Merge branch 'develop' into uif2-initial

CMakeLists.txt

@@ -27,20 +27,18 @@ if("${CMAKE_SOURCE_DIR}" STREQUAL "${CMAKE_BINARY_DIR}")
     message(FATAL_ERROR "The binary and source directroy cannot be the same")
 endif()
 
-get_property(_GENERATOR_IS_MULTI_CONFIG GLOBAL PROPERTY GENERATOR_IS_MULTI_CONFIG)
+# Setup valid strings for build type
+if (NOT CMAKE_CONFIGURATION_TYPES)
+    set(CMAKE_CONFIGURATION_TYPES "Debug;Release;RelWithDebInfo;MinSizeRel" CACHE STRING "Configs")
+endif()
 
+get_property(MIGRAPHX_GENERATOR_IS_MULTI_CONFIG GLOBAL PROPERTY GENERATOR_IS_MULTI_CONFIG)
 # This has to be initialized before the project() command appears
 # Set the default of CMAKE_BUILD_TYPE to be release, unless user specifies with -D.  MSVC_IDE does not use CMAKE_BUILD_TYPE
-if(_GENERATOR_IS_MULTI_CONFIG)
-    if (NOT CMAKE_CONFIGURATION_TYPES)
-        set(CMAKE_CONFIGURATION_TYPES "Debug;Release;RelWithDebInfo;MinSizeRel" CACHE STRING
-            "Available build types (configurations) on multi-config generators")
-    endif()
-else()
-    if(NOT CMAKE_BUILD_TYPE)
-        set(CMAKE_BUILD_TYPE Release CACHE STRING
-            "Choose the type of build, options are: None Debug Release RelWithDebInfo MinSizeRel.")
-    endif()
+if(NOT MIGRAPHX_GENERATOR_IS_MULTI_CONFIG)
+    set(CMAKE_BUILD_TYPE Release CACHE STRING
+        "Choose the type of build, options are: None Debug Release RelWithDebInfo MinSizeRel.")
+    set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS ${CMAKE_CONFIGURATION_TYPES})
 endif()
 
 set(CMAKE_INSTALL_PREFIX "/opt/rocm" CACHE PATH "")
@@ -87,7 +85,7 @@ include(ROCMSetupVersion)
 option(BUILD_DEV "Build for development purpose only" OFF)
 
 rocm_setup_version(VERSION 2.8.0)
-set(MIGRAPHX_SO_VERSION ${PROJECT_VERSION_MAJOR}.${PROJECT_VERSION_MINOR}.${PROJECT_VERSION_PATCH})
+set(MIGRAPHX_SO_VERSION ${PROJECT_VERSION_MAJOR}.${PROJECT_VERSION_MINOR})
 
 option( BUILD_SHARED_LIBS "Build as a shared library" ON )
 

Jenkinsfile

@@ -109,12 +109,15 @@ def rocmnode(name, body) {
     }
 }
 
-rocmtest clang_debug: rocmnode('cdna') { cmake_build ->
+rocmtest clang_debug: rocmnode('mi100+') { cmake_build ->
     stage('hipRTC Debug') {
-        def sanitizers = "undefined"
-        def debug_flags = "-g -O2 -fsanitize=${sanitizers} -fno-sanitize-recover=${sanitizers}"
-        def gpu_targets = getgputargets()
-        cmake_build(flags: "-DCMAKE_BUILD_TYPE=debug -DMIGRAPHX_ENABLE_PYTHON=Off -DCMAKE_CXX_FLAGS_DEBUG='${debug_flags}' -DCMAKE_C_FLAGS_DEBUG='${debug_flags}' -DMIGRAPHX_USE_HIPRTC=On -DGPU_TARGETS='${gpu_targets}'", gpu_debug: true)
+        // Disable MLIR since it doesnt work with all ub sanitizers
+        withEnv(['MIGRAPHX_DISABLE_MLIR=1']) {
+            def sanitizers = "undefined"
+            def debug_flags = "-g -O2 -fsanitize=${sanitizers} -fno-sanitize-recover=${sanitizers}"
+            def gpu_targets = getgputargets()
+            cmake_build(flags: "-DCMAKE_BUILD_TYPE=debug -DMIGRAPHX_ENABLE_PYTHON=Off -DCMAKE_CXX_FLAGS_DEBUG='${debug_flags}' -DCMAKE_C_FLAGS_DEBUG='${debug_flags}' -DMIGRAPHX_USE_HIPRTC=On -DGPU_TARGETS='${gpu_targets}'", gpu_debug: true)
+        }
     }
 }, clang_release: rocmnode('mi100+') { cmake_build ->
     stage('Hip Clang Release') {
@@ -126,14 +129,14 @@ rocmtest clang_debug: 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 ->
+}, all_targets_debug : rocmnode('mi100+') { cmake_build ->
     stage('All targets Release') {
         def gpu_targets = getgputargets()
         cmake_build(flags: "-DCMAKE_BUILD_TYPE=release -DMIGRAPHX_ENABLE_GPU=On -DMIGRAPHX_ENABLE_CPU=On -DMIGRAPHX_ENABLE_FPGA=On -DGPU_TARGETS='${gpu_targets}'")
     }
-}, mlir_debug: rocmnode('cdna') { cmake_build ->
+}, mlir_debug: rocmnode('mi100+') { cmake_build ->
     stage('MLIR Debug') {
-        withEnv(['MIGRAPHX_ENABLE_MLIR=1']) {
+        withEnv(['MIGRAPHX_ENABLE_EXTRA_MLIR=1']) {
             def sanitizers = "undefined"
             // Note: the -fno-sanitize= is copied from upstream LLVM_UBSAN_FLAGS.
             def debug_flags_cxx = "-g -O2 -fsanitize=${sanitizers} -fno-sanitize=vptr,function -fno-sanitize-recover=${sanitizers}"
@@ -144,7 +147,7 @@ rocmtest clang_debug: rocmnode('cdna') { cmake_build ->
     }
 }, ck_hiprtc: rocmnode('mi100+') { cmake_build ->
     stage('CK hipRTC') {
-        withEnv(['MIGRAPHX_ENABLE_CK=1', 'MIGRAPHX_TUNE_CK=1']) {
+        withEnv(['MIGRAPHX_ENABLE_CK=1', 'MIGRAPHX_TUNE_CK=1', 'MIGRAPHX_DISABLE_MLIR=1']) {
             def gpu_targets = getgputargets()
             cmake_build(flags: "-DCMAKE_BUILD_TYPE=release -DMIGRAPHX_USE_HIPRTC=On -DGPU_TARGETS='${gpu_targets}'")
         }

docs/.sphinx/requirements.txt

@@ -35,7 +35,7 @@ fastjsonschema==2.16.3
     # via rocm-docs-core
 gitdb==4.0.10
     # via gitpython
-gitpython==3.1.32
+gitpython==3.1.37
     # via rocm-docs-core
 idna==3.4
     # via requests
@@ -87,7 +87,7 @@ requests==2.28.2
     # via
     #   pygithub
     #   sphinx
-rocm-docs-core==0.24.2
+rocm-docs-core==0.26.0
     # via -r requirements.in
 smmap==5.0.0
     # via gitdb
@@ -130,7 +130,7 @@ sphinxcontrib-serializinghtml==1.1.5
     # via sphinx
 typing-extensions==4.5.0
     # via pydata-sphinx-theme
-urllib3==1.26.15
+urllib3==1.26.18
     # via requests
 wrapt==1.15.0
     # via deprecated

requirements.txt

@@ -28,5 +28,5 @@ ROCmSoftwarePlatform/half@rocm-5.6.0
 pybind/pybind11@d159a563383d10c821ba7b2a71905d1207db6de4 --build
 msgpack/msgpack-c@cpp-3.3.0 -DMSGPACK_BUILD_TESTS=Off
 sqlite3@3.17 -DCMAKE_POSITION_INDEPENDENT_CODE=On
-ROCmSoftwarePlatform/composable_kernel@a22e479b8e1557961039db2d5c5ff89cff35e86b -DCK_BUILD_JIT_LIB=On -DCMAKE_POSITION_INDEPENDENT_CODE=On
-ROCmSoftwarePlatform/rocMLIR@12748a3402c069f733ea7f2ba1f8d8a070b3622a -DBUILD_FAT_LIBROCKCOMPILER=On
+ROCmSoftwarePlatform/composable_kernel@70eefcf4f263aa5c25f3c9ff0db8f6f199ef0fb9 -DCK_BUILD_JIT_LIB=On -DCMAKE_POSITION_INDEPENDENT_CODE=On
+ROCmSoftwarePlatform/rocMLIR@507bb94ce7873786486d296ec81d2eadaab49003 -DBUILD_FAT_LIBROCKCOMPILER=On
\ No newline at end of file

src/driver/argument_parser.hpp

@@ -187,6 +187,13 @@ struct value_parser
     }
 };
 
+// version for std::optional object
+template <class T>
+struct value_parser<std::optional<T>>
+{
+    static T apply(const std::string& x) { return value_parser<T>::apply(x); }
+};
+
 struct argument_parser
 {
     struct argument

src/driver/main.cpp

@@ -540,19 +540,17 @@ struct params : command<params>
 struct verify : command<verify>
 {
     compiler c;
-    migraphx::verify::tolerance tols;
+    std::optional<double> rms_tol;
+    std::optional<double> atol;
+    std::optional<double> rtol;
     bool per_instruction = false;
     bool reduce          = false;
     void parse(argument_parser& ap)
     {
         c.parse(ap);
-        ap(tols.rms_tol, {"--rms-tol"}, ap.help("Tolerance for the RMS error (Default: 0.001)"));
-        ap(tols.atol,
-           {"--atol"},
-           ap.help("Tolerance for the elementwise absolute difference (Default: 0.001)"));
-        ap(tols.rtol,
-           {"--rtol"},
-           ap.help("Tolerance for the elementwise relative difference (Default: 0.001)"));
+        ap(rms_tol, {"--rms-tol"}, ap.help("Tolerance for the RMS error"));
+        ap(atol, {"--atol"}, ap.help("Tolerance for the elementwise absolute difference"));
+        ap(rtol, {"--rtol"}, ap.help("Tolerance for the elementwise relative difference"));
         ap(per_instruction,
            {"-i", "--per-instruction"},
            ap.help("Verify each instruction"),
@@ -571,9 +569,18 @@ struct verify : command<verify>
 
         auto quantize = precision::fp32;
         if(c.to_fp16)
+        {
             quantize = precision::fp16;
+        }
         if(c.to_int8)
+        {
             quantize = precision::int8;
+        }
+
+        auto tols = get_tolerances(p, quantize, rms_tol, atol, rtol);
+        std::cout << "rms_tol: " << tols.rms_tol << std::endl;
+        std::cout << "atol: " << tols.atol << std::endl;
+        std::cout << "rtol: " << tols.rtol << std::endl;
 
         if(per_instruction)
         {

src/driver/verify.cpp

@@ -36,6 +36,42 @@ namespace migraphx {
 namespace driver {
 inline namespace MIGRAPHX_INLINE_NS {
 
+/**
+ * Gives tolerances based on user input (`rms_tol`, `atol`, `rtol` parameters) and defaults.
+ * Sets to fp16 tolerances if `quantize` input is fp16 or any fp16 instruction in found in the
+ * model.
+ */
+verify::tolerance get_tolerances(const program& p,
+                                 precision quantize,
+                                 std::optional<double> rms_tol,
+                                 std::optional<double> atol,
+                                 std::optional<double> rtol)
+{
+    bool has_fp16 = any_of(p.get_modules(), [](auto&& m) {
+        return any_of(*m, [](auto&& ins) { return (ins.get_shape().type() == shape::half_type); });
+    });
+    migraphx::verify::tolerance result{};
+    if(has_fp16 or quantize == precision::fp16)
+    {
+        result.rms_tol = 8e-2;
+        result.atol    = 4e-2;
+        result.rtol    = 4e-2;
+    }
+    if(rms_tol)
+    {
+        result.rms_tol = *rms_tol;
+    }
+    if(atol)
+    {
+        result.atol = *atol;
+    }
+    if(rtol)
+    {
+        result.rtol = *rtol;
+    }
+    return result;
+}
+
 std::vector<argument> run_ref(program p, const parameter_map& inputs)
 {
     p.compile(migraphx::make_target("ref"));

src/driver/verify.hpp

@@ -32,6 +32,12 @@ namespace migraphx {
 namespace driver {
 inline namespace MIGRAPHX_INLINE_NS {
 
+verify::tolerance get_tolerances(const program& p,
+                                 precision quantize,
+                                 std::optional<double> rms_tol,
+                                 std::optional<double> atol,
+                                 std::optional<double> rtol);
+
 void verify_program(const std::string& name,
                     const program& p,
                     const target& t,

src/include/migraphx/argument.hpp

@@ -46,7 +46,7 @@ struct MIGRAPHX_EXPORT argument : raw_data<argument>
 {
     argument() = default;
 
-    argument(const shape& s);
+    explicit argument(const shape& s);
 
     template <class F, MIGRAPHX_REQUIRES(std::is_pointer<decltype(std::declval<F>()())>{})>
     argument(shape s, F d)

src/include/migraphx/instruction_ref.hpp

@@ -57,7 +57,7 @@ struct instruction_ref : std::list<instruction>::iterator
                                 std::is_same<U, instruction_ref>{})>
     friend bool operator!=(const T& x, const U& y)
     {
-        return !(x == y);
+        return not(x == y);
     }
 };
 #else

src/include/migraphx/op/allocate.hpp

@@ -88,13 +88,13 @@ struct allocate
     {
         if(args.empty())
         {
-            return {output_shape};
+            return argument{output_shape};
         }
         else
         {
             std::vector<std::size_t> output_dims(output_shape.ndim());
             args.at(0).visit([&](auto a) { output_dims.assign(a.begin(), a.end()); });
-            return {shape{buf_type, output_dims}};
+            return argument{shape{buf_type, output_dims}};
         }
     }
 };

src/include/migraphx/op/argmax.hpp

 /*
  * The MIT License (MIT)
  *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+ * Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to deal
@@ -31,6 +31,7 @@
 #include <migraphx/value.hpp>
 #include <migraphx/op/normalize_attribute.hpp>
 #include <migraphx/dyn_output.hpp>
+#include <migraphx/float_equal.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -38,12 +39,13 @@ namespace op {
 
 struct argmax
 {
-    int64_t axis = 0;
+    int64_t axis           = 0;
+    bool select_last_index = false;
 
     template <class Self, class F>
     static auto reflect(Self& self, F f)
     {
-        return pack(f(self.axis, "axis"));
+        return pack(f(self.axis, "axis"), f(self.select_last_index, "select_last_index"));
     }
 
     value attributes() const
@@ -87,6 +89,10 @@ struct argmax
                 max_val   = cur_val;
                 max_index = i;
             }
+            else if(select_last_index and float_equal(max_val, cur_val))
+            {
+                max_index = i;
+            }
         }
         return max_index;
     }

src/include/migraphx/op/argmin.hpp

 /*
  * The MIT License (MIT)
  *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+ * Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to deal
@@ -30,6 +30,7 @@
 #include <migraphx/config.hpp>
 #include <migraphx/value.hpp>
 #include <migraphx/op/normalize_attribute.hpp>
+#include <migraphx/float_equal.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -38,11 +39,12 @@ namespace op {
 struct argmin
 {
     int64_t axis = 0;
+    bool select_last_index = false;
 
     template <class Self, class F>
     static auto reflect(Self& self, F f)
     {
-        return pack(f(self.axis, "axis"));
+        return pack(f(self.axis, "axis"), f(self.select_last_index, "select_last_index"));
     }
 
     value attributes() const
@@ -78,6 +80,10 @@ struct argmin
                 min_val   = cur_val;
                 min_index = i;
             }
+            else if(select_last_index and float_equal(min_val, cur_val))
+            {
+                min_index = i;
+            }
         }
 
         return min_index;

src/include/migraphx/op/pooling.hpp

@@ -411,7 +411,7 @@ struct pooling
             // for dynamic GlobalPooling, there's no padding
             kernel_dims.insert(kernel_dims.end(), input_lens.begin() + 2, input_lens.end());
             output_shape = dyn_out.computed_shape;
-            result       = dyn_out.computed_shape;
+            result       = argument{dyn_out.computed_shape};
         }
         else if((padding_mode != op::padding_mode_t::default_))
         {
@@ -439,7 +439,7 @@ struct pooling
         {
             kernel_dims  = this->lengths;
             output_shape = dyn_out.computed_shape;
-            result       = dyn_out.computed_shape;
+            result       = argument{dyn_out.computed_shape};
         }
 
         // Perform the computation and populate result

src/include/migraphx/op/reshape.hpp

@@ -36,6 +36,22 @@ namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace op {
 
+/**
+ * 1 input version:
+ * reshape(input_data)
+ * this.dims = output_dims
+ * Makes a copy of input_data to the output shape.
+ *
+ * 2 input version:
+ * reshape(input_data, output_buffer)
+ * this.dims = unset
+ * Copies input_data to output_buffer; output_buffer already has the output shape.
+ * This version will not fail gracefully if the input shape and output_buffer shape are
+ * incompatible. There's a throw that will catch when the number of elements do not match at
+ * runtime. This version should only be used for dynamic reshapes (output dimensions only known at
+ * runtime). If output_buffer has a static shape during compile/parse, you can use the 1 input
+ * version.
+ */
 struct reshape
 {
     std::vector<int64_t> dims;
@@ -215,32 +231,56 @@ struct reshape
 
     shape compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this, true}.has(1);
+        check_shapes{inputs, *this, true}.has(1, 2);
 
         auto n_neg_dims = std::count(dims.begin(), dims.end(), -1);
         if(n_neg_dims > 1)
             MIGRAPHX_THROW("reshape: Dimensions for reshape can only have one -1 dim");
 
         auto s0 = inputs.front();
-        if(s0.dynamic())
+        if(inputs.size() == 1)
         {
-            return dyn_compute_shape(s0);
+            if(s0.dynamic())
+            {
+                return dyn_compute_shape(s0);
+            }
+            else
+            {
+                return static_compute_shape(inputs, n_neg_dims);
+            }
         }
         else
         {
-            return static_compute_shape(inputs, n_neg_dims);
+            return inputs.back();
         }
     }
 
     argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
     {
         assert(dyn_out.computed_shape.standard());
-        argument result{dyn_out.computed_shape};
+        if(args.size() == 1)
+        {
+            argument result{dyn_out.computed_shape};
 
-        visit_all(result, args[0])([&](auto output, auto input) {
-            std::copy(input.begin(), input.end(), output.begin());
-        });
-        return result;
+            visit_all(result, args[0])([&](auto output, auto input) {
+                std::copy(input.begin(), input.end(), output.begin());
+            });
+            return result;
+        }
+        else
+        {
+            // 2 arg
+            if(args[0].get_shape().elements() != args[1].get_shape().elements())
+            {
+                MIGRAPHX_THROW("Reshape: Number of elements must match at runtime. Input: " +
+                               std::to_string(args[0].get_shape().elements()) +
+                               " Output buffer: " + std::to_string(args[1].get_shape().elements()));
+            }
+            visit_all(args[1], args[0])([&](auto output, auto input) {
+                std::copy(input.begin(), input.end(), output.begin());
+            });
+            return args[1];
+        }
     }
 };
 

src/include/migraphx/op/scatter.hpp

@@ -66,7 +66,7 @@ struct scatter : op_name<Derived>
 
     shape normalize_compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.has(3).standard();
+        check_shapes{inputs, *this}.has(3);
         // If non-packed, this converts to a packed output while preserving permutation of tensor
         return inputs.front().with_lens(inputs.front().lens());
     }

src/onnx/padding.cpp

@@ -47,7 +47,7 @@ void cal_auto_padding_size(onnx_parser::node_info info,
         return;
     }
 
-    auto auto_pad = info.attributes["auto_pad"].s();
+    auto auto_pad = to_upper(info.attributes["auto_pad"].s());
     if(auto_pad.find("SAME") != std::string::npos)
     {
         bool is_same_upper = (auto_pad.find("SAME_UPPER") != std::string::npos);

src/onnx/parse_arg_op.cpp

 /*
  * The MIT License (MIT)
  *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+ * Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to deal
@@ -50,14 +50,25 @@ struct parse_arg_op : op_parser<parse_arg_op>
             keep_dims = parser.parse_value(info.attributes.at("keepdims")).at<int>();
         }
 
+        bool select_last_index = false;
+        if(contains(info.attributes, "select_last_index"))
+        {
+            select_last_index = static_cast<bool>(
+                parser.parse_value(info.attributes.at("select_last_index")).at<int>());
+        }
+
         if(keep_dims == 0)
         {
-            auto ins = info.add_instruction(make_op(opd.op_name, {{"axis", axis}}), args);
+            auto ins = info.add_instruction(
+                make_op(opd.op_name, {{"axis", axis}, {"select_last_index", select_last_index}}),
+                args);
             return info.add_instruction(make_op("squeeze", {{"axes", {axis}}}), ins);
         }
         else
         {
-            return info.add_instruction(make_op(opd.op_name, {{"axis", axis}}), args);
+            return info.add_instruction(
+                make_op(opd.op_name, {{"axis", axis}, {"select_last_index", select_last_index}}),
+                args);
         }
     }
 };

src/onnx/parse_groupnorm.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 <migraphx/onnx/op_parser.hpp>
+#include <migraphx/ranges.hpp>
+#include <migraphx/make_op.hpp>
+#include <migraphx/instruction.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace onnx {
+
+struct parse_groupnorm : op_parser<parse_groupnorm>
+{
+    std::vector<op_desc> operators() const { return {{"GroupNormalization"}}; }
+
+    instruction_ref parse(const op_desc& /*opd*/,
+                          const onnx_parser& parser,
+                          const onnx_parser::node_info& info,
+                          std::vector<instruction_ref> args) const
+    {
+        float epsilon = 1e-5f;
+        if(contains(info.attributes, "epsilon"))
+        {
+            epsilon = parser.parse_value(info.attributes.at("epsilon")).at<float>();
+        }
+        size_t num_groups;
+        if(contains(info.attributes, "num_groups"))
+        {
+            num_groups = parser.parse_value(info.attributes.at("num_groups")).at<size_t>();
+        }
+        else
+        {
+            MIGRAPHX_THROW("PARSE_GROUPNORM: num_groups must be available");
+        }
+
+        if(args.size() != 3)
+        {
+            MIGRAPHX_THROW("PARSE_GROUPNORM: invalid input count");
+        }
+
+        auto x     = args.at(0);
+        auto scale = args.at(1);
+        auto bias  = args.at(2);
+
+        auto x_shape = x->get_shape();
+        auto x_dtype = x_shape.type();
+        auto x_dims  = x_shape.lens();
+
+        if(x_shape.ndim() <= 2)
+        {
+            MIGRAPHX_THROW("PARSE_GROUPNORM: invalid input shape");
+        }
+
+        auto c = x_shape.lens().at(1);
+        if(c % num_groups != 0)
+        {
+            MIGRAPHX_THROW(
+                "PARSE_GROUPNORM: num_groups should be a divisor of the number of channels");
+        }
+        auto group_size = c / num_groups;
+        if(scale->get_shape().ndim() != 1 or scale->get_shape().lens().at(0) != num_groups)
+        {
+            MIGRAPHX_THROW("PARSE_GROUPNORM: scale tensor shape should be num_groups");
+        }
+        if(bias->get_shape().ndim() != 1 or bias->get_shape().lens().at(0) != num_groups)
+        {
+            MIGRAPHX_THROW("PARSE_GROUPNORM: bias tensor shape should be num_groups");
+        }
+
+        // Original shape: N x C x D1 x ... x Dn
+        // New shape: N x num_groups x C // num_groups x D1 x ... x Dn
+
+        std::vector<size_t> dims = {x_dims.at(0), num_groups, group_size};
+        std::copy(x_dims.begin() + 2, x_dims.end(), std::back_inserter(dims));
+        auto x_reshaped = info.add_instruction(make_op("reshape", {{"dims", dims}}), x);
+
+        // Axes for D1 x ... x Dn
+        std::vector<size_t> axes(dims.size() - 2);
+        std::iota(axes.begin(), axes.end(), 2);
+
+        // y = (x - mean) * rsqrt(variance + epsilon) * scale + bias
+        // mean = reduce_mean({D1, D2, ... Dk}, x)
+        // variance = reduce_mean({D1, D2, ... Dk}, (x - mean)^2)
+
+        auto mean = info.add_instruction(make_op("reduce_mean", {{"axes", axes}}), x_reshaped);
+        auto x_sub_mean    = info.add_common_op("sub", x_reshaped, mean);
+        auto x_sqdiff_mean = info.add_common_op("sqdiff", x_reshaped, mean);
+        auto variance =
+            info.add_instruction(make_op("reduce_mean", {{"axes", axes}}), x_sqdiff_mean);
+        epsilon =
+            (x_dtype == migraphx::shape::half_type and std::abs(epsilon) < 1e-7) ? 1e-7 : epsilon;
+        auto eps     = info.add_literal(migraphx::literal{migraphx::shape{x_dtype}, {epsilon}});
+        auto var_eps = info.add_common_op("add", variance, eps);
+        auto rsqrt   = info.add_instruction(make_op("rsqrt"), var_eps);
+        auto result  = info.add_common_op("mul", x_sub_mean, rsqrt);
+        auto scale_bcast =
+            info.add_instruction(make_op("broadcast", {{"axis", 1}, {"out_lens", dims}}), scale);
+        auto bias_bcast =
+            info.add_instruction(make_op("broadcast", {{"axis", 1}, {"out_lens", dims}}), bias);
+        auto scaled     = info.add_instruction(make_op("mul"), result, scale_bcast);
+        auto y          = info.add_instruction(make_op("add"), scaled, bias_bcast);
+        auto y_reshaped = info.add_instruction(make_op("reshape", {{"dims", x_dims}}), y);
+        return y_reshaped;
+    }
+};
+
+} // namespace onnx
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/onnx/parse_layernorm.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 <migraphx/onnx/op_parser.hpp>
+#include <migraphx/ranges.hpp>
+#include <migraphx/make_op.hpp>
+#include <migraphx/instruction.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace onnx {
+
+struct parse_layernorm : op_parser<parse_layernorm>
+{
+    std::vector<op_desc> operators() const { return {{"LayerNormalization"}}; }
+
+    std::vector<instruction_ref> parse(const op_desc& /*opd*/,
+                                       const onnx_parser& parser,
+                                       const onnx_parser::node_info& info,
+                                       std::vector<instruction_ref> args) const
+    {
+        int64_t axis = -1;
+        if(contains(info.attributes, "axis"))
+        {
+            axis = parser.parse_value(info.attributes.at("axis")).at<int64_t>();
+        }
+        float epsilon = 1e-5f;
+        if(contains(info.attributes, "epsilon"))
+        {
+            epsilon = parser.parse_value(info.attributes.at("epsilon")).at<float>();
+        }
+        if(contains(info.attributes, "stash_type"))
+        {
+            std::cerr << "WARNING: LAYERNORM does not support stash_type, it will be ignored.\n";
+        }
+
+        if(args.size() < 2 or args.size() > 3)
+        {
+            MIGRAPHX_THROW("PARSE_LAYERNORM: invalid input count");
+        }
+
+        auto x         = args.at(0);
+        auto scale     = args.at(1);
+        bool skip_bias = args.size() == 2;
+        instruction_ref bias;
+        if(not skip_bias)
+        {
+            bias = args.at(2);
+        }
+
+        auto x_shape   = x->get_shape();
+        auto x_dtype   = x_shape.type();
+        int64_t x_rank = x_shape.ndim();
+
+        if(x_rank < 2)
+        {
+            MIGRAPHX_THROW("PARSE_LAYERNORM: invalid input shape");
+        }
+
+        // If rank(X) is r, axis' allowed range is [-r, r)
+        if(axis < -x_rank or axis >= x_rank)
+        {
+            MIGRAPHX_THROW("PARSE_LAYERNORM: invalid axis");
+        }
+
+        // y = (x - mean) * rsqrt(variance + epsilon) * scale + bias
+        // mean = reduce_mean({D1, D2, ... Dk}, x)
+        // variance = reduce_mean({D1, D2, ... Dk}, (x - mean)^2)
+
+        // axis can be negative
+        axis = axis < 0 ? axis + x_rank : axis;
+
+        auto kdims = x_rank - axis;
+        std::vector<int64_t> axes(kdims);
+        std::iota(axes.begin(), axes.end(), axis);
+        auto skipped_axes = x_rank - kdims;
+
+        auto mean          = info.add_instruction(make_op("reduce_mean", {{"axes", axes}}), x);
+        auto x_sub_mean    = info.add_common_op("sub", x, mean);
+        auto x_sqdiff_mean = info.add_common_op("sqdiff", x, mean);
+        auto variance =
+            info.add_instruction(make_op("reduce_mean", {{"axes", axes}}), x_sqdiff_mean);
+        epsilon =
+            (x_dtype == migraphx::shape::half_type and std::abs(epsilon) < 1e-7) ? 1e-7 : epsilon;
+        auto eps     = info.add_literal(migraphx::literal{migraphx::shape{x_dtype}, {epsilon}});
+        auto var_eps = info.add_common_op("add", variance, eps);
+        auto rsqrt   = info.add_instruction(make_op("rsqrt"), var_eps);
+        auto result  = info.add_common_op("mul", x_sub_mean, rsqrt);
+
+        instruction_ref scale_bcast = scale;
+        instruction_ref bias_bcast  = bias;
+        if(skipped_axes > 0)
+        {
+            auto x_dims = x_shape.lens();
+            scale_bcast = info.add_instruction(
+                make_op("broadcast", {{"axis", skipped_axes}, {"out_lens", x_dims}}), scale);
+            if(not skip_bias)
+            {
+                bias_bcast = info.add_instruction(
+                    make_op("broadcast", {{"axis", skipped_axes}, {"out_lens", x_dims}}), bias);
+            }
+        }
+        auto scaled = info.add_instruction(make_op("mul"), result, scale_bcast);
+        auto y      = skip_bias ? scaled : info.add_instruction(make_op("add"), scaled, bias_bcast);
+        return {y, mean, rsqrt};
+    }
+};
+
+} // namespace onnx
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx