Merge branch 'develop' of https://github.com/ROCmSoftwarePlatform/AMDMIGraphX into mlir_gconv_msg

src/targets/gpu/include/migraphx/gpu/mlir.hpp

@@ -37,7 +37,7 @@ struct module;
 namespace gpu {
 
 MIGRAPHX_GPU_EXPORT std::string dump_mlir(const module& m);
-MIGRAPHX_GPU_EXPORT code_object_op compile_mlir(const context& ctx,
+MIGRAPHX_GPU_EXPORT code_object_op compile_mlir(const context& migraphx_ctx,
                                                 module m,
                                                 const std::vector<instruction_ref>& inputs,
                                                 const value& solution);
@@ -47,7 +47,8 @@ MIGRAPHX_GPU_EXPORT instruction_ref insert_mlir(module& m,
                                                 code_object_op co,
                                                 const std::vector<instruction_ref>& inputs);
 
-MIGRAPHX_GPU_EXPORT tuning_config get_tuning_config_mlir(module m,
+MIGRAPHX_GPU_EXPORT tuning_config get_tuning_config_mlir(const context& migraphx_ctx,
+                                                         module m,
                                                          const std::vector<shape>& inputs);
 
 } // namespace gpu

src/targets/gpu/jit/ck_gemm.cpp

@@ -300,7 +300,8 @@ struct ck_gemm_compiler : compiler<ck_gemm_compiler>
         const auto& b_shape = inputs[1];
         const auto& c_shape = inputs.back();
 
-        auto rank = a_shape.lens().size();
+        // cppcheck-suppress unreadVariable
+        auto rank = a_shape.ndim();
 
         auto batch_count = get_batch_count(c_shape);
         auto m           = c_shape.lens()[rank - 2];

src/targets/gpu/jit/mlir.cpp

@@ -37,7 +37,7 @@ struct mlir_compiler : compiler<mlir_compiler>
     operation compile_op(context&, const std::vector<shape>&, const value&) const { return {}; }
 
     compiler_replace
-    compile(context& ctx, instruction_ref ins, const operation&, const value& solution) const
+    compile(const context& ctx, instruction_ref ins, const operation&, const value& solution) const
     {
         auto* smod = ins->module_inputs().front();
         assert(smod->get_parameter_names().size() == ins->inputs().size() - 1);
@@ -52,14 +52,16 @@ struct mlir_compiler : compiler<mlir_compiler>
                 }};
     }
 
-    optional<tuning_config>
-    get_tuning_config(context&, instruction_ref ins, const operation&, bool exhaustive) const
+    optional<tuning_config> get_tuning_config(const context& ctx,
+                                              instruction_ref ins,
+                                              const operation&,
+                                              bool exhaustive) const
     {
         if(not exhaustive)
             return nullopt;
         auto shapes = to_shapes(ins->inputs());
         auto* smod  = ins->module_inputs().front();
-        return get_tuning_config_mlir(*smod, shapes);
+        return get_tuning_config_mlir(ctx, *smod, shapes);
     }
 };
 

src/targets/gpu/mlir.cpp

@@ -36,7 +36,10 @@
 #include <mutex>
 #if !defined(MLIR_MIGRAPHX_DIALECT_API_VERSION) || MLIR_MIGRAPHX_DIALECT_API_VERSION != 3
 #warning "Incompatible version of rocMLIR library used, disabling"
+// Only undefine when not using cppcheck
+#ifndef CPPCHECK
 #undef MIGRAPHX_MLIR
+#endif
 #else
 #include <mlir-c/RegisterRocMLIR.h>
 #endif
@@ -50,6 +53,7 @@
 #include <migraphx/ranges.hpp>
 #include <migraphx/gpu/code_object_op.hpp>
 #include <migraphx/gpu/context.hpp>
+#include <migraphx/gpu/compile_gen.hpp>
 #include <migraphx/gpu/device_name.hpp>
 #include <migraphx/gpu/perfdb.hpp>
 #include <migraphx/gpu/tuning_config.hpp>
@@ -172,12 +176,6 @@ std::string mlir_print(F f, T x)
     return ss.str();
 }
 
-bool has_xdlops(const std::string& target_arch)
-{
-    const auto device_name = trim(split_string(target_arch, ':').front());
-    return (starts_with(device_name, "gfx9") and device_name >= "gfx908");
-}
-
 struct mlir_program
 {
     mlir_program()
@@ -512,7 +510,8 @@ struct mlir_program
         ops.add_attributes({{"function_type", make_function_type(inputs, outputs)},
                             {"sym_name", sym_name},
                             {"kernel", std::string("mixr")},
-                            {"arch", target_arch}});
+                            {"arch", target_arch},
+                            {"num_cu", num_cu}});
         ops.add_region(std::move(region));
         insert(body, std::move(ops));
 
@@ -559,14 +558,7 @@ struct mlir_program
 
     static std::string get_symbol_name(const module& m)
     {
-        for(auto ins : iterator_for(m))
-        {
-            if(ins->name() == "convolution" or ins->name() == "dot")
-            {
-                return "mlir_" + ins->name();
-            }
-        }
-        return "main";
+        return "mlir_" + gen::generate_name_from_ops(m);
     }
 
     void parse(const module& m)
@@ -602,9 +594,6 @@ struct mlir_program
             {
                 pp =
                     problem_params{ins->get_operator(), to_shapes(ins->inputs()), ins->get_shape()};
-                // check if HW supports xdlops
-                if(has_xdlops(target_arch))
-                    ops.add_attributes({{"xdlopsV2", true}});
             }
 
             std::vector<MlirValue> inputs;
@@ -653,7 +642,12 @@ struct mlir_program
         return op;
     }
 
-    void find_target() { target_arch = get_device_name(); }
+    void set_gpu_properties(const context& migraphx_ctx)
+    {
+        const auto& device = migraphx_ctx.get_current_device();
+        target_arch  = device.get_device_name();
+        num_cu       = device.get_cu_count();
+    }
 
     std::pair<std::size_t, std::size_t> get_launch_params() const
     {
@@ -667,7 +661,7 @@ struct mlir_program
 
     value::binary get_binary() const
     {
-        int size = 0;
+        size_t size = 0;
         mlirGetBinary(mmodule.get(), &size, nullptr);
         value::binary result(size);
         if(mlirGetBinary(mmodule.get(), &size, reinterpret_cast<char*>(result.data())))
@@ -675,30 +669,41 @@ struct mlir_program
         MIGRAPHX_THROW("Failed to compile mlir program");
     }
 
-    void set_tuning(const value& v)
+    void set_tuning(const value& v) MIGRAPHX_TIDY_CONST
     {
-        auto str = v.to<std::string>();
-        // We need to make a copy of the buffer since mlirRockTuningSetFromStr may modify the string
-        std::vector<char> buffer(str.begin(), str.end());
-        buffer.push_back(0);
-        if(not mlirRockTuningSetFromStr(mmodule.get(), buffer.data()))
-            MIGRAPHX_THROW("Failed setting tuning key: " + str);
+        const auto* str = v.if_string();
+        if(str == nullptr)
+            MIGRAPHX_THROW("mlir tuning solutions must be strings");
+        if(not mlirRockTuningSetFromStr(mmodule.get(), make_mlir_string_ref(*str)))
+            MIGRAPHX_THROW("Failed setting tuning key: " + *str);
     }
 
     tuning_config get_tuning_config() MIGRAPHX_TIDY_CONST
     {
         tuning_config tc;
         run_high_level_pipeline();
-        mlir_tuning_space params{mlirRockTuningSpaceCreate(mmodule.get())};
-        for(auto i : range(mlirRockTuningGetNumParamsFull(params.get())))
+        mlir_tuning_space params{
+            mlirRockTuningSpaceCreate(mmodule.get(), RocmlirTuningParamSetKindFull)};
+        for(auto i : range(mlirRockTuningGetNumParams(params.get())))
         {
             mlir_tuning_param param{mlirRockTuningParamCreate()};
             if(not mlirRockTuningParamGet(params.get(), i, param.get()))
                 MIGRAPHX_THROW("Incorrect mlir tuning parameter: " + std::to_string(i));
-            tc.solutions.push_back(std::string{mlirRockTuningGetParamStr(param.get())});
-        }
-        mlir_tuning_table tuning_table{mlirRockTuningTableCreate()};
-        tc.problem = std::string{mlirRockTuningGetKey(tuning_table.get(), mmodule.get())};
+            std::array<char, ROCMLIR_TUNING_KEY_BUFSZ> perf_key;
+            size_t perf_key_bytes =
+                mlirRockTuningParamToString(param.get(), perf_key.data(), perf_key.size());
+            if(perf_key_bytes > perf_key.size())
+                MIGRAPHX_THROW("Tuning perf key was " + std::to_string(perf_key_bytes) +
+                               " bytes and thus too long");
+            tc.solutions.emplace_back(perf_key.begin(), perf_key.begin() + perf_key_bytes);
+        }
+        std::array<char, ROCMLIR_TUNING_KEY_BUFSZ> tuning_key;
+        size_t tuning_key_bytes =
+            mlirRockTuningGetKey(mmodule.get(), tuning_key.data(), tuning_key.size());
+        if(tuning_key_bytes > tuning_key.size())
+            MIGRAPHX_THROW("Tuning table key was " + std::to_string(tuning_key_bytes) +
+                           " bytes and thus too long");
+        tc.problem = std::string(tuning_key.begin(), tuning_key.begin() + tuning_key_bytes);
         return tc;
     }
 
@@ -706,10 +711,10 @@ struct mlir_program
 
     // This function appends to tuning cfg file that could be
     // used with rocMLIR tuning scripts.
-    void dump_tuning_cfg(const char* prob_config) const
+    void dump_tuning_cfg(const std::string& prob_config) const
     {
         std::string tuning_cfg_path = string_value_of(MIGRAPHX_MLIR_TUNING_CFG{});
-        if(!tuning_cfg_path.empty())
+        if(not tuning_cfg_path.empty())
         {
             std::vector<std::string> tokens = split_string(prob_config, '\t');
             std::string prob                = tokens[1];
@@ -726,51 +731,66 @@ struct mlir_program
         }
     }
 
-    static mlir_tuning_table create_tuning_table()
+    static std::pair<mlir_tuning_table, bool> load_tuning_table()
     {
         mlir_tuning_table tuning_table{mlirRockTuningTableCreate()};
+        bool found_table           = false;
         std::string tuning_db_path = string_value_of(MIGRAPHX_MLIR_TUNING_DB{});
-        if(!tuning_db_path.empty())
+        if(not tuning_db_path.empty())
         {
             std::ifstream tuning_db_tsv(tuning_db_path);
             if(tuning_db_tsv)
             {
+                found_table = true;
                 std::string line;
                 while(std::getline(tuning_db_tsv, line))
                 {
                     std::vector<std::string> tokens = split_string(line, '\t');
                     std::string arch                = tokens[0];
-                    std::string prob                = tokens[1];
-                    std::string perf                = tokens[2];
-                    std::string key                 = arch.append("\t").append(prob);
-                    mlirRockTuningUpdateTable(tuning_table.get(), key.c_str(), perf.c_str(), 1.0);
+                    std::string num_cu              = tokens[1];
+                    std::string prob                = tokens[2];
+                    std::string perf                = tokens[3];
+                    std::string key = arch.append("\t").append(num_cu).append("\t").append(prob);
+                    mlirRockTuningUpdateTable(tuning_table.get(),
+                                              make_mlir_string_ref(key),
+                                              make_mlir_string_ref(perf),
+                                              1.0);
                 }
             }
         }
         else
         {
+            found_table = false;
             std::cerr
                 << "WARNING: MLIR tuning db not found. Please set MIGRAPHX_MLIR_TUNING_DB for "
                    "optimal performance."
                 << std::endl;
         }
-        return tuning_table;
+        return std::make_pair(std::move(tuning_table), found_table);
     }
 
     bool get_module_tuned() const
     {
-        static mlir_tuning_table tuning_table = create_tuning_table();
-        // The tuning table as currently implemented is currently not
-        // thread safe. This will be fixed in the future. For now,
-        // stick a mutex around all tuning table interaction.
-        static std::mutex lock;
-        std::lock_guard<std::mutex> guard(lock);
-        if(!mlirRockTuningSetFromTable(tuning_table.get(), mmodule.get()))
-        {
-            const char* prob_config = mlirRockTuningGetKey(tuning_table.get(), mmodule.get());
-            std::stringstream key(prob_config);
-            std::cerr << "fails to set param on" << prob_config << std::endl;
-            dump_tuning_cfg(prob_config);
+        static std::pair<mlir_tuning_table, bool> tuning_table = load_tuning_table();
+        if(not mlirRockTuningSetFromTable(tuning_table.first.get(), mmodule.get()))
+        {
+            std::array<char, ROCMLIR_TUNING_KEY_BUFSZ> prob_config;
+            size_t prob_config_bytes =
+                mlirRockTuningGetKey(mmodule.get(), prob_config.data(), prob_config.size());
+            if(prob_config_bytes >= prob_config.size())
+            {
+                std::cerr << "MLIR tuning key overflowed buffer, needed " << prob_config_bytes
+                          << " bytes" << std::endl;
+                return false;
+            }
+            std::string prob_config_str(prob_config.begin(),
+                                        prob_config.begin() + prob_config_bytes);
+            if(tuning_table.second)
+            {
+                std::cerr << "NOTE: MLIR tuning table did not include a key for " << prob_config_str
+                          << std::endl;
+            }
+            dump_tuning_cfg(prob_config_str);
             return false;
         }
         return true;
@@ -781,7 +801,8 @@ struct mlir_program
     mlir_module mmodule;
     problem_params pp;
     std::deque<std::string> strings{};
-    std::string target_arch;
+    std::string target_arch = "";
+    std::size_t num_cu      = 0;
     std::string sym_name;
 };
 
@@ -838,7 +859,7 @@ void adjust_param_shapes(module& m, const std::vector<shape>& inputs)
     }
 }
 
-code_object_op compile_mlir(const context&,
+code_object_op compile_mlir(const context& migraphx_ctx,
                             module m,
                             const std::vector<instruction_ref>& inputs,
                             const value& solution)
@@ -850,7 +871,7 @@ code_object_op compile_mlir(const context&,
         std::cout << m << std::endl;
 
     mlir_program mp;
-    mp.find_target();
+    mp.set_gpu_properties(migraphx_ctx);
     mp.parse(m);
     auto mod_op = mlirModuleGetOperation(mp.mmodule.get());
     if(trace)
@@ -877,12 +898,13 @@ instruction_ref insert_mlir(module& m,
     return m.insert_instruction(ins, co, refs);
 }
 
-tuning_config get_tuning_config_mlir(module m, const std::vector<shape>& inputs)
+tuning_config
+get_tuning_config_mlir(const context& migraphx_ctx, module m, const std::vector<shape>& inputs)
 {
     adjust_param_shapes(m, inputs);
 
     mlir_program mp;
-    mp.find_target();
+    mp.set_gpu_properties(migraphx_ctx);
     mp.parse(m);
     return mp.get_tuning_config();
 }
@@ -909,10 +931,14 @@ instruction_ref
 insert_mlir(module& m, instruction_ref, code_object_op co, const std::vector<instruction_ref>&)
 {
     use(co);
+    use(m);
     return m.end();
 }
 
-tuning_config get_tuning_config_mlir(module, const std::vector<shape>&) { return {}; }
+tuning_config get_tuning_config_mlir(const context&, module, const std::vector<shape>&)
+{
+    return {};
+}
 // NOLINTEND(performance-unnecessary-value-param)
 
 #endif

src/targets/gpu/time_op.cpp

@@ -34,7 +34,7 @@ namespace gpu {
 std::vector<argument> generate_arguments(const std::vector<shape>& shapes, unsigned long seed = 0)
 {
     std::vector<argument> args;
-    std::transform(shapes.begin(), shapes.end(), std::back_inserter(args), [&](auto& s) {
+    std::transform(shapes.begin(), shapes.end(), std::back_inserter(args), [&](const auto& s) {
         return to_gpu(generate_argument(s, seed++));
     });
     return args;

src/tf/tf_parser.cpp

@@ -338,7 +338,7 @@ void tf_parser::parse_node(const std::string& name)
             std::string input_name = input;
             // if input has trailing `:0` index then remove it
             auto multi_out_idx = input.find(':');
-            if(multi_out_idx != std::string::npos && input.substr(multi_out_idx + 1) == "0")
+            if(multi_out_idx != std::string::npos and input.substr(multi_out_idx + 1) == "0")
             {
                 input_name = input.substr(0, multi_out_idx);
             }

src/value.cpp

@@ -285,7 +285,7 @@ bool value::contains(const std::string& pkey) const
 }
 std::size_t value::size() const
 {
-    auto* a = if_array_impl(x);
+    const auto* a = if_array_impl(x);
     if(a == nullptr)
         return 0;
     return a->size();

test/CMakeLists.txt

@@ -98,17 +98,11 @@ endfunction()
 
 function(add_test_executable TEST_NAME)
     add_executable(${TEST_NAME} EXCLUDE_FROM_ALL ${ARGN})
-    target_link_libraries(${TEST_NAME} ${CMAKE_THREAD_LIBS_INIT})
-
-    # Cmake does not add flags correctly for gcc
-    if(CMAKE_CXX_COMPILER_ID MATCHES "GNU")
-        set_target_properties(${TEST_NAME} PROPERTIES COMPILE_FLAGS -pthread LINK_FLAGS -pthread)
-    endif()
     set(TEST_COMMAND ${TEST_NAME})
     add_test_command(${TEST_NAME} ${TEST_COMMAND})
     add_dependencies(tests ${TEST_NAME})
     add_dependencies(check ${TEST_NAME})
-    target_link_libraries(${TEST_NAME} migraphx migraphx_onnx migraphx_ref)
+    target_link_libraries(${TEST_NAME} Threads::Threads migraphx migraphx_onnx migraphx_ref)
     target_include_directories(${TEST_NAME} PUBLIC include)
 endfunction(add_test_executable)
 
@@ -208,11 +202,16 @@ endif()
 
 
 function(test_header NAME HEADER)
-    file(WRITE ${CMAKE_CURRENT_BINARY_DIR}/header-main-include-${NAME}.cpp
-        "#include <${HEADER}>\nint main() {}\n"
+    file(WRITE ${CMAKE_CURRENT_BINARY_DIR}/header-main-include-${NAME}.cpp "
+#include <${HEADER}>
+int main() {}\n"
     )
-    file(WRITE ${CMAKE_CURRENT_BINARY_DIR}/header-static-include-${NAME}.cpp
-        "#include <${HEADER}>\n"
+    file(WRITE ${CMAKE_CURRENT_BINARY_DIR}/header-static-include-${NAME}.cpp "
+#include <${HEADER}>
+#if defined(min) || defined(max) || defined(near) || defined(far)
+#error \"Do not include windows.h in header files\"
+#endif
+\n"
     )
     add_test_executable(${NAME}
         ${CMAKE_CURRENT_BINARY_DIR}/header-main-include-${NAME}.cpp

test/api/test_cpu.cpp

@@ -145,15 +145,15 @@ TEST_CASE(zero_parameter)
 
 TEST_CASE(set_scalar_parameter)
 {
-    auto p1 = migraphx::parse_onnx("add_bcast_test.onnx");
-    migraphx::shape s1(migraphx_shape_float_type, {3, 4});
+    auto p1 = migraphx::parse_onnx("implicit_add_bcast_test.onnx");
+    migraphx::shape s1(migraphx_shape_float_type, {3, 4, 1});
     auto param_shapes = p1.get_parameter_shapes();
     auto s1_orig      = param_shapes["1"];
     CHECK(bool{s1 == s1_orig});
 
     migraphx::onnx_options option;
     option.set_input_parameter_shape("1", {});
-    auto p2 = migraphx::parse_onnx("add_bcast_test.onnx", option);
+    auto p2 = migraphx::parse_onnx("implicit_add_bcast_test.onnx", option);
     migraphx::shape s_scalar(migraphx_shape_float_type);
     auto param_shapes_1 = p2.get_parameter_shapes();
     auto s_scalar_after = param_shapes_1["1"];

test/eliminate_contiguous_test.cpp

@@ -196,15 +196,47 @@ TEST_CASE(contiguous_pointwise)
             migraphx::make_op("broadcast", {{"axis", 1}, {"out_lens", {2, 3, 8, 8}}}), y);
         auto yc  = mm->add_instruction(migraphx::make_op("contiguous"), yb);
         auto add = add_pointwise(p, "main:pointwise0", {x, yc}, single_pointwise("add"));
-        mm->add_instruction(pass_op{}, add);
+        auto cadd = mm->add_instruction(migraphx::make_op("contiguous"), add);
+        mm->add_instruction(pass_op{}, cadd);
     }
     auto count = std::distance(mm->begin(), mm->end());
     run_pass(*mm);
-    EXPECT(std::distance(mm->begin(), mm->end()) == (count - 1));
+    EXPECT(std::distance(mm->begin(), mm->end()) == (count - 2));
     EXPECT(std::none_of(
         mm->begin(), mm->end(), [](auto&& ins) { return ins.name() == "contiguous"; }));
 }
 
+TEST_CASE(contiguous_nhwc_pointwise)
+{
+    auto s =
+        migraphx::shape::from_permutation(migraphx::shape::float_type, {2, 3, 8, 8}, {0, 2, 3, 1});
+    migraphx::program p1;
+    {
+        auto* mm = p1.get_main_module();
+        auto x   = mm->add_parameter("x", s);
+        auto y   = mm->add_parameter("y", migraphx::shape{migraphx::shape::float_type, {3}});
+        auto yb  = mm->add_instruction(
+            migraphx::make_op("broadcast", {{"axis", 1}, {"out_lens", {2, 3, 8, 8}}}), y);
+        auto yc   = mm->add_instruction(migraphx::make_op("contiguous"), yb);
+        auto add  = add_pointwise(p1, "main:pointwise0", {x, yc}, single_pointwise("add"));
+        auto cadd = mm->add_instruction(migraphx::make_op("contiguous"), add);
+        mm->add_instruction(pass_op{}, cadd);
+    }
+    run_pass(*p1.get_main_module());
+    migraphx::program p2;
+    {
+        auto* mm = p2.get_main_module();
+        auto x   = mm->add_parameter("x", s);
+        auto y   = mm->add_parameter("y", migraphx::shape{migraphx::shape::float_type, {3}});
+        auto yb  = mm->add_instruction(
+            migraphx::make_op("broadcast", {{"axis", 1}, {"out_lens", {2, 3, 8, 8}}}), y);
+        auto add  = add_pointwise(p2, "main:pointwise0", {x, yb}, single_pointwise("add"));
+        auto cadd = mm->add_instruction(migraphx::make_op("contiguous"), add);
+        mm->add_instruction(pass_op{}, cadd);
+    }
+    EXPECT(p1 == p2);
+}
+
 TEST_CASE(slice_contiguous)
 {
     migraphx::module m;

test/eliminate_pad_test.cpp

@@ -27,7 +27,7 @@
 #include <migraphx/pass_manager.hpp>
 #include <migraphx/instruction.hpp>
 #include <basic_ops.hpp>
-#include <migraphx/operators.hpp>
+#include <migraphx/op/common.hpp>
 #include <migraphx/make_op.hpp>
 
 #include <test.hpp>
@@ -58,9 +58,8 @@ create_conv(migraphx::instruction_ref& l_img,
     migraphx::shape s_weights{migraphx::shape::int32_type, {4, channels, 3, 3}};
     std::vector<int32_t> weights(4 * channels * 3 * 3);
     auto l_weights = m.add_literal(migraphx::literal{s_weights, weights});
-    migraphx::op::convolution op;
-    op.padding_mode = padding_mode;
-    return m.add_instruction(op, l_img, l_weights);
+    return m.add_instruction(
+        migraphx::make_op("convolution", {{"padding_mode", padding_mode}}), l_img, l_weights);
 }
 
 TEST_CASE(rewrite_pad)

test/gpu/mlir.cpp

@@ -140,7 +140,7 @@ TEST_CASE(conv)
 {
     const std::string mlir_output = R"__migraphx__(
 module {
-  func.func @mlir_convolution(%arg0: tensor<2x8x3x3xf32>, %arg1: tensor<1x8x4x4xf32>) -> tensor<1x2x2x2xf32> attributes {arch = "", kernel = "mixr"} {
+  func.func @mlir_convolution(%arg0: tensor<2x8x3x3xf32>, %arg1: tensor<1x8x4x4xf32>) -> tensor<1x2x2x2xf32> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
     %0 = migraphx.convolution(%arg1, %arg0) {dilation = [1, 1], group = 1 : i64, padding = [0, 0, 0, 0], padding_mode = 0 : i64, stride = [1, 1]} : (tensor<1x8x4x4xf32>, tensor<2x8x3x3xf32>) -> tensor<1x2x2x2xf32>
     return %0 : tensor<1x2x2x2xf32>
   }
@@ -163,7 +163,7 @@ TEST_CASE(conv_add_relu)
 {
     const std::string mlir_output = R"__migraphx__(
 module {
-  func.func @mlir_convolution(%arg0: tensor<1x2x2x2xf32>, %arg1: tensor<2x8x3x3xf32>, %arg2: tensor<1x8x4x4xf32>) -> tensor<1x2x2x2xf32> attributes {arch = "", kernel = "mixr"} {
+  func.func @mlir_convolution_add_relu(%arg0: tensor<1x2x2x2xf32>, %arg1: tensor<2x8x3x3xf32>, %arg2: tensor<1x8x4x4xf32>) -> tensor<1x2x2x2xf32> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
     %0 = migraphx.convolution(%arg2, %arg1) {dilation = [1, 1], group = 1 : i64, padding = [0, 0, 0, 0], padding_mode = 0 : i64, stride = [1, 1]} : (tensor<1x8x4x4xf32>, tensor<2x8x3x3xf32>) -> tensor<1x2x2x2xf32>
     %1 = migraphx.add(%0, %arg0) : (tensor<1x2x2x2xf32>, tensor<1x2x2x2xf32>) -> tensor<1x2x2x2xf32>
     %2 = migraphx.relu(%1) : (tensor<1x2x2x2xf32>) -> tensor<1x2x2x2xf32>
@@ -191,7 +191,7 @@ TEST_CASE(quant_dot_add)
 {
     const std::string mlir_output = R"__migraphx__(
 module {
-  func.func @main(%arg0: tensor<1x5x4xi8>, %arg1: tensor<1x4x3xi8>, %arg2: tensor<1x5x3xi32>) -> tensor<1x5x3xi32> attributes {arch = "", kernel = "mixr"} {
+  func.func @mlir_quant_dot_add(%arg0: tensor<1x5x4xi8>, %arg1: tensor<1x4x3xi8>, %arg2: tensor<1x5x3xi32>) -> tensor<1x5x3xi32> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
     %0 = migraphx.quant_dot(%arg0, %arg1) : (tensor<1x5x4xi8>, tensor<1x4x3xi8>) -> tensor<1x5x3xi32>
     %1 = migraphx.add(%0, %arg2) : (tensor<1x5x3xi32>, tensor<1x5x3xi32>) -> tensor<1x5x3xi32>
     return %1 : tensor<1x5x3xi32>
@@ -218,7 +218,7 @@ TEST_CASE(dot_add)
 {
     const std::string mlir_output = R"__migraphx__(
 module {
-  func.func @mlir_dot(%arg0: tensor<1x5x4xf32>, %arg1: tensor<1x4x3xf32>, %arg2: tensor<1x5x3xf32>) -> tensor<1x5x3xf32> attributes {arch = "", kernel = "mixr"} {
+  func.func @mlir_dot_add(%arg0: tensor<1x5x4xf32>, %arg1: tensor<1x4x3xf32>, %arg2: tensor<1x5x3xf32>) -> tensor<1x5x3xf32> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
     %0 = migraphx.dot(%arg0, %arg1) : (tensor<1x5x4xf32>, tensor<1x4x3xf32>) -> tensor<1x5x3xf32>
     %1 = migraphx.add(%0, %arg2) : (tensor<1x5x3xf32>, tensor<1x5x3xf32>) -> tensor<1x5x3xf32>
     return %1 : tensor<1x5x3xf32>
@@ -244,7 +244,7 @@ TEST_CASE(conv_int8_dequantize_quantize)
 {
     const std::string mlir_output = R"__migraphx__(
 module {
-  func.func @main(%arg0: tensor<2x8x3x3xi8>, %arg1: tensor<1x8x4x4xi8>, %arg2: tensor<1x2x2x2xf32>, %arg3: tensor<1x2x2x2xi32>) -> tensor<1x2x2x2xi32> attributes {arch = "", kernel = "mixr"} {
+  func.func @mlir_quant_convolution_dequantizelinear_quantizelinear(%arg0: tensor<2x8x3x3xi8>, %arg1: tensor<1x8x4x4xi8>, %arg2: tensor<1x2x2x2xf32>, %arg3: tensor<1x2x2x2xi32>) -> tensor<1x2x2x2xi32> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
       %0 = migraphx.quant_convolution(%arg1, %arg0) {dilation = [1, 1], group = 1 : i64, padding = [0, 0, 0, 0], padding_mode = 0 : i64, stride = [1, 1]} : (tensor<1x8x4x4xi8>, tensor<2x8x3x3xi8>) -> tensor<1x2x2x2xi32>
       %1 = migraphx.dequantizelinear(%0, %arg2, %arg3) : (tensor<1x2x2x2xi32>, tensor<1x2x2x2xf32>, tensor<1x2x2x2xi32>) -> tensor<1x2x2x2xf32>
       %2 = migraphx.quantizelinear(%1, %arg2, %arg3) : (tensor<1x2x2x2xf32>, tensor<1x2x2x2xf32>, tensor<1x2x2x2xi32>) -> tensor<1x2x2x2xi32>
@@ -277,7 +277,7 @@ TEST_CASE(dot_convert)
 {
     const std::string mlir_output = R"__migraphx__(
 module {
-  func.func @mlir_dot(%arg0: tensor<1x5x4xf32>, %arg1: tensor<1x4x3xf32>) -> tensor<1x5x3xf16> attributes {arch = "", kernel = "mixr"} {
+  func.func @mlir_dot_convert(%arg0: tensor<1x5x4xf32>, %arg1: tensor<1x4x3xf32>) -> tensor<1x5x3xf16> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
     %0 = migraphx.dot(%arg0, %arg1) : (tensor<1x5x4xf32>, tensor<1x4x3xf32>) -> tensor<1x5x3xf32>
     %1 = migraphx.convert(%0) {target_type  =  1  :  i64} : (tensor<1x5x3xf32>) -> tensor<1x5x3xf16>
     return %1 : tensor<1x5x3xf16>
@@ -303,7 +303,7 @@ TEST_CASE(dot_where)
 {
     const std::string mlir_output = R"__migraphx__(
 module {
-  func.func @mlir_dot(%arg0: tensor<1x5x4xf32>, %arg1: tensor<1x4x3xf32>, %arg2: tensor<1x5x3xi8>, %arg3: tensor<1x5x3xf32>) -> tensor<1x5x3xf32> attributes {arch = "", kernel = "mixr"} {
+  func.func @mlir_dot_where(%arg0: tensor<1x5x4xf32>, %arg1: tensor<1x4x3xf32>, %arg2: tensor<1x5x3xi8>, %arg3: tensor<1x5x3xf32>) -> tensor<1x5x3xf32> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
     %0 = migraphx.dot(%arg0, %arg1) : (tensor<1x5x4xf32>, tensor<1x4x3xf32>) -> tensor<1x5x3xf32>
     %1 = migraphx.where(%arg2, %0, %arg3) : (tensor<1x5x3xi8>, tensor<1x5x3xf32>, tensor<1x5x3xf32>) -> tensor<1x5x3xf32>
     return %1 : tensor<1x5x3xf32>

test/gpu/quantization.cpp

@@ -24,7 +24,7 @@
 #include <iostream>
 #include <vector>
 #include <migraphx/gpu/fuse_mlir.hpp>
-#include <migraphx/operators.hpp>
+#include <migraphx/make_op.hpp>
 #include <migraphx/instruction.hpp>
 #include <migraphx/quantization.hpp>
 #include <migraphx/generate.hpp>
@@ -90,7 +90,7 @@ TEST_CASE(int8_quantization)
         migraphx::shape sc{migraphx::shape::float_type, {5, 8}};
         auto pa = mm->add_parameter("a", sa);
         auto pb = mm->add_parameter("b", sb);
-        mm->add_instruction(migraphx::op::dot{}, pa, pb);
+        mm->add_instruction(migraphx::make_op("dot"), pa, pb);
 
         return p;
     };

test/include/test.hpp

@@ -22,6 +22,7 @@
  * THE SOFTWARE.
  */
 
+#include <atomic>
 #include <algorithm>
 #include <cassert>
 #include <cstdio>
@@ -342,11 +343,19 @@ inline std::ostream& operator<<(std::ostream& os, const color& c)
     return os;
 }
 
+inline std::atomic<int>& failures()
+{
+    // NOLINTNEXTLINE
+    static std::atomic<int> f = 0;
+    return f;
+}
+
 template <class T, class F>
 void failed(T x, const char* msg, const char* func, const char* file, int line, F f)
 {
     if(not bool(x.value()))
     {
+        failures()++;
         std::cout << func << std::endl;
         std::cout << file << ":" << line << ":" << std::endl;
         std::cout << color::bold << color::fg_red << "    FAILED: " << color::reset << msg << " "
@@ -586,13 +595,21 @@ struct driver
         {
             try
             {
+                failures() = 0;
                 f();
             }
+            // cppcheck-suppress EmptyCatchStatement
             catch(const failure_error&)
             {
-                msg = "Test failure";
             }
         }
+        if(msg.empty() and failures() != 0)
+        {
+            if(failures() == 1)
+                msg = "Test failure";
+            else
+                msg = std::to_string(failures()) + " test failures";
+        }
         if(msg.empty())
         {
             out() << color::fg_green << "[ COMPLETE ] " << color::reset << color::bold << name
@@ -685,8 +702,8 @@ inline void run(int argc, const char* argv[])
 // NOLINTNEXTLINE
 #define CHECK(...) \
     test::failed(  \
-        test::capture{}->*__VA_ARGS__, #__VA_ARGS__, __PRETTY_FUNCTION__, __FILE__, __LINE__, [] { \
-        })
+        TEST_CAPTURE(__VA_ARGS__), #__VA_ARGS__, __PRETTY_FUNCTION__, __FILE__, __LINE__, [] {})
+
 // NOLINTNEXTLINE
 #define EXPECT(...)                         \
     test::failed(TEST_CAPTURE(__VA_ARGS__), \

test/inline_module_test.cpp

@@ -26,7 +26,6 @@
 #include <migraphx/pass_manager.hpp>
 #include <migraphx/instruction.hpp>
 #include <basic_ops.hpp>
-#include <migraphx/operators.hpp>
 #include <migraphx/make_op.hpp>
 
 #include <test.hpp>

test/insert_pad_test.cpp

@@ -26,8 +26,8 @@
 #include <migraphx/insert_pad.hpp>
 #include <migraphx/pass_manager.hpp>
 #include <migraphx/instruction.hpp>
+#include <migraphx/op/common.hpp>
 #include <basic_ops.hpp>
-#include <migraphx/operators.hpp>
 #include <migraphx/make_op.hpp>
 
 #include <test.hpp>
@@ -58,10 +58,11 @@ create_conv(migraphx::instruction_ref& l_img,
     migraphx::shape s_weights{migraphx::shape::int32_type, {4, channels, 3, 3}};
     std::vector<int32_t> weights(4 * channels * 3 * 3);
     auto l_weights = m.add_literal(migraphx::literal{s_weights, weights});
-    migraphx::op::convolution op;
-    op.padding_mode = padding_mode;
-    op.padding      = {0, 0, 1, 1};
-    return m.add_instruction(op, l_img, l_weights);
+    return m.add_instruction(
+        migraphx::make_op("convolution",
+                          {{"padding_mode", padding_mode}, {"padding", {0, 0, 1, 1}}}),
+        l_img,
+        l_weights);
 }
 
 TEST_CASE(rewrite_pad)

test/layout_nhwc.cpp

@@ -24,7 +24,6 @@
 #include <migraphx/layout_nhwc.hpp>
 #include <migraphx/dead_code_elimination.hpp>
 #include <migraphx/pass_manager.hpp>
-#include <migraphx/operators.hpp>
 #include <migraphx/generate.hpp>
 #include <migraphx/ranges.hpp>
 #include <migraphx/instruction.hpp>

test/memory_coloring_test.cpp

@@ -89,17 +89,13 @@ bool is_overlap_load(migraphx::instruction_ref a, migraphx::instruction_ref b)
 
 bool is_disjoint(const std::vector<migraphx::instruction_ref>& inss)
 {
-    for(auto ins1 : inss)
-    {
-        for(auto ins2 : inss)
-        {
+    return std::none_of(inss.begin(), inss.end(), [&](auto ins1) {
+        return std::none_of(inss.begin(), inss.end(), [&](auto ins2) {
             if(ins1 == ins2)
-                continue;
-            if(is_overlap_load(ins1, ins2))
-                return false;
-        }
-    }
                 return true;
+            return is_overlap_load(ins1, ins2);
+        });
+    });
 }
 
 TEST_CASE(test1)

test/onnx/.onnxrt-commit

-21a71d52bd2074b770807b209939ec11e2c64fa7
+a476dbf430ac8315550474a78d47bf182f202d7c

test/onnx/gen_onnx.py

@@ -6414,6 +6414,30 @@ def slice_test():
     return ([node], [x], [y])
 
 
+@onnx_test()
+def slice_constant_test():
+    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 2])
+
+    x_tensor = helper.make_tensor(name='x_tensor',
+                                  data_type=TensorProto.FLOAT,
+                                  dims=[3, 2],
+                                  vals=[0, 1, 2, 3, 4, 5])
+
+    x = onnx.helper.make_node('Constant',
+                              inputs=[],
+                              outputs=['x'],
+                              value=x_tensor)
+
+    node = onnx.helper.make_node('Slice',
+                                 inputs=['x'],
+                                 axes=[0, 1],
+                                 starts=[1, 0],
+                                 ends=[2, 2],
+                                 outputs=['1'])
+
+    return ([x, node], [], [y])
+
+
 @onnx_test()
 def slice_dyn_test():
     x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [None, None, 2])
@@ -6746,6 +6770,92 @@ def slice_max_end_test():
     return ([node], [x], [y])
 
 
+@onnx_test()
+def slice_var_input_static0():
+    data = helper.make_tensor_value_info('data', TensorProto.FLOAT, [3, 2])
+    starts = helper.make_tensor_value_info('starts', TensorProto.INT32, [2])
+    ends = helper.make_tensor_value_info('ends', TensorProto.INT32, [2])
+    output = helper.make_tensor_value_info('output', TensorProto.FLOAT, [1, 2])
+
+    node = onnx.helper.make_node('Slice',
+                                 inputs=['data', 'starts', 'ends'],
+                                 axes=[0, 1],
+                                 outputs=['output'])
+
+    return ([node], [data, starts, ends], [output])
+
+
+@onnx_test()
+def slice_var_input_static1():
+    data = helper.make_tensor_value_info('data', TensorProto.FLOAT, [3, 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'],
+                                 outputs=['output'])
+
+    return ([node], [data, starts, ends, axes], [output])
+
+
+@onnx_test()
+def slice_var_input_dyn0():
+    data = helper.make_tensor_value_info('data', TensorProto.FLOAT, [None, 2])
+    starts = helper.make_tensor_value_info('starts', TensorProto.INT32, [2])
+    ends = helper.make_tensor_value_info('ends', TensorProto.INT32, [2])
+    output = helper.make_tensor_value_info('output', TensorProto.FLOAT, [1, 2])
+
+    node = onnx.helper.make_node('Slice',
+                                 inputs=['data', 'starts', 'ends'],
+                                 axes=[0, 1],
+                                 outputs=['output'])
+
+    return ([node], [data, starts, ends], [output])
+
+
+@onnx_test()
+def slice_var_input_dyn1():
+    data = helper.make_tensor_value_info('data', TensorProto.FLOAT, [None, 2])
+    starts = helper.make_tensor_value_info('starts', TensorProto.INT32, [2])
+    ends = helper.make_tensor_value_info('ends', TensorProto.INT32, [2])
+    axes = helper.make_tensor_value_info('axes', TensorProto.INT32, [2])
+    output = helper.make_tensor_value_info('output', TensorProto.FLOAT, [1, 2])
+
+    node = onnx.helper.make_node('Slice',
+                                 inputs=['data', 'starts', 'ends', 'axes'],
+                                 outputs=['output'])
+
+    return ([node], [data, starts, ends, axes], [output])
+
+
+@onnx_test()
+def slice_var_input_steps_error():
+    step = np.array([2, 1])
+    step_tensor = helper.make_tensor(name="step",
+                                     data_type=TensorProto.INT32,
+                                     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, [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])
+    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 softmax_test():
     x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3])