8_test.onnx");
p.compile(migraphx::make_target("ref"));
migraphx::shape s{migraphx::shape::uint8_type, {3, 3}};
std::vector<uint8_t> data{1, 2, 3, 4, 5, 6, 7, 8, 9};
migraphx::parameter_map pp;
pp["x"] = migraphx::argument(s, data.data());
auto result = p.eval(pp).back();
std::vector<uint8_t> result_vector;
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
std::vector<uint8_t> gold = {0, 0, 0, 0, 0, 10, 11, 12, 13};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(size_verify_test)
{
migraphx::program p = migraphx::parse_onnx("size_verify_test.onnx");
......@@ -1637,9 +2430,10 @@ std::vector<float> gen_trilu_test(const migraphx::shape& s, const migraphx::prog
result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
return result_vector;
}
TEST_CASE(trilu_test)
TEST_CASE(triu_test)
{
migraphx::program p = migraphx::parse_onnx("trilu_test.onnx");
migraphx::program p = migraphx::parse_onnx("triu_test.onnx");
std::vector<float> result_vector = gen_trilu_test({migraphx::shape::float_type, {3, 4}}, p);
......@@ -1648,9 +2442,9 @@ TEST_CASE(trilu_test)
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(trilu_batch_diff_k_test)
TEST_CASE(triu_batch_diff_k_test)
{
migraphx::program p = migraphx::parse_onnx("trilu_batch_diff_k_test.onnx");
migraphx::program p = migraphx::parse_onnx("triu_batch_diff_k_test.onnx");
std::vector<float> result_vector = gen_trilu_test({migraphx::shape::float_type, {2, 2, 3}}, p);
......@@ -1659,9 +2453,42 @@ TEST_CASE(trilu_batch_diff_k_test)
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(trilu_lower_test)
TEST_CASE(tril_test)
{
migraphx::program p = migraphx::parse_onnx("tril_test.onnx");
std::vector<float> result_vector = gen_trilu_test({migraphx::shape::float_type, {3, 4}}, p);
std::vector<float> gold = {1, 0, 0, 0, 5, 6, 0, 0, 9, 10, 11, 0};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(tril_batch_diff_k_test)
{
migraphx::program p = migraphx::parse_onnx("tril_batch_diff_k_test.onnx");
std::vector<float> result_vector = gen_trilu_test({migraphx::shape::float_type, {2, 2, 3}}, p);
std::vector<float> gold = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(triu_neg_k_test)
{
migraphx::program p = migraphx::parse_onnx("trilu_lower_test.onnx");
migraphx::program p = migraphx::parse_onnx("triu_neg_k_test.onnx");
std::vector<float> result_vector = gen_trilu_test({migraphx::shape::float_type, {3, 4}}, p);
std::vector<float> gold = {1, 2, 3, 4, 5, 6, 7, 8, 0, 10, 11, 12};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(tril_neg_k_test)
{
migraphx::program p = migraphx::parse_onnx("tril_neg_k_test.onnx");
std::vector<float> result_vector = gen_trilu_test({migraphx::shape::float_type, {3, 4}}, p);
......@@ -1670,9 +2497,9 @@ TEST_CASE(trilu_lower_test)
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(trilu_out_k_test)
TEST_CASE(triu_out_k_test)
{
migraphx::program p = migraphx::parse_onnx("trilu_out_k_test.onnx");
migraphx::program p = migraphx::parse_onnx("triu_out_k_test.onnx");
std::vector<float> result_vector = gen_trilu_test({migraphx::shape::float_type, {3, 4}}, p);
......@@ -1681,9 +2508,20 @@ TEST_CASE(trilu_out_k_test)
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(trilu_row_one_test)
TEST_CASE(tril_out_k_test)
{
migraphx::program p = migraphx::parse_onnx("tril_out_k_test.onnx");
std::vector<float> result_vector = gen_trilu_test({migraphx::shape::float_type, {3, 4}}, p);
std::vector<float> gold = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(triu_row_one_test)
{
migraphx::program p = migraphx::parse_onnx("trilu_row_one_test.onnx");
migraphx::program p = migraphx::parse_onnx("triu_row_one_test.onnx");
std::vector<float> result_vector = gen_trilu_test({migraphx::shape::float_type, {1, 4}}, p);
......@@ -1692,4 +2530,15 @@ TEST_CASE(trilu_row_one_test)
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
TEST_CASE(tril_row_one_test)
{
migraphx::program p = migraphx::parse_onnx("tril_row_one_test.onnx");
std::vector<float> result_vector = gen_trilu_test({migraphx::shape::float_type, {1, 4}}, p);
std::vector<float> gold = {1, 2, 0, 0};
EXPECT(migraphx::verify::verify_rms_range(result_vector, gold));
}
int main(int argc, const char* argv[]) { test::run(argc, argv); }
test/op_shape_test.cpp
View file @ 4ea39116
......@@ -88,7 +88,7 @@ TEST_CASE(allocate_static)
expect_shape(out_shape, migraphx::make_op("allocate", {{"shape", to_value(out_shape)}}));
}
TEST_CASE(allocate_static_input_error)
TEST_CASE(allocate_static_input)
{
migraphx::shape input{migraphx::shape::int64_type, {3}};
migraphx::shape out_shape{migraphx::shape::float_type, {2, 3, 4}};
......@@ -116,7 +116,7 @@ TEST_CASE(allocate_dyn_with_shape_attr)
input);
}
TEST_CASE(allocate_dyn_no_input_error)
TEST_CASE(allocate_dyn_no_input)
{
migraphx::shape shape_attr{migraphx::shape::float_type,
{{1, 4}, {3, 3}, {4, 8, {4, 6}}, {4, 8}, {4, 6}}};
......@@ -124,6 +124,21 @@ TEST_CASE(allocate_dyn_no_input_error)
migraphx::make_op("allocate", {{"shape", migraphx::to_value(shape_attr)}}));
}
TEST_CASE(allocate_shape_and_buf_type_error)
{
migraphx::shape shape_attr{migraphx::shape::float_type,
{{1, 4}, {3, 3}, {4, 8, {4, 6}}, {4, 8}, {4, 6}}};
throws_shape(migraphx::make_op(
"allocate",
{{"shape", migraphx::to_value(shape_attr)}, {"buf_type", migraphx::shape::half_type}}));
}
TEST_CASE(allocate_no_attr_error)
{
migraphx::shape input{migraphx::shape::int64_type, {4}};
throws_shape(migraphx::make_op("allocate"), input);
}
TEST_CASE(argmax_axis0)
{
migraphx::shape input{migraphx::shape::half_type, {2, 3, 4, 5}};
......@@ -1942,12 +1957,42 @@ TEST_CASE(multibroadcast_3in_dyn_dyn)
expect_shape(expected_shape, migraphx::make_op("multibroadcast"), c_shape, a_shape, b_shape);
}
TEST_CASE(multinomial)
TEST_CASE(multinomial_bool_type)
{
migraphx::shape s{migraphx::shape::float_type, {2, 5}};
migraphx::shape s1{migraphx::shape::float_type, {1, 2}};
migraphx::shape s2{migraphx::shape::float_type, {3, 4}};
int dtype = 0;
throws_shape(migraphx::make_op("multinomial", {{"dtype", dtype}}), s, s);
throws_shape(migraphx::make_op("multinomial", {{"dtype", dtype}}), s1, s2);
}
TEST_CASE(multinomial)
{
migraphx::shape s1{migraphx::shape::float_type, {1, 2}};
migraphx::shape s2{migraphx::shape::float_type, {3, 4}};
migraphx::shape s3{migraphx::shape::float_type, {1, 4}};
int dtype = 2;
expect_shape(s3, migraphx::make_op("multinomial", {{"dtype", dtype}}), s1, s2);
}
TEST_CASE(multinomial_0size_input)
{
migraphx::shape s1{migraphx::shape::float_type, {1, 2}};
migraphx::shape s2{migraphx::shape::float_type, {}};
int dtype = 2;
throws_shape(migraphx::make_op("multinomial", {{"dtype", dtype}}), s1, s2);
}
TEST_CASE(multinomial_dyn)
{
migraphx::shape s1{migraphx::shape::int32_type, {{2, 3}, {5, 6}}};
migraphx::shape s2{migraphx::shape::int32_type, {{7, 8}, {9, 10}}};
migraphx::shape s3{migraphx::shape::int32_type, {{2, 3}, {9, 10}}};
expect_shape(
s3, migraphx::make_op("multinomial", {{"dtype", migraphx::shape::int32_type}}), s1, s2);
}
TEST_CASE(nms_shape)
......@@ -2684,7 +2729,7 @@ TEST_CASE(reshape_broadcast_squeeze_memlayout_change)
expect_shape(output, migraphx::make_op("reshape", {{"dims", output.lens()}}), input);
}
TEST_CASE(reshape_dyn_shape)
TEST_CASE(reshape_dyn_1in)
{
migraphx::shape input{migraphx::shape::float_type, {{1, 4}, {24, 24}, {1, 1}, {1, 1}}};
for(auto&& new_shape : std::vector<std::vector<int64_t>>{
......@@ -2708,6 +2753,27 @@ TEST_CASE(reshape_dyn_shape)
}
}
TEST_CASE(reshape_dyn_2in_0)
{
migraphx::shape input{migraphx::shape::float_type, {{1, 4}, {24, 24}, {1, 1}, {1, 1}}};
migraphx::shape output{migraphx::shape::float_type, {{1, 4}, {8, 8}, {3, 3}, {1, 1}}};
expect_shape(output, migraphx::make_op("reshape"), input, output);
}
TEST_CASE(reshape_dyn_2in_1)
{
migraphx::shape input{migraphx::shape::float_type, {{1, 4}, {24, 24}, {1, 1}, {1, 1}}};
migraphx::shape output{migraphx::shape::float_type, {{12, 12}, {2, 2}, {1, 1}, {1, 4}}};
expect_shape(output, migraphx::make_op("reshape"), input, output);
}
TEST_CASE(reshape_dyn_2in_2)
{
migraphx::shape input{migraphx::shape::float_type, {2, 24, 1, 1}};
migraphx::shape output{migraphx::shape::float_type, {{1, 2}, {6, 12}, {1, 1}, {4, 4}}};
expect_shape(output, migraphx::make_op("reshape"), input, output);
}
TEST_CASE(reshape_multiple_non_fixed_error)
{
migraphx::shape input{migraphx::shape::float_type, {{1, 4}, {24, 24}, {10, 20}, {1, 1}}};
......@@ -3166,7 +3232,65 @@ TEST_CASE(slice_static_shape)
}
TEST_CASE(slice_var_inputs_static_shape0)
{
// attr ends and axes set; inputs are (data, input_starts)
migraphx::shape input{migraphx::shape::float_type, {3, 4, 4}};
migraphx::shape starts{migraphx::shape::int64_type, {2}};
expect_shape(migraphx::shape{migraphx::shape::float_type, {{3, 3}, {0, 4}, {0, 4}}},
migraphx::make_op("slice", {{"ends", {2, 3}}, {"axes", {1, 2}}}),
input,
starts);
}
TEST_CASE(slice_var_inputs_static_mismatch_error0)
{
migraphx::shape input{migraphx::shape::float_type, {3, 4, 4}};
migraphx::shape starts{migraphx::shape::int64_type, {2}};
throws_shape(
migraphx::make_op("slice", {{"ends", {2, 3, 4}}, {"axes", {0, 1, 2}}}), input, starts);
}
TEST_CASE(slice_var_inputs_static_shape1)
{
// attr starts and axes set; inputs are (data, input_ends)
migraphx::shape input{migraphx::shape::float_type, {3, 4, 4}};
migraphx::shape ends{migraphx::shape::int64_type, {2}};
expect_shape(migraphx::shape{migraphx::shape::float_type, {{3, 3}, {0, 4}, {0, 4}}},
migraphx::make_op("slice", {{"starts", {0, 1}}, {"axes", {1, 2}}}),
input,
ends);
}
TEST_CASE(slice_var_inputs_static_mismatch_error1)
{
migraphx::shape input{migraphx::shape::float_type, {3, 4, 4}};
migraphx::shape ends{migraphx::shape::int64_type, {2}};
throws_shape(
migraphx::make_op("slice", {{"starts", {0, 1, 2}}, {"axes", {0, 1, 2}}}), input, ends);
}
TEST_CASE(slice_var_inputs_static_shape2)
{
// attr starts and ends set; inputs are (data, input_axes)
migraphx::shape input{migraphx::shape::float_type, {3, 4, 4}};
migraphx::shape axes{migraphx::shape::int64_type, {2}};
expect_shape(migraphx::shape{migraphx::shape::float_type, {{0, 3}, {0, 4}, {0, 4}}},
migraphx::make_op("slice", {{"starts", {0, 1}}, {"ends", {1, 2}}}),
input,
axes);
}
TEST_CASE(slice_var_inputs_static_mismatch_error2)
{
migraphx::shape input{migraphx::shape::float_type, {3, 4, 4}};
migraphx::shape axes{migraphx::shape::int64_type, {2}};
throws_shape(
migraphx::make_op("slice", {{"starts", {0, 1, 2}}, {"ends", {3, 4, 4}}}), input, axes);
}
TEST_CASE(slice_var_inputs_static_shape3)
{
// attr axes set; inputs are (data, input_starts, input_ends)
migraphx::shape input{migraphx::shape::float_type, {3, 4, 4}};
migraphx::shape starts{migraphx::shape::int64_type, {2}};
migraphx::shape ends{migraphx::shape::int64_type, {2}};
......@@ -3177,7 +3301,57 @@ TEST_CASE(slice_var_inputs_static_shape0)
ends);
}
TEST_CASE(slice_var_inputs_static_shape1)
TEST_CASE(slice_var_inputs_static_mismatch_error3)
{
migraphx::shape input{migraphx::shape::float_type, {3, 4, 4}};
migraphx::shape starts{migraphx::shape::int64_type, {2}};
migraphx::shape ends{migraphx::shape::int64_type, {2}};
throws_shape(migraphx::make_op("slice", {{"axes", {0, 1, 2}}}), input, starts, ends);
}
TEST_CASE(slice_var_inputs_static_shape4)
{
// attr ends set; inputs are (data, input_starts, input_axes)
migraphx::shape input{migraphx::shape::float_type, {3, 4, 4}};
migraphx::shape starts{migraphx::shape::int64_type, {2}};
migraphx::shape axes{migraphx::shape::int64_type, {2}};
expect_shape(migraphx::shape{migraphx::shape::float_type, {{0, 3}, {0, 4}, {0, 4}}},
migraphx::make_op("slice", {{"ends", {3, 4}}}),
input,
starts,
axes);
}
TEST_CASE(slice_var_inputs_static_mismatch_error4)
{
migraphx::shape input{migraphx::shape::float_type, {3, 4, 4}};
migraphx::shape starts{migraphx::shape::int64_type, {2}};
migraphx::shape axes{migraphx::shape::int64_type, {2}};
throws_shape(migraphx::make_op("slice", {{"ends", {3, 3, 3}}}), input, starts, axes);
}
TEST_CASE(slice_var_inputs_static_shape5)
{
// attr starts set; inputs are (data, input_ends, input_axes)
migraphx::shape input{migraphx::shape::float_type, {3, 4, 4}};
migraphx::shape ends{migraphx::shape::int64_type, {2}};
migraphx::shape axes{migraphx::shape::int64_type, {2}};
expect_shape(migraphx::shape{migraphx::shape::float_type, {{0, 3}, {0, 4}, {0, 4}}},
migraphx::make_op("slice", {{"starts", {0, 2}}}),
input,
ends,
axes);
}
TEST_CASE(slice_var_inputs_static_mismatch_error5)
{
migraphx::shape input{migraphx::shape::float_type, {3, 4, 4}};
migraphx::shape ends{migraphx::shape::int64_type, {2}};
migraphx::shape axes{migraphx::shape::int64_type, {2}};
throws_shape(migraphx::make_op("slice", {{"starts", {0, 1, 2}}}), input, ends, axes);
}
TEST_CASE(slice_var_inputs_static_shape6)
{
migraphx::shape input{migraphx::shape::float_type, {3, 4, 4}};
migraphx::shape starts{migraphx::shape::int64_type, {2}};
......@@ -3191,7 +3365,7 @@ TEST_CASE(slice_var_inputs_static_shape1)
axes);
}
TEST_CASE(slice_var_inputs_static_error0)
TEST_CASE(slice_var_inputs_static_mismatch_error6)
{
migraphx::shape input{migraphx::shape::float_type, {3, 4, 4}};
migraphx::shape starts{migraphx::shape::int64_type, {2}};
......@@ -3202,17 +3376,125 @@ TEST_CASE(slice_var_inputs_static_error0)
TEST_CASE(slice_var_inputs_dyn_shape0)
{
migraphx::shape input{migraphx::shape::float_type, {{3, 6}, {2, 4, {2, 4}}, {2, 4, {2, 4}}}};
// attr ends and axes set; inputs are (data, input_starts)
migraphx::shape input{migraphx::shape::float_type, {{3, 6}, {4, 6}, {4, 6}}};
migraphx::shape starts{migraphx::shape::int64_type, {2}};
expect_shape(migraphx::shape{migraphx::shape::float_type, {{3, 6}, {0, 6}, {0, 6}}},
migraphx::make_op("slice", {{"ends", {2, 3}}, {"axes", {1, 2}}}),
input,
starts);
}
TEST_CASE(slice_var_inputs_dyn_mismatch_error0)
{
migraphx::shape input{migraphx::shape::float_type, {{3, 6}, {4, 6}, {4, 6}}};
migraphx::shape starts{migraphx::shape::int64_type, {2}};
throws_shape(
migraphx::make_op("slice", {{"ends", {2, 3, 4}}, {"axes", {0, 1, 2}}}), input, starts);
}
TEST_CASE(slice_var_inputs_dyn_shape1)
{
// attr starts and axes set; inputs are (data, input_ends)
migraphx::shape input{migraphx::shape::float_type, {{3, 6}, {4, 6}, {4, 6}}};
migraphx::shape ends{migraphx::shape::int64_type, {2}};
expect_shape(migraphx::shape{migraphx::shape::float_type, {{3, 6}, {0, 6}, {0, 6}}},
migraphx::make_op("slice", {{"starts", {0, 1}}, {"axes", {1, 2}}}),
input,
ends);
}
TEST_CASE(slice_var_inputs_dyn_mismatch_error1)
{
migraphx::shape input{migraphx::shape::float_type, {{3, 6}, {4, 6}, {4, 6}}};
migraphx::shape ends{migraphx::shape::int64_type, {2}};
throws_shape(
migraphx::make_op("slice", {{"starts", {0, 1, 2}}, {"axes", {0, 1, 2}}}), input, ends);
}
TEST_CASE(slice_var_inputs_dyn_shape2)
{
// attr starts and ends set; inputs are (data, input_axes)
migraphx::shape input{migraphx::shape::float_type, {{3, 6}, {4, 6}, {4, 6}}};
migraphx::shape axes{migraphx::shape::int64_type, {2}};
expect_shape(migraphx::shape{migraphx::shape::float_type, {{0, 6}, {0, 6}, {0, 6}}},
migraphx::make_op("slice", {{"starts", {0, 1}}, {"ends", {8, 8}}}),
input,
axes);
}
TEST_CASE(slice_var_inputs_dyn_mismatch_error2)
{
migraphx::shape input{migraphx::shape::float_type, {{3, 6}, {4, 6}, {4, 6}}};
migraphx::shape axes{migraphx::shape::int64_type, {2}};
throws_shape(
migraphx::make_op("slice", {{"starts", {0, 1, 2}}, {"ends", {3, 4, 4}}}), input, axes);
}
TEST_CASE(slice_var_inputs_dyn_shape3)
{
// attr axes set; inputs are (data, input_starts, input_ends)
migraphx::shape input{migraphx::shape::float_type, {{3, 6}, {4, 6}, {4, 6}}};
migraphx::shape starts{migraphx::shape::int64_type, {2}};
migraphx::shape ends{migraphx::shape::int64_type, {2}};
expect_shape(migraphx::shape{migraphx::shape::float_type, {{3, 6}, {0, 4}, {0, 4}}},
expect_shape(migraphx::shape{migraphx::shape::float_type, {{3, 6}, {0, 6}, {0, 6}}},
migraphx::make_op("slice", {{"axes", {1, 2}}}),
input,
starts,
ends);
}
TEST_CASE(slice_var_inputs_dyn_shape1)
TEST_CASE(slice_var_inputs_dyn_mismatch_error3)
{
migraphx::shape input{migraphx::shape::float_type, {{3, 6}, {4, 6}, {4, 6}}};
migraphx::shape starts{migraphx::shape::int64_type, {2}};
migraphx::shape ends{migraphx::shape::int64_type, {2}};
throws_shape(migraphx::make_op("slice", {{"axes", {0, 1, 2}}}), input, starts, ends);
}
TEST_CASE(slice_var_inputs_dyn_shape4)
{
// attr ends set; inputs are (data, input_starts, input_axes)
migraphx::shape input{migraphx::shape::float_type, {{3, 6}, {4, 6}, {4, 6}}};
migraphx::shape starts{migraphx::shape::int64_type, {2}};
migraphx::shape axes{migraphx::shape::int64_type, {2}};
expect_shape(migraphx::shape{migraphx::shape::float_type, {{0, 6}, {0, 6}, {0, 6}}},
migraphx::make_op("slice", {{"ends", {3, 4}}}),
input,
starts,
axes);
}
TEST_CASE(slice_var_inputs_dyn_mismatch_error4)
{
migraphx::shape input{migraphx::shape::float_type, {{3, 6}, {4, 6}, {4, 6}}};
migraphx::shape starts{migraphx::shape::int64_type, {2}};
migraphx::shape axes{migraphx::shape::int64_type, {2}};
throws_shape(migraphx::make_op("slice", {{"ends", {3, 3, 3}}}), input, starts, axes);
}
TEST_CASE(slice_var_inputs_dyn_shape5)
{
// attr starts set; inputs are (data, input_ends, input_axes)
migraphx::shape input{migraphx::shape::float_type, {{3, 6}, {4, 6}, {4, 6}}};
migraphx::shape ends{migraphx::shape::int64_type, {2}};
migraphx::shape axes{migraphx::shape::int64_type, {2}};
expect_shape(migraphx::shape{migraphx::shape::float_type, {{0, 6}, {0, 6}, {0, 6}}},
migraphx::make_op("slice", {{"starts", {0, 2}}}),
input,
ends,
axes);
}
TEST_CASE(slice_var_inputs_dyn_mismatch_error5)
{
migraphx::shape input{migraphx::shape::float_type, {{3, 6}, {4, 6}, {4, 6}}};
migraphx::shape ends{migraphx::shape::int64_type, {2}};
migraphx::shape axes{migraphx::shape::int64_type, {2}};
throws_shape(migraphx::make_op("slice", {{"starts", {0, 1, 2}}}), input, ends, axes);
}
TEST_CASE(slice_var_inputs_dyn_shape6)
{
migraphx::shape input{migraphx::shape::float_type, {{3, 6}, {2, 4, {2, 4}}, {2, 4, {2, 4}}}};
migraphx::shape starts{migraphx::shape::int64_type, {2}};
......@@ -3226,6 +3508,15 @@ TEST_CASE(slice_var_inputs_dyn_shape1)
axes);
}
TEST_CASE(slice_var_inputs_dyn_mismatch_error6)
{
migraphx::shape input{migraphx::shape::float_type, {{3, 6}, {4, 6}, {4, 6}}};
migraphx::shape starts{migraphx::shape::int64_type, {2}};
migraphx::shape ends{migraphx::shape::int64_type, {2}};
migraphx::shape axes{migraphx::shape::int64_type, {3}};
throws_shape(migraphx::make_op("slice"), input, starts, ends, axes);
}
TEST_CASE(slice_dyn_shape0)
{
migraphx::shape input{migraphx::shape::int32_type, {{2, 3}, {7, 7}, {2, 3}}};
......
test/py/CMakeLists.txt
View file @ 4ea39116
#####################################################################################
# The MIT License (MIT)
#
# Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
# Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
......@@ -23,8 +23,15 @@
#####################################################################################
include(PythonModules)
set(VENV ${CMAKE_BINARY_DIR}/test/py/venv)
set(VENV_ONNX ${CMAKE_BINARY_DIR}/test/py/venv-onnx)
set(REQUIREMENTS ${CMAKE_CURRENT_SOURCE_DIR}/requirements.txt)
set(REQUIREMENTS_ONNX ${CMAKE_CURRENT_SOURCE_DIR}/requirements-onnx.txt)
set(PYTHON_VERSION_TO_DISABLE_ONNX 3.6)
option(MIGRAPHX_DISABLE_VIRTUAL_ENV "Disable python virtual environments" OFF)
function(add_py_test NAME SCRIPT)
function(add_py_venv_fixture FIXTURE_NAME VIRTUAL_ENV_DIR REQUIREMENTS_FILE)
foreach(PYTHON_VERSION ${PYTHON_VERSIONS})
set (ENV_COMMAND ${CMAKE_COMMAND} -E env
"PYTHONPATH=$<TARGET_FILE_DIR:migraphx_pybind_${PYTHON_VERSION}>"
......@@ -32,28 +39,73 @@ function(add_py_test NAME SCRIPT)
"MALLOC_CHECK_=3"
)
set(PYTHON_EXECUTABLE ${PYTHON_${PYTHON_VERSION}_EXECUTABLE})
if(NOT TEST py_${PYTHON_VERSION}_${FIXTURE_NAME}_initialize_env)
if (NOT (${FIXTURE_NAME} STREQUAL "onnx" AND ${PYTHON_VERSION} STREQUAL ${PYTHON_VERSION_TO_DISABLE_ONNX}))
add_test(NAME py_${PYTHON_VERSION}_${FIXTURE_NAME}_initialize_env COMMAND ${PYTHON_EXECUTABLE} -m venv ${VIRTUAL_ENV_DIR}/${PYTHON_VERSION} --clear)
set_tests_properties(py_${PYTHON_VERSION}_${FIXTURE_NAME}_initialize_env PROPERTIES FIXTURES_SETUP ${FIXTURE_NAME}_${PYTHON_VERSION}_INIT_VENV)
set(PYTHON_EXECUTABLE ${VIRTUAL_ENV_DIR}/${PYTHON_VERSION}/bin/python)
if(EXISTS ${REQUIREMENTS_FILE})
add_test(
NAME py_${PYTHON_VERSION}_${FIXTURE_NAME}_setup_env
COMMAND ${PYTHON_EXECUTABLE} -m pip install -r ${REQUIREMENTS_FILE})
else()
# If there is no requirements file, then there are no packages to install in the virtual env.
# Just create a placeholder test for setting up the required fixture for running the tests.
add_test(
NAME py_${PYTHON_VERSION}_${FIXTURE_NAME}_setup_env
COMMAND ${PYTHON_EXECUTABLE} -m pip install --help)
endif()
set_tests_properties(py_${PYTHON_VERSION}_${FIXTURE_NAME}_setup_env PROPERTIES FIXTURES_REQUIRED ${FIXTURE_NAME}_${PYTHON_VERSION}_INIT_VENV)
set_tests_properties(py_${PYTHON_VERSION}_${FIXTURE_NAME}_setup_env PROPERTIES FIXTURES_SETUP ${FIXTURE_NAME}_${PYTHON_VERSION}_VENV)
endif()
endif()
endforeach()
endfunction()
function(add_py_test NAME SCRIPT FIXTURE_NAME VENV_DIR)
foreach(PYTHON_VERSION ${PYTHON_VERSIONS})
set (ENV_COMMAND ${CMAKE_COMMAND} -E env
"PYTHONPATH=$<TARGET_FILE_DIR:migraphx_pybind_${PYTHON_VERSION}>"
"PYTHONMALLOC=debug"
"MALLOC_CHECK_=3"
)
if(MIGRAPHX_DISABLE_VIRTUAL_ENV)
set(PYTHON_EXECUTABLE ${PYTHON_${PYTHON_VERSION}_EXECUTABLE})
else()
set(PYTHON_EXECUTABLE ${VENV_DIR}/${PYTHON_VERSION}/bin/python)
endif()
if(NOT (${FIXTURE_NAME} STREQUAL "onnx" AND ${PYTHON_VERSION} STREQUAL ${PYTHON_VERSION_TO_DISABLE_ONNX}))
add_test(
NAME test_py_${PYTHON_VERSION}_${NAME}
COMMAND ${ENV_COMMAND} ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/${SCRIPT} ${ARGN})
add_custom_target(test_py_${PYTHON_VERSION}_${NAME}
COMMAND ${ENV_COMMAND} ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/${SCRIPT} ${ARGN}
COMMENT "${PYTHON_EXECUTABLE} ${SCRIPT}")
if(NOT MIGRAPHX_DISABLE_VIRTUAL_ENV)
set_tests_properties(test_py_${PYTHON_VERSION}_${NAME} PROPERTIES FIXTURES_REQUIRED ${FIXTURE_NAME}_${PYTHON_VERSION}_VENV)
endif()
endif()
endforeach()
endfunction()
add_dependencies(tests migraphx_py)
add_dependencies(check migraphx_py)
add_py_test(ref test_cpu.py WORKING_DIRECTORY ${TEST_ONNX_DIR})
add_py_test(save_load test_save_load.py WORKING_DIRECTORY ${TEST_ONNX_DIR})
add_py_test(op test_op.py WORKING_DIRECTORY ${TEST_ONNX_DIR})
add_py_test(shape test_shape.py WORKING_DIRECTORY ${TEST_ONNX_DIR})
add_py_test(module_construct test_module_construct.py WORKING_DIRECTORY ${TEST_ONNX_DIR})
add_py_test(literal test_literal.py WORKING_DIRECTORY ${TEST_ONNX_DIR})
if(NOT MIGRAPHX_DISABLE_VIRTUAL_ENV)
add_py_venv_fixture(common ${VENV} ${REQUIREMENTS})
add_py_venv_fixture(onnx ${VENV_ONNX} ${REQUIREMENTS_ONNX})
endif()
add_py_test(ref test_cpu.py common ${VENV} WORKING_DIRECTORY ${TEST_ONNX_DIR})
add_py_test(save_load test_save_load.py common ${VENV} WORKING_DIRECTORY ${TEST_ONNX_DIR})
add_py_test(op test_op.py common ${VENV} WORKING_DIRECTORY ${TEST_ONNX_DIR})
add_py_test(shape test_shape.py common ${VENV} WORKING_DIRECTORY ${TEST_ONNX_DIR})
add_py_test(module_construct test_module_construct.py common ${VENV} WORKING_DIRECTORY ${TEST_ONNX_DIR})
add_py_test(literal test_literal.py common ${VENV} WORKING_DIRECTORY ${TEST_ONNX_DIR})
if(MIGRAPHX_ENABLE_GPU)
add_py_test(gpu_offload test_gpu_offload.py WORKING_DIRECTORY ${TEST_ONNX_DIR})
add_py_test(gpu test_gpu.py WORKING_DIRECTORY ${TEST_ONNX_DIR})
add_py_test(array test_array.py WORKING_DIRECTORY ${TEST_ONNX_DIR})
add_py_test(backend onnx_backend_test.py WORKING_DIRECTORY ${TEST_ONNX_DIR})
add_py_test(gpu_async test_gpu_async.py WORKING_DIRECTORY ${TEST_ONNX_DIR})
add_py_test(gpu_offload test_gpu_offload.py common ${VENV} WORKING_DIRECTORY ${TEST_ONNX_DIR})
add_py_test(gpu test_gpu.py common ${VENV} WORKING_DIRECTORY ${TEST_ONNX_DIR})
add_py_test(array test_array.py common ${VENV} WORKING_DIRECTORY ${TEST_ONNX_DIR})
add_py_test(backend onnx_backend_test.py onnx ${VENV_ONNX} WORKING_DIRECTORY ${TEST_ONNX_DIR})
add_py_test(gpu_async test_gpu_async.py common ${VENV} WORKING_DIRECTORY ${TEST_ONNX_DIR})
endif()
test/py/onnx_backend_test.py
View file @ 4ea39116
......@@ -66,16 +66,6 @@ class MIGraphXBackendTest(onnx.backend.test.BackendTest):
def disabled_tests_onnx_1_7_0(backend_test):
# fails
# from OnnxBackendNodeModelTest
backend_test.exclude(r'test_argmax_keepdims_example_select_last_index_cpu')
backend_test.exclude(
r'test_argmax_negative_axis_keepdims_example_select_last_index_cpu')
backend_test.exclude(
r'test_argmax_no_keepdims_example_select_last_index_cpu')
backend_test.exclude(r'test_argmin_keepdims_example_select_last_index_cpu')
backend_test.exclude(
r'test_argmin_negative_axis_keepdims_example_select_last_index_cpu')
backend_test.exclude(
r'test_argmin_no_keepdims_example_select_last_index_cpu')
backend_test.exclude(r'test_logsoftmax_axis_0_cpu')
backend_test.exclude(r'test_logsoftmax_axis_1_cpu')
backend_test.exclude(r'test_logsoftmax_default_axis_cpu')
......@@ -93,7 +83,6 @@ def disabled_tests_onnx_1_7_0(backend_test):
backend_test.exclude(r'test_nonmaxsuppression_two_batches_cpu')
backend_test.exclude(r'test_nonmaxsuppression_two_classes_cpu')
backend_test.exclude(r'test_nonzero_example_cpu')
backend_test.exclude(r'test_round_cpu')
backend_test.exclude(r'test_softmax_axis_0_cpu')
backend_test.exclude(r'test_softmax_axis_1_cpu')
backend_test.exclude(r'test_softmax_default_axis_cpu')
......@@ -145,16 +134,12 @@ def disabled_tests_onnx_1_7_0(backend_test):
backend_test.exclude(r'test_hardmax_example_cpu')
backend_test.exclude(r'test_hardmax_negative_axis_cpu')
backend_test.exclude(r'test_hardmax_one_hot_cpu')
backend_test.exclude(r'test_isinf_cpu')
backend_test.exclude(r'test_isinf_negative_cpu')
backend_test.exclude(r'test_isinf_positive_cpu')
backend_test.exclude(r'test_matmulinteger_cpu')
backend_test.exclude(r'test_maxpool_2d_uint8_cpu')
backend_test.exclude(r'test_maxunpool_export_with_output_shape_cpu')
backend_test.exclude(r'test_maxunpool_export_without_output_shape_cpu')
backend_test.exclude(r'test_mod_mixed_sign_int32_cpu')
backend_test.exclude(r'test_mod_mixed_sign_int8_cpu')
backend_test.exclude(r'test_mvn_cpu')
backend_test.exclude(
r'test_negative_log_likelihood_loss_iinput_shape_is_NCd1_weight_ignore_index_cpu'
)
......@@ -249,8 +234,6 @@ def disabled_tests_onnx_1_7_0(backend_test):
backend_test.exclude(r'test_reversesequence_time_cpu')
backend_test.exclude(r'test_scan9_sum_cpu')
backend_test.exclude(r'test_scan_sum_cpu')
backend_test.exclude(r'test_shrink_hard_cpu')
backend_test.exclude(r'test_shrink_soft_cpu')
backend_test.exclude(r'test_slice_cpu')
backend_test.exclude(r'test_slice_default_axes_cpu')
backend_test.exclude(r'test_slice_default_steps_cpu')
......@@ -463,7 +446,6 @@ def disabled_tests_onnx_1_7_0(backend_test):
backend_test.exclude(r'test_sequence_model6_cpu')
backend_test.exclude(r'test_sequence_model7_cpu')
backend_test.exclude(r'test_sequence_model8_cpu')
backend_test.exclude(r'test_shrink_cpu')
backend_test.exclude(r'test_strnorm_model_monday_casesensintive_lower_cpu')
backend_test.exclude(
r'test_strnorm_model_monday_casesensintive_nochangecase_cpu')
......@@ -594,9 +576,6 @@ def disabled_tests_onnx_1_9_0(backend_test):
backend_test.exclude(r'test_gru_batchwise_cpu')
backend_test.exclude(r'test_lstm_batchwise_cpu')
backend_test.exclude(r'test_simple_rnn_batchwise_cpu')
backend_test.exclude(r'test_tril_cpu')
backend_test.exclude(r'test_tril_one_row_neg_cpu')
backend_test.exclude(r'test_tril_square_cpu')
# from OnnxBackendPyTorchConvertedModelTest
backend_test.exclude(r'test_MaxPool1d_stride_padding_dilation_cpu')
backend_test.exclude(r'test_MaxPool2d_stride_padding_dilation_cpu')
......@@ -806,7 +785,6 @@ def disabled_tests_onnx_1_13_0(backend_test):
backend_test.exclude(r'test_group_normalization_example_cpu')
backend_test.exclude(r'test_group_normalization_example_expanded_cpu')
backend_test.exclude(r'test_mish_cpu')
backend_test.exclude(r'test_mvn_expanded_ver18_cpu')
backend_test.exclude(r'test_optional_get_element_optional_sequence_cpu')
backend_test.exclude(r'test_optional_get_element_optional_tensor_cpu')
backend_test.exclude(r'test_optional_get_element_tensor_cpu')
......@@ -853,10 +831,6 @@ def disabled_tests_onnx_1_13_0(backend_test):
backend_test.exclude(r'test_scatter_elements_with_reduction_max_cpu')
backend_test.exclude(r'test_scatter_elements_with_reduction_min_cpu')
# The following tests fail due to the CastLike operator being unsupported
backend_test.exclude(r'test_split_1d_uneven_split_opset18_cpu')
backend_test.exclude(r'test_split_2d_uneven_split_opset18_cpu')
def disabled_tests_onnx_1_14_0(backend_test):
# fails
......
tools/generate.sh → test/py/requirements-onnx.txt 100755 → 100644
View file @ 4ea39116
#####################################################################################
# The MIT License (MIT)
#
# Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
# Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
......@@ -21,23 +21,9 @@
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#####################################################################################
DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
CLANG_FORMAT=/opt/rocm/llvm/bin/clang-format
SRC_DIR=$DIR/../src
PYTHON=python3
if type -p python3.6 > /dev/null ; then
PYTHON=python3.6
fi
if type -p python3.8 > /dev/null ; then
PYTHON=python3.8
fi
ls -1 $DIR/include/ | xargs -n 1 -P $(nproc) -I{} -t bash -c "$PYTHON $DIR/te.py $DIR/include/{} | $CLANG_FORMAT -style=file > $SRC_DIR/include/migraphx/{}"
function api {
$PYTHON $DIR/api.py $SRC_DIR/api/migraphx.py $1 | $CLANG_FORMAT -style=file > $2
}
api $DIR/api/migraphx.h $SRC_DIR/api/include/migraphx/migraphx.h
echo "Finished generating header migraphx.h"
api $DIR/api/api.cpp $SRC_DIR/api/api.cpp
echo "Finished generating source api.cpp "
onnx==1.14.1
protobuf==3.20.2
numpy==1.21.6
packaging==23.0
pytest==6.0.1
test/py/test_gpu.py
View file @ 4ea39116
#####################################################################################
# The MIT License (MIT)
#
# Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
# Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
......@@ -21,12 +21,7 @@
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#####################################################################################
import sys
import migraphx
try:
import numpy as np
except:
sys.exit()
def test_conv_relu():
......@@ -55,8 +50,12 @@ def test_sub_uint64():
params = {}
shapes = p.get_parameter_shapes()
params["0"] = np.arange(120).reshape(shapes["0"].lens()).astype(np.uint64)
params["1"] = np.arange(20).reshape(shapes["1"].lens()).astype(np.uint64)
params["0"] = migraphx.create_argument(
migraphx.shape(type='uint64_type', lens=shapes["0"].lens()),
list(range(120)))
params["1"] = migraphx.create_argument(
migraphx.shape(type='uint64_type', lens=shapes["1"].lens()),
list(range(20)))
r = p.run(params)
print(r)
......@@ -71,7 +70,9 @@ def test_neg_int64():
params = {}
shapes = p.get_parameter_shapes()
params["0"] = np.arange(6).reshape(shapes["0"].lens()).astype(np.int64)
params["0"] = migraphx.create_argument(
migraphx.shape(type='int64_type', lens=shapes["0"].lens()),
list(range(6)))
r = p.run(params)
print(r)
......@@ -86,8 +87,9 @@ def test_nonzero():
params = {}
shapes = p.get_parameter_shapes()
params["data"] = np.array([1, 1, 0,
1]).reshape(shapes["data"].lens()).astype(bool)
params["data"] = migraphx.create_argument(
migraphx.shape(type='bool_type', lens=shapes["data"].lens()),
[1, 1, 0, 1])
r = p.run(params)
print(r)
......@@ -105,8 +107,8 @@ def test_fp16_imagescaler():
params = {}
shapes = p.get_parameter_shapes()
params["0"] = np.random.randn(768).reshape(shapes["0"].lens()).astype(
np.float16)
params["0"] = migraphx.generate_argument(
migraphx.shape(type='half_type', lens=shapes["0"].lens()), 768)
r = p.run(params)[-1]
print(r)
......@@ -124,10 +126,12 @@ def test_if_pl():
params = {}
shapes = p.get_parameter_shapes()
params["x"] = np.ones(6).reshape(shapes["x"].lens()).astype(np.float32)
params["y"] = np.array([2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0
]).reshape(shapes["y"].lens()).astype(np.float32)
params["cond"] = np.array([1]).reshape(()).astype(bool)
params["x"] = migraphx.fill_argument(
migraphx.shape(type='float_type', lens=shapes["x"].lens()), 1)
params["y"] = migraphx.fill_argument(
migraphx.shape(type='float_type', lens=shapes["y"].lens()), 2.0)
params["cond"] = migraphx.fill_argument(
migraphx.shape(type="bool", lens=[1], strides=[0]), 1)
r = p.run(params)[-1]
print(r)
......
test/py/test_numpy.py
View file @ 4ea39116
#####################################################################################
# The MIT License (MIT)
#
# Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
# Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
......@@ -21,11 +21,8 @@
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#####################################################################################
import migraphx, sys
try:
import numpy as np
except:
sys.exit()
import migraphx
import numpy as np
def test_add_op():
......
test/ref/allocate.cpp
View file @ 4ea39116
......@@ -30,7 +30,7 @@
#include <test.hpp>
TEST_CASE(allocate_dyn)
TEST_CASE(allocate_dyn0)
{
migraphx::program p;
auto* mm = p.get_main_module();
......@@ -47,3 +47,21 @@ TEST_CASE(allocate_dyn)
migraphx::shape sresult{migraphx::shape::float_type, {2, 3, 4, 4}};
result.visit([&](auto output) { EXPECT(output.get_shape() == sresult); });
}
TEST_CASE(allocate_dyn1)
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape s{migraphx::shape::int64_type, {4}};
migraphx::shape out_shape{migraphx::shape::float_type, {2, 3, 4, 4}};
auto out_dims = mm->add_parameter("out_dims", s);
mm->add_instruction(migraphx::make_op("allocate", {{"shape", migraphx::to_value(out_shape)}}),
out_dims);
p.compile(migraphx::make_target("ref"));
migraphx::parameter_map params;
std::vector<int64_t> data = {2, 3, 4, 4};
params["out_dims"] = migraphx::argument(s, data.data());
auto result = p.eval(params).back();
result.visit([&](auto output) { EXPECT(output.get_shape() == out_shape); });
}
test/ref/argmax.cpp
View file @ 4ea39116
......@@ -147,3 +147,37 @@ TEST_CASE(argmax_test_nonstd_shape)
res_gold.visit([&](auto output) { res_gold_vec.assign(output.begin(), output.end()); });
EXPECT(migraphx::verify::verify_rms_range(result_vec, res_gold_vec));
}
TEST_CASE(argmax_test_select_last_index_0)
{
migraphx::program p;
auto* mm = p.get_main_module();
std::vector<float> data = {2.0305, -1.853, 2.0305, -1.5706, 0.7545, 0.7545};
std::vector<int64_t> res_gold = {2, 2};
migraphx::shape data_shape{migraphx::shape::float_type, {2, 3}};
auto dl = mm->add_literal(migraphx::literal{data_shape, data});
mm->add_instruction(migraphx::make_op("argmax", {{"axis", 1}, {"select_last_index", true}}),
dl);
p.compile(migraphx::make_target("ref"));
auto result = p.eval({}).back();
std::vector<int64_t> result_vec;
result.visit([&](auto output) { result_vec.assign(output.begin(), output.end()); });
EXPECT(migraphx::verify::verify_rms_range(result_vec, res_gold));
}
TEST_CASE(argmax_test_select_last_index_1)
{
migraphx::program p;
auto* mm = p.get_main_module();
std::vector<float> data = {2.0305, -1.853, 2.0305, -1.5706, 0.7545, 0.7545};
std::vector<int64_t> res_gold = {0, 1};
migraphx::shape data_shape{migraphx::shape::float_type, {2, 3}};
auto dl = mm->add_literal(migraphx::literal{data_shape, data});
mm->add_instruction(migraphx::make_op("argmax", {{"axis", 1}, {"select_last_index", false}}),
dl);
p.compile(migraphx::make_target("ref"));
auto result = p.eval({}).back();
std::vector<int64_t> result_vec;
result.visit([&](auto output) { result_vec.assign(output.begin(), output.end()); });
EXPECT(migraphx::verify::verify_rms_range(result_vec, res_gold));
}
test/ref/argmin.cpp
View file @ 4ea39116
......@@ -125,3 +125,37 @@ TEST_CASE(argmin_test_nonstd_shape)
res_gold.visit([&](auto output) { res_gold_vec.assign(output.begin(), output.end()); });
EXPECT(migraphx::verify::verify_rms_range(result_vec, res_gold_vec));
}
TEST_CASE(argmin_test_select_last_index_0)
{
migraphx::program p;
auto* mm = p.get_main_module();
std::vector<float> data = {-2.0305, 0.853, -2.0305, 1.5706, 0.7545, 0.7545};
std::vector<int64_t> res_gold = {2, 2};
migraphx::shape data_shape{migraphx::shape::float_type, {2, 3}};
auto dl = mm->add_literal(migraphx::literal{data_shape, data});
mm->add_instruction(migraphx::make_op("argmin", {{"axis", 1}, {"select_last_index", true}}),
dl);
p.compile(migraphx::make_target("ref"));
auto result = p.eval({}).back();
std::vector<int64_t> result_vec;
result.visit([&](auto output) { result_vec.assign(output.begin(), output.end()); });
EXPECT(migraphx::verify::verify_rms_range(result_vec, res_gold));
}
TEST_CASE(argmin_test_select_last_index_1)
{
migraphx::program p;
auto* mm = p.get_main_module();
std::vector<float> data = {-2.0305, 0.853, -2.0305, 1.5706, 0.7545, 0.7545};
std::vector<int64_t> res_gold = {0, 1};
migraphx::shape data_shape{migraphx::shape::float_type, {2, 3}};
auto dl = mm->add_literal(migraphx::literal{data_shape, data});
mm->add_instruction(migraphx::make_op("argmin", {{"axis", 1}, {"select_last_index", false}}),
dl);
p.compile(migraphx::make_target("ref"));
auto result = p.eval({}).back();
std::vector<int64_t> result_vec;
result.visit([&](auto output) { result_vec.assign(output.begin(), output.end()); });
EXPECT(migraphx::verify::verify_rms_range(result_vec, res_gold));
}
test/ref/isinf.cpp 0 → 100644
View file @ 4ea39116
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#include <migraphx/instruction.hpp>
#include <migraphx/literal.hpp>
#include <migraphx/make_op.hpp>
#include <migraphx/program.hpp>
#include <migraphx/register_target.hpp>
#include <migraphx/verify.hpp>
#include <test.hpp>
TEST_CASE(isinf_double_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape s{migraphx::shape::double_type, {2, 3}};
auto inf_val = std::numeric_limits<double>::infinity();
std::vector<double> data0 = {1.2, 5.2, inf_val, -inf_val, 0., 100.};
auto l1 = mm->add_literal(migraphx::literal{s, data0});
mm->add_instruction(migraphx::make_op("isinf"), l1);
p.compile(migraphx::make_target("ref"));
auto result = p.eval({}).back();
std::vector<double> results_vector;
result.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); });
std::vector<double> gold = {0, 0, 1, 1, 0, 0};
EXPECT(migraphx::verify::verify_rms_range(results_vector, gold));
}
TEST_CASE(isinf_float_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape s{migraphx::shape::float_type, {2, 3}};
auto inf_val = std::numeric_limits<float>::infinity();
std::vector<float> data0 = {1.2, 5.2, inf_val, -inf_val, 0., 100.};
auto l1 = mm->add_literal(migraphx::literal{s, data0});
mm->add_instruction(migraphx::make_op("isinf"), l1);
p.compile(migraphx::make_target("ref"));
auto result = p.eval({}).back();
std::vector<float> results_vector;
result.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); });
std::vector<float> gold = {0, 0, 1, 1, 0, 0};
EXPECT(migraphx::verify::verify_rms_range(results_vector, gold));
}
TEST_CASE(isinf_half_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape s{migraphx::shape::half_type, {2, 3}};
auto inf_val = std::numeric_limits<migraphx::half>::infinity();
migraphx::half a{1.2};
migraphx::half b{5.2};
std::vector<migraphx::half> data0 = {a, b, inf_val, -inf_val, b, a};
auto l1 = mm->add_literal(migraphx::literal{s, data0});
mm->add_instruction(migraphx::make_op("isinf"), l1);
p.compile(migraphx::make_target("ref"));
auto result = p.eval({}).back();
std::vector<float> results_vector;
result.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); });
std::vector<float> gold = {0, 0, 1, 1, 0, 0};
EXPECT(migraphx::verify::verify_rms_range(results_vector, gold));
}
TEST_CASE(isinf_dyn_test)
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape s{migraphx::shape::float_type, {{2, 2}, {3, 8}}};
auto input = mm->add_parameter("X", s);
auto inf_val = std::numeric_limits<migraphx::half>::infinity();
mm->add_instruction(migraphx::make_op("isinf"), input);
p.compile(migraphx::make_target("ref"));
std::vector<float> input_data = {1.2, 5.2, inf_val, -inf_val, 0., 100.};
migraphx::parameter_map params0;
migraphx::shape input_fixed_shape0{migraphx::shape::float_type, {2, 3}};
params0["X"] = migraphx::argument(input_fixed_shape0, input_data.data());
auto result = p.eval(params0).back();
std::vector<float> results_vector;
result.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); });
std::vector<float> gold = {0, 0, 1, 1, 0, 0};
EXPECT(migraphx::verify::verify_rms_range(results_vector, gold));
}
test/ref/multinomial.cpp
View file @ 4ea39116
......@@ -24,9 +24,10 @@
#include <migraphx/instruction.hpp>
#include <migraphx/literal.hpp>
#include <migraphx/make_op.hpp>
#include <migraphx/program.hpp>
#include <migraphx/onnx.hpp>
#include <migraphx/register_target.hpp>
#include <migraphx/verify.hpp>
#include <numeric>
#include <random>
#include <test.hpp>
......@@ -48,27 +49,37 @@ TEST_CASE(multinomial_test)
migraphx::shape s{migraphx::shape::float_type, {1, 5}};
std::vector<int> dist{15, 25, 15, 25, 20};
std::vector<float> data(5);
std::transform(dist.begin(), dist.end(), data.begin(), [&](auto d) { return std::log(d); });
auto input = mm->add_literal(migraphx::literal(s, data));
std::vector<float> sum(5);
// convert to float
std::transform(dist.begin(), dist.end(), data.begin(), [&](auto d) { return d; });
// take cumulative sum
std::partial_sum(data.begin(), data.end(), sum.begin(), std::plus<float>());
// scale probabilities arbitrarily
float odd_scale = 10000.;
std::transform(sum.begin(), sum.end(), data.begin(), [&](auto d) { return d * odd_scale; });
auto maxes = mm->add_instruction(migraphx::make_op("reduce_max", {{"axes", {1}}}), input);
auto mb_maxes =
mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {1, 5}}}), maxes);
auto cdf = mm->add_instruction(migraphx::make_op("sub"), input, mb_maxes);
cdf = mm->add_instruction(migraphx::make_op("exp"), cdf);
cdf = mm->add_instruction(
migraphx::make_op("prefix_scan_sum", {{"axis", 1}, {"exclusive", false}}), cdf);
auto input = mm->add_literal(migraphx::literal(s, data));
mm->add_instruction(migraphx::make_op("multinomial"), cdf, rs_lit);
mm->add_instruction(migraphx::make_op("multinomial"), input, rs_lit);
p.compile(migraphx::make_target("ref"));
auto result = p.eval({}).back();
// result_vec contains an index, or category label, for each random input value
std::vector<int32_t> result_vec(sample_size);
result.visit([&](auto output) { result_vec.assign(output.begin(), output.end()); });
// res_dist is a count, or histogram, of the number of samples in each category. This is the
// sampled distribution.
std::vector<int> res_dist(5, 0);
for(const auto& r : result_vec)
res_dist[r]++;
// To check the result, normalize the original probability distribution dist
// and the sampling result res_dist; they should be close
// Total the unnormalized probabilities
auto dist_sum = std::accumulate(dist.begin(), dist.end(), 0);
// Total the number of values returned
auto res_dist_sum = std::accumulate(res_dist.begin(), res_dist.end(), 0);
std::vector<float> norm(5);
std::vector<float> res_norm(5);
......@@ -78,6 +89,204 @@ TEST_CASE(multinomial_test)
std::transform(res_dist.begin(), res_dist.end(), res_norm.begin(), [&](auto n) {
return static_cast<double>(n) / res_dist_sum;
});
EXPECT(migraphx::verify::verify_range_with_tolerance(
res_norm, migraphx::verify::expected{norm}, migraphx::verify::tolerance{0.01}));
}
TEST_CASE(multinomial_dyn_test)
{
// Invokes random_uniform and multinomial ops together, to verify the interface
// Dynamic Batch dimension input of 2 means there are 2 different probability
// distribution functions contained in Input_2
migraphx::program p;
auto* mm = p.get_main_module();
size_t sample_size = 100000;
size_t batch_size = 2;
// Shape of the random data
migraphx::shape rs{migraphx::shape::float_type, {{1, 2}, {2, sample_size + 1}}};
auto input = mm->add_parameter("Input_1", rs);
// Runtime randomization seed
// To seed the random_uniform, we can provide a value by literal or input,
// or ask the system to auto-seed with random_seed op.
migraphx::shape seed_shape{migraphx::shape::uint32_type,
{migraphx::shape::dynamic_dimension{0, 1}}};
auto seed_input = mm->add_parameter("Seed", seed_shape);
// Shape of the probability distribution, which also defines the number of categories
migraphx::shape s{migraphx::shape::float_type, {{2, 2}, {5, 6}}};
// Unnormalized distributions for batch size 2:
// 15, 25, 15, 15, 20
// 20, 20, 10, 25, 25
std::vector<int> dist{15, 25, 15, 25, 20, 20, 20, 10, 25, 25};
// Hard-coded non-normalized, accumulated distribution follows:
std::vector<float> data{.15f, .40f, .55f, .80f, 1.0f, 20.f, 40.f, 50.f, 75.f, 100.f};
auto input2 = mm->add_parameter("Input_2", s);
auto randoms = mm->add_instruction(migraphx::make_op("random_uniform"), seed_input, input);
mm->add_instruction(migraphx::make_op("multinomial"), input2, randoms);
p.compile(migraphx::make_target("ref"));
// Create a dummy input in the shape we want for the random data
std::vector<float> dummy(sample_size, 0);
migraphx::shape input_fixed_shape1{migraphx::shape::float_type, {batch_size, sample_size}};
migraphx::shape input_fixed_shape2{migraphx::shape::float_type, {batch_size, 5}};
migraphx::parameter_map params0;
params0["Input_1"] = migraphx::argument(input_fixed_shape1, dummy.data());
migraphx::shape seed_fixed_shape{migraphx::shape::uint32_type, {1}};
std::vector<uint32_t> seed_data = {4};
params0["Seed"] = migraphx::argument(seed_fixed_shape, seed_data.data());
params0["Input_2"] = migraphx::argument(input_fixed_shape2, data.data());
auto result = p.eval(params0).back();
std::vector<float> result_vec(input_fixed_shape2.elements());
result.visit([&](auto output) { result_vec.assign(output.begin(), output.end()); });
// Make a categorical histogram of output
std::vector<int> res_dist(5, 0);
size_t r = 0;
for(r = 0; r < result_vec.size() / 2; r++)
res_dist[result_vec[r]]++;
// histogram for second set of batch
std::vector<int> res_dist2(5, 0);
for(; r < result_vec.size(); r++)
res_dist2[result_vec[r]]++;
// Rescale or normalize both the input probability distribution and the output
// histogram, and compare. Should be close but not identical.
auto dist_sum = std::accumulate(dist.begin(), dist.begin() + 5, 0);
auto res_dist_sum = std::accumulate(res_dist.begin(), res_dist.end(), 0);
std::vector<float> norm(5);
std::vector<float> res_norm(5);
std::transform(dist.begin(), dist.begin() + 5, norm.begin(), [&](auto n) {
return static_cast<double>(n) / dist_sum;
});
std::transform(res_dist.begin(), res_dist.end(), res_norm.begin(), [&](auto n) {
return static_cast<double>(n) / res_dist_sum;
});
EXPECT(migraphx::verify::verify_range_with_tolerance(
res_norm, migraphx::verify::expected{norm}, migraphx::verify::tolerance{0.01}));
// Do the same rescaling for the 2nd in batch, which has a different probability distribution
dist_sum = std::accumulate(dist.begin() + 5, dist.end(), 0);
res_dist_sum = std::accumulate(res_dist2.begin(), res_dist2.end(), 0);
std::transform(dist.begin() + 5, dist.end(), norm.begin(), [&](auto n) {
return static_cast<double>(n) / dist_sum;
});
std::transform(res_dist2.begin(), res_dist2.end(), res_norm.begin(), [&](auto n) {
return static_cast<double>(n) / res_dist_sum;
});
EXPECT(migraphx::verify::verify_range_with_tolerance(
res_norm, migraphx::verify::expected{norm}, migraphx::verify::tolerance{0.01}));
}
TEST_CASE(multinomial_float_dyn_test)
{
// int data type for random_uniform op and float data type for multinomial.
migraphx::program p;
auto* mm = p.get_main_module();
size_t sample_size = 100000;
size_t batch_size = 2;
// Shape of the random data
migraphx::shape rs{migraphx::shape::int32_type, {{1, 2}, {2, sample_size + 1}}};
auto input = mm->add_parameter("Input_1", rs);
// Runtime randomization seed
// To seed the random_uniform, we can provide a value by literal or input,
// or ask the system to auto-seed with random_seed op.
migraphx::shape seed_shape{migraphx::shape::uint32_type,
{migraphx::shape::dynamic_dimension{0, 1}}};
auto seed_input = mm->add_parameter("Seed", seed_shape);
// Shape of the probability distribution, which also defines the number of categories
migraphx::shape s{migraphx::shape::float_type, {{2, 2}, {5, 6}}};
// Unnormalized distributions for batch size 2:
// 15, 25, 15, 15, 20
// 20, 20, 10, 25, 25
std::vector<int> dist{15, 25, 15, 25, 20, 20, 20, 10, 25, 25};
// Hard-coded normalized, accumulated distribution follows:
std::vector<float> data{.15f, .40f, .55f, .80f, 1.0f, .20f, .40f, .50f, .75f, 1.0f};
auto input2 = mm->add_parameter("Input_2", s);
auto randoms = mm->add_instruction(migraphx::make_op("random_uniform"), seed_input, input);
mm->add_instruction(migraphx::make_op("multinomial", {{"dtype", migraphx::shape::float_type}}),
input2,
randoms);
p.compile(migraphx::make_target("ref"));
// Create a dummy input in the shape we want for the random data
std::vector<float> dummy(sample_size, 0);
migraphx::shape input_fixed_shape1{migraphx::shape::float_type, {batch_size, sample_size}};
migraphx::shape input_fixed_shape2{migraphx::shape::float_type, {batch_size, 5}};
migraphx::parameter_map params0;
params0["Input_1"] = migraphx::argument(input_fixed_shape1, dummy.data());
migraphx::shape seed_fixed_shape{migraphx::shape::uint32_type, {1}};
std::vector<uint32_t> seed_data = {4};
params0["Seed"] = migraphx::argument(seed_fixed_shape, seed_data.data());
params0["Input_2"] = migraphx::argument(input_fixed_shape2, data.data());
auto result = p.eval(params0).back();
std::vector<float> result_vec(input_fixed_shape2.elements());
result.visit([&](auto output) { result_vec.assign(output.begin(), output.end()); });
// Make a categorical histogram of output
std::vector<int> res_dist(5, 0);
size_t r = 0;
for(r = 0; r < result_vec.size() / 2; r++)
res_dist[result_vec[r]]++;
// histogram for second set of batch
std::vector<int> res_dist2(5, 0);
for(; r < result_vec.size(); r++)
res_dist2[result_vec[r]]++;
// Rescale or normalize both the input probability distribution and the output
// histogram, and compare. Should be close but not identical.
auto dist_sum = std::accumulate(dist.begin(), dist.begin() + 5, 0);
auto res_dist_sum = std::accumulate(res_dist.begin(), res_dist.end(), 0);
std::vector<float> norm(5);
std::vector<float> res_norm(5);
std::transform(dist.begin(), dist.begin() + 5, norm.begin(), [&](auto n) {
return static_cast<double>(n) / dist_sum;
});
std::transform(res_dist.begin(), res_dist.end(), res_norm.begin(), [&](auto n) {
return static_cast<double>(n) / res_dist_sum;
});
EXPECT(migraphx::verify::verify_range_with_tolerance(
res_norm, migraphx::verify::expected{norm}, migraphx::verify::tolerance{0.01}));
// Do the same rescaling for the 2nd in batch, which has a different probability distribution
dist_sum = std::accumulate(dist.begin() + 5, dist.end(), 0);
res_dist_sum = std::accumulate(res_dist2.begin(), res_dist2.end(), 0);
std::transform(dist.begin() + 5, dist.end(), norm.begin(), [&](auto n) {
return static_cast<double>(n) / dist_sum;
});
std::transform(res_dist2.begin(), res_dist2.end(), res_norm.begin(), [&](auto n) {
return static_cast<double>(n) / res_dist_sum;
});
EXPECT(migraphx::verify::verify_range_with_tolerance(
res_norm, migraphx::verify::expected{norm}, migraphx::verify::tolerance{0.01}));
}
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