Merge branch 'umang_msgpack' of github.com:ROCmSoftwarePlatform/AMDMIGraphX into umang_msgpack

Dockerfile

@@ -114,7 +114,7 @@ RUN git clone --single-branch --branch ${ONNXRUNTIME_BRANCH} --recursive ${ONNXR
 ADD tools/build_and_test_onnxrt.sh /onnxruntime/build_and_test_onnxrt.sh
 
 # Use the /opt/cmake install because LLVM/MLIR need cmake >= 3.20
-RUN env PATH=/opt/cmake/bin:$PATH cget -p /usr/local install ROCmSoftwarePlatform/rocMLIR@1ad9d6df32acc6d29d58e8ed6710e36746d0a4d6 -DBUILD_FAT_LIBROCKCOMPILER=On
+RUN env PATH=/opt/cmake/bin:$PATH cget -p /usr/local install ROCmSoftwarePlatform/rocMLIR@ea15b3597ce55b9088621818228595dd48fb6ec0 -DBUILD_FAT_LIBROCKCOMPILER=On
 
 ENV MIOPEN_FIND_DB_PATH=/tmp/miopen/find-db
 ENV MIOPEN_USER_DB_PATH=/tmp/miopen/user-db

Jenkinsfile

@@ -114,6 +114,10 @@ rocmtest clang_debug: rocmnode('cdna') { cmake_build ->
         cmake_build(flags: "-DCMAKE_BUILD_TYPE=release")
         stash includes: 'build/*.deb', name: 'migraphx-package'
     }
+}, hidden_symbols: rocmnode('cdna') { cmake_build ->
+    stage('Hidden symbols') {
+        cmake_build(flags: "-DMIGRAPHX_ENABLE_PYTHON=Off -DMIGRAPHX_ENABLE_GPU=On -DMIGRAPHX_ENABLE_CPU=On -DCMAKE_CXX_VISIBILITY_PRESET=hidden -DCMAKE_C_VISIBILITY_PRESET=hidden")
+    }
 }, all_targets_debug : rocmnode('cdna') { cmake_build ->
     stage('All targets Release') {
         cmake_build(flags: "-DCMAKE_BUILD_TYPE=release -DMIGRAPHX_ENABLE_GPU=On -DMIGRAPHX_ENABLE_CPU=On -DMIGRAPHX_ENABLE_FPGA=On") 

cmake/ExportHeader.cmake

@@ -29,7 +29,10 @@ endif()
 include(GenerateExportHeader)
 
 function(migraphx_generate_export_header TARGET)
-    cmake_parse_arguments(PARSE "" "DIRECTORY" "" ${ARGN})
+    set(options)
+    set(oneValueArgs DIRECTORY)
+    set(multiValueArgs)
+    cmake_parse_arguments(PARSE "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
     if(PARSE_DIRECTORY)
         set(__directory ${PARSE_DIRECTORY})
     else()

src/py/CMakeLists.txt

@@ -24,6 +24,7 @@
 
 option(MIGRAPHX_ENABLE_PYTHON "Enable python bindings" ON)
 add_library(migraphx_py py_loader.cpp)
+migraphx_generate_export_header(migraphx_py)
 target_include_directories(migraphx_py PRIVATE include)
 target_link_libraries(migraphx_py PUBLIC migraphx)
 rocm_install_targets(TARGETS migraphx_py INCLUDE include)

src/py/include/migraphx/py.hpp

@@ -26,11 +26,12 @@
 
 #include <migraphx/config.hpp>
 #include <migraphx/program.hpp>
+#include <migraphx/py/export.h>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 
-program load_py(const std::string& filename);
+MIGRAPHX_PY_EXPORT program load_py(const std::string& filename);
 
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx

src/py/py_loader.cpp

@@ -64,7 +64,7 @@ static dynamic_loader py_lib()
     return lib;
 }
 
-program load_py(const std::string& filename)
+MIGRAPHX_PY_EXPORT program load_py(const std::string& filename)
 {
     static auto f = py_lib().get_function<program(const std::string&)>("migraphx_load_py");
     return f(filename);

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/mlir.cpp

@@ -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

@@ -176,12 +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()
@@ -516,7 +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));
 
@@ -599,9 +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;
@@ -650,7 +642,12 @@ struct mlir_program
         return op;
     }
 
-    void find_target() { target_arch = get_device_name(); }
+    void set_gpu_properties(const context& migraphx_ctx)
+    {
+        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
     {
@@ -664,7 +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())))
@@ -674,28 +671,39 @@ struct mlir_program
 
     void set_tuning(const value& v)
     {
-        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);
+        auto* str = v.if_string();
+        if(not str)
+            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;
     }
 
@@ -703,7 +711,7 @@ 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(not tuning_cfg_path.empty())
@@ -723,51 +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(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 numCU               = tokens[1];
+                    std::string prob                = tokens[2];
+                    std::string perf                = tokens[3];
+                    std::string key = arch.append("\t").append(numCU).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(not 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;
@@ -778,7 +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;
 };
 
@@ -835,7 +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)
@@ -847,7 +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)
@@ -874,12 +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();
 }
@@ -910,7 +935,10 @@ insert_mlir(module& m, instruction_ref, code_object_op co, const std::vector<ins
     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

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_add_relu(%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 @mlir_quant_dot_add(%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_add(%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 @mlir_quant_convolution_dequantizelinear_quantizelinear(%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_convert(%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_where(%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>