Merge branch 'develop' of https://github.com/ROCmSoftwarePlatform/AMDMIGraphX into mlir-attention

src/version.h.in

@@ -25,5 +25,5 @@
 #define MIGRAPHX_VERSION_MAJOR @PROJECT_VERSION_MAJOR@
 #define MIGRAPHX_VERSION_MINOR @PROJECT_VERSION_MINOR@
 #define MIGRAPHX_VERSION_PATCH @PROJECT_VERSION_PATCH@
-#define MIGRAPHX_VERSION_TWEAK @PROJECT_VERSION_TWEAK@
+#define MIGRAPHX_VERSION_TWEAK "@PROJECT_VERSION_TWEAK@"
 // clang-format on

test/CMakeLists.txt

@@ -150,6 +150,7 @@ function(test_headers PREFIX)
         list(REMOVE_ITEM HEADERS
               ${CMAKE_SOURCE_DIR}/src/targets/gpu/include/migraphx/gpu/ck.hpp)
     endif()
+    list(REMOVE_ITEM HEADERS ${CMAKE_SOURCE_DIR}/src/include/migraphx/float8_impl.hpp)
     foreach(HEADER ${HEADERS})
         file(RELATIVE_PATH HEADER_REL ${CMAKE_SOURCE_DIR} ${HEADER})
         string(MAKE_C_IDENTIFIER ${HEADER_REL} TEST_NAME)

test/api/CMakeLists.txt

@@ -30,6 +30,9 @@ function(add_api_test TEST_NAME TEST_SRC TEST_DIR)
     add_test(NAME ${NAME} COMMAND $<TARGET_FILE:${NAME}> WORKING_DIRECTORY ${TEST_DIR}) 
     add_dependencies(tests ${NAME})
     add_dependencies(check ${NAME})
+    if(WIN32)
+        target_compile_definitions(${NAME} PRIVATE _CRT_SECURE_NO_WARNINGS)
+    endif()
 endfunction()
 
 # Workaround: C file dont work with clang-tidy right now, need a fix in rocm-cmake
@@ -41,6 +44,9 @@ function(add_c_api_test TEST_NAME TEST_SRC TEST_DIR)
     add_test(NAME ${NAME} COMMAND $<TARGET_FILE:${NAME}> WORKING_DIRECTORY ${TEST_DIR}) 
     add_dependencies(tests ${NAME})
     add_dependencies(check ${NAME})
+    if(WIN32)
+        target_compile_definitions(${NAME} PRIVATE _CRT_SECURE_NO_WARNINGS)
+    endif()
 endfunction()
 
 add_api_test(array_base test_array_base.cpp ${TEST_ONNX_DIR})
@@ -57,10 +63,6 @@ add_api_test(custom_op test_custom_op.cpp ${TEST_ONNX_DIR})
 add_api_test(tf_parser test_tf_parser.cpp ${TEST_TF_DIR})
 # GPU-based tests
 if(MIGRAPHX_ENABLE_GPU)
-list(APPEND CMAKE_PREFIX_PATH /opt/rocm)
-find_package(hip)
 add_api_test(gpu test_gpu.cpp ${TEST_ONNX_DIR})
-target_link_libraries(test_api_gpu)
 add_api_test(custom_op_gpu test_custom_op_gpu.cpp ${TEST_ONNX_DIR})
-target_link_libraries(test_api_custom_op_gpu)
 endif()

test/api/test_cpu.cpp

@@ -198,4 +198,29 @@ TEST_CASE(set_loop_default_iter_num)
     EXPECT(out_shapes[1].lengths() == out_lens1);
 }
 
+TEST_CASE(set_loop_limit_iterations)
+{
+    migraphx::onnx_options option;
+    option.set_default_loop_iterations(15);
+    option.set_limit_loop_iterations(10);
+    auto p                             = migraphx::parse_onnx("loop_default_test.onnx", option);
+    auto out_shapes                    = p.get_output_shapes();
+    std::vector<std::size_t> out_lens0 = {1};
+    EXPECT(out_shapes[0].lengths() == out_lens0);
+    std::vector<std::size_t> out_lens1 = {10, 1};
+    EXPECT(out_shapes[1].lengths() == out_lens1);
+}
+
+TEST_CASE(set_loop_limit_iterations2)
+{
+    migraphx::onnx_options option;
+    option.set_limit_loop_iterations(10);
+    auto p          = migraphx::parse_onnx("loop_test_implicit_tripcnt.onnx", option);
+    auto out_shapes = p.get_output_shapes();
+    std::vector<std::size_t> out_lens0 = {1};
+    EXPECT(out_shapes[0].lengths() == out_lens0);
+    std::vector<std::size_t> out_lens1 = {10, 1};
+    EXPECT(out_shapes[1].lengths() == out_lens1);
+}
+
 int main(int argc, const char* argv[]) { test::run(argc, argv); }

test/api/test_gpu.cpp

@@ -317,4 +317,59 @@ TEST_CASE(loop_test)
     }
 }
 
+TEST_CASE(loop_test_limit_max_iter)
+{
+    auto run_prog = [&](int64_t limit_max_iterations) {
+        migraphx::onnx_options parse_options;
+        parse_options.set_limit_loop_iterations(limit_max_iterations);
+        auto p             = migraphx::parse_onnx("loop_test_implicit_tripcnt.onnx", parse_options);
+        auto shapes_before = p.get_output_shapes();
+        migraphx::compile_options options;
+        options.set_offload_copy();
+        p.compile(migraphx::target("gpu"), options);
+        auto shapes_after = p.get_output_shapes();
+        CHECK(shapes_before.size() == 2);
+        CHECK(bool{shapes_before.front() == shapes_after.front()});
+
+        migraphx::program_parameters pp;
+        auto param_shapes     = p.get_parameter_shapes();
+        auto aas              = param_shapes["a"];
+        std::vector<float> xd = {1.0f};
+        pp.add("a", migraphx::argument(aas, xd.data()));
+        auto bbs              = param_shapes["b"];
+        std::vector<float> yd = {2.0};
+        pp.add("b", migraphx::argument(bbs, yd.data()));
+
+        auto cs   = param_shapes["keep_going_cond"];
+        bool cond = true;
+        pp.add("keep_going_cond", migraphx::argument(cs, &cond));
+
+        auto outputs = p.eval(pp);
+        auto output  = outputs[0];
+        std::vector<std::vector<float>> ret;
+        ret.push_back(output.as_vector<float>());
+
+        output = outputs[1];
+        ret.push_back(output.as_vector<float>());
+
+        return ret;
+    };
+
+    {
+        auto result_vector       = run_prog(5);
+        std::vector<float> gold0 = {2.0f};
+        EXPECT(result_vector.at(0) == gold0);
+        std::vector<float> gold1 = {-2, 4, 0, 0, 0};
+        EXPECT(result_vector.at(1) == gold1);
+    }
+
+    {
+        auto result_vector       = run_prog(20);
+        std::vector<float> gold0 = {2.0f};
+        EXPECT(result_vector.at(0) == gold0);
+        std::vector<float> gold1 = {-2, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
+        EXPECT(result_vector.at(1) == gold1);
+    }
+}
+
 int main(int argc, const char* argv[]) { test::run(argc, argv); }

test/float_equal.cpp

@@ -22,6 +22,7 @@
  * THE SOFTWARE.
  */
 #include <migraphx/float_equal.hpp>
+#include <migraphx/float8.hpp>
 #include <migraphx/half.hpp>
 #include "test.hpp"
 
@@ -53,7 +54,7 @@ auto test_float_equal(T x, U y)
 template <class T, class U>
 void test_equality()
 {
-    auto x1 = T(0.1);
+    auto x1 = T(0.125);
     auto x2 = U(0.0);
     auto x3 = U(1.0);
     EXPECT(test_float_equal(x1, x1));
@@ -71,8 +72,12 @@ void test_equality()
 TEST_CASE_REGISTER(test_equality<double, float>);
 TEST_CASE_REGISTER(test_equality<double, int>);
 TEST_CASE_REGISTER(test_equality<double, migraphx::half>);
+TEST_CASE_REGISTER(test_equality<double, migraphx::fp8::fp8e4m3fnuz>);
 TEST_CASE_REGISTER(test_equality<float, int>);
+TEST_CASE_REGISTER(test_equality<float, migraphx::fp8::fp8e4m3fnuz>);
 TEST_CASE_REGISTER(test_equality<migraphx::half, int>);
+TEST_CASE_REGISTER(test_equality<migraphx::half, migraphx::fp8::fp8e4m3fnuz>);
+TEST_CASE_REGISTER(test_equality<migraphx::fp8::fp8e4m3fnuz, int>);
 
 template <class T, class U>
 void test_limits()
@@ -110,8 +115,13 @@ void test_limits()
 TEST_CASE_REGISTER(test_limits<double, float>);
 TEST_CASE_REGISTER(test_limits<double, int>);
 TEST_CASE_REGISTER(test_limits<double, migraphx::half>);
+TEST_CASE_REGISTER(test_limits<double, migraphx::fp8::fp8e4m3fnuz>);
 TEST_CASE_REGISTER(test_limits<float, int>);
+TEST_CASE_REGISTER(test_limits<float, migraphx::fp8::fp8e4m3fnuz>);
 TEST_CASE_REGISTER(test_limits<int, migraphx::half>);
+TEST_CASE_REGISTER(test_limits<int, migraphx::fp8::fp8e4m3fnuz>);
+TEST_CASE_REGISTER(test_limits<migraphx::fp8::fp8e4m3fnuz, migraphx::half>);
+
 #ifndef _WIN32
 // On Windows, types int and long have the same min and max values.
 TEST_CASE_REGISTER(test_limits<long, int>);

test/fp8e4m3fn.cpp

+/*
+ * The MIT License (MIT)
+ *
+ * Copyright (c) 2015-2022 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 <cmath>
+#include <migraphx/float_equal.hpp>
+#include <migraphx/float8.hpp>
+#include <migraphx/half.hpp>
+#include <migraphx/ranges.hpp>
+#include "test.hpp"
+
+#include <limits>
+
+float fp8e4m3fn_to_fp32_value(uint8_t input)
+{
+    constexpr std::array<float, 256> e4m3fnuz_lut = {
+        0.0,        0.001953125,  0.00390625,  0.005859375,
+        0.0078125,  0.009765625,  0.01171875,  0.013671875,
+        0.015625,   0.017578125,  0.01953125,  0.021484375,
+        0.0234375,  0.025390625,  0.02734375,  0.029296875,
+        0.03125,    0.03515625,   0.0390625,   0.04296875,
+        0.046875,   0.05078125,   0.0546875,   0.05859375,
+        0.0625,     0.0703125,    0.078125,    0.0859375,
+        0.09375,    0.1015625,    0.109375,    0.1171875,
+        0.125,      0.140625,     0.15625,     0.171875,
+        0.1875,     0.203125,     0.21875,     0.234375,
+        0.25,       0.28125,      0.3125,      0.34375,
+        0.375,      0.40625,      0.4375,      0.46875,
+        0.5,        0.5625,       0.625,       0.6875,
+        0.75,       0.8125,       0.875,       0.9375,
+        1.0,        1.125,        1.25,        1.375,
+        1.5,        1.625,        1.75,        1.875,
+        2.0,        2.25,         2.5,         2.75,
+        3.0,        3.25,         3.5,         3.75,
+        4.0,        4.5,          5.0,         5.5,
+        6.0,        6.5,          7.0,         7.5,
+        8.0,        9.0,          10.0,        11.0,
+        12.0,       13.0,         14.0,        15.0,
+        16.0,       18.0,         20.0,        22.0,
+        24.0,       26.0,         28.0,        30.0,
+        32.0,       36.0,         40.0,        44.0,
+        48.0,       52.0,         56.0,        60.0,
+        64.0,       72.0,         80.0,        88.0,
+        96.0,       104.0,        112.0,       120.0,
+        128.0,      144.0,        160.0,       176.0,
+        192.0,      208.0,        224.0,       240.0,
+        256.0,      288.0,        320.0,       352.0,
+        384.0,      416.0,        448.0,       std::numeric_limits<float>::quiet_NaN(),
+        -0.0,       -0.001953125, -0.00390625, -0.005859375,
+        -0.0078125, -0.009765625, -0.01171875, -0.013671875,
+        -0.015625,  -0.017578125, -0.01953125, -0.021484375,
+        -0.0234375, -0.025390625, -0.02734375, -0.029296875,
+        -0.03125,   -0.03515625,  -0.0390625,  -0.04296875,
+        -0.046875,  -0.05078125,  -0.0546875,  -0.05859375,
+        -0.0625,    -0.0703125,   -0.078125,   -0.0859375,
+        -0.09375,   -0.1015625,   -0.109375,   -0.1171875,
+        -0.125,     -0.140625,    -0.15625,    -0.171875,
+        -0.1875,    -0.203125,    -0.21875,    -0.234375,
+        -0.25,      -0.28125,     -0.3125,     -0.34375,
+        -0.375,     -0.40625,     -0.4375,     -0.46875,
+        -0.5,       -0.5625,      -0.625,      -0.6875,
+        -0.75,      -0.8125,      -0.875,      -0.9375,
+        -1.0,       -1.125,       -1.25,       -1.375,
+        -1.5,       -1.625,       -1.75,       -1.875,
+        -2.0,       -2.25,        -2.5,        -2.75,
+        -3.0,       -3.25,        -3.5,        -3.75,
+        -4.0,       -4.5,         -5.0,        -5.5,
+        -6.0,       -6.5,         -7.0,        -7.5,
+        -8.0,       -9.0,         -10.0,       -11.0,
+        -12.0,      -13.0,        -14.0,       -15.0,
+        -16.0,      -18.0,        -20.0,       -22.0,
+        -24.0,      -26.0,        -28.0,       -30.0,
+        -32.0,      -36.0,        -40.0,       -44.0,
+        -48.0,      -52.0,        -56.0,       -60.0,
+        -64.0,      -72.0,        -80.0,       -88.0,
+        -96.0,      -104.0,       -112.0,      -120.0,
+        -128.0,     -144.0,       -160.0,      -176.0,
+        -192.0,     -208.0,       -224.0,      -240.0,
+        -256.0,     -288.0,       -320.0,      -352.0,
+        -384.0,     -416.0,       -448.0,      std::numeric_limits<float>::quiet_NaN(),
+
+    };
+
+    return e4m3fnuz_lut[input];
+}
+
+TEST_CASE(test_fp8_cast_to_float)
+{
+    std::vector<uint8_t> bit_vals(256);
+    std::iota(bit_vals.begin(), bit_vals.end(), 0);
+    EXPECT(bool{std::all_of(bit_vals.begin(), bit_vals.end(), [](uint8_t bit_val) {
+        migraphx::fp8::fp8e4m3fn fp8_val(bit_val, migraphx::fp8::fp8e4m3fn::from_bits());
+        if(std::isnan(float(fp8_val)) and std::isnan(fp8e4m3fn_to_fp32_value(bit_val)))
+        {
+            return true;
+        }
+        return migraphx::float_equal(float(fp8_val), fp8e4m3fn_to_fp32_value(bit_val));
+    })});
+}
+
+TEST_CASE(test_fp8_cast_from_float)
+{
+    std::unordered_map<float, uint8_t> test_vals = {
+        {{512, 0x7e},        {-512, 0xfe},        {448, 0x7e},        {-448, 0xfe},
+         {256, 0x78},        {-256, 0xf8},        {240, 0x77},        {-240, 0xf7},
+         {1e-07, 0x0},       {1e+07, 0x7e},       {1, 0x38},          {-1, 0xb8},
+         {0.1, 0x1d},        {0.11, 0x1e},        {0.111, 0x1e},      {0.1111, 0x1e},
+         {-0.1, 0x9d},       {-0.11, 0x9e},       {-0.111, 0x9e},     {-0.1111, 0x9e},
+         {0.2, 0x25},        {2, 0x40},           {20, 0x5a},         {200, 0x74},
+         {-0.2, 0xa5},       {-2, 0xc0},          {-20, 0xda},        {-200, 0xf4},
+         {0.5, 0x30},        {-0.5, 0xb0},        {1.17549e-38, 0x0}, {1.4013e-45, 0x0},
+         {0.0078125, 0x4},   {-0.0078125, 0x84},  {0.000976562, 0x0}, {-0.000976562, 0x80},
+         {0.000488281, 0x0}, {-0.000488281, 0x80}}};
+
+    EXPECT(bool{std::all_of(test_vals.begin(), test_vals.end(), [](const auto sample) {
+        return migraphx::float_equal(
+            migraphx::fp8::fp8e4m3fn(sample.first),
+            migraphx::fp8::fp8e4m3fn(sample.second, migraphx::fp8::fp8e4m3fn::from_bits()));
+    })});
+}
+
+TEST_CASE(test_positive_zero)
+{
+    float zero = 0.0;
+    migraphx::fp8::fp8e4m3fn fp8_zero(zero);
+    EXPECT(fp8_zero.is_zero());
+    EXPECT(migraphx::float_equal(zero, float(fp8_zero)));
+}
+
+TEST_CASE(test_negative_zero)
+{
+    float nzero = -0.0;
+    migraphx::fp8::fp8e4m3fn fp8_nzero(nzero);
+    EXPECT(fp8_nzero.is_zero());
+    //  negative zero is preserved for fp8e4m3fn
+    EXPECT(migraphx::float_equal(nzero, float(fp8_nzero)));
+}
+
+TEST_CASE(test_pos_zero_eq_neg_zero)
+{
+    float nzero = -0.0;
+    float pzero = 0.0;
+    migraphx::fp8::fp8e5m2 fp8_nzero(nzero);
+    migraphx::fp8::fp8e5m2 fp8_pzero(pzero);
+    EXPECT(fp8_nzero == fp8_pzero);
+}
+
+TEST_CASE(test_nan_1)
+{
+    float fnan = std::numeric_limits<float>::quiet_NaN();
+    migraphx::fp8::fp8e4m3fn fp8_nan(fnan);
+    EXPECT(fp8_nan.is_nan());
+    EXPECT(std::isnan(fp8_nan));
+}
+
+TEST_CASE(test_nan_2)
+{
+    auto fnan = std::numeric_limits<migraphx::fp8::fp8e4m3fn>::quiet_NaN();
+    migraphx::fp8::fp8e4m3fn fp8_nan(fnan.data, migraphx::fp8::fp8e4m3fn::from_bits());
+    EXPECT(fp8_nan.is_nan());
+    EXPECT(std::isnan(fp8_nan));
+    EXPECT(std::isnan(float(fp8_nan)));
+}
+
+TEST_CASE(test_infinity_1)
+{
+    float finf = std::numeric_limits<float>::infinity();
+    // no inf in fp8e4m3fn, it gets clipped to max()
+    migraphx::fp8::fp8e4m3fn fp8_max(finf);
+    EXPECT(fp8_max == std::numeric_limits<migraphx::fp8::fp8e4m3fn>::max());
+}
+
+TEST_CASE(test_infinity_2)
+{
+    // neg inf
+    float finf = -1.0 * std::numeric_limits<float>::infinity();
+    // no inf in fp8e4m3fn, it gets clipped to lowest
+    migraphx::fp8::fp8e4m3fn fp8_lowest(finf);
+    EXPECT(bool{fp8_lowest == std::numeric_limits<migraphx::fp8::fp8e4m3fn>::lowest()});
+}
+
+TEST_CASE(test_numeric_max_1)
+{
+    float fmax = std::numeric_limits<float>::max();
+    migraphx::fp8::fp8e4m3fn fp8_max(fmax);
+    EXPECT(fp8_max == std::numeric_limits<migraphx::fp8::fp8e4m3fn>::max());
+}
+
+TEST_CASE(test_numeric_max_2)
+{
+    // gets clipped to max
+    float fmax = 2 * std::numeric_limits<migraphx::fp8::fp8e4m3fn>::max();
+    migraphx::fp8::fp8e4m3fn fp8_max(fmax);
+    EXPECT(fp8_max == std::numeric_limits<migraphx::fp8::fp8e4m3fn>::max());
+}
+
+TEST_CASE(test_numeric_lowest_1)
+{
+    float flowest = std::numeric_limits<float>::lowest();
+    migraphx::fp8::fp8e4m3fn fp8_lowest(flowest);
+    EXPECT(fp8_lowest == std::numeric_limits<migraphx::fp8::fp8e4m3fn>::lowest());
+}
+
+TEST_CASE(test_numeric_lowest_2)
+{
+    // gets clipped to lowest
+    float fmin = 2.0 * std::numeric_limits<migraphx::fp8::fp8e4m3fn>::lowest();
+    migraphx::fp8::fp8e4m3fn fp8_lowest(fmin);
+    EXPECT(fp8_lowest == std::numeric_limits<migraphx::fp8::fp8e4m3fn>::lowest());
+}
+
+TEST_CASE(test_max_eq_lowest)
+{
+    EXPECT(migraphx::float_equal(std::numeric_limits<migraphx::fp8::fp8e4m3fn>::lowest(),
+                                 -1 * std::numeric_limits<migraphx::fp8::fp8e4m3fn>::max()));
+}
+
+TEST_CASE(test_isfinite)
+{
+    EXPECT(std::isfinite(migraphx::fp8::fp8e4m3fn(0.0)));
+    EXPECT(std::isfinite(migraphx::fp8::fp8e4m3fn(-0.0)));
+    EXPECT(not std::isfinite(
+        migraphx::fp8::fp8e4m3fn(std::numeric_limits<migraphx::fp8::fp8e4m3fn>::quiet_NaN())));
+}
+
+TEST_CASE(test_no_infinity)
+{
+    EXPECT(not bool{std::numeric_limits<migraphx::fp8::fp8e4m3fn>::has_infinity});
+}
+
+TEST_CASE(test_binary_ops)
+{
+    auto a = migraphx::fp8::fp8e4m3fn(-1.0);
+    auto b = migraphx::fp8::fp8e4m3fn(1.0);
+    auto c = migraphx::fp8::fp8e4m3fn(0.0);
+    auto d = migraphx::fp8::fp8e4m3fn(-0.0);
+    EXPECT(migraphx::float_equal((c + d), c));
+    EXPECT(migraphx::float_equal((c + d), d));
+    EXPECT(migraphx::float_equal((a + b), c));
+    EXPECT(migraphx::float_equal((a + b), d));
+
+    auto e = migraphx::fp8::fp8e4m3fn(10.0);
+    auto f = migraphx::fp8::fp8e4m3fn(-10.0);
+    EXPECT(bool{e > f});
+    EXPECT(bool{f < e});
+    EXPECT(bool{f <= e});
+    EXPECT(bool{e >= f});
+    EXPECT(bool{e <= e});
+    EXPECT(bool{f >= f});
+    EXPECT(not migraphx::float_equal(f, e));
+}
+
+TEST_CASE(test_fabs)
+{
+    auto a = migraphx::fp8::fp8e4m3fn(-1.0);
+    auto b = migraphx::fp8::fp8e4m3fn(1.0);
+    EXPECT(migraphx::float_equal(b, migraphx::fp8::fabs(a)));
+}
+
+TEST_CASE(test_stream_op)
+{
+    auto a = migraphx::fp8::fp8e4m3fn(-1.0);
+    std::stringstream ss;
+    ss << a;
+    EXPECT(std::string("-1") == ss.str());
+    ss     = std::stringstream();
+    auto b = std::numeric_limits<migraphx::fp8::fp8e4m3fn>::quiet_NaN();
+    ss << b;
+    EXPECT(std::string("nan") == ss.str());
+}
+
+int main(int argc, const char* argv[]) { test::run(argc, argv); }

test/fp8e4m3fnuz.cpp

+/*
+ * The MIT License (MIT)
+ *
+ * Copyright (c) 2015-2022 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 <cmath>
+#include <migraphx/float_equal.hpp>
+#include <migraphx/float8.hpp>
+#include <migraphx/half.hpp>
+#include <migraphx/ranges.hpp>
+#include "test.hpp"
+
+#include <limits>
+
+float fp8e4m3fnuz_to_fp32_value(uint8_t input)
+{
+    constexpr std::array<float, 256> e4m3fnuz_lut = {
+        0.0f,           0.0009765625f,  0.001953125f,
+        0.0029296875f,  0.00390625f,    0.0048828125f,
+        0.005859375f,   0.0068359375f,  0.0078125f,
+        0.0087890625f,  0.009765625f,   0.0107421875f,
+        0.01171875f,    0.0126953125f,  0.013671875f,
+        0.0146484375f,  0.015625f,      0.017578125f,
+        0.01953125f,    0.021484375f,   0.0234375f,
+        0.025390625f,   0.02734375f,    0.029296875f,
+        0.03125f,       0.03515625f,    0.0390625f,
+        0.04296875f,    0.046875f,      0.05078125f,
+        0.0546875f,     0.05859375f,    0.0625f,
+        0.0703125f,     0.078125f,      0.0859375f,
+        0.09375f,       0.1015625f,     0.109375f,
+        0.1171875f,     0.125f,         0.140625f,
+        0.15625f,       0.171875f,      0.1875f,
+        0.203125f,      0.21875f,       0.234375f,
+        0.25f,          0.28125f,       0.3125f,
+        0.34375f,       0.375f,         0.40625f,
+        0.4375f,        0.46875f,       0.5f,
+        0.5625f,        0.625f,         0.6875f,
+        0.75f,          0.8125f,        0.875f,
+        0.9375f,        1.0f,           1.125f,
+        1.25f,          1.375f,         1.5f,
+        1.625f,         1.75f,          1.875f,
+        2.0f,           2.25f,          2.5f,
+        2.75f,          3.0f,           3.25f,
+        3.5f,           3.75f,          4.0f,
+        4.5f,           5.0f,           5.5f,
+        6.0f,           6.5f,           7.0f,
+        7.5f,           8.0f,           9.0f,
+        10.0f,          11.0f,          12.0f,
+        13.0f,          14.0f,          15.0f,
+        16.0f,          18.0f,          20.0f,
+        22.0f,          24.0f,          26.0f,
+        28.0f,          30.0f,          32.0f,
+        36.0f,          40.0f,          44.0f,
+        48.0f,          52.0f,          56.0f,
+        60.0f,          64.0f,          72.0f,
+        80.0f,          88.0f,          96.0f,
+        104.0f,         112.0f,         120.0f,
+        128.0f,         144.0f,         160.0f,
+        176.0f,         192.0f,         208.0f,
+        224.0f,         240.0f,         std::numeric_limits<float>::quiet_NaN(),
+        -0.0009765625f, -0.001953125f,  -0.0029296875f,
+        -0.00390625f,   -0.0048828125f, -0.005859375f,
+        -0.0068359375f, -0.0078125f,    -0.0087890625f,
+        -0.009765625f,  -0.0107421875f, -0.01171875f,
+        -0.0126953125f, -0.013671875f,  -0.0146484375f,
+        -0.015625f,     -0.017578125f,  -0.01953125f,
+        -0.021484375f,  -0.0234375f,    -0.025390625f,
+        -0.02734375f,   -0.029296875f,  -0.03125f,
+        -0.03515625f,   -0.0390625f,    -0.04296875f,
+        -0.046875f,     -0.05078125f,   -0.0546875f,
+        -0.05859375f,   -0.0625f,       -0.0703125f,
+        -0.078125f,     -0.0859375f,    -0.09375f,
+        -0.1015625f,    -0.109375f,     -0.1171875f,
+        -0.125f,        -0.140625f,     -0.15625f,
+        -0.171875f,     -0.1875f,       -0.203125f,
+        -0.21875f,      -0.234375f,     -0.25f,
+        -0.28125f,      -0.3125f,       -0.34375f,
+        -0.375f,        -0.40625f,      -0.4375f,
+        -0.46875f,      -0.5f,          -0.5625f,
+        -0.625f,        -0.6875f,       -0.75f,
+        -0.8125f,       -0.875f,        -0.9375f,
+        -1.0f,          -1.125f,        -1.25f,
+        -1.375f,        -1.5f,          -1.625f,
+        -1.75f,         -1.875f,        -2.0f,
+        -2.25f,         -2.5f,          -2.75f,
+        -3.0f,          -3.25f,         -3.5f,
+        -3.75f,         -4.0f,          -4.5f,
+        -5.0f,          -5.5f,          -6.0f,
+        -6.5f,          -7.0f,          -7.5f,
+        -8.0f,          -9.0f,          -10.0f,
+        -11.0f,         -12.0f,         -13.0f,
+        -14.0f,         -15.0f,         -16.0f,
+        -18.0f,         -20.0f,         -22.0f,
+        -24.0f,         -26.0f,         -28.0f,
+        -30.0f,         -32.0f,         -36.0f,
+        -40.0f,         -44.0f,         -48.0f,
+        -52.0f,         -56.0f,         -60.0f,
+        -64.0f,         -72.0f,         -80.0f,
+        -88.0f,         -96.0f,         -104.0f,
+        -112.0f,        -120.0f,        -128.0f,
+        -144.0f,        -160.0f,        -176.0f,
+        -192.0f,        -208.0f,        -224.0f,
+        -240.0f,
+    };
+
+    return e4m3fnuz_lut[input];
+}
+
+TEST_CASE(test_fp8_cast_to_float)
+{
+    std::vector<uint8_t> bit_vals(256);
+    std::iota(bit_vals.begin(), bit_vals.end(), 0);
+    EXPECT(bool{std::all_of(bit_vals.begin(), bit_vals.end(), [](uint8_t bit_val) {
+        migraphx::fp8::fp8e4m3fnuz fp8_val(bit_val, migraphx::fp8::fp8e4m3fnuz::from_bits());
+        if(std::isnan(float(fp8_val)) and std::isnan(fp8e4m3fnuz_to_fp32_value(bit_val)))
+        {
+            return true;
+        }
+        return migraphx::float_equal(float(fp8_val), fp8e4m3fnuz_to_fp32_value(bit_val));
+    })});
+}
+
+TEST_CASE(test_fp8_cast_from_float)
+{
+    std::unordered_map<float, uint8_t> test_vals = {{256, 0x7f},        {-256, 0xff},
+                                                    {240, 0x7f},        {-240, 0xff},
+                                                    {1e-07, 0x0},       {1e+07, 0x7f},
+                                                    {1, 0x40},          {-1, 0xc0},
+                                                    {0.1, 0x25},        {0.11, 0x26},
+                                                    {0.111, 0x26},      {0.1111, 0x26},
+                                                    {-0.1, 0xa5},       {-0.11, 0xa6},
+                                                    {-0.111, 0xa6},     {-0.1111, 0xa6},
+                                                    {0.2, 0x2d},        {2, 0x48},
+                                                    {20, 0x62},         {200, 0x7c},
+                                                    {-0.2, 0xad},       {-2, 0xc8},
+                                                    {-20, 0xe2},        {-200, 0xfc},
+                                                    {0.5, 0x38},        {-0.5, 0xb8},
+                                                    {1.17549e-38, 0x0}, {1.4013e-45, 0x0},
+                                                    {0.00390625, 0x4},  {-0.00390625, 0x84},
+                                                    {0.00195312, 0x2},  {-0.00195312, 0x82},
+                                                    {0.000976562, 0x1}, {-0.000976562, 0x81},
+                                                    {0.000488281, 0x0}, {-0.000488281, 0x0}};
+
+    EXPECT(bool{std::all_of(test_vals.begin(), test_vals.end(), [](const auto sample) {
+        return migraphx::float_equal(
+            migraphx::fp8::fp8e4m3fnuz(sample.first),
+            migraphx::fp8::fp8e4m3fnuz(sample.second, migraphx::fp8::fp8e4m3fnuz::from_bits()));
+    })});
+}
+
+TEST_CASE(test_positive_zero)
+{
+    float zero = 0.0;
+    migraphx::fp8::fp8e4m3fnuz fp8_zero(zero);
+    EXPECT(fp8_zero.is_zero());
+    EXPECT(migraphx::float_equal(zero, float(fp8_zero)));
+}
+
+TEST_CASE(test_negative_zero)
+{
+    float nzero = -0.0;
+    float pzero = 0.0;
+    migraphx::fp8::fp8e4m3fnuz fp8_nzero(nzero);
+    EXPECT(fp8_nzero.is_zero());
+    //  negative zero gets converted to positive zero
+    EXPECT(migraphx::float_equal(pzero, float(fp8_nzero)));
+}
+
+TEST_CASE(test_nan_1)
+{
+    float fnan = std::numeric_limits<float>::quiet_NaN();
+    migraphx::fp8::fp8e4m3fnuz fp8_nan(fnan);
+    EXPECT(fp8_nan.is_nan());
+    EXPECT(std::isnan(fp8_nan));
+}
+
+TEST_CASE(test_nan_2)
+{
+    auto fnan = std::numeric_limits<migraphx::fp8::fp8e4m3fnuz>::quiet_NaN();
+    migraphx::fp8::fp8e4m3fnuz fp8_nan(fnan.data, migraphx::fp8::fp8e4m3fnuz::from_bits());
+    EXPECT(fp8_nan.is_nan());
+    EXPECT(std::isnan(fp8_nan));
+    EXPECT(std::isnan(float(fp8_nan)));
+}
+
+TEST_CASE(test_infinity_1)
+{
+    float finf = std::numeric_limits<float>::infinity();
+    // no inf in fp8e4m3fnuz it gets clipped to Nans
+    migraphx::fp8::fp8e4m3fnuz fp8_nan(finf);
+    EXPECT(fp8_nan.is_nan());
+    EXPECT(std::isnan(float(fp8_nan)));
+}
+
+TEST_CASE(test_infinity_2)
+{
+    // neg inf
+    float finf = -1.0 * std::numeric_limits<float>::infinity();
+    // no inf in fp8e4m3fnuz it gets clipped to NaNs
+    migraphx::fp8::fp8e4m3fnuz fp8_nan(finf);
+    EXPECT(fp8_nan.is_nan());
+    EXPECT(std::isnan(float(fp8_nan)));
+}
+
+TEST_CASE(test_numeric_max_1)
+{
+    float fmax = std::numeric_limits<float>::max();
+    migraphx::fp8::fp8e4m3fnuz fp8_max(fmax);
+    EXPECT(fp8_max == std::numeric_limits<migraphx::fp8::fp8e4m3fnuz>::max());
+}
+
+TEST_CASE(test_numeric_max_2)
+{
+    // gets clipped to max
+    float fmax = 2 * std::numeric_limits<migraphx::fp8::fp8e4m3fnuz>::max();
+    migraphx::fp8::fp8e4m3fnuz fp8_max(fmax);
+    EXPECT(fp8_max == std::numeric_limits<migraphx::fp8::fp8e4m3fnuz>::max());
+}
+
+TEST_CASE(test_numeric_lowest_1)
+{
+    float flowest = std::numeric_limits<float>::lowest();
+    migraphx::fp8::fp8e4m3fnuz fp8_lowest(flowest);
+    EXPECT(fp8_lowest == std::numeric_limits<migraphx::fp8::fp8e4m3fnuz>::lowest());
+}
+
+TEST_CASE(test_numeric_lowest_2)
+{
+    // gets clipped to lowest
+    float fmin = 2.0 * std::numeric_limits<migraphx::fp8::fp8e4m3fnuz>::lowest();
+    migraphx::fp8::fp8e4m3fnuz fp8_lowest(fmin);
+    EXPECT(fp8_lowest == std::numeric_limits<migraphx::fp8::fp8e4m3fnuz>::lowest());
+}
+
+TEST_CASE(test_max_eq_lowest)
+{
+    EXPECT(migraphx::float_equal(std::numeric_limits<migraphx::fp8::fp8e4m3fnuz>::lowest(),
+                                 -1 * std::numeric_limits<migraphx::fp8::fp8e4m3fnuz>::max()));
+}
+
+TEST_CASE(test_isfinite)
+{
+    EXPECT(std::isfinite(migraphx::fp8::fp8e4m3fnuz(0.0)));
+    EXPECT(std::isfinite(migraphx::fp8::fp8e4m3fnuz(-0.0)));
+    EXPECT(not std::isfinite(
+        migraphx::fp8::fp8e4m3fnuz(std::numeric_limits<migraphx::fp8::fp8e4m3fnuz>::quiet_NaN())));
+}
+
+TEST_CASE(test_no_infinity)
+{
+    EXPECT(not bool{std::numeric_limits<migraphx::fp8::fp8e4m3fnuz>::has_infinity});
+}
+
+TEST_CASE(test_binary_ops)
+{
+    auto a = migraphx::fp8::fp8e4m3fnuz(-1.0);
+    auto b = migraphx::fp8::fp8e4m3fnuz(1.0);
+    auto c = migraphx::fp8::fp8e4m3fnuz(0.0);
+    auto d = migraphx::fp8::fp8e4m3fnuz(-0.0);
+    EXPECT(migraphx::float_equal((c + d), c));
+    EXPECT(migraphx::float_equal((c + d), d));
+    EXPECT(migraphx::float_equal((a + b), c));
+    EXPECT(migraphx::float_equal((a + b), d));
+
+    auto e = migraphx::fp8::fp8e4m3fnuz(10.0);
+    auto f = migraphx::fp8::fp8e4m3fnuz(-10.0);
+    EXPECT(bool{e > f});
+    EXPECT(bool{f < e});
+    EXPECT(bool{f <= e});
+    EXPECT(bool{e >= f});
+    EXPECT(bool{e <= e});
+    EXPECT(bool{f >= f});
+    EXPECT(not migraphx::float_equal(f, e));
+}
+
+TEST_CASE(test_fabs)
+{
+    auto a = migraphx::fp8::fp8e4m3fnuz(-1.0);
+    auto b = migraphx::fp8::fp8e4m3fnuz(1.0);
+    EXPECT(migraphx::float_equal(b, migraphx::fp8::fabs(a)));
+}
+
+TEST_CASE(test_stream_op)
+{
+    auto a = migraphx::fp8::fp8e4m3fnuz(-1.0);
+    std::stringstream ss;
+    ss << a;
+    EXPECT(std::string("-1") == ss.str());
+    ss     = std::stringstream();
+    auto b = std::numeric_limits<migraphx::fp8::fp8e4m3fnuz>::quiet_NaN();
+    ss << b;
+    EXPECT(std::string("nan") == ss.str());
+}
+
+int main(int argc, const char* argv[]) { test::run(argc, argv); }

test/fp8e5m2.cpp

+/*
+ * The MIT License (MIT)
+ *
+ * Copyright (c) 2015-2022 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 <cmath>
+#include <migraphx/float_equal.hpp>
+#include <migraphx/float8.hpp>
+#include <migraphx/half.hpp>
+#include <migraphx/ranges.hpp>
+#include "test.hpp"
+
+#include <limits>
+#include <sstream>
+
+float fp8e5m2_to_fp32_value(uint8_t input)
+{
+    constexpr std::array<float, 256> e4m3fnuz_lut = {
+        0.0,
+        1.52587890625e-05,
+        3.0517578125e-05,
+        4.57763671875e-05,
+        6.103515625e-05,
+        7.62939453125e-05,
+        9.1552734375e-05,
+        0.0001068115234375,
+        0.0001220703125,
+        0.000152587890625,
+        0.00018310546875,
+        0.000213623046875,
+        0.000244140625,
+        0.00030517578125,
+        0.0003662109375,
+        0.00042724609375,
+        0.00048828125,
+        0.0006103515625,
+        0.000732421875,
+        0.0008544921875,
+        0.0009765625,
+        0.001220703125,
+        0.00146484375,
+        0.001708984375,
+        0.001953125,
+        0.00244140625,
+        0.0029296875,
+        0.00341796875,
+        0.00390625,
+        0.0048828125,
+        0.005859375,
+        0.0068359375,
+        0.0078125,
+        0.009765625,
+        0.01171875,
+        0.013671875,
+        0.015625,
+        0.01953125,
+        0.0234375,
+        0.02734375,
+        0.03125,
+        0.0390625,
+        0.046875,
+        0.0546875,
+        0.0625,
+        0.078125,
+        0.09375,
+        0.109375,
+        0.125,
+        0.15625,
+        0.1875,
+        0.21875,
+        0.25,
+        0.3125,
+        0.375,
+        0.4375,
+        0.5,
+        0.625,
+        0.75,
+        0.875,
+        1.0,
+        1.25,
+        1.5,
+        1.75,
+        2.0,
+        2.5,
+        3.0,
+        3.5,
+        4.0,
+        5.0,
+        6.0,
+        7.0,
+        8.0,
+        10.0,
+        12.0,
+        14.0,
+        16.0,
+        20.0,
+        24.0,
+        28.0,
+        32.0,
+        40.0,
+        48.0,
+        56.0,
+        64.0,
+        80.0,
+        96.0,
+        112.0,
+        128.0,
+        160.0,
+        192.0,
+        224.0,
+        256.0,
+        320.0,
+        384.0,
+        448.0,
+        512.0,
+        640.0,
+        768.0,
+        896.0,
+        1024.0,
+        1280.0,
+        1536.0,
+        1792.0,
+        2048.0,
+        2560.0,
+        3072.0,
+        3584.0,
+        4096.0,
+        5120.0,
+        6144.0,
+        7168.0,
+        8192.0,
+        10240.0,
+        12288.0,
+        14336.0,
+        16384.0,
+        20480.0,
+        24576.0,
+        28672.0,
+        32768.0,
+        40960.0,
+        49152.0,
+        57344.0,
+        std::numeric_limits<float>::infinity(),
+        std::numeric_limits<float>::quiet_NaN(),
+        std::numeric_limits<float>::quiet_NaN(),
+        std::numeric_limits<float>::quiet_NaN(),
+        -0.0,
+        -1.52587890625e-05,
+        -3.0517578125e-05,
+        -4.57763671875e-05,
+        -6.103515625e-05,
+        -7.62939453125e-05,
+        -9.1552734375e-05,
+        -0.0001068115234375,
+        -0.0001220703125,
+        -0.000152587890625,
+        -0.00018310546875,
+        -0.000213623046875,
+        -0.000244140625,
+        -0.00030517578125,
+        -0.0003662109375,
+        -0.00042724609375,
+        -0.00048828125,
+        -0.0006103515625,
+        -0.000732421875,
+        -0.0008544921875,
+        -0.0009765625,
+        -0.001220703125,
+        -0.00146484375,
+        -0.001708984375,
+        -0.001953125,
+        -0.00244140625,
+        -0.0029296875,
+        -0.00341796875,
+        -0.00390625,
+        -0.0048828125,
+        -0.005859375,
+        -0.0068359375,
+        -0.0078125,
+        -0.009765625,
+        -0.01171875,
+        -0.013671875,
+        -0.015625,
+        -0.01953125,
+        -0.0234375,
+        -0.02734375,
+        -0.03125,
+        -0.0390625,
+        -0.046875,
+        -0.0546875,
+        -0.0625,
+        -0.078125,
+        -0.09375,
+        -0.109375,
+        -0.125,
+        -0.15625,
+        -0.1875,
+        -0.21875,
+        -0.25,
+        -0.3125,
+        -0.375,
+        -0.4375,
+        -0.5,
+        -0.625,
+        -0.75,
+        -0.875,
+        -1.0,
+        -1.25,
+        -1.5,
+        -1.75,
+        -2.0,
+        -2.5,
+        -3.0,
+        -3.5,
+        -4.0,
+        -5.0,
+        -6.0,
+        -7.0,
+        -8.0,
+        -10.0,
+        -12.0,
+        -14.0,
+        -16.0,
+        -20.0,
+        -24.0,
+        -28.0,
+        -32.0,
+        -40.0,
+        -48.0,
+        -56.0,
+        -64.0,
+        -80.0,
+        -96.0,
+        -112.0,
+        -128.0,
+        -160.0,
+        -192.0,
+        -224.0,
+        -256.0,
+        -320.0,
+        -384.0,
+        -448.0,
+        -512.0,
+        -640.0,
+        -768.0,
+        -896.0,
+        -1024.0,
+        -1280.0,
+        -1536.0,
+        -1792.0,
+        -2048.0,
+        -2560.0,
+        -3072.0,
+        -3584.0,
+        -4096.0,
+        -5120.0,
+        -6144.0,
+        -7168.0,
+        -8192.0,
+        -10240.0,
+        -12288.0,
+        -14336.0,
+        -16384.0,
+        -20480.0,
+        -24576.0,
+        -28672.0,
+        -32768.0,
+        -40960.0,
+        -49152.0,
+        -57344.0,
+        -1.0f * std::numeric_limits<float>::infinity(),
+        std::numeric_limits<float>::quiet_NaN(),
+        std::numeric_limits<float>::quiet_NaN(),
+        std::numeric_limits<float>::quiet_NaN(),
+
+    };
+
+    return e4m3fnuz_lut[input];
+}
+
+TEST_CASE(test_fp8_cast_to_float)
+{
+    std::vector<uint8_t> bit_vals(256);
+    std::iota(bit_vals.begin(), bit_vals.end(), 0);
+    EXPECT(bool{std::all_of(bit_vals.begin(), bit_vals.end(), [](uint8_t bit_val) {
+        migraphx::fp8::fp8e5m2 fp8_val(bit_val, migraphx::fp8::fp8e5m2::from_bits());
+        if(std::isnan(float(fp8_val)) and std::isnan(fp8e5m2_to_fp32_value(bit_val)))
+        {
+            return true;
+        }
+        else if(std::isinf(float(fp8_val)) and std::isinf(fp8e5m2_to_fp32_value(bit_val)))
+        {
+            return true;
+        }
+        return migraphx::float_equal(float(fp8_val), fp8e5m2_to_fp32_value(bit_val));
+    })});
+}
+
+TEST_CASE(test_fp8_cast_from_float)
+{
+    std::unordered_map<float, uint8_t> test_vals = {
+        {-60000, 0xfb},
+        {-57344, 0xfb},
+        {-448, 0xdf},
+        {-256, 0xdc},
+        {-240, 0xdc},
+        {-200, 0xda},
+        {-20, 0xcd},
+        {-2, 0xc0},
+        {-1, 0xbc},
+        {-0.5, 0xb8},
+        {-0.2, 0xb2},
+        {-0.1111, 0xaf},
+        {-0.111, 0xaf},
+        {-0.11, 0xaf},
+        {-0.1, 0xae},
+        {6.10351e-05, 0x4},
+        {-6.10351e-05, 0x84},
+        {3.05176e-05, 0x2},
+        {-3.05176e-05, 0x82},
+        {1.52588e-05, 0x1},
+        {-1.52588e-05, 0x81},
+        {7.62939e-06, 0x0},
+        {-7.62939e-06, 0x80},
+        {0.1, 0x2e},
+        {0.11, 0x2f},
+        {0.111, 0x2f},
+        {0.1111, 0x2f},
+        {0.2, 0x32},
+        {0.5, 0x38},
+        {1, 0x3c},
+        {2, 0x40},
+        {20, 0x4d},
+        {200, 0x5a},
+        {240, 0x5c},
+        {256, 0x5c},
+        {448, 0x5f},
+        {57344, 0x7b},
+        {60000, 0x7b},
+        {1e+07, 0x7b},
+    };
+
+    EXPECT(bool{std::all_of(test_vals.begin(), test_vals.end(), [](const auto sample) {
+        return migraphx::float_equal(
+            migraphx::fp8::fp8e5m2(sample.first),
+            migraphx::fp8::fp8e5m2(sample.second, migraphx::fp8::fp8e5m2::from_bits()));
+    })});
+}
+
+TEST_CASE(test_positive_zero)
+{
+    float zero = 0.0;
+    migraphx::fp8::fp8e5m2 fp8_zero(zero);
+    EXPECT(fp8_zero.is_zero());
+    EXPECT(migraphx::float_equal(zero, float(fp8_zero)));
+}
+
+TEST_CASE(test_negative_zero)
+{
+    float nzero = -0.0;
+    migraphx::fp8::fp8e5m2 fp8_nzero(nzero);
+    EXPECT(fp8_nzero.is_zero());
+    //  negative zero is preserved for fp8e5m2
+    EXPECT(migraphx::float_equal(nzero, float(fp8_nzero)));
+}
+
+TEST_CASE(test_pos_zero_eq_neg_zero)
+{
+    float nzero = -0.0;
+    float pzero = 0.0;
+    migraphx::fp8::fp8e5m2 fp8_nzero(nzero);
+    migraphx::fp8::fp8e5m2 fp8_pzero(pzero);
+    EXPECT(fp8_nzero == fp8_pzero);
+}
+
+TEST_CASE(test_nan_1)
+{
+    float fnan = std::numeric_limits<float>::quiet_NaN();
+    migraphx::fp8::fp8e5m2 fp8_nan(fnan);
+    EXPECT(fp8_nan.is_nan());
+    EXPECT(std::isnan(fp8_nan));
+}
+
+TEST_CASE(test_nan_2)
+{
+    auto fnan = std::numeric_limits<migraphx::fp8::fp8e5m2>::quiet_NaN();
+    migraphx::fp8::fp8e5m2 fp8_nan(fnan.data, migraphx::fp8::fp8e5m2::from_bits());
+    EXPECT(fp8_nan.is_nan());
+    EXPECT(std::isnan(fp8_nan));
+    EXPECT(std::isnan(float(fp8_nan)));
+}
+
+TEST_CASE(test_infinity_1)
+{
+    // float infinity should get clipped to max
+    float finf = std::numeric_limits<float>::infinity();
+    migraphx::fp8::fp8e5m2 fp8_max(finf);
+    EXPECT(fp8_max == std::numeric_limits<migraphx::fp8::fp8e5m2>::max());
+}
+
+TEST_CASE(test_infinity_2)
+{
+    // neg inf
+    float finf = -1.0 * std::numeric_limits<float>::infinity();
+    // no inf in fp8e5m2, it gets clipped to lowest
+    migraphx::fp8::fp8e5m2 fp8_lowest(finf);
+    EXPECT(bool{fp8_lowest == std::numeric_limits<migraphx::fp8::fp8e5m2>::lowest()});
+}
+
+TEST_CASE(test_numeric_max_1)
+{
+    float fmax = std::numeric_limits<float>::max();
+    migraphx::fp8::fp8e5m2 fp8_max(fmax);
+    EXPECT(fp8_max == std::numeric_limits<migraphx::fp8::fp8e5m2>::max());
+}
+
+TEST_CASE(test_numeric_max_2)
+{
+    // gets clipped to max
+    float fmax = 2 * std::numeric_limits<migraphx::fp8::fp8e5m2>::max();
+    migraphx::fp8::fp8e5m2 fp8_max(fmax);
+    EXPECT(fp8_max == std::numeric_limits<migraphx::fp8::fp8e5m2>::max());
+}
+
+TEST_CASE(test_numeric_lowest_1)
+{
+    float flowest = std::numeric_limits<float>::lowest();
+    migraphx::fp8::fp8e5m2 fp8_lowest(flowest);
+    EXPECT(fp8_lowest == std::numeric_limits<migraphx::fp8::fp8e5m2>::lowest());
+}
+
+TEST_CASE(test_numeric_lowest_2)
+{
+    // gets clipped to lowest
+    float fmin = 2.0 * std::numeric_limits<migraphx::fp8::fp8e5m2>::lowest();
+    migraphx::fp8::fp8e5m2 fp8_lowest(fmin);
+    EXPECT(fp8_lowest == std::numeric_limits<migraphx::fp8::fp8e5m2>::lowest());
+}
+
+TEST_CASE(test_max_eq_lowest)
+{
+    EXPECT(migraphx::float_equal(std::numeric_limits<migraphx::fp8::fp8e5m2>::lowest(),
+                                 -1 * std::numeric_limits<migraphx::fp8::fp8e5m2>::max()));
+}
+
+TEST_CASE(test_isfinite)
+{
+    EXPECT(std::isfinite(migraphx::fp8::fp8e5m2(0.0)));
+    EXPECT(std::isfinite(migraphx::fp8::fp8e5m2(-0.0)));
+    EXPECT(not std::isfinite(
+        migraphx::fp8::fp8e5m2(std::numeric_limits<migraphx::fp8::fp8e5m2>::quiet_NaN())));
+    EXPECT(not std::isfinite(std::numeric_limits<migraphx::fp8::fp8e5m2>::infinity()));
+    // -1.0 * inf  is float(-inf) which with clipping/saturation gets converted into fp8::lowest()
+    EXPECT(std::isfinite(
+        migraphx::fp8::fp8e5m2(-1.0 * std::numeric_limits<migraphx::fp8::fp8e5m2>::infinity())));
+    EXPECT(not std::isfinite(migraphx::fp8::fp8e5m2(0xFC, migraphx::fp8::fp8e5m2::from_bits())));
+}
+
+TEST_CASE(test_binary_ops)
+{
+    auto a = migraphx::fp8::fp8e5m2(-1.0);
+    auto b = migraphx::fp8::fp8e5m2(1.0);
+    auto c = migraphx::fp8::fp8e5m2(0.0);
+    auto d = migraphx::fp8::fp8e5m2(-0.0);
+    EXPECT(migraphx::float_equal((c + d), c));
+    EXPECT(migraphx::float_equal((c + d), d));
+    EXPECT(migraphx::float_equal((a + b), c));
+    EXPECT(migraphx::float_equal((a + b), d));
+
+    auto e = migraphx::fp8::fp8e5m2(10.0);
+    auto f = migraphx::fp8::fp8e5m2(-10.0);
+    EXPECT(bool{e > f});
+    EXPECT(bool{f < e});
+    EXPECT(bool{f <= e});
+    EXPECT(bool{e >= f});
+    EXPECT(bool{e <= e});
+    EXPECT(bool{f >= f});
+    EXPECT(not migraphx::float_equal(f, e));
+}
+
+TEST_CASE(test_fabs)
+{
+    auto a = migraphx::fp8::fp8e5m2(-1.0);
+    auto b = migraphx::fp8::fp8e5m2(1.0);
+    EXPECT(migraphx::float_equal(b, migraphx::fp8::fabs(a)));
+}
+
+TEST_CASE(test_stream_op)
+{
+    auto a = migraphx::fp8::fp8e5m2(-1.0);
+    std::stringstream ss;
+    ss << a;
+    EXPECT(std::string("-1") == ss.str());
+    ss     = std::stringstream();
+    auto b = std::numeric_limits<migraphx::fp8::fp8e5m2>::quiet_NaN();
+    ss << b;
+    EXPECT(std::string("nan") == ss.str());
+}
+
+int main(int argc, const char* argv[]) { test::run(argc, argv); }

test/fp8e5m2fnuz.cpp

+/*
+ * The MIT License (MIT)
+ *
+ * Copyright (c) 2015-2022 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 <cmath>
+#include <migraphx/float_equal.hpp>
+#include <migraphx/float8.hpp>
+#include <migraphx/half.hpp>
+#include <migraphx/ranges.hpp>
+#include "test.hpp"
+
+#include <limits>
+
+float fp8e5m2fnuz_to_fp32_value(uint8_t input)
+{
+    constexpr std::array<float, 256> e4m3fnuz_lut = {
+        0.0,
+        7.62939453125e-06,
+        1.52587890625e-05,
+        2.288818359375e-05,
+        3.0517578125e-05,
+        3.814697265625e-05,
+        4.57763671875e-05,
+        5.340576171875e-05,
+        6.103515625e-05,
+        7.62939453125e-05,
+        9.1552734375e-05,
+        0.0001068115234375,
+        0.0001220703125,
+        0.000152587890625,
+        0.00018310546875,
+        0.000213623046875,
+        0.000244140625,
+        0.00030517578125,
+        0.0003662109375,
+        0.00042724609375,
+        0.00048828125,
+        0.0006103515625,
+        0.000732421875,
+        0.0008544921875,
+        0.0009765625,
+        0.001220703125,
+        0.00146484375,
+        0.001708984375,
+        0.001953125,
+        0.00244140625,
+        0.0029296875,
+        0.00341796875,
+        0.00390625,
+        0.0048828125,
+        0.005859375,
+        0.0068359375,
+        0.0078125,
+        0.009765625,
+        0.01171875,
+        0.013671875,
+        0.015625,
+        0.01953125,
+        0.0234375,
+        0.02734375,
+        0.03125,
+        0.0390625,
+        0.046875,
+        0.0546875,
+        0.0625,
+        0.078125,
+        0.09375,
+        0.109375,
+        0.125,
+        0.15625,
+        0.1875,
+        0.21875,
+        0.25,
+        0.3125,
+        0.375,
+        0.4375,
+        0.5,
+        0.625,
+        0.75,
+        0.875,
+        1.0,
+        1.25,
+        1.5,
+        1.75,
+        2.0,
+        2.5,
+        3.0,
+        3.5,
+        4.0,
+        5.0,
+        6.0,
+        7.0,
+        8.0,
+        10.0,
+        12.0,
+        14.0,
+        16.0,
+        20.0,
+        24.0,
+        28.0,
+        32.0,
+        40.0,
+        48.0,
+        56.0,
+        64.0,
+        80.0,
+        96.0,
+        112.0,
+        128.0,
+        160.0,
+        192.0,
+        224.0,
+        256.0,
+        320.0,
+        384.0,
+        448.0,
+        512.0,
+        640.0,
+        768.0,
+        896.0,
+        1024.0,
+        1280.0,
+        1536.0,
+        1792.0,
+        2048.0,
+        2560.0,
+        3072.0,
+        3584.0,
+        4096.0,
+        5120.0,
+        6144.0,
+        7168.0,
+        8192.0,
+        10240.0,
+        12288.0,
+        14336.0,
+        16384.0,
+        20480.0,
+        24576.0,
+        28672.0,
+        32768.0,
+        40960.0,
+        49152.0,
+        57344.0,
+        std::numeric_limits<float>::quiet_NaN(),
+        -7.62939453125e-06,
+        -1.52587890625e-05,
+        -2.288818359375e-05,
+        -3.0517578125e-05,
+        -3.814697265625e-05,
+        -4.57763671875e-05,
+        -5.340576171875e-05,
+        -6.103515625e-05,
+        -7.62939453125e-05,
+        -9.1552734375e-05,
+        -0.0001068115234375,
+        -0.0001220703125,
+        -0.000152587890625,
+        -0.00018310546875,
+        -0.000213623046875,
+        -0.000244140625,
+        -0.00030517578125,
+        -0.0003662109375,
+        -0.00042724609375,
+        -0.00048828125,
+        -0.0006103515625,
+        -0.000732421875,
+        -0.0008544921875,
+        -0.0009765625,
+        -0.001220703125,
+        -0.00146484375,
+        -0.001708984375,
+        -0.001953125,
+        -0.00244140625,
+        -0.0029296875,
+        -0.00341796875,
+        -0.00390625,
+        -0.0048828125,
+        -0.005859375,
+        -0.0068359375,
+        -0.0078125,
+        -0.009765625,
+        -0.01171875,
+        -0.013671875,
+        -0.015625,
+        -0.01953125,
+        -0.0234375,
+        -0.02734375,
+        -0.03125,
+        -0.0390625,
+        -0.046875,
+        -0.0546875,
+        -0.0625,
+        -0.078125,
+        -0.09375,
+        -0.109375,
+        -0.125,
+        -0.15625,
+        -0.1875,
+        -0.21875,
+        -0.25,
+        -0.3125,
+        -0.375,
+        -0.4375,
+        -0.5,
+        -0.625,
+        -0.75,
+        -0.875,
+        -1.0,
+        -1.25,
+        -1.5,
+        -1.75,
+        -2.0,
+        -2.5,
+        -3.0,
+        -3.5,
+        -4.0,
+        -5.0,
+        -6.0,
+        -7.0,
+        -8.0,
+        -10.0,
+        -12.0,
+        -14.0,
+        -16.0,
+        -20.0,
+        -24.0,
+        -28.0,
+        -32.0,
+        -40.0,
+        -48.0,
+        -56.0,
+        -64.0,
+        -80.0,
+        -96.0,
+        -112.0,
+        -128.0,
+        -160.0,
+        -192.0,
+        -224.0,
+        -256.0,
+        -320.0,
+        -384.0,
+        -448.0,
+        -512.0,
+        -640.0,
+        -768.0,
+        -896.0,
+        -1024.0,
+        -1280.0,
+        -1536.0,
+        -1792.0,
+        -2048.0,
+        -2560.0,
+        -3072.0,
+        -3584.0,
+        -4096.0,
+        -5120.0,
+        -6144.0,
+        -7168.0,
+        -8192.0,
+        -10240.0,
+        -12288.0,
+        -14336.0,
+        -16384.0,
+        -20480.0,
+        -24576.0,
+        -28672.0,
+        -32768.0,
+        -40960.0,
+        -49152.0,
+        -57344.0,
+    };
+
+    return e4m3fnuz_lut[input];
+}
+
+TEST_CASE(test_fp8_cast_to_float)
+{
+    std::vector<uint8_t> bit_vals(256);
+    std::iota(bit_vals.begin(), bit_vals.end(), 0);
+    EXPECT(bool{std::all_of(bit_vals.begin(), bit_vals.end(), [](uint8_t bit_val) {
+        migraphx::fp8::fp8e5m2fnuz fp8_val(bit_val, migraphx::fp8::fp8e5m2fnuz::from_bits());
+        if(std::isnan(float(fp8_val)) and std::isnan(fp8e5m2fnuz_to_fp32_value(bit_val)))
+        {
+            return true;
+        }
+        return migraphx::float_equal(float(fp8_val), fp8e5m2fnuz_to_fp32_value(bit_val));
+    })});
+}
+
+TEST_CASE(test_fp8_cast_from_float)
+{
+    std::unordered_map<float, uint8_t> test_vals = {
+        {57344, 0x7f},      {-57344, 0xff},       {60000, 0x7f},      {-60000, 0xff},
+        {448, 0x63},        {-448, 0xe3},         {256, 0x60},        {-256, 0xe0},
+        {240, 0x60},        {-240, 0xe0},         {3.05176e-05, 0x4}, {-3.05176e-05, 0x84},
+        {1.52588e-05, 0x2}, {-1.52588e-05, 0x82}, {7.62939e-06, 0x1}, {-7.62939e-06, 0x81},
+        {3.81469e-06, 0x0}, {-3.81469e-06, 0x0},  {1e+07, 0x7f},      {1, 0x40},
+        {-1, 0xc0},         {0.1, 0x32},          {0.11, 0x33},       {0.111, 0x33},
+        {0.1111, 0x33},     {-0.1, 0xb2},         {-0.11, 0xb3},      {-0.111, 0xb3},
+        {-0.1111, 0xb3},    {0.2, 0x36},          {2, 0x44},          {20, 0x51},
+        {200, 0x5e},        {-0.2, 0xb6},         {-2, 0xc4},         {-20, 0xd1},
+        {-200, 0xde},       {0.5, 0x3c},          {-0.5, 0xbc},       {1.17549e-38, 0x0},
+        {1.4013e-45, 0x0},
+    };
+
+    EXPECT(bool{std::all_of(test_vals.begin(), test_vals.end(), [](const auto sample) {
+        return migraphx::float_equal(
+            migraphx::fp8::fp8e5m2fnuz(sample.first),
+            migraphx::fp8::fp8e5m2fnuz(sample.second, migraphx::fp8::fp8e5m2fnuz::from_bits()));
+    })});
+}
+
+TEST_CASE(test_positive_zero)
+{
+    float zero = 0.0;
+    migraphx::fp8::fp8e5m2fnuz fp8_zero(zero);
+    EXPECT(fp8_zero.is_zero());
+    EXPECT(migraphx::float_equal(zero, float(fp8_zero)));
+}
+
+TEST_CASE(test_negative_zero)
+{
+    float nzero = -0.0;
+    float pzero = 0.0;
+    migraphx::fp8::fp8e5m2fnuz fp8_nzero(nzero);
+    EXPECT(fp8_nzero.is_zero());
+    //  negative zero gets converted to positive zero
+    EXPECT(migraphx::float_equal(pzero, float(fp8_nzero)));
+}
+
+TEST_CASE(test_nan_1)
+{
+    float fnan = std::numeric_limits<float>::quiet_NaN();
+    migraphx::fp8::fp8e5m2fnuz fp8_nan(fnan);
+    EXPECT(fp8_nan.is_nan());
+    EXPECT(std::isnan(fp8_nan));
+}
+
+TEST_CASE(test_nan_2)
+{
+    auto fnan = std::numeric_limits<migraphx::fp8::fp8e5m2fnuz>::quiet_NaN();
+    migraphx::fp8::fp8e5m2fnuz fp8_nan(fnan.data, migraphx::fp8::fp8e5m2fnuz::from_bits());
+    EXPECT(fp8_nan.is_nan());
+    EXPECT(std::isnan(fp8_nan));
+    EXPECT(std::isnan(float(fp8_nan)));
+}
+
+TEST_CASE(test_infinity_1)
+{
+    float finf = std::numeric_limits<float>::infinity();
+    // no inf in fp8e5m2fnuz it gets clipped to Nans
+    migraphx::fp8::fp8e5m2fnuz fp8_nan(finf);
+    EXPECT(fp8_nan.is_nan());
+    EXPECT(std::isnan(float(fp8_nan)));
+}
+
+TEST_CASE(test_infinity_2)
+{
+    // neg inf
+    float finf = -1.0 * std::numeric_limits<float>::infinity();
+    // no inf in fp8e5m2fnuz it gets clipped to NaNs
+    migraphx::fp8::fp8e5m2fnuz fp8_nan(finf);
+    EXPECT(fp8_nan.is_nan());
+    EXPECT(std::isnan(float(fp8_nan)));
+}
+
+TEST_CASE(test_numeric_max_1)
+{
+    float fmax = std::numeric_limits<float>::max();
+    migraphx::fp8::fp8e5m2fnuz fp8_max(fmax);
+    EXPECT(fp8_max == std::numeric_limits<migraphx::fp8::fp8e5m2fnuz>::max());
+}
+
+TEST_CASE(test_numeric_max_2)
+{
+    // gets clipped to max
+    float fmax = 2 * std::numeric_limits<migraphx::fp8::fp8e5m2fnuz>::max();
+    migraphx::fp8::fp8e5m2fnuz fp8_max(fmax);
+    EXPECT(fp8_max == std::numeric_limits<migraphx::fp8::fp8e5m2fnuz>::max());
+}
+
+TEST_CASE(test_numeric_lowest_1)
+{
+    float flowest = std::numeric_limits<float>::lowest();
+    migraphx::fp8::fp8e5m2fnuz fp8_lowest(flowest);
+    EXPECT(fp8_lowest == std::numeric_limits<migraphx::fp8::fp8e5m2fnuz>::lowest());
+}
+
+TEST_CASE(test_numeric_lowest_2)
+{
+    // gets clipped to lowest
+    float fmin = 2.0 * std::numeric_limits<migraphx::fp8::fp8e5m2fnuz>::lowest();
+    migraphx::fp8::fp8e5m2fnuz fp8_lowest(fmin);
+    EXPECT(fp8_lowest == std::numeric_limits<migraphx::fp8::fp8e5m2fnuz>::lowest());
+}
+
+TEST_CASE(test_max_eq_lowest)
+{
+    EXPECT(migraphx::float_equal(std::numeric_limits<migraphx::fp8::fp8e5m2fnuz>::lowest(),
+                                 -1 * std::numeric_limits<migraphx::fp8::fp8e5m2fnuz>::max()));
+}
+
+TEST_CASE(test_isfinite)
+{
+    EXPECT(std::isfinite(migraphx::fp8::fp8e5m2fnuz(0.0)));
+    EXPECT(std::isfinite(migraphx::fp8::fp8e5m2fnuz(-0.0)));
+    EXPECT(not std::isfinite(
+        migraphx::fp8::fp8e5m2fnuz(std::numeric_limits<migraphx::fp8::fp8e5m2fnuz>::quiet_NaN())));
+}
+
+TEST_CASE(test_no_infinity)
+{
+    EXPECT(not bool{std::numeric_limits<migraphx::fp8::fp8e5m2fnuz>::has_infinity});
+}
+
+TEST_CASE(test_binary_ops)
+{
+    auto a = migraphx::fp8::fp8e5m2fnuz(-1.0);
+    auto b = migraphx::fp8::fp8e5m2fnuz(1.0);
+    auto c = migraphx::fp8::fp8e5m2fnuz(0.0);
+    auto d = migraphx::fp8::fp8e5m2fnuz(-0.0);
+    EXPECT(migraphx::float_equal((c + d), c));
+    EXPECT(migraphx::float_equal((c + d), d));
+    EXPECT(migraphx::float_equal((a + b), c));
+    EXPECT(migraphx::float_equal((a + b), d));
+
+    auto e = migraphx::fp8::fp8e5m2fnuz(10.0);
+    auto f = migraphx::fp8::fp8e5m2fnuz(-10.0);
+    EXPECT(bool{e > f});
+    EXPECT(bool{f < e});
+    EXPECT(bool{f <= e});
+    EXPECT(bool{e >= f});
+    EXPECT(bool{e <= e});
+    EXPECT(bool{f >= f});
+    EXPECT(not migraphx::float_equal(f, e));
+}
+
+TEST_CASE(test_fabs)
+{
+    auto a = migraphx::fp8::fp8e5m2fnuz(-1.0);
+    auto b = migraphx::fp8::fp8e5m2fnuz(1.0);
+    EXPECT(migraphx::float_equal(b, migraphx::fp8::fabs(a)));
+}
+
+TEST_CASE(test_stream_op)
+{
+    auto a = migraphx::fp8::fp8e5m2fnuz(-1.0);
+    std::stringstream ss;
+    ss << a;
+    EXPECT(std::string("-1") == ss.str());
+    ss     = std::stringstream();
+    auto b = std::numeric_limits<migraphx::fp8::fp8e5m2fnuz>::quiet_NaN();
+    ss << b;
+    EXPECT(std::string("nan") == ss.str());
+}
+int main(int argc, const char* argv[]) { test::run(argc, argv); }

test/fuse_pointwise.cpp

@@ -414,8 +414,8 @@ TEST_CASE(add_reshape_add_nonstandard)
         auto y       = mm->add_parameter("y", s1);
         auto z       = mm->add_parameter("z", s2);
         auto add1    = mm->add_instruction(migraphx::make_op("add"), x, y);
-        auto c       = mm->add_instruction(migraphx::make_op("contiguous"), add1);
-        auto reshape = mm->add_instruction(migraphx::make_op("reshape", {{"dims", s2.lens()}}), c);
+        auto reshape =
+            mm->add_instruction(migraphx::make_op("reshape", {{"dims", s2.lens()}}), add1);
         auto add2    = mm->add_instruction(migraphx::make_op("add"), reshape, z);
         mm->add_return({add2});
     }
@@ -426,10 +426,8 @@ TEST_CASE(add_reshape_add_nonstandard)
         auto x   = mm->add_parameter("x", s1);
         auto y   = mm->add_parameter("y", s1);
         auto z   = mm->add_parameter("z", s2);
-        auto cx  = mm->add_instruction(migraphx::make_op("contiguous"), x);
-        auto cy  = mm->add_instruction(migraphx::make_op("contiguous"), y);
-        auto x2  = mm->add_instruction(migraphx::make_op("reshape", {{"dims", s3.lens()}}), cx);
-        auto y2  = mm->add_instruction(migraphx::make_op("reshape", {{"dims", s3.lens()}}), cy);
+        auto x2  = mm->add_instruction(migraphx::make_op("reshape", {{"dims", s3.lens()}}), x);
+        auto y2  = mm->add_instruction(migraphx::make_op("reshape", {{"dims", s3.lens()}}), y);
         auto z2  = mm->add_instruction(migraphx::make_op("reshape", {{"dims", s3.lens()}}), z);
         auto fadd =
             add_pointwise(p2, "main:pointwise0", {x2, y2, z2}, [=](auto* pm, const auto& inputs) {
@@ -466,10 +464,8 @@ TEST_CASE(add_unsqueeze_add_nonstandard)
         auto x   = mm->add_parameter("x", s1);
         auto y   = mm->add_parameter("y", s1);
         auto z   = mm->add_parameter("z", s2);
-        auto cx  = mm->add_instruction(migraphx::make_op("contiguous"), x);
-        auto cy  = mm->add_instruction(migraphx::make_op("contiguous"), y);
-        auto x2  = mm->add_instruction(migraphx::make_op("reshape", {{"dims", s2.lens()}}), cx);
-        auto y2  = mm->add_instruction(migraphx::make_op("reshape", {{"dims", s2.lens()}}), cy);
+        auto x2  = mm->add_instruction(migraphx::make_op("reshape", {{"dims", s2.lens()}}), x);
+        auto y2  = mm->add_instruction(migraphx::make_op("reshape", {{"dims", s2.lens()}}), y);
         auto fadd =
             add_pointwise(p2, "main:pointwise0", {x2, y2, z}, [=](auto* pm, const auto& inputs) {
                 auto add1 = pm->add_instruction(migraphx::make_op("add"), inputs[0], inputs[1]);

test/gpu/codegen_literal.cpp

@@ -64,7 +64,7 @@ TEST_CASE(mul_literal_round_test)
     auto l1 = mm->add_literal(1 / 0.00787402f);
 
     auto mul   = mm->add_instruction(migraphx::make_op("mul"), l0, l1);
-    auto round = mm->add_instruction(migraphx::make_op("round"), mul);
+    auto round = mm->add_instruction(migraphx::make_op("nearbyint"), mul);
 
     mm->add_return({round});
 

test/gpu/gemm_tune.cpp

+/*
+ * 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 <iostream>
+#include <vector>
+#include <migraphx/gpu/gemm.hpp>
+#include <hip/hip_runtime_api.h>
+#include <migraphx/gpu/target.hpp>
+#include <migraphx/verify.hpp>
+#include <test.hpp>
+#include <migraphx/make_op.hpp>
+#include <migraphx/iterator_for.hpp>
+
+// includes needed for run_lowering
+#include <migraphx/gpu/lowering.hpp>
+#include <migraphx/auto_contiguous.hpp>
+#include <migraphx/instruction.hpp>
+#include <migraphx/pass_manager.hpp>
+
+// Abbreviated lowering; we don't need the usual cleanup passes for this test
+void run_lowering(migraphx::program& p, bool offload_copy = false)
+{
+    auto ctx = migraphx::gpu::context{};
+    migraphx::run_passes(
+        *p.get_main_module(),
+        {migraphx::auto_contiguous{}, migraphx::gpu::lowering{&ctx, offload_copy}});
+}
+
+/**
+ * Tests the automatic GEMM tuning feature.  In the finalize() method of the gemm op,
+ * rocBLAS API functions are called to quickly benchmark all the GEMM solutions
+ * available in the currently installed rocBLAS library and choose the index of the fastest.
+ */
+TEST_CASE(gemm_tune_with_rocblas)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+
+    migraphx::shape sa{migraphx::shape::float_type, {4, 2}};
+    migraphx::shape sb{migraphx::shape::float_type, {2, 3}};
+    auto a = mm->add_parameter("a", sa);
+    auto b = mm->add_parameter("b", sb);
+
+    migraphx::operation dot_op = migraphx::make_op("dot");
+    mm->add_instruction(dot_op, a, b);
+
+    // lowering adds gemm implementation for dot operator
+    run_lowering(p);
+
+    migraphx::target gpu_t = migraphx::gpu::target{};
+    migraphx::compile_options options;
+    options.exhaustive_tune = true;
+    p.compile(gpu_t, options);
+
+    migraphx::value solution_idx(0);
+    for(auto ins : iterator_for(*p.get_main_module()))
+    {
+        if(ins->name() == "gpu::gemm")
+        {
+            auto gemm_op = migraphx::get_operation(ins);
+
+            // tuned solution index is not deterministic, but anything other than 0
+            // (default, invalid, or not available) is good.
+            // gemm_op.to_value().debug_print();
+            solution_idx = gemm_op.to_value()["solution_idx"];
+            break;
+        }
+    }
+#ifdef MIGRAPHX_USE_ROCBLAS_TUNING_API
+    EXPECT(0 != solution_idx.to<std::size_t>());
+#else
+    EXPECT(0 == solution_idx.to<std::size_t>());
+#endif
+}
+
+// GEMM tuning of a strided-batch matrix; invokes rocblas_gemm_strided_batched_ex
+TEST_CASE(gemm_tune_strided)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+
+    migraphx::shape sa{migraphx::shape::float_type, {4, 2, 2}};
+    migraphx::shape sb{migraphx::shape::float_type, {4, 2, 2}};
+    migraphx::shape s_output{migraphx::shape::float_type, {4, 2, 2}};
+    auto a      = mm->add_parameter("a", sa);
+    auto b      = mm->add_parameter("b", sb);
+    auto output = mm->add_parameter("out", s_output);
+
+    auto gemm_oper = migraphx::make_op("gpu::gemm", {{"beta", 2}});
+    mm->add_instruction(gemm_oper, a, b, output);
+
+    migraphx::target gpu_t = migraphx::gpu::target{};
+    migraphx::compile_options options;
+    options.exhaustive_tune = true;
+    p.compile(gpu_t, options);
+
+    migraphx::value solution_idx(0);
+    for(auto ins : iterator_for(*p.get_main_module()))
+    {
+        if(ins->name() == "gpu::gemm")
+        {
+            auto gemm_op = migraphx::get_operation(ins);
+            auto gemmv   = gemm_op.to_value();
+
+            // tuned solution index is not deterministic, but anything other than 0
+            // (default, invalid, or not available) is good.
+            solution_idx = gemm_op.to_value()["solution_idx"];
+            break;
+        }
+    }
+#ifdef MIGRAPHX_USE_ROCBLAS_TUNING_API
+    EXPECT(0 != solution_idx.to<std::size_t>());
+#else
+    EXPECT(0 == solution_idx.to<std::size_t>());
+#endif
+}
+
+// GEMM tuning of a strided-batch matrix; created by lowering
+TEST_CASE(gemm_tune_strided_lowered)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+
+    // At time of writing this test, gemm_impl considers a shape is strided if it has
+    // at least three dimensions and the 3rd-to-last is nonzero, invoking
+    // rocblas_gemm_strided_batched_ex. Also, DOT operator requires all dimensions except the last
+    // two to be equal.
+    migraphx::shape sa{migraphx::shape::float_type, {4, 2, 5}};
+    migraphx::shape sb{migraphx::shape::float_type, {4, 5, 3}};
+    auto a = mm->add_parameter("a", sa);
+    auto b = mm->add_parameter("b", sb);
+
+    migraphx::operation dot_op = migraphx::make_op("dot");
+    mm->add_instruction(dot_op, a, b);
+
+    // lowering adds gemm implementation for dot operator
+    run_lowering(p);
+
+    migraphx::target gpu_t = migraphx::gpu::target{};
+    migraphx::compile_options options;
+    options.exhaustive_tune = true;
+    p.compile(gpu_t, options);
+
+    migraphx::value solution_idx(0);
+    for(auto ins : iterator_for(*p.get_main_module()))
+    {
+        if(ins->name() == "gpu::gemm")
+        {
+            auto gemm_op = migraphx::get_operation(ins);
+
+            // tuned solution index is not deterministic, but anything other than 0
+            // (default, invalid, or not available) is good.
+            solution_idx = gemm_op.to_value()["solution_idx"];
+            break;
+        }
+    }
+#ifdef MIGRAPHX_USE_ROCBLAS_TUNING_API
+    EXPECT(0 != solution_idx.to<std::size_t>());
+#else
+    EXPECT(0 == solution_idx.to<std::size_t>());
+#endif
+}
+
+TEST_CASE(gemm_tune_invalid_sol_index)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+
+    migraphx::shape sa{migraphx::shape::float_type, {4, 2}};
+    migraphx::shape sb{migraphx::shape::float_type, {2, 3}};
+    migraphx::shape s_output{migraphx::shape::float_type, {4, 3}};
+    auto a      = mm->add_parameter("a", sa);
+    auto b      = mm->add_parameter("b", sb);
+    auto output = mm->add_parameter("out", s_output);
+
+    auto gemm_oper = migraphx::make_op("gpu::gemm", {{"solution_idx", 987654321}});
+    mm->add_instruction(gemm_oper, a, b, output);
+
+    migraphx::target gpu_t = migraphx::gpu::target{};
+    migraphx::compile_options options;
+    options.exhaustive_tune = true;
+    p.compile(gpu_t, options);
+
+    migraphx::value solution_idx(0);
+    for(auto ins : iterator_for(*p.get_main_module()))
+    {
+        if(ins->name() == "gpu::gemm")
+        {
+            auto gemm_op = migraphx::get_operation(ins);
+            auto gemmv   = gemm_op.to_value();
+
+            // given invalid starting index, should return default 0
+            solution_idx = gemm_op.to_value()["solution_idx"];
+            break;
+        }
+    }
+#ifdef MIGRAPHX_USE_ROCBLAS_TUNING_API
+    EXPECT(0 == solution_idx.to<std::size_t>());
+#else
+    EXPECT(0 != solution_idx.to<std::size_t>());
+#endif
+}
+
+int main(int argc, const char* argv[]) { test::run(argc, argv); }

test/gpu/jit.cpp

@@ -237,12 +237,12 @@ TEST_CASE(code_object_hip)
 
     std::vector<migraphx::shape> expected_inputs = {input, input};
     auto co                                      = migraphx::make_op("gpu::code_object",
-                                {{"code_object", migraphx::value::binary{binaries.front()}},
-                                 {"symbol_name", "add_2"},
-                                 {"global", input.elements()},
-                                 {"local", 1024},
-                                 {"expected_inputs", migraphx::to_value(expected_inputs)},
-                                 {"output", migraphx::to_value(input)}});
+                                                                     {{"code_object", migraphx::value::binary{binaries.front()}},
+                                                                      {"symbol_name", "add_2"},
+                                                                      {"global", input.elements()},
+                                                                      {"local", 1024},
+                                                                      {"expected_inputs", migraphx::to_value(expected_inputs)},
+                                                                      {"output", migraphx::to_value(input)}});
 
     migraphx::program p;
     auto* mm            = p.get_main_module();
@@ -348,7 +348,10 @@ TEST_CASE(compile_math)
     auto vec_sizes = {2, 4, 6};
     for(auto&& t : migraphx::shape::types())
     {
-        if(contains({migraphx::shape::bool_type, migraphx::shape::tuple_type}, t))
+        if(contains({migraphx::shape::bool_type,
+                     migraphx::shape::fp8e4m3fnuz_type,
+                     migraphx::shape::tuple_type},
+                    t))
             continue;
         auto name = migraphx::shape::cpp_type(t);
         if(t == migraphx::shape::half_type)
@@ -396,7 +399,10 @@ TEST_CASE(assert_type_min_max)
     migraphx::gpu::hip_compile_options options;
     for(auto&& t : migraphx::shape::types())
     {
-        if(contains({migraphx::shape::bool_type, migraphx::shape::tuple_type}, t))
+        if(contains({migraphx::shape::bool_type,
+                     migraphx::shape::fp8e4m3fnuz_type,
+                     migraphx::shape::tuple_type},
+                    t))
             continue;
         auto name = migraphx::shape::cpp_type(t);
         if(t == migraphx::shape::half_type)

test/jit.cpp

@@ -46,8 +46,12 @@ std::function<F>
 compile_function(const std::string& src, const std::string& flags, const std::string& fname)
 {
     migraphx::src_compiler compiler;
-    compiler.flags  = flags + "-std=c++14 -fPIC -shared";
+    compiler.flags = flags + "-std=c++14 -fPIC -shared";
+#ifdef _WIN32
+    compiler.output = "simple.dll";
+#else
     compiler.output = "libsimple.so";
+#endif
     migraphx::src_file f{"main.cpp", src};
     auto image = compiler.compile({f});
     return migraphx::dynamic_loader{image}.get_function<F>(fname);

test/onnx/.onnxrt-commit

-2eeafc37bca21dc8bf337dda7020b486543162d7
+a5537f2f563d4975c7e6121a7eb260bbbfd9455a

test/onnx/gen_onnx.py

@@ -3858,6 +3858,64 @@ def instance_norm_val_3d_test():
     return ([node], [], [y], [x_tensor, scale_tensor, bias_tensor])
 
 
+@onnx_test()
+def isinf_half_test():
+    t1 = helper.make_tensor_value_info('t1', TensorProto.FLOAT16, [2, 3])
+    t2 = helper.make_tensor_value_info('t2', TensorProto.BOOL, [2, 3])
+
+    node = onnx.helper.make_node(
+        'IsInf',
+        inputs=['t1'],
+        outputs=['t2'],
+    )
+    return ([node], [t1], [t2])
+
+
+@onnx_test()
+def isinf_neg_test():
+    t1 = helper.make_tensor_value_info('t1', TensorProto.FLOAT, [2, 3])
+    t2 = helper.make_tensor_value_info('t2', TensorProto.BOOL, [2, 3])
+
+    node = onnx.helper.make_node(
+        'IsInf',
+        detect_negative=[1],
+        detect_positive=[0],
+        inputs=['t1'],
+        outputs=['t2'],
+    )
+    return ([node], [t1], [t2])
+
+
+@onnx_test()
+def isinf_double_pos_test():
+    t1 = helper.make_tensor_value_info('t1', TensorProto.DOUBLE, [2, 3])
+    t2 = helper.make_tensor_value_info('t2', TensorProto.BOOL, [2, 3])
+
+    node = onnx.helper.make_node(
+        'IsInf',
+        detect_negative=[0],
+        detect_positive=[1],
+        inputs=['t1'],
+        outputs=['t2'],
+    )
+    return ([node], [t1], [t2])
+
+
+@onnx_test()
+def isinf_no_detect_test():
+    t1 = helper.make_tensor_value_info('t1', TensorProto.FLOAT, [2, 3])
+    t2 = helper.make_tensor_value_info('t2', TensorProto.BOOL, [2, 3])
+
+    node = onnx.helper.make_node(
+        'IsInf',
+        detect_negative=[0],
+        detect_positive=[0],
+        inputs=['t1'],
+        outputs=['t2'],
+    )
+    return ([node], [t1], [t2])
+
+
 @onnx_test()
 def isnan_float_test():
     t1 = helper.make_tensor_value_info('t1', TensorProto.FLOAT, [2, 3])
@@ -4276,6 +4334,50 @@ def loop_test():
     return ([node], [iter, cond, a, b], [b_loop, uout])
 
 
+@onnx_test()
+def loop_test_implicit_tripcnt():
+    body = helper.make_graph([
+        helper.make_node("Add", ["a", "b_in"], ["my_local"]),
+        helper.make_node("Sub", ["a", "b_in"], ["a_sub_b_in"]),
+        helper.make_node("Greater", ["my_local", "a_sub_b_in"],
+                         ["keep_going"]),
+        helper.make_node("Add", ["a_sub_b_in", "a_sub_b_in"],
+                         ["user_defined_vals"]),
+    ], "body", [
+        helper.make_tensor_value_info('iteration_num', TensorProto.INT64, [1]),
+        helper.make_tensor_value_info('keep_going_inp', TensorProto.BOOL, [1]),
+        helper.make_tensor_value_info('b_in', TensorProto.FLOAT, [1])
+    ], [
+        helper.make_tensor_value_info('keep_going', TensorProto.BOOL, [1]),
+        helper.make_tensor_value_info('a_sub_b_in', TensorProto.FLOAT, [1]),
+        helper.make_tensor_value_info('my_local', TensorProto.FLOAT, [1]),
+        helper.make_tensor_value_info('user_defined_vals', TensorProto.FLOAT,
+                                      [1]),
+    ])
+
+    iter = helper.make_tensor(name='max_trip_count',
+                              data_type=TensorProto.INT64,
+                              dims=[1],
+                              vals=[15])
+
+    node = helper.make_node(
+        "Loop",
+        inputs=["max_trip_count", "keep_going_cond", "b"],
+        outputs=["b_loop", "my_local_loop", "user_defined_vals_loop"],
+        body=body)
+
+    a = helper.make_tensor_value_info('a', TensorProto.FLOAT, [1])
+    b = helper.make_tensor_value_info('b', TensorProto.FLOAT, [1])
+    cond = helper.make_tensor_value_info('keep_going_cond', TensorProto.BOOL,
+                                         [1])
+
+    b_loop = helper.make_tensor_value_info('b_loop', TensorProto.FLOAT, [1])
+    uout = helper.make_tensor_value_info('user_defined_vals_loop',
+                                         TensorProto.FLOAT, [2, 1])
+
+    return ([node], [cond, a, b], [b_loop, uout], [iter])
+
+
 @onnx_test()
 def lpnormalization_axis_error_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 3])
@@ -4382,6 +4484,177 @@ def lrn_test():
     return ([node], [x], [y])
 
 
+@onnx_test()
+def lstm_bi_layout_cell_test():
+    seq = helper.make_tensor_value_info('seq', TensorProto.FLOAT, [3, 5, 10])
+    w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [2, 80, 10])
+    r = helper.make_tensor_value_info('r', TensorProto.FLOAT, [2, 80, 20])
+    bias = helper.make_tensor_value_info('bias', TensorProto.FLOAT, [2, 160])
+    seq_len = helper.make_tensor_value_info('seq_len', TensorProto.INT32, [3])
+    h0 = helper.make_tensor_value_info('h0', TensorProto.FLOAT, [3, 2, 20])
+    c0 = helper.make_tensor_value_info('c0', TensorProto.FLOAT, [3, 2, 20])
+    pph = helper.make_tensor_value_info('pph', TensorProto.FLOAT, [2, 60])
+
+    cellout = helper.make_tensor_value_info('cellout', TensorProto.FLOAT,
+                                            [3, 2, 20])
+
+    node = onnx.helper.make_node(
+        'LSTM',
+        inputs=['seq', 'w', 'r', 'bias', 'seq_len', 'h0', 'c0', 'pph'],
+        outputs=['', '', 'cellout'],
+        activations=['sigmoid', 'tanh', 'tanh'],
+        clip=0,
+        direction='bidirectional',
+        hidden_size=20,
+        input_forget=1,
+        layout=1)
+
+    return ([node], [seq, w, r, bias, seq_len, h0, c0, pph], [cellout])
+
+
+@onnx_test()
+def lstm_bi_layout_last_test():
+    seq = helper.make_tensor_value_info('seq', TensorProto.FLOAT, [3, 5, 10])
+    w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [2, 80, 10])
+    r = helper.make_tensor_value_info('r', TensorProto.FLOAT, [2, 80, 20])
+    bias = helper.make_tensor_value_info('bias', TensorProto.FLOAT, [2, 160])
+    seq_len = helper.make_tensor_value_info('seq_len', TensorProto.INT32, [3])
+    h0 = helper.make_tensor_value_info('h0', TensorProto.FLOAT, [3, 2, 20])
+    c0 = helper.make_tensor_value_info('c0', TensorProto.FLOAT, [3, 2, 20])
+    pph = helper.make_tensor_value_info('pph', TensorProto.FLOAT, [2, 60])
+
+    hs = helper.make_tensor_value_info('hs', TensorProto.FLOAT, [3, 5, 2, 20])
+    output = helper.make_tensor_value_info('output', TensorProto.FLOAT,
+                                           [3, 2, 20])
+
+    node = onnx.helper.make_node(
+        'LSTM',
+        inputs=['seq', 'w', 'r', 'bias', 'seq_len', 'h0', 'c0', 'pph'],
+        outputs=['hs', 'output'],
+        activations=['sigmoid', 'tanh', 'tanh'],
+        clip=0,
+        direction='bidirectional',
+        hidden_size=20,
+        input_forget=1,
+        layout=1)
+
+    return ([node], [seq, w, r, bias, seq_len, h0, c0, pph], [hs, output])
+
+
+@onnx_test()
+def lstm_f_layout_hs_test():
+    seq = helper.make_tensor_value_info('seq', TensorProto.FLOAT, [3, 5, 10])
+    w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 80, 10])
+    r = helper.make_tensor_value_info('r', TensorProto.FLOAT, [1, 80, 20])
+    bias = helper.make_tensor_value_info('bias', TensorProto.FLOAT, [1, 160])
+    seq_len = helper.make_tensor_value_info('seq_len', TensorProto.INT32, [3])
+    h0 = helper.make_tensor_value_info('h0', TensorProto.FLOAT, [3, 1, 20])
+    c0 = helper.make_tensor_value_info('c0', TensorProto.FLOAT, [3, 1, 20])
+    pph = helper.make_tensor_value_info('pph', TensorProto.FLOAT, [1, 60])
+
+    hs = helper.make_tensor_value_info('hs', TensorProto.FLOAT, [3, 5, 1, 20])
+    output = helper.make_tensor_value_info('output', TensorProto.FLOAT,
+                                           [3, 1, 20])
+
+    node = onnx.helper.make_node(
+        'LSTM',
+        inputs=['seq', 'w', 'r', 'bias', 'seq_len', 'h0', 'c0', 'pph'],
+        outputs=['hs', 'output'],
+        activations=['sigmoid', 'tanh', 'tanh'],
+        clip=0,
+        direction='forward',
+        hidden_size=20,
+        input_forget=1,
+        layout=1)
+
+    return ([node], [seq, w, r, bias, seq_len, h0, c0, pph], [hs, output])
+
+
+@onnx_test()
+def lstm_f_layout_cell_test():
+    seq = helper.make_tensor_value_info('seq', TensorProto.FLOAT, [3, 5, 10])
+    w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 80, 10])
+    r = helper.make_tensor_value_info('r', TensorProto.FLOAT, [1, 80, 20])
+    bias = helper.make_tensor_value_info('bias', TensorProto.FLOAT, [1, 160])
+    seq_len = helper.make_tensor_value_info('seq_len', TensorProto.INT32, [3])
+    h0 = helper.make_tensor_value_info('h0', TensorProto.FLOAT, [3, 1, 20])
+    c0 = helper.make_tensor_value_info('c0', TensorProto.FLOAT, [3, 1, 20])
+    pph = helper.make_tensor_value_info('pph', TensorProto.FLOAT, [1, 60])
+
+    cellout = helper.make_tensor_value_info('cellout', TensorProto.FLOAT,
+                                            [3, 1, 20])
+
+    node = onnx.helper.make_node(
+        'LSTM',
+        inputs=['seq', 'w', 'r', 'bias', 'seq_len', 'h0', 'c0', 'pph'],
+        outputs=['', '', 'cellout'],
+        activations=['sigmoid', 'tanh', 'tanh'],
+        clip=0,
+        direction='forward',
+        hidden_size=20,
+        input_forget=1,
+        layout=1)
+
+    return ([node], [seq, w, r, bias, seq_len, h0, c0, pph], [cellout])
+
+
+@onnx_test()
+def lstm_r_layout_test():
+    seq = helper.make_tensor_value_info('seq', TensorProto.FLOAT, [3, 5, 10])
+    w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 80, 10])
+    r = helper.make_tensor_value_info('r', TensorProto.FLOAT, [1, 80, 20])
+    bias = helper.make_tensor_value_info('bias', TensorProto.FLOAT, [1, 160])
+    seq_len = helper.make_tensor_value_info('seq_len', TensorProto.INT32, [3])
+    h0 = helper.make_tensor_value_info('h0', TensorProto.FLOAT, [3, 1, 20])
+    c0 = helper.make_tensor_value_info('c0', TensorProto.FLOAT, [3, 1, 20])
+    pph = helper.make_tensor_value_info('pph', TensorProto.FLOAT, [1, 60])
+
+    hs = helper.make_tensor_value_info('hs', TensorProto.FLOAT, [3, 5, 1, 20])
+
+    node = onnx.helper.make_node(
+        'LSTM',
+        inputs=['seq', 'w', 'r', 'bias', 'seq_len', 'h0', 'c0', 'pph'],
+        outputs=['hs'],
+        activations=['sigmoid', 'tanh', 'tanh'],
+        clip=0,
+        direction='reverse',
+        hidden_size=20,
+        input_forget=1,
+        layout=1)
+
+    return ([node], [seq, w, r, bias, seq_len, h0, c0, pph], [hs])
+
+
+@onnx_test()
+def lstm_r_layout_hs_cell_test():
+    seq = helper.make_tensor_value_info('seq', TensorProto.FLOAT, [3, 5, 10])
+    w = helper.make_tensor_value_info('w', TensorProto.FLOAT, [1, 80, 10])
+    r = helper.make_tensor_value_info('r', TensorProto.FLOAT, [1, 80, 20])
+    bias = helper.make_tensor_value_info('bias', TensorProto.FLOAT, [1, 160])
+    seq_len = helper.make_tensor_value_info('seq_len', TensorProto.INT32, [3])
+    h0 = helper.make_tensor_value_info('h0', TensorProto.FLOAT, [3, 1, 20])
+    c0 = helper.make_tensor_value_info('c0', TensorProto.FLOAT, [3, 1, 20])
+    pph = helper.make_tensor_value_info('pph', TensorProto.FLOAT, [1, 60])
+
+    output = helper.make_tensor_value_info('output', TensorProto.FLOAT,
+                                           [3, 1, 20])
+    cellout = helper.make_tensor_value_info('cellout', TensorProto.FLOAT,
+                                            [3, 1, 20])
+
+    node = onnx.helper.make_node(
+        'LSTM',
+        inputs=['seq', 'w', 'r', 'bias', 'seq_len', 'h0', 'c0', 'pph'],
+        outputs=['', 'output', 'cellout'],
+        activations=['sigmoid', 'tanh', 'tanh'],
+        clip=0,
+        direction='reverse',
+        hidden_size=20,
+        input_forget=1,
+        layout=1)
+
+    return ([node], [seq, w, r, bias, seq_len, h0, c0, pph], [output, cellout])
+
+
 @onnx_test()
 def matmul_bmbm_test():
     m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 6, 7])
@@ -4883,9 +5156,9 @@ def mod_test_fmod_different_dtypes():
 
 @onnx_test()
 def multinomial_test():
-    sample_size = 10
-    seed = 0.0
-    input = helper.make_tensor_value_info("input", TensorProto.FLOAT, [1, 10])
+    sample_size = 13
+    seed = 0.
+    input = helper.make_tensor_value_info("input", TensorProto.FLOAT, [3, 10])
     output = helper.make_tensor_value_info("output", TensorProto.INT32,
                                            [1, 10])
 
@@ -4898,6 +5171,44 @@ def multinomial_test():
     return ([node], [input], [output])
 
 
+@onnx_test()
+def multinomial_dyn_test():
+    sample_size = 100000
+    seed = 1.3
+    categories = 5
+    input = helper.make_tensor_value_info("input", TensorProto.FLOAT,
+                                          [None, categories])
+    output = helper.make_tensor_value_info("output", TensorProto.FLOAT,
+                                           [None, categories])
+
+    node = onnx.helper.make_node(
+        'Multinomial',
+        inputs=['input'],
+        sample_size=sample_size,
+        dtype=1,  # shape::float_type
+        seed=seed,
+        outputs=['output'])
+
+    return ([node], [input], [output])
+
+
+@onnx_test()
+def multinomial_autoseed_dyn_test():
+    # If seed attribute is not given, device should auto generate one at runtime
+    sample_size = 12
+    input = helper.make_tensor_value_info("input", TensorProto.FLOAT,
+                                          [None, 10])
+    output = helper.make_tensor_value_info("output", TensorProto.INT32,
+                                           [None, 10])
+
+    node = onnx.helper.make_node('Multinomial',
+                                 inputs=['input'],
+                                 sample_size=sample_size,
+                                 outputs=['output'])
+
+    return ([node], [input], [output])
+
+
 @onnx_test()
 def multinomial_generated_seed_test():
     sample_size = 10
@@ -5868,6 +6179,61 @@ def qlinearmatmul_3D_test():
             [sc_a, zero_pt_a, sc_b, zero_pt_b, sc_c, zero_pt_c])
 
 
+@onnx_test()
+def qlinearmul_test():
+    a = helper.make_tensor_value_info('A', TensorProto.UINT8, [64])
+    sc_a = helper.make_tensor('A_scale', TensorProto.FLOAT, [], [0.05])
+    zero_pt_a = helper.make_tensor('A_zero_point', TensorProto.UINT8, [], [0])
+
+    b = helper.make_tensor_value_info('B', TensorProto.UINT8, [64])
+    sc_b = helper.make_tensor('B_scale', TensorProto.FLOAT, [], [0.05])
+    zero_pt_b = helper.make_tensor('B_zero_point', TensorProto.UINT8, [], [16])
+
+    sc_c = helper.make_tensor('C_scale', TensorProto.FLOAT, [], [0.05])
+    zero_pt_c = helper.make_tensor('C_zero_point', TensorProto.UINT8, [],
+                                   [100])
+
+    c = helper.make_tensor_value_info('C', TensorProto.UINT8, [64])
+
+    node = onnx.helper.make_node(
+        'QLinearMul',
+        inputs=[
+            'A', 'A_scale', 'A_zero_point', 'B', 'B_scale', 'B_zero_point',
+            'C_scale', 'C_zero_point'
+        ],
+        outputs=['C'],
+    )
+    return ([node], [a, b], [c],
+            [sc_a, zero_pt_a, sc_b, zero_pt_b, sc_c, zero_pt_c])
+
+
+@onnx_test()
+def qlinearmul_bcast_test():
+    a = helper.make_tensor_value_info('A', TensorProto.INT8, [64])
+    sc_a = helper.make_tensor('A_scale', TensorProto.FLOAT, [], [0.05])
+    zero_pt_a = helper.make_tensor('A_zero_point', TensorProto.INT8, [], [0])
+
+    b = helper.make_tensor_value_info('B', TensorProto.INT8, [1, 1, 64])
+    sc_b = helper.make_tensor('B_scale', TensorProto.FLOAT, [], [0.05])
+    zero_pt_b = helper.make_tensor('B_zero_point', TensorProto.INT8, [], [128])
+
+    sc_c = helper.make_tensor('C_scale', TensorProto.FLOAT, [], [0.15])
+    zero_pt_c = helper.make_tensor('C_zero_point', TensorProto.INT8, [], [32])
+
+    c = helper.make_tensor_value_info('C', TensorProto.INT8, [1, 1, 64])
+
+    node = onnx.helper.make_node(
+        'QLinearMul',
+        inputs=[
+            'A', 'A_scale', 'A_zero_point', 'B', 'B_scale', 'B_zero_point',
+            'C_scale', 'C_zero_point'
+        ],
+        outputs=['C'],
+    )
+    return ([node], [a, b], [c],
+            [sc_a, zero_pt_a, sc_b, zero_pt_b, sc_c, zero_pt_c])
+
+
 @onnx_test()
 def quantizelinear_test():
     arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [5])
@@ -6947,6 +7313,16 @@ def roialign_test():
     return ([node], [x, roi, bi], [y])
 
 
+@onnx_test()
+def round_half_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT16, [4, 4])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT16, [4, 4])
+
+    node = onnx.helper.make_node('Round', inputs=['x'], outputs=['y'])
+
+    return ([node], [x], [y])
+
+
 @onnx_test()
 def scatter_add_test():
     x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [3, 4, 5, 6])
@@ -7866,6 +8242,32 @@ def slice_var_input_dyn1():
     return ([node], [data, starts, ends, axes], [output])
 
 
+@onnx_test()
+def slice_var_input_default_steps():
+    step = np.array([1, 1])
+    step_tensor = helper.make_tensor(name="step",
+                                     data_type=TensorProto.INT64,
+                                     dims=step.shape,
+                                     vals=step.astype(int))
+    arg_step = helper.make_node("Constant",
+                                inputs=[],
+                                outputs=['arg_step'],
+                                value=step_tensor)
+
+    data = helper.make_tensor_value_info('data', TensorProto.FLOAT, [None, 2])
+    starts = helper.make_tensor_value_info('starts', TensorProto.INT64, [2])
+    ends = helper.make_tensor_value_info('ends', TensorProto.INT64, [2])
+    axes = helper.make_tensor_value_info('axes', TensorProto.INT64, [2])
+    output = helper.make_tensor_value_info('output', TensorProto.FLOAT, [1, 2])
+
+    node = onnx.helper.make_node(
+        'Slice',
+        inputs=['data', 'starts', 'ends', 'axes', 'arg_step'],
+        outputs=['output'])
+
+    return ([arg_step, node], [data, starts, ends, axes], [output])
+
+
 @onnx_test()
 def slice_var_input_steps_error():
     step = np.array([2, 1])
@@ -7879,9 +8281,9 @@ def slice_var_input_steps_error():
                                 value=step_tensor)
 
     data = helper.make_tensor_value_info('data', TensorProto.FLOAT, [3, 2])
-    starts = helper.make_tensor_value_info('starts', TensorProto.FLOAT, [2])
-    ends = helper.make_tensor_value_info('ends', TensorProto.FLOAT, [2])
-    axes = helper.make_tensor_value_info('axes', TensorProto.FLOAT, [2])
+    starts = helper.make_tensor_value_info('starts', TensorProto.INT64, [2])
+    ends = helper.make_tensor_value_info('ends', TensorProto.INT64, [2])
+    axes = helper.make_tensor_value_info('axes', TensorProto.INT64, [2])
     output = helper.make_tensor_value_info('output', TensorProto.FLOAT, [1, 2])
 
     node = onnx.helper.make_node(
@@ -8042,6 +8444,42 @@ def split_test_no_attribute():
     return ([const_node, node], [x], [y1, y2, y3, y4])
 
 
+@onnx_test()
+def split_test_uneven():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [12, 15])
+    y1 = helper.make_tensor_value_info('y1', TensorProto.FLOAT, [3, 15])
+    y2 = helper.make_tensor_value_info('y2', TensorProto.FLOAT, [3, 15])
+    y3 = helper.make_tensor_value_info('y3', TensorProto.FLOAT, [3, 15])
+    y4 = helper.make_tensor_value_info('y4', TensorProto.FLOAT, [3, 15])
+    y5 = helper.make_tensor_value_info('y5', TensorProto.FLOAT, [0, 15])
+
+    node = onnx.helper.make_node(
+        'Split',
+        inputs=['x'],
+        outputs=['y1', 'y2', 'y3', 'y4', 'y5'],
+    )
+
+    return ([node], [x], [y1, y2, y3, y4, y5])
+
+
+@onnx_test()
+def split_test_uneven_num_outputs():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [11, 15])
+    y1 = helper.make_tensor_value_info('y1', TensorProto.FLOAT, [3, 15])
+    y2 = helper.make_tensor_value_info('y2', TensorProto.FLOAT, [3, 15])
+    y3 = helper.make_tensor_value_info('y3', TensorProto.FLOAT, [3, 15])
+    y4 = helper.make_tensor_value_info('y4', TensorProto.FLOAT, [2, 15])
+
+    node = onnx.helper.make_node(
+        'Split',
+        inputs=['x'],
+        outputs=['y1', 'y2', 'y3', 'y4'],
+        num_outputs=4,
+    )
+
+    return ([node], [x], [y1, y2, y3, y4])
+
+
 @onnx_test()
 def split_test_no_attribute_invalid_split():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [300, 15])
@@ -8101,6 +8539,24 @@ def split_test_no_attribute_invalid_input_split():
     return ([node], [x], [y1, y2, y3])
 
 
+@onnx_test()
+def split_test_invalid_num_outputs():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [11, 15])
+    y1 = helper.make_tensor_value_info('y1', TensorProto.FLOAT, [3, 15])
+    y2 = helper.make_tensor_value_info('y2', TensorProto.FLOAT, [3, 15])
+    y3 = helper.make_tensor_value_info('y3', TensorProto.FLOAT, [3, 15])
+    y4 = helper.make_tensor_value_info('y4', TensorProto.FLOAT, [2, 15])
+
+    node = onnx.helper.make_node(
+        'Split',
+        inputs=['x'],
+        outputs=['y1', 'y2', 'y3', 'y4'],
+        num_outputs=5,
+    )
+
+    return ([node], [x], [y1, y2, y3, y4])
+
+
 @onnx_test()
 def sqrt_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10, 15])
@@ -8837,6 +9293,20 @@ def upsample_test():
     return ([node], [X], [Y], [scale_tensor])
 
 
+@onnx_test()
+def upsample_ver7_test():
+    X = helper.make_tensor_value_info('X', TensorProto.FLOAT, [1, 1, 2, 2])
+    Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [1, 1, 4, 6])
+
+    node = onnx.helper.make_node('Upsample',
+                                 inputs=['X'],
+                                 outputs=['Y'],
+                                 mode='nearest',
+                                 scales=[1.0, 1.0, 2.0, 3.0])
+
+    return ([node], [X], [Y])
+
+
 @onnx_test()
 def variable_batch_test():
     x = helper.make_tensor_value_info('0', TensorProto.FLOAT,