manual merge

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/streamutils.hpp

@@ -30,6 +30,7 @@
 #include <migraphx/rank.hpp>
 #include <migraphx/requires.hpp>
 #include <migraphx/config.hpp>
+#include <migraphx/optional.hpp>
 #include <vector>
 
 namespace migraphx {
@@ -68,6 +69,19 @@ auto stream_write_value_impl(rank<1>, std::ostream& os, const T& x) -> decltype(
     os << x;
 }
 
+template <class T>
+auto stream_write_value_impl(rank<1>, std::ostream& os, const optional<T>& x)
+{
+    if(x.has_value())
+    {
+        os << *x;
+    }
+    else
+    {
+        os << "nullopt";
+    }
+}
+
 template <class T>
 void stream_write_value_impl(rank<1>, std::ostream& os, const std::vector<T>& r)
 {

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/normalize_attributes.cpp

@@ -66,15 +66,15 @@ auto tune_attribute(const std::vector<int64_t>& vec,
     {
         if(input_shape.dynamic())
         {
-            std::transform(axes.begin(), axes.end(), max_vals.begin(), [&](auto i) {
-                const auto& dd = input_shape.dyn_dims().at(i);
-                if(not dd.is_fixed())
+            // return the unchanged `vec` if the dynamic_dimensions at `axes` are not fixed
+            if(std::any_of(axes.begin(), axes.end(), [&](auto ax) {
+                   return not input_shape.dyn_dims().at(ax).is_fixed();
+               }))
             {
-                    MIGRAPHX_THROW(
-                        "NORMALIZE_ATTR: 'use_lens' on a non-fixed dynamic dimension, axis=" +
-                        std::to_string(i));
+                return vec;
             }
-                return dd.max;
+            std::transform(axes.begin(), axes.end(), max_vals.begin(), [&](auto i) {
+                return input_shape.dyn_dims().at(i).max;
             });
         }
         else

src/targets/gpu/int8_conv_pack.cpp → src/onnx/broadcast_qdq.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
@@ -21,58 +21,56 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
  * THE SOFTWARE.
  */
-#include <migraphx/gpu/int8_conv_pack.hpp>
-#include <migraphx/gpu/context.hpp>
+
+#include <migraphx/onnx/broadcast_qdq.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
-namespace gpu {
+namespace onnx {
 
-shape pack_int8_shape(const shape& s)
+// 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)
 {
-    if(s.type() != shape::int8_type)
-    {
-        MIGRAPHX_THROW("PACK_INT8_ARGS: only process int8_type");
-    }
+    auto in_lens = x_in->get_shape().lens();
 
-    auto lens    = s.lens();
-    auto strides = s.strides();
-    lens[1]      = (lens[1] + 3) / 4 * 4;
-    strides[0]   = strides[1] * lens[1];
+    // 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);
 
-    return {s.type(), lens, strides};
-}
+    // 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);
 
-shape miopen_int8_conv_pack::compute_shape(const std::vector<shape>& inputs) const
-{
-    check_shapes{{inputs.at(0)}, *this}.has(1).standard();
-    return pack_int8_shape(inputs.at(0));
+    // op_name is either quantizelinear or dequantizelinear:
+    return info.add_instruction(migraphx::make_op(op_name), x_in, bcast_scale, bcast_zero_pt);
 }
 
-argument
-miopen_int8_conv_pack::compute(context& ctx, const shape&, const std::vector<argument>& args) const
+// Multibroadcast a scaler..
+instruction_ref bcast_scalar_instr(const migraphx::shape& shape_out,
+                                   instruction_ref arg_in,
+                                   const onnx_parser::node_info& info)
 {
-    auto arg_desc      = make_tensor(args[0].get_shape());
-    auto arg_desc_vec4 = make_tensor(args[0].get_shape(), true);
-
-    float alpha = 1;
-    float beta  = 0;
-    // pack input to vec4 format
-    auto status = miopenTransformTensor(ctx.get_stream().get_miopen(),
-                                        &alpha,
-                                        arg_desc.get(),
-                                        args[0].implicit(),
-                                        &beta,
-                                        arg_desc_vec4.get(),
-                                        args[1].implicit());
-    if(status != miopenStatusSuccess)
-    {
-        MIGRAPHX_THROW("INT8_CONV_PACK: transform input tensor failed");
-    }
-
-    return args[1];
+    auto bcast_instr_out = info.add_instruction(
+        migraphx::make_op("multibroadcast", {{"out_lens", shape_out.lens()}}), arg_in);
+    return bcast_instr_out;
 }
 
-} // namespace gpu
+} // namespace onnx
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx

src/targets/gpu/include/migraphx/gpu/int8_gemm_pack.hpp → src/onnx/include/migraphx/onnx/broadcast_qdq.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
@@ -21,42 +21,35 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
  * THE SOFTWARE.
  */
-#ifndef MIGRAPHX_GUARD_RTGLIB_INT8_GEMM_PACK_HPP
-#define MIGRAPHX_GUARD_RTGLIB_INT8_GEMM_PACK_HPP
 
-#include <migraphx/argument.hpp>
-#include <migraphx/config.hpp>
-#include <utility>
+#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 gpu {
-
-struct context;
-
-struct hip_int8_gemm_pack_a
-{
-    std::string name() const { return "gpu::int8_gemm_pack_a"; }
-    shape compute_shape(const std::vector<shape>& inputs) const;
-    argument compute(context& ctx, const shape&, const std::vector<argument>& args) const;
-    std::ptrdiff_t output_alias(const std::vector<shape>& shapes) const
-    {
-        return shapes.size() - 1;
-    }
-};
-
-struct hip_int8_gemm_pack_b
-{
-    std::string name() const { return "gpu::int8_gemm_pack_b"; }
-    shape compute_shape(const std::vector<shape>& inputs) const;
-    argument compute(context& ctx, const shape&, const std::vector<argument>& args) const;
-    std::ptrdiff_t output_alias(const std::vector<shape>& shapes) const
-    {
-        return shapes.size() - 1;
-    }
-};
-
-} // namespace gpu
+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
 

src/onnx/include/migraphx/onnx/onnx_parser.hpp

@@ -100,6 +100,7 @@ struct onnx_parser
     bool use_dyn_output          = false;
     bool skip_unknown_operators  = false;
     int64_t max_loop_iterations  = 10;
+    int64_t limit_max_iterations = std::numeric_limits<uint16_t>::max();
     int64_t opset_version        = 13;
 
     std::unordered_map<std::string, op_func> ops;

src/onnx/onnx.cpp

@@ -67,6 +67,7 @@ program parse_onnx_from(const onnx_options& options, Ts&&... xs)
     }
     parser.skip_unknown_operators = options.skip_unknown_operators;
     parser.max_loop_iterations    = options.max_loop_iterations;
+    parser.limit_max_iterations   = options.limit_max_iterations;
     parser.use_dyn_output         = options.use_dyn_output;
 
     if(options.print_program_on_error)

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_clip.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

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_generic_op.cpp

@@ -60,7 +60,7 @@ struct parse_generic_op : op_parser<parse_generic_op>
                 {"Neg", "neg"},
                 {"Reciprocal", "recip"},
                 {"Relu", "relu"},
-                {"Round", "round"},
+                {"Round", "nearbyint"},
                 {"Sigmoid", "sigmoid"},
                 {"Sign", "sign"},
                 {"Sin", "sin"},

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_isinf.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_isinf : op_parser<parse_isinf>
+{
+    std::vector<op_desc> operators() const { return {{"IsInf", "isinf"}}; }
+
+    instruction_ref parse(const op_desc& /*opd*/,
+                          const onnx_parser& parser,
+                          onnx_parser::node_info info,
+                          const std::vector<instruction_ref>& args) const
+    {
+        bool detect_negative = true;
+        bool detect_positive = true;
+        if(contains(info.attributes, "detect_negative"))
+        {
+            detect_negative = static_cast<bool>(
+                parser.parse_value(info.attributes.at("detect_negative")).at<int>());
+        }
+
+        if(contains(info.attributes, "detect_positive"))
+        {
+            detect_positive = static_cast<bool>(
+                parser.parse_value(info.attributes.at("detect_positive")).at<int>());
+        }
+
+        auto x_shape = args[0]->get_shape();
+        if(not detect_negative and not detect_positive)
+        {
+            return info.add_instruction(
+                make_op("multibroadcast", {{"out_lens", x_shape.lens()}}),
+                info.add_literal(migraphx::literal{migraphx::shape{shape::bool_type}, {false}}));
+        }
+
+        auto is_inf = info.add_instruction(make_op("isinf"), args[0]);
+        if(detect_negative and detect_positive)
+        {
+            return is_inf;
+        }
+
+        auto zero_l = info.add_literal(migraphx::literal{migraphx::shape{x_shape.type()}, {0}});
+        auto mb_zero =
+            info.add_instruction(make_op("multibroadcast", {{"out_lens", x_shape.lens()}}), zero_l);
+
+        auto cond = info.add_broadcastable_binary_op(
+            detect_negative ? "less" : "greater", args[0], mb_zero);
+        if(cond->get_shape().type() != shape::bool_type)
+        {
+            cond =
+                info.add_instruction(make_op("convert", {{"target_type", shape::bool_type}}), cond);
+        }
+        return info.add_instruction(make_op("logical_and"), is_inf, cond);
+    }
+};
+
+} // 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