Merge branch 'develop' into enable_navi_32_ci

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/include/migraphx/optional.hpp

@@ -29,6 +29,17 @@
 #if defined(CPPCHECK)
 #define MIGRAPHX_HAS_OPTIONAL 1
 #define MIGRAPHX_HAS_OPTIONAL_TS 1
+#elif defined(_WIN32)
+#if _MSC_VER >= 1920
+#define MIGRAPHX_HAS_OPTIONAL 1
+#define MIGRAPHX_HAS_OPTIONAL_TS 0
+#elif _MSC_VER >= 1900
+#define MIGRAPHX_HAS_OPTIONAL 0
+#define MIGRAPHX_HAS_OPTIONAL_TS 1
+#else
+#define MIGRAPHX_HAS_OPTIONAL 0
+#define MIGRAPHX_HAS_OPTIONAL_TS 0
+#endif
 #elif defined(__has_include)
 #if __has_include(<optional>) && __cplusplus >= 201703L
 #define MIGRAPHX_HAS_OPTIONAL 1

src/include/migraphx/pad_calc.hpp

@@ -64,6 +64,7 @@ shape compute_padded_shape(const shape& input,
 
 // Used for dynamic auto padding of pooling operators where padding needs to be computed at
 // evaulation time.
+MIGRAPHX_EXPORT
 shape compute_padded_pool_shape(const shape& input,
                                 const shape& kernel,
                                 const std::vector<std::size_t>& padding,

src/include/migraphx/run_loop.hpp

@@ -31,6 +31,7 @@
 #include <migraphx/module.hpp>
 #include <migraphx/config.hpp>
 #include <migraphx/ranges.hpp>
+#include <array>
 #include <string>
 
 namespace migraphx {

src/include/migraphx/source_location.hpp

@@ -24,6 +24,7 @@
 #ifndef MIGRAPHX_GUARD_MIGRAPHX_SOURCE_LOCATION_HPP
 #define MIGRAPHX_GUARD_MIGRAPHX_SOURCE_LOCATION_HPP
 
+#include <cstdint>
 #include <migraphx/config.hpp>
 
 #if defined(CPPCHECK)

src/include/migraphx/tmp_dir.hpp

@@ -34,6 +34,7 @@ struct MIGRAPHX_EXPORT tmp_dir
 {
     fs::path path;
     tmp_dir(const std::string& prefix = "");
+    tmp_dir(tmp_dir&&) = default;
 
     void execute(const std::string& exe, const std::string& args) const;
 

src/include/migraphx/type_name.hpp

@@ -34,7 +34,7 @@ template <class PrivateMigraphTypeNameProbe>
 std::string compute_type_name()
 {
     std::string name;
-#ifdef _MSC_VER
+#if defined(_MSC_VER) && !defined(__clang__)
     name = typeid(PrivateMigraphTypeNameProbe).name();
     name = name.substr(7);
 #else

src/include/migraphx/verify.hpp

@@ -90,8 +90,7 @@ struct not_finite_fn
     template <class T>
     bool operator()(T x) const
     {
-        using std::isfinite;
-        return not isfinite(x);
+        return not std::isfinite(static_cast<double>(x));
     }
 };
 static constexpr not_finite_fn not_finite{};
@@ -101,8 +100,7 @@ struct compare_mag_fn
     template <class T, class U>
     bool operator()(T x, U y) const
     {
-        using std::fabs;
-        return fabs(x) < fabs(y);
+        return std::fabs(x) < std::fabs(y);
     }
 };
 static constexpr compare_mag_fn compare_mag{};

src/onnx/broadcast_qdq.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/broadcast_qdq.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace onnx {
+
+// This method is to prep for quantizelinear or dequantizelinear operation for
+// either the broadcasting of weight-scale or zero-points of qlinearadd operator
+// outputs: operator op (inputs x, broadcasted: scale (float) & zero_pt (8-bit))
+instruction_ref bcast_qdq_instr(const std::string& op_name,
+                                instruction_ref x_in,
+                                instruction_ref arg_fscale,
+                                instruction_ref arg_z_pt,
+                                const onnx_parser::node_info& info)
+{
+    auto in_lens = x_in->get_shape().lens();
+
+    // prep 1: broadcast scale. it can come as a scalar or a 1-D tensor.
+    instruction_ref bcast_scale;
+    if(arg_fscale->get_shape().elements() > 1)
+        bcast_scale = info.add_instruction(
+            migraphx::make_op("broadcast", {{"axis", 0}, {"out_lens", in_lens}}), arg_fscale);
+    else
+        bcast_scale = info.add_instruction(
+            migraphx::make_op("multibroadcast", {{"out_lens", in_lens}}), arg_fscale);
+
+    // prep 2: broadcast zero point. it can come as a scalar or a 1-D tensor.
+    instruction_ref bcast_zero_pt;
+    if(arg_z_pt->get_shape().elements() > 1)
+        bcast_zero_pt = info.add_instruction(
+            migraphx::make_op("broadcast", {{"axis", 0}, {"out_lens", in_lens}}), arg_z_pt);
+    else
+        bcast_zero_pt = info.add_instruction(
+            migraphx::make_op("multibroadcast", {{"out_lens", in_lens}}), arg_z_pt);
+
+    // op_name is either quantizelinear or dequantizelinear:
+    return info.add_instruction(migraphx::make_op(op_name), x_in, bcast_scale, bcast_zero_pt);
+}
+
+// Multibroadcast a scaler..
+instruction_ref bcast_scalar_instr(const migraphx::shape& shape_out,
+                                   instruction_ref arg_in,
+                                   const onnx_parser::node_info& info)
+{
+    auto bcast_instr_out = info.add_instruction(
+        migraphx::make_op("multibroadcast", {{"out_lens", shape_out.lens()}}), arg_in);
+    return bcast_instr_out;
+}
+
+} // namespace onnx
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/onnx/include/migraphx/onnx/broadcast_qdq.hpp

+/*
+ * 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.
+ */
+
+#ifndef MIGRAPHX_GUARD_AMDMIGRAPHX_ONNX_BROADCAST_QDQ_HPP
+#define MIGRAPHX_GUARD_AMDMIGRAPHX_ONNX_BROADCAST_QDQ_HPP
+
+#include <string>
+
+#include <migraphx/onnx/op_parser.hpp>
+#include <migraphx/make_op.hpp>
+#include <migraphx/instruction.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace onnx {
+
+// This method is to prep for quantizelinear or dequantizelinear operation for
+// either the broadcasting of weight-scale or zero-points of qlinearadd operator
+// outputs: operator op (inputs x, broadcasted: scale (float) & zero_pt (8-bit))
+instruction_ref bcast_qdq_instr(const std::string& op_name,
+                                instruction_ref x_in,
+                                instruction_ref arg_fscale,
+                                instruction_ref arg_z_pt,
+                                const onnx_parser::node_info& info);
+
+// Multibroadcast a scaler..
+instruction_ref bcast_scalar_instr(const migraphx::shape& shape_out,
+                                   instruction_ref arg_in,
+                                   const onnx_parser::node_info& info);
+
+} // namespace onnx
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/onnx/onnx_parser.cpp

@@ -244,7 +244,7 @@ void onnx_parser::parse_from(std::istream& is, std::string name)
     this->filename   = std::move(name);
     auto parent_path = fs::path(this->filename).parent_path();
     if(not parent_path.empty())
-        this->path = parent_path;
+        this->path = parent_path.string();
 
     onnx::ModelProto model;
     if(model.ParseFromIstream(&is))

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_castlike.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_castlike : op_parser<parse_castlike>
+{
+    std::vector<op_desc> operators() const { return {{"CastLike"}}; }
+
+    instruction_ref parse(const op_desc& /*opd*/,
+                          const onnx_parser& /*parser*/,
+                          const onnx_parser::node_info& info,
+                          const std::vector<instruction_ref>& args) const
+    {
+        if(not(args.size() == 2))
+        {
+            MIGRAPHX_THROW("PARSE_CASTLIKE: CastLike must have exactly 2 inputs!");
+        }
+        shape::type_t target_type = args[1]->get_shape().type();
+        return info.add_instruction(make_op("convert", {{"target_type", target_type}}), args[0]);
+    }
+};
+
+} // namespace onnx
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/onnx/parse_constant_of_shape.cpp

@@ -49,6 +49,8 @@ struct parse_constant_of_shape : op_parser<parse_constant_of_shape>
             {
                 MIGRAPHX_THROW("ConstantOfShape: attribute value can contain only 1 elements!");
             }
+            // convert to a scalar literal
+            l_val = literal(shape{l_val.get_shape().type(), {1}, {0}}, l_val.data());
         }
         else
         {
@@ -64,6 +66,9 @@ struct parse_constant_of_shape : op_parser<parse_constant_of_shape>
             migraphx::shape s;
             // input is empty, output is a scalar
             auto type = l_val.get_shape().type();
+            migraphx::argument input = args[0]->eval();
+            if(not input.empty())
+            {
                 // empty input tensor, output is a scalar
                 if(args[0]->get_shape().elements() == 0)
                 {
@@ -71,14 +76,10 @@ struct parse_constant_of_shape : op_parser<parse_constant_of_shape>
                 }
                 else
                 {
-                migraphx::argument in = args[0]->eval();
-                check_arg_empty(in, "ConstantOfShape: dynamic shape is not supported");
-
                     std::vector<std::size_t> dims;
-                in.visit([&](auto input) { dims.assign(input.begin(), input.end()); });
+                    input.visit([&](auto ia) { dims.assign(ia.begin(), ia.end()); });
                     s = migraphx::shape{type, dims};
                 }
-
                 literal l_out{};
                 l_val.visit([&](auto val) {
                     using val_type = std::remove_cv_t<typename decltype(val)::value_type>;
@@ -86,9 +87,17 @@ struct parse_constant_of_shape : op_parser<parse_constant_of_shape>
                     std::vector<val_type> out_vec(s.elements(), val.front());
                     l_out = literal(s, out_vec);
                 });
-
                 return info.add_literal(l_out);
             }
+            // has variable input (dynamic shape buffer)
+            else
+            {
+                auto dv_lit = info.add_literal(l_val);
+                auto alloc_ins =
+                    info.add_instruction(make_op("allocate", {{"buf_type", type}}), args[0]);
+                return info.add_instruction(make_op("fill"), dv_lit, alloc_ins);
+            }
+        }
     }
 };
 

src/onnx/parse_depthtospace.cpp

@@ -87,8 +87,7 @@ struct parse_depthtospace : op_parser<parse_depthtospace>
 
         auto temp1 = info.add_instruction(make_op("reshape", {{"dims", lens1}}), args[0]);
         auto temp2 = info.add_instruction(make_op("transpose", {{"permutation", perm}}), temp1);
-        return info.add_instruction(make_op("reshape", {{"dims", lens2}}),
-                                    info.make_contiguous(temp2));
+        return info.add_instruction(make_op("reshape", {{"dims", lens2}}), temp2);
     }
 };
 

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

src/onnx/parse_mean_variance_normalization.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/instruction.hpp>
+#include <migraphx/make_op.hpp>
+#include <migraphx/onnx/checks.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace onnx {
+
+struct parse_mean_variance_normalization : op_parser<parse_mean_variance_normalization>
+{
+    std::vector<op_desc> operators() const { return {{"MeanVarianceNormalization"}}; }
+
+    instruction_ref parse(const op_desc& /*opd*/,
+                          const onnx_parser& /*parser*/,
+                          onnx_parser::node_info info,
+                          std::vector<instruction_ref> args) const
+    {
+        auto&& data    = args.front();
+        auto data_rank = data->get_shape().ndim();
+        std::vector<int64_t> axes{0, 2, 3};
+
+        if(contains(info.attributes, "axes"))
+        {
+            const auto& axes_attr = info.attributes["axes"].ints();
+            axes.assign(axes_attr.begin(), axes_attr.end());
+        }
+        else if(data_rank != 4)
+        {
+            MIGRAPHX_THROW(
+                "Input tensor needs to be rank 4 when axes is not specified. Instead it is rank " +
+                std::to_string(data_rank));
+        }
+
+        if(axes.size() != data_rank - 1)
+        {
+            MIGRAPHX_THROW("Length of axes array needs to be equal to input tensor rank - 1");
+        }
+
+        auto data_mean = info.add_instruction(make_op("reduce_mean", {{"axes", axes}}), data);
+        auto data_mean_squared = info.add_common_op("mul", data_mean, data_mean);
+
+        auto data_squared = info.add_common_op("mul", data, data);
+        auto data_squared_mean =
+            info.add_instruction(make_op("reduce_mean", {{"axes", axes}}), data_squared);
+
+        auto mean_sub = info.add_common_op("sub", data_squared_mean, data_mean_squared);
+        auto std      = info.add_common_op("sqrt", mean_sub);
+
+        auto dividend = info.add_common_op("sub", data, data_mean);
+        auto epsilon =
+            info.add_literal({data->get_shape().type(),
+                              {data->get_shape().type() == shape::half_type ? 1e-7 : 1e-9}});
+        auto divisor = info.add_common_op("add", std, epsilon);
+
+        return info.add_common_op("div", dividend, divisor);
+    }
+};
+
+} // namespace onnx
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/onnx/parse_pad.cpp

@@ -115,65 +115,71 @@ struct parse_pad : op_parser<parse_pad>
 {
     std::vector<op_desc> operators() const { return {{"Pad"}}; }
 
-    instruction_ref parse(const op_desc& /*opd*/,
-                          const onnx_parser& parser,
-                          onnx_parser::node_info info,
-                          std::vector<instruction_ref> args) const
+    std::string parse_mode(const onnx_parser::node_info& info,
+                           const std::vector<instruction_ref>& args) const
     {
-        std::vector<int64_t> pads{};
-        if(args.size() >= 2)
+        if(contains(info.attributes, "mode"))
         {
-            auto pad_arg = args.at(1)->eval();
-            check_arg_empty(pad_arg, "PARSE_PAD: pad input must be constant");
-            pad_arg.visit([&](auto v) { pads.assign(v.begin(), v.end()); });
+            auto mode = info.attributes.at("mode").s();
+            if(mode == "reflect")
+            {
+                if(args.front()->get_shape().dynamic())
+                {
+                    MIGRAPHX_THROW("PARSE_PAD: reflect padding with dynamic shape not supported");
                 }
-        else if(contains(info.attributes, "pads"))
+            }
+            else if(mode != "constant")
             {
-            auto&& pad_vals = info.attributes["pads"].ints();
-            pads            = std::vector<int64_t>(pad_vals.begin(), pad_vals.end());
+                MIGRAPHX_THROW(
+                    "PARSE_PAD: migraphx currently only supports constant and reflect padding");
+            }
+            return mode;
         }
         else
         {
-            MIGRAPHX_THROW("PARSE_PAD: pad must be available");
+            // default mode
+            return "constant";
         }
-
-        // check if padding is actually being done (at least one value is nonzero)
-        if(std::all_of(pads.begin(), pads.end(), [](const int& i) { return i == 0; }))
-        {
-            return info.add_instruction(make_op("identity"), args.front());
     }
 
-        if(contains(info.attributes, "mode"))
+    std::vector<int64_t> parse_pads(const onnx_parser::node_info& info,
+                                    const std::vector<instruction_ref>& args) const
     {
-            auto mode = info.attributes.at("mode").s();
-            if(mode == "reflect")
-            {
-                if(args.front()->get_shape().dynamic())
+        std::vector<int64_t> pads{};
+        if(args.size() >= 2)
         {
-                    MIGRAPHX_THROW("PARSE_PAD: reflect padding with dynamic shape not supported");
+            auto pad_arg = args.at(1)->eval();
+            check_arg_empty(pad_arg, "PARSE_PAD: `pads` input must be constant");
+            pad_arg.visit([&](auto v) { pads.assign(v.begin(), v.end()); });
         }
-                return reflect_pad(info, pads, args.front());
+        else if(contains(info.attributes, "pads"))
+        {
+            auto&& pad_vals = info.attributes.at("pads").ints();
+            pads            = std::vector<int64_t>(pad_vals.begin(), pad_vals.end());
         }
-            if(mode != "constant")
+        else
         {
-                MIGRAPHX_THROW(
-                    "PARSE_PAD: migraphx currently only supports constant and reflect padding");
+            MIGRAPHX_THROW("PARSE_PAD: `pads` must be available");
         }
+        return pads;
     }
 
+    float parse_constant_value(const onnx_parser& parser,
+                               const onnx_parser::node_info& info,
+                               const std::vector<instruction_ref>& args) const
+    {
         float value = 0.0f;
-        // third input is the value
-        if(args.size() == 3)
+        if(args.size() >= 3 and args.at(2)->get_shape().scalar())
         {
             auto val_ins = args.at(2);
             if(not val_ins->can_eval())
             {
-                MIGRAPHX_THROW("PARSE_PAD: input value must be constant");
+                MIGRAPHX_THROW("PARSE_PAD: input `value` must be constant");
             }
             auto val_arg = val_ins->eval();
             if(val_arg.get_shape().elements() != 1)
             {
-                MIGRAPHX_THROW("PARSE_PAD: value should contain only one element");
+                MIGRAPHX_THROW("PARSE_PAD: `value` should contain only one element");
             }
             value = val_arg.at<float>();
         }
@@ -181,6 +187,81 @@ struct parse_pad : op_parser<parse_pad>
         {
             value = parser.parse_value(info.attributes.at("value")).at<float>();
         }
+        return value;
+    }
+
+    std::vector<int64_t> parse_axes(const std::vector<instruction_ref>& args,
+                                    bool is_constant_mode) const
+    {
+        std::vector<int64_t> axes{};
+        // axes is 3rd or 4th, depending on constant mode
+        auto pos = is_constant_mode ? 4 : 3;
+        if(args.size() >= pos)
+        {
+            auto axes_arg = args.at(pos - 1)->eval();
+            check_arg_empty(axes_arg, "PARSE_PAD: variable `axes` input not supported");
+            axes_arg.visit([&](auto v) { axes.assign(v.begin(), v.end()); });
+        }
+        return axes;
+    }
+
+    std::vector<int64_t> calculate_pads_with_axes(const std::vector<int64_t>& pads,
+                                                  const std::vector<int64_t>& axes,
+                                                  size_t input_rank) const
+    {
+        size_t num_axes = axes.size();
+        if(num_axes * 2 != pads.size())
+        {
+            MIGRAPHX_THROW("PARSE_PAD: number of elements of pads should be equal to 2 * "
+                           "number of elements of axes");
+        }
+
+        std::vector<int64_t> new_pads(input_rank * 2);
+        for(size_t idx{0}; idx < num_axes; ++idx)
+        {
+            // axis can be negative
+            int64_t axis = axes[idx] < 0 ? input_rank + axes[idx] : axes[idx];
+            // pad format is x1_begin, x2_begin, ... , x3_end, x4_end
+            new_pads[axis]              = pads[idx];
+            new_pads[axis + input_rank] = pads[idx + num_axes];
+        }
+        return new_pads;
+    }
+
+    instruction_ref parse(const op_desc& /*opd*/,
+                          const onnx_parser& parser,
+                          const onnx_parser::node_info& info,
+                          const std::vector<instruction_ref>& args) const
+    {
+        std::vector<int64_t> pads = parse_pads(info, args);
+
+        // check if padding is actually being done (at least one value is nonzero)
+        if(std::all_of(pads.begin(), pads.end(), [](const int& i) { return i == 0; }))
+        {
+            return info.add_instruction(make_op("identity"), args.front());
+        }
+
+        std::string mode      = parse_mode(info, args);
+        bool is_constant_mode = mode == "constant";
+        float value           = is_constant_mode ? parse_constant_value(parser, info, args) : 0.0f;
+        std::vector<int64_t> axes = parse_axes(args, is_constant_mode);
+        size_t input_rank         = args.front()->get_shape().ndim();
+
+        if(not axes.empty())
+        {
+            pads = calculate_pads_with_axes(pads, axes, input_rank);
+        }
+
+        if(pads.size() != input_rank * 2)
+        {
+            MIGRAPHX_THROW("PARSE_PAD: number of elements of pads should be equal to 2 * "
+                           "input rank");
+        }
+
+        if(mode == "reflect")
+        {
+            return reflect_pad(info, pads, args.front());
+        }
 
         return info.add_instruction(migraphx::make_op("pad", {{"pads", pads}, {"value", value}}),
                                     args.front());