Merge branch 'develop' into multiply-add

CMakeLists.txt

@@ -81,6 +81,7 @@ rocm_enable_clang_tidy(
         -modernize-use-override
         -modernize-pass-by-value
         -modernize-use-default-member-init
+        -modernize-use-trailing-return-type
         -modernize-use-transparent-functors
         -performance-type-promotion-in-math-fn
         -readability-braces-around-statements

Dockerfile

@@ -20,6 +20,7 @@ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-
     clang-format-5.0 \
     clang-tidy-5.0 \
     cmake \
+    comgr \
     curl \
     doxygen \
     g++-7 \
@@ -32,14 +33,16 @@ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-
     libncurses5-dev \
     libnuma-dev \
     libpthread-stubs0-dev \
+    libssl-dev \
     python \
     python-dev \
     python-pip \
+    rocm-device-libs \
     rocm-opencl \
     rocm-opencl-dev \
-    rocminfo \
     software-properties-common \
-    wget && \
+    wget \
+    zlib1g-dev && \
     apt-get clean && \
     rm -rf /var/lib/apt/lists/*
 
@@ -50,7 +53,7 @@ RUN pip install cget
 RUN pip install https://github.com/pfultz2/rclone/archive/master.tar.gz
 
 # Install hcc
-RUN rclone -b roc-2.3.x -c fd93baed7dcc4fe8019b5fdc90213bfe7c298245 https://github.com/RadeonOpenCompute/hcc.git /hcc
+RUN rclone -b roc-2.6.x -c 0f4c96b7851af2663a7f3ac16ecfb76c7c78a5bf https://github.com/RadeonOpenCompute/hcc.git /hcc
 RUN cget -p $PREFIX install hcc,/hcc
 
 # Use hcc

dev-requirements.txt

 pfultz2/rocm-recipes
 danmar/cppcheck@8aa68ee297c2d9ebadf5bcfd00c66ea8d9291e35 -DHAVE_RULES=1
-ROCm-Developer-Tools/HIP@e21df3058728ad8e73708bc99b82e0bdd3509c97
+ROCm-Developer-Tools/HIP@2490e42baa7d90458f0632fd9fbead2d395f41b9
 python/cpython@v3.6.6 -X autotools -H sha256:92aa914572c695c0aeb01b0a214813f414da4b51a371234df514a74761f2bb36
 -f requirements.txt

requirements.txt

 google/protobuf@v3.8.0 -DCMAKE_POSITION_INDEPENDENT_CODE=On -X subdir -Dprotobuf_BUILD_TESTS=Off
 RadeonOpenCompute/rocm-cmake@42f6740 --build
-ROCmSoftwarePlatform/rocBLAS@30a992ae02fda568688bcd190edd5e277d6674d9
-ROCmSoftwarePlatform/MIOpen@1.7.0
+ROCmSoftwarePlatform/rocBLAS@7197df74e5a1ba64ff967065872e5f86a3516637
+ROCmSoftwarePlatform/MIOpen@2.0.0
 blaze,https://bitbucket.org/blaze-lib/blaze/get/f0755dea0e03.tar.gz -X header -DHEADER_DIR=blaze
 half,https://github.com/pfultz2/half/archive/1.12.0.tar.gz -X header -H sha256:0a08660b68abb176ebc2a0cdf8de46e3182a7f46c66443bb80dbfaaec98cf969
 pybind/pybind11@v2.2.4 -DPYBIND11_TEST=Off --build

src/include/migraphx/op/pow.hpp

+#ifndef MIGRAPHX_GUARD_OPERATORS_POW_HPP
+#define MIGRAPHX_GUARD_OPERATORS_POW_HPP
+
+#include <migraphx/op/binary.hpp>
+#include <migraphx/config.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
+
+struct pow : binary<pow>
+{
+    auto apply() const
+    {
+        return [](auto x, auto y) { return std::pow(x, y); };
+    }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/op/reduce_mean.hpp

+#ifndef MIGRAPHX_GUARD_OPERATORS_MEAN_HPP
+#define MIGRAPHX_GUARD_OPERATORS_MEAN_HPP
+
+#include <migraphx/check_shapes.hpp>
+#include <migraphx/argument.hpp>
+#include <migraphx/shape_for_each.hpp>
+#include <migraphx/par_for.hpp>
+#include <migraphx/config.hpp>
+#include <vector>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
+
+struct reduce_mean
+{
+    std::vector<std::int64_t> axes{};
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(self.axes, "axes"));
+    }
+
+    std::string name() const { return "reduce_mean"; }
+
+    std::vector<int64_t> tune_axes(std::size_t n_dim) const
+    {
+        auto tuned_axes = axes;
+        if(tuned_axes.empty())
+        {
+            tuned_axes.resize(n_dim);
+            std::iota(tuned_axes.begin(), tuned_axes.end(), 0);
+        }
+        else
+        {
+            for(auto& axis : tuned_axes)
+            {
+                int64_t s_dim = static_cast<int64_t>(n_dim);
+                if(axis >= s_dim or axis < -s_dim)
+                {
+                    MIGRAPHX_THROW("REDUCE_MEAN: axis out of range");
+                }
+                if(axis < 0)
+                {
+                    axis += n_dim;
+                }
+            }
+        }
+
+        return tuned_axes;
+    }
+
+    shape compute_shape(std::vector<shape> inputs) const
+    {
+        check_shapes{inputs, *this}.has(1);
+        auto s          = inputs.at(0);
+        auto lens       = s.lens();
+        auto tuned_axes = tune_axes(lens.size());
+        for(auto axis : tuned_axes)
+        {
+            lens[axis] = 1;
+        }
+
+        return {s.type(), lens};
+    }
+
+    template <class T>
+    void calc_mean(tensor_view<T>& input,
+                   shape& batch_shape,
+                   std::vector<int64_t>& tuned_axes,
+                   std::vector<std::size_t>& out_idx,
+                   tensor_view<T>& output) const
+    {
+        auto data_idx = out_idx;
+        T val         = T{0};
+        shape_for_each(batch_shape, [&](auto b_idx) {
+            for(auto axis : tuned_axes)
+            {
+                data_idx[axis] = b_idx[axis];
+            }
+            val += input(data_idx.begin(), data_idx.end());
+        });
+
+        output(out_idx.begin(), out_idx.end()) = val / batch_shape.elements();
+    }
+
+    argument compute(const shape& output_shape, std::vector<argument> args) const
+    {
+        argument result{output_shape};
+        auto arg_lens   = args.front().get_shape().lens();
+        auto tuned_axes = tune_axes(arg_lens.size());
+        std::vector<std::size_t> batch_lens(output_shape.lens().size(), 1);
+        for(auto axis : tuned_axes)
+        {
+            batch_lens[axis] = arg_lens[axis];
+        }
+        shape batch_shape{output_shape.type(), batch_lens};
+        visit_all(result, args[0])([&](auto output, auto input) {
+            par_for(output_shape.elements(), [&](auto i) {
+                auto out_idx = output_shape.multi(i);
+                this->calc_mean(input, batch_shape, tuned_axes, out_idx, output);
+            });
+        });
+
+        return result;
+    }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/op/reduce_sum.hpp

@@ -4,6 +4,7 @@
 #include <migraphx/check_shapes.hpp>
 #include <migraphx/argument.hpp>
 #include <migraphx/shape_for_each.hpp>
+#include <migraphx/par_for.hpp>
 #include <migraphx/config.hpp>
 #include <vector>
 
@@ -13,7 +14,7 @@ namespace op {
 
 struct reduce_sum
 {
-    std::vector<std::size_t> axes;
+    std::vector<int64_t> axes{};
 
     template <class Self, class F>
     static auto reflect(Self& self, F f)
@@ -23,25 +24,82 @@ struct reduce_sum
 
     std::string name() const { return "reduce_sum"; }
 
+    std::vector<int64_t> tune_axes(std::size_t n_dim) const
+    {
+        auto tuned_axes = axes;
+        if(tuned_axes.empty())
+        {
+            tuned_axes.resize(n_dim);
+            std::iota(tuned_axes.begin(), tuned_axes.end(), 0);
+        }
+        else
+        {
+            for(auto& axis : tuned_axes)
+            {
+                int64_t s_dim = static_cast<int64_t>(n_dim);
+                if(axis >= s_dim or axis < -s_dim)
+                {
+                    MIGRAPHX_THROW("REDUCE_MEAN: axis out of range");
+                }
+                if(axis < 0)
+                {
+                    axis += n_dim;
+                }
+            }
+        }
+
+        return tuned_axes;
+    }
+
     shape compute_shape(std::vector<shape> inputs) const
     {
         check_shapes{inputs, *this}.has(1);
-        auto s    = inputs.at(0);
-        auto lens = s.lens();
-        for(auto axis : axes)
+        auto s          = inputs.at(0);
+        auto lens       = s.lens();
+        auto tuned_axes = tune_axes(lens.size());
+        for(auto axis : tuned_axes)
+        {
             lens[axis] = 1;
+        }
+
         return {s.type(), lens};
     }
 
+    template <class T>
+    void calc_sum(tensor_view<T>& input,
+                  shape& batch_shape,
+                  std::vector<int64_t>& tuned_axes,
+                  std::vector<std::size_t>& out_idx,
+                  tensor_view<T>& output) const
+    {
+        auto data_idx = out_idx;
+        T val         = T{0};
+        shape_for_each(batch_shape, [&](auto b_idx) {
+            for(auto axis : tuned_axes)
+            {
+                data_idx[axis] = b_idx[axis];
+            }
+            val += input(data_idx.begin(), data_idx.end());
+        });
+
+        output(out_idx.begin(), out_idx.end()) = val;
+    }
+
     argument compute(const shape& output_shape, std::vector<argument> args) const
     {
         argument result{output_shape};
+        auto arg_lens                   = args.front().get_shape().lens();
+        std::vector<int64_t> tuned_axes = tune_axes(arg_lens.size());
+        std::vector<std::size_t> batch_lens(output_shape.lens().size(), 1);
+        for(auto axis : tuned_axes)
+        {
+            batch_lens[axis] = arg_lens[axis];
+        }
+        shape batch_shape{output_shape.type(), batch_lens};
         visit_all(result, args[0])([&](auto output, auto input) {
-            shape_for_each(input.get_shape(), [&](auto&& in_idx) {
-                auto out_idx = in_idx;
-                for(auto axis : axes)
-                    out_idx[axis] = 0;
-                output(out_idx.begin(), out_idx.end()) += input(in_idx.begin(), in_idx.end());
+            par_for(output_shape.elements(), [&](auto i) {
+                auto out_idx = output_shape.multi(i);
+                this->calc_sum(input, batch_shape, tuned_axes, out_idx, output);
             });
         });
 

src/include/migraphx/op/rsqrt.hpp

+#ifndef MIGRAPHX_GUARD_OPERATORS_RSQRT_HPP
+#define MIGRAPHX_GUARD_OPERATORS_RSQRT_HPP
+
+#include <migraphx/op/unary.hpp>
+#include <cmath>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
+
+struct rsqrt : unary<rsqrt>
+{
+    auto apply() const
+    {
+        return [](auto x) { return 1 / std::sqrt(x); };
+    }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/op/sqdiff.hpp

+#ifndef MIGRAPHX_GUARD_OPERATORS_SQDIFF_HPP
+#define MIGRAPHX_GUARD_OPERATORS_SQDIFF_HPP
+
+#include <migraphx/op/binary.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
+
+struct sqdiff : binary<sqdiff>
+{
+    auto apply() const
+    {
+        return [](auto x, auto y) { return (x - y) * (x - y); };
+    }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/op/sqrt.hpp

+#ifndef MIGRAPHX_GUARD_OPERATORS_SQRT_HPP
+#define MIGRAPHX_GUARD_OPERATORS_SQRT_HPP
+
+#include <migraphx/op/unary.hpp>
+#include <migraphx/config.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
+
+struct sqrt : unary<sqrt>
+{
+    auto apply() const
+    {
+        return [](auto x) { return std::sqrt(x); };
+    }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/operators.hpp

@@ -45,18 +45,23 @@
 #include <migraphx/op/outline.hpp>
 #include <migraphx/op/pad.hpp>
 #include <migraphx/op/pooling.hpp>
+#include <migraphx/op/pow.hpp>
 #include <migraphx/op/reduce_sum.hpp>
+#include <migraphx/op/reduce_mean.hpp>
 #include <migraphx/op/relu.hpp>
 #include <migraphx/op/reshape.hpp>
 #include <migraphx/op/rnn.hpp>
 #include <migraphx/op/rnn_last_cell_output.hpp>
 #include <migraphx/op/rnn_last_output.hpp>
+#include <migraphx/op/rsqrt.hpp>
 #include <migraphx/op/scalar.hpp>
 #include <migraphx/op/sigmoid.hpp>
 #include <migraphx/op/sinh.hpp>
 #include <migraphx/op/sin.hpp>
 #include <migraphx/op/slice.hpp>
 #include <migraphx/op/softmax.hpp>
+#include <migraphx/op/sqrt.hpp>
+#include <migraphx/op/sqdiff.hpp>
 #include <migraphx/op/squeeze.hpp>
 #include <migraphx/op/sub.hpp>
 #include <migraphx/op/tanh.hpp>

src/onnx/onnx.cpp

@@ -54,11 +54,13 @@ struct onnx_parser
         add_generic_op("Asin", op::asin{});
         add_generic_op("Acos", op::acos{});
         add_generic_op("Atan", op::atan{});
+        add_generic_op("Sqrt", op::sqrt{});
 
         add_binary_op("Add", op::add{});
         add_binary_op("Div", op::div{});
         add_binary_op("Mul", op::mul{});
         add_binary_op("Sub", op::sub{});
+        add_binary_op("Pow", op::pow{});
 
         add_variadic_op("Sum", op::add{});
         add_variadic_op("Max", op::max{});
@@ -66,11 +68,13 @@ struct onnx_parser
 
         add_mem_op("ArgMax", &onnx_parser::parse_argmax);
         add_mem_op("ArgMin", &onnx_parser::parse_argmin);
+        add_mem_op("Cast", &onnx_parser::parse_cast);
         add_mem_op("Clip", &onnx_parser::parse_clip);
         add_mem_op("LRN", &onnx_parser::parse_lrn);
         add_mem_op("ImageScaler", &onnx_parser::parse_imagescaler);
         add_mem_op("LeakyRelu", &onnx_parser::parse_leaky_relu);
         add_mem_op("Elu", &onnx_parser::parse_elu);
+        add_mem_op("Expand", &onnx_parser::parse_expand);
         add_mem_op("Constant", &onnx_parser::parse_constant);
         add_mem_op("Conv", &onnx_parser::parse_conv);
         add_mem_op("MaxPool", &onnx_parser::parse_pooling);
@@ -91,12 +95,14 @@ struct onnx_parser
         add_mem_op("Gather", &onnx_parser::parse_gather);
         add_mem_op("Shape", &onnx_parser::parse_shape);
         add_mem_op("ConstantFill", &onnx_parser::parse_constant_fill);
+        add_mem_op("ConstantOfShape", &onnx_parser::parse_constant_of_shape);
         add_mem_op("Transpose", &onnx_parser::parse_transpose);
         add_mem_op("RNN", &onnx_parser::parse_rnn);
         add_mem_op("GRU", &onnx_parser::parse_gru);
         add_mem_op("LSTM", &onnx_parser::parse_lstm);
         add_mem_op("Pad", &onnx_parser::parse_pad);
-        add_mem_op("ReduceSum", &onnx_parser::parse_reduce_sum);
+        add_mem_op("ReduceSum", &onnx_parser::parse_reduce_oper<op::reduce_sum>);
+        add_mem_op("ReduceMean", &onnx_parser::parse_reduce_oper<op::reduce_mean>);
 
         // init the activation function map
         init_actv_func();
@@ -461,8 +467,7 @@ struct onnx_parser
         if(args.size() == 2)
         {
             auto s = args[1]->eval();
-            if(s.empty())
-                MIGRAPHX_THROW("Dynamic shape is not supported.");
+            check_arg_empty(s, "Reshape: dynamic shape is not supported");
             s.visit([&](auto v) { copy(v, std::back_inserter(op.dims)); });
         }
         return prog.add_instruction(op, args[0]);
@@ -541,7 +546,13 @@ struct onnx_parser
                                    attribute_map attributes,
                                    const std::vector<instruction_ref>&)
     {
-        literal v     = parse_value(attributes.at("value"));
+        literal v = parse_value(attributes.at("value"));
+        // return empty literal
+        if(v.get_shape().elements() == 0)
+        {
+            return prog.add_literal(literal{});
+        }
+
         auto dim_size = attributes.at("value").t().dims_size();
         // if dim_size is 0, it is a scalar
         if(dim_size == 0)
@@ -869,10 +880,7 @@ struct onnx_parser
             }
 
             migraphx::argument in = args[0]->eval();
-            if(in.empty())
-            {
-                MIGRAPHX_THROW("ConstantFill: cannot handle dynamic shape as input");
-            }
+            check_arg_empty(in, "ConstantFill: dynamic shape is not supported");
 
             std::vector<std::size_t> dims;
             in.visit([&](auto input) { dims.assign(input.begin(), input.end()); });
@@ -900,6 +908,74 @@ struct onnx_parser
         }
     }
 
+    instruction_ref parse_constant_of_shape(const std::string&,
+                                            attribute_map attributes,
+                                            std::vector<instruction_ref> args)
+    {
+        literal l_val{};
+        if(contains(attributes, "value"))
+        {
+            l_val = parse_value(attributes.at("value"));
+            if(l_val.get_shape().elements() != 1)
+            {
+                MIGRAPHX_THROW("ConstantOfShape: attribute value can contain only 1 elements!");
+            }
+        }
+        else
+        {
+            l_val = literal({shape::float_type, {1}, {0}}, {0.0f});
+        }
+
+        // input is empty, output is a scalar
+        auto type = l_val.get_shape().type();
+
+        if(args.empty())
+        {
+            MIGRAPHX_THROW("ConstantOfShape : must have 1 input!");
+        }
+        else
+        {
+            migraphx::shape s;
+            // empty input tensor, output is a scalar
+            if(args[0]->get_shape().elements() == 0)
+            {
+                s = migraphx::shape{type, {1}, {0}};
+            }
+            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()); });
+                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>;
+                // l_val contains only one element
+                std::vector<val_type> out_vec(s.elements(), *val.begin());
+                l_out = literal(s, out_vec);
+            });
+
+            return prog.add_literal(l_out);
+        }
+    }
+
+    instruction_ref
+    parse_expand(const std::string&, const attribute_map&, std::vector<instruction_ref> args)
+    {
+        auto in_lens             = args[0]->get_shape().lens();
+        migraphx::argument arg_s = args[1]->eval();
+        check_arg_empty(arg_s, "Expand: dynamic shape is not supported");
+        std::vector<std::size_t> dims;
+        arg_s.visit([&](auto input) { dims.assign(input.begin(), input.end()); });
+        auto out_lens = compute_broadcasted_lens(in_lens, dims);
+
+        return prog.add_instruction(op::multibroadcast{out_lens}, args[0]);
+    }
+
     std::vector<instruction_ref>
     parse_rnn(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
     {
@@ -1288,20 +1364,21 @@ struct onnx_parser
         return {hidden_states, last_output, last_cell_output};
     }
 
-    instruction_ref parse_reduce_sum(const std::string&,
-                                     attribute_map attributes,
-                                     std::vector<instruction_ref> args)
+    template <class T>
+    instruction_ref parse_reduce_oper(const std::string&,
+                                      attribute_map attributes,
+                                      std::vector<instruction_ref> args)
     {
         std::size_t n_dim = args.front()->get_shape().lens().size();
 
         // default to reduce over all dimensions
-        std::vector<std::size_t> axes(n_dim);
+        std::vector<int64_t> axes(n_dim);
         std::iota(axes.begin(), axes.end(), 0);
         if(contains(attributes, "axes"))
         {
             axes.clear();
             auto&& attr_axes = attributes["axes"].ints();
-            axes             = std::vector<std::size_t>(attr_axes.begin(), attr_axes.end());
+            axes             = std::vector<int64_t>(attr_axes.begin(), attr_axes.end());
         }
 
         int keep_dims = 1;
@@ -1312,16 +1389,28 @@ struct onnx_parser
 
         if(keep_dims == 1)
         {
-            return prog.add_instruction(op::reduce_sum{axes}, std::move(args));
+            return prog.add_instruction(T{axes}, std::move(args));
         }
         else
         {
-            auto ins = prog.add_instruction(op::reduce_sum{axes}, std::move(args));
-            std::vector<int64_t> squeeze_axes{axes.begin(), axes.end()};
-            return prog.add_instruction(op::squeeze{squeeze_axes}, ins);
+            auto ins = prog.add_instruction(T{axes}, std::move(args));
+            return prog.add_instruction(op::squeeze{axes}, ins);
         }
     }
 
+    instruction_ref
+    parse_cast(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
+    {
+        if(!contains(attributes, "to"))
+        {
+            MIGRAPHX_THROW("PARSE_CAST: missing to type attribute!");
+        }
+
+        int to_type        = parse_value(attributes.at("to")).at<int>();
+        shape::type_t type = get_type(to_type);
+        return prog.add_instruction(op::convert{type}, std::move(args));
+    }
+
     void parse_from(std::istream& is)
     {
         onnx::ModelProto model;
@@ -1468,16 +1557,16 @@ struct onnx_parser
     {
         switch(attr.type())
         {
-        case onnx::AttributeProto::UNDEFINED: return {};
         case onnx::AttributeProto::FLOAT: return literal{attr.f()};
         case onnx::AttributeProto::INT: return literal{attr.i()};
-        case onnx::AttributeProto::STRING: return {};
         case onnx::AttributeProto::TENSOR: return parse_tensor(attr.t());
-        case onnx::AttributeProto::GRAPH: return {};
         case onnx::AttributeProto::FLOATS: return from_repeated(shape::float_type, attr.floats());
         case onnx::AttributeProto::INTS: return from_repeated(shape::int64_type, attr.ints());
-        case onnx::AttributeProto::STRINGS: return {};
-        case onnx::AttributeProto::TENSORS: return {};
+        case onnx::AttributeProto::UNDEFINED:
+        case onnx::AttributeProto::GRAPH:
+        case onnx::AttributeProto::STRING:
+        case onnx::AttributeProto::STRINGS:
+        case onnx::AttributeProto::TENSORS:
         case onnx::AttributeProto::GRAPHS: return {};
         }
         MIGRAPHX_THROW("Invalid attribute type");
@@ -1491,47 +1580,41 @@ struct onnx_parser
             const std::string& s = t.raw_data();
             switch(t.data_type())
             {
-            case onnx::TensorProto::UNDEFINED: throw std::runtime_error("");
             case onnx::TensorProto::FLOAT: return create_literal(shape::float_type, dims, s.data());
-            case onnx::TensorProto::UINT8: throw std::runtime_error("");
-            case onnx::TensorProto::INT8: return create_literal(shape::int32_type, dims, s.data());
-            case onnx::TensorProto::UINT16:
-                return create_literal(shape::int32_type, dims, s.data());
-            case onnx::TensorProto::INT16: return create_literal(shape::int32_type, dims, s.data());
-            case onnx::TensorProto::INT32: return create_literal(shape::int32_type, dims, s.data());
-            case onnx::TensorProto::INT64: return create_literal(shape::int64_type, dims, s.data());
-            case onnx::TensorProto::STRING: throw std::runtime_error("");
-            case onnx::TensorProto::BOOL: return create_literal(shape::int32_type, dims, s.data());
             case onnx::TensorProto::FLOAT16:
                 return create_literal(shape::half_type, dims, s.data());
             case onnx::TensorProto::DOUBLE:
                 return create_literal(shape::double_type, dims, s.data());
-            case onnx::TensorProto::UINT32: throw std::runtime_error("");
-            case onnx::TensorProto::UINT64: throw std::runtime_error("");
-            case onnx::TensorProto::COMPLEX64: throw std::runtime_error("");
+            case onnx::TensorProto::INT64: return create_literal(shape::int64_type, dims, s.data());
+            case onnx::TensorProto::INT8:
+            case onnx::TensorProto::UINT16:
+            case onnx::TensorProto::INT16:
+            case onnx::TensorProto::INT32:
+            case onnx::TensorProto::BOOL: return create_literal(shape::int32_type, dims, s.data());
+            case onnx::TensorProto::UINT8:
+            case onnx::TensorProto::STRING:
+            case onnx::TensorProto::UNDEFINED:
+            case onnx::TensorProto::UINT32:
+            case onnx::TensorProto::UINT64:
+            case onnx::TensorProto::COMPLEX64:
             case onnx::TensorProto::COMPLEX128: throw std::runtime_error("");
             }
             MIGRAPHX_THROW("Invalid tensor type");
         }
         switch(t.data_type())
         {
-        case onnx::TensorProto::UNDEFINED: throw std::runtime_error("");
-        case onnx::TensorProto::FLOAT:
-            return create_literal(shape::float_type, dims, t.float_data());
-        case onnx::TensorProto::UINT8: throw std::runtime_error("");
         case onnx::TensorProto::INT8:
-            return create_literal(shape::int32_type, dims, t.int32_data());
         case onnx::TensorProto::UINT16:
-            return create_literal(shape::int32_type, dims, t.int32_data());
         case onnx::TensorProto::INT16:
-            return create_literal(shape::int32_type, dims, t.int32_data());
         case onnx::TensorProto::INT32:
+        case onnx::TensorProto::BOOL:
             return create_literal(shape::int32_type, dims, t.int32_data());
         case onnx::TensorProto::INT64:
             return create_literal(shape::int64_type, dims, t.int64_data());
-        case onnx::TensorProto::STRING: throw std::runtime_error("");
-        case onnx::TensorProto::BOOL:
-            return create_literal(shape::int32_type, dims, t.int32_data());
+        case onnx::TensorProto::DOUBLE:
+            return create_literal(shape::double_type, dims, t.double_data());
+        case onnx::TensorProto::FLOAT:
+            return create_literal(shape::float_type, dims, t.float_data());
         case onnx::TensorProto::FLOAT16:
         {
             std::vector<uint16_t> data_uint16(t.int32_data().begin(), t.int32_data().end());
@@ -1542,11 +1625,12 @@ struct onnx_parser
                            [](uint16_t raw_val) { return *reinterpret_cast<half*>(&raw_val); });
             return create_literal(shape::half_type, dims, data_half);
         }
-        case onnx::TensorProto::DOUBLE:
-            return create_literal(shape::double_type, dims, t.double_data());
-        case onnx::TensorProto::UINT32: throw std::runtime_error("");
-        case onnx::TensorProto::UINT64: throw std::runtime_error("");
-        case onnx::TensorProto::COMPLEX64: throw std::runtime_error("");
+        case onnx::TensorProto::UNDEFINED:
+        case onnx::TensorProto::UINT8:
+        case onnx::TensorProto::STRING:
+        case onnx::TensorProto::UINT32:
+        case onnx::TensorProto::UINT64:
+        case onnx::TensorProto::COMPLEX64:
         case onnx::TensorProto::COMPLEX128: throw std::runtime_error("");
         }
         MIGRAPHX_THROW("Invalid tensor type");
@@ -1574,28 +1658,23 @@ struct onnx_parser
         shape::type_t shape_type{};
         switch(t.tensor_type().elem_type())
         {
-        case onnx::TensorProto::UNDEFINED:
-            break; // throw std::runtime_error("Unsupported type UNDEFINED");
         case onnx::TensorProto::FLOAT: shape_type = shape::float_type; break;
-        case onnx::TensorProto::UINT8:
-            break; // throw std::runtime_error("Unsupported type UINT8");
         case onnx::TensorProto::INT8: shape_type = shape::int8_type; break;
         case onnx::TensorProto::UINT16: shape_type = shape::uint16_type; break;
         case onnx::TensorProto::INT16: shape_type = shape::int16_type; break;
         case onnx::TensorProto::INT32: shape_type = shape::int32_type; break;
         case onnx::TensorProto::INT64: shape_type = shape::int64_type; break;
-        case onnx::TensorProto::STRING:
-            break; // throw std::runtime_error("Unsupported type STRING");
-        case onnx::TensorProto::BOOL:
-            break; // throw std::runtime_error("Unsupported type BOOL");
         case onnx::TensorProto::FLOAT16: shape_type = shape::half_type; break;
         case onnx::TensorProto::DOUBLE: shape_type = shape::double_type; break;
         case onnx::TensorProto::UINT32: shape_type = shape::uint32_type; break;
         case onnx::TensorProto::UINT64: shape_type = shape::uint64_type; break;
+        case onnx::TensorProto::UINT8:
+        case onnx::TensorProto::STRING:
+        case onnx::TensorProto::BOOL:
+        case onnx::TensorProto::UNDEFINED:
         case onnx::TensorProto::COMPLEX64:
-            break; // throw std::runtime_error("Unsupported type COMPLEX64");
         case onnx::TensorProto::COMPLEX128:
-            break; // throw std::runtime_error("Unsupported type COMPLEX128");
+            break; // throw std::runtime_error("Unsupported type");
         }
         std::vector<std::size_t> dims;
         auto&& tensor_dims = t.tensor_type().shape().dim();
@@ -1634,6 +1713,14 @@ struct onnx_parser
         }
         }
     }
+
+    void check_arg_empty(const argument& arg, const std::string& msg)
+    {
+        if(arg.empty())
+        {
+            MIGRAPHX_THROW(msg);
+        }
+    }
 };
 
 program parse_onnx(const std::string& name)

src/targets/gpu/CMakeLists.txt

@@ -37,8 +37,14 @@ add_library(migraphx_device
     device/pad.cpp
     device/gather.cpp
     device/sub.cpp
+    device/div.cpp
     device/clip.cpp
     device/reduce_sum.cpp
+    device/rsqrt.cpp
+    device/sqrt.cpp
+    device/reduce_mean.cpp
+    device/pow.cpp
+    device/sqdiff.cpp
 )
 set_target_properties(migraphx_device PROPERTIES EXPORT_NAME device)
 rocm_clang_tidy_check(migraphx_device)
@@ -77,6 +83,7 @@ add_library(migraphx_gpu
     adjust_allocation.cpp
     clip.cpp
     reduce_sum.cpp
+    reduce_mean.cpp
 )
 set_target_properties(migraphx_gpu PROPERTIES EXPORT_NAME gpu)
 rocm_clang_tidy_check(migraphx_gpu)

src/targets/gpu/device/div.cpp

+#include <migraphx/gpu/device/div.hpp>
+#include <migraphx/gpu/device/nary.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+namespace device {
+
+void div(hipStream_t stream, const argument& result, const argument& arg1, const argument& arg2)
+{
+    nary(stream, result, arg1, arg2)([](auto x, auto y) { return x / y; });
+}
+
+} // namespace device
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/targets/gpu/device/include/migraphx/gpu/device/reduce.hpp

@@ -28,6 +28,16 @@ struct id
     }
 };
 
+struct mean
+{
+    size_t item_num = 1;
+    template <class T>
+    MIGRAPHX_DEVICE_CONSTEXPR auto operator()(T x) const
+    {
+        return static_cast<T>(x / item_num);
+    }
+};
+
 struct max
 {
     template <class T, class U>

src/targets/gpu/device/pow.cpp

+#include <migraphx/gpu/device/pow.hpp>
+#include <migraphx/gpu/device/nary.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+namespace device {
+
+void pow(hipStream_t stream, const argument& result, const argument& arg1, const argument& arg2)
+{
+    nary(stream, result, arg1, arg2)(
+        [](auto b, auto e) { return ::pow(to_hip_type(b), to_hip_type(e)); });
+}
+
+} // namespace device
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/targets/gpu/device/reduce_mean.cpp

+#include <migraphx/gpu/device/reduce_mean.hpp>
+#include <migraphx/gpu/device/reduce.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+namespace device {
+
+void reduce_mean(hipStream_t stream, const argument& result, const argument& arg)
+{
+    std::size_t item_num = arg.get_shape().elements() / result.get_shape().elements();
+    reduce(stream, result, arg, sum{}, 0, id{}, mean{item_num});
+}
+
+} // namespace device
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/targets/gpu/device/reduce_sum.cpp

@@ -8,6 +8,7 @@ namespace device {
 
 void reduce_sum(hipStream_t stream, const argument& result, const argument& arg)
 {
+
     reduce(stream, result, arg, sum{}, 0, id{}, id{});
 }
 

src/targets/gpu/device/rsqrt.cpp

+#include <migraphx/gpu/device/rsqrt.hpp>
+#include <migraphx/gpu/device/nary.hpp>
+#include <migraphx/gpu/device/types.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+namespace device {
+
+void rsqrt(hipStream_t stream, const argument& result, const argument& arg)
+{
+    nary(stream, result, arg)([](auto x) __device__ { return ::rsqrt(to_hip_type(x)); });
+}
+
+} // namespace device
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/targets/gpu/device/sqdiff.cpp

+#include <migraphx/gpu/device/sqdiff.hpp>
+#include <migraphx/gpu/device/nary.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+namespace device {
+
+void sqdiff(hipStream_t stream, const argument& result, const argument& arg1, const argument& arg2)
+{
+    nary(stream, result, arg1, arg2)([](auto x, auto y) { return (x - y) * (x - y); });
+}
+
+} // namespace device
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx